An RCT on 12 weeks of cognitive, motor or combined cognitive-motor exercise to improve dual-task walking in older adults: The role of baseline cognitive and motor fitness

An RCT on 12 weeks of cognitive, motor or combined cognitive-motor exercise to improve dual-task walking in older adults: The role of baseline cognitive and motor fitness | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An RCT on 12 weeks of cognitive, motor or combined cognitive-motor exercise to improve dual-task walking in older adults: The role of baseline cognitive and motor fitness Melanie Mack, Robert Stojan, Nicole Hudl, Otmar Bock, Claudia Voelcker-Rehage This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6185287/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cognitive-motor dual-tasking, essential for daily activities like walking in busy spaces, declines with age. Research suggests that cognitive (cogT), motor (motT), and cognitive-motor dual-task training (DTT) can improve dual-task performance in older adults, yet studies report heterogeneous effects. This RCT examined whether baseline cognitive (cf) and motor fitness (mf) moderates training effects of these interventions on cognitive-motor dual-task performance in older adults. Methods Participants ( N = 97, aged 65–75) completed 12-week interventions in cogT, motT, or DTT. A battery of cognitive and motor tests was conducted at pre-test to create composite scores of cf and mf. Cognitive-motor performance was assessed at pre- and post-test using a Serial Threes task (S3), a Stroop task (STR), and a walking task. For the cognitive domain, outcomes included correct responses (S3) and inverted RT inhibition costs expressed as percentage (STR); for the motor domain, step variability (inverted to step stability) was used. Outcomes were assessed under single-task (ST) and dual-task (DT) conditions. Results In summary, linear mixed model results indicated that for both S3 as STR, cogT and DTT led to greater increases in cognitive performance than in motor performance across both ST and DT conditions, while the motT showed greater increases in motor performance than in cognitive performance (S3: cogT vs. motT: t = -2.25, DTT vs. motT: t = -2.69; STR: cogT vs. motT: t = -2.41, DTT vs. motT: t = -2.08). The results also showed that mf and the interaction between cf and mf did not moderate pre-post changes in cognitive or motor performance. However, cf did play a significant moderating role for the S3. When comparing the groups, in particular, cogT and motT showed opposing effects ( t = 5.35). For individuals with higher cf, motor performance increased more in the cogT than in the motT. However, their cognitive performance increased more in the cogT than in the motT. Conclusions The results emphasize the complex relationship between cognitive and motor outcomes in cognitive-motor interventions and the key role of baseline fitness in moderating intervention effects. Trial registration This trial was retrospectively registered at German Clinical Trials Register (DRKS00022407). dual-task walking exercise intervention neuroplasticity multitasking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Cognitive-motor dual-tasking – also named multitasking – is pervasive in our daily lives. It includes activities such as going on a sidewalk while avoiding collision with other people, crossing busy streets, or driving a car. These tasks require simultaneous cognitive and motor processing, including route planning, anticipating others’ movements, adhering to traffic rules, controlling one’s locomotion, and avoiding both stationary and moving obstacles. Moreover, other activities are frequently performed concurrently, such as reading billboards, observing storefronts, or having a conversation. These additional tasks can lead to reduced performance in the primary tasks and are associated with a higher risk of falls and accidents. Cognitive-motor dual-tasking therefore is an important prerequisite for everyday mobility, independence and, ultimately, our quality of life. It is well documented that dual-task performance tends to deteriorate in older age [1–4]. The observed age-related decline of dual-task performance has typically been interpreted as a reflection of cognitive capacity limitations [5–7]. Both cognitive and motor performances require cognitive resources. Older adults, however, reach cognitive capacity limits sooner than young adults due to age-related decline in cognitive and motor functions. This age-related decline has been documented not only in typical laboratory paradigms, but also in paradigms that mimic everyday scenarios, such as simulated car driving [8,9], simulated sidewalk walking [3,10,11], and simulated street crossing [12–14]. Counteracting these detrimental changes in everyday behaviors through effective interventions is crucial, in particular in light of an aging society. Numerous interventions aiming to improve cognitive-motor dual-tasking in older adults have been conducted [15,16]. These interventions typically involve either cognitive training, motor training, or cognitive-motor dual-task training, each addressing distinct but also overlapping underlying mechanisms. Cognitive training regimens usually involve repetitive practice of cognitive laboratory tasks aimed at enhancing various fluid cognitive functions, such as attention, memory, and executive functions [17]. Enhancements in cognitive-motor dual-task performance may arise from various cognitive adaptations, such as increased efficiency (e.g., increases in grey matter volume) or greater capacity (e.g., higher levels of task automatization or faster information processing) [18,19]. Motor training generally involves repetitive exercises targeting physical abilities such as strength, balance, and coordination. Strength exercises are designed to enhance neuromuscular control and muscle force, which are significantly diminished with aging and linked to declines in brain health and cognitive performance [20–22]. Balance and coordination exercises include the practice of complex movements that require the coordination of multiple joints and limbs, involving attentional and executive processes for accurate execution and control [23,24]. Enhancements in cognitive-motor dual-task performance through motor training may arise from cognitive adaptations as well as adaptations in the movement control systems relevant to walking [25–27]. Changes in the movement control systems may lead to the automatization of walking. As walking becomes more automatic, fewer cognitive resources are required to maintain postural control and gait stability. Cognitive-motor dual-task training combines both cognitive and motor training by involving the simultaneous or consecutive execution of cognitive tasks and motor exercises. In addition to the adaptations achieved through cognitive and motor single-task training, it is proposed that dual-task training helps participants to develop attentional control capacities which refers to the ability to coordinate and monitor information processing [28,29]. These processes allow one to select the most efficient strategy to optimize the distribution of limited cognitive resources across multiple competing tasks based on environmental and task demands [30–35] Despite the overall finding that cognitive-motor dual-task training is more effective than single-task training in improving cognitive-motor dual-tasking in older adults [15,16], the emergence and magnitude of benefits from cognitive-motor dual-task training compared to single-task training are not consistent in the literature [16]. For instance, in some studies, cognitive-motor dual-task training is more beneficial than single-task motor training [36,37] or single-task cognitive training [38] for improving cognitive-motor dual-tasking. In other studies, cognitive-motor dual-task training is similarly effective as motor single-task training [39,40] Given that interindividual differences in cognitive functioning and motor fitness tend to increase with advancing age [41], it is likely that some participants in the aforementioned studies had higher levels of cognitive functioning and/or motor fitness, while others had lower levels. The varying baseline levels of individual cognitive and motor fitness before the start of the interventions may be a potential factor contributing to this heterogeneity [42]. For instance, recent evidence suggests that individuals with low baseline cognitive fitness tend to benefit more from cognitive training compared to those with higher baseline levels [43,44]. Similar findings have been reported for the association between baseline physical fitness and benefits from physical training [45–47]. This phenomenon, referred to as as compensation, has been attributed to a ceiling effect, where high-baseline participants have less room for improvement than low-baseline participants. Conversely, low-baseline participants were also found to benefit less from training compared to high-baseline participants [48,49], resulting in the differences between high- and low-performing individuals at baseline being further enlarged by training. This so-called magnification phenomenon could represent a floor effect, where low-baseline participants are overwhelmed by higher task demands that exceed their individual capacity. When it comes to dual-task training, the evidence is less clear due to the limited number of studies on this [50]. To our knowledge, only one study has investigated the effect of baseline performance in dual-tasking, and even then, only for cognitive dual-tasking. This study demonstrated that reaction time variability (as a proxy for inefficient neural processing) moderates the intervention effects of cognitive dual-task training on cognitive dual-task performance in both young and older adults [51]. Rationale of the study The aforementioned studies documented that baseline cognitive fitness may modulate the benefits of cognitive training [43,44], baseline motor fitness modulates the benefits of motor training [45–47], and baseline dual-tasking fitness modulates the benefits of dual-task training [51]. Adding to these results, the present work explored possible cross-effects between domains. Specifically, we investigated whether baseline cognitive and motor fitness modulated the benefits of cognitive, motor and cognitive-motor dual-task training in improving cognitive-motor dual-tasking. For instance, an individual with balance problems may benefit little from dual-task training that involves walking while simultaneously counting backwards. This is because a large portion of their available processing resources must be allocated to the walking exercise, leaving only limited resources for the concurrent counting backwards task (i.e., task prioritization hypothesis [10,52]). These individuals may benefit little from cognitive-motor dual-task training, not because their task coordination skills are poor, but rather because their balance skills are degraded. However, if those participants undergo single-task balance and walking, their walking skills might improve to the point where fewer processing resources are needed for walking. The freed-up resources could then support concurrent cognitive tasks. If so, purely motor training might enhance not only motor performance, but also cognitive-motor dual-task performance in these people. In a broader sense, we proposed that the benefits of training depend on the interplay between participants’ baseline cognitive and motor fitness on one side, and the training stimulus on the other side. More specifically, we hypothesized that cognitive-motor dual-task performance in persons with low baseline cognitive fitness benefited more from cognitive training than from motor or cognitive-motor dual-task training (H1). We further posited that cognitive-motor dual-task performance in persons with low baseline motor fitness benefited more from motor training than from cognitive or cognitive-motor dual-task training (H2). Finally, we reasoned that cognitive-motor dual-task performance in persons with higher baseline cognitive and motor fitness benefited more from cognitive-motor dual-task training than from cognitive training alone or motor training alone (H3). Methods This study was part of a larger project within the Priority Program SPP 1772 “Multitasking”, funded by the German Research Foundation (DFG). Assessments were conducted at the xx and xx. Participants Older adults, (N = 128) between 65 and 75 years of age took part in this study. Participants were recruited via homepage announcements, local senior networks, newspaper articles, and posting at public places and social media. All information on inclusion and exclusion criteria was self-reported during a telephone interview. Inclusion criteria comprised: (1) aged between 65 and 75 years (minor exceptions were made for couples for ethical reasons: 75 years), (2) right-handed, (3) active car driving at least once a week within the last 6 months, (4) ability to walk unassisted without self-reported problems (e.g., difficulty to breath, pain, and cardiac palpitations), and (5) community-dwelling. Exclusion criteria comprised: (6) BMI > 30, (7) red-green deficiency or red-green-color blindness, (8) orthopedic impairments, (9) perceived health concerns, (10) neurological diseases, (11) cardiovascular disorders, (12) previous heart attack or stroke, or (13) previous head/brain surgery. In addition, all participants had to obtain a physician’s health clearance (exercise electrocardiogram, ECG) within the last six months. Subsequent screening assessed: (1) overall cognition by the Mini-Mental State Examination (MMSE) with a cutoff score of 27/30 [53,54], (2) visual acuity by the Freiburg vision test (FrACT 3.9.0) with a cutoff score of 20/60 [55,56], and (4) handedness by the Edinburgh Handedness Inventory [57]. No person had to be excluded because of these screening outcomes. Participants who regularly wore vision or hearing aids kept doing so during testing. This study was approved by the Institutional Review Board of the TUC, was carried out in accordance with the guidelines of the Declaration of Helsinki, was registered at the German Clinical Trials Register (DRKS), and the study protocol has been published [58]. Written informed consent was obtained from each participant. Participants’ flow through the study is illustrated in Figure 1. *** Figure 1 *** Measures Screening Cognitive impairment of participants was evaluated using the MMSE, which assesses a range of cognitive abilities including attention, arithmetical skills, verbal fluency, memory, and spatial orientation, on a scale from 0 to 30. All participants in this study scored above the threshold of 27 and were included in the analysis. Visual acuity was measured using the Freiburg Visual Acuity Test (FrACT v 3.9.3). Participants, seated 3 m away from the computer screen, had to identify the orientation of small openings in little circles, i.e., Landolt rings, displayed at the center of the computer screen. The test dynamically adjusted the size of the rings based on participant response accuracy. Performance was expressed in both decimal acuity (VAdec) and the logarithm of the minimum angle of resolution (LogMAR). No participants were excluded based on the visual acuity criterion of scoring below 20/60. Tests of baseline cognitive fitness The N-back and the Simon test were used to assess executive functions [59,60], following standardized protocols and instructions. They were programmed with E-Prime and were displayed on a 24-inch screen with a resolution of 1920 × 1080 pixels, positioned approximately 65 cm from the participants. Each test lasted about 10 minutes and included up to three preliminary practice trials lasting between 1 and 2 minutes each. Feedback on responses was provided after the practice trials, but not during the actual test sessions. The tests presented stimuli across six blocks, with brief inter-block intervals of 5 seconds (extending to 20 seconds after the third block). Following a response or after 2000 ms, a central fixation cross (0.3 cm wide and high) appeared for a variable interval ranging from 800 to 1200 ms. All stimuli were presented in black on a white screen background. Participants responded by depressing the "X" or "M" key on a German keyboard with their left and right index fingers, and they were instructed to respond as quickly and accurately as possible. Reaction times and correctness of responses were recorded. N-back test: A black 4×4 grid (18.4 cm wide and high) was continuously displayed, within which dots (n = 19 per block, 2.6 cm diameter) appeared sequentially in the center of various grid cells (4.6 cm wide and high) for 500 ms each. Participants were required to memorize the positions of these dots. When a dot's position was identical to that of the dot two positions earlier (target), participants pressed the right key "M"; when it differed (non-target), they pressed the left key "X". In total, 30 targets and 72 non-targets were presented across blocks. Simon test: A black fixation cross remained visible on a white screen throughout the test. Arrows (2 cm long, 0.5 cm high) pointing left or right (n = 32 per block) appeared sequentially for 500 ms on either the left or the right side of the fixation cross, with a 3.1 cm distance between arrows and fixation cross. In half of the cases, the arrow's direction and position were congruent (e.g., a leftward arrow on the left side), while in the other half, they were incongruent (e.g., a leftward arrow on the right side). In total, 192 arrows were presented across blocks (96 congruent, 96 incongruent). Participants were instructed to press the left key "X" for leftward arrows and the right key "M" for rightward arrows. Tests of baseline motor fitness A battery of four established tests was utilized to evaluate various aspects of motor fitness, following standardized procedures and instructions [23,61]. Timekeeping was managed using a regular stopwatch. Two to five practice trials were performed before each test. In addition, Spiroergometry was conducted to measure cardiovascular fitness. We included cardiovascular fitness as it is closely associated with motor fitness; it enhances the efficiency of the cardiovascular system, which in turn supports sustained physical activity and improved motor performance. Higher levels of cardiovascular fitness contribute to better coordination and overall motor skills, facilitating more effective execution of motor tasks. Chair stand test: Participants sat on a height-adjustable chair without armrests, with arms crossed and hands resting on opposite shoulders. They repeatedly rose to a full standing position and returned to a fully seated posture as many times as possible within 30 seconds. Throughout the test, participants were required to keep their arms crossed and both feet firmly on the floor. Purdue Pegboard test: The test involved a board with two rows of 25 small holes, extending from top to bottom, with small metal pins located at the upper left and right of the board. Participants were instructed to simultaneously pick up a pin with the right hand from the right side and a pin with the left hand from the left side, place both pins into the topmost empty holes of the respective rows and repeat this action as often as possible within 30 seconds. Three trials were conducted. One-legged stand test: The test was performed with both open and closed eyes in the GRAIL (Gait Real-time Analysis Interactive Lab, Motekforce Link, Amsterdam, The Netherlands) environment, though without using a safety harness to avoid influencing participants' posture. Eight trials were conducted, alternating between the right and left leg, with the first four trials with eyes open and the subsequent four trials with eyes closed. Participants stood on one leg with the other leg slightly flexed and arms at their sides, without hopping, touching the ground with the lifted foot, or pressing the lifted leg against the standing leg. They were instructed to maintain balance as long as possible without opening their eyes during the closed-eye trials. Timekeeping started when a participant lifted one leg and stopped upon any standard violation or after 20 seconds. Feet tapping test: Participants sat on a stationary chair without armrests and were tasked with moving both feet concurrently back and forth across a mid-sagittal line on the floor as quickly as possible, ensuring full contact of the soles with the floor at each tap. Two 20-second trials were conducted. V02max test: Spiroergometry (ZAN600 CPET, nSpire Health, Oberthulba, Germany) was performed on a stationary bicycle (Lode Corival cpet, Groningen, the Netherlands). Participants were instructed to abstain from caffeine and alcohol for 12 hours prior to testing and to avoid vigorous exercise the day before. Each session was either supervised by a physician or participants were required to present a medical clearance certificate, which included exercise electrocardiography (ECG) and clinical history. Respiration, specifically oxygen (VO2) and carbon dioxide (VCO2) consumption, was measured on a breath-by-breath basis. Heart rate was monitored using an integrated digital twelve-lead electrocardiogram (Kiss, GE Healthcare, Munich, Germany). Blood pressure was continuously monitored with a sphygmomanometer. Participants underwent a ramp protocol, with male participants starting at 20 W and increasing by 20 W/min, while female participants started at 10 W and increased by 15 W/min. All participants were instructed to maintain a cycling frequency of 60 to 80 rpm. Each protocol began with a 3-minute resting period and concluded with a 5-minute cool-down period (1 minute at the initial load and 4 minutes without load). The protocol was terminated if the participant’s respiratory exchange ratio (RER = VCO2/VO2) remained above 1.05 for at least 30 seconds or exceeded 1.10, or in cases of subjective fatigue, or the occurrence of physiological risk factors such as blood pressure exceeding 230/115 mmHg, dizziness, a heart rate greater than roughly 220 minus their age, cardiac arrhythmia, or other abnormalities. Each test was conducted by an experienced sport scientist. Peak oxygen uptake (VO2 peak: VO2 consumption during the maximum load level achieved), RER, and the maximum load level (wattage) were analyzed and considered to evaluate the measurement's validity. Pre-post tests of cognitive-motor dual-tasking System hardware and software: The dual-task walking test was performed with the GRAIL system (Gait Real-time Analysis Interactive Lab, Motekforce Link, Amsterdam, The Netherlands). The GRAIL is a valid and reliable gait assessment device [62] that integrated two embedded force plates in an instrumented 3D split-belt treadmill platform (0.8 × 1.5 m). A semi-cylindrical 240° projection screen (2.4 × 5 m) was located in front of the treadmill. Four RGB projectors connected in series project a virtual scenario onto the projection screen. A photodiode was placed on the projection screen to precisely measure the visual onset of stimuli, thus considering any unsystematic variations in the onset times of the RGB projectors. A custom-made ergonomic key switch in the participants’ dominant hand was used to record manual responses, and a voice recorder was used to assess verbal responses. The system's safety measures included two handrails attached to the side of the treadmill and two laser barriers at the front and rear of the treadmill. Participants also wore a safety harness during walking that was attached to the ceiling to prevent injury in case of a fall. The experimenters had a stop button available to stop the treadmill immediately in case of an emergency. However, no falls or emergency stops occurred. The systems standard software D-Flow (Motekforce Link, Amsterdam, the Netherlands) was used to customize the virtual scenario. It depicted an industrial-like virtual landscape. Motor and cognitive tasks were also designed and integrated within D-Flow. All instructions and tasks were presented at eye level in small rectangular grey and brownish boxes. Motor and cognitive tasks: Motor and cognitive tasks were presented in a mixed sequence comprising six different tasks, with five trials each. The sequence remained consistent across all participants and for both the pre-test and post-test. No task was repeated consecutively for more than two trials. Each trial lasted 30 seconds and was preceded by an additional 3-second introductory text (for example “Standing only” or “Walking only,” in German). The entire set of tasks spanned 16.5 minutes (30 x 30 seconds + 30 x 3 seconds). Testing consisted of one baseline task, three tasks in ST condition encompassing one motor and two cognitive tasks, and two combined tasks in DT condition. Outcome measures for the two combined tasks in DT condition were the same as described for the tasks in ST condition. The tasks were as follows: (1) Standing task (baseline): Participants remained stationary, standing with both feet on the treadmill and maintaining a forward gaze directed at a fixation cross. Ground reaction forces were recorded. This task was not analyzed in this study. (2) Walking task (ST condition): Participants walked at a fixed treadmill speed of 1 m/s, focusing their gaze straight ahead on a fixation cross. Since the treadmill accelerated and decelerated at a rate of 0.2 m/s², it necessitated a 5-second transition between standing and walking trials. Ground reaction forces were recorded. (3) Serial Threes task (ST condition): Participants maintained a stationary position on the treadmill while focusing on the fixation cross at the center of the projection screen. A three-digit number was presented at the start of the trial for 5 seconds. Based on this number, participants were required to count backwards in threes from this number, as rapidly and accurately as they could, and to verbalize each resultant number. They had to keep their eyes open throughout the task, to articulate each number in full (for instance, stating “177” rather than “77”), and to refrain from correcting any mistakes. That is, they had to continue counting from the last number stated, even if incorrect. All verbal responses were documented by the experimenter and additionally captured via a voice recorder. (4) Color Word Stroop task (ST condition): This task evaluated inhibitory control by presenting the four color-naming words yellow, red, blue, green in a randomized sequence. Each word appeared for 500 ms, followed by a fixation cross for 1800 to 2200 ms, such that the average inter-stimulus interval (ISI) was 2500 ms. Stimulus words were congruent, i.e., the color of the word matched its meaning (e.g., "green" appeared in green), or incongruent i.e., the color and meaning differed (e.g., "green" appeared in blue). Two response options were shown for 1500 ms, aligned with the onset of the stimulus. These options were displayed in two rectangular areas, one to the left and one to the right below the stimulus word, both in white font. One response indicated the color of the font of the stimulus word, and the other named one of the three other possible colors. Participants had to decide which of the two response words corresponded to the color of the font by pressing either the left or the right button on a handheld key switch. They were instructed to respond as quickly and accurately as possible. The Stroop task design maintained a balance across various factors: 50% of the trials were congruent and 50% were incongruent, each font color was used in 25% of trials, and the positioning of correct and incorrect answers, as well as the frequency of these answers per color, were equally distributed at 50%. Reaction times and accuracy of responses were meticulously recorded via the handheld key switch responses. (5) Walking + Serial Threes task (DT condition): Participants concurrently performed the Walking and Serial Threes tasks, without prioritizing either. (6) Walking + Color Word Stroop task (DT condition): Participants concurrently performed the Walking and Color Word Stroop task, without prioritizing either. Procedure: Participants familiarized themselves with the treadmill by walking through a simulated forest environment for approximately 5 to 10 minutes, during which the walking speed gradually increased to 1 m/s. The familiarization phase concluded once participants were able to walk steadily, while maintaining their focus on the center of the projection screen. Subsequently, cognitive tests such as the MMSE and DSST were conducted. These lasted about 12 to 15 minutes in total, allowing participants to return to a physical resting state. Following these assessments, participants engaged in a brief practice session lasting about 2 minoutes, which included a shortened version of each task in a predetermined sequence. Intervention Three different training programs (cognitive training, motor training, cognitive-motor dual-task training) were conducted at TUC and MU facilities. They lasted twelve weeks and included two one-hour training sessions per week (24 training sessions in total). Each of the three training programs included a total of 72 15-minute training blocks (18 hours in total). The blocks were performed several times in a predefined sequence that was the same for all participants. In each training session, three of those blocks were provided to the participants. To ensure continuous training progress, the difficulty level of the training was continuously adjusted to individuals’ performance. The three different training programs are briefly described below. For a more detailed description of the training programs and exemplary training sessions, please refer to our study protocol [58]. To ensure that training effects were not confounded by the effects of cardiovascular practice on brain functions, the training intensity of the motor and multitask training did not exceed 60% of VO2-peak. Apart from participating in the training, participants were asked not to alter their regular daily routines, including social, physical, and cognitive activities. See Figure 2 for a photographic illustration of the three training programs. Cognitive training The training program was conducted in a computer pool with a separate computer per participant. The exercises were presented on a computer monitor and hand-held trackball mice (YUMQUA Y-01, YUMQUA, Shenzhen, China) were used to control the cursor. This input device was chosen for compatibility with cognitive-motor training, where conventional computer mice would not be practical (see below). The training program included 22 different cognitive exercises from three different software applications: NeuroNation (NeuroNation, Berlin, Germany), Happyneuron (Scientific Brain Training, Lyon, France), and Neuropeak [63]. The exercises trained different fluid cognitive functions, specifically inhibitory control, updating, shifting, multitasking and action planning which are essential for everyday life functioning. One exercise was performed in each of the 72 training blocks. Throughout the training, the different exercises were performed several times (approximately three times), and exercise difficulty increased adaptively with participants’ proficiency. Group sizes ranged from 10 to 15 participants at TUC and one to ten at UM. Motor training The training program was conducted in a customized exercise room and consisted of 15-minute blocks of floor exercises and walking exercises. The floor program included various exercises that train either strength or balance. The difficulty of the exercises was varied with different surfaces (e.g. AIREX-Pad, Balance Board). Various flexibility exercises were performed for recovery between and after the strength and balance exercises. The walking program was performed on a non-motorized treadmill with curved belt (Speedfit SpT-1000C, Tobeone, Korea). It included different walking exercises with varying degrees of difficulty. Throughout the training, the different exercises were performed several times, and exercise difficulty increased adaptively with participants’ proficiency. Cognitive-motor dual-tasking training This training was conducted in the same exercise room as motor training. Participants performed the cognitive and motor exercises simultaneously (e.g., they performed a cognitive exercise while standing on one leg). Thereby, the execution and sequence of exercises stayed exactly the same as in the other two training groups. Again, exercise difficulty increased adaptively with participants’ proficiency. The cognitive exercises were presented on a 48″ screen placed at eye level in front of the participant. *** Figure 2 *** Research design This study was a three-arm, double-blind, randomized controlled trial. Following eligibility screening, all participants completed a battery of behavioral tests to assess their baseline cognitive and motor fitness, and their cognitive-motor dual-task performance during walking and driving (dual-task while driving will be considered in subsequent reports). Tests and screening were conducted in three sessions within two weeks and at least one day apart. The first testing sessions included spiroergometry and lasted about 45 minutes. The second and third session included the remaining behavioral tests in four different orders to which the participants were randomly assigned. Approximately one week after completing the pre-test, all participants began the training. They were randomly assigned to one of three intervention groups: (1) cognitive training, (2) motor training, (3) cognitive-motor dual-task training. Randomization was conducted at a 1:1:1 ratio using a computer-generated random allocation schedule. A research assistant sealed the random assignments in envelopes, which were given to participants after completing the pre-tests. Outcome assessors were blinded to group allocation, and personnel delivering the intervention were blinded to outcome assessments and individual performances. To minimize experimental contamination from social interaction and communication among participants, they were instructed not to discuss their assigned interventions with each other. Close friends and spouses were assigned to the same intervention groups. Within about two weeks after completing their respective training programs, all participants were again given the same battery of behavioral tests in the same order as they had completed prior to training. At TUC, groups of five participants were randomly assigned to one of three training groups, with all members of the group starting their training simultaneously. At UM, a rolling start approach was used. Each week, four to eight participants were invited to begin the study. Assessments were conducted at TUC between March 2019 and December 2019, and at UM between September 2020 and August 2021. Preprocessing and final outcome variables Data preprocessing and statistical analysis was performed using R version 4.2.2 [64]. Baseline cognitive fitness The outcome variable was a composite score of executive functions that included measures of processing speed, working memory, and inhibition, assessed at pre-test. Processing speed and inhibition were measured using the Simon test, while working memory performance was measured with the N-back test. To remove unreliable responses and outliers in both tests, trials with reaction times below 80 ms or above 1300 ms were excluded, and then the ±3.29 SD criterion was applied for each participant. To verify participants' understanding of the tests, we checked whether the mean accuracy across all stimuli exceeded 55%. In the Simon test, three of the final 103 participants did not reach this threshold, and in the N-back test, 20 participants did not reach this threshold. The pertinent scores of these participants were excluded from further analysis. Then, processing speed was calculated as the mean reaction time for correct responses in congruent trials of the Simon test. Inhibition was derived by subtracting the mean reaction time for correct responses in incongruent trials from the mean reaction time for correct responses in congruent trials of the Simon test. Working memory performance was quantified as the mean reaction time for correct responses across both target and non-target trials in the N-back test. The composite score was calculated as the mean of the z-transformed individual measures of processing speed, working memory, and inhibition. Composite scores for participants with more than one missing value (n = 23) were excluded from further analysis. Baseline motor fitness The outcome variable was a composite score for strength, movement speed, dexterity, balance, and cardiovascular fitness, assessed at pre-test. Strength was quantified by the number of correctly executed chair stands in the Chair Stand test. Movement speed was measured by the trial with the highest number of correctly performed crossings in the Feet Tapping test. Fine-motor control was assessed by the average number of rows with correctly placed pegs across three trials in the Purdue Pegboard test. Balance was evaluated with the One-legged Stand test, measuring the standing duration (in seconds) in the eyes-closed balancing condition, averaged across two trials: one with the longest standing duration for the right leg and one for the left leg. Due to ceiling effects in the eyes-open trials, only the eyes-closed trials were analyzed. Preprocessing spiroergometry data followed a structured pipeline. Initially, the minimum breath-to-breath interval (Tmin) was first determined as approximately one breath per second, providing the baseline time interval for interpolation. Time series data for oxygen uptake (VO₂), carbon dioxide output (VCO₂), expiratory ventilation (VE), and inspiratory ventilation (VI) were interpolated using Tmin as the reference to create evenly spaced datasets. To reduce noise and fluctuations typically present in breath-by-breath data, a third-order low-pass Butterworth filter was applied with a low-cutoff frequency of 0.04 Hz. The filtering process followed established guidelines and references [65]. Following this step, filtered VCO₂ and VO₂ data were used to calculate the respiratory exchange ratio (RER), defined as RER = VCO₂ / VO₂ (filtered data). The filtered, evenly spaced data were then transformed back to the original measured time intervals to retain their physiological timing for further analyses. Threshold detection was performed by applying a methodology using Wasserman plots [66]. This involved slope analyses of VCO₂ versus VO₂ and VE/VCO₂ versus VCO₂, as well as the identification of the nadir point in VE/VCO₂ over time. For each ventilatory threshold (VT1 and VT2), the mean of the two respective analysis values was calculated. To ensure accuracy, all threshold detections were visually inspected and corrected as needed. The final preprocessing step produced key outputs at defined time points, including heart rate, VO₂, VO₂/kg, VCO₂, workload, and RER. These were reported for resting conditions (mean of the last 10 measurements before exercise onset), VT1, VT2, RER = 1.00, RER = 1.05, VO₂ peak, and at the end of the exercise test. The composite score was calculated as the mean of the z-transformed individual measures for strength, movement speed, fine motor control, balance, and cardiorespiratory fitness. Composite scores for participants with more than one missing value were excluded from further analysis, which applied to none of our 103 final participants. Pre- and post tests of cognitive-motor dual-tasking: cognitive performance For the Serial Threes task, the outcome variable was the number of correct calculations (S3cog) per trial per condition (ST condition: 5 trials per person, 30s each; DT condition: 5 trials per person, 30 seconds each), assessed at pre-test and at post-test. As accuracy rate (number of correct calculations divided by the number of total calculations) showed substantial ceiling effects close to 100% correctness, it was not regarded as an outcome variable in this study. For the Stroop task, the outcome variable was inhibition costs for reaction times of correctly responded stimuli in percentage (STRcog) per trial per condition (ST condition: 5 trials per person, 30 seconds each; DT condition: 5 trials per person, 30 seconds each), assessed at pre-test and at post-test. Correct trials were determined by evaluating whether participants pressed the key that matched the location of the correct response word on the projection screen. Prior to calculating the outcome variable, reaction times were inspected for plausibility and excluded if they were below 80 ms or above 2500 ms. After that, outliers were removed according to the ±3.29 SD criterion across all trials for each participant [63]. Pre- and post-test of cognitive-motor dual-tasking: motor performance For both the Serial Threes task and the Stroop task, the outcome variable was step time variability (S3gait, STR gait ) per trail per condition (ST condition: 5 trials per person per task, 30 seconds each; DT condition: 5 trials per person per task, 30 seconds each), assessed at pre-test and post-test. Step time variability is known to be related to mobility restrictions and the risk of falls in the elderly [67,68] and exhibit changes under DT conditions. Increased step time variability is suggested to be a marker of neural function degradation (e.g., the pattern generator for motor control) and declines in executive function [69]. Step time variability was calculated for each trial from 5 to 25 seconds after trial onset, using the kinetic data from force plates. Kinetic data were collected at a frequency of 1000 Hz. In the first step, the global center of pressure was calculated using the data from each force plate [70]. The resulting X and Y coordinates were filtered using a second-order Butterworth filter with a cutoff frequency of 13 Hz, which is an appropriate filter configuration for gait analysis using center of pressure data [68]. Subsequently, heel strikes were determined as local maxima of the center of pressure trajectory along the anterior-posterior axis [71]. Step time variability was calculated as the standard deviation of the differences in time between subsequent heel strikes [72]. Data preparation for final analysis Baseline cognitive and motor fitness: In summary, there were no missing values for the baseline motor fitness composite score but 23 of the 103 subjects had missing values for the baseline cognitive fitness composite score. For the imputation of the missing values for baseline cognitive fitness, we mean-centered the covariates age and education and analyzed the effects of the covariates sex, age, education and baseline motor fitness on baseline cognitive motor fitness. There were sex differences in baseline cognitive fitness, but no effects of age and education (see supplements). Therefore, we replaced missing baseline cognitive fitness values with the mean baseline cognitive fitness of participants’ sex group. An analysis of the effects of sex, age, education, and baseline cognitive fitness on baseline motor fitness revealed a significant effect of age and sex, but no effects of education and cognitive fitness (see supplements). For the final data set, the variables baseline cognitive, and motor fitness were centered within men and women to reduce the correlation between sex and these two covariates. Pre- and post-tests of cognitive-motor dual-tasking: As data were not normally distributed, based on a Box-Cox distributional analysis [73], a square root transformation of S3cog and STRcog, and a reciprocal transformation of S3gait and STRgait brought model residuals in line with normal distribution. We then converted S3cog, STRcog, S3gait, and STRgait to z-scores using means and SDs of ST condition at pre-test as reference for the rest. For the Serial Threes task, from the original 3,801 trials (including both outcomes: S3cog and S3gait, both conditions: ST and DT, and both measurement points: pre and post) from 103 participants after preprocessing, data from six participants were excluded. Three participants had missing data for all ten trials at either pre-test or post-test for both S3cog and S3gait outcomes across both ST and DT conditions. Additionally, three participants exhibited abnormal change scores and were identified as outliers based on the conditional mode analysis [74]. This resulted in a final dataset of 3,622 trials from 97 participants. For the Stroop task, from the original 3,634 trials (including both outcomes: STRcog and STRgait, both conditions: ST and DT, and both measurement points: pre and post) from 103 participants after preprocessing, data from 22 participants were excluded. 21 participants had missing data for all ten trials at either pre-test or post-test for both STRcog and STRgait outcomes across both ST and DT conditions. Additionally, one participant exhibited abnormal change scores and were identified as outliers based on the conditional mode analysis. This resulted in a final dataset of 2,201 trials from 81 participants. Statistical analysis A power analysis was conducted before data collection and reported in detail in the study protocol [58]. The estimated required sample size to provide sufficient power to detect a small to moderate effect was N = 118. With our final sample of N = 97 participants for the Serial Threes task and N = 81 participants for the Stroop task, we did not reach this calculated sample size, due to higher drop-outs and missing data than expected. However, since we conducted our analysis using linear mixed models (LMMs) we are optimistic this will mitigate the shortfall. LMMs are advantageous because they leverage the repeated measures within subjects using disaggregated data, effectively increasing statistical power and requiring fewer participants compared to traditional mean-based statistical analyses on aggregated data. Additionally, it is possible to further increase statistical power by reducing model complexity and selecting the most parsimonious model, while also effectively balancing type I and type II error [75] For all analyses, we applied LMMs using the lme4 package [76]. All models were fitted using maximum likelihood estimation (ML), which is assumed to provide better estimates for fixed effects than restricted maximum likelihood estimation (REML). We further followed the parsimonious model selection procedure proposed by Bates et al. [77], which is described in more detail in the supplementary material. All models were built separately for the combination of walking and Serial Threes (S3), and for the combination of walking and Stroop (STR), administered as pre- and post-tests. This was done across both the ST and DT conditions, with performance (cognitive performance and motor performance) as the dependent variable. The following independent variables were included in the models: Time (pre-test [pre] vs. post-test [post]), condition (single-task [ST] vs. dual-task [DT]), domain (cognitive performance [cog] vs. motor performance [gait]), training group (cognitive training [cogT], motor training [motT], cognitive-motor dual-task training [DTT]), baseline cognitive fitness (cf), and baseline motor fitness (mf). Contrasts were specified with the MASS package [78] and the hypr package [79]. Time and condition were dummy coded with pre-test and ST condition as the reference level, allowing these categories to serve as the baseline for comparison. Domain was sum (or effect) coded by cog as negative and gait as positive, with zero as the mean of the two levels. This approach simplifies interpretation of main effects and interactions by balancing the levels [80]. For group, we specified three contrasts comparing (1) DTT vs. cogT, (2) DTT vs. motT, and (3) cogT vs. motT. In a first step, we tested for the differential effects of the three interventions (cogT, motT, DTT) on cognitive-motor performance during both ST and DT conditions. Following a pre-specified model selection process (for details see supplements), the final models for this analysis included fixed effects for time (pre, post), group (motT, cogT, DTT), domain (cog, gait), condition (ST, DT), the interaction between condition x domain and the triple-interaction between time x group x domain. The random effects term included time, domain and condition which were allowed to vary within participants. Including factors as both fixed and random effects is a widely accepted and recommended approach in mixed-effects modeling, as it allows for the estimation of population-level effects while accounting for within-subject variability [81]However, to avoid overfitting and ensure model stability, we evaluated the random effects structure by checking model fit using the diagnostic functions isSingular and rePCA as part of the lm4 package [76]. The final model was as follows: cognitive and motor performance ~ 1 + time * group * domain + condition + condition : domain + (1 + time + domain + condition | Subj), in the model formula, * denotes all main effects and interactions between the variables, : specifies only the interaction between two variables without their main effects. Because we were not only interested in the time x group x domain interaction, but also in the time x group interaction within the separate domains (cog vs. gait), we rebuilt the final models to have these effects nested within the levels of the variable domain [82]: cognitive and motor performance ~ 1 + domain / (time * group) + condition + condition : domain + (1 + time + domain + condition | Subj). In a second step, we evaluated the influence of baseline cognitive and motor fitness on the intervention effects using model comparisons with log-likelihood ratio tests. Specifically, we constructed a model similar to the one detailed above. However, the fixed effect of the three-way interaction of time x group x domain was replaced with the fixed effects of two four-way interactions of time x group x domain x cf and time x group x domain x mf. This new model (cognitive and motor performance ~ 1 + time * group * domain * (cf + mf) + condition + condition : domain + (1 + time + domain + condition | Subj)) was then compared to the original model to test whether cognitive and motor fitness influence the outcomes. If model comparison showed significance, it was further compared to the following models: 1) a model including only the four-way interaction of time x group x domain x cf, to test the moderation effect of cf alone, and 2) a model including only the four-way interaction of time x group x domain x mf, to test the moderation effect of mf alone. Additionally, 3) a model including a five-way interaction of time x group x domain x cf x mf was compared to a model with the two four-way interactions to test the moderation effect of the interaction between baseline cognitive and motor fitness Because we were not only interested in the time x group x domain x cf interaction, but also in the (1) time x group x cf interaction within the separate domains (cog vs. gait), and the (2) time x domain x cf interaction within the separate training groups (motT vs. cogT vs. DTT), we rebuilt the final models to have these effects nested within the levels of the variable domain: (1) cognitive and motor performance ~ 1 + domain / (time * group * cf) + condition + condition : domain + (1 + time + domain + condition | Subj); (2) cognitive-motor performance ~ 1 + group / (time * domain * cf) + condition + condition : domain + (1 + time + domain + condition | Subj); For all LMM analyses, t-values above t = 2.00 were considered significant [83,84]. Only significant results are reported with statistical values in the text of the results section, while non-significant results are provided in the tables. Results Participants Data from 97 participants were analyzed for the Serial Threes task, and data from 81 participants for the Stroop task (Figure 1). Exclusion from the data analysis was due to missing data from participants dropping out because of illness or time constraints, as well as data collection issues caused by technical problems or participants having difficulties performing the tasks. Demographic characteristics of the final sample and excluded participants are presented in Table 1. For both the Serial Threes and Stroop tasks, there were no significant differences in age, sex, education, MMSE, or subjective health between analyzed participants across the different groups and those who were excluded. However, for the Serial Threes task – but not for the Stroop task – BMI showed significant group differences, F (3, 126) = 3.28, p = .034. Tukey-adjusted post-hoc tests revealed that excluded participants showed a higher BMI than participants in the cognitive training group ( p = .029) and in the cognitive-motor dual-task training group ( p = .017). Table 1 Participants` demographic characteristics at baseline cogT motT DTT Excluded from analysis Statistics M (SD) / n M (SD) / n M (SD) / n M (SD) / n F / χ² p Serial Threes task Sample size 32 32 33 31 Age (years) 69.19 (4.06) 69.43 (3.95) 68.85 (2.88) 70.32 (4.23) 0.87 .462 Sex (f / m) 20 / 12 19 / 13 17 / 16 12 / 15 1.68 .643 Education (years) 15.61 (2.92) 16.34 (3.32) 16.28 (2.54) 16.25 (3.67) 0.39 .760 BMI (kg/m2) 25.56 (3.09) 24.55 (2.97) 25.37 (3.38) 23.32 (2.98) 3.28 .023 MMSE (0-30) 28.98 (1.00) 28.97 (1.2) 28.90 (1.40) 29.22 (0.75) 0.42 .740 Subjective health 3.90 (0.54) 3.88 (0.49) 3.88 (0.48) 3.94 (0.57) 0.23 .973 Stroop task Sample size 22 27 32 47 Age (years) 68.90 (3.74) 69.41 (4.05) 68.81 (3.00) 70.13 (4.15) 0.95 .423 Sex (f / m) 13 / 9 16 / 11 17 / 15 22 / 21 0.44 .931 Education (years) 16.39 (2.81) 16.63 (3.45) 16.44 (2.52) 15.50 (3.39) 1.03 .384 BMI (kg/m2) 25.43 (2.358) 24.39 (2.99) 25.59 (2.98) 23.99 (3.65) 2.07 .115 MMSE (0-30) 29.09 (1.11) 28.78 (1.22) 29.00 (1.41) 29.13 (0.79) 0.58 .636 Subjective health 3.86 (0.56) 3.85 (0.53) 3.88 (0.49) 3.96 (0.51) 0.97 .809 Note. F-statistic for age, education, BMI, and MMSE, χ² statistics for sex and subjective health. Serial Threes task Changes from single- to dual-tasking For the Serial Threes task, results showed a significant domain x condition interaction ( Est = 0.41, SE = 0.04, t = 10.10; Table 2). While S3cog substantially decreased from ST to DT conditions, S3gait increased slightly from ST to DT (Figure 3). Changes from pre- to post-test Results further revealed a significant effect of time ( Est = 0.11, SE = 0.04, t = 2.67; Table 2) and a significant time x domain interaction ( Est = -0.23, SE = 0.04, t = -5.32; Table 2). While performance (across S3cog and S3gait) overall increased from pre- to post-test, S3cog increased more than S3gait (Figure 3). Furthermore, the condition did not influence pre-to post-test changes, i.e., S3cog and S3gait showed similar pre-post changes for both, ST and DT conditions. This was indicated by a non-significant interaction effect of condition x time and condition x time x group in a primary model (Table 3 in the supplementary materials). Consequently, these interactions were excluded during the parsimonious model selection process as detailed in the Statistical analysis section and reported in Table 3 in the supplementary materials. Moreover, the results demonstrated no significant time x group interaction, but a significant time x group x domain interaction: pre-post changes varied for S3cog and S3gait for DTT vs. motT ( Est = -0.28, SE = 0.10, t = -2.69) and motT vs. cogT ( Est = -0.24, SE = 0.11, t = -2.25), but not for DTT vs. cogT, (Table 2). The results also indicated a significant time x group interaction for S3gait, but not for S3cog. For S3cog, all interventions showed comparable positive pre-post changes (Table 2 and Figure 4). In contrast, for S3gait, significant differences were observed between cogT and motT ( Est = -0.27, SE = 0.12, t = -2.34), but not between DTT and motT or between cogT and DTT (Table 2). S3gait increased from pre to post for motT, remained stable for DTT, and declined for cogT (Figure 4). Table 2 Fixed-effects estimates of the final linear mixed models testing the time x group x domain interaction on cognitive and motor performance in the Serial Threes task during ST and DT conditions Effect Est SE t p m0a_S3 (Intercept) 0.00 0.06 0.05 .961 Time (pre/post) 0.11 0.04 2.67 .009 Domain (motor/cognitive) 0.04 0.12 0.31 .760 Condition (ST/DT) -0.14 0.02 -6.40 < .001 Group (DTT vs. cogT) -0.04 0.16 -0.24 .812 Group (DTT vs, motT) 0.04 0.16 0.26 .793 Group (cogT vs, motT) 0.08 0.16 0.50 .620 Time x group (DTT vs. cogT) 0.08 0.10 0.82 .416 Time x group (DTT vs. motT) -0.07 0.10 -0.73 .129 Time x group (cogT vs. motT) -0.15 0.10 -1.53 .464 Time x domain -0.23 0.04 -5.32 < .001 Group x domain (DTT vs. cogT) -0.06 0.29 -0.20 .843 Group x domain (DTT vs. motT) 0.34 0.29 1.17 .245 Group x domain (cogT vs. motT) 0.40 0.29 1.35 .179 Domain x condition 0.41 0.04 10.10 < .001 Time x group x domain (DTT vs. cogT) -0.04 0.11 -0.34 .733 Time x group x domain (DTT vs. motT) -0.28 0.10 -2.69 .007 Time x group x domain (cogT vs. motT) -0.24 0.11 -2.25 .025 m0a_S3/domain (effects for time x group nested in domain) Domain S3cog: time x group (DTT vs. cogT) 0.10 0.11 0.91 .362 Domain S3gait: time x group (DTT vs. cogT) 0.06 0.11 0.53 .595 Domain S3cog: time x group (DTT vs. motT) 0.07 0.11 0.65 .518 Domain S3gait: time x group (DTT vs. motT) -0.21 0.11 -1.88 .062 Domain S3cog: time x group (cogT vs. motT) -0.03 0.11 -0.27 .788 Domain S3gait: time x group (cogT vs. motT) -0.27 0.12 -2.34 .021 Note. Linear mixed model formula for m0a_S3 in lm4 syntax in R: cognitive and motor performance ~ 1 + domain*time*group + condition + condition : domain + (1 + time + domain + condition | Subj); Linear mixed model formula for m0a_S3/domain (effects of time and time x group nested in domain) in lm4 syntax in R: cognitive and motor performance ~ 1 + domain / (time * group) + condition + condition : domain + (1 + time + domain + condition | Subj); the dependent variable was z-scaled. Bold values are significant t-values above 2; S3cog = cognitive performance, S3gait = motor performance; cogT = cognitive training, motT = motor training, DTT = cognitive-motor dual-task training. *** Figure 3 *** *** Figure 4 *** Moderation of baseline motor and cognitive fitness Results of the model comparisons indicated an influence of cf, but no influence of mf or the interaction between cf and mf on pre-post changes of performance (across S3cog and S3gait, Table 6 and Table 8 in the supplementary materials). The final model examining the moderation of cf is presented in Table 3. Results indicated no significant time x cf or group x time x cf interactions, suggesting that cf did not influence pre-post changes, neither overall nor depending on group (Table 4). However, the time x cf x domain interaction was significant ( Est = -0.0, SE = 0.0, t = -3.14; Table 4). While for S3cog, a higher cf led to larger increases than a lower cf, for S3gait, a lower cf led to larger increases than a higher cf (Figure 5). Moreover, results demonstrated significant time x group x domain x cf interactions: for DTT vs. motT ( Est = 0.01, SE = 0.00, t = 3.64) and motT vs. cogT ( Est = 0.01, SE = 0.00, t = 5.35), but not for DTT vs. cogT, pre-post changes of the two domains were moderated differently by cf (Table 4). The results further indicated significant time x group x cf interactions for both S3cog and S3gait (Table 4). For S3cog, the time x group x cf interaction was significant for cogT vs. motT ( Est = -0.01, SE = 0.00, t = -2.87), but not for DTT vs. cogT and for DTT vs. motT (Table 4): higher cf led to greater increases in the motT groups but to lower increases in the cogT group. In contrast, for S3gait, the time x group x cf interaction was significant for cogT and motT ( Est = 0.00, SE = 0.00, t = 2.33) and DTT and motT ( Est = 0.00, SE = 0.00, t = 2.66; Table 4): higher cf led to greater increases in the DTT and cogT groups but to lower increases in the motT group. Results further showed significant time x domain x cf interactions for the motT ( Est = -0.0, SE = 0.0, t = -6.05), but not for DTT ( Est = -0.0, SE = 0.0, t = -0.68) and cogT ( Est = -0.0, SE = 0.0, t = 1.39). For motT, while for S3cog, a high cf led to larger increases than a lower cf, for S3gait, a lower cf led to larger increases than a higher cf (Figure 5). Table 3 Fixed-effects estimates of final linear mixed models testing the time x group x domain x cf interaction on cognitive and motor performance in the Serial Threes task Effect Est SE t p m1a_cf_S3 (Intercept) -0.01 0.06 -0.13 .900 Time 0.11 0.04 2.89 .005 Group (DTT vs. cogT) -0.02 0.15 -0.13 .899 Group (DTT vs. motT) 0.06 0.15 0.4 .693 Group (cogT vs. motT) 0.08 0.15 0.52 .605 Domain 0.04 0.12 0.35 .730 Cf 0.0 0.0 1.88 .063 Condition -0.14 0.02 -6.39 < .001 Time x group (DTT vs. cogT) 0.08 0.1 0.86 .394 Time x group (DTT vs. motT) -0.07 0.09 -0.77 .443 Time x group (cogT vs. motT) -0.15 0.1 -1.6 .112 Time x domain -0.22 0.04 -5.05 < .001 Group x domain (DTT vs. cogT) -0.05 0.28 -0.16 .871 Group x domain (DTT vs. motT) 0.37 0.28 1.35 .180 Group x domain (cogT vs. motT) 0.42 0.28 1.5 .137 Time x cf 0.0 0.0 0.49 .628 Group x cf (DTT vs. cogT) -0.01 0.0 -2.46 .016 Group x cf (DTT vs. motT) -0.0 0.0 -0.62 .539 Group x cf (cogT vs. motT) 0.01 0.0 1.98 .051 Domain x cf 0.0 0.0 0.42 .678 Domain x condition 0.41 0.04 10.16 < .001 Time x group x domain (DTT vs. cogT) -0.04 0.11 -0.34 .733 Time x group x domain (DTT vs. motT) -0.32 0.1 -3.13 .002 Time x group x domain (cogT vs. motT) -0.28 0.11 -2.67 .008 Group x time x cf (DTT vs. cogT) 0.0 0.0 1.33 .187 Group x time x cf (DTT vs. motT) 0.0 0.0 1.13 .262 Group x time x cf (cogT vs. motT) -0.0 0.0 -0.21 .834 Time x domain x cf -0.0 0.0 -3.14 .002 Group x domain x cf (DTT vs. cogT) 0.01 0.01 2.07 .041 Group x domain x cf (DTT vs. motT) 0.01 0.01 2.02 .046 Group x domain x cf (cogT vs. motT) -0.0 0.0 -0.06 .954 Time x group x domain x cf (DTT vs. cogT) -0.0 0.0 -1.43 .153 Time x group x domain x cf (DTT vs. motT) 0.01 0.0 3.64 < .001 Time x group x domain x cf (cogT vs. motT) 0.01 0.0 5.35 < .001 m1a_cf_S3/outcome (effects for time x group x cf nested in outcome) Domain S3cog: time x group x cf (DTT vs. cogT) 0.0 0.0 1.88 .062 Domain S3gait: time x group x cf (DTT vs. cogT) 0.0 0.0 0.48 .630 Domain S3cog: time x group x cf (DTT vs. motT) -0.0 0.0 -0.8 .427 Domain S3gait: time x group x cf (DTT vs. motT) 0.01 0.0 2.66 .008 Domain S3cog: time x group x cf (cogT vs. motT) -0.01 0.0 -2.87 .005 Domain S3gait: time x group x cf (cogT vs. motT) 0.0 0.0 2.33 .021 m1a_cf_S3/group (effects for time x domain x cf nested in group) Group DTT: time x domain x cf -0.0 0.0 -0.68 .496 Group cogT: time x domain x cf -0.0 0.0 -0.68 .165 Group motT: time x domain x cf -0.0 0.0 -6.05 < .001 Note. Linear mixed model formula for m1a_cf_S3 in lm4 syntax in R: cognitive and motor performance ~ 1 + time * group * domain * cf + condition + condition : domain + (1 + time + domain + condition | Subj); Linear mixed model formula for m1a_cf_S3/domain in lm4 syntax in R: cognitive and motor performance ~ 1 + domain / (time * group * cf) + condition + condition : domain + (1 + time + domain + condition | Subj); Linear mixed model formula for m1a_cf_S3/group in lm4 syntax in R: cognitive and motor performance ~ 1 + group / (time * domain * cf) + condition + condition : domain + (1 + time + domain + condition | Subj); The dependent variable was z-scaled. Bold values are significant t-values below above 2; S3cog = cognitive performance, S3gait = motor performance; cogT = cognitive training, motT = motor training, DTT = cognitive-motor dual-task training. *** Figure 5 *** Stroop task Changes from single- to dual-tasking For the Stroop task, results showed a significant domain x condition interaction ( Est = 0.19, SE = 0.07, t = 2.86; Table 2). While STRcog decreased from ST to DT conditions, STRgait increased from ST to DT (Figure 6). Changes from pre- to post-test Results further revealed no significant effect of time, but a significant time x domain interaction ( Est = 0.14, SE = 0.08, t = 1.80; Table 4). Similar to the Serial Threes task, STRcog increased more than STRgait from pre- to post-test (Figure 7). Furthermore, again similar to the Serial Threes task, while the domain influenced pre-post changes, the condition did not, i.e., STRcog and STRgait showed similar pre-post changes for both DT and ST conditions, which was indicated by the non-significant interaction effects of condition x time and condition x time x group in a primary model (Table 11 in the supplementary material). Consequently, these interactions were excluded during the parsimonious model selection process as detailed in the Statistical analysis section and reported in Table 11 in the supplementary materials. Moreover, similar to the Serial Threes task, the results demonstrated no significant time x group interaction, but significant time x group x domain interaction: for DTT vs. motT ( Est = -0.37, SE = 0.17, t = -2.13) and motT vs. cogT ( Est = -0.45, SE = 0.19, t = -2.42), but not for DTT vs. cogT, pre-post changes varied for the STRcog and STRgait (Table 4). However, unlike the Serial Threes task, the results did not demonstrate significant time x group interactions for neither STRcog nor STRgait (Table 4), indicating similar pre-post changes across the different training groups within the cognitive and motor domains (see Figure 7) Table 4 Fixed-effects estimates of final linear mixed models testing the time x group x domain interaction on cognitive and motor performance in the Stroop task Effect Est SE t p m0a_STR (Intercept) 0.01 0.06 0.17 .866 Time 0.14 0.08 1.80 .075 Domain 0.02 0.11 0.16 .874 Condition -0.03 0.04 -0.69 .493 Group (DTT vs. cogT) -0.06 0.14 -0.45 .655 Group (DTT vs. motT) 0.10 0.13 0.75 .455 Group (cogT vs. motT) 0.16 0.15 1.10 .273 Time x group (DTT vs. cogT) 0.07 0.19 0.39 .699 Time x group (DTT vs. motT) 0.08 0.18 0.46 .646 Time x group (cogT vs. motT) 0.01 0.20 0.04 .966 Time x domain -0.21 0.07 -2.84 .005 Group x domain (DTT vs. cogT) -0.19 0.26 -0.74 .462 Group x domain (DTT vs. motT) 0.42 0.24 1.71 .091 Group x domain (cogT vs. motT) 0.62 0.27 2.26 .023 Outcome x condition 0.19 0.07 2.68 .007 Time x group x domain (DTT vs. cogT) 0.09 0.18 0.49 .621 Time x group x domain (DTT vs. motT) -0.37 0.17 -2.13 .033 Time x group x domain (cogT vs. motT) -0.45 0.19 -2.42 .016 m0a_STR/outcome (effects for time x group nested in outcome) Domain STRcog: time x group (DTT vs. cogT) 0.03 0.23 0.13 .996 Domain STRgait: time x group (DTT vs. cogT) 0.12 0.19 0.61 .540 Domain STRcog: time x group (DTT vs. motT) 0.27 0.22 1.23 .221 Domain STRgait: time x group (DTT vs. motT) -0.10 0.18 -0.56 .590 Domain STRcog: time x group (cogT vs. motT) 0.23 0.24 0.98 .322 Domain STRgait: time x group (cogT vs. motT) -0.22 0.20 -1.10 .275 Note. Linear mixed model formula for m0a_STR in lm4 syntax in R: cognitive and motor performance ~ 1 + domain * time * group + condition + condition : domain + (1 + time + domain + condition | Subj); Linear mixed model formula for m0a_STR/domain in lm4 syntax in R: cognitive and motor performance ~ 1 + domain / (time * group) + condition + condition : domain + (1 + time + domain + condition | Subj); the dependent variable was z-scaled. Bold values are significant t-values below above 2; STRcog = cognitive performance, STRgait = motor performance; cogT = cognitive training, motT = motor training, DTT = cognitive-motor dual-task training. *** Figure 6 *** *** Figure 7 *** Moderation of baseline motor and cognitive fitness Results of the model comparisons indicated no moderation of cf, mf, or their interaction on pre-post changes in cognitive and motor performance (Table 14, 15, and 16 in the supplementary materials). Discussion This randomized controlled trial is the first to examine how baseline levels of cognitive and motor fitness moderate the benefits of three different training regimes (cognitive training, motor training, cognitive-motor dual-task training) on cognitive-motor dual-task performance in older adults. Additionally, to draw conclusions about the generalizability of our results, we tested the effects of two different cognitive tasks administered during walking (Serial Threes, Stroop). In summary, the results indicated that for both the Serial Threes task and the Stroop task, cognitive and dual-task training led to greater increases in cognitive performance than in motor performance across both ST and DT conditions, while the motor training group showed greater increases in motor performance than in cognitive performance. The results also showed that baseline motor fitness and the interaction between baseline cognitive and motor fitness did not moderate pre-post changes in cognitive or motor performance. However, baseline cognitive fitness did play a significant moderating role for the Serial Threes task. When comparing the groups, in particular, cognitive and motor training showed opposing effects. For individuals with higher cognitive fitness, motor performance increased more in the cognitive training group than in the motor training. However, their cognitive performance increased more in the cognitive training than in the motor training group. For both, the Serial Threes task and the Stroop task, cognitive performance declined when transitioning from ST to DT conditions, while gait variability decreases. These findings align with existing literature supporting the "posture-first" strategy, where resources are allocated to maintain motor performance at the expense of cognitive performance (e.g., prioritization theory [ 10 , 85 , 86 ]. Dual-tasking can occasionally also result in improved motor performance [ 87 ]. This may occur in specific situations where maintaining motor performance is essential for the successful completion of the cognitive task. For instance, walking steadily (i.e., a lower gait variability) may help individuals to focus attention and respond accurately to a cognitive challenge during dual-task walking. It has been suggested that the improved motor performance observed in certain DT conditions reflects an adaptive strategy to support the cognitive demands of the task [ 87 , 88 ]. Our study revealed comparable improvements in both single-task and dual-task performance, suggesting the training benefits extended beyond task integration to enhance individual task performance and overall cognitive resources. These findings align with existing literature on cognitive, motor, and cognitive-motor training, demonstrating increased efficiency, greater capacity, and improved motor control [ 16 , 89 , 90 ]. Notably, our cognitive single-task assessment was conducted while standing, introducing an element of postural control. This approach may have created a de facto DT condition, potentially underestimating pure cognitive performance due to cognitive-motor interference. The observed improvements in cognitive single-task performance likely reflect a combination of enhanced cognitive function and improved postural control. However, it also enhances ecological validity by more closely resembling real-world conditions. The observed similar performance increases in cognitive task performance during dual-tasking across all three training groups (no significant time x group interaction within outcome) align with recent studies on cognitive-motor interventions for older adults. This finding is consistent with results reported by Ansai and colleagues [ 37 ], who found no significant differences in cognitive dual-task performance between multicomponent exercise alone and multicomponent exercise combined with cognitive tasks. Similarly, Downey and colleagues [ 88 ]compared cognitive, motor, and aerobic training and found similar intervention effects of cognitive performance during dual-tasking, supporting the idea that various training approaches can enhance cognitive-motor dual-tasking in older adults. These findings reinforce the concept of diverse pathways for improving cognitive-motor dual-task performance in the aging population. Downey and colleagues [ 91 ], for example, noted that while executive function improvements were specific to cognitive training, energy efficiency during walking showed particular enhancement following motor training. This suggests that different training modalities may target distinct aspects of cognitive-motor performance. However, our findings diverge from those of Falbo and colleagues [ 39 ]. who observed a more substantial increase in cognitive dual-task performance in their cognitive-motor group compared to a group that received only motor training. Notably, their cognitive-motor intervention integrated higher cognitive demands directly into motor tasks, such as associating equipment features (e.g., color or size of obstacles) with specific motor requirements and incorporating random switching between stimulus-response sets during physical tasks. This integrated approach, embedding cognitive challenges within motor tasks, may be more effective than simply adding a separate cognitive task to a motor activity [ 92 ]. With respect to gait performance, our findings reveal a different pattern of results across the three intervention groups. Notably, only the motor group demonstrated improvements in gait over time, while the cognitive and dual-task groups either declined or maintained their baseline performance levels. Here it is important to note that differences between groups were significant only for the Serial Threes task, with significant group-by-time interaction effects observed for cognitive training compared to motor training, and a nearly significant result ( t = 1.88) for dual-task training compared to motor training. However, the Stroop tasks showed a similar but non-significant pattern of results. The improvement observed in the motor training group's gait performance under DT conditions aligns with existing literature [ 16 ] and can be attributed to several factors. Firstly, the motor training program included a walking component, which directly addressed the primary outcome measure, supporting the principle of training specificity. Additionally, the training of coordination, strength, and flexibility are all relevant for postural control and walking [ 93 ]. Also, the lack of improvement in gait performance after dual-task training is consistent with recent reviews investigating cognitive-motor training effects on gait speed under DT conditions in older adults [ 94 , 95 ]. One possible explanation for this result is that participants might have prioritized cognitive performance at the expense of gait quality during training, a strategy that may have persisted during the outcome measurements. This prioritization is plausible, as most cognitive-motor dual-task training programs, especially those including cognitive tasks, have a high engagement potential for the cognitive but not for the motor tasks. In other words, participants may have perceived the cognitive aspects of the training as more challenging or important, leading to a focus on cognitive performance at the expense of gait quality. The absence of improvement in the cognitive training group is similar to findings reported by Downey and colleagues [ 91 ] and can also be explained by task prioritization. Participants who underwent purely cognitive training may have lacked the necessary practice in integrating cognitive and motor tasks, leading to prioritization of cognitive performance when faced with a dual-task situation during assessment. The task prioritization hypothesis is further supported by the results showing that, for both the Serial Threes and Stroop tasks, the time-by-outcome interaction within individual groups was significant for the dual-task training group and the cognitive training group, but not for the motor training group. It could be hypothesized that decreases in gait performance for the cognitive training and motor training groups were less pronounced in the Stroop task compared to the Serial Threes task because performing a dual task naturally reduces step variability compared to a single task. This effect may be attributed to the nature of the Stroop task, which requires more focused attention on the cognitive component. This increased focus could facilitate a more stable gait pattern, making it less susceptible to training effects [ 87 , 96 ]. Our findings suggest that baseline motor fitness, as well as the interaction between baseline cognitive and motor fitness, did not impact the intervention effects for either task. In contrast, baseline cognitive fitness was found to significantly moderate the intervention effects for the Serial Threes task. This differential impact of baseline cognitive and motor abilities on training outcomes aligns with and extends prior research. The moderation effect of cognitive fitness is consistent with the findings of Strobach and colleagues [ 51 ], who demonstrated that reaction time variability (as a proxy for inefficient neural processing) moderates the intervention effects of cognitive dual-task training on cognitive dual-task performance in both young and older adults. The absence of a moderating effect of motor fitness further supports the notion that dual-task walking performance is more strongly linked to cognitive, rather than motor capabilities in older adults [ 97 , 98 ]. The moderation of cognitive fitness for the Serial Threes task, but not for the Stroop task, may be explained by specific key features of the tasks. One potential explanation is that the self-paced mode of the Serial Threes task allows participants to apply personalized strategies learned during training to optimize their performance (e.g., counting backwards in sync with their walking rhythm). In contrast, the fixed-paced Stroop task imposes strict temporal constraints, limiting opportunities for strategy deployment and reducing the moderating influence of cognitive fitness. Another explanation is that the Serial Threes task relies heavily on verbal responses, whereas the Stroop task depends on manual motor responses. These manual motor responses primarily rely on simpler sensorimotor pathways, making the Stroop task less directly associated with cognitive fitness compared to the Serial Threes task. Similar to the results of the time x group x outcome interaction, the four-way interaction between cognitive fitness x time x group x outcome was significant for dual-task vs. motor training and for cognitive vs. motor training but not for dual-task vs. cognitive training. This highlights the similarities between cognitive and dual-task training, as well as the distinct effects of motor training on cognitive-motor dual-task performance in older adults. Baseline cognitive fitness influenced cognitive and gait performance differently in the motor training group (significant time x outcome x cognitive fitness interaction for motor training) but had similar effects in the cognitive and dual-task training groups (no significant time x outcome x cognitive fitness interaction for cognitive and dual-task training). In the motor training group, higher cognitive fitness was associated with greater improvements in cognitive performance but smaller improvements in gait performance compared to individuals with lower cognitive fitness. Motor training benefits both cognitive and motor skills [ 24 , 99 , 100 ]. However, individuals with lower baseline cognitive fitness may utilize their newly gained cognitive resources differently than individuals with higher baseline cognitive fitness. People with higher baseline cognitive fitness are often more engaged in cognitive activities throughout their lives [ 101 , 102 ]. This engagement makes them more familiar with and confident in performing cognitively demanding tasks [ 103 ]. As a result, after gaining additional cognitive resources, individuals with lower baseline cognitive fitness may allocate fewer resources to cognitive tasks and prioritize motor tasks when faced with competing demands, as motor tasks are often more automatic and essential for safety (e.g., maintaining balance while walking). Conversely, individuals with high baseline cognitive fitness might exhibit the opposite pattern, directing their newly gained cognitive resources toward cognitive tasks rather than motor tasks. Since both cognitive and dual-task training involve cognitive tasks similar to those assessed in the outcomes, individuals with lower cognitive fitness may benefit from a sense of familiarity with these tasks. This familiarity could enhance their confidence in handling such tasks, thereby reducing the influence of baseline cognitive fitness on both cognitive and motor task performance. This RCT has several strengths but also some limitations. One key strength is that, by including motor, cognitive, and dual-task training in our intervention design, we were the first to directly compare the effects of these three training programs on cognitive and motor dual-task performance. Earlier trials only compared two of these training approaches. Additionally, by structuring the dual-task training to include exactly the same exercises as those used in the motor and cognitive training groups, we eliminated the possibility that the effects of dual-task training were due to different types of exercises rather than the dual-task nature of the training itself. However, our design and setup also come with some limitations. First, we cannot draw conclusions on how the effects would compare to an active or passive control group, such as stretching or waitlist control. However, this was not our primary goal, as there is already substantial literature on this topic. Instead, we aimed to examine the differential effects of cognitive, motor, and cognitive-motor dual-task training, as well as the moderating role of baseline cognitive and motor fitness. Second, during dual-task training, participants received direct feedback on their performance for the cognitive exercises, with progress visualized on a colorful screen. In contrast, instructions for the motor exercises were verbally communicated by the trainers. This difference may have made it more challenging for some participants to focus equally on both motor and cognitive training, potentially leading them to prioritize the cognitive tasks. Conclusion In conclusion, this study highlights the complex relationship between cognitive and motor outcomes in older adults undergoing cognitive, motor, or cognitive-motor dual-task training. The findings reveal that cognitive and dual-task training have comparable effects on cognitive-motor dual-task performance, primarily enhancing cognitive outcomes. In contrast, motor training demonstrates a unique advantage in improving gait performance, highlighting its critical role in mobility enhancement and fall prevention. Notably, baseline cognitive fitness emerged as a key moderator of training effects, particularly in motor training. While cognitive and dual-task training outcomes appeared less influenced by baseline cognitive fitness, higher cognitive fitness significantly shaped the effects of motor training. This suggests that baseline cognitive fitness may determine how newly acquired cognitive resources are allocated between cognitive and motor tasks, especially under DT conditions. Additionally, the observed improvements in both single-task and DT conditions suggest that the benefits of training extend beyond task-specific integration, enhancing general cognitive and motor capacities. These findings provide valuable insights for designing targeted interventions to support mobility, cognitive health, and quality of life in aging populations. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of xx and xx and was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent before participation. Consent for publication All participants provided written informed consent for the publication of images. Funding The study was funded by the German Research Foundation (DFG) and is part of the DFG Priority Program SPP 1772 (grant VO 1432/22–1). RS is supported by an ESF (European Social Fund) and SAB (Development Bank of Saxony) doctoral scholarship (100342331). The funding body doesn’t play any role in the design of the study, the collection, analysis and interpretation of data and the deci‑sion to write and publish manuscripts. The study protocol has not been peer reviewed by the funding body. Author Contribution Author Contributions StatementConceptualisation: MM, RS, OB, CVR; Methodology: MM, RS, NH, OB, CVR; Software: RS; Validation: MM, RS, OB, CVR; Formal analysis: MM; Investigation: MM, NH, RS; Ressources: CVR; Data curation: MM, RS; Writing – original draft: MM; Writing – review and editing: MM, RS, NH, OB, CVR; Visualisation: MM; Supervision: OB, CVR; Project administration: OB, CVR; Funding acquisition: OB, CVRFundingThe study was funded by the German Research Foundation (DFG) and is part of the DFG Priority Program SPP 1772 (grant VO 1432/22–1). RS is supported by an ESF (European Social Fund) and SAB (Development Bank of Saxony) doctoral scholarship (100342331). The funding body doesn’t play any role in the design of the study, the collection, analysis and interpretation of data and the decision to write and publish manuscripts. The study protocol has not been peer reviewed by the funding body. 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Roerdink M, Cutti AG, Summa A, Monari D, Veronesi D, van Ooijen MW, et al. Gaitography applied to prosthetic walking. Med Biol Eng Comput. 2014;52:963–9. 10.1007/S11517-014-1195-1/TABLES/2 . Hollman JH, McDade EM, Petersen RC. Normative spatiotemporal gait parameters in older adults. Gait Posture. 2011;34:111–8. 10.1016/J.GAITPOST.2011.03.024 . Box GEP, Cox DR. An analysis of transformations. J R Stat Soc Ser B Stat Methodol. 1964;26:211–43. 10.1111/J.2517-6161.1964.TB00553.X . Kliegl. Experimental effects and individual differences in linear mixed models: Estimating the relationship between spatial, object, and attraction effects in visual attention. Front Psychol. 2010;1. 10.3389/fpsyg.2010.00238 . Matuschek H, Kliegl R, Vasishth S, Baayen H, Bates D. Balancing Type I error and power in linear mixed models. J Mem Lang. 2017;94:305–15. 10.1016/J.JML.2017.01.001 . Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. 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Fernández G, Shalom DE, Kliegl R, Sigman M. Eye movements during reading proverbs and regular sentences: the incoming word predictability effect. Lang Cogn Neurosci. 2014;29:260–73. 10.1080/01690965.2012.760745 . Bianchi B, Shalom DE, Kamienkowski JE. Predicting known sentences: Neural basis of proverb reading using non-parametric statistical testing and mixed-effects models. Front Hum Neurosci. 2019;13:425683. 10.3389/fnhum.2019.00082 . Schaefer S, Schumacher V. The Interplay between cognitive and motor functioning in healthy older adults: Findings from dual-task studies and suggestions for intervention. Gerontology. 2011;57:239–46. 10.1159/000322197 . Shumway-Cook A, Woollacott M. Attentional demands and postural control: the effect of sensorycontext. J Gerontol Biol Sci Med Sci. 2000;55:M10–6. 10.1093/gerona/55.1.M10 . Swan L, Otani H, Loubert PV, Sheffert SM, Dunbar GL. Improving balance by performing a secondary cognitive task. Br J Psychol. 2004;95:31–40. 10.1348/000712604322779442 . Wollesen B, Wanstrath M, van Schooten KS, Delbaere K. A taxonomy of cognitive tasks to evaluate cognitive-motor interference on spatiotemoporal gait parameters in older people: A systematic review and meta-analysis. Eur Rev Aging Phys Activity. 2019;16:12. 10.1186/s11556-019-0218-1 . Falck RS, Davis JC, Best JR, Crockett RA, Liu-Ambrose T. Impact of exercise training on physical and cognitive function among older adults: a systematic review and meta-analysis. Neurobiol Aging. 2019;79:119–30. 10.1016/J.NEUROBIOLAGING.2019.03.007 . Pichierri G, Wolf P, Murer K, de Bruin ED. Cognitive and cognitive-motor interventions affecting physical functioning: A systematic review. BMC Geriatr. 2011;11:29. 10.1186/1471-2318-11-29 . Downey R, Bherer L, Pothier K, Vrinceanu T, Intzandt B, Berryman N, et al. Multiple routes to help you roam: A comparison of training interventions to improve cognitive-motor dual-tasking in healthy older adults. Front Aging Neurosci. 2022;14:710958. 10.3389/FNAGI.2022.710958 . Herold F, Hamacher D, Schega L, Müller NG. Thinking while moving or moving while thinking - concepts of motor-cognitive training for cognitive performance enhancement. Front Aging Neurosci. 2018;10:364696. 10.3389/FNAGI.2018.00228/BIBTEX . Plummer P, Zukowski LA, Giuliani C, Hall AM, Zurakowski D. Effects of physical exercise interventions on gait-related dual-task interference in older adults: a systematic review and meta-analysis. Gerontology. 2015/02/28. 2015;62: 94–117. 10.1159/000371577 Gavelin HM, Dong C, Minkov R, Bahar-Fuchs A, Ellis KA, Lautenschlager NT, et al. Combined physical and cognitive training for older adults with and without cognitive impairment: A systematic review and network meta-analysis of randomized controlled trials. Ageing Res Rev. 2021;66:101232. https://doi.org/10.1016/j.arr.2020.101232 . Teraz K, Šlosar L, Paravlić AH, de Bruin ED, Marusic U. Impact of motor-cognitive interventions on selected gait and balance outcomes in older adults: A systematic review and meta-analysis of randomized controlled trials. Front Psychol. 2022;13:837710. 10.3389/fpsyg.2022.837710 . Wollesen B, Voelcker-Rehage C. Differences in cognitive-motor interference in older adults while walking and performing a visual-verbal stroop task. Front Aging Neurosci. 2019;10:426. 10.3389/fnagi.2018.00426 . Li KZH, Bherer L, Mirelman A, Maidan I, Hausdorff JM. Cognitive involvement in balance, gait and dual-tasking in aging: A focused review from a neuroscience of aging perspective. Front Neurol. 2018;9:413669. 10.3389/FNEUR.2018.00913/BIBTEX . Bishnoi A, Hernandez ME. Dual task walking costs in older adults with mild cognitive impairment: a systematic review and meta-analysis. Aging Ment Health. 2021;25:1618–29. 10.1080/13607863.2020.1802576 . Voelcker-Rehage C. Motor-skill learning in older adults-a review of studies on age-related differences. Eur Rev Aging Phys Activity. 2008;5:5–16. 10.1007/S11556-008-0030-9/FIGURES/3 . Berryman N, Bherer L, Nadeau S, Lauzière S, Lehr L, Bobeuf F, et al. Multiple roads lead to Rome: combined high-intensity aerobic and strength training vs. gross motor activities leads to equivalent improvement in executive functions in a cohort of healthy older adults. Age (Omaha). 2014;36:9710. 10.1007/s11357-014-9710-8 . Verghese J, Lipton RB, Katz MJ, Hall CB, Derby CA, Kuslansky G, et al. Leisure activities and the risk of dementia in the elderly. N Engl J Med. 2003;348:2508–16. 10.1056/NEJMoa022252 . Fritsch T, McClendon MJ, Smyth KA, Lerner AJ, Friedland RP, Larsen JD. Cognitive functioning in healthy aging: The role of reserve and lifestyle factors early in life. Gerontologist. 2007;47:307–22. 10.1093/geront/47.3.307 . Hess TM. Selective engagement of cognitive resources: Motivational influences on older adults’ cognitive functioning. Perspect Psychol Sci. 2014;9:388–407. 10.1177/1745691614527465 . 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6185287","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430165220,"identity":"b23fa63f-cd83-4fb7-b60c-c34d25f2f796","order_by":0,"name":"Melanie Mack","email":"","orcid":"","institution":"Centre for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"Mack","suffix":""},{"id":430165221,"identity":"b5b841b8-198f-429c-a68d-8e897bf364b6","order_by":1,"name":"Robert Stojan","email":"","orcid":"","institution":"Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, University of Münster","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Stojan","suffix":""},{"id":430165222,"identity":"62234084-8f17-4321-b918-dc82dabf0da1","order_by":2,"name":"Nicole Hudl","email":"","orcid":"","institution":"Institute of Human Movement Science and Health, Chemnitz University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"","lastName":"Hudl","suffix":""},{"id":430165223,"identity":"e7e53447-8295-4f58-9e66-147f98b21a57","order_by":3,"name":"Otmar Bock","email":"","orcid":"","institution":"Institute of Exercise Training and Sport Informatics, German Sport University","correspondingAuthor":false,"prefix":"","firstName":"Otmar","middleName":"","lastName":"Bock","suffix":""},{"id":430165224,"identity":"dda0527b-7be2-428b-aa6c-d5c3eb15ada9","order_by":4,"name":"Claudia Voelcker-Rehage","email":"data:image/png;base64,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","orcid":"","institution":"Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, University of Münster","correspondingAuthor":true,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Voelcker-Rehage","suffix":""}],"badges":[],"createdAt":"2025-03-08 17:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6185287/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6185287/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78920716,"identity":"60569fa8-1a83-4c9c-a2ce-70481b8d885e","added_by":"auto","created_at":"2025-03-20 20:38:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132941,"visible":true,"origin":"","legend":"\u003cp\u003eConsort flow diagram\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e UM = xx, TUC = xx, S3 = Serial Threes task STR = Stroop task.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6185287/v1/eee0644c8dfd8434cffd8c37.png"},{"id":78920510,"identity":"6c3cf37c-6ddd-416c-9ec4-4aee67ea6f04","added_by":"auto","created_at":"2025-03-20 20:30:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1308273,"visible":true,"origin":"","legend":"\u003cp\u003ePhotographic illustration of three different training programs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e a = cognitive training, b = motor training, c = cognitive-motor dual-task training.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6185287/v1/c99893fe043ad378a39cd778.png"},{"id":78920507,"identity":"e1fdabf6-5ee4-490d-b049-58d1a50c7ced","added_by":"auto","created_at":"2025-03-20 20:30:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78924,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of condition on cognitive and motor performance in the Serial Threes task across domains\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Error bars in the figure represent the standard error (SE). S3cog = cognitive performance; S3gait = motor performance.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6185287/v1/30643280b3f3ad36c4154009.jpg"},{"id":78920838,"identity":"da057f00-4d8a-4443-93e4-e5dad3cd27b7","added_by":"auto","created_at":"2025-03-20 20:46:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94590,"visible":true,"origin":"","legend":"\u003cp\u003ePre-post changes in cognitive and motor performance for the Serial Threes task across domains following cognitive, motor and cognitive-motor dual-task training\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Error bars in the figure represent the standard error (SE); cogT = cognitive training; motT = motor training; DTT = cognitive-motor dual-task training; S3cog = cognitive performance, S3gait = motor performance.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6185287/v1/7b4a468af0a1a381691012f3.jpg"},{"id":78920513,"identity":"3ae62572-dfc1-4263-a2cd-db8b676ff8c9","added_by":"auto","created_at":"2025-03-20 20:30:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":189294,"visible":true,"origin":"","legend":"\u003cp\u003eModeration of baseline cognitive fitness on pre-post changes of cognitive and motor performance for the Serial Threes tasks across domains following cognitive, motor and cognitive-motor dual-task training\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e The shaded areas around the regression lines represent the standard error of the estimates; S3cog = cognitive performance, S3gait = motor performance; cogT = cognitive training, motT = motor training, DTT = cognitive-motor dual-task training.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6185287/v1/fc68d5d4fd328508a4ed72fd.jpg"},{"id":78920837,"identity":"b7634ffd-66f9-4347-8899-0c76305a02e2","added_by":"auto","created_at":"2025-03-20 20:46:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":82175,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of condition on cognitive and motor performance in the Stroop task across domains\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Error bars in the figure represent the standard error (SE); STRcog = cognitive performance, STRgait = motor performance.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6185287/v1/22813d2db99e18575fa601a4.jpg"},{"id":78920719,"identity":"718dbbab-7f7d-433b-99a6-57a122e2ec3b","added_by":"auto","created_at":"2025-03-20 20:38:29","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":95373,"visible":true,"origin":"","legend":"\u003cp\u003ePre-post changes in cognitive and motor performance for the Stroop task across domains following cognitive, motor and cognitive-motor dual-task training\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Error bars in the figure represent the standard error (SE); STRcog = cognitive performance, STRgait = motor performance; cogT = cognitive training, motT = motor training, DTT = cognitive-motor dual-task training.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6185287/v1/bb9433dfffd3d226e7d068ce.jpg"},{"id":81834103,"identity":"4b97d662-d5a8-441a-b599-fbed73b4b363","added_by":"auto","created_at":"2025-05-02 14:23:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3856656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6185287/v1/930914f7-02c3-4b5c-a00e-3c16f8337ac1.pdf"},{"id":78920717,"identity":"1c2cb77d-e7cd-4b22-b0b5-be7cf03f0e4c","added_by":"auto","created_at":"2025-03-20 20:38:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":43528,"visible":true,"origin":"","legend":"","description":"","filename":"supplements.docx","url":"https://assets-eu.researchsquare.com/files/rs-6185287/v1/4c39b19d8cbdc2b97c687f9c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"An RCT on 12 weeks of cognitive, motor or combined cognitive-motor exercise to improve dual-task walking in older adults: The role of baseline cognitive and motor fitness","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCognitive-motor dual-tasking – also named multitasking – is pervasive in our daily lives. It includes activities such as going on a sidewalk while avoiding collision with other people, crossing busy streets, or driving a car. These tasks require simultaneous cognitive and motor processing, including route planning, anticipating others’ movements, adhering to traffic rules, controlling one’s locomotion, and avoiding both stationary and moving obstacles. Moreover, other activities are frequently performed concurrently, such as reading billboards, observing storefronts, or having a conversation. These additional tasks can lead to reduced performance in the primary tasks and are associated with a higher risk of falls and accidents. Cognitive-motor dual-tasking therefore is an important prerequisite for everyday mobility, independence and, ultimately, our quality of life.\u003c/p\u003e\n\u003cp\u003eIt is well documented that dual-task performance tends to deteriorate in older age [1–4]. The observed age-related decline of dual-task performance has typically been interpreted as a reflection of cognitive capacity limitations [5–7]. Both cognitive and motor performances require cognitive resources. Older adults, however, reach cognitive capacity limits sooner than young adults due to age-related decline in cognitive and motor functions. This age-related decline has been documented not only in typical laboratory paradigms, but also in paradigms that mimic everyday scenarios, such as simulated car driving [8,9], simulated sidewalk walking [3,10,11], and simulated street crossing [12–14]. Counteracting these detrimental changes in everyday behaviors through effective interventions is crucial, in particular in light of an aging society.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNumerous interventions aiming to improve cognitive-motor dual-tasking in older adults have been conducted [15,16]. These interventions typically involve either cognitive training, motor training, or cognitive-motor dual-task training, each addressing distinct but also overlapping underlying mechanisms. Cognitive training regimens usually involve repetitive practice of cognitive laboratory tasks aimed at enhancing various fluid cognitive functions, such as attention, memory, and executive functions [17]. Enhancements in cognitive-motor dual-task performance may arise from various cognitive adaptations, such as increased efficiency (e.g., increases in grey matter volume) or greater capacity (e.g., higher levels of task automatization or faster information processing) [18,19].\u003c/p\u003e\n\u003cp\u003eMotor training generally involves repetitive exercises targeting physical abilities such as strength, balance, and coordination. Strength exercises are designed to enhance neuromuscular control and muscle force, which are significantly diminished with aging and linked to declines in brain health and cognitive performance [20–22]. Balance and coordination exercises include the practice of complex movements that require the coordination of multiple joints and limbs, involving attentional and executive processes for accurate execution and control [23,24]. Enhancements in cognitive-motor dual-task performance through motor training may arise from cognitive adaptations as well as adaptations in the movement control systems relevant to walking [25–27]. Changes in the movement control systems may lead to the automatization of walking. As walking becomes more automatic, fewer cognitive resources are required to maintain postural control and gait stability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCognitive-motor dual-task training combines both cognitive and motor training by involving the simultaneous or consecutive execution of cognitive tasks and motor exercises. In addition to the adaptations achieved through cognitive and motor single-task training, it is proposed that dual-task training helps participants to develop attentional control capacities which refers to the ability to coordinate and monitor information processing [28,29]. These processes allow one to select the most efficient strategy to optimize the distribution of limited cognitive resources across multiple competing tasks based on environmental and task demands\u0026nbsp;[30–35]\u003c/p\u003e\n\u003cp\u003eDespite the overall finding that cognitive-motor dual-task training is more effective than single-task training in improving cognitive-motor dual-tasking in older adults [15,16], the emergence and magnitude of benefits from cognitive-motor dual-task training compared to single-task training are not consistent in the literature [16]. For instance, in some studies, cognitive-motor dual-task training is more beneficial than single-task motor training [36,37] or single-task cognitive training [38] for improving cognitive-motor dual-tasking. In other studies, cognitive-motor dual-task training is similarly effective as motor single-task training\u0026nbsp;[39,40]\u003c/p\u003e\n\u003cp\u003eGiven that interindividual differences in cognitive functioning and motor fitness tend to increase with advancing age [41], it is likely that some participants in the aforementioned studies had higher levels of cognitive functioning and/or motor fitness, while others had lower levels. The varying baseline levels of individual cognitive and motor fitness before the start of the interventions may be a potential factor contributing to this heterogeneity [42]. For instance, recent evidence suggests that individuals with low baseline cognitive fitness tend to benefit more from cognitive training compared to those with higher baseline levels [43,44]. Similar findings have been reported for the association between baseline physical fitness and benefits from physical training [45–47]. This phenomenon, referred to as as compensation, has been attributed to a ceiling effect, where high-baseline participants have less room for improvement than low-baseline participants. Conversely, low-baseline participants were also found to benefit less from training compared to high-baseline participants [48,49], resulting in the differences between high- and low-performing individuals at baseline being further enlarged by training. This so-called magnification phenomenon could represent a floor effect, where low-baseline participants are overwhelmed by higher task demands that exceed their individual capacity. When it comes to dual-task training, the evidence is less clear due to the limited number of studies on this [50]. To our knowledge, only one study has investigated the effect of baseline performance in dual-tasking, and even then, only for cognitive dual-tasking. This study demonstrated that reaction time variability (as a proxy for inefficient neural processing) moderates the intervention effects of cognitive dual-task training on cognitive dual-task performance in both young and older adults [51].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRationale of the study\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aforementioned studies documented that baseline cognitive fitness may modulate the benefits of cognitive training [43,44], baseline motor fitness modulates the benefits of motor training [45–47], and baseline dual-tasking fitness modulates the benefits of dual-task training [51]. Adding to these results, the present work explored possible cross-effects between domains. Specifically, we investigated whether baseline cognitive and motor fitness modulated the benefits of cognitive, motor and cognitive-motor dual-task training in improving cognitive-motor dual-tasking. For instance, an individual with balance problems may benefit little from dual-task training that involves walking while simultaneously counting backwards. This is because a large portion of their available processing resources must be allocated to the walking exercise, leaving only limited resources for the concurrent counting backwards task (i.e., task prioritization hypothesis [10,52]). These individuals may benefit little from cognitive-motor dual-task training, not because their task coordination skills are poor, but rather because their balance skills are degraded. However, if those participants undergo single-task balance and walking, their walking skills might improve to the point where fewer processing resources are needed for walking. The freed-up resources could then support concurrent cognitive tasks. If so, purely motor training might enhance not only motor performance, but also cognitive-motor dual-task performance in these people. In a broader sense, we proposed that the benefits of training depend on the interplay between participants’ baseline cognitive and motor fitness on one side, and the training stimulus on the other side. More specifically, we hypothesized that cognitive-motor dual-task performance in persons with low baseline cognitive fitness benefited more from cognitive training than from motor or cognitive-motor dual-task training (H1). We further posited that cognitive-motor dual-task performance in persons with low baseline motor fitness benefited more from motor training than from cognitive or cognitive-motor dual-task training (H2). Finally, we reasoned that cognitive-motor dual-task performance in persons with higher baseline cognitive and motor fitness benefited more from cognitive-motor dual-task training than from cognitive training alone or motor training alone (H3).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was part of a larger project within the Priority Program SPP 1772 \u0026ldquo;Multitasking\u0026rdquo;, funded by the German Research Foundation (DFG). Assessments were conducted at the\u0026nbsp;xx\u0026nbsp;and\u0026nbsp;xx.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOlder adults, (N = 128) between 65 and 75 years of age took part in this study. Participants were recruited via homepage announcements, local senior networks, newspaper articles, and posting at public places and social media. All information on inclusion and exclusion criteria was self-reported during a telephone interview. Inclusion criteria comprised: (1) aged between 65 and 75 years (minor exceptions were made for couples for ethical reasons: \u0026lt; 65 and \u0026gt; 75 years), (2) right-handed, (3) active car driving at least once a week within the last 6 months, (4) ability to walk unassisted without self-reported problems (e.g., difficulty to breath, pain, and cardiac palpitations), and (5) community-dwelling. Exclusion criteria comprised: (6) BMI \u0026gt; 30, (7) red-green deficiency or red-green-color blindness, (8) orthopedic impairments, (9) perceived health concerns, (10) neurological diseases, (11) cardiovascular disorders, (12) previous heart attack or stroke, or (13) previous head/brain surgery. In addition, all participants had to obtain a physician\u0026rsquo;s health clearance (exercise electrocardiogram, ECG) within the last six months. Subsequent screening assessed: (1) overall cognition by the Mini-Mental State Examination (MMSE) with a cutoff score of 27/30 [53,54], (2) visual acuity by the Freiburg vision test (FrACT 3.9.0) with a cutoff score of 20/60 [55,56], and (4) handedness by the Edinburgh Handedness Inventory [57]. No person had to be excluded because of these screening outcomes. Participants who regularly wore vision or hearing aids kept doing so during testing. This study was approved by the Institutional Review Board of the TUC, was carried out in accordance with the guidelines of the Declaration of Helsinki, was registered at the German Clinical Trials Register (DRKS), and the study protocol has been published [58]. Written informed consent was obtained from each participant. Participants\u0026rsquo; flow through the study is illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*** Figure 1 ***\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScreening\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCognitive impairment of participants was evaluated using the MMSE, which assesses a range of cognitive abilities including attention, arithmetical skills, verbal fluency, memory, and spatial orientation, on a scale from 0 to 30. All participants in this study scored above the threshold of 27 and were included in the analysis.\u003c/p\u003e\n\u003cp\u003eVisual acuity was measured using the Freiburg Visual Acuity Test (FrACT v 3.9.3). Participants, seated 3 m away from the computer screen, had to identify the orientation of small openings in little circles, i.e., Landolt rings, displayed at the center of the computer screen. The test dynamically adjusted the size of the rings based on participant response accuracy. Performance was expressed in both decimal acuity (VAdec) and the logarithm of the minimum angle of resolution (LogMAR). No participants were excluded based on the visual acuity criterion of scoring below 20/60.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTests of baseline cognitive fitness\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe N-back and the Simon test were used to assess executive functions [59,60], following standardized protocols and instructions. They were programmed with E-Prime and were displayed on a 24-inch screen with a resolution of 1920 \u0026times; 1080 pixels, positioned approximately 65 cm from the participants. Each test lasted about 10 minutes and included up to three preliminary practice trials lasting between 1 and 2 minutes each. Feedback on responses was provided after the practice trials, but not during the actual test sessions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe tests presented stimuli across six blocks, with brief inter-block intervals of 5 seconds (extending to 20 seconds after the third block). Following a response or after 2000 ms, a central fixation cross (0.3 cm wide and high) appeared for a variable interval ranging from 800 to 1200 ms. All stimuli were presented in black on a white screen background. Participants responded by depressing the \u0026quot;X\u0026quot; or \u0026quot;M\u0026quot; key on a German keyboard with their left and right index fingers, and they were instructed to respond as quickly and accurately as possible. Reaction times and correctness of responses were recorded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN-back test:\u003c/strong\u003e A black 4\u0026times;4 grid (18.4 cm wide and high) was continuously displayed, within which dots (n = 19 per block, 2.6 cm diameter) appeared sequentially in the center of various grid cells (4.6 cm wide and high) for 500 ms each. Participants were required to memorize the positions of these dots. When a dot\u0026apos;s position was identical to that of the dot two positions earlier (target), participants pressed the right key \u0026quot;M\u0026quot;; when it differed (non-target), they pressed the left key \u0026quot;X\u0026quot;. In total, 30 targets and 72 non-targets were presented across blocks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSimon test:\u003c/strong\u003e A black fixation cross remained visible on a white screen throughout the test. Arrows (2 cm long, 0.5 cm high) pointing left or right (n = 32 per block) appeared sequentially for 500 ms on either the left or the right side of the fixation cross, with a 3.1 cm distance between arrows and fixation cross. In half of the cases, the arrow\u0026apos;s direction and position were congruent (e.g., a leftward arrow on the left side), while in the other half, they were incongruent (e.g., a leftward arrow on the right side). In total, 192 arrows were presented across blocks (96 congruent, 96 incongruent). Participants were instructed to press the left key \u0026quot;X\u0026quot; for leftward arrows and the right key \u0026quot;M\u0026quot; for rightward arrows.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTests of baseline motor fitness\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA battery of four established tests was utilized to evaluate various aspects of motor fitness, following standardized procedures and instructions [23,61]. Timekeeping was managed using a regular stopwatch. Two to five practice trials were performed before each test. In addition, Spiroergometry was conducted to measure cardiovascular fitness. We included cardiovascular fitness as it is closely associated with motor fitness; it enhances the efficiency of the cardiovascular system, which in turn supports sustained physical activity and improved motor performance. Higher levels of cardiovascular fitness contribute to better coordination and overall motor skills, facilitating more effective execution of motor tasks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChair stand test:\u003c/strong\u003e Participants sat on a height-adjustable chair without armrests, with arms crossed and hands resting on opposite shoulders. They repeatedly rose to a full standing position and returned to a fully seated posture as many times as possible within 30 seconds. Throughout the test, participants were required to keep their arms crossed and both feet firmly on the floor.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurdue Pegboard test:\u003c/strong\u003e The test involved a board with two rows of 25 small holes, extending from top to bottom, with small metal pins located at the upper left and right of the board. Participants were instructed to simultaneously pick up a pin with the right hand from the right side and a pin with the left hand from the left side, place both pins into the topmost empty holes of the respective rows and repeat this action as often as possible within 30 seconds. Three trials were conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOne-legged stand test:\u003c/strong\u003e The test was performed with both open and closed eyes in the GRAIL (Gait Real-time Analysis Interactive Lab, Motekforce Link, Amsterdam, The Netherlands) environment, though without using a safety harness to avoid influencing participants\u0026apos; posture. Eight trials were conducted, alternating between the right and left leg, with the first four trials with eyes open and the subsequent four trials with eyes closed. Participants stood on one leg with the other leg slightly flexed and arms at their sides, without hopping, touching the ground with the lifted foot, or pressing the lifted leg against the standing leg. They were instructed to maintain balance as long as possible without opening their eyes during the closed-eye trials. Timekeeping started when a participant lifted one leg and stopped upon any standard violation or after 20 seconds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeet tapping test:\u003c/strong\u003e Participants sat on a stationary chair without armrests and were tasked with moving both feet concurrently back and forth across a mid-sagittal line on the floor as quickly as possible, ensuring full contact of the soles with the floor at each tap. Two 20-second trials were conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eV02max test:\u003c/strong\u003e Spiroergometry (ZAN600 CPET, nSpire Health, Oberthulba, Germany) was performed on a stationary bicycle (Lode Corival cpet, Groningen, the Netherlands). Participants were instructed to abstain from caffeine and alcohol for 12 hours prior to testing and to avoid vigorous exercise the day before. Each session was either supervised by a physician or participants were required to present a medical clearance certificate, which included exercise electrocardiography (ECG) and clinical history. Respiration, specifically oxygen (VO2) and carbon dioxide (VCO2) consumption, was measured on a breath-by-breath basis. Heart rate was monitored using an integrated digital twelve-lead electrocardiogram (Kiss, GE Healthcare, Munich, Germany). Blood pressure was continuously monitored with a sphygmomanometer. Participants underwent a ramp protocol, with male participants starting at 20 W and increasing by 20 W/min, while female participants started at 10 W and increased by 15 W/min. All participants were instructed to maintain a cycling frequency of 60 to 80 rpm. Each protocol began with a 3-minute resting period and concluded with a 5-minute cool-down period (1 minute at the initial load and 4 minutes without load). The protocol was terminated if the participant\u0026rsquo;s respiratory exchange ratio (RER = VCO2/VO2) remained above 1.05 for at least 30 seconds or exceeded 1.10, or in cases of subjective fatigue, or the occurrence of physiological risk factors such as blood pressure exceeding 230/115 mmHg, dizziness, a heart rate greater than roughly 220 minus their age, cardiac arrhythmia, or other abnormalities. Each test was conducted by an experienced sport scientist. Peak oxygen uptake (VO2 peak: VO2 consumption during the maximum load level achieved), RER, and the maximum load level (wattage) were analyzed and considered to evaluate the measurement\u0026apos;s validity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePre-post tests of cognitive-motor dual-tasking\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSystem hardware and software:\u0026nbsp;\u003c/strong\u003eThe dual-task walking test was performed with the GRAIL system (Gait Real-time Analysis Interactive Lab, Motekforce Link, Amsterdam, The Netherlands). The GRAIL is a valid and reliable gait assessment device [62] that integrated two embedded force plates in an instrumented 3D split-belt treadmill platform (0.8 \u0026times; 1.5 m). A semi-cylindrical 240\u0026deg; projection screen (2.4 \u0026times; 5 m) was located in front of the treadmill. Four RGB projectors connected in series project a virtual scenario onto the projection screen. A photodiode was placed on the projection screen to precisely measure the visual onset of stimuli, thus considering any unsystematic variations in the onset times of the RGB projectors. A custom-made ergonomic key switch in the participants\u0026rsquo; dominant hand was used to record manual responses, and a voice recorder was used to assess verbal responses. The system\u0026apos;s safety measures included two handrails attached to the side of the treadmill and two laser barriers at the front and rear of the treadmill. Participants also wore a safety harness during walking that was attached to the ceiling to prevent injury in case of a fall. The experimenters had a stop button available to stop the treadmill immediately in case of an emergency. However, no falls or emergency stops occurred.\u003c/p\u003e\n\u003cp\u003eThe systems standard software D-Flow (Motekforce Link, Amsterdam, the Netherlands) was used to customize the virtual scenario. It depicted an industrial-like virtual landscape. Motor and cognitive tasks were also designed and integrated within D-Flow. All instructions and tasks were presented at eye level in small rectangular grey and brownish boxes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMotor and cognitive tasks:\u003c/strong\u003e Motor and cognitive tasks were presented in a mixed sequence comprising six different tasks, with five trials each. The sequence remained consistent across all participants and for both the pre-test and post-test. No task was repeated consecutively for more than two trials. Each trial lasted 30 seconds and was preceded by an additional 3-second introductory text (for example \u0026ldquo;Standing only\u0026rdquo; or \u0026ldquo;Walking only,\u0026rdquo; in German). The entire set of tasks spanned 16.5 minutes (30 x 30 seconds + 30 x 3 seconds).\u003c/p\u003e\n\u003cp\u003eTesting consisted of one baseline task, three tasks in ST condition encompassing one motor and two cognitive tasks, and two combined tasks in DT condition. Outcome measures for the two combined tasks in DT condition were the same as described for the tasks in ST condition. The tasks were as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(1) Standing task (baseline): Participants remained stationary, standing with both feet on the treadmill and maintaining a forward gaze directed at a fixation cross. Ground reaction forces were recorded. This task was not analyzed in this study.\u003c/p\u003e\n\u003cp\u003e(2) Walking task (ST condition): Participants walked at a fixed treadmill speed of 1 m/s, focusing their gaze straight ahead on a fixation cross. Since the treadmill accelerated and decelerated at a rate of 0.2 m/s\u0026sup2;, it necessitated a 5-second transition between standing and walking trials. Ground reaction forces were recorded.\u003c/p\u003e\n\u003cp\u003e(3) Serial Threes task (ST condition): Participants maintained a stationary position on the treadmill while focusing on the fixation cross at the center of the projection screen. A three-digit number was presented at the start of the trial for 5 seconds. Based on this number, participants were required to count backwards in threes from this number, as rapidly and accurately as they could, and to verbalize each resultant number. They had to keep their eyes open throughout the task, to articulate each number in full (for instance, stating \u0026ldquo;177\u0026rdquo; rather than \u0026ldquo;77\u0026rdquo;), and to refrain from correcting any mistakes. That is, they had to continue counting from the last number stated, even if incorrect. All verbal responses were documented by the experimenter and additionally captured via a voice recorder.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(4) Color Word Stroop task (ST condition): This task evaluated inhibitory control by presenting the four color-naming words yellow, red, blue, green in a randomized sequence. Each word appeared for 500 ms, followed by a fixation cross for 1800 to 2200 ms, such that the average inter-stimulus interval (ISI) was 2500 ms. Stimulus words were congruent, i.e., the color of the word matched its meaning (e.g., \u0026quot;green\u0026quot; appeared in green), or incongruent i.e., the color and meaning differed (e.g., \u0026quot;green\u0026quot; appeared in blue). Two response options were shown for 1500 ms, aligned with the onset of the stimulus. These options were displayed in two rectangular areas, one to the left and one to the right below the stimulus word, both in white font. One response indicated the color of the font of the stimulus word, and the other named one of the three other possible colors. Participants had to decide which of the two response words corresponded to the color of the font by pressing either the left or the right button on a handheld key switch. They were instructed to respond as quickly and accurately as possible. The Stroop task design maintained a balance across various factors: 50% of the trials were congruent and 50% were incongruent, each font color was used in 25% of trials, and the positioning of correct and incorrect answers, as well as the frequency of these answers per color, were equally distributed at 50%. Reaction times and accuracy of responses were meticulously recorded via the handheld key switch responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(5) Walking + Serial Threes task (DT condition): Participants concurrently performed the Walking and Serial Threes tasks, without prioritizing either.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(6) Walking + Color Word Stroop task (DT condition): Participants concurrently performed the Walking and Color Word Stroop task, without prioritizing either.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure:\u003c/strong\u003e Participants familiarized themselves with the treadmill by walking through a simulated forest environment for approximately 5 to 10 minutes, during which the walking speed gradually increased to 1 m/s. The familiarization phase concluded once participants were able to walk steadily, while maintaining their focus on the center of the projection screen. Subsequently, cognitive tests such as the MMSE and DSST were conducted. These lasted about 12 to 15 minutes in total, allowing participants to return to a physical resting state. Following these assessments, participants engaged in a brief practice session lasting about 2 minoutes, which included a shortened version of each task in a predetermined sequence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIntervention\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree different training programs (cognitive training, motor training, cognitive-motor dual-task training) were conducted at TUC and MU facilities. They lasted twelve weeks and included two one-hour training sessions per week (24 training sessions in total). Each of the three training programs included a total of 72 15-minute training blocks (18 hours in total). The blocks were performed several times in a predefined sequence that was the same for all participants. In each training session, three of those blocks were provided to the participants. To ensure continuous training progress, the difficulty level of the training was continuously adjusted to individuals\u0026rsquo; performance. The three different training programs are briefly described below. For a more detailed description of the training programs and exemplary training sessions, please refer to our study protocol\u0026nbsp;[58]. To ensure that training effects were not confounded by the effects of cardiovascular practice on brain functions, the training intensity of the motor and multitask training did not exceed 60% of VO2-peak. Apart from participating in the training, participants were asked not to alter their regular daily routines, including social, physical, and cognitive activities. See Figure 2 for a photographic illustration of the three training programs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCognitive training\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe training program was conducted in a computer pool with a separate computer per participant. The exercises were presented on a computer monitor and hand-held trackball mice (YUMQUA Y-01, YUMQUA, Shenzhen, China) were used to control the cursor. This input device was chosen for compatibility with cognitive-motor training, where conventional computer mice would not be practical (see below). The training program included 22 different cognitive exercises from three different software applications: NeuroNation (NeuroNation, Berlin, Germany), Happyneuron (Scientific Brain Training, Lyon, France), and Neuropeak [63]. The exercises trained different fluid cognitive functions, specifically inhibitory control, updating, shifting, multitasking and action planning which are essential for everyday life functioning. One exercise was performed in each of the 72 training blocks. Throughout the training, the different exercises were performed several times (approximately three times), and exercise difficulty increased adaptively with participants\u0026rsquo; proficiency. Group sizes ranged from 10 to 15 participants at TUC and one to ten at UM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMotor training\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe training program was conducted in a customized exercise room and consisted of 15-minute blocks of floor exercises and walking exercises. The floor program included various exercises that train either strength or balance. The difficulty of the exercises was varied with different surfaces (e.g. AIREX-Pad, Balance Board). Various flexibility exercises were performed for recovery between and after the strength and balance exercises. The walking program was performed on a non-motorized treadmill with curved belt (Speedfit SpT-1000C, Tobeone, Korea). It included different walking exercises with varying degrees of difficulty. Throughout the training, the different exercises were performed several times, and exercise difficulty increased adaptively with participants\u0026rsquo; proficiency.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCognitive-motor dual-tasking training\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis training was conducted in the same exercise room as motor training. Participants performed the cognitive and motor exercises simultaneously (e.g., they performed a cognitive exercise while standing on one leg). Thereby, the execution and sequence of exercises stayed exactly the same as in the other two training groups. Again, exercise difficulty increased adaptively with participants\u0026rsquo; proficiency. The cognitive exercises were presented on a 48\u0026Prime; screen placed at eye level in front of the participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*** Figure 2 ***\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eResearch design\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a three-arm, double-blind, randomized controlled trial. Following eligibility screening, all participants completed a battery of behavioral tests to assess their baseline cognitive and motor fitness, and their cognitive-motor dual-task performance during walking and \u0026nbsp; driving (dual-task while driving will be considered in subsequent reports). Tests and screening were conducted in three sessions within two weeks and at least one day apart. The first testing sessions included spiroergometry and lasted about 45 minutes. The second and third session included the remaining behavioral tests in four different orders to which the participants were randomly assigned. Approximately one week after completing the pre-test, all participants began the training. They were randomly assigned to one of three intervention groups: (1) cognitive training, (2) motor training, (3) cognitive-motor dual-task training. \u0026nbsp; Randomization was conducted at a 1:1:1 ratio using a computer-generated random allocation schedule. A research assistant sealed the random assignments in envelopes, which were given to participants after completing the pre-tests. Outcome assessors were blinded to group allocation, and personnel delivering the intervention were blinded to outcome assessments and individual performances. To minimize experimental contamination from social interaction and communication among participants, they were instructed not to discuss their assigned interventions with each other. Close friends and spouses were assigned to the same intervention groups. Within about two weeks after completing their respective training programs, all participants were again given the same battery of behavioral tests in the same order as they had completed prior to training. \u0026nbsp;At TUC, groups of five participants were randomly assigned to one of three training groups, with all members of the group starting their training simultaneously. At UM, a rolling start approach was used. Each week, four to eight participants were invited to begin the study. Assessments were conducted at TUC between March 2019 and December 2019, and at UM between September 2020 and August 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePreprocessing and final outcome variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData preprocessing and statistical analysis was performed using R version 4.2.2 [64].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBaseline cognitive fitness\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe outcome variable was a composite score of executive functions that included measures of processing speed, working memory, and inhibition, assessed at pre-test. Processing speed and inhibition were measured using the Simon test, while working memory performance was measured with the N-back test. To remove unreliable responses and outliers in both tests, trials with reaction times below 80 ms or above 1300 ms were excluded, and then the \u0026plusmn;3.29 SD criterion was applied for each participant. To verify participants\u0026apos; understanding of the tests, we checked whether the mean accuracy across all stimuli exceeded 55%. In the Simon test, three of the final 103 participants did not reach this threshold, and in the N-back test, 20 participants did not reach this threshold. The pertinent scores of these participants\u0026nbsp;were excluded from further analysis. Then, processing speed was calculated as the mean reaction time for correct responses in congruent trials of the Simon test. Inhibition was derived by subtracting the mean reaction time for correct responses in incongruent trials from the mean reaction time for correct responses in congruent trials of the Simon test. Working memory performance was quantified as the mean reaction time for correct responses across both target and non-target trials in the N-back test. The composite score was calculated as the mean of the z-transformed individual measures of processing speed, working memory, and inhibition. Composite scores for participants with more than one missing value (n = 23) were excluded from further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBaseline motor fitness\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe outcome variable was a composite score for strength, movement speed, dexterity, balance, and cardiovascular fitness, assessed at pre-test. Strength was quantified by the number of correctly executed chair stands in the Chair Stand test. Movement speed was measured by the trial with the highest number of correctly performed crossings in the Feet Tapping test. Fine-motor control was assessed by the average number of rows with correctly placed pegs across three trials in the Purdue Pegboard test. Balance was evaluated with the One-legged Stand test, measuring the standing duration (in seconds) in the eyes-closed balancing condition, averaged across two trials: one with the longest standing duration for the right leg and one for the left leg. Due to ceiling effects in the eyes-open trials, only the eyes-closed trials were analyzed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePreprocessing spiroergometry data followed a structured pipeline. Initially, the minimum breath-to-breath interval (Tmin) was first determined as approximately one breath per second, providing the baseline time interval for interpolation. Time series data for oxygen uptake (VO₂), carbon dioxide output (VCO₂), expiratory ventilation (VE), and inspiratory ventilation (VI) were interpolated using Tmin as the reference to create evenly spaced datasets. To reduce noise and fluctuations typically present in breath-by-breath data, a third-order low-pass Butterworth filter was applied with a low-cutoff frequency of 0.04 Hz. The filtering process followed established guidelines and references [65]. Following this step, filtered VCO₂ and VO₂ data were used to calculate the respiratory exchange ratio (RER), defined as RER = VCO₂ / VO₂ (filtered data). The filtered, evenly spaced data were then transformed back to the original measured time intervals to retain their physiological timing for further analyses. Threshold detection was performed by applying a methodology using Wasserman plots [66]. This involved slope analyses of VCO₂ versus VO₂ and VE/VCO₂ versus VCO₂, as well as the identification of the nadir point in VE/VCO₂ over time. For each ventilatory threshold (VT1 and VT2), the mean of the two respective analysis values was calculated. To ensure accuracy, all threshold detections were visually inspected and corrected as needed.\u0026nbsp;The final preprocessing step produced key outputs at defined time points, including heart rate, VO₂, VO₂/kg, VCO₂, workload, and RER. These were reported for resting conditions (mean of the last 10 measurements before exercise onset), VT1, VT2, RER = 1.00, RER = 1.05, VO₂ peak, and at the end of the exercise test.\u003c/p\u003e\n\u003cp\u003eThe composite score was calculated as the mean of the z-transformed individual measures for strength, movement speed, fine motor control, balance, and cardiorespiratory fitness. Composite scores for participants with more than one missing value were excluded from further analysis, which applied to none of our 103 final participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePre- and post tests of cognitive-motor dual-tasking: cognitive performance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the Serial Threes task, the outcome variable was the number of correct calculations (S3cog) per trial per condition (ST condition: 5 trials per person, 30s each; DT condition: 5 trials per person, 30 seconds each), assessed at pre-test and at post-test. As accuracy rate (number of correct calculations divided by the number of total calculations) showed substantial ceiling effects close to 100% correctness, it was not regarded as an outcome variable in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the Stroop task, the outcome variable was inhibition costs for reaction times of correctly responded stimuli in percentage (STRcog) per trial per condition (ST condition: 5 trials per person, 30 seconds each; DT condition: 5 trials per person, 30 seconds each), assessed at pre-test and at post-test. \u0026nbsp;Correct trials were determined by evaluating whether participants pressed the key that matched the location of the correct response word on the projection screen. Prior to calculating the outcome variable, reaction times were inspected for plausibility and excluded if they were below 80 ms or above 2500 ms. After that, outliers were removed according to the \u0026plusmn;3.29 SD criterion across all trials for each participant\u0026nbsp;[63].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePre- and post-test of cognitive-motor dual-tasking: motor performance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor both the Serial Threes task and the Stroop task, the outcome variable was step time variability (S3gait, STR\u003csub\u003egait\u003c/sub\u003e) per trail per condition (ST condition: 5 trials per person per task, 30 seconds each; DT condition: 5 trials per person per task, 30 seconds each), assessed at pre-test and post-test. Step time variability is known to be related to mobility restrictions and the risk of falls in the elderly [67,68] and exhibit changes under DT conditions. Increased step time variability is suggested to be a marker of neural function degradation (e.g., the pattern generator for motor control) and declines in executive function [69]. Step time variability was calculated for each trial from 5 to 25 seconds after trial onset, using the kinetic data from force plates. Kinetic data were collected at a frequency of 1000 Hz. In the first step, the global center of pressure was calculated using the data from each force plate [70]. The resulting X and Y coordinates were filtered using a second-order Butterworth filter with a cutoff frequency of 13 Hz, which is an appropriate filter configuration for gait analysis using center of pressure data\u0026nbsp;[68]. Subsequently, heel strikes were determined\u0026nbsp;as local maxima of the\u0026nbsp;center of pressure\u0026nbsp;trajectory along the anterior-posterior axis [71]. Step time variability was calculated as the standard deviation of the differences in time between subsequent heel strikes [72].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData preparation for final analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline cognitive and motor fitness:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eIn summary, there were no missing values for the baseline motor fitness composite score but 23 of the 103 subjects had missing values for the baseline cognitive fitness composite score. For the imputation of the missing values for baseline cognitive fitness, we mean-centered the covariates age and education and analyzed the effects of the covariates sex, age, education and baseline motor fitness on baseline cognitive motor fitness. There were sex differences in baseline cognitive fitness, but no effects of age and education (see supplements). Therefore, we replaced missing baseline cognitive fitness values with the mean baseline cognitive fitness of participants\u0026rsquo; sex group. An analysis of the effects of sex, age, education, and baseline cognitive fitness on baseline motor fitness revealed a significant effect of age and sex, but no effects of education and cognitive fitness (see supplements). For the final data set, the variables baseline cognitive, and motor fitness were centered within men and women to reduce the correlation between sex and these two covariates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePre- and post-tests of cognitive-motor dual-tasking:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eAs data were not normally distributed,\u003cem\u003e\u0026nbsp;\u003c/em\u003ebased on a Box-Cox distributional analysis [73], a square root transformation of S3cog and STRcog, and a reciprocal transformation of S3gait and STRgait brought model residuals in line with normal distribution. We then converted S3cog, STRcog, S3gait, and STRgait to z-scores using means and SDs of ST condition at pre-test as reference for the rest. For the Serial Threes task, from the original 3,801 trials (including both outcomes: S3cog and S3gait, both conditions: ST and DT, and both measurement points: pre and post) from 103 participants after preprocessing, data from six participants were excluded. Three participants had missing data for all ten trials at either pre-test or post-test for both S3cog and S3gait outcomes across both ST and DT conditions. Additionally, three participants exhibited abnormal change scores and were identified as outliers based on the conditional mode analysis [74].\u0026nbsp;This resulted in a final dataset of 3,622 trials from 97 participants. For the Stroop task, from the original 3,634 trials (including both outcomes: STRcog and STRgait, both conditions: ST and DT, and both measurement points: pre and post) from 103 participants after preprocessing, data from 22 participants were excluded. 21 participants had missing data for all ten trials at either pre-test or post-test for both STRcog and STRgait outcomes across both ST and DT conditions. Additionally, one participant exhibited abnormal change scores and were identified as outliers based on the conditional mode analysis. This resulted in a final dataset of 2,201 trials from 81 participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA power analysis was conducted before data collection and reported in detail in the study protocol [58].\u0026nbsp;The estimated required sample size to provide sufficient power to detect a small to moderate effect was N = 118. With our final sample of N = 97 participants for the Serial Threes task and N = 81 participants for the Stroop task, we did not reach this calculated sample size, due to higher drop-outs and missing data than expected. However, since we conducted our analysis using linear mixed models (LMMs) we are optimistic this will mitigate the shortfall. LMMs are advantageous because they leverage the repeated measures within subjects using disaggregated data, effectively increasing statistical power and requiring fewer participants compared to traditional mean-based statistical analyses on aggregated data. Additionally, it is possible to further increase statistical power by reducing model complexity and selecting the most parsimonious model, while also effectively balancing type I and type II error [75]\u003c/p\u003e\n\u003cp\u003eFor all analyses, we applied LMMs using the \u003cem\u003elme4\u003c/em\u003e package [76]. All models were fitted using maximum likelihood estimation (ML), which is assumed to provide better estimates for fixed effects than restricted maximum likelihood estimation (REML). We further followed the parsimonious model selection procedure proposed by Bates et al. [77], which is described in more detail in the supplementary material.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll models were built separately for the combination of walking and Serial Threes (S3), and for the combination of walking and Stroop (STR), administered as pre- and post-tests. This was done across both the ST and DT conditions, with performance (cognitive performance and motor performance) as the dependent variable. The following independent variables were included in the models: Time (pre-test [pre] vs. post-test [post]), condition (single-task [ST] vs. dual-task [DT]), domain (cognitive performance [cog] vs. motor performance [gait]), training group (cognitive training [cogT], motor training [motT], cognitive-motor dual-task training [DTT]), baseline cognitive fitness (cf), and baseline motor fitness (mf). Contrasts were specified with the MASS package [78] and the \u003cem\u003ehypr\u003c/em\u003e package [79].\u0026nbsp;Time and condition were dummy coded with pre-test and ST condition as the reference level, allowing these categories to serve as the baseline for comparison. Domain was sum (or effect) coded by cog as negative and gait as positive, with zero as the mean of the two levels. This approach simplifies interpretation of main effects and interactions by balancing the levels [80]. For group, we specified three contrasts comparing (1) DTT vs. cogT, (2) DTT vs. motT, and (3) cogT vs. motT.\u003c/p\u003e\n\u003cp\u003eIn a first step, we tested for the differential effects of the three interventions (cogT, motT, DTT) on cognitive-motor performance during both ST and DT conditions. Following a pre-specified model selection process (for details see supplements), the final models for this analysis included fixed effects for time (pre, post), group (motT, cogT, DTT), domain (cog, gait), condition (ST, DT), the interaction between condition x domain and the triple-interaction between time x group x domain. The random effects term included time, domain and condition which were allowed to vary within participants. Including factors as both fixed and random effects is a widely accepted and recommended approach in mixed-effects modeling, as it allows for the estimation of population-level effects while accounting for within-subject variability [81]However, to avoid overfitting and ensure model stability, we evaluated the random effects structure by checking model fit using the diagnostic functions \u003cem\u003eisSingular\u003c/em\u003e and \u003cem\u003erePCA\u003c/em\u003e as part of the \u003cem\u003elm4\u003c/em\u003e package [76]. The final model was as follows: cognitive and motor performance ~ 1 + time * group * domain + condition + condition : domain + (1 + time + domain + condition | Subj), in the model formula, * denotes all main effects and interactions between the variables, : specifies only the interaction between two variables without their main effects. Because we were not only interested in the time x group x domain interaction, but also in the time x group interaction within the separate domains (cog vs. gait), we rebuilt the final models to have these effects nested within the levels of the variable domain [82]: \u0026nbsp;cognitive and motor performance ~ 1 + domain / (time * group) + condition + condition : domain + (1 + time + domain + condition | Subj).\u003c/p\u003e\n\u003cp\u003eIn a second step, we evaluated the influence of baseline cognitive and motor fitness on the intervention effects using model comparisons with log-likelihood ratio tests. Specifically, we constructed a model similar to the one detailed above. However, the fixed effect of the three-way interaction of time x group x domain was replaced with the fixed effects of two four-way interactions of time x group x domain x cf and time x group x domain x mf. This new model (cognitive and motor performance ~ 1 + time * group * domain * (cf + mf) + condition + condition : domain + (1 + time + domain + condition | Subj)) was then compared to the original model to test whether cognitive and motor fitness influence the outcomes. If model comparison showed significance, it was further compared to the following models: 1) a model including only the four-way interaction of time x group x domain x cf, to test the moderation effect of cf alone, and 2) a model including only the four-way interaction of time x group x domain x mf, to test the moderation effect of mf alone. Additionally, 3) a model including a five-way interaction of time x group x domain x cf x mf was compared to a model with the two four-way interactions to test the moderation effect of the interaction between baseline cognitive and motor fitness\u003c/p\u003e\n\u003cp\u003eBecause we were not only interested in the time x group x domain x cf interaction, but also in the (1) time x group x cf interaction within the separate domains (cog vs. gait), and the (2) time x domain x cf interaction within the separate training groups (motT vs. cogT vs. DTT), we rebuilt the final models to have these effects nested within the levels of the variable domain: \u0026nbsp;(1) cognitive and motor performance ~ 1 + domain / (time * group * cf) + condition + condition : domain + (1 + time + domain + condition | Subj); (2) cognitive-motor performance ~ 1 + group / (time * domain * cf) + condition + condition : domain + (1 + time + domain + condition | Subj);\u003c/p\u003e\n\u003cp\u003eFor all LMM analyses, t-values above\u003cem\u003e\u0026nbsp;t\u003c/em\u003e = 2.00 were considered significant [83,84]. Only significant results are reported with statistical values in the text of the results section, while non-significant results are provided in the tables.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from 97 participants were analyzed for the Serial Threes task, and data from 81 participants for the Stroop task (Figure 1). Exclusion from the data analysis was due to missing data from participants dropping out because of illness or time constraints, as well as data collection issues caused by technical problems or participants having difficulties performing the tasks. \u0026nbsp;Demographic characteristics of the final sample and excluded participants are presented in Table 1. For both the Serial Threes and Stroop tasks, there were no significant differences in age, sex, education, MMSE, or subjective health between analyzed participants across the different groups and those who were excluded. However, for the Serial Threes task \u0026ndash; but not for the Stroop task \u0026ndash; BMI showed significant group differences, \u003cem\u003eF\u003c/em\u003e(3, 126) = 3.28, \u003cem\u003ep\u003c/em\u003e = .034. Tukey-adjusted post-hoc tests revealed that excluded participants showed a higher BMI than participants in the cognitive training group (\u003cem\u003ep\u003c/em\u003e = .029) and in the cognitive-motor dual-task training group (\u003cem\u003ep\u003c/em\u003e = .017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants` demographic characteristics at baseline\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecogT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emotT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDTT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExcluded from analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM (SD) / n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM (SD) / n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM (SD) / n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM (SD) / n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF / \u0026chi;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 604px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerial Threes task\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e69.19 (4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e69.43 (3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e68.85 (2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e70.32 (4.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSex (f / m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e20 / 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e19 / 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e17 / 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e12 / 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eEducation (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e15.61 (2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e16.34 (3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e16.28 (2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e16.25 (3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e25.56 (3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e24.55 (2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e25.37 (3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e23.32 (2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMMSE (0-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e28.98 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e28.97 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e28.90 (1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e29.22 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSubjective health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3.90 (0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.88 (0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.88 (0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.94 (0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 604px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroop task\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e68.90 (3.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e69.41 (4.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e68.81 (3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e70.13 (4.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSex (f / m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e13 / 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e16 / 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e17 / 15 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e22 / 21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eEducation (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e16.39 (2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e16.63 (3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e16.44 (2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e15.50 (3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e25.43 (2.358)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e24.39 (2.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e25.59 (2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e23.99 (3.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMMSE (0-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e29.09 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e28.78 (1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e29.00 (1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e29.13 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSubjective health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3.86 (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.85 (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.88 (0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.96 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u0026nbsp;\u003c/strong\u003eF-statistic for age, education, BMI, and MMSE, \u0026chi;\u0026sup2; statistics for sex and subjective health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSerial Threes task\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChanges from single- to dual-tasking\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the Serial Threes task, results showed a significant domain x condition interaction (\u003cem\u003eEst\u003c/em\u003e = 0.41, \u003cem\u003eSE\u003c/em\u003e = 0.04, \u003cem\u003et\u0026nbsp;\u003c/em\u003e= 10.10; Table 2). While S3cog substantially decreased from ST to DT conditions, S3gait increased slightly from ST to DT (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChanges from pre- to post-test\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eResults further revealed a significant effect of time (\u003cem\u003eEst\u0026nbsp;\u003c/em\u003e= 0.11, \u003cem\u003eSE\u003c/em\u003e = 0.04, \u003cem\u003et\u003c/em\u003e = 2.67; Table 2) and a significant time x domain interaction (\u003cem\u003eEst\u0026nbsp;\u003c/em\u003e= -0.23, \u003cem\u003eSE\u003c/em\u003e = 0.04, \u003cem\u003et\u0026nbsp;\u003c/em\u003e= -5.32; Table 2). While performance (across S3cog and S3gait) overall increased from pre- to post-test, S3cog increased more than S3gait (Figure 3). Furthermore, the condition did not influence pre-to post-test changes, i.e., S3cog and S3gait showed similar pre-post changes for both, ST and DT conditions. This was indicated by a non-significant interaction effect of condition x time and condition x time x group in a primary model (Table 3 in the supplementary materials). Consequently, these interactions were excluded during the parsimonious model selection process as detailed in the \u003cem\u003eStatistical analysis\u003c/em\u003e section and reported in Table 3 in the supplementary materials.\u003c/p\u003e\n\u003cp\u003eMoreover, the results demonstrated no significant time x group interaction, but a significant time x group x domain interaction: pre-post changes varied for S3cog and S3gait for DTT vs. motT (\u003cem\u003eEst\u003c/em\u003e = -0.28, \u003cem\u003eSE\u003c/em\u003e = 0.10, \u003cem\u003et\u003c/em\u003e = -2.69) and motT vs. cogT (\u003cem\u003eEst\u003c/em\u003e = -0.24, \u003cem\u003eSE\u003c/em\u003e = 0.11, \u003cem\u003et\u003c/em\u003e = -2.25), but not for DTT vs. cogT, (Table 2). The results also indicated a significant time x group interaction for S3gait, but not for S3cog. For S3cog, all interventions showed comparable positive pre-post changes (Table 2 and Figure 4). In contrast, for S3gait, significant differences were observed between cogT and motT (\u003cem\u003eEst\u0026nbsp;\u003c/em\u003e= -0.27, \u003cem\u003eSE\u0026nbsp;\u003c/em\u003e= 0.12, \u003cem\u003et\u003c/em\u003e = -2.34), but not between DTT and motT or between cogT and DTT (Table 2). S3gait increased from pre to post for motT, remained stable for DTT, and declined for cogT (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFixed-effects estimates of the final linear mixed models testing the time x group x domain interaction on cognitive and motor performance in the Serial Threes task during ST and DT conditions\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"519\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEst\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 519px;\"\u003e\n \u003cp\u003e\u003cstrong\u003em0a_S3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTime (pre/post)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDomain (motor/cognitive)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eCondition (ST/DT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-6.40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eGroup (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eGroup (DTT vs, motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eGroup (cogT vs, motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTime x group (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTime x group (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTime x group (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTime x domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-5.32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eGroup x domain (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eGroup x domain (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.245\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eGroup x domain (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDomain x condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTime x group x domain (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTime x group x domain (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTime x group x domain (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 519px;\"\u003e\n \u003cp\u003e\u003cstrong\u003em0a_S3/domain (effects for time x group nested in domain)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDomain S3cog: time x group (DTT vs. cogT) \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDomain S3gait: time x group (DTT vs. cogT) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDomain S3cog: time x group (DTT vs. motT) \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDomain S3gait: time x group (DTT vs. motT) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDomain S3cog: time x group (cogT vs. motT) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDomain S3gait: time x group (cogT vs. motT) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e Linear mixed model formula for m0a_S3 in lm4 syntax in R: cognitive and motor performance ~ 1 + domain*time*group + condition + condition : domain + (1 + time + domain + condition | Subj); Linear mixed model formula for m0a_S3/domain (effects of time and time x group nested in domain) in lm4 syntax in R: cognitive and motor performance ~ 1 + domain / (time * group) + condition + condition : domain + (1 + time + domain + condition | Subj); the dependent variable was z-scaled. Bold values are significant t-values above 2; S3cog = cognitive performance, S3gait = motor performance; cogT = cognitive training, motT = motor training, DTT = cognitive-motor dual-task training.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*** Figure 3 ***\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*** Figure 4 ***\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eModeration of baseline motor and cognitive fitness\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eResults of the model comparisons indicated an influence of cf, but no influence of mf or the interaction between cf and mf on pre-post changes of performance (across S3cog and S3gait, Table 6 and Table 8 in the supplementary materials). The final model examining the moderation of cf is presented in Table 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults indicated no significant time x cf or group x time x cf interactions, suggesting that cf did not influence pre-post changes, neither overall nor depending on group (Table 4). However, the time x cf x domain interaction was significant (\u003cem\u003eEst\u003c/em\u003e = -0.0, \u003cem\u003eSE\u003c/em\u003e = 0.0,\u003cem\u003e\u0026nbsp;t\u003c/em\u003e = -3.14; Table 4). While for S3cog, a higher cf led to larger increases than a lower cf, for S3gait, a lower cf led to larger increases than a higher cf (Figure 5).\u003c/p\u003e\n\u003cp\u003eMoreover, results demonstrated significant time x group x domain x cf interactions: for DTT vs. motT (\u003cem\u003eEst\u0026nbsp;\u003c/em\u003e= 0.01, \u003cem\u003eSE\u003c/em\u003e = 0.00, \u003cem\u003et\u003c/em\u003e = 3.64) and motT vs. cogT (\u003cem\u003eEst\u003c/em\u003e = 0.01, \u003cem\u003eSE\u003c/em\u003e = 0.00, \u003cem\u003et\u003c/em\u003e = 5.35), but not for DTT vs. cogT, pre-post changes of the two domains were moderated differently by cf (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results further indicated significant time x group x cf interactions for both S3cog and S3gait (Table 4). For S3cog, the time x group x cf interaction was significant for cogT vs. motT (\u003cem\u003eEst\u003c/em\u003e = -0.01, \u003cem\u003eSE\u003c/em\u003e = 0.00, \u003cem\u003et\u003c/em\u003e = -2.87), but not for DTT vs. cogT and for DTT vs. motT (Table 4): higher cf led to greater increases in the motT groups but to lower increases in the cogT group. In contrast, for S3gait, the time x group x cf interaction was significant for cogT and motT (\u003cem\u003eEst\u0026nbsp;\u003c/em\u003e= 0.00, \u003cem\u003eSE\u003c/em\u003e = 0.00, \u003cem\u003et\u003c/em\u003e = 2.33) and DTT and motT (\u003cem\u003eEst\u003c/em\u003e = 0.00, \u003cem\u003eSE\u003c/em\u003e = 0.00, \u003cem\u003et\u003c/em\u003e = 2.66; Table 4): higher cf led to greater increases in the DTT and cogT groups but to lower increases in the motT group.\u003c/p\u003e\n\u003cp\u003eResults further showed significant time x domain x cf interactions for the motT (\u003cem\u003eEst\u003c/em\u003e = -0.0, \u003cem\u003eSE\u003c/em\u003e = 0.0, \u003cem\u003et\u0026nbsp;\u003c/em\u003e= -6.05), but not for DTT (\u003cem\u003eEst\u003c/em\u003e = -0.0, \u003cem\u003eSE\u003c/em\u003e = 0.0, \u003cem\u003et\u003c/em\u003e = -0.68) and cogT (\u003cem\u003eEst\u003c/em\u003e = -0.0, \u003cem\u003eSE\u0026nbsp;\u003c/em\u003e= 0.0, \u003cem\u003et\u003c/em\u003e = 1.39). For motT, while for S3cog, a high cf led to larger increases than a lower cf, for S3gait, a lower cf led to larger increases than a higher cf (Figure 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFixed-effects estimates of final linear mixed models testing the time x group x domain x cf interaction on cognitive and motor performance in the Serial Threes task\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEst\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 604px;\"\u003e\n \u003cp\u003e\u003cstrong\u003em1a_cf_S3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.89\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.605\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eDomain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eCf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-6.39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x group (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x group (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x group (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-5.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x domain (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x domain (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x domain (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x cf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x cf (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.46\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x cf (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x cf (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eDomain x cf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eDomain x condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.41\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x group x domain (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x group x domain (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x group x domain (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x time x cf (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x time x cf (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x time x cf (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.834\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x domain x cf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x domain x cf (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x domain x cf (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup x domain x cf (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x group x domain x cf (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x group x domain x cf (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eTime x group x domain x cf (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 604px;\"\u003e\n \u003cp\u003e\u003cstrong\u003em1a_cf_S3/outcome (effects for time x group x cf nested in outcome)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eDomain S3cog: time x group x cf (DTT vs. cogT)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eDomain S3gait: time x group x cf (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eDomain S3cog: time x group x cf (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eDomain S3gait: time x group x cf (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eDomain S3cog: time x group x cf (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eDomain S3gait: time x group x cf (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 531px;\"\u003e\n \u003cp\u003e\u003cstrong\u003em1a_cf_S3/group (effects for time x domain x cf nested in group)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup DTT: time x domain x cf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup cogT: time x domain x cf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 321px;\"\u003e\n \u003cp\u003eGroup motT: time x domain x cf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-6.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e Linear mixed model formula for m1a_cf_S3 in lm4 syntax in R: cognitive and motor performance ~ 1 + time * group * domain * cf + condition + condition : domain + (1 + time + domain + condition | Subj); Linear mixed model formula for m1a_cf_S3/domain in lm4 syntax in R: cognitive and motor performance ~ 1 + domain / (time * group * cf) + condition + condition : domain + (1 + time + domain + condition | Subj); Linear mixed model formula for m1a_cf_S3/group in lm4 syntax in R: cognitive and motor performance ~ 1 + group / (time * domain * cf) + condition + condition : domain + (1 + time + domain + condition | Subj); The dependent variable was z-scaled. Bold values are significant t-values below above 2; S3cog = cognitive performance, S3gait = motor performance; cogT = cognitive training, motT = motor training, DTT = cognitive-motor dual-task training.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*** Figure 5 ***\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStroop task\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChanges from single- to dual-tasking\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the Stroop task, results showed a significant domain x condition interaction (\u003cem\u003eEst\u003c/em\u003e = 0.19, \u003cem\u003eSE\u003c/em\u003e = 0.07, \u003cem\u003et\u0026nbsp;\u003c/em\u003e= 2.86; Table 2). While STRcog decreased from ST to DT conditions, STRgait increased from ST to DT (Figure 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChanges from pre- to post-test\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eResults further revealed no significant effect of time, but a significant time x domain interaction (\u003cem\u003eEst\u003c/em\u003e = 0.14, \u003cem\u003eSE\u003c/em\u003e = 0.08, \u003cem\u003et\u0026nbsp;\u003c/em\u003e= 1.80; Table 4). Similar to the Serial Threes task, STRcog increased more than STRgait from pre- to post-test (Figure 7). Furthermore, again similar to the Serial Threes task, while the domain influenced pre-post changes, the condition did not, i.e., STRcog and STRgait showed similar pre-post changes for both DT and ST conditions, which was indicated by the non-significant interaction effects of condition x time and condition x time x group in a primary model (Table 11 in the supplementary material). Consequently, these interactions were excluded during the parsimonious model selection process as detailed in the \u003cem\u003eStatistical analysis\u003c/em\u003e section and reported in Table 11 in the supplementary materials.\u003c/p\u003e\n\u003cp\u003eMoreover, similar to the Serial Threes task, the results demonstrated no significant time x group interaction, but significant time x group x domain interaction: \u0026nbsp;for DTT vs. motT (\u003cem\u003eEst\u0026nbsp;\u003c/em\u003e= -0.37, \u003cem\u003eSE\u0026nbsp;\u003c/em\u003e= 0.17, \u003cem\u003et\u0026nbsp;\u003c/em\u003e= -2.13) and motT vs. cogT (\u003cem\u003eEst\u0026nbsp;\u003c/em\u003e= -0.45, \u003cem\u003eSE\u0026nbsp;\u003c/em\u003e= 0.19, \u003cem\u003et\u003c/em\u003e = -2.42), but not for DTT vs. cogT, pre-post changes varied for the STRcog and STRgait (Table 4). However, unlike the Serial Threes task, the results did not demonstrate significant time x group interactions for neither STRcog nor STRgait (Table 4), indicating similar pre-post changes across the different training groups within the cognitive and motor domains (see Figure 7)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFixed-effects estimates of final linear mixed models testing the time x group x domain interaction on cognitive and motor performance in the Stroop task\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"519\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEst\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003e\u003cstrong\u003em0a_STR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.866\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eDomain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eGroup (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eGroup (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eGroup (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.273 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTime x group (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTime x group (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTime x group (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.966\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTime x domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eGroup x domain (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.462\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eGroup x domain (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eGroup x domain (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.62\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eOutcome x condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTime x group x domain (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTime x group x domain (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eTime x group x domain (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 463px;\"\u003e\n \u003cp\u003e\u003cstrong\u003em0a_STR/outcome (effects for time x group nested in outcome)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eDomain STRcog: time x group (DTT vs. cogT) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eDomain STRgait: time x group (DTT vs. cogT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eDomain STRcog: time x group (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eDomain STRgait: time x group (DTT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.590\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eDomain STRcog: time x group (cogT vs. motT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.322\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003eDomain STRgait: time x group (cogT vs. motT) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.275\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u003c/strong\u003e Linear mixed model formula for m0a_STR in lm4 syntax in R: cognitive and motor performance ~ 1 + domain * time * group + condition + condition : domain + (1 + time + domain + condition | Subj); Linear mixed model formula for m0a_STR/domain in lm4 syntax in R: cognitive and motor performance ~ 1 + domain / (time * group) + condition + condition : domain + (1 + time + domain + condition | Subj); the dependent variable was z-scaled. Bold values are significant t-values below above 2; STRcog = cognitive performance, STRgait = motor performance; cogT = cognitive training, motT = motor training, DTT = cognitive-motor dual-task training.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*** Figure 6 ***\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*** Figure 7 ***\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eModeration of baseline motor and cognitive fitness\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eResults of the model comparisons indicated no moderation of cf, mf, or their interaction on pre-post changes in cognitive and motor performance (Table 14, 15, and 16 in the supplementary materials).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis randomized controlled trial is the first to examine how baseline levels of cognitive and motor fitness moderate the benefits of three different training regimes (cognitive training, motor training, cognitive-motor dual-task training) on cognitive-motor dual-task performance in older adults. Additionally, to draw conclusions about the generalizability of our results, we tested the effects of two different cognitive tasks administered during walking (Serial Threes, Stroop).\u003c/p\u003e \u003cp\u003eIn summary, the results indicated that for both the Serial Threes task and the Stroop task, cognitive and dual-task training led to greater increases in cognitive performance than in motor performance across both ST and DT conditions, while the motor training group showed greater increases in motor performance than in cognitive performance. The results also showed that baseline motor fitness and the interaction between baseline cognitive and motor fitness did not moderate pre-post changes in cognitive or motor performance. However, baseline cognitive fitness did play a significant moderating role for the Serial Threes task. When comparing the groups, in particular, cognitive and motor training showed opposing effects. For individuals with higher cognitive fitness, motor performance increased more in the cognitive training group than in the motor training. However, their cognitive performance increased more in the cognitive training than in the motor training group.\u003c/p\u003e \u003cp\u003eFor both, the Serial Threes task and the Stroop task, cognitive performance declined when transitioning from ST to DT conditions, while gait variability decreases. These findings align with existing literature supporting the \"posture-first\" strategy, where resources are allocated to maintain motor performance at the expense of cognitive performance (e.g., prioritization theory [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Dual-tasking can occasionally also result in improved motor performance [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. This may occur in specific situations where maintaining motor performance is essential for the successful completion of the cognitive task. For instance, walking steadily (i.e., a lower gait variability) may help individuals to focus attention and respond accurately to a cognitive challenge during dual-task walking. It has been suggested that the improved motor performance observed in certain DT conditions reflects an adaptive strategy to support the cognitive demands of the task [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study revealed comparable improvements in both single-task and dual-task performance, suggesting the training benefits extended beyond task integration to enhance individual task performance and overall cognitive resources. These findings align with existing literature on cognitive, motor, and cognitive-motor training, demonstrating increased efficiency, greater capacity, and improved motor control [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Notably, our cognitive single-task assessment was conducted while standing, introducing an element of postural control. This approach may have created a de facto DT condition, potentially underestimating pure cognitive performance due to cognitive-motor interference. The observed improvements in cognitive single-task performance likely reflect a combination of enhanced cognitive function and improved postural control. However, it also enhances ecological validity by more closely resembling real-world conditions.\u003c/p\u003e \u003cp\u003eThe observed similar performance increases in cognitive task performance during dual-tasking across all three training groups (no significant time x group interaction within outcome) align with recent studies on cognitive-motor interventions for older adults. This finding is consistent with results reported by Ansai and colleagues [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], who found no significant differences in cognitive dual-task performance between multicomponent exercise alone and multicomponent exercise combined with cognitive tasks. Similarly, Downey and colleagues [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]compared cognitive, motor, and aerobic training and found similar intervention effects of cognitive performance during dual-tasking, supporting the idea that various training approaches can enhance cognitive-motor dual-tasking in older adults. These findings reinforce the concept of diverse pathways for improving cognitive-motor dual-task performance in the aging population. Downey and colleagues [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e], for example, noted that while executive function improvements were specific to cognitive training, energy efficiency during walking showed particular enhancement following motor training. This suggests that different training modalities may target distinct aspects of cognitive-motor performance. However, our findings diverge from those of Falbo and colleagues [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. who observed a more substantial increase in cognitive dual-task performance in their cognitive-motor group compared to a group that received only motor training. Notably, their cognitive-motor intervention integrated higher cognitive demands directly into motor tasks, such as associating equipment features (e.g., color or size of obstacles) with specific motor requirements and incorporating random switching between stimulus-response sets during physical tasks. This integrated approach, embedding cognitive challenges within motor tasks, may be more effective than simply adding a separate cognitive task to a motor activity [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith respect to gait performance, our findings reveal a different pattern of results across the three intervention groups. Notably, only the motor group demonstrated improvements in gait over time, while the cognitive and dual-task groups either declined or maintained their baseline performance levels. Here it is important to note that differences between groups were significant only for the Serial Threes task, with significant group-by-time interaction effects observed for cognitive training compared to motor training, and a nearly significant result (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.88) for dual-task training compared to motor training. However, the Stroop tasks showed a similar but non-significant pattern of results. The improvement observed in the motor training group's gait performance under DT conditions aligns with existing literature [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and can be attributed to several factors. Firstly, the motor training program included a walking component, which directly addressed the primary outcome measure, supporting the principle of training specificity. Additionally, the training of coordination, strength, and flexibility are all relevant for postural control and walking [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Also, the lack of improvement in gait performance after dual-task training is consistent with recent reviews investigating cognitive-motor training effects on gait speed under DT conditions in older adults [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. One possible explanation for this result is that participants might have prioritized cognitive performance at the expense of gait quality during training, a strategy that may have persisted during the outcome measurements. This prioritization is plausible, as most cognitive-motor dual-task training programs, especially those including cognitive tasks, have a high engagement potential for the cognitive but not for the motor tasks. In other words, participants may have perceived the cognitive aspects of the training as more challenging or important, leading to a focus on cognitive performance at the expense of gait quality. The absence of improvement in the cognitive training group is similar to findings reported by Downey and colleagues [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e] and can also be explained by task prioritization. Participants who underwent purely cognitive training may have lacked the necessary practice in integrating cognitive and motor tasks, leading to prioritization of cognitive performance when faced with a dual-task situation during assessment. The task prioritization hypothesis is further supported by the results showing that, for both the Serial Threes and Stroop tasks, the time-by-outcome interaction within individual groups was significant for the dual-task training group and the cognitive training group, but not for the motor training group. It could be hypothesized that decreases in gait performance for the cognitive training and motor training groups were less pronounced in the Stroop task compared to the Serial Threes task because performing a dual task naturally reduces step variability compared to a single task. This effect may be attributed to the nature of the Stroop task, which requires more focused attention on the cognitive component. This increased focus could facilitate a more stable gait pattern, making it less susceptible to training effects [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings suggest that baseline motor fitness, as well as the interaction between baseline cognitive and motor fitness, did not impact the intervention effects for either task. In contrast, baseline cognitive fitness was found to significantly moderate the intervention effects for the Serial Threes task. This differential impact of baseline cognitive and motor abilities on training outcomes aligns with and extends prior research. The moderation effect of cognitive fitness is consistent with the findings of Strobach and colleagues [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], who demonstrated that reaction time variability (as a proxy for inefficient neural processing) moderates the intervention effects of cognitive dual-task training on cognitive dual-task performance in both young and older adults. The absence of a moderating effect of motor fitness further supports the notion that dual-task walking performance is more strongly linked to cognitive, rather than motor capabilities in older adults [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe moderation of cognitive fitness for the Serial Threes task, but not for the Stroop task, may be explained by specific key features of the tasks. One potential explanation is that the self-paced mode of the Serial Threes task allows participants to apply personalized strategies learned during training to optimize their performance (e.g., counting backwards in sync with their walking rhythm). In contrast, the fixed-paced Stroop task imposes strict temporal constraints, limiting opportunities for strategy deployment and reducing the moderating influence of cognitive fitness. Another explanation is that the Serial Threes task relies heavily on verbal responses, whereas the Stroop task depends on manual motor responses. These manual motor responses primarily rely on simpler sensorimotor pathways, making the Stroop task less directly associated with cognitive fitness compared to the Serial Threes task.\u003c/p\u003e \u003cp\u003eSimilar to the results of the time x group x outcome interaction, the four-way interaction between cognitive fitness x time x group x outcome was significant for dual-task vs. motor training and for cognitive vs. motor training but not for dual-task vs. cognitive training. This highlights the similarities between cognitive and dual-task training, as well as the distinct effects of motor training on cognitive-motor dual-task performance in older adults. Baseline cognitive fitness influenced cognitive and gait performance differently in the motor training group (significant time x outcome x cognitive fitness interaction for motor training) but had similar effects in the cognitive and dual-task training groups (no significant time x outcome x cognitive fitness interaction for cognitive and dual-task training). In the motor training group, higher cognitive fitness was associated with greater improvements in cognitive performance but smaller improvements in gait performance compared to individuals with lower cognitive fitness. Motor training benefits both cognitive and motor skills [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. However, individuals with lower baseline cognitive fitness may utilize their newly gained cognitive resources differently than individuals with higher baseline cognitive fitness. People with higher baseline cognitive fitness are often more engaged in cognitive activities throughout their lives [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. This engagement makes them more familiar with and confident in performing cognitively demanding tasks [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. As a result, after gaining additional cognitive resources, individuals with lower baseline cognitive fitness may allocate fewer resources to cognitive tasks and prioritize motor tasks when faced with competing demands, as motor tasks are often more automatic and essential for safety (e.g., maintaining balance while walking). Conversely, individuals with high baseline cognitive fitness might exhibit the opposite pattern, directing their newly gained cognitive resources toward cognitive tasks rather than motor tasks. Since both cognitive and dual-task training involve cognitive tasks similar to those assessed in the outcomes, individuals with lower cognitive fitness may benefit from a sense of familiarity with these tasks. This familiarity could enhance their confidence in handling such tasks, thereby reducing the influence of baseline cognitive fitness on both cognitive and motor task performance.\u003c/p\u003e \u003cp\u003eThis RCT has several strengths but also some limitations. One key strength is that, by including motor, cognitive, and dual-task training in our intervention design, we were the first to directly compare the effects of these three training programs on cognitive and motor dual-task performance. Earlier trials only compared two of these training approaches. Additionally, by structuring the dual-task training to include exactly the same exercises as those used in the motor and cognitive training groups, we eliminated the possibility that the effects of dual-task training were due to different types of exercises rather than the dual-task nature of the training itself. However, our design and setup also come with some limitations. First, we cannot draw conclusions on how the effects would compare to an active or passive control group, such as stretching or waitlist control. However, this was not our primary goal, as there is already substantial literature on this topic. Instead, we aimed to examine the differential effects of cognitive, motor, and cognitive-motor dual-task training, as well as the moderating role of baseline cognitive and motor fitness. Second, during dual-task training, participants received direct feedback on their performance for the cognitive exercises, with progress visualized on a colorful screen. In contrast, instructions for the motor exercises were verbally communicated by the trainers. This difference may have made it more challenging for some participants to focus equally on both motor and cognitive training, potentially leading them to prioritize the cognitive tasks.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study highlights the complex relationship between cognitive and motor outcomes in older adults undergoing cognitive, motor, or cognitive-motor dual-task training. The findings reveal that cognitive and dual-task training have comparable effects on cognitive-motor dual-task performance, primarily enhancing cognitive outcomes. In contrast, motor training demonstrates a unique advantage in improving gait performance, highlighting its critical role in mobility enhancement and fall prevention. Notably, baseline cognitive fitness emerged as a key moderator of training effects, particularly in motor training. While cognitive and dual-task training outcomes appeared less influenced by baseline cognitive fitness, higher cognitive fitness significantly shaped the effects of motor training. This suggests that baseline cognitive fitness may determine how newly acquired cognitive resources are allocated between cognitive and motor tasks, especially under DT conditions. Additionally, the observed improvements in both single-task and DT conditions suggest that the benefits of training extend beyond task-specific integration, enhancing general cognitive and motor capacities. These findings provide valuable insights for designing targeted interventions to support mobility, cognitive health, and quality of life in aging populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of xx and xx and was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent before participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent for the publication of images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the German Research Foundation (DFG) and is part of the DFG Priority Program SPP 1772 (grant VO 1432/22\u0026ndash;1). RS is supported by an ESF (European Social Fund) and SAB (Development Bank of Saxony) doctoral scholarship (100342331). The funding body doesn\u0026rsquo;t play any role in the design of the study, the collection, analysis and interpretation of data and the deci‑sion to write and publish manuscripts. The study protocol has not been peer reviewed by the funding body.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAuthor Contributions StatementConceptualisation: MM, RS, OB, CVR; Methodology: MM, RS, NH, OB, CVR; Software: RS; Validation: MM, RS, OB, CVR; Formal analysis: MM; Investigation: MM, NH, RS; Ressources: CVR; Data curation: MM, RS; Writing \u0026ndash; original draft: MM; Writing \u0026ndash; review and editing: MM, RS, NH, OB, CVR; Visualisation: MM; Supervision: OB, CVR; Project administration: OB, CVR; Funding acquisition: OB, CVRFundingThe study was funded by the German Research Foundation (DFG) and is part of the DFG Priority Program SPP 1772 (grant VO 1432/22\u0026ndash;1). RS is supported by an ESF (European Social Fund) and SAB (Development Bank of Saxony) doctoral scholarship (100342331). The funding body doesn\u0026rsquo;t play any role in the design of the study, the collection, analysis and interpretation of data and the decision to write and publish manuscripts. The study protocol has not been peer reviewed by the funding body.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe would like to thank all former members and associates of the project, along with research group members, technicians, and student members, for their assistance with hardware and software implementation, data acquisition, analysis, training administration, and implementation. We also thank all participants for their involvement in this project. Further, we are grateful to Reinhold Kliegl for his excellent support in the statistical analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVoelcker-Rehage C, Stronge AJ, Alberts JL. Age-related differences in working memory and force control under dual-task conditions. 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Perspect Psychol Sci. 2014;9:388\u0026ndash;407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1745691614527465\u003c/span\u003e\u003cspan address=\"10.1177/1745691614527465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"dual-task walking, exercise intervention, neuroplasticity, multitasking","lastPublishedDoi":"10.21203/rs.3.rs-6185287/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6185287/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCognitive-motor dual-tasking, essential for daily activities like walking in busy spaces, declines with age. Research suggests that cognitive (cogT), motor (motT), and cognitive-motor dual-task training (DTT) can improve dual-task performance in older adults, yet studies report heterogeneous effects. This RCT examined whether baseline cognitive (cf) and motor fitness (mf) moderates training effects of these interventions on cognitive-motor dual-task performance in older adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eParticipants (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;97, aged 65\u0026ndash;75) completed 12-week interventions in cogT, motT, or DTT. A battery of cognitive and motor tests was conducted at pre-test to create composite scores of cf and mf. Cognitive-motor performance was assessed at pre- and post-test using a Serial Threes task (S3), a Stroop task (STR), and a walking task. For the cognitive domain, outcomes included correct responses (S3) and inverted RT inhibition costs expressed as percentage (STR); for the motor domain, step variability (inverted to step stability) was used. Outcomes were assessed under single-task (ST) and dual-task (DT) conditions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn summary, linear mixed model results indicated that for both S3 as STR, cogT and DTT led to greater increases in cognitive performance than in motor performance across both ST and DT conditions, while the motT showed greater increases in motor performance than in cognitive performance (S3: cogT vs. motT: \u003cem\u003et\u003c/em\u003e = -2.25, DTT vs. motT: \u003cem\u003et\u003c/em\u003e = -2.69; STR: cogT vs. motT: \u003cem\u003et\u003c/em\u003e = -2.41, DTT vs. motT: \u003cem\u003et\u003c/em\u003e = -2.08). The results also showed that mf and the interaction between cf and mf did not moderate pre-post changes in cognitive or motor performance. However, cf did play a significant moderating role for the S3. When comparing the groups, in particular, cogT and motT showed opposing effects (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.35). For individuals with higher cf, motor performance increased more in the cogT than in the motT. However, their cognitive performance increased more in the cogT than in the motT.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe results emphasize the complex relationship between cognitive and motor outcomes in cognitive-motor interventions and the key role of baseline fitness in moderating intervention effects.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eThis trial was retrospectively registered at German Clinical Trials Register (DRKS00022407).\u003c/p\u003e","manuscriptTitle":"An RCT on 12 weeks of cognitive, motor or combined cognitive-motor exercise to improve dual-task walking in older adults: The role of baseline cognitive and motor fitness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 20:30:24","doi":"10.21203/rs.3.rs-6185287/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"588c1512-2021-4b7a-9940-0b2c287fd911","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-02T14:23:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-20 20:30:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6185287","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6185287","identity":"rs-6185287","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}