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Low levels of physical activity (PA) and high levels of sedentary behavior in individuals with spinal cord injury (SCI) have been associated with secondary conditions such as pain, fatigue, weight gain, and deconditioning. One strategy for promoting regular PA is to provide people with an accurate estimate of everyday PA level. The objective of this research was to use a mobile health-based PA measurement system to track PA levels of individuals with SCI in the community and provide them with a behavior-sensitive, just-in-time-adaptive intervention (JITAI) to improve their PA levels. The first, second, and third phases of the study, each with a duration of one month, involved collecting baseline PA levels, providing near-real-time feedback on PA level (PA Feedback), and providing PA Feedback with JITAI, respectively. PA levels in terms of energy expenditure in kilocalories, and minutes of light- and moderate- or vigorous-intensity PA were assessed by an activity monitor during the study. Twenty participants with SCI took part in this research study with a mean (SD) age of 39.4 (12.8) years and 12.4 (12.5) years since injury. Sixteen participants completed the study. Sixteen were male, 16 had paraplegia, and 12 had complete injury. Within-participant comparisons indicated that only two participants had higher energy expenditure (>10%) or lower energy expenditure (<-10%) during PA Feedback with JITAI compared to the baseline. However, eleven participants (69.0%) had higher light- and/or moderate-intensity PA during PA Feedback with JITAI compared to the baseline. To our knowledge, this is the first study to test a PA JITAI for individuals with SCI that responds automatically to monitored PA levels. The results of this pilot study suggest that a sensor-enabled mobile JITAI has potential to improve PA levels of individuals with SCI. Future research should investigate the efficacy of JITAI through a clinical trial.

Intensive longitudinal data (ILD) collection and analytic strategies are well-positioned to capture and interpret within-person shifts between reactive and reflective systems underlying behavior maintenance. Abstract Interventions that promote long-term maintenance of behaviors such as exercise, healthy eating, and avoidance of tobacco and excessive alcohol are critical to reduce noncommunicable disease burden. Theories of health behavior maintenance tend to address reactive (i.e., automatic) or reflective (i.e., deliberative) decision-making processes, but rarely both. Progress in this area has been stalled by theories that say little about when, why, where, and how reactive and reflective systems interact to promote or derail a positive health behavior change. In this commentary, we discuss factors influencing the timing and circumstances under which an individual may shift between the two systems such as (a) limited availability of psychological assets, (b) interruption in exposure to established contextual cues, and (c) lack of intrinsic or appetitive motives. To understand the putative factors that regulate the interface between these systems, research methods are needed that are able to capture properties such as (a) fluctuation over short periods of time, (b) change as a function of time, (c) context dependency, (d) implicit and physiological channels, and (e) idiographic phenomenology. These properties are difficult to assess with static, cross-sectional, laboratory-based, or retrospective research methods. We contend that intensive longitudinal data (ILD) collection and analytic strategies such as smartphone and sensor-based real-time activity and location monitoring, ecological momentary assessment (EMA), machine learning, and systems modeling are well-positioned to capture and interpret within-person shifts between reactive and reflective systems underlying behavior maintenance. We conclude with examples of how ILD can accelerate the development of theories and interventions to sustain health behavior over the long term.

[This corrects the article DOI: 10.1371/journal.pone.0223762.].

Missing data are inevitable in mobile health (mHealth) and ubiquitous health (uHealth) research and are often driven by distinct within- and between-person factors that influence compliance. Understanding these distinct mechanisms underlying nonresponse can inform strategies to improve compliance and strengthen the validity of inferences about health behaviors. However, current missing data handling techniques rarely disentangle these different sources of nonresponse, especially when data are missing not at random. We demonstrate the usability of joint modeling in the mHealth context, showing how simultaneously accounting for the dynamics of health behavior and both within- and between-person missingness mechanisms can affect the validity of health behavior inferences. We also illustrate how joint modeling can inform distinct sources of (possibly nonignorable) missingness in studies using ecological momentary assessment and wearable devices. We provide a practical workflow for applying joint models to empirical data. We applied joint modeling on empirical data comprising 1 year of daily smartphone-based ecological momentary assessment data (affect and energetic feeling) and smartwatch-tracked physical activity (PA). The approach combined (1) a multilevel vector autoregressive model for examining the reciprocal influences between daily affect and PA, and (2) a multilevel probit model for missingness. Unlike conventional 2-stage imputation methods-which first impute missing data before fitting the main model-joint modeling handles missingness during model fitting without explicit imputation. Sensitivity analyses compared results from the proposed method to other missing data approaches that do not explicitly model missingness. A simulation study designed to mirror the temporally clustered (eg, consecutive days of missing data) and person-specific missingness patterns of the empirical data validated the feasibility of the proposed approach. Sensitivity analysis indicated relative robustness of the autoregressive effects across missing data handling approaches, whereas cross-regressive effects could be detected only under the joint modeling but not with methods that did not simultaneously model missingness mechanisms. Specifically, under joint modeling approaches, participants had higher levels of PA on days following a previous day with higher self-report energy levels (95% credible interval [CrI] 0.012-0.049). Furthermore, the missing data model revealed both missing not at random and missing at random mechanisms. For example, lower PA predicted higher missingness in PA at the within-person level (95% CrI -1.528 to -1.441). Being employed was associated with higher missingness in device-tracked PA at the between-person level (95% CrI 0.148-0.574). Finally, simulation showed that joint modeling could improve the accuracy of estimates and identify nonignorable missingness. We recommend joint modeling with multilevel decomposition for addressing nonignorable missingness in mHealth/uHealth studies collecting intensive longitudinal data. We also suggest using a missing data model to explore the missingness mechanism and inform data collection strategies.

The lack of regular physical activity (PA) in individuals with spinal cord injury (SCI) in the United States is an ongoing health crisis. Regular PA and exercise-based interventions have been linked with improved outcomes and healthier lifestyles among those with SCI. Providing people with an accurate estimate of their everyday PA level can promote PA. Furthermore, PA tracking can be combined with mobile health technology such as smartphones and smartwatches to provide a just-in-time adaptive intervention (JITAI) for individuals with SCI as they go about everyday life. A JITAI can prompt an individual to set a PA goal or provide feedback about their PA levels. The primary aim of this study is to investigate whether minutes of moderate-intensity PA among individuals with SCI can be increased by integrating a JITAI with a web-based PA intervention (WI) program. The WI program is a 14-week web-based PA program widely recommended for individuals with disabilities. A secondary aim is to investigate the benefit of a JITAI on proximal PA, defined as minutes of moderate-intensity PA within 120 minutes of a PA feedback prompt. Individuals with SCI (N=196) will be randomized to a WI arm or a WI+JITAI arm. Within the WI+JITAI arm, a microrandomized trial will be used to randomize participants several times a day to different tailored feedback and PA recommendations. Participants will take part in the 24-week study from their home environment in the community. The study has three phases: (1) baseline, (2) WI program with or without JITAI, and (3) PA sustainability. Participants will provide survey-based information at the initial meeting and at the end of weeks 2, 8, 16, and 24. Participants will be asked to wear a smartwatch every day for ≥12 hours for the duration of the study. Recruitment and enrollment began in May 2023. Data analysis is expected to be completed within 6 months of finishing participant data collection. The JITAI has the potential to achieve long-term PA performance by delivering tailored, just-in-time feedback based on the person's actual PA behavior rather than a generic PA recommendation. New insights from this study may guide intervention designers to develop engaging PA interventions for individuals with disability. ClinicalTrials.gov NCT05317832; https://clinicaltrials.gov/study/NCT05317832. DERR1-10.2196/57699.

Purpose This study used Ecological Momentary Assessment (EMA), a real-time self-report strategy, to describe the physical and social contexts of adults’ physical activity and sedentary activity during their everyday lives and to determine whether these patterns and relationships differ for men and women. Methods Data from 114 adults were collected through mobile phones across 4 days. Eight electronic EMA surveys were randomly prompted each day asking about current activities (e.g., physical or sedentary activity), physical and social contexts, and perceived outdoor environmental features (e.g., greenness/vegetation, safety, and traffic). All participants also wore accelerometers during this period to objectively measure moderate-to-vigorous physical activity (MVPA) and sedentary activity. Results Home was the most common physical context for EMA-reported physical and sedentary activity. Most of these activities occurred when participants were alone. When alone, the most commonly EMA-reported physical activity and sedentary activity was walking and reading/using computer, respectively. When in outdoor home locations (e.g., yard and driveway) women demonstrated higher levels of MVPA, whereas men demonstrated higher levels of MVPA when in outdoor park settings ( p s < .05). Men but not women demonstrated higher levels of MVPA in settings with a greater degree of perceived greenness and vegetation ( p  < .05). Conclusions The current study shows how EMA via mobile phones and accelerometers can be combined to offer an innovative approach to assess the contexts of adults’ daily physical and sedentary activity. Future studies could consider utilizing this method in more representative samples to gather context-specific information to inform the development of physical activity interventions.

To overcome problems with traditional methods for measuring stereotypical motor movements in persons with Autism Spectrum Disorders (ASD), we evaluated the use of wireless three-axis accelerometers and pattern recognition algorithms to automatically detect body rocking and hand flapping in children with ASD. Findings revealed that, on average, pattern recognition algorithms correctly identified approximately 90% of stereotypical motor movements repeatedly observed in both laboratory and classroom settings. Precise and efficient recording of stereotypical motor movements could enable researchers and clinicians to systematically study what functional relations exist between these behaviors and specific antecedents and consequences. These measures could also facilitate efficacy studies of behavioral and pharmacologic interventions intended to replace or decrease the incidence or severity of stereotypical motor movements.

Research examined how acute affect dynamics, including stability and context-dependency, contribute to changes in children’s physical activity levels as they transition from late-childhood to early-adolescence. Children (N = 151) (ages 8–12 years at baseline) participated in an ecological momentary assessment and accelerometry study with six semi-annual bursts (7 days each) across three years. A two-stage mixed-effects multiple location-scale model tested random intercept, variance, and slope estimates for positive affect as predictors of moderate-to-vigorous physical activity (MVPA). Multi-year declines in MVPA were greater for children who had greater subject-level variance in positive affect. Children who experienced more positive affect when alone did not experience steeper declines in physical activity. Interventions aiming for long-term modifications in children’s physical activity may focus on buffering the effects of within-day fluctuations in affect or tailoring programs to fit the needs of “acute dynamic process phenotypes.”

Ecological momentary assessment (EMA) is a valuable method for capturing real-time data on behaviors and experiences in naturalistic settings. However, maintaining participant engagement in longitudinal (ie, multiburst) EMA studies remains challenging, particularly when collecting intensive data over extended periods. Understanding factors affecting completion rates is essential for designing more effective EMA protocols and interpreting results accurately. This study investigated factors influencing EMA completion rates in a 12-month intensive longitudinal study among young adults in the United States, examining both time-varying factors and stable individual characteristics. Young adults (N=246, ages 18-29 years) participated in the Temporal Influences on Movement and Exercise (TIME) study, responding to smartphone-based EMA prompts during biweekly measurement bursts (4-day periods of intensive sampling), with continuous passive data collection via smartwatches. Each burst included signal-contingent prompts delivered approximately once per hour during waking hours, resulting in an average of 12.1 (SD 1.3) prompts per day. Multilevel logistic regression models examined the effects of time-varying temporal factors (time of day, day of week, season, and time in study), contextual factors (phone screen status, phone usage, and location), behavioral factors (sleep duration, physical activity levels, and travel status), and psychological factors (momentary affect and stress) on prompt completion. Models also included time-invariant demographic characteristics (sex, race, ethnicity, education, and employment) and tested interactions between time in study and other predictors. Mean completion rate was 77% (SD 13%). Hispanic participants showed lower odds of completion compared to non-Hispanic participants (odds ratio [OR] 0.79, 95% CI 0.63-0.99; P=.04) and employed participants were less likely to complete prompts than unemployed participants (OR 0.75, 95% CI 0.61-0.92; P<.01). Having the phone screen on at prompt delivery substantially increased completion odds (OR 3.39, 95% CI 2.81-4.09; P<.001), while being away from home reduced completion likelihood, with particularly low odds when at sports facilities (OR 0.58, 95% CI 0.47-0.74; P<.001) or restaurants and shops (OR 0.61, 95% CI 0.51-0.72; P<.001). Short sleep duration the previous night (OR 0.92, 95% CI 0.87-0.99; P=.02) and traveling status (OR 0.78, 95% CI 0.75-0.82; P<.001) were associated with lower completion odds. Higher momentary stress levels predicted lower completion of subsequent prompts (OR 0.85, 95% CI 0.78-0.93; P<.001). Completion odds declined over the 12-month study (OR 0.95, 95% CI 0.94-0.96; P<.001), with significant interactions between time in study and various predictors, indicating changing patterns of engagement over time. Findings highlight the dynamic nature of EMA engagement in longitudinal multiburst studies and underscore the importance of considering time-varying and time-invariant factors in study design and analysis. This study provides valuable insights for researchers designing intensive longitudinal studies in behavioral science and digital health. Potential strategies for optimizing EMA protocols could include tailoring prompt schedules to individual contexts and developing adaptive sampling techniques.

A method for solving the stereo matching problem in the presence of large occlusion is presented. A data structure--the disparity space image--is defined to facilitate the description of the effects of occlusion on the stereo matching process and in particular on dynamic programming (DP) solutions that find matches and occlusions simultaneously. We significantly improve upon existing DP stereo matching methods by showing that while some cost must be assigned to unmatched pixels, sensitivity to occlusion-cost and algorithmic complexity can be significantly reduced when highly-reliable matches, or ground control points, are incorporated into the matching process. The use of ground control points eliminates both the need for biasing the process towards a smooth solution and the task of selecting critical prior probabilities describing image formation. Finally, we describe how the detection of intensity edges can be used to bias the recovered solution such that occlusion boundaries will tend to be proposed along such edges, reflecting the observation that occlusion boundaries usually cause intensity discontinuities.[PUBLICATION ABSTRACT]