Do autonomy, self-efficacy, vitality, and fatigue predict daily morning heart rate variability? A running intervention study in healthy women

Do autonomy, self-efficacy, vitality, and fatigue predict daily morning heart rate variability? 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A running intervention study in healthy women Laura Buchner, Günter Amesberger, Sabine Würth, Thomas Finkenzeller This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6285008/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Feb, 2026 Read the published version in Sport Sciences for Health → Version 1 posted You are reading this latest preprint version Abstract Self-regulation, self-efficacy, and motivation are critical correlates for exercise maintenance and play a significant role in sustaining a physically active lifestyle. Vitality and fatigue, recognized as unipolar affective states, also impact these processes by influencing exercise-induced affective responses and action initiation. This ambulatory assessment study investigates how trait self-efficacy and self-concordance, as well as daily morning fatigue and vitality, effect cardiac activity (heart period and vagally-mediated heart rate variability (vmHRV)) measured every morning in response to running. Over eight weeks, 18 young healthy women new to running followed either a prescribed or self-determined intensity intervention. Results from multilevel analyses revealed that individuals with autonomy in choosing their running intensity exhibited increased vmHRV compared to the prescribed intensity group. Higher trait self-efficacy was associated with better self-regulation, indicated by elevated vmHRV. The effects remain stable upon controlling for heart period. However, the effects vanished when predicting heart period, indicating a mediating role for parasympathetic nervous system activity concerning vmHRV modulation. Direct effects of running, morning vitality, fatigue, or motivation on cardiac activity were not detected. The results of this study suggest that interventions promoting physical activity should enhance feelings of competence and allow self-determination to achieve activity goals. The promotion of autonomy in exercise intensity and fostering self-efficacy are pivotal for enhancing self-regulation, as evidenced by the improvement in morning vmHRV. These strategies have the potential to result in more effective and sustainable physical activity behaviors, thereby contributing to enhanced overall health outcomes. vagal tone self-determination incidental affect self-regulation ambulatory assessment physical activity Introduction It is well known that regular physical activity has positive benefits on physical and mental health (Marquez et al., 2020 ). The growing interest in psychophysiological mechanisms to improve health, foster an active lifestyle, and improve well-being led to several theories about the interconnectivity between the brain, autonomous nervous system, heart, and affective states (Grosicki et al., 2022 ; Kogan et al., 2013 ; Kreibig, 2010 ; Mosley & Laborde, 2022 ). In this sense, the measure of heart rate variability (HRV) is of particular interest. The following introduction will give an overview of theories and current research findings trying to explain the association between psychophysiological measures, incidental affect, and behavior change toward an active lifestyle. HRV is a non-invasive measure of the neurovegetative activity and autonomic function of the heart. It describes the variability of time intervals between consecutive heartbeats (RR intervals measured in milliseconds) primarily driven by the dynamic interplay between parasympathetic and sympathetic influences on the heart via the sinoatrial node. Increased variability explains the ability of the heart to react and adjust according to external and internal circumstances and is correlated with higher parasympathetic modulation (Sammito & Bockelmann, 2015 ; Thayer et al., 2009 ). Parasympathetic nervous activity, also known as cardiac vagal activity or vagal tone, is operationalized by the following parameters of vagally-mediated HRV (vmHRV): root mean square of successive differences (RMSSD); percentage of successive normal sinus RR intervals more than 50ms (pNN50); high-frequency power (HF-HRV) (Laborde et al., 2017 ). The interaction of physiological and psychological processes is described by the Neurovisceral Integration Model (Thayer & Lane, 2000 ). The model posits a reciprocal connection between a network of brain regions (central autonomic network (CAN: such as prefrontal cortex, amygdala, and hypothalamus) with cardiac regulation through the stellate ganglia and vagus nerve. The CAN coordinates cognitive, attentional, affective, and autonomic processes that are relevant to support adaptability, behavioral flexibility, and goal-directed behavior (Smith et al., 2017 ; Thayer et al., 2009 ). The primary function of the prefrontal cortex is related to inhibitory processes associated with executive functions to suppress undesirable reactions or motivational tendencies to pursue overarching action goals (Goschke, 2017 ). The amygdala contribute to sensory and emotional processing, essential for affective regulation (Thayer et al., 2009 ). Empirical evidence demonstrates a positive association between enhanced vmHRV and improved executive functioning and self-regulatory capacity. Improved self-regulation can support emotional and physiological regulation, which are essential for behavior control and decision-making (Forte et al., 2022 ; Holzman & Bridgett, 2017 ; Thayer & Lane, 2000 ). Thus, vmHRV reflects the functional capacity of the central autonomic network to adapt to various situational stimuli effectively, which could support maintaining a healthy lifestyle and facilitating behavior change (Porges, 2007 ; Shaffer et al., 2014 ; Thayer et al., 2009 ). Maintaining a healthy lifestyle such as being physically active serves several benefits. Regular physical activity reduces the risk of cardiovascular diseases by enhancing regulatory capacities to adaptively respond to changes in homeostasis changes (Amekran & El Hangouche, 2024 ; Bechke et al., 2017 ). Endurance athletes, in particular, exhibit greater RMSSD and HF-HRV at rest compared to healthy untrained individuals (Chihaoui Mamlouk et al., 2021 ). Aerobic intervention studies have demonstrated that moderate to high exercise intensities can significantly enhance vmHRV (Forte et al., 2022 ; Soltani et al., 2021 ) whereas low-intensity or low-volume training does not sufficiently impact parasympathetic activity (Grässler et al., 2021 ; Soltani et al., 2021 ). Although knowing about the benefits of physical activity, individuals frequently face challenges in translating their exercise intentions into actions. To overcome the so-called “intention-behavior gap”, several theories and models were developed (Rhodes & Sui, 2021 ). From the perspective of action-control theories, behavioral regulation skills such as planning and intention implementation, are considered essential for initiating and maintaining physical activity (Rhodes & Yao, 2015 ). A recent review emphasized that self-efficacy is a relevant moderator in the effectiveness of such action planning (Kompf, 2020 ). Self-efficacy describes the belief in one’s own capabilities to pursue a goal. These beliefs represent an individual’s confidence in their personal competencies to succeed (Bandura, 1977 ). Furthermore, Rhodes et al. ( 2022 ) highlighted in their review of predictors of the intention-behavior gap, that self-efficacy is a key determinant in bridging this discordance. Additionally, self-efficacy has been proven to be associated with functional connectivity of the right anterior cingulate cortex – a cortical region of the CAN (Wang et al., 2022 ). Besides the presence of social-cognitive theories to explain physical activity behavior, models based on hedonic principles are gaining prominence in research (Stevens et al., 2020 ). Empirical findings indicate that positive affective responses during exercise substantially influence future exercise adherence (Hevel & Maher, 2023 ; Rhodes & Kates, 2015 ). The relationship between affect and physical activity appears to be bidirectional rather than unidirectional (Ruissen et al., 2022 ; Timm et al., 2024 ). Consequently, the engagement in physical activity has been shown to result in an elevation of positive activated affect and on the other hand, individuals are more likely to engage in physical activity when they feel positive and energized (Fiedler et al., 2022 ; Timm et al., 2024 ). To foster positive affective outcomes associated with physical activity, the self-determination of exercise intensity can play a crucial role. While a self-determined exercise intensity intervention led to higher pleasant feelings compared to the prescribed-intensity group in low-active overweight adults (Williams et al., 2016 ), this evidence wasn’t supported by results from Buchner et al. ( 2024 ) in a sample of healthy untrained women. However, higher levels of perceived self-determination are strongly associated with greater intrinsic motivation, enhanced autonomy, and improved adherence to physical activity (Baldwin et al., 2016 ; Ryan & Deci, 2008 ). Therefore, further research is warranted to explore the interplay between reflective motivation (e.g., self-efficacy, intention), affective responses towards exercise (e.g., vitality, fatigue), satisfaction of basic psychological needs (e.g., autonomy), and cardiac autonomic regulation (e.g., RMSSD). Deeper insights into these relationships could enhance the development of effective interventions for long-term physical activity engagement. In addition to its role in physiological adaptation, cardiac vagal tone is increasingly recognized as a biological marker of psychological well-being (Kogan et al., 2013 ). Based on the Neurovisceral Integration Model, better emotion regulation is indicated with enhanced vmHRV (Thayer et al., 2009 ). Research implies that individuals with lower resting vmHRV are more prone to experience greater increases in negative affect (Bylsma et al., 2024 ; Sloan et al., 2017 ), and report higher levels of depressive symptoms (e.g., Rottenberg, 2007 ), and anxiety disorders (e.g., Chalmers et al., 2014 ). By contrast, several studies have shown, that higher values of vmHRV at rest are related to positive affect (Dang et al., 2021 ), improved emotion regulation (Cai et al., 2019 ), and other measures of trait subjective well-being (Geisler et al., 2010 ; Laborde et al., 2015 ). Interestingly, results from Bylsma et al. ( 2024 ) and Sloan et al. ( 2017 ) cannot support significant correlations between vmHRV and positive affect. Moreover, a review by Mosley and Laborde ( 2022 ) summarizes mixed findings regarding the relationship between HRV measures and affect. The inconsistent results may be explained by differences in study designs and sample characteristics. Additionally, emerging evidence points to a potential curvilinear, rather than linear, relationship between resting vmHRV and various measures of subjective well-being (Dang et al., 2021 ; Kogan et al., 2013 ). Whereas Kogan et al. ( 2013 ) demonstrated a negative quadratic trend between vmHRV and life satisfaction, Dang et al. ( 2021 ) posit a positive quadratic trend between vmHRV and meaning in life. Another study by Duarte and Pinto-Gouveia ( 2017 ) highlighted that various positive emotions may be differently associated with autonomic nervous system functioning. Comparing different positive emotional correlates (positive affect, activating positive affect, relaxed positive affect, and safe/content positive affect), only safe/content positive affect was found to predict HF-HRV following a negative quadratic trend. No significant linear relationships or associations with other positive affective measures or vmHRV as a predictor were identified (Duarte & Pinto-Gouveia, 2017 ). This possibility underscores the complexity of the relationship between physiological markers and psychological states, highlighting the need for further research to better understand these dynamics. Knowing about the separate effects between physical activity (endurance) and measures of subjective well-being on vmHRV, little research provides insights into the interplay between these three correlates. Often considered in pre-post intervention designs, ambulatory assessment studies are rare although they offer valuable insights into the dynamic interrelationships between variables that fluctuate on a daily basis. For instance, Crawford et al. ( 2020 ) examined the relationship between daily resting vmHRV, motivation to exercise, and perceived fatigue in participants following either an HRV-guided CrossFit high-intensity training (HIT) program or a prescribed HIT program. The seven-day rolling average of RMSSD remained stable throughout the 6-week intervention, indicating no overall long-term changes. However, meaningful daily shifts in RMSSD, measured by the smallest worthwhile change, revealed an association with increased fatigue when RMSSD values were either above or below the normal range. For the control group, this was also apparent for daily motivation – showing lower motivational states, when vmHRV was different from normal (Crawford et al., 2020 ). This outcome highlights the association between vmHRV and adherence-related correlates for long-term physical engagement such as motivation and fatigue. Another study by da Silva et al. ( 2020 ) compared HRV-guided training programs with pre-defined polarized training in untrained women during an eight-week running intervention study. On the one hand, HRV-guided training resulted in a reduction in fatigue and stress, alongside improved mood and lack of energy. On the other hand, the pre-defined training group reported a decrease in vigor but experienced better-perceived recovery. Both training programs were effective in improving self-regulation and various stress-related symptoms, albeit in different ways (da Silva et al., 2020 ). These findings underscore the value of tracking HRV changes and affective measures on a daily basis to better understand short-term variations that might be missed by pre-post-study designs. There is a need for further studies investigating the relationship between incidental affect, vmHRV, and behavior-adopting parameters toward regular physical activity. Exercise-related motivation, self-efficacy, perceived autonomy, and positive affective outcomes all predict physical activity maintenance. However, research linking all of these aspects with cardiac autonomic functioning is missing. Ambulatory assessment provides the state-of-the-art methodology for exploring the dynamic interplay between subjective experiences and behavior in participants' natural environments (Trull & Ebner-Priemer, 2013 ). Understanding the day-to-day variations in affective states and autonomic nervous system activity with physical activity highlights the importance of expanding research using this method. Additionally, analyzing ambulatory assessment data allows not only assumptions about between-person differences, but also about within-person variations (Nezlek & Mroziński, 2020 ). This is important to better understand the interplay of different aspects on the individual level over time. When measuring resting state vmHRV in ambulatory settings, it is important to note, that changes in body posture (supine, seated, standing), time of day, routine (before/after getting up), and breathing habit (just like slow-paced breathing) can have substantial influences on HRV parameters (Laborde et al., 2017 ; Shao et al., 2024 ; Tisdell et al., 2024 ). For the latter, it is recommended to allow spontaneous breathing (Larsen et al., 2010 ) and participants should be well instructed to guarantee good data quality. Current Study Vitality and fatigue – considered as unipolar affective states (Boolani et al., 2019 ; Buchner et al., 2024 ) – play an important role in exercise-induced affective responses (Buchner et al., 2024 ; Timm et al., 2024 ), action initiation (Buchner et al., 2022 ; Dodge et al., 2022 ; Ryan & Deci, 2008 ), self-efficacy (Buchner et al., 2022 ; Matsuo et al., 2015 ), and self-determination (Oliveira et al., 2022 ). In particular, feelings of energy support the maintenance of a physically active lifestyle (Timm et al., 2024 ). The current study investigates how morning fatigue and vitality affect morning vmHRV (RMSSD) and heart period (RR) in response to prescribed and self-determined running intensity in young women over 8 weeks. Running as a form of endurance training is widely recognized as one of the most effective methods for lowering the risk of cardiovascular disease and enhancing parasympathetic activity. Additionally, running is particularly well-suited for ambulatory assessment studies due to its accessibility. Its adaptability to various training programs makes it an ideal choice for individuals at different fitness levels, including novices. Based on other research outcomes showing stable vagal tone over time, we expect short-term effects of running bouts on daily vmHRV (H1a), but no long-term changes (H1b). Building on previous mixed findings examining the impact of positive and negative affective responses on vmHRV, we are interested in evaluating the relationship of fatigue and vitality with vmHRV. Additionally, we aim to clarify whether this relationship is linear (H2) or quadratic (H3). Moreover, we control for the type of intervention, to identify group-effects between prescribed and self-determined running intensity (H4). Given the role of self-efficacy and motivational states in long-term physical activity behavior and subcortical representation, we assume that increased trait self-efficacy and running-related motivation will predict enhanced vmHRV (H5). Following recommendations by de Geus et al. ( 2019 ), we control for the bias of heart period on vmHRV. Changes in heart rate/heart period (RR) could be due to either increased sympathetic activity or reduced parasympathetic activity. Thus, given the strong correlation between HRV and heart rate/heart period, this procedure allows to identify vagally-mediated effects from chronotropic states. Methods Sample Twenty-eight young healthy female novices who were interested in starting a running routine were recruited at the University of Salzburg between August and December 2020. Ten participants were excluded from the analysis due to the following reasons: n = 1 due to malfunction of the diary app; n = 2 due to bad quality of HRV morning measures; n = 4 due to inconsistent measurement routine of HRV morning measures, n = 3 due to low ratio of valid data < 50%. The final sample used for analysis consists of 18 women (19–29 years) with eight participants in the self-determined intensity group, and ten participants receiving prescribed-polarized intensity instructions. Procedure The local ethics committee granted approval (GZ 372020) and participants gave their written informed consent before participation. The study was divided into six different phases and a detailed description of the procedure is explained elsewhere (Buchner et al., 2024 ). Phase 1 was held in the lab to collect demographic, anthropometric measures, and exercise-related self-efficacy and motivation. Two smartphone applications were installed on the participants' private smartphones. 1) a self-developed custom smartphone e-diary app (Android Version 6.0 or higher) for reporting incidental subjective vitality and fatigue, and 2) a modified version of ABIOS GmbH training app for collecting HRV measures in the morning and heart rate data during the running sessions. Participants were instructed on how to collect daily morning HRV at rest and how to use the diary app to collect affective measures during the day. The 5-minute seated familiarization session for HRV was used as a baseline measure to control for group differences (self-determined: 61.8 ms ± 29.5; prescribed: 72.3 ms ± 59.2; t (10) = -0.46, p = 0.15). Participants familiarized themselves for one week with the two apps during Phase 2. Daily diary prompts reminded participants three times per day (morning: 7.00–11:00 am; noon: 12h30-3:30 pm; evening: 6h30-9:30 pm) to report their incidental affect. If the e-diary was not fully completed after the initial notification, participants received follow-up reminders every 30 minutes, up to a maximum of five reminders. However, for the current study, only morning measures were taken into consideration for data analysis to compare against HRV. HRV was measured every morning for 5 minutes. During the first week (Phase 2), participants were allowed to establish their own routine to maintain consistency throughout the remaining study phases: either measuring while seated or lying & before or immediately after getting up & with eyes open or closed. Phase 3 took place in the lab to assess VO 2max and respiratory thresholds using spiroergometric methods through a standardized graded treadmill test. Phase 4 started with a supervised running session to introduce the 8-week running program and how to record the sessions via the training app. Participants were randomly assigned to the polarized-prescribed or the self-determined intensity group. Both groups were instructed to complete three 30-minute runs per week. The self-determined group focused on choosing a running intensity that made one feel good, while the polarized-prescribed group followed a standardized polarized training regime, consisting of 80% low-intensity, 20% high-intensity runs. Throughout the 8-week intervention, participants continued recording daily morning HRV at rest and incidental affective states using the e-diary. In Phase 5, participants repeated the treadmill test, followed by the continuation of the e-diary procedure in Phase 6 for another week. Upon finishing Phase 6, participants returned the devices and received a 50€ voucher for local stores, along with updated running recommendations based on their VO 2max results from Phase 5. Measures Self-efficacy was measured with the German exercise self-efficacy scale (Krämer & Fuchs, 2010 ) which assesses the conviction to start, maintain, and restart physical activity on a regular basis. The scale consists of three items with 1 = “not true at all” to 6 = “a 100% true”. The mean value of the three items is related to the extent of self-efficacy beliefs towards physical activity. Running-related motivation was assessed with the self-concordance scale (SSK; Seelig & Fuchs, 2006 ). The 12-item scale evaluates four dimensions of motivation, each measured by three items: intrinsic, identified, introjected, and extrinsic motivation. The wording of the items was adapted specifically for running. Responses are rated on a 6-point Likert scale from 1 = “not true at all” to 6 = “exactly true”. The SSK-index, ranging from − 10 to + 10, represents self-concordance and is calculated by subtracting introjected and extrinsic ratings from the sum of intrinsic and identified ratings. Incidental subjective vitality (Ryan & Frederick, 1997 ) was assessed using the German adaptation of the Subjective Vitality Scale (Buchner et al., 2022 ). This three-item scale is validated for the use of ambulatory assessment. Participants' current vitality is measured on an 11-point scale (0 = “not true at all” to 10 = “completely true”) describing the perceived energy available to the self, aliveness, drive, and spirit. Reliability coefficients (Shrout & Lane, 2012 ) of morning vitality demonstrate strong consistency with within-person reliability R C = .88 (Phase 4) and between-person reliability R KR = .99 (Phase 4). Incidental fatigue is measured via the German translation (Buchner et al., 2022 ) of the Rate of Fatigue (Micklewright et al., 2017 ). The one-item scale, which assesses global fatigue on an 11-point scale (0 = “not fatigued at all” to 10 = “total fatigue and exhaustion—nothing left”), is validated across diverse contexts (e.g., daily life, rest, exercise). Daily morning HRV was measured upon waking, either while sitting or lying. A Polar H10 (130 Hz) chest strap was connected via Bluetooth with the training app. The Polar H10 is a valid and reliable heart rate belt to detect raw ECG and RR intervals during rest and exercise (Gilgen-Ammann et al., 2019 ). The instructions for the 5-minute measurement were as follows: “Breathe calmly and evenly during the 5-minute rest measurement and try to relax.” Data from HRV measurements were stored in RR intervals and processed with Kubios Scientific Lite 4.1.1 (Tarvainen et al., 2014 ). We selected 4-minute intervals (the first and last 30 seconds of recording were not used for data extraction) and applied low- or medium-threshold filtering when needed. The pre-processing settings for the RR detrending method were set to smoothness priors (lambda = 500) with an interpolation rate of 4 Hz. To ensure the validity of the measure, corrected segments never surpassed 5% of the analyzed data. The parameters of interest were RMSSD and RR from the time domain. Parameters were transformed with the natural logarithm to account for the non-normality distribution tested with the Kolmogorow-Smirnow-Test. In the following RMSSD log is related to vmHRV and RR log to heart period. Heart rate during running sessions was measured with a Polar H10 chest belt connected via Bluetooth with the training app. Statistical analysis To examine the intensive longitudinal effects of running on vmHRV, we performed autoregressive (AR) multilevel modeling using restricted maximum likelihood estimation with the lme4 package in R (R Core Team, 2024 ). Repeated measures (Level 1) are nested within participants (Level 2) and missing data were considered missing at random. Level-1 variables are person-mean centered and Level-2 variables (self-efficacy, motivation, VO 2max ) are grand-mean centered (Nezlek & Mroziński, 2020 ). Kenward Roger approximation were applied to mitigate the increased risk of Type I errors associated with underestimating standard errors of fixed effects in small sample sizes. Effect sizes for the model and predictors were calculated using Cohen's f 2 , with values of 0.02, 0.15, and 0.35 indicating small, medium, and large effect sizes, respectively (Lorah, 2018 ). To test whether vmHRV varies over the short-term in accordance to running (H1a), the RMSSD log was entered as the dependent variable with days categorized into 3 day types : (1) runDay – participants ran on that day; (2) nextDay – the subsequent day after a running session without running; (3) neutralDay – neither a running day nor type of subsequent day. Long-term changes (H1b) were analyzed with the predictor time , calculated by the day within the intervention. Person-mean centered values of morning subjective vitality and fatigue were entered to evaluate the effects of affective states on vmHRV (H2). To control for a quadratic trend of this relationship, we also included a polynomial effect of second order (H3). To distinguish between the type of intervention, the predictor group was entered as a categorical variable (H4). Grand-mean centered predictors of trait self-efficacy and motivation were included to analyze their effect on state vmHRV (H5). The best model fits were assessed using chi-square difference statistics. As an autoregressive predictor, the person-mean RMSSD log value from the previous morning was entered into the model. We also controlled for VO 2max measured in Phase 3. Given expected changes in resting heart rate due to the interventions, and the guidance to co-analyze heart period within vmHRV research (de Geus et al., 2019 ), we consider the above-mentioned predictors in a second model, with resting RR log as the dependent variable. Moreover, we control for the influence of heart period on vmHRV and vice versa, by adding the corresponding variable as a covariate to each of the two models. Due to the relatively small sample size, we did not analyze any between-person differences via random effects (Arend & Schäfer, 2019 ). Model assumptions of the final model, including the normal distribution and homoscedasticity of residuals, were examined. Detailed model equations are provided in the supplementary material (Online Resource 1). Results Participants completed in total 1061 morning diary observations and 1056 morning HRV measures. Over 8 weeks, on average 22.56 ( SD = 3.88) running sessions were completed. Mean Training Impulse (TRIMP; Lucía et al., 2003 ) was at 1.61 ( SD = 0.34) with no differences between groups. For more details about compliance, mean incidental measures, and aggregated affect scores, see Table 1 . A graphical illustration of the distributions of vmHRV and affective measures is provided in Online Resource 2 (Figure ESM 1–3). Table 2 represents the results of the multilevel analysis to identify AR effects and the short- to long-term prediction of running, incidental vitality and fatigue, group effects, trait self-efficacy, and motivation on daily morning vmHRV indicated by RMSSD log . Table 1 Participant’s characteristics Variable M SD Age [years] a 23.72 3.20 BMI [kg/m2] a 23.19 4.21 VO 2max [ml O2/min)/kg] a 39.16 6.35 Self-efficacy a 5.22 0.58 Motivation a 3.56 1.94 Morning incidental affect (Compliance [%]) 93.74 7.06 Vitality b 6.17 1.77 Fatigue b 2.9 1.81 Morning HRV (Compliance [%]) 93.39 7.62 HR [bpm] b 64.54 8.96 RR [ms] b 948.76 145.03 RMSSD [ms] b 63.31 30.13 pNN50 [%] b 37.08 20.85 SDNN [ms] b 59.05 23.07 HF power [nu] b 47.04 18.71 a no significant differences between Groups at baseline b averaged scores aggregated within participants and days of phase 4 The first model showed a small- to medium-sized AR effect on vmHRV. Thus, measures completed on the previous morning predicted positively values the morning after. Moreover, vmHRV remained stable over time, with no significant time effects in the short- (H1a) or the long-term (H1b). Running had no direct effects on vmHRV morning measures. In addition, neither subjective vitality nor fatigue predicted vmHRV in either a linear or quadratic term. The predictor group became significant after adding self-efficacy and motivation to the model. The prescribed-intensity group showed lower vmHRV values compared to the self-determined intensity group with a medium-sized effect (Online Resource 2, Figure ESM9). In contrast, VO 2max measured at Phase 3 became non-significant, after adding other trait variables to the model, showing a small- to medium-sized effect. As such, trait self-efficacy predicts vmHRV to a large effect, whereas motivational measures do not predict vmHRV. When adding heart period (RR log ) as an additional covariate, heart period predicts vmHRV ( b = 2.86, p < 0.001, f 2 = 0.82), with a negative time trend of small effect size ( b = -0.00, p = 0.005, f 2 = 0.003). All other effects remain stable. Table 2 Model parameters for the multilevel analysis (H1-H5) with RMSSD log (left) and RR log (right) as the dependent measures Outcome RMSSD log RR log Fixed effects b (SE) Std. ß -coefficient 95% CI for b f 2 b (SE) Std. ß-coefficient 95% CI for b f 2 Intercept 4.10 (0.12)** 0.00 3.85–4.35 6.85 (0.04)** 0.00 6.77–6.92 Autoregressive predictor 0.23 (0.04)** 0.06 0.16–0.31 0.02 0.29 (0.04)** 0.00 0.21–0.36 0.03 time 0.00 (0.00) 0.01 -0.00–0.00 0.00 0.00 (0.00)* 0.01 0.00–0.00 0.00 Day type [neutralDay] a 0.00 (0.03) 0.00 -0.05–0.06 0.00 -0.01 (0.01) 0.00 -0.02–0.01 0.01 Day type [runDay ] a 0.02 (0.02) 0.01 -0.02–0.07 0.00 0.01 (0.01) 0.00 -0.01–0.02 0.01 Vitality 0.00 (0.01) 0.00 -0.02–0.02 0.00 -0.00 (0.00) 0.00 -0.01–0.00 0.00 Fatigue -0.01 (0.01) -0.02 -0.03–0.00 0.00 -0.00 (0.00) 0.01 -0.01–0.00 0.00 Group b -0.39 (0.18)* -0.20 -0.78 – -0.00 0.16 -0-05 (0.06) 0.03 -0.17–0.07 0.01 VO 2max 0.02 (0.01) 0.10 -0.01–0.05 0.02 -0.00 (0.00) -0.01 -0.01–0.01 0.04 Self-efficacy 0.56 (0.18)* 0.33 0.18–0.95 0.40 0.12 (0.06)* 0.07 0.00–0.24 0.20 Motivation -0.09 (0.05) -0.17 -0.20–0.02 0.10 -0.02 (0.02) -0.04 -0.05–0.01 0.04 Random effects Intercept 0.12 0.21–0.42 0.01 0.08–0.13 Residual 0.08 0.26–0.29 0.01 0.07–0.08 ICC c 0.73 0.69 Conditional R 2 0.60 0.57 0.70 0.11 Note : Unstandardized estimates and standard errors; f 2 = effect size related to the variance explained by the single predictor and for the overall model. a the reference category is type of day [nextDay] b the reference category is the self-determined intensity group c from the unconditional model * p < 0.05, and **p < 0.01. The second model showed a small- to medium-sized AR effect on heart period and a significant time effect in heart period over the 8-week intervention period of negligible effect size. Neither type of day, subjective vitality, nor fatigue were significant predictors. With heart period as the dependent variable, the significant group effect diminished. Moreover, VO 2max does not influence heart period, nor does trait self-concordance. A medium to large significant predictive effect is seen for self-efficacy. After including vmHRV as an additional covariate, the effect of self-efficacy diminished as well ( b = 0.19, p < 0.001, f 2 = 0.20). This suggests that fluctuation in vmHRV could explain the link between self-efficacy and heart period. Discussion The present 8-week running intervention study investigates the short- to long-term effects (H1) of self-determined and prescribed running intensity on vmHRV (H4), its relationship with vitality and fatigue (linear: H2; quadratic: H3), and the influence of trait components like self-efficacy and self-concordance (H5). The results reveal that not situational factors (incidental affect, physical activity), but especially stable personality traits like self-efficacy play an important role in self-regulatory mechanisms, displayed in higher vmHRV (indicated by RMSSD log ) and lower heart period (indicated by RR log ). Our sample consists of 18 healthy females, with the tendency of increased aggregated mean values of subjective vitality and decreased fatigue (Table 1 ), typical for healthy subjects (Ryan & Frederick, 1997 ). Moreover, the aggregated HRV values observed in our study are comparable to the upper percentiles of the normative data reported by Dantas et al. ( 2018 ). The research group provides reference HRV values for short-term measures for both sexes between 35 to 74 years. Given that HRV typically declines with age, the relatively high percentile classification of our population can be attributed to its younger age. Further, our sample has high self-efficacy beliefs to start, maintain, and restart running after an extended break. This is no surprise since we recruited novices, who specifically wanted to start with running. The positive SSK-index implies a positive explicit association towards running moderately spread between participants. Results of multilevel analyses show no long-term changes in vmHRV, however heart period decreased slightly over time (H1b). Both models are time-dependent, seen in the significant AR effect of first order. This outcome supports the assumption of stable vagal tone over time. These results are in line to those of the control group in the study by da Silva et al. ( 2019 ), in which women aged 18–35 followed a comparable 8-week training regimen to that of our prescribed intensity group. No shifts in resting vmHRV were neither observed in a 6-week high-intensity CrossFit intervention with healthy men and women of the same age (Crawford et al., 2020 ). Additionally, previous research by Duarte et al. ( 2015 ) has shown that individuals with a high resting vagal tone at baseline are less likely to experience further increases in heart rate variability (HRV) following a 12-week aerobic training intervention compared to those with lower initial levels. Notably, Duarte et al. ( 2015 ) conducted their study on men, whereas our research focuses on women. While our sample, on average, already shows relatively high vmHRV, we did not account for potential initial differences in vagal tone, which could influence the extent of HRV adaptations. Considering short-term effects of running, multilevel analyses in our study do not identify effects by categorizing neutralDays, runDays, and nextDays. Thus, vmHRV or heart period does not differ between different types of days and running is not proven to influence vmHRV or heart period in the short term (H1a). Possibly, the training intensity was not high enough to induce any adaptations in vagal tone at rest. Furthermore, Duarte and colleagues ( 2015 ) recommend that resting measurements of vagal tone are not suitable for assessing vagal reactivation; instead, measurements should be taken during the immediate recovery phase after an exercise session. This suggests that potential improvements in autonomic function may have occurred in our population but were not captured by the selected procedure of resting cardiac activity assessments. Looking at the effects of vitality and fatigue on morning vmHRV, neither a linear (H2) nor a quadratic trend (H3) becomes significant. Although visually a positive quadratic (U-shaped) effect for vitality (Online Resource 2, Figure ESM7) and a negative cubic trend for fatigue was expected to predict vmHRV (Online Resource 2, Figure ESM8), this effect is not strong enough to remain in the model. Thus, the results of our study cannot support recently published findings of a quadratic relationship between vmHRV and affective measures (Dang et al., 2021 ; Duarte & Pinto-Gouveia, 2017 ; Kogan et al., 2013 ). In particular, the work of Spangler et al. ( 2021 ) investigated gender differences, highlighting that a non-linear relationship between vmHRV and positive affect is only apparent in women, but not in men. However, it is important to note that together with other studies, previous results are based on laboratory measurements focusing on trait affect and state vmHRV. Therefore, comparisons with ambulatory data may be limited, and the polynomial relationship between vmHRV and affective measures may be true at the between-person level. However, at the within-person level, this relationship appears to be more sensitive to intraindividual variability. Evidence towards this assumption provides the plots per participant, suggesting individual polynomial relationships between affective states and vmHRV (Online Resource 2, Figure ESM7-8). Investigating differences in intervention methods on vmHRV postulates a clear association of parasympathetic activity with self-determination (H4). Participants self-determining running intensity with the goal of feeling good have significantly higher morning vmHRV throughout the intervention compared to the prescribed-intensity group. This effect is independent from heart period when entered as a covariate. Interestingly, the lack of a significant group effect when heart period was analyzed as the dependent variable reinforces the notion that the observed changes are specifically tied to parasympathetic modulation rather than general heart rate dynamics. The increase in vmHRV without changes in heart period suggests that the self-determined group experienced enhanced autonomic regulation without significant alterations in chronotropic regulation, further supporting the idea that self-determination fosters better self-regulation. The findings imply that addressing fundamental psychological needs, such as autonomy, could be important for supporting self-regulation in females who are new to running. The ability for autonomous decision-making regarding running intensity might allow subjects to make choices considering individual preferences. This could have contributed to higher vmHRV, potentially indicating greater autonomic adaptability and aligning with the principles of the Neurovisceral Integration Model (Thayer et al., 2009 ). Enhanced self-regulation might have supported individuals to better cope with physiological and psychological stressors associated with a new exercise regimen. Besides this, self-regulation plays a central role in initiating and sustaining goal-directed behavior (Geldhof et al., 2017 ). Consequently, our findings highlight the importance of considering individual preferences and self-regulation in exercise programs. Personalized exercise prescriptions that allow for self-determined intensity may be more effective in promoting long-term adherence and improving physiological outcomes compared to rigid, prescribed intensity programs. The outcome corresponds with self-determination theory (Deci & Ryan, 2000 ), and is further supported by a meta-analysis, pointing towards the relevance of autonomy support (large effect) in facilitating health behavior change (Gillison et al., 2019 ). Other important predictors of health behavior change are self-efficacy and motivation (Larsen et al., 2021 ; Nurmi et al., 2023 ). Adding these two predictors to the model results in increased vmHRV when reporting higher trait self-efficacy but shows no association with motivational traits. These effects remain stable when heart period is added as a covariate, again indicating the influence of parasympathetic activation relevant to vmHRV modulation. Interestingly, trait self-efficacy also predicts heart period, but this effect diminishes when vmHRV is added as a covariate. This phenomenon supports the notion of the relevance of parasympathetic activity on social-cognitive behavior (e.g. self. efficacy). It suggests that increased self-efficacy enhances self-regulation. Individuals with strong convictions to start, maintain, and resume physical activity appear better ability to balance external and internal situational stimuli, as indicated by elevated vmHRV. Consistent with previous studies (Kompf, 2020 ; Rebar et al., 2016 ), we agree that future interventions should aim to enhance self-efficacy for exercise by promoting competence and autonomy. Our findings of increased self-regulation, as indicated by increased vagal tone, hold promise for promoting long-term changes in physical activity behavior. Limitations and future directions Despite the valuable insights provided, there are several limitations that should be acknowledged. Firstly, daily HRV measurements were collected via a chest belt and stored in RR intervals, instead of the gold-standard ECG method. This allowed easy, low-cost implementation in an ecological ambulatory setting. However, this approach could lead to more artifacts and processing steps lack of correction for R-peak detection (Laborde et al., 2017 ). To mitigate some of these limitations and to ensure data quality, we excluded participants whose data did not meet the required standards. Secondly, self-efficacy and self-concordance were not assessed daily, limiting direct comparison with daily HRV measures. However, studies such as Crawford et al. ( 2020 ), Dunton ( 2013 ), and Maher et al. ( 2025 ) highlight daily fluctuations and individual variability in self-efficacy and physical activity intentions. While this variability is relevant, our focus was on trait-like factors influencing self-regulatory mechanisms. Notably, individuals with stable positive state-like factors, such as consistent self-efficacy and exercise intentions, often exhibit higher physical activity levels, suggesting enhanced self-regulation (Dunton, 2013 ; Maher et al., 2025 ). For future studies, it would be interesting to examine the stability of self-efficacy, motivation, and exercise intentions, how they evolve with HRV fluctuations and their influence on long-term behavioral patterns. Thirdly, minimization of the sample resulted in 8 vs. 10. subjects per group not allowing us to capture between-person differences. Moreover, the small- to medium-sized effects observed in our study should be interpreted with caution. However, the large effects detected in our study may permit generalization to the population at the within-person level. Fourthly, to eliminate any sex differences in emotion regulation, resting HRV (Min et al., 2023 ), and affective response towards exercise (Tavares et al., 2021 ), we focused on young females only. Nevertheless, we did not control for the menstrual cycle, which also modulates vmHRV metrics (Schmalenberger et al., 2024 ; Schmalenberger et al., 2019 ). Therefore, future studies focusing on different sexes and considering vmHRV fluctuations across phases of the menstrual cycle will provide a deeper understanding of the relationship between exercise, affect, and vmHRV. Lastly, data collection was held during the COVID-19 pandemic, and we cannot exclude bias of infection or pandemic restrictions on our outcomes (Asarcikli et al., 2022 ). However, running may have led to positive affective outcomes that were diminished by the pandemic, like the effects of leisure walks (Reuter et al., 2021 ).In this context, running could have facilitated better self-regulation, thereby supporting increased vmHRV. Conclusion This ambulatory assessment study focused on elaborating the effects of two different running interventions (self-determined vs. prescribed intensity) on daily morning vmHRV. Considering personality distributions of self-efficacy for exercise and self-concordance as well as fluctuations of daily vitality and fatigue, the study aimed to better understand the interplay of these correlates on self-regulation, indicated by increased morning vmHRV. Direct effects of motivation, running, morning vitality, or fatigue could not be detected. Interestingly, subjects allowing for autonomy of their running intensity showed increased vmHRV compared to the prescribed intensity group. Moreover, increased trait self-efficacy supported better self-regulation. The findings of this study suggest that both of these effects are predominantly influenced by parasympathetic activity. Therefore, in the design of future interventions promoting physical activity behavior, it is recommended that efforts are made to help individuals feel competent and allow a certain sense of self-determination to reach their activity goal. Declarations Funding This research was funded by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry for Digital and Economic Affairs, and the federal state of Salzburg under the research program COMET – Competence Center for Excellent Technologies – in the project Digital Motion in Sports, Fitness, and Well-being (DiMo) (Österreichische Forschungsförderungsgesellschaft). Acknowledgments We would like to thank Jana Schnabel for her consistent and extra ordinary support in data storage. Moreover, we would like to thank all our participants who took part in our running intervention study. Conflict-of-Interest Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics approval The study was conducted in accordance with the ethical standards of the institutional research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The protocol was approved by the Ethics Committee of Paris Lodron-University Salzburg, approval number EK-GZ: 37/2020. Informed consent Informed consent was obtained from all participants, who had the right to withdraw at any time. All data were anonymized to ensure confidentiality. Consent to publish Patients signed informed consent regarding publishing their data. Data availability statement The datasets presented in this article are not readily available because of sharing agreements in the funded project. Requests to access the datasets should be directed to [email protected] . References Amekran, Y., & El Hangouche, A. J. (2024). 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Neuroscience and Biobehavioral Reviews 75 , 274-96, https://doi.org/10.1016/j.neubiorev.2017.02.003 Soltani, M., Baluchi, M. J., Boullosa, D., Daraei, A., Doyle-Baker, P. K., Saeidi, A., Knechtle, B., Dehbaghi, K. M., Mollabashi, S. S., Vandusseldorp, T. A., & Zouhal, H. (2021). Effect of Intensity on Changes in Cardiac Autonomic Control of Heart Rate and Arterial Stiffness After Equated Continuous Running Training Programs. Frontiers in Physiology , 12 , https://doi.org/10.3389/fphys.2021.758299 Spangler, D. P., Dunn, E. J., Aldao, A., Feeling, N. R., Free, M. L., Gillie, B. L., Vasey, M. W., Williams, D. P., Koenig, J., & Thayer, J. F. (2021). Gender Matters: Nonlinear Relationships Between Heart Rate Variability and Depression and Positive Affect. Frontiers in Neuroscience , 15 , https://doi.org/10.3389/fnins.2021.612566 Stevens, C. J., Baldwin, A. S., Bryan, A. D., Conner, M., Rhodes, R. E., & Williams, D. M. (2020). Affective Determinants of Physical Activity: A Conceptual Framework and Narrative Review. Frontiers in Psychology , 11 , https://doi.org/10.3389/fpsyg.2020.568331 Tarvainen, M. P., Niskanen, J. P., Lipponen, J. A., Ranta-Aho, P. O., & Karjalainen, P. A. (2014). Kubios HRV--heart rate variability analysis software. Computer Methods & Programs in Biomedicine , 113 (1), 210-20, https://doi.org/10.1016/j.cmpb.2013.07.024 Tavares, V. D. D. O., Schuch, F. B., Tempest, G., Parfitt, G., Oliveira Neto, L., Galvão-Coelho, N. L., & Hackett, D. (2021). Exercisers’ Affective and Enjoyment Responses: A Meta-Analytic and Meta-Regression Review. Perceptual & Motor Skills , 128 (5), https://doi.org/10.1177/00315125211024212 R Core Team. (2024). R: A Language and Environment for Statistical Computing . In R Foundation for Statistical Computing. https://www.R-project.org/ Thayer, J. F., Hansen, A. L., Saus-Rose, E., & Johnsen, B. H. (2009). Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health. Annals of Behavioral Medicine , 37 (2), 141-53, https://doi.org/10.1007/s12160-009-9101-z Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders , 61 , 201-16, Timm, I., Giurgiu, M., Ebner-Priemer, U., & Reichert, M. (2024). The Within-Subject Association of Physical Behavior and Affective Well-Being in Everyday Life: A Systematic Literature Review. Sports Medicine , https://doi.org/10.1007/s40279-024-02016-1 Tisdell, E. J., Lukic, B., Banerjee, R., Liao, D., & Palmer, C. (2024). The Effects of Heart Rhythm Meditation on Vagal Tone and Well-being: A Mixed Methods Research Study. Applied Psychophysiology & Biofeedback , 49 (3), 439-55, https://doi.org/10.1007/s10484-024-09639-0 Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology , 9 , https://doi.org/10.1146/annurev-clinpsy-050212-185510 Wang, X., Chen, Q., Li, Y., Ding, K., & Qiu, J. (2022). The brain functional connectivity in the default mode network is associated with self-efficacy in young adults. Brain Imaging & Behavior , 16 (1), 107-17, https://doi.org/10.1007/s11682-021-00480-1 Williams, D. M., Dunsiger, S., Emerson, J. A., Gwaltney, C. J., Monti, P. M., & Miranda, R., Jr. (2016). Self-paced exercise, affective response, and exercise adherence: A preliminary investigation using ecological momentary assessment. Journal of Sport and Exercise Psychology , 38 (3), https://doi.org/10.1123/jsep.2015-0232 Additional Declarations No competing interests reported. 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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-6285008","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441877050,"identity":"5d8524e9-ce9b-4ed9-b221-31055fd0c362","order_by":0,"name":"Laura Buchner","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACPgh1gIFBAkRXMMgQ1MKGquUMAw9MRoI4LYxtxGhhP2P24QfDncT+2b0HH1fOu8NjcP4AI1DErg6nFp4c45k9DM8SZ9w5l2x4dtszHoMbCcySPQzJeByWYwx0zOHEDRI5ZpKN2w4DtTAwSDMwMOPWwv/GmPEPRIv5z8Y5h0EOY/7NwFCPW4tEjjEzzBbGxgaglgMJbEBbDuPR8qyYWcbgmfGMGznGkg3HnvFI3khss+wxOC7ZgEMLP3/yZsY3FXdk+2fkGH5sqLkjx3f+8OEbPyqq+XHZAgEGcNYBIGZsQBYhCA4Qr3QUjIJRMApGDAAAXbtSAqOLqGEAAAAASUVORK5CYII=","orcid":"","institution":"University of Salzburg","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"","lastName":"Buchner","suffix":""},{"id":441877052,"identity":"9ca2dcd4-32e2-4161-830b-0e0af78f0a3b","order_by":1,"name":"Günter Amesberger","email":"","orcid":"","institution":"University of Salzburg","correspondingAuthor":false,"prefix":"","firstName":"Günter","middleName":"","lastName":"Amesberger","suffix":""},{"id":441877053,"identity":"d6e92073-88f4-47b6-938c-b0813286e631","order_by":2,"name":"Sabine Würth","email":"","orcid":"","institution":"University of Salzburg","correspondingAuthor":false,"prefix":"","firstName":"Sabine","middleName":"","lastName":"Würth","suffix":""},{"id":441877055,"identity":"c84fe50c-a5f9-4e3e-804c-4951ee309472","order_by":3,"name":"Thomas Finkenzeller","email":"","orcid":"","institution":"University of Salzburg","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Finkenzeller","suffix":""}],"badges":[],"createdAt":"2025-03-22 17:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6285008/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6285008/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11332-026-01668-y","type":"published","date":"2026-02-10T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102880260,"identity":"15b03c62-974d-4571-a722-1e9185d2cfe2","added_by":"auto","created_at":"2026-02-17 22:22:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":873965,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6285008/v1/9ed7095d-a5c4-4523-8e09-66de94854d5e.pdf"},{"id":81110630,"identity":"f6782302-8754-48da-b265-5ecdb38328fc","added_by":"auto","created_at":"2025-04-22 10:25:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":114822,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6285008/v1/f690913115cb63c105da61dd.pdf"},{"id":81109720,"identity":"90369525-78a9-4061-a8f3-8c27251585ed","added_by":"auto","created_at":"2025-04-22 10:17:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2085056,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6285008/v1/66cf87a22964fc01333fb543.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Do autonomy, self-efficacy, vitality, and fatigue predict daily morning heart rate variability? A running intervention study in healthy women","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIt is well known that regular physical activity has positive benefits on physical and mental health (Marquez et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The growing interest in psychophysiological mechanisms to improve health, foster an active lifestyle, and improve well-being led to several theories about the interconnectivity between the brain, autonomous nervous system, heart, and affective states (Grosicki et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kogan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kreibig, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mosley \u0026amp; Laborde, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this sense, the measure of heart rate variability (HRV) is of particular interest. The following introduction will give an overview of theories and current research findings trying to explain the association between psychophysiological measures, incidental affect, and behavior change toward an active lifestyle. HRV is a non-invasive measure of the neurovegetative activity and autonomic function of the heart. It describes the variability of time intervals between consecutive heartbeats (RR intervals measured in milliseconds) primarily driven by the dynamic interplay between parasympathetic and sympathetic influences on the heart via the sinoatrial node. Increased variability explains the ability of the heart to react and adjust according to external and internal circumstances and is correlated with higher parasympathetic modulation (Sammito \u0026amp; Bockelmann, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thayer et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Parasympathetic nervous activity, also known as cardiac vagal activity or vagal tone, is operationalized by the following parameters of vagally-mediated HRV (vmHRV): root mean square of successive differences (RMSSD); percentage of successive normal sinus RR intervals more than 50ms (pNN50); high-frequency power (HF-HRV) (Laborde et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe interaction of physiological and psychological processes is described by the Neurovisceral Integration Model (Thayer \u0026amp; Lane, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The model posits a reciprocal connection between a network of brain regions (central autonomic network (CAN: such as prefrontal cortex, amygdala, and hypothalamus) with cardiac regulation through the stellate ganglia and vagus nerve. The CAN coordinates cognitive, attentional, affective, and autonomic processes that are relevant to support adaptability, behavioral flexibility, and goal-directed behavior (Smith et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Thayer et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The primary function of the prefrontal cortex is related to inhibitory processes associated with executive functions to suppress undesirable reactions or motivational tendencies to pursue overarching action goals (Goschke, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The amygdala contribute to sensory and emotional processing, essential for affective regulation (Thayer et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Empirical evidence demonstrates a positive association between enhanced vmHRV and improved executive functioning and self-regulatory capacity. Improved self-regulation can support emotional and physiological regulation, which are essential for behavior control and decision-making (Forte et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Holzman \u0026amp; Bridgett, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Thayer \u0026amp; Lane, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Thus, vmHRV reflects the functional capacity of the central autonomic network to adapt to various situational stimuli effectively, which could support maintaining a healthy lifestyle and facilitating behavior change (Porges, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Shaffer et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Thayer et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMaintaining a healthy lifestyle such as being physically active serves several benefits. Regular physical activity reduces the risk of cardiovascular diseases by enhancing regulatory capacities to adaptively respond to changes in homeostasis changes (Amekran \u0026amp; El Hangouche, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bechke et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Endurance athletes, in particular, exhibit greater RMSSD and HF-HRV at rest compared to healthy untrained individuals (Chihaoui Mamlouk et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Aerobic intervention studies have demonstrated that moderate to high exercise intensities can significantly enhance vmHRV (Forte et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Soltani et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) whereas low-intensity or low-volume training does not sufficiently impact parasympathetic activity (Grässler et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Soltani et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although knowing about the benefits of physical activity, individuals frequently face challenges in translating their exercise intentions into actions. To overcome the so-called “intention-behavior gap”, several theories and models were developed (Rhodes \u0026amp; Sui, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom the perspective of action-control theories, behavioral regulation skills such as planning and intention implementation, are considered essential for initiating and maintaining physical activity (Rhodes \u0026amp; Yao, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A recent review emphasized that self-efficacy is a relevant moderator in the effectiveness of such action planning (Kompf, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Self-efficacy describes the belief in one’s own capabilities to pursue a goal. These beliefs represent an individual’s confidence in their personal competencies to succeed (Bandura, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Furthermore, Rhodes et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlighted in their review of predictors of the intention-behavior gap, that self-efficacy is a key determinant in bridging this discordance. Additionally, self-efficacy has been proven to be associated with functional connectivity of the right anterior cingulate cortex – a cortical region of the CAN (Wang et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Besides the presence of social-cognitive theories to explain physical activity behavior, models based on hedonic principles are gaining prominence in research (Stevens et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Empirical findings indicate that positive affective responses during exercise substantially influence future exercise adherence (Hevel \u0026amp; Maher, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rhodes \u0026amp; Kates, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The relationship between affect and physical activity appears to be bidirectional rather than unidirectional (Ruissen et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Timm et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, the engagement in physical activity has been shown to result in an elevation of positive activated affect and on the other hand, individuals are more likely to engage in physical activity when they feel positive and energized (Fiedler et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Timm et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo foster positive affective outcomes associated with physical activity, the self-determination of exercise intensity can play a crucial role. While a self-determined exercise intensity intervention led to higher pleasant feelings compared to the prescribed-intensity group in low-active overweight adults (Williams et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), this evidence wasn’t supported by results from Buchner et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in a sample of healthy untrained women. However, higher levels of perceived self-determination are strongly associated with greater intrinsic motivation, enhanced autonomy, and improved adherence to physical activity (Baldwin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ryan \u0026amp; Deci, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Therefore, further research is warranted to explore the interplay between reflective motivation (e.g., self-efficacy, intention), affective responses towards exercise (e.g., vitality, fatigue), satisfaction of basic psychological needs (e.g., autonomy), and cardiac autonomic regulation (e.g., RMSSD). Deeper insights into these relationships could enhance the development of effective interventions for long-term physical activity engagement.\u003c/p\u003e \u003cp\u003eIn addition to its role in physiological adaptation, cardiac vagal tone is increasingly recognized as a biological marker of psychological well-being (Kogan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Based on the Neurovisceral Integration Model, better emotion regulation is indicated with enhanced vmHRV (Thayer et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Research implies that individuals with lower resting vmHRV are more prone to experience greater increases in negative affect (Bylsma et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sloan et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and report higher levels of depressive symptoms (e.g., Rottenberg, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and anxiety disorders (e.g., Chalmers et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). By contrast, several studies have shown, that higher values of vmHRV at rest are related to positive affect (Dang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), improved emotion regulation (Cai et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and other measures of trait subjective well-being (Geisler et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Laborde et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Interestingly, results from Bylsma et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Sloan et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) cannot support significant correlations between vmHRV and positive affect. Moreover, a review by Mosley and Laborde (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) summarizes mixed findings regarding the relationship between HRV measures and affect. The inconsistent results may be explained by differences in study designs and sample characteristics. Additionally, emerging evidence points to a potential curvilinear, rather than linear, relationship between resting vmHRV and various measures of subjective well-being (Dang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kogan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Whereas Kogan et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) demonstrated a negative quadratic trend between vmHRV and life satisfaction, Dang et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) posit a positive quadratic trend between vmHRV and meaning in life. Another study by Duarte and Pinto-Gouveia (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) highlighted that various positive emotions may be differently associated with autonomic nervous system functioning. Comparing different positive emotional correlates (positive affect, activating positive affect, relaxed positive affect, and safe/content positive affect), only safe/content positive affect was found to predict HF-HRV following a negative quadratic trend. No significant linear relationships or associations with other positive affective measures or vmHRV as a predictor were identified (Duarte \u0026amp; Pinto-Gouveia, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This possibility underscores the complexity of the relationship between physiological markers and psychological states, highlighting the need for further research to better understand these dynamics.\u003c/p\u003e \u003cp\u003eKnowing about the separate effects between physical activity (endurance) and measures of subjective well-being on vmHRV, little research provides insights into the interplay between these three correlates. Often considered in pre-post intervention designs, ambulatory assessment studies are rare although they offer valuable insights into the dynamic interrelationships between variables that fluctuate on a daily basis. For instance, Crawford et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) examined the relationship between daily resting vmHRV, motivation to exercise, and perceived fatigue in participants following either an HRV-guided CrossFit high-intensity training (HIT) program or a prescribed HIT program. The seven-day rolling average of RMSSD remained stable throughout the 6-week intervention, indicating no overall long-term changes. However, meaningful daily shifts in RMSSD, measured by the smallest worthwhile change, revealed an association with increased fatigue when RMSSD values were either above or below the normal range. For the control group, this was also apparent for daily motivation – showing lower motivational states, when vmHRV was different from normal (Crawford et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This outcome highlights the association between vmHRV and adherence-related correlates for long-term physical engagement such as motivation and fatigue. Another study by da Silva et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) compared HRV-guided training programs with pre-defined polarized training in untrained women during an eight-week running intervention study. On the one hand, HRV-guided training resulted in a reduction in fatigue and stress, alongside improved mood and lack of energy. On the other hand, the pre-defined training group reported a decrease in vigor but experienced better-perceived recovery. Both training programs were effective in improving self-regulation and various stress-related symptoms, albeit in different ways (da Silva et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These findings underscore the value of tracking HRV changes and affective measures on a daily basis to better understand short-term variations that might be missed by pre-post-study designs.\u003c/p\u003e \u003cp\u003eThere is a need for further studies investigating the relationship between incidental affect, vmHRV, and behavior-adopting parameters toward regular physical activity. Exercise-related motivation, self-efficacy, perceived autonomy, and positive affective outcomes all predict physical activity maintenance. However, research linking all of these aspects with cardiac autonomic functioning is missing. Ambulatory assessment provides the state-of-the-art methodology for exploring the dynamic interplay between subjective experiences and behavior in participants' natural environments (Trull \u0026amp; Ebner-Priemer, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Understanding the day-to-day variations in affective states and autonomic nervous system activity with physical activity highlights the importance of expanding research using this method. Additionally, analyzing ambulatory assessment data allows not only assumptions about between-person differences, but also about within-person variations (Nezlek \u0026amp; Mroziński, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is important to better understand the interplay of different aspects on the individual level over time. When measuring resting state vmHRV in ambulatory settings, it is important to note, that changes in body posture (supine, seated, standing), time of day, routine (before/after getting up), and breathing habit (just like slow-paced breathing) can have substantial influences on HRV parameters (Laborde et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shao et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tisdell et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For the latter, it is recommended to allow spontaneous breathing (Larsen et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and participants should be well instructed to guarantee good data quality.\u003c/p\u003e\n\u003ch3\u003eCurrent Study\u003c/h3\u003e\n\u003cp\u003eVitality and fatigue – considered as unipolar affective states (Boolani et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Buchner et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) – play an important role in exercise-induced affective responses (Buchner et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Timm et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), action initiation (Buchner et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dodge et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ryan \u0026amp; Deci, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), self-efficacy (Buchner et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Matsuo et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and self-determination (Oliveira et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In particular, feelings of energy support the maintenance of a physically active lifestyle (Timm et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The current study investigates how morning fatigue and vitality affect morning vmHRV (RMSSD) and heart period (RR) in response to prescribed and self-determined running intensity in young women over 8 weeks. Running as a form of endurance training is widely recognized as one of the most effective methods for lowering the risk of cardiovascular disease and enhancing parasympathetic activity. Additionally, running is particularly well-suited for ambulatory assessment studies due to its accessibility. Its adaptability to various training programs makes it an ideal choice for individuals at different fitness levels, including novices.\u003c/p\u003e \u003cp\u003eBased on other research outcomes showing stable vagal tone over time, we expect short-term effects of running bouts on daily vmHRV (H1a), but no long-term changes (H1b). Building on previous mixed findings examining the impact of positive and negative affective responses on vmHRV, we are interested in evaluating the relationship of fatigue and vitality with vmHRV. Additionally, we aim to clarify whether this relationship is linear (H2) or quadratic (H3). Moreover, we control for the type of intervention, to identify group-effects between prescribed and self-determined running intensity (H4). Given the role of self-efficacy and motivational states in long-term physical activity behavior and subcortical representation, we assume that increased trait self-efficacy and running-related motivation will predict enhanced vmHRV (H5). Following recommendations by de Geus et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we control for the bias of heart period on vmHRV. Changes in heart rate/heart period (RR) could be due to either increased sympathetic activity or reduced parasympathetic activity. Thus, given the strong correlation between HRV and heart rate/heart period, this procedure allows to identify vagally-mediated effects from chronotropic states.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003eSample\u003c/h2\u003e\u003cp\u003eTwenty-eight young healthy female novices who were interested in starting a running routine were recruited at the \u003cem\u003eUniversity of Salzburg\u003c/em\u003e between August and December 2020. Ten participants were excluded from the analysis due to the following reasons: n = 1 due to malfunction of the diary app; n = 2 due to bad quality of HRV morning measures; n = 4 due to inconsistent measurement routine of HRV morning measures, n = 3 due to low ratio of valid data \u0026lt; 50%. The final sample used for analysis consists of 18 women (19–29 years) with eight participants in the self-determined intensity group, and ten participants receiving prescribed-polarized intensity instructions.\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003e The local ethics committee granted approval (GZ 372020) and participants gave their written informed consent before participation. The study was divided into six different phases and a detailed description of the procedure is explained elsewhere (Buchner et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Phase 1 was held in the lab to collect demographic, anthropometric measures, and exercise-related self-efficacy and motivation. Two smartphone applications were installed on the participants' private smartphones. 1) a self-developed custom smartphone e-diary app (Android Version 6.0 or higher) for reporting incidental subjective vitality and fatigue, and 2) a modified version of ABIOS GmbH training app for collecting HRV measures in the morning and heart rate data during the running sessions. Participants were instructed on how to collect daily morning HRV at rest and how to use the diary app to collect affective measures during the day. The 5-minute seated familiarization session for HRV was used as a baseline measure to control for group differences (self-determined: 61.8 ms\u0026thinsp;\u0026plusmn;\u0026thinsp;29.5; prescribed: 72.3 ms\u0026thinsp;\u0026plusmn;\u0026thinsp;59.2; \u003cem\u003et\u003c/em\u003e(10) = -0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15). Participants familiarized themselves for one week with the two apps during Phase 2. Daily diary prompts reminded participants three times per day (morning: 7.00\u0026ndash;11:00 am; noon: 12h30-3:30 pm; evening: 6h30-9:30 pm) to report their incidental affect. If the e-diary was not fully completed after the initial notification, participants received follow-up reminders every 30 minutes, up to a maximum of five reminders. However, for the current study, only morning measures were taken into consideration for data analysis to compare against HRV. HRV was measured every morning for 5 minutes. During the first week (Phase 2), participants were allowed to establish their own routine to maintain consistency throughout the remaining study phases: either measuring while seated or lying \u0026amp; before or immediately after getting up \u0026amp; with eyes open or closed. Phase 3 took place in the lab to assess VO\u003csub\u003e2max\u003c/sub\u003e and respiratory thresholds using spiroergometric methods through a standardized graded treadmill test. Phase 4 started with a supervised running session to introduce the 8-week running program and how to record the sessions via the training app. Participants were randomly assigned to the polarized-prescribed or the self-determined intensity group. Both groups were instructed to complete three 30-minute runs per week. The self-determined group focused on choosing a running intensity that made one feel good, while the polarized-prescribed group followed a standardized polarized training regime, consisting of 80% low-intensity, 20% high-intensity runs. Throughout the 8-week intervention, participants continued recording daily morning HRV at rest and incidental affective states using the e-diary. In Phase 5, participants repeated the treadmill test, followed by the continuation of the e-diary procedure in Phase 6 for another week. Upon finishing Phase 6, participants returned the devices and received a 50\u0026euro; voucher for local stores, along with updated running recommendations based on their VO\u003csub\u003e2max\u003c/sub\u003e results from Phase 5.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eSelf-efficacy\u003c/em\u003e was measured with the German exercise self-efficacy scale (Kr\u0026auml;mer \u0026amp; Fuchs, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) which assesses the conviction to start, maintain, and restart physical activity on a regular basis. The scale consists of three items with 1 = \u0026ldquo;not true at all\u0026rdquo; to 6 = \u0026ldquo;a 100% true\u0026rdquo;. The mean value of the three items is related to the extent of self-efficacy beliefs towards physical activity.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRunning-related motivation\u003c/em\u003e was assessed with the self-concordance scale (SSK; Seelig \u0026amp; Fuchs, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The 12-item scale evaluates four dimensions of motivation, each measured by three items: intrinsic, identified, introjected, and extrinsic motivation. The wording of the items was adapted specifically for running. Responses are rated on a 6-point Likert scale from 1 = \u0026ldquo;not true at all\u0026rdquo; to 6 = \u0026ldquo;exactly true\u0026rdquo;. The SSK-index, ranging from \u0026minus;\u0026thinsp;10 to +\u0026thinsp;10, represents self-concordance and is calculated by subtracting introjected and extrinsic ratings from the sum of intrinsic and identified ratings.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIncidental subjective vitality\u003c/em\u003e (Ryan \u0026amp; Frederick, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) was assessed using the German adaptation of the Subjective Vitality Scale (Buchner et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This three-item scale is validated for the use of ambulatory assessment. Participants' current vitality is measured on an 11-point scale (0 = \u0026ldquo;not true at all\u0026rdquo; to 10 = \u0026ldquo;completely true\u0026rdquo;) describing the perceived energy available to the self, aliveness, drive, and spirit. Reliability coefficients (Shrout \u0026amp; Lane, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) of morning vitality demonstrate strong consistency with within-person reliability R\u003csub\u003eC\u003c/sub\u003e = .88 (Phase 4) and between-person reliability R\u003csub\u003eKR\u003c/sub\u003e = .99 (Phase 4).\u003c/p\u003e \u003cp\u003e \u003cem\u003eIncidental fatigue\u003c/em\u003e is measured via the German translation (Buchner et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) of the Rate of Fatigue (Micklewright et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The one-item scale, which assesses global fatigue on an 11-point scale (0 = \u0026ldquo;not fatigued at all\u0026rdquo; to 10 = \u0026ldquo;total fatigue and exhaustion\u0026mdash;nothing left\u0026rdquo;), is validated across diverse contexts (e.g., daily life, rest, exercise).\u003c/p\u003e \u003cp\u003e \u003cem\u003eDaily morning HRV\u003c/em\u003e was measured upon waking, either while sitting or lying. A Polar H10 (130 Hz) chest strap was connected via Bluetooth with the training app. The Polar H10 is a valid and reliable heart rate belt to detect raw ECG and RR intervals during rest and exercise (Gilgen-Ammann et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The instructions for the 5-minute measurement were as follows: \u0026ldquo;Breathe calmly and evenly during the 5-minute rest measurement and try to relax.\u0026rdquo; Data from HRV measurements were stored in RR intervals and processed with Kubios Scientific Lite 4.1.1 (Tarvainen et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We selected 4-minute intervals (the first and last 30 seconds of recording were not used for data extraction) and applied low- or medium-threshold filtering when needed. The pre-processing settings for the RR detrending method were set to smoothness priors (lambda\u0026thinsp;=\u0026thinsp;500) with an interpolation rate of 4 Hz. To ensure the validity of the measure, corrected segments never surpassed 5% of the analyzed data. The parameters of interest were RMSSD and RR from the time domain. Parameters were transformed with the natural logarithm to account for the non-normality distribution tested with the Kolmogorow-Smirnow-Test. In the following RMSSD\u003csub\u003elog\u003c/sub\u003e is related to vmHRV and RR\u003csub\u003elog\u003c/sub\u003e to heart period.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHeart rate during running sessions\u003c/em\u003e was measured with a Polar H10 chest belt connected via Bluetooth with the training app.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo examine the intensive longitudinal effects of running on vmHRV, we performed autoregressive (AR) multilevel modeling using restricted maximum likelihood estimation with the lme4 package in R (R Core Team, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Repeated measures (Level 1) are nested within participants (Level 2) and missing data were considered missing at random. Level-1 variables are person-mean centered and Level-2 variables (self-efficacy, motivation, VO\u003csub\u003e2max\u003c/sub\u003e) are grand-mean centered (Nezlek \u0026amp; Mroziński, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Kenward Roger approximation were applied to mitigate the increased risk of Type I errors associated with underestimating standard errors of fixed effects in small sample sizes. Effect sizes for the model and predictors were calculated using Cohen's \u003cem\u003ef\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e, with values of 0.02, 0.15, and 0.35 indicating small, medium, and large effect sizes, respectively (Lorah, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo test whether vmHRV varies over the short-term in accordance to running (H1a), the RMSSD\u003csub\u003elog\u003c/sub\u003e was entered as the dependent variable with days categorized into 3 \u003cem\u003eday types\u003c/em\u003e: (1) runDay \u0026ndash; participants ran on that day; (2) nextDay \u0026ndash; the subsequent day after a running session without running; (3) neutralDay \u0026ndash; neither a running day nor type of subsequent day. Long-term changes (H1b) were analyzed with the predictor \u003cem\u003etime\u003c/em\u003e, calculated by the day within the intervention. Person-mean centered values of \u003cem\u003emorning subjective vitality\u003c/em\u003e and \u003cem\u003efatigue\u003c/em\u003e were entered to evaluate the effects of affective states on vmHRV (H2). To control for a quadratic trend of this relationship, we also included a \u003cem\u003epolynomial effect\u003c/em\u003e of second order (H3). To distinguish between the type of intervention, the predictor \u003cem\u003egroup\u003c/em\u003e was entered as a categorical variable (H4). Grand-mean centered predictors of trait \u003cem\u003eself-efficacy\u003c/em\u003e and \u003cem\u003emotivation\u003c/em\u003e were included to analyze their effect on state vmHRV (H5). The best model fits were assessed using chi-square difference statistics. As an autoregressive predictor, the person-mean RMSSD\u003csub\u003elog\u003c/sub\u003e value from the previous morning was entered into the model. We also controlled for VO\u003csub\u003e2max\u003c/sub\u003e measured in Phase 3. Given expected changes in resting heart rate due to the interventions, and the guidance to co-analyze heart period within vmHRV research (de Geus et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we consider the above-mentioned predictors in a second model, with resting RR\u003csub\u003elog\u003c/sub\u003e as the dependent variable. Moreover, we control for the influence of heart period on vmHRV and vice versa, by adding the corresponding variable as a covariate to each of the two models. Due to the relatively small sample size, we did not analyze any between-person differences via random effects (Arend \u0026amp; Sch\u0026auml;fer, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Model assumptions of the final model, including the normal distribution and homoscedasticity of residuals, were examined. Detailed model equations are provided in the supplementary material (Online Resource 1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eParticipants completed in total 1061 morning diary observations and 1056 morning HRV measures. Over 8 weeks, on average 22.56 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.88) running sessions were completed. Mean Training Impulse (TRIMP; Luc\u0026iacute;a et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) was at 1.61 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.34) with no differences between groups. For more details about compliance, mean incidental measures, and aggregated affect scores, see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A graphical illustration of the distributions of vmHRV and affective measures is provided in Online Resource 2 (Figure ESM 1\u0026ndash;3). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e represents the results of the multilevel analysis to identify AR effects and the short- to long-term prediction of running, incidental vitality and fatigue, group effects, trait self-efficacy, and motivation on daily morning vmHRV indicated by RMSSD\u003csub\u003elog\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eParticipant\u0026rsquo;s characteristics\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge [years] \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI [kg/m2] \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2max\u003c/sub\u003e [ml\u0026nbsp;O2/min)/kg] \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-efficacy \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotivation \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorning incidental affect (Compliance [%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitality \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorning HRV (Compliance [%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR [bpm] \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR [ms] \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e948.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD [ms] \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epNN50 [%] \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN [ms] \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF power [nu] \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003e no significant differences between Groups at baseline\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003eb\u003c/sup\u003e averaged scores aggregated within participants and days of phase 4\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe first model showed a small- to medium-sized AR effect on vmHRV. Thus, measures completed on the previous morning predicted positively values the morning after. Moreover, vmHRV remained stable over time, with no significant time effects in the short- (H1a) or the long-term (H1b). Running had no direct effects on vmHRV morning measures. In addition, neither subjective vitality nor fatigue predicted vmHRV in either a linear or quadratic term. The predictor group became significant after adding self-efficacy and motivation to the model. The prescribed-intensity group showed lower vmHRV values compared to the self-determined intensity group with a medium-sized effect (Online Resource 2, Figure ESM9). In contrast, VO\u003csub\u003e2max\u003c/sub\u003e measured at Phase 3 became non-significant, after adding other trait variables to the model, showing a small- to medium-sized effect. As such, trait self-efficacy predicts vmHRV to a large effect, whereas motivational measures do not predict vmHRV. When adding heart period (RR\u003csub\u003elog\u003c/sub\u003e) as an additional covariate, heart period predicts vmHRV (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.82), with a negative time trend of small effect size (\u003cem\u003eb\u003c/em\u003e = -0.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, \u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.003). All other effects remain stable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eModel parameters for the multilevel analysis (H1-H5) with RMSSD\u003c/em\u003e\u003csub\u003e\u003cem\u003elog\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(left) and RR\u003c/em\u003e\u003csub\u003e\u003cem\u003elog\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(right) as the dependent measures\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eRMSSD\u003csub\u003elog\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eRR\u003csub\u003elog\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFixed effects\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. \u003cem\u003e\u0026szlig;\u003c/em\u003e-coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI for b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eb (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd. \u0026szlig;-coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI for b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.10 (0.12)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.85\u0026ndash;4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.85 (0.04)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.77\u0026ndash;6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutoregressive predictor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23 (0.04)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u0026ndash;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29 (0.04)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u0026ndash;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00\u0026ndash;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00 (0.00)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u0026ndash;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay type [neutralDay] \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u0026ndash;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02\u0026ndash;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay type [runDay ] \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u0026ndash;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u0026ndash;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u0026ndash;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u0026ndash;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.39 (0.18)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.78 \u0026ndash; -0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0-05 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.17\u0026ndash;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2max\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u0026ndash;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u0026ndash;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.18)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12 (0.06)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u0026ndash;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.09 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.20\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.05\u0026ndash;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRandom effects\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u0026ndash;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u0026ndash;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u0026ndash;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u0026ndash;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConditional R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote\u003c/em\u003e: Unstandardized estimates and standard errors; \u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;effect size related to the variance explained by the single predictor and for the overall model.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e the reference category is type of day [nextDay]\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eb\u003c/sup\u003e the reference category is the self-determined intensity group\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ec\u003c/sup\u003e from the unconditional model\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003cp\u003eThe second model showed a small- to medium-sized AR effect on heart period and a significant time effect in heart period over the 8-week intervention period of negligible effect size. Neither type of day, subjective vitality, nor fatigue were significant predictors. With heart period as the dependent variable, the significant group effect diminished. Moreover, VO\u003csub\u003e2max\u003c/sub\u003e does not influence heart period, nor does trait self-concordance. A medium to large significant predictive effect is seen for self-efficacy. After including vmHRV as an additional covariate, the effect of self-efficacy diminished as well (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.20). This suggests that fluctuation in vmHRV could explain the link between self-efficacy and heart period.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present 8-week running intervention study investigates the short- to long-term effects (H1) of self-determined and prescribed running intensity on vmHRV (H4), its relationship with vitality and fatigue (linear: H2; quadratic: H3), and the influence of trait components like self-efficacy and self-concordance (H5). The results reveal that not situational factors (incidental affect, physical activity), but especially stable personality traits like self-efficacy play an important role in self-regulatory mechanisms, displayed in higher vmHRV (indicated by RMSSD\u003csub\u003elog\u003c/sub\u003e) and lower heart period (indicated by RR\u003csub\u003elog\u003c/sub\u003e). Our sample consists of 18 healthy females, with the tendency of increased aggregated mean values of subjective vitality and decreased fatigue (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), typical for healthy subjects (Ryan \u0026amp; Frederick, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Moreover, the aggregated HRV values observed in our study are comparable to the upper percentiles of the normative data reported by Dantas et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The research group provides reference HRV values for short-term measures for both sexes between 35 to 74 years. Given that HRV typically declines with age, the relatively high percentile classification of our population can be attributed to its younger age. Further, our sample has high self-efficacy beliefs to start, maintain, and restart running after an extended break. This is no surprise since we recruited novices, who specifically wanted to start with running. The positive SSK-index implies a positive explicit association towards running moderately spread between participants.\u003c/p\u003e \u003cp\u003eResults of multilevel analyses show no long-term changes in vmHRV, however heart period decreased slightly over time (H1b). Both models are time-dependent, seen in the significant AR effect of first order. This outcome supports the assumption of stable vagal tone over time. These results are in line to those of the control group in the study by da Silva et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), in which women aged 18\u0026ndash;35 followed a comparable 8-week training regimen to that of our prescribed intensity group. No shifts in resting vmHRV were neither observed in a 6-week high-intensity CrossFit intervention with healthy men and women of the same age (Crawford et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, previous research by Duarte et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) has shown that individuals with a high resting vagal tone at baseline are less likely to experience further increases in heart rate variability (HRV) following a 12-week aerobic training intervention compared to those with lower initial levels. Notably, Duarte et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) conducted their study on men, whereas our research focuses on women. While our sample, on average, already shows relatively high vmHRV, we did not account for potential initial differences in vagal tone, which could influence the extent of HRV adaptations. Considering short-term effects of running, multilevel analyses in our study do not identify effects by categorizing neutralDays, runDays, and nextDays. Thus, vmHRV or heart period does not differ between different types of days and running is not proven to influence vmHRV or heart period in the short term (H1a). Possibly, the training intensity was not high enough to induce any adaptations in vagal tone at rest. Furthermore, Duarte and colleagues (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) recommend that resting measurements of vagal tone are not suitable for assessing vagal reactivation; instead, measurements should be taken during the immediate recovery phase after an exercise session. This suggests that potential improvements in autonomic function may have occurred in our population but were not captured by the selected procedure of resting cardiac activity assessments.\u003c/p\u003e \u003cp\u003eLooking at the effects of vitality and fatigue on morning vmHRV, neither a linear (H2) nor a quadratic trend (H3) becomes significant. Although visually a positive quadratic (U-shaped) effect for vitality (Online Resource 2, Figure ESM7) and a negative cubic trend for fatigue was expected to predict vmHRV (Online Resource 2, Figure ESM8), this effect is not strong enough to remain in the model. Thus, the results of our study cannot support recently published findings of a quadratic relationship between vmHRV and affective measures (Dang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Duarte \u0026amp; Pinto-Gouveia, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kogan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In particular, the work of Spangler et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) investigated gender differences, highlighting that a non-linear relationship between vmHRV and positive affect is only apparent in women, but not in men. However, it is important to note that together with other studies, previous results are based on laboratory measurements focusing on trait affect and state vmHRV. Therefore, comparisons with ambulatory data may be limited, and the polynomial relationship between vmHRV and affective measures may be true at the between-person level. However, at the within-person level, this relationship appears to be more sensitive to intraindividual variability. Evidence towards this assumption provides the plots per participant, suggesting individual polynomial relationships between affective states and vmHRV (Online Resource 2, Figure ESM7-8).\u003c/p\u003e \u003cp\u003eInvestigating differences in intervention methods on vmHRV postulates a clear association of parasympathetic activity with self-determination (H4). Participants self-determining running intensity with the goal of feeling good have significantly higher morning vmHRV throughout the intervention compared to the prescribed-intensity group. This effect is independent from heart period when entered as a covariate. Interestingly, the lack of a significant group effect when heart period was analyzed as the dependent variable reinforces the notion that the observed changes are specifically tied to parasympathetic modulation rather than general heart rate dynamics. The increase in vmHRV without changes in heart period suggests that the self-determined group experienced enhanced autonomic regulation without significant alterations in chronotropic regulation, further supporting the idea that self-determination fosters better self-regulation. The findings imply that addressing fundamental psychological needs, such as autonomy, could be important for supporting self-regulation in females who are new to running. The ability for autonomous decision-making regarding running intensity might allow subjects to make choices considering individual preferences. This could have contributed to higher vmHRV, potentially indicating greater autonomic adaptability and aligning with the principles of the Neurovisceral Integration Model (Thayer et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Enhanced self-regulation might have supported individuals to better cope with physiological and psychological stressors associated with a new exercise regimen. Besides this, self-regulation plays a central role in initiating and sustaining goal-directed behavior (Geldhof et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Consequently, our findings highlight the importance of considering individual preferences and self-regulation in exercise programs. Personalized exercise prescriptions that allow for self-determined intensity may be more effective in promoting long-term adherence and improving physiological outcomes compared to rigid, prescribed intensity programs. The outcome corresponds with self-determination theory (Deci \u0026amp; Ryan, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), and is further supported by a meta-analysis, pointing towards the relevance of autonomy support (large effect) in facilitating health behavior change (Gillison et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther important predictors of health behavior change are self-efficacy and motivation (Larsen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nurmi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Adding these two predictors to the model results in increased vmHRV when reporting higher trait self-efficacy but shows no association with motivational traits. These effects remain stable when heart period is added as a covariate, again indicating the influence of parasympathetic activation relevant to vmHRV modulation. Interestingly, trait self-efficacy also predicts heart period, but this effect diminishes when vmHRV is added as a covariate. This phenomenon supports the notion of the relevance of parasympathetic activity on social-cognitive behavior (e.g. self. efficacy). It suggests that increased self-efficacy enhances self-regulation. Individuals with strong convictions to start, maintain, and resume physical activity appear better ability to balance external and internal situational stimuli, as indicated by elevated vmHRV. Consistent with previous studies (Kompf, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rebar et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), we agree that future interventions should aim to enhance self-efficacy for exercise by promoting competence and autonomy. Our findings of increased self-regulation, as indicated by increased vagal tone, hold promise for promoting long-term changes in physical activity behavior.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future directions\u003c/h2\u003e \u003cp\u003eDespite the valuable insights provided, there are several limitations that should be acknowledged. Firstly, daily HRV measurements were collected via a chest belt and stored in RR intervals, instead of the gold-standard ECG method. This allowed easy, low-cost implementation in an ecological ambulatory setting. However, this approach could lead to more artifacts and processing steps lack of correction for R-peak detection (Laborde et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To mitigate some of these limitations and to ensure data quality, we excluded participants whose data did not meet the required standards. Secondly, self-efficacy and self-concordance were not assessed daily, limiting direct comparison with daily HRV measures. However, studies such as Crawford et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Dunton (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and Maher et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) highlight daily fluctuations and individual variability in self-efficacy and physical activity intentions. While this variability is relevant, our focus was on trait-like factors influencing self-regulatory mechanisms. Notably, individuals with stable positive state-like factors, such as consistent self-efficacy and exercise intentions, often exhibit higher physical activity levels, suggesting enhanced self-regulation (Dunton, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Maher et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For future studies, it would be interesting to examine the stability of self-efficacy, motivation, and exercise intentions, how they evolve with HRV fluctuations and their influence on long-term behavioral patterns.\u003c/p\u003e \u003cp\u003eThirdly, minimization of the sample resulted in 8 vs. 10. subjects per group not allowing us to capture between-person differences. Moreover, the small- to medium-sized effects observed in our study should be interpreted with caution. However, the large effects detected in our study may permit generalization to the population at the within-person level. Fourthly, to eliminate any sex differences in emotion regulation, resting HRV (Min et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and affective response towards exercise (Tavares et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we focused on young females only. Nevertheless, we did not control for the menstrual cycle, which also modulates vmHRV metrics (Schmalenberger et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Schmalenberger et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, future studies focusing on different sexes and considering vmHRV fluctuations across phases of the menstrual cycle will provide a deeper understanding of the relationship between exercise, affect, and vmHRV. Lastly, data collection was held during the COVID-19 pandemic, and we cannot exclude bias of infection or pandemic restrictions on our outcomes (Asarcikli et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, running may have led to positive affective outcomes that were diminished by the pandemic, like the effects of leisure walks (Reuter et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).In this context, running could have facilitated better self-regulation, thereby supporting increased vmHRV.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis ambulatory assessment study focused on elaborating the effects of two different running interventions (self-determined vs. prescribed intensity) on daily morning vmHRV. Considering personality distributions of self-efficacy for exercise and self-concordance as well as fluctuations of daily vitality and fatigue, the study aimed to better understand the interplay of these correlates on self-regulation, indicated by increased morning vmHRV. Direct effects of motivation, running, morning vitality, or fatigue could not be detected. Interestingly, subjects allowing for autonomy of their running intensity showed increased vmHRV compared to the prescribed intensity group. Moreover, increased trait self-efficacy supported better self-regulation. The findings of this study suggest that both of these effects are predominantly influenced by parasympathetic activity. Therefore, in the design of future interventions promoting physical activity behavior, it is recommended that efforts are made to help individuals feel competent and allow a certain sense of self-determination to reach their activity goal.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was funded by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry for Digital and Economic Affairs, and the federal state of Salzburg under the research program COMET \u0026ndash; Competence Center for Excellent Technologies \u0026ndash; in the project Digital Motion in Sports, Fitness, and Well-being (DiMo) (\u0026Ouml;sterreichische Forschungsf\u0026ouml;rderungsgesellschaft).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Jana Schnabel for her consistent and extra ordinary support in data storage. Moreover, we would like to thank all our participants who took part in our running intervention study.\u003c/p\u003e\n\u003ch2\u003eConflict-of-Interest Statement\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eThe study was conducted in accordance with the ethical standards of the institutional research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The protocol was approved by the Ethics Committee of Paris Lodron-University Salzburg, approval number EK-GZ: 37/2020.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eInformed consent\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from all participants, who had the right to withdraw at any time. All data were anonymized to ensure confidentiality.\u003c/p\u003e\n\u003ch2\u003eConsent to publish\u003c/h2\u003e\n\u003cp\u003ePatients signed informed consent regarding publishing their data.\u003c/p\u003e\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eThe datasets presented in this article are not readily available because of sharing agreements in the funded project. Requests to access the datasets should be directed to [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmekran, Y., \u0026amp; El Hangouche, A. J. (2024). Effects of Exercise Training on Heart Rate Variability in Healthy Adults: A Systematic Review and Meta-analysis of Randomized Controlled Trials. \u003cem\u003eCureus\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(6), e62465, https://doi.org/10.7759/cureus.62465 \u003c/li\u003e\n\u003cli\u003eArend, M. G., \u0026amp; Sch\u0026auml;fer, T. (2019). 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M., \u0026amp; Miranda, R., Jr. (2016). Self-paced exercise, affective response, and exercise adherence: A preliminary investigation using ecological momentary assessment. \u003cem\u003eJournal of Sport and Exercise Psychology\u003c/em\u003e,\u003cem\u003e 38\u003c/em\u003e(3), https://doi.org/10.1123/jsep.2015-0232 \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":"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":"vagal tone, self-determination, incidental affect, self-regulation, ambulatory assessment, physical activity","lastPublishedDoi":"10.21203/rs.3.rs-6285008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6285008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSelf-regulation, self-efficacy, and motivation are critical correlates for exercise maintenance and play a significant role in sustaining a physically active lifestyle. Vitality and fatigue, recognized as unipolar affective states, also impact these processes by influencing exercise-induced affective responses and action initiation. This ambulatory assessment study investigates how trait self-efficacy and self-concordance, as well as daily morning fatigue and vitality, effect cardiac activity (heart period and vagally-mediated heart rate variability (vmHRV)) measured every morning in response to running. Over eight weeks, 18 young healthy women new to running followed either a prescribed or self-determined intensity intervention. Results from multilevel analyses revealed that individuals with autonomy in choosing their running intensity exhibited increased vmHRV compared to the prescribed intensity group. Higher trait self-efficacy was associated with better self-regulation, indicated by elevated vmHRV. The effects remain stable upon controlling for heart period. However, the effects vanished when predicting heart period, indicating a mediating role for parasympathetic nervous system activity concerning vmHRV modulation. Direct effects of running, morning vitality, fatigue, or motivation on cardiac activity were not detected. The results of this study suggest that interventions promoting physical activity should enhance feelings of competence and allow self-determination to achieve activity goals. The promotion of autonomy in exercise intensity and fostering self-efficacy are pivotal for enhancing self-regulation, as evidenced by the improvement in morning vmHRV. These strategies have the potential to result in more effective and sustainable physical activity behaviors, thereby contributing to enhanced overall health outcomes.\u003c/p\u003e","manuscriptTitle":"Do autonomy, self-efficacy, vitality, and fatigue predict daily morning heart rate variability? A running intervention study in healthy women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 10:17:09","doi":"10.21203/rs.3.rs-6285008/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":"7056f4ae-132c-424d-9332-a10f8280a601","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-17T22:22:16+00:00","versionOfRecord":{"articleIdentity":"rs-6285008","link":"https://doi.org/10.1007/s11332-026-01668-y","journal":{"identity":"sport-sciences-for-health","isVorOnly":false,"title":"Sport Sciences for Health"},"publishedOn":"2026-02-10 00:00:00","publishedOnDateReadable":"February 10th, 2026"},"versionCreatedAt":"2025-04-22 10:17:09","video":"","vorDoi":"10.1007/s11332-026-01668-y","vorDoiUrl":"https://doi.org/10.1007/s11332-026-01668-y","workflowStages":[]},"version":"v1","identity":"rs-6285008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6285008","identity":"rs-6285008","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}