Functional Lower Extremity Strength Influences Stepping Strategy in Community-Dwelling Older Adults During Single and Dual-Task Walking

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Peoples, Kenneth D. Harrison, Keven G. Santamaria-Guzman, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3983607/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jun, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract As age increases, a decline in lower extremity strength leads to reduced mobility and increased fall risks. This decline outpaces the age-related reduction in muscle mass, resulting in mobility limitations. Older adults with varying degrees of mobility-disability use different stepping strategies. However, the link between functional lower extremity strength and stepping strategy is unknown. Therefore, understanding how age-related reductions in functional lower extremity strength influence stepping strategy is vital to unraveling mobility limitations. Participants were recruited and tested at a local community event, where they were outfitted with IMUs and walked across a pressurized walkway. Our study reveals that older adults with normal strength prefer adjusting their step time during walking tasks, while those with reduced strength do not exhibit a preferred stepping strategy. This study provides valuable insights into the influence of functional lower extremity strength on stepping strategy in community-dwelling older adults during simple and complex walking tasks. These findings could aid in diagnosing gait deviations and developing appropriate treatment or management plans for mobility disability in older adults. mobility lower extremity strength gait dual-task stepping strategy Figures Figure 1 Figure 2 Figure 3 Introduction Maintaining mobility is vital for older adults to preserve their independence, self-reliance, community involvement, and overall health. Nevertheless, the aging process has a notable impact on this ability. As individuals cross their fifties, they may experience a gradual decline in their lower extremity strength, leading to reduced functional mobility, slower walking speeds, increased sedentary behavior, social isolation, higher fall risks, and a decline in their quality of life across physical, cognitive, emotional, and social domains 1–3 . Alarmingly, lower extremity strength deteriorates faster than age-related reductions in muscle mass, prompting mobility limitations since walking requires supporting body weight through sit-to-stand transitions and propelling the body forward during walking using the lower body muscles 3–5 . Therefore, mitigating deficits in lower extremity strength is paramount as society grays as it requires elucidating complex interactions between reduced lower extremity strength, destabilized gait, and heightened mobility-disability. Functional lower extremity strength plays a critical role in preserving mobility but inevitably declines with advanced age, resulting in a nearly three-fold increase in fall risks in older adults and costing over $ 50 billion in annual geriatric injury expenditures from government programs such as Medicare 6,7 . Quantifying decrements to functional lower extremity strength before outright mobility-disability occurs is critical but requires direct assessments linking functional lower extremity strength to gait safety. The 5-repetition Sit-to-Stand test fills this need since itis a clinically feasible test of functional lower extremity strength that holds validity and strongly predicts mobility limitations in older adults 8–11 . Poor performance on the 5xSTS is associated with reduced gait speed, functional impairment, frailty, and fall risk in community-dwelling adults 1,8,11–14 . Moreover, older adults with reduced functional mobility use different stepping strategies (increasing step length or step time); however, the link between functional lower extremity strength and stepping strategy is unknown 15 . Therefore, understanding how age-related reductions in functional lower extremity strength influence stepping strategy is vital to unraveling mobility limitations. Stepping strategy is important to contemplate while examining normal and dual-task walking patterns. In order to gain a comprehensive understanding of how older adults navigate their environment, we need to consider two important factors: the ability to regulate walking pace and the aptitude to perform multiple tasks simultaneously while walking 16,17 . Regulating walking pace is important to meet the demands of the environment. However, the ability to walk and multitask is essential for maintaining independence, as many daily activities require the ability to walk while performing other tasks, such as carrying a plate of food or engaging in conversation 16,18–20 . Thus, examining the relationship between functional lower extremity and stepping strategy in older adults during simple or complex walking tasks can provide valuable insights into gait safety. This study investigates whether functional lower extremity strength affects stepping strategy in community-dwelling older adults across normal and dual-task walking. We hypothesize: 1) Older adults with reduced functional lower extremity strength will use a step time dominant stepping strategy than those with normal strength, and 2) Stepping strategies will be similar between groups during walking conditions where multitasking is required. Thus, investigating functional lower extremity strength and stepping strategy can enhance our understanding of the factors that impact gait stability and help devise effective interventions to proactively address mobility-disability. Methods Participants Our study's participant recruitment and data collection were conducted at a local community health fair event instead of a laboratory. This was done to improve ecological validity and demonstrate the feasibility of implementing assessment procedures in a practical, real-world context. The recruitment and data collection took place over two consecutive years (2022 and 2023) as part of our ongoing research engagement and service partnership to address health needs in a rural region. Our study focused on a sample from an underserved rural community. It was in response to a call from the National Institute of Aging (NIA) workshop on age-related changes in gait biomechanics. The workshop stressed the importance of diverse sampling and building community partnerships 21 . Twenty older adults (17F, 72 ± 6) living in the Lee County community participated in this study during a local health fair. Participants who were free from lower extremity injuries such as bone fractures, muscle strains, and joint dislocations were included in the study. Participants who did not report any severe problems that may affect their ability to walk were also included. However, those who reported any neurological disease such as Parkinson’s disease, Essential Tremor, Multiple Sclerosis, Stroke, or Traumatic Brain injury were excluded from the study. Almost 50% of the participants in the study were from communities of color. Material This study was approved by the Auburn University Institutional Review Board on the basis of minimal risk, and a waiver for informed consent was granted. All participants were given a detailed explanation of the study's purpose, procedures, potential risks, and benefits, and provided informed consent before their inclusion in the study. The study was conducted according to the ethical principles outlined in the Declaration of Helsinki. Following consent participants underwent a series of assessments, including a set of questionnaires, two strength assessments, three functional mobility assessments, and four gait assessments. The questionnaire included questions about their demographic information, living status, education, sleep habits, retrospective fall history, and self-reported physical activity history. Fear of falling was measured using the Fall Efficacy Scale (FES) developed by Tinetti et al. (1990), and barriers to physical activity were assessed using the CDC Barrier to Being Active Quiz (BBAQ) (Control & Prevention, 2013). Functional Lower Extremity Strength measured by the Instrumented 5-Rep Sit-to-Stand (i5xSTS) The i5xSTS device was employed to measure the functional strength of the lower extremities using wireless inertial measurement units (IMU) from APDM Opals (APDM Inc, Portland, OR). The Opal sensor has triaxial accelerometers, gyroscopes, and magnetometers, capturing signal data at 128 Hz. To obtain readings, researchers placed six IMUs at four different body locations: 1) atop the sternum centered over the manubrium, 2) on the superior aspect of the posterior sacral surface (below the fifth lumbar vertebrae L5), 3) on the posterior aspect of the distal radius and ulna, and 4) on the dorsal surface of the foot, centered on the intermediate and lateral cuneiform. Participants were instructed to sit with their backs against the chair, arms crossed, and hands touching the anterior aspect of their deltoid. They were then asked to stand and sit as quickly as possible for five repetitions. The duration of this exercise was used to determine the strength status of the participants in this study. Gait Testing All participants completed the walking trials on a walkway next to the main health fair venues and were exposed to roughly ~ 75dB of sound. All assessments were conducted during vendor exhibitions, demonstrations, and community events to reflect real-world complexity. Participants were asked to walk under four conditions during the community health fair. These were: 1)Walking at their normal speed, 2) Walking at their fastest speed, 3) Motoric Dual Task at their normal speed (Walking at their normal speed while holding a tray with a cup of water), and 4) Motoric Dual Task at their fastest feed (Walking at their fastest speed while holding a tray with a cup of water. The researcher instructed the participants to walk at either a "comfortable, natural walking speed" or the "fastest, safe walking speed without jogging or running," as prompted. Additionally, the researchers provided non-prioritizing instructions while the participants walked with the tray to prevent participants from prioritizing the tray task over the walking task, and vice versa. The GAITRite instrumented walkway was used to capture all spatial and temporal gait cycle parameters used for this study. Our GAITRite system (GAITRite Gold, CIR Systems, Clifton, NJ) consists of an electronic walkway approximately 8.2 meters long, connected to a personal computer via an interface cable. The walkway comprises a series of sensor pads inserted in a grid formation between a layer of vinyl (top cover) and foam rubber (bottom cover). The active area of the walkway is 61 cm wide and 732 cm long, while the sensors are placed 1.27 cm apart, consisting of a total of 27,648 sensors that are activated by mechanical pressure. The data from the activated sensors is collected by a series of onboard processors and transferred to the computer through a serial port. The system has a sampling rate of 80 Hz. Data analysis Functional Lower Extremity Strength measured by the Instrumented 5-Rep Sit-to-Stand (i5xSTS) i5xSTS was collected from APDM Opals and participant data was processed in Moveo Explorer version 1.0.0.202206 (APDM Inc, Portland, OR). Total duration was calculated using the average duration of the sit-to-stand and stand-to-sit transitions. Participants were classified as low strength (LS) if their 5xSTS duration exceeded normative performance values for their age range of 11.4 seconds (60 to 69 years), 12.6 seconds (70 to 79 years), and 14.8 seconds (80 to 89 years) 22 . Gait Cycle Parameters Gait speed, step length, and step time for all walking conditions were exported from the GAITRite software (GAITRite, CIR Systems Inc., Clifton, NJ). Step length was measured using the heel center of the current footprint to the heel center of the previous footprint on the opposite foot. Step time is the elapsed time from the first contact of one foot to the first contact of the opposite foot. Gait Speed was measured using the distance traveled divided by the total ambulation time. Step length and step time from each condition was used to calculate the stepping strategy measured by Length-Time Difference (see below). Length-Time Difference (LTD) LTD was derived from a previous study evaluating how strategy, adjustments to cadence or stride length when walking at a normal speed and fast speed, influence lower extremity joint moments 23 . The equation used to calculate strategy for cadence and stride length are below: Ardestani’s Strategy Eq. (1) $$\varDelta {Cadence}_{i}=\left(\frac{{Cadence}_{Fast}-{ Cadence}_{Normal}}{{Cadence}_{Normal}} \right)*100\% i = \text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\text{o}\text{f}\text{s}\text{u}\text{b}\text{j}\text{e}\text{c}\text{t}\text{s}$$ $$\varDelta {Stride}_{i}=\left(\frac{{Stride}_{Fast}-{ Stride}_{Normal}}{{Stride}_{Normal}} \right)*100\%$$ LTD represents relative change in step length and step time when comparing two conditions (i.e. fast vs preferred. An LTD > 0 indicates a step length dominant strategy (increasing step length), an LTD < 0 indicates a step time dominant strategy, while an LTD = 0 indicates a neutral strategy, implying equal contribution from both step time and step length (see Fig. 1 ) 15,23 . Length-Time Difference Eq. (2) $$LTD=\left(\frac{{Length}_{Fast}-{ Length}_{preferred}}{{length}_{preferred}} \right)+\left(\frac{{Time}_{Fast}-{ Time}_{preferred}}{{Time}_{preferred}}\right)*100\%$$ Modified Length-Time Difference Eq. (3) $$LTD=\left(\frac{{Length}_{Tray+ Walk Speed}-{ Length}_{Walk Speed}}{{length}_{Walk Speed}} \right)+\left(\frac{{Time}_{Tray+Walk Speed}-{ Time}_{Walk Speed}}{{Time}_{Walk Speed}}\right)*100\%$$ Following the equations above, we calculated the stepping strategy for four distinct comparisons: Walking at a Fast Speed vs Walking at a Normal Speed (Walking Comparison 1), Motoric Dual Task at a Fast Walking Speed vs Motoric Dual Task at a Normal Walking Speed (Walking Comparison 2), Motoric Dual Task at a Normal Walking Speed vs Walking at a Normal Speed (Walking Comparison 3), and Motoric Dual Task at a Fast Walking Speed vs Walking at a Fast Speed (Walking Comparison 4). Statistical Analysis Power Analysis An a priori power analysis was conducted using G*Power version 3.1.9.7 24 to attain 80% power and determine the minimum sample size based on data from 15 , which observed nearly large effect size ( d = 0.77) between the 5-repetition chair stand duration of the Short Physical Performance Battery and stepping strategy using Length-Time difference in older adults with mobility limitations. With the significance criterion set at α = .05 and power = .80, the minimum sample size needed with this effect size is N = 12 for a repeated measures ANOVA. Descriptive Statistics Descriptive statistics are provided for each group for the following variables: gait speed (m/s), step length (m), and step time (s). Independent Samples T-Test An independent samples t-test was conducted to compare age, height, mass, 5x sit-to-stand test duration, sit-to-stand duration, stand-to-sit duration, sit-to-stand angle, stand-to-sit angle, and Falls Efficacy Scale (FES) score between the low strength and normal strength groups. 2x4 Repeated Measures ANOVA A 2x4 repeated measures ANOVA was conducted to examine the effect of group (Low vs. Normal) and walking comparisons: Walking at a Fast Speed vs Walking at a Normal Speed (Walking Comparison 1), Motoric Dual Task at a Fast Walking Speed vs Motoric Dual Task at a Normal Walking Speed (Walking Comparison 2), Motoric Dual Task at a Normal Walking Speed vs Walking at a Normal Speed (Walking Comparison 3), and Motoric Dual Task at a Fast Walking Speed vs Walking at a Fast Speed (Walking Comparison 4) measured by LTD. Mauchly’s test indicated that the assumption of sphericity was violated for condition (p < .05); therefore, we used Greenhouse-Geisser correction. Post-hoc test comparisons were performed using the Tukey HSD test. Results Descriptive Statistics The low functional strength group had a slower gait speed ( M = 1.26, SD .25) across the single and motoric dual task walking trials compared to the normal strength group ( M = 1.37, SD .28). The step lengths between each group were similar ( M = .65, SD .11), however, the normal functional strength group had longer overall stepping times ( M = .48, SD .05) in comparison to the low functional strength group ( M = .38, SD .77), summarized in Table 1 . Figure 2 depicts mean gait speed (m/s), step length (m), and step for each group across single and motoric dual task walking conditions. Table 1 Means and Standard Deviations for Gait Speed (m/s), Step Time (s), and Step Length (m) for each group across all walking conditions. Low Strength Normal Strength Gait Speed (m/s) 1.26 ± .25 .77–1.96 1.37 ± .28 0.831–2.14 Step Length (m) 0.65 ± .11 .44 − .93 .65 ± .11 .45–1.0 Step Time (s) 0.38 ± 0.77 1.45–1.73 .48 ± .05 .39 – .56 Independent Samples T-Tests An independent samples T-test was conducted to compare group characteristics summarized in Table 1 . There was a significant difference in 5x sit-to-stand test duration between the low strength group ( M = 19.35, SD = 4.73) and the normal strength group ( M = 11.27, SD = 2.21), t ( 18 ) = 5.05, p < .001, d = 2.27. This indicates that the low-strength group took significantly longer to complete the 5x sit-to-stand test than the normal-strength group with an extremely large effect size. Longer 5x sit-to-stand test duration may be attributed to the long sit-stand phases ( p < .006) and greater sit-to-stand lean angles ( p < .006). No significant differences existed between age, height, mass, Stand-to-sit duration, Stand-to-Sit lean angle, or FES score, summarized in Table 2 . Table 2 All Participant Characteristics Low Strength Normal Strength p Cohen’s d Sex (M/F) 2M/7F 1M/10F .46 0.36 Age (years) 74 ± 7 65–87 69 ± 5 61–78 .07 0.87 Height (m) 1.64 ± 0.1 1.45–1.73 1.61 ± .1 1.47–1.79 .29 0.29 Mass (kg) 84.1 ± 15.5 63.4–113.8 74.7 ± 14.6 52.6–101.3 .24 0.62 5xSTS Duration (s) 19.3 ± 4.7 15.3–27.6 11.3 ± 2.2 7.7–14.1 < .001 2.27 Sit-to Stand duration 1.4 ± 0.6 0.9–2.8 0.9 ± 0.17 0.7–1.2 .006 1.39 Stand-to-Sit duration 0.90 ± 0.4 0.6–1.8 0.75 ± 0.3 0.6–1.5 .32 0.46 Sit-to-Stand lean angle 57 ± 28 21–104 29 ± 6 21–38 .006 1.41 Stand-to-Sit lean angle 32 ± 10 14–101 28 ± 6 23–28 .08 0.83 FES Score 17 ± 8 10–29 14 ± 6 10–30 .36 0.46 p- values in bold indicate statistically significant differences between groups. Values indicate mean ± sd. Values indicate range: minimum-maximum. M, male, F, female. FES-Falls Efficacy Scale 2 X 4 Repeated Measures ANOVA A 2 x 4 repeated measures ANOVA showed statistically significant differences in the main effects of stepping strategy, functional lower extremity strength (Low vs. Normal) and interaction between functional lower extremity strength and stepping strategy measured by LTD, summarized in Table 3 . The normal strength group showed a clear preference for a step time dominant strategy ( M = -7.17, SD = 3.82) compared to the low strength group. The low strength group vacillated between step length and step time across walking comparisons ( M = -0.4 , SD = 4.94) see Fig. 3 . Table 3 Main Effects of Repeated Measures ANOVA Factor F (df) p η² p Walking Comparison 5.205 (2,36) 0.010 0.224 Strength Status 11.773 ( 1 , 18 ) 0.003 0.395 Walking Comparison * Strength Status 3.487 (2,36) 0.041 0.162 Figure Post hoc analysis with a Tukey’s HSD revealed there was a mean difference of 14.32 (95% CI: 0.68 to 3.60) between the Normal Strength Walking and Low Strength groups during Walking Comparison 3 (p < .001). Tests of simple main effects showed the interaction was driven by differences between strength groups in Walking Comparison 3 (Motoric Dual Task at a Normal Walking Speed vs Walking at a Normal Speed ), F ( 1 , 18 ) = 9.66, p < .05, summarized in Table 4 . Table 4 Simple Main Effects - Strength Status Level of Condition Sum of Squares df Mean Square F p Walking Comparison 1 310.211 1 310.211 4.057 0.059 Walking Comparison 2 147.660 1 147.660 2.933 0.104 Walking Comparison 3 1025.323 1 1025.323 9.661 0.006 Walking Comparison 4 4.166 1 4.166 0.308 0.586 Discussion The present study investigated if functional lower extremity strength influences stepping strategy in community-dwelling older adults during simple and complex walking. Our main findings are: 1) Older adults with normal lower extremity strength preferred adjusting their step time when walking in both single and dual task conditions, 2) Older adults with reduced functional strength did not exhibit a preferred stepping strategy while walking, either with or without a motoric dual task (i.e., walking while holding a tray balancing a cup of water), 3) The interaction of stepping strategy was mostly influenced by the motoric dual task. Our findings demonstrate that adults with reduced functional lower strength lack a preferred stepping strategy across all walking comparisons, whereas stronger older adults prioritize adjusting step timing over step length. Adjusting step timing may be a safer compensation for older adults to regulate walking given age-related declines in neuromuscular control, balance, proprioception, and reaction time 25,26 . Relying on adjusting step length likely reduces instability by bringing the center of mass closer to the leading foot to improve stability 27 . Contrary to our hypothesis, the low strength group did not demonstrate a step time strategy. Upon examination of the individual data, over half of the people in the low strength group utilized a step length strategy. Our findings disagree with Baudendistel et al. (2021) who reported in a sample of people with mobility disability, a longer sit to stand time relates to more step timing adjustments when comparing a walking trial at a comfortable pace versus a walking trial at a faster pace. Our differences between studies can be explained by the walking conditions used, and population sampled. Specifically, Baudendistel et al. recruited people who met requirements for mobility disability, as determined by physical inactivity, worse physical function, and slower preferred gait speed. Our study did not have the same exclusion and inclusion criteria, and we recruited people at a local health fair hosted in a community recreation center, and 13 of 20 participants reported being physically active 28 . Additionally, Baudendistel et al. compared a single task preferred-walking speed to a single task fast-walking speed. Our study adds complexity to this comparison by having participants complete a motoric dual task during fast and normal walking, in addition to single task fast and normal walking. Interestingly, the interaction effect in our study reveals functional lower extremity strength impacts stepping strategy primarily when attention is divided during walking - an essential skill for navigation and avoidance of fall risk hazards 29 . Elucidating connections between functional lower extremity strength, attentional resources, and stepping strategies provides clinically meaningful patient profiles of gait instability often overlooked in older adults without overt mobility disability. Tailoring gait retraining programs by strength capacity and attention underscores a precision rehabilitation approach to improve neurocognitive control of gait and mitigate fall risk 30 . Future research should explore how strength training combined with stepping strategy re-education impacts gait variability and dual-task. A strength of our study lies in its community-based approach, which fosters inclusivity compared to traditional university settings. By engaging individuals in community settings away from the university, we enhance the generalizability of our findings. This approach addresses limitations associated with convenience samples from local university communities 21 . Actively seeking participation from the community ensures our research reflects a broader spectrum of experiences, enriching the validity and applicability of our results. Our community-based approach fosters collaboration, enhances diversity, and ensures interventions meet specific needs, aligning with principles of community engagement. Conclusion In our study, we found that functional lower extremity strength assessed by the instrument 5-repetition Sit-to-Stand influences stepping strategy in community-dwelling older adults. Older adults with normal functional lower extremity strength utilize a step-time dominant strategy, adjusting step time under normal and dual-task conditions. In contrast, older adults with reduced lower extremity strength lack a clear preference for modulating step length or time to walk faster. When faced with an added cognitive demand during gait, older adults with reduced lower extremity strength switch towards a step-length dominant strategy, compared to older adults with normal strength who maintain a time-based strategy. Our findings can help clinicians proactively identify community-dwelling older adults with reduced lower extremity strength who may have difficulty safely adjusting gait patterns in response to environmental demands. Future research should test whether strength training programs can also optimize stepping strategies during walking in older adults with reduced lower extremity strength. Declarations Availability of materials and data The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. Acknowledgements BMP is supported by the NIH T-32 grant (5T32GM141739-02). The National Institutes of Health grants listed had no role in the design, methods, subject recruitment, data collections, analysis, and preparation of paper. The authors would like to thank Wendi Weimar and the Sports Biomechanics Laboratory for the use of their GAITRite Instrumented Walkway. The authors would also like to thank Julia Christl, Madi Malone, and Charlie Gossett for their help. Author information Authors and Affiliations School of Kinesiology, Auburn University, Auburn, AL, USA Brandon M. Peoples, Kenneth D. Harrison, Keven G. Santa-Maria-Guzman, & Jaimie A Roper College of Pharmacy and Health Sciences, Wayne State University , Detroit, MI, USA Patrick G. Monaghan University of Costa Rica, San Jose Province, San Pedro, Costa Rica Silvia E. Campos-Vargas Contributions Study conception and design: B.M.P. and J.A.R. Data analysis: B.M.P, K.S.G., and S.E.C Interpretation: B.M.P., K.S.G.., and J.A.R. Drafting manuscript: B.M.P. Critical revision: B.M.P, K.D.H., P.G.M, K.S.G, and J.A.R. All authors read and approved the final manuscript. Competing interests The author(s) declare no competing interests. References Buatois, S. et al. Five times sit to stand test is a predictor of recurrent falls in healthy community-living subjects aged 65 and older. J Am Geriatr Soc 56 , 1575-1577 (2008). https://doi.org/10.1111/j.1532-5415.2008.01777.x Ambrose, A. F., Paul, G. & Hausdorff, J. M. Risk factors for falls among older adults: a review of the literature. 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Gait Posture 44 , 250-258 (2016). https://doi.org/10.1016/j.gaitpost.2015.12.017 Smith, E., Walsh, L., Doyle, J., Greene, B. & Blake, C. Effect of a dual task on quantitative Timed Up and Go performance in community-dwelling older adults: A preliminary study. Geriatr Gerontol Int 17 , 1176-1182 (2017). https://doi.org/10.1111/ggi.12845 Boyer, K. A. et al. Age-related changes in gait biomechanics and their impact on the metabolic cost of walking: Report from a National Institute on Aging workshop. Exp Gerontol 173 , 112102 (2023). https://doi.org/10.1016/j.exger.2023.112102 Bohannon, R. W. Reference values for the five-repetition sit-to-stand test: a descriptive meta-analysis of data from elders. Percept Mot Skills 103 , 215-222 (2006). https://doi.org/10.2466/pms.103.1.215-222 Ardestani, M. M., Ferrigno, C., Moazen, M. & Wimmer, M. A. From normal to fast walking: Impact of cadence and stride length on lower extremity joint moments. Gait Posture 46 , 118-125 (2016). https://doi.org/10.1016/j.gaitpost.2016.02.005 Faul, F., Erdfelder, E., Lang, A. G. & Buchner, A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 39 , 175-191 (2007). https://doi.org/10.3758/bf03193146 Amboni, M., Barone, P. & Hausdorff, J. M. Cognitive contributions to gait and falls: evidence and implications. Mov Disord 28 , 1520-1533 (2013). https://doi.org/10.1002/mds.25674 Brach, J. S., Berlin, J. E., VanSwearingen, J. M., Newman, A. B. & Studenski, S. A. Too much or too little step width variability is associated with a fall history in older persons who walk at or near normal gait speed. J Neuroeng Rehabil 2 , 21 (2005). https://doi.org/10.1186/1743-0003-2-21 Espy, D. D., Yang, F., Bhatt, T. & Pai, Y. C. Independent influence of gait speed and step length on stability and fall risk. Gait Posture 32 , 378-382 (2010). https://doi.org/10.1016/j.gaitpost.2010.06.013 Piercy, K. L. et al. The Physical Activity Guidelines for Americans. JAMA 320 , 2020-2028 (2018). https://doi.org/10.1001/jama.2018.14854 Woollacott, M. & Shumway-Cook, A. Attention and the control of posture and gait: a review of an emerging area of research. Gait Posture 16 , 1-14 (2002). https://doi.org/10.1016/s0966-6362(01)00156-4 Zhang, W., Low, L. F., Gwynn, J. D. & Clemson, L. Interventions to Improve Gait in Older Adults with Cognitive Impairment: A Systematic Review. J Am Geriatr Soc 67 , 381-391 (2019). https://doi.org/10.1111/jgs.15660 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Jun, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Apr, 2024 Reviews received at journal 24 Apr, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviewers agreed at journal 25 Mar, 2024 Reviews received at journal 19 Mar, 2024 Reviewers agreed at journal 19 Mar, 2024 Reviewers invited by journal 15 Mar, 2024 Editor assigned by journal 14 Mar, 2024 Editor invited by journal 13 Mar, 2024 Submission checks completed at journal 13 Mar, 2024 First submitted to journal 23 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3983607","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":279106666,"identity":"25271574-36c1-430d-a4da-eb9de2eac3ff","order_by":0,"name":"Brandon M. Peoples","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"","firstName":"Brandon","middleName":"M.","lastName":"Peoples","suffix":""},{"id":279106667,"identity":"269d326c-8e59-4dc4-aee3-c00e5ceb810a","order_by":1,"name":"Kenneth D. Harrison","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"D.","lastName":"Harrison","suffix":""},{"id":279106668,"identity":"feb6eec2-1dd6-453e-b4a7-a5245ef62c10","order_by":2,"name":"Keven G. Santamaria-Guzman","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"","firstName":"Keven","middleName":"G.","lastName":"Santamaria-Guzman","suffix":""},{"id":279106669,"identity":"3289d7b5-7839-4e4e-976d-f13dd17e895b","order_by":3,"name":"Silvia E. Campos-Varga","email":"","orcid":"","institution":"University of Costa Rica","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"E.","lastName":"Campos-Varga","suffix":""},{"id":279106671,"identity":"e8ecffdb-8bf4-40bc-8eb6-5b6ae94a4341","order_by":4,"name":"Patrick G. Monaghan","email":"","orcid":"","institution":"Wayne State University","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"G.","lastName":"Monaghan","suffix":""},{"id":279106673,"identity":"80ebc494-f595-4146-ac81-8171777b2f61","order_by":5,"name":"Jaimie A. Roper","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACxgYwZcPAIIEiQFhLGglaoOAwCVqYZx8+9uDjnvPyBrd7zD4XMNjIbjhAyGF9aemGM57dNtxw54zx7BkMacaEtfTwmEnzHLjNuOFGjjEzD8PhROK0/Dlwzh6q5T+RWhgOHEiEajlAjBa2dMOeA8nJM+8cK2bmMUg2nklIi2EP87EHPw7Y2fbdbt7MzFNhJ9tHUEsDAxsS14CAchCQZ0DRMgpGwSgYBaMACwAAupFC4EESeMgAAAAASUVORK5CYII=","orcid":"","institution":"Auburn University","correspondingAuthor":true,"prefix":"","firstName":"Jaimie","middleName":"A.","lastName":"Roper","suffix":""}],"badges":[],"createdAt":"2024-02-24 02:33:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3983607/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3983607/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-64293-0","type":"published","date":"2024-06-11T14:52:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52793681,"identity":"26a47f98-2a4d-4d76-8d17-6d4577ebd6b7","added_by":"auto","created_at":"2024-03-15 20:34:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":674902,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic demonstrating the different stepping strategies used when comparing multiple walking conditions.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3983607/v1/44f5fbbb7256abd99f76365f.png"},{"id":52793074,"identity":"18d5b872-2d2c-47f0-bf40-745aac89d8ba","added_by":"auto","created_at":"2024-03-15 20:26:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":268174,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots of stepping strategy across walking comparison.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3983607/v1/e26c88f4896fccad9aff77c1.png"},{"id":52793079,"identity":"ce83e0a6-a499-4c15-a11c-7e02518a8027","added_by":"auto","created_at":"2024-03-15 20:26:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165463,"visible":true,"origin":"","legend":"\u003cp\u003ePaneled Bar graphs of gait speed, step time, and step length for each group across walking conditions.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3983607/v1/b0e5a8920f0186fe4c76e32f.png"},{"id":58822681,"identity":"154df9c2-5155-4001-9a54-42cb27e7b3a9","added_by":"auto","created_at":"2024-06-21 16:46:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1834300,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3983607/v1/789a38e9-154d-4d98-b204-6b98051c9ce1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Functional Lower Extremity Strength Influences Stepping Strategy in Community-Dwelling Older Adults During Single and Dual-Task Walking","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaintaining mobility is vital for older adults to preserve their independence, self-reliance, community involvement, and overall health. Nevertheless, the aging process has a notable impact on this ability. As individuals cross their fifties, they may experience a gradual decline in their lower extremity strength, leading to reduced functional mobility, slower walking speeds, increased sedentary behavior, social isolation, higher fall risks, and a decline in their quality of life across physical, cognitive, emotional, and social domains \u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. Alarmingly, lower extremity strength deteriorates faster than age-related reductions in muscle mass, prompting mobility limitations since walking requires supporting body weight through sit-to-stand transitions and propelling the body forward during walking using the lower body muscles \u003csup\u003e3\u0026ndash;5\u003c/sup\u003e. Therefore, mitigating deficits in lower extremity strength is paramount as society grays as it requires elucidating complex interactions between reduced lower extremity strength, destabilized gait, and heightened mobility-disability.\u003c/p\u003e \u003cp\u003eFunctional lower extremity strength plays a critical role in preserving mobility but inevitably declines with advanced age, resulting in a nearly three-fold increase in fall risks in older adults and costing over \u003cspan\u003e$\u003c/span\u003e50\u0026nbsp;billion in annual geriatric injury expenditures from government programs such as Medicare \u003csup\u003e6,7\u003c/sup\u003e. Quantifying decrements to functional lower extremity strength before outright mobility-disability occurs is critical but requires direct assessments linking functional lower extremity strength to gait safety. The 5-repetition Sit-to-Stand test fills this need since itis a clinically feasible test of functional lower extremity strength that holds validity and strongly predicts mobility limitations in older adults \u003csup\u003e8\u0026ndash;11\u003c/sup\u003e. Poor performance on the 5xSTS is associated with reduced gait speed, functional impairment, frailty, and fall risk in community-dwelling adults \u003csup\u003e1,8,11\u0026ndash;14\u003c/sup\u003e. Moreover, older adults with reduced functional mobility use different stepping strategies (increasing step length or step time); however, the link between functional lower extremity strength and stepping strategy is unknown \u003csup\u003e15\u003c/sup\u003e. Therefore, understanding how age-related reductions in functional lower extremity strength influence stepping strategy is vital to unraveling mobility limitations.\u003c/p\u003e \u003cp\u003eStepping strategy is important to contemplate while examining normal and dual-task walking patterns. In order to gain a comprehensive understanding of how older adults navigate their environment, we need to consider two important factors: the ability to regulate walking pace and the aptitude to perform multiple tasks simultaneously while walking \u003csup\u003e16,17\u003c/sup\u003e. Regulating walking pace is important to meet the demands of the environment. However, the ability to walk and multitask is essential for maintaining independence, as many daily activities require the ability to walk while performing other tasks, such as carrying a plate of food or engaging in conversation \u003csup\u003e16,18\u0026ndash;20\u003c/sup\u003e. Thus, examining the relationship between functional lower extremity and stepping strategy in older adults during simple or complex walking tasks can provide valuable insights into gait safety.\u003c/p\u003e \u003cp\u003eThis study investigates whether functional lower extremity strength affects stepping strategy in community-dwelling older adults across normal and dual-task walking. We hypothesize: 1) Older adults with reduced functional lower extremity strength will use a step time dominant stepping strategy than those with normal strength, and 2) Stepping strategies will be similar between groups during walking conditions where multitasking is required. Thus, investigating functional lower extremity strength and stepping strategy can enhance our understanding of the factors that impact gait stability and help devise effective interventions to proactively address mobility-disability.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eParticipants\u003c/span\u003e \u003c/p\u003e \u003cp\u003eOur study's participant recruitment and data collection were conducted at a local community health fair event instead of a laboratory. This was done to improve ecological validity and demonstrate the feasibility of implementing assessment procedures in a practical, real-world context. The recruitment and data collection took place over two consecutive years (2022 and 2023) as part of our ongoing research engagement and service partnership to address health needs in a rural region. Our study focused on a sample from an underserved rural community. It was in response to a call from the National Institute of Aging (NIA) workshop on age-related changes in gait biomechanics. The workshop stressed the importance of diverse sampling and building community partnerships \u003csup\u003e21\u003c/sup\u003e. Twenty older adults (17F, 72\u0026thinsp;\u0026plusmn;\u0026thinsp;6) living in the Lee County community participated in this study during a local health fair. Participants who were free from lower extremity injuries such as bone fractures, muscle strains, and joint dislocations were included in the study. Participants who did not report any severe problems that may affect their ability to walk were also included. However, those who reported any neurological disease such as Parkinson\u0026rsquo;s disease, Essential Tremor, Multiple Sclerosis, Stroke, or Traumatic Brain injury were excluded from the study. Almost 50% of the participants in the study were from communities of color.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMaterial\u003c/span\u003e \u003c/p\u003e \u003cp\u003e This study was approved by the Auburn University Institutional Review Board on the basis of minimal risk, and a waiver for informed consent was granted. All participants were given a detailed explanation of the study's purpose, procedures, potential risks, and benefits, and provided informed consent before their inclusion in the study. The study was conducted according to the ethical principles outlined in the Declaration of Helsinki. Following consent participants underwent a series of assessments, including a set of questionnaires, two strength assessments, three functional mobility assessments, and four gait assessments. The questionnaire included questions about their demographic information, living status, education, sleep habits, retrospective fall history, and self-reported physical activity history. Fear of falling was measured using the Fall Efficacy Scale (FES) developed by Tinetti et al. (1990), and barriers to physical activity were assessed using the CDC Barrier to Being Active Quiz (BBAQ) (Control \u0026amp; Prevention, 2013).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFunctional Lower Extremity Strength measured by the Instrumented 5-Rep Sit-to-Stand (i5xSTS)\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe i5xSTS device was employed to measure the functional strength of the lower extremities using wireless inertial measurement units (IMU) from APDM Opals (APDM Inc, Portland, OR). The Opal sensor has triaxial accelerometers, gyroscopes, and magnetometers, capturing signal data at 128 Hz. To obtain readings, researchers placed six IMUs at four different body locations: 1) atop the sternum centered over the manubrium, 2) on the superior aspect of the posterior sacral surface (below the fifth lumbar vertebrae L5), 3) on the posterior aspect of the distal radius and ulna, and 4) on the dorsal surface of the foot, centered on the intermediate and lateral cuneiform. Participants were instructed to sit with their backs against the chair, arms crossed, and hands touching the anterior aspect of their deltoid. They were then asked to stand and sit as quickly as possible for five repetitions. The duration of this exercise was used to determine the strength status of the participants in this study.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGait Testing\u003c/span\u003e \u003c/p\u003e \u003cp\u003eAll participants completed the walking trials on a walkway next to the main health fair venues and were exposed to roughly\u0026thinsp;~\u0026thinsp;75dB of sound. All assessments were conducted during vendor exhibitions, demonstrations, and community events to reflect real-world complexity. Participants were asked to walk under four conditions during the community health fair. These were: 1)Walking at their normal speed, 2) Walking at their fastest speed, 3) Motoric Dual Task at their normal speed (Walking at their normal speed while holding a tray with a cup of water), and 4) Motoric Dual Task at their fastest feed (Walking at their fastest speed while holding a tray with a cup of water. The researcher instructed the participants to walk at either a \"comfortable, natural walking speed\" or the \"fastest, safe walking speed without jogging or running,\" as prompted. Additionally, the researchers provided non-prioritizing instructions while the participants walked with the tray to prevent participants from prioritizing the tray task over the walking task, and vice versa. The GAITRite instrumented walkway was used to capture all spatial and temporal gait cycle parameters used for this study. Our GAITRite system (GAITRite Gold, CIR Systems, Clifton, NJ) consists of an electronic walkway approximately 8.2 meters long, connected to a personal computer via an interface cable. The walkway comprises a series of sensor pads inserted in a grid formation between a layer of vinyl (top cover) and foam rubber (bottom cover). The active area of the walkway is 61 cm wide and 732 cm long, while the sensors are placed 1.27 cm apart, consisting of a total of 27,648 sensors that are activated by mechanical pressure. The data from the activated sensors is collected by a series of onboard processors and transferred to the computer through a serial port. The system has a sampling rate of 80 Hz.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFunctional Lower Extremity Strength measured by the Instrumented 5-Rep Sit-to-Stand (i5xSTS)\u003c/span\u003e \u003c/p\u003e \u003cp\u003ei5xSTS was collected from APDM Opals and participant data was processed in Moveo Explorer version 1.0.0.202206 (APDM Inc, Portland, OR). Total duration was calculated using the average duration of the sit-to-stand and stand-to-sit transitions. Participants were classified as low strength (LS) if their 5xSTS duration exceeded normative performance values for their age range of 11.4 seconds (60 to 69 years), 12.6 seconds (70 to 79 years), and 14.8 seconds (80 to 89 years) \u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGait Cycle Parameters\u003c/span\u003e \u003c/p\u003e \u003cp\u003eGait speed, step length, and step time for all walking conditions were exported from the GAITRite software (GAITRite, CIR Systems Inc., Clifton, NJ). Step length was measured using the heel center of the current footprint to the heel center of the previous footprint on the opposite foot. Step time is the elapsed time from the first contact of one foot to the first contact of the opposite foot. Gait Speed was measured using the distance traveled divided by the total ambulation time. Step length and step time from each condition was used to calculate the stepping strategy measured by Length-Time Difference (see below).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLength-Time Difference (LTD)\u003c/span\u003e \u003c/p\u003e \u003cp\u003eLTD was derived from a previous study evaluating how strategy, adjustments to cadence or stride length when walking at a normal speed and fast speed, influence lower extremity joint moments \u003csup\u003e23\u003c/sup\u003e. The equation used to calculate strategy for cadence and stride length are below:\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eArdestani\u0026rsquo;s Strategy Eq.\u0026nbsp;(1)\u003c/span\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\varDelta {Cadence}_{i}=\\left(\\frac{{Cadence}_{Fast}-{ Cadence}_{Normal}}{{Cadence}_{Normal}} \\right)*100\\% i = \\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\text{o}\\text{f}\\text{s}\\text{u}\\text{b}\\text{j}\\text{e}\\text{c}\\text{t}\\text{s}$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\varDelta {Stride}_{i}=\\left(\\frac{{Stride}_{Fast}-{ Stride}_{Normal}}{{Stride}_{Normal}} \\right)*100\\%$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eLTD represents relative change in step length and step time when comparing two conditions (i.e. fast vs preferred. An LTD\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates a step length dominant strategy (increasing step length), an LTD\u0026thinsp;\u0026lt;\u0026thinsp;0 indicates a step time dominant strategy, while an LTD\u0026thinsp;=\u0026thinsp;0 indicates a neutral strategy, implying equal contribution from both step time and step length (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) \u003csup\u003e15,23\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLength-Time Difference Eq.\u0026nbsp;(2)\u003c/span\u003e \u003cdiv id=\"Equc\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$LTD=\\left(\\frac{{Length}_{Fast}-{ Length}_{preferred}}{{length}_{preferred}} \\right)+\\left(\\frac{{Time}_{Fast}-{ Time}_{preferred}}{{Time}_{preferred}}\\right)*100\\%$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModified Length-Time Difference Eq.\u0026nbsp;(3)\u003c/span\u003e \u003cdiv id=\"Equd\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$LTD=\\left(\\frac{{Length}_{Tray+ Walk Speed}-{ Length}_{Walk Speed}}{{length}_{Walk Speed}} \\right)+\\left(\\frac{{Time}_{Tray+Walk Speed}-{ Time}_{Walk Speed}}{{Time}_{Walk Speed}}\\right)*100\\%$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eFollowing the equations above, we calculated the stepping strategy for four distinct comparisons: Walking at a Fast Speed vs Walking at a Normal Speed (Walking Comparison 1), Motoric Dual Task at a Fast Walking Speed vs Motoric Dual Task at a Normal Walking Speed (Walking Comparison 2), Motoric Dual Task at a Normal Walking Speed vs Walking at a Normal Speed (Walking Comparison 3), and Motoric Dual Task at a Fast Walking Speed vs Walking at a Fast Speed (Walking Comparison 4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003ePower Analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAn \u003cem\u003ea priori\u003c/em\u003e power analysis was conducted using G*Power version 3.1.9.7 \u003csup\u003e24\u003c/sup\u003e to attain 80% power and determine the minimum sample size based on data from \u003csup\u003e15\u003c/sup\u003e, which observed nearly large effect size (\u003cem\u003ed\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.77) between the 5-repetition chair stand duration of the Short Physical Performance Battery and stepping strategy using Length-Time difference in older adults with mobility limitations. With the significance criterion set at \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.05 and power\u0026thinsp;=\u0026thinsp;.80, the minimum sample size needed with this effect size is \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12 for a repeated measures ANOVA.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDescriptive Statistics\u003c/span\u003e \u003c/p\u003e \u003cp\u003eDescriptive statistics are provided for each group for the following variables: gait speed (m/s), step length (m), and step time (s).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIndependent Samples T-Test\u003c/span\u003e \u003c/p\u003e \u003cp\u003eAn independent samples t-test was conducted to compare age, height, mass, 5x sit-to-stand test duration, sit-to-stand duration, stand-to-sit duration, sit-to-stand angle, stand-to-sit angle, and Falls Efficacy Scale (FES) score between the low strength and normal strength groups.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2x4 Repeated Measures ANOVA\u003c/span\u003e \u003c/p\u003e \u003cp\u003eA 2x4 repeated measures ANOVA was conducted to examine the effect of group (Low vs. Normal) and walking comparisons: Walking at a Fast Speed vs Walking at a Normal Speed (Walking Comparison 1), Motoric Dual Task at a Fast Walking Speed vs Motoric Dual Task at a Normal Walking Speed (Walking Comparison 2), Motoric Dual Task at a Normal Walking Speed vs Walking at a Normal Speed (Walking Comparison 3), and Motoric Dual Task at a Fast Walking Speed vs Walking at a Fast Speed (Walking Comparison 4) measured by LTD. Mauchly\u0026rsquo;s test indicated that the assumption of sphericity was violated for condition (p\u0026thinsp;\u0026lt;\u0026thinsp;.05); therefore, we used Greenhouse-Geisser correction. Post-hoc test comparisons were performed using the Tukey HSD test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive Statistics\u003c/p\u003e\n\u003cp\u003eThe low functional strength group had a slower gait speed (\u003cem\u003eM\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1.26, \u003cem\u003eSD\u003c/em\u003e .25) across the single and motoric dual task walking trials compared to the normal strength group (\u003cem\u003eM\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1.37, \u003cem\u003eSD\u003c/em\u003e .28). The step lengths between each group were similar (\u003cem\u003eM\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.65, \u003cem\u003eSD\u003c/em\u003e .11), however, the normal functional strength group had longer overall stepping times (\u003cem\u003eM\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.48, \u003cem\u003eSD\u003c/em\u003e .05) in comparison to the low functional strength group (\u003cem\u003eM\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.38, \u003cem\u003eSD\u003c/em\u003e .77), summarized in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. Figure\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e depicts mean gait speed (m/s), step length (m), and step for each group across single and motoric dual task walking conditions.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMeans and Standard Deviations for Gait Speed (m/s), Step Time (s), and Step Length (m) for each group across all walking conditions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow Strength\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNormal Strength\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGait Speed (m/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e.77\u0026ndash;1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.831\u0026ndash;2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStep Length (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e.44 \u0026minus;\u0026thinsp;.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e.65\u0026thinsp;\u0026plusmn;\u0026thinsp;.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e.45\u0026ndash;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStep Time (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.45\u0026ndash;1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e.48\u0026thinsp;\u0026plusmn;\u0026thinsp;.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e.39 \u0026ndash; .56\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\u003eIndependent Samples T-Tests\u003c/p\u003e\n\u003cp\u003eAn independent samples T-test was conducted to compare group characteristics summarized in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. There was a significant difference in 5x sit-to-stand test duration between the low strength group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19.35, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.73) and the normal strength group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.27, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.21), \u003cem\u003et\u003c/em\u003e(\u003cspan\u003e18\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;5.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.27. This indicates that the low-strength group took significantly longer to complete the 5x sit-to-stand test than the normal-strength group with an extremely large effect size. Longer 5x sit-to-stand test duration may be attributed to the long sit-stand phases (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.006) and greater sit-to-stand lean angles (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.006). No significant differences existed between age, height, mass, Stand-to-sit duration, Stand-to-Sit lean angle, or FES score, summarized in Table \u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAll Participant Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow Strength\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNormal Strength\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (M/F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2M/7F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1M/10F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u0026ndash;78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u0026ndash;1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47\u0026ndash;1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMass (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.4\u0026ndash;113.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.6\u0026ndash;101.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5xSTS Duration (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.3\u0026ndash;27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7\u0026ndash;14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSit-to Stand duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u0026ndash;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u0026ndash;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStand-to-Sit duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u0026ndash;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u0026ndash;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSit-to-Stand lean angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u0026ndash;104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u0026ndash;38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStand-to-Sit lean angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026ndash;101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u0026ndash;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFES Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalues in bold indicate statistically significant differences between groups. Values indicate mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd. Values indicate range: minimum-maximum. M, male, F, female. FES-Falls Efficacy Scale\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\u003e2 X 4 Repeated Measures ANOVA\u003c/p\u003e\n\u003cp\u003eA 2 x 4 repeated measures ANOVA showed statistically significant differences in the main effects of stepping strategy, functional lower extremity strength (Low vs. Normal) and interaction between functional lower extremity strength and stepping strategy measured by LTD, summarized in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. The normal strength group showed a clear preference for a step time dominant strategy (\u003cem\u003eM =\u003c/em\u003e -7.17, \u003cem\u003eSD\u0026thinsp;=\u003c/em\u003e\u0026thinsp;3.82) compared to the low strength group. The low strength group vacillated between step length and step time across walking comparisons (\u003cem\u003eM = -0.4\u003c/em\u003e, \u003cem\u003eSD\u0026thinsp;=\u003c/em\u003e\u0026thinsp;4.94) see Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMain Effects of Repeated Measures ANOVA\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e (df)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026eta;\u0026sup2;\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWalking Comparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.205 (2,36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrength Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.773 (\u003cspan\u003e1\u003c/span\u003e, \u003cspan\u003e18\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWalking Comparison * Strength Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.487 (2,36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162\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\u003eFigure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePost hoc analysis with a Tukey\u0026rsquo;s HSD revealed there was a mean difference of 14.32 (95% CI: 0.68 to 3.60) between the Normal Strength Walking and Low Strength groups during Walking Comparison 3 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Tests of simple main effects showed the interaction was driven by differences between strength groups in Walking Comparison 3 (Motoric Dual Task at a Normal Walking Speed vs Walking at a Normal Speed ), \u003cem\u003eF\u003c/em\u003e(\u003cspan\u003e1\u003c/span\u003e, \u003cspan\u003e18\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;9.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, summarized in Table \u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSimple Main Effects - Strength Status\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLevel of Condition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWalking Comparison 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e310.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e310.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWalking Comparison 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWalking Comparison 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1025.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1025.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWalking Comparison 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study investigated if functional lower extremity strength influences stepping strategy in community-dwelling older adults during simple and complex walking. Our main findings are: 1) Older adults with normal lower extremity strength preferred adjusting their step time when walking in both single and dual task conditions, 2) Older adults with reduced functional strength did not exhibit a preferred stepping strategy while walking, either with or without a motoric dual task (i.e., walking while holding a tray balancing a cup of water), 3) The interaction of stepping strategy was mostly influenced by the motoric dual task.\u003c/p\u003e \u003cp\u003eOur findings demonstrate that adults with reduced functional lower strength lack a preferred stepping strategy across all walking comparisons, whereas stronger older adults prioritize adjusting step timing over step length. Adjusting step timing may be a safer compensation for older adults to regulate walking given age-related declines in neuromuscular control, balance, proprioception, and reaction time \u003csup\u003e25,26\u003c/sup\u003e. Relying on adjusting step length likely reduces instability by bringing the center of mass closer to the leading foot to improve stability \u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eContrary to our hypothesis, the low strength group did not demonstrate a step time strategy. Upon examination of the individual data, over half of the people in the low strength group utilized a step length strategy. Our findings disagree with Baudendistel et al. (2021) who reported in a sample of people with mobility disability, a longer sit to stand time relates to more step timing adjustments when comparing a walking trial at a comfortable pace versus a walking trial at a faster pace. Our differences between studies can be explained by the walking conditions used, and population sampled. Specifically, Baudendistel et al. recruited people who met requirements for mobility disability, as determined by physical inactivity, worse physical function, and slower preferred gait speed. Our study did not have the same exclusion and inclusion criteria, and we recruited people at a local health fair hosted in a community recreation center, and 13 of 20 participants reported being physically active \u003csup\u003e28\u003c/sup\u003e. Additionally, Baudendistel et al. compared a single task preferred-walking speed to a single task fast-walking speed. Our study adds complexity to this comparison by having participants complete a motoric dual task during fast and normal walking, in addition to single task fast and normal walking. Interestingly, the interaction effect in our study reveals functional lower extremity strength impacts stepping strategy primarily when attention is divided during walking - an essential skill for navigation and avoidance of fall risk hazards \u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eElucidating connections between functional lower extremity strength, attentional resources, and stepping strategies provides clinically meaningful patient profiles of gait instability often overlooked in older adults without overt mobility disability. Tailoring gait retraining programs by strength capacity and attention underscores a precision rehabilitation approach to improve neurocognitive control of gait and mitigate fall risk \u003csup\u003e30\u003c/sup\u003e. Future research should explore how strength training combined with stepping strategy re-education impacts gait variability and dual-task.\u003c/p\u003e \u003cp\u003eA strength of our study lies in its community-based approach, which fosters inclusivity compared to traditional university settings. By engaging individuals in community settings away from the university, we enhance the generalizability of our findings. This approach addresses limitations associated with convenience samples from local university communities\u003csup\u003e21\u003c/sup\u003e. Actively seeking participation from the community ensures our research reflects a broader spectrum of experiences, enriching the validity and applicability of our results. Our community-based approach fosters collaboration, enhances diversity, and ensures interventions meet specific needs, aligning with principles of community engagement.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn our study, we found that functional lower extremity strength assessed by the instrument 5-repetition Sit-to-Stand influences stepping strategy in community-dwelling older adults. Older adults with normal functional lower extremity strength utilize a step-time dominant strategy, adjusting step time under normal and dual-task conditions. In contrast, older adults with reduced lower extremity strength lack a clear preference for modulating step length or time to walk faster. When faced with an added cognitive demand during gait, older adults with reduced lower extremity strength switch towards a step-length dominant strategy, compared to older adults with normal strength who maintain a time-based strategy. Our findings can help clinicians proactively identify community-dwelling older adults with reduced lower extremity strength who may have difficulty safely adjusting gait patterns in response to environmental demands. Future research should test whether strength training programs can also optimize stepping strategies during walking in older adults with reduced lower extremity strength.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of materials and data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Acknowledgements\u003c/p\u003e\n\u003cp\u003eBMP is supported by the NIH T-32 grant (5T32GM141739-02). The National Institutes of Health grants listed had no role in the design, methods, subject recruitment, data collections, analysis, and preparation of paper. The authors would like to thank Wendi Weimar and the Sports Biomechanics Laboratory for the use of their GAITRite Instrumented Walkway. The authors would also like to thank Julia Christl, Madi Malone, and Charlie Gossett for their help.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSchool of Kinesiology, Auburn University, Auburn, AL, USA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBrandon M. Peoples, Kenneth D. Harrison, Keven G. Santa-Maria-Guzman, \u0026amp; Jaimie A Roper\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollege of Pharmacy and Health Sciences, Wayne State University\u003c/strong\u003e\u003cstrong\u003e, Detroit, MI, USA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatrick G. Monaghan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUniversity of Costa Rica, San Jose Province, San Pedro, Costa Rica\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSilvia E. Campos-Vargas\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eStudy conception and design: B.M.P. and J.A.R. Data analysis: B.M.P, K.S.G., and S.E.C Interpretation: B.M.P., K.S.G.., and J.A.R. Drafting manuscript: B.M.P. Critical revision: B.M.P, K.D.H., P.G.M, K.S.G, and J.A.R. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBuatois, S.\u003cem\u003e et al.\u003c/em\u003e Five times sit to stand test is a predictor of recurrent falls in healthy community-living subjects aged 65 and older. \u003cem\u003eJ Am Geriatr Soc\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 1575-1577 (2008). https://doi.org/10.1111/j.1532-5415.2008.01777.x\u003c/li\u003e\n\u003cli\u003eAmbrose, A. F., Paul, G. \u0026amp; Hausdorff, J. M. 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Interventions to Improve Gait in Older Adults with Cognitive Impairment: A Systematic Review. \u003cem\u003eJ Am Geriatr Soc\u003c/em\u003e \u003cstrong\u003e67\u003c/strong\u003e, 381-391 (2019). https://doi.org/10.1111/jgs.15660\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mobility, lower extremity strength, gait, dual-task, stepping strategy","lastPublishedDoi":"10.21203/rs.3.rs-3983607/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3983607/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs age increases, a decline in lower extremity strength leads to reduced mobility and increased fall risks. This decline outpaces the age-related reduction in muscle mass, resulting in mobility limitations. Older adults with varying degrees of mobility-disability use different stepping strategies. However, the link between functional lower extremity strength and stepping strategy is unknown. Therefore, understanding how age-related reductions in functional lower extremity strength influence stepping strategy is vital to unraveling mobility limitations. Participants were recruited and tested at a local community event, where they were outfitted with IMUs and walked across a pressurized walkway. Our study reveals that older adults with normal strength prefer adjusting their step time during walking tasks, while those with reduced strength do not exhibit a preferred stepping strategy. This study provides valuable insights into the influence of functional lower extremity strength on stepping strategy in community-dwelling older adults during simple and complex walking tasks. These findings could aid in diagnosing gait deviations and developing appropriate treatment or management plans for mobility disability in older adults.\u003c/p\u003e","manuscriptTitle":"Functional Lower Extremity Strength Influences Stepping Strategy in Community-Dwelling Older Adults During Single and Dual-Task Walking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-15 20:25:47","doi":"10.21203/rs.3.rs-3983607/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-26T09:07:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-24T14:50:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92adb83c-d4a3-4267-873d-c73ec549c4c5","date":"2024-04-24T13:25:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b053fe51-3e62-456b-90b4-a0d81064ce12","date":"2024-03-25T11:02:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-19T15:03:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3403a847-ee85-41dd-96fb-0d9abe6c9645","date":"2024-03-19T14:15:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-15T15:07:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-15T03:55:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-13T09:32:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-13T09:23:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-02-24T01:57:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f3411482-7938-49ba-9608-24471f7e13e3","owner":[],"postedDate":"March 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T14:52:16+00:00","versionOfRecord":{"articleIdentity":"rs-3983607","link":"https://doi.org/10.1038/s41598-024-64293-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-06-11 14:52:16","publishedOnDateReadable":"June 11th, 2024"},"versionCreatedAt":"2024-03-15 20:25:47","video":"","vorDoi":"10.1038/s41598-024-64293-0","vorDoiUrl":"https://doi.org/10.1038/s41598-024-64293-0","workflowStages":[]},"version":"v1","identity":"rs-3983607","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3983607","identity":"rs-3983607","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}