Engineering Person-Specific Behavioral Interventions to Promote Physical Activity

Exploring Regular Exercisers' Experiences with Readiness/Recovery Scores Produced by Wearable Devices: A Descriptive Qualitative Study

Meta-session autoregulation, a person-adaptive exercise programming approach, is characterized by individuals' matching exercise demands specifically to their current readiness states. Some consumer wearables provide 'recovery' or 'readiness' scores, computed primarily based on heart rate variability. Despite the growing popularity of consumer wearables and interest in person-adaptive programming, limited research exists on how exercisers interact, interpret and use these scores. This study explores individuals' experiences with wearable devices and their associated readiness or recovery scores. Seventeen regular exercisers who owned and used a Whoop™ band or Oura™ ring for at least 3 months participated in a one-on-one virtual semi-structured interview. Interviews were analyzed using reflexive thematic analysis, with themes supported by 'in-vivo' quotes. This paper focuses on three key themes for a comprehensive demonstration. Theme 1, 'It's more about how I can make adjustments to optimize my programming,' (MPR) highlights users' intended use of wearables for guiding training decisions. Theme 2, 'So many things outside of training modifications have changed,' (Misty) reveals that users also modify non-exercise behaviors to manage and optimize their scores. Theme 3, 'You can't really capture the complexities of a human on a device' (Letty) underscores users' recognition of the limitations and errors associated with these devices emphasizing self-reliance to further direct behavioral adjustments. While wearable devices provide a numeric approach to measuring readiness and recovery, users prioritize self-awareness, flexibility, and personal judgment for exercise decisions. Understanding these experiences, in addition to exploring the psycho-behavioral aspects of user interactions, can contribute to refining meta-session autoregulation.

Exploring person-specific associations of situational motivation and readiness with leisure-time physical activity effort and experience

Identifying determinants of leisure-time physical activity (LTPA) often relies on population-level (nomothetic) averages, potentially overlooking person-specific (idiographic) associations. This study uses an idiographic perspective to explore how subjective readiness and motives for LTPA relate to volitional effort (duration, intensity) and affective experience (pleasure, displeasure). We also highlight the potential for different interpretations when data are averaged within individuals and assessed using a variable-centered approach. Participants (N = 22, 25±8 years old, 54.5% women) were asked to continue their regular PA patterns for 10 weeks. Ecological momentary assessment procedures allowed participants to provide pre-activity reports (physical, cognitive, emotional readiness and situational motive for activity) and post-activity reports (activity type, duration, perceived exertion, ratings of affective valence). Spearman rank correlation was implemented to interpret within- and between-person associations. Data visualization approaches were used to showcase person-specific differences in associations. Participants provided 519 reports of LTPA (24±11 events/person), which displayed between- and within-person variety in type, duration, intensity, and affective experience. Exemplar cases highlight discrepancies in interpretation based on level of analysis, such that the nomothetic association (rho = .42, p = .05; 95% CI -.02, .72) between motive to replenish energy and LTPA duration was observed in only one within-person analysis (41% were weak-to-large inverse effects). Alternatively, the negligible nomothetic association (rho = .02, p = .93; 95% CI -.41, .44) between physical readiness and LTPA-related affect did not reflect the 59% of within-person analyses showing moderate-to-large, positive effects. Future research aiming to identify determinants of LTPA effort and experience should integrate contemporary, idiographic analyses in early-stage research for developing person-specific strategies for LTPA promotion.

The Specification of a Computational Model of Physical Activity Habit

The influence of habit on physical activity is computationally modeled as the aggregated influence of past behavioral choices a person makes in a given context. We hypothesize that the influence of habit on behavior can be enhanced through engagement of the target behavior in a particular context or weakened through engagement of alternative behaviors in that context.

Contextualising flexible nonlinear periodization as a person-adaptive behavioral model for exercise maintenance

There is a growing focus on developing person-adaptive strategies to support sustained exercise behaviour, necessitating conceptual models to guide future research and applications. This paper introduces Flexible nonlinear periodisation (FNLP) - a proposed, but underdeveloped person-adaptive model originating in sport-specific conditioning - that, pending empirical refinement and evaluation, may be applied in health promotion and disease prevention settings. To initiate such efforts, the procedures of FNLP (i.e., acutely and dynamically matching exercise demand to individual assessments of mental and physical readiness) are integrated with contemporary health behaviour evidence and theory to propose a modified FNLP model and to show hypothesised pathways by which FNLP may support exercise adherence (e.g., flexible goal setting, management of affective responses, and provision of autonomy/variety-support). Considerations for future research are also provided to guide iterative, evidence-based efforts for further development, acceptability, implementation, and evaluation.

Implementing Meta-Session Autoregulation Strategies for Exercise - A Scoping Review

Meta-session autoregulation, a person-adaptive form of exercise prescription that adjusts training variables according to daily fluctuations in performance considering an individual's daily fitness, fatigue, and readiness-to-exercise is commonly used in sports-related training and may be beneficial for non-athlete populations to promote exercise adherence. To guide refinement of meta-session autoregulation, it is crucial to examine the existing literature and synthesize how these procedures have been practically implemented. Following PRIMSA guidelines a scoping review of two databases was conducted from August 2021 to September 2021 to identify and summarize the selected measures of readiness-to-exercise and decision-making processes used to match workload to participants in meta-session autoregulatory strategies, while also evaluating the methodological quality of existing study designs using a validated checklist. Eleven studies reported utilizing a form of meta-session autoregulation for exercise. Primary findings include: (i) readiness-to-exercise measures have been divided into either objective or subjective measures, (ii) measures of subjective readiness measures lacked evidence of validity, and (iii) fidelity to autoregulatory strategies was not reported. Results of the risk of bias assessment indicated that 45% of the studies had a poor-quality score. Existing implementations of meta-session autoregulation are not directly translatable for use in health promotion and disease prevention settings. Considerable refinement research is required to optimize this person-adaptive strategy prior to estimating effects related to exercise adherence and/or health and fitness outcomes. Based on the methodological deficits uncovered, researchers implementing autoregulation strategies would benefit reviewing existing models and frameworks created to guide behavioral intervention development.

Adaptive Content Tuning of Social Network Digital Health Interventions Using Control Systems Engineering for Precision Public Health: Cluster Randomized Controlled Trial

BACKGROUND

Social media has emerged as an effective tool to mitigate preventable and costly health issues with social network interventions (SNIs), but a precision public health approach is still lacking to improve health equity and account for population disparities.

OBJECTIVE

This study aimed to (1) develop an SNI framework for precision public health using control systems engineering to improve the delivery of digital educational interventions for health behavior change and (2) validate the SNI framework to increase organ donation awareness in California, taking into account underlying population disparities.

METHODS

This study developed and tested an SNI framework that uses publicly available data at the ZIP Code Tabulation Area (ZCTA) level to uncover demographic environments using clustering analysis, which is then used to guide digital health interventions using the Meta business platform. The SNI delivered 5 tailored organ donation-related educational contents through Facebook to 4 distinct demographic environments uncovered in California with and without an Adaptive Content Tuning (ACT) mechanism, a novel application of the Proportional Integral Derivative (PID) method, in a cluster randomized trial (CRT) over a 3-month period. The daily number of impressions (ie, exposure to educational content) and clicks (ie, engagement) were measured as a surrogate marker of awareness. A stratified analysis per demographic environment was conducted.

RESULTS

Four main clusters with distinctive sociodemographic characteristics were identified for the state of California. The ACT mechanism significantly increased the overall click rate per 1000 impressions (β=.2187; P<.001), with the highest effect on cluster 1 (β=.3683; P<.001) and the lowest effect on cluster 4 (β=.0936; P=.053). Cluster 1 is mainly composed of a population that is more likely to be rural, White, and have a higher rate of Medicare beneficiaries, while cluster 4 is more likely to be urban, Hispanic, and African American, with a high employment rate without high income and a higher proportion of Medicaid beneficiaries.

CONCLUSIONS

The proposed SNI framework, with its ACT mechanism, learns and delivers, in real time, for each distinct subpopulation, the most tailored educational content and establishes a new standard for precision public health to design novel health interventions with the use of social media, automation, and machine learning in a form that is efficient and equitable.

TRIAL REGISTRATION

ClinicalTrials.gov NTC04850287; https://clinicaltrials.gov/ct2/show/NCT04850287.

Motivational Message Framing Effects on Physical Activity Dynamics in a Digital Messaging Intervention: Secondary Analysis

BACKGROUND

Digital smartphone messaging can be used to promote physical activity to large populations with limited cost. It is not clear which psychological constructs should be targeted by digital messages to promote physical activity. This gap presents a challenge for developing optimal content for digital messaging interventions.

OBJECTIVE

The aim of this study is to compare affectively framed and social cognitively framed messages on subsequent changes in physical activity using dynamical modeling techniques.

METHODS

We conducted a secondary analysis of data collected from a digital messaging intervention in insufficiently active young adults (18-29 years) recruited between April 2019 and July 2020 who wore a Fitbit smartwatch for 6 months. Participants received 0 to 6 messages at random per day across the intervention period. Messages were drawn from 3 content libraries: affectively framed, social cognitively framed, or inspirational quotes. Person-specific dynamical models were identified, and model features of impulse response and cumulative step response were extracted for comparison. Two-way repeated-measures ANOVAs evaluated the main effects and interaction of message type and day type on model features. This early-phase work with novel dynamic features may have been underpowered to detect differences between message types so results were interpreted descriptively.

RESULTS

Messages (n=20,689) were paired with valid physical activity monitoring data from 45 participants for analysis. Received messages were distributed as 40% affective (8299/20,689 messages), 39% social-cognitive (8187/20,689 messages), and 20% inspirational quotes (4219/20,689 messages). There were no statistically significant main effects for message type when evaluating the steady state of step responses. Participants demonstrated heterogeneity in intervention response: some had their strongest responses to affectively framed messages, some had their strongest responses to social cognitively framed messages, and some had their strongest responses to the inspirational quote messages.

CONCLUSIONS

No single type of digital message content universally promotes physical activity. Future work should evaluate the effects of multiple message types so that content can be continuously tuned based on person-specific responses to each message type.

Analysis of dynamic psychological processes to understand and promote physical activity behaviour using intensive longitudinal methods: a primer

Physical activity behaviour displays temporal variability, and is influenced by a range of dynamic psychological processes (e.g., affect) and shaped by various co-occurring events (e.g., social/environmental factors, interpersonal dynamics). Yet, most physical activity research tends not to examine the dynamic psychological processes implicated in adopting and maintaining physical activity. Intensive longitudinal methods (ILM) represent one particularly salient means of studying the complex psychological dynamics that underlie and result from physical activity behaviour. With the increased recent interest in using intensive longitudinal data to understand specific dynamic psychological processes, the field of exercise and health psychology is well-positioned to draw from state-of-the-art measurement and statistical approaches that have been developed and operationalised in other fields of enquiry. The purpose of this review is to provide an overview of some of the fundamental dynamic measurement and modelling approaches applicable to the study of physical activity behaviour change, as well as the dynamic psychological processes that contribute to such change.

Physical Activity Dynamics During a Digital Messaging Intervention Changed After the Pandemic Declaration

BACKGROUND

The COVID-19 pandemic adversely impacted physical activity, but little is known about how contextual changes following the pandemic declaration impacted either the dynamics of people's physical activity or their responses to micro-interventions for promoting physical activity.

PURPOSE

This paper explored the effect of the COVID-19 pandemic on the dynamics of physical activity responses to digital message interventions.

METHODS

Insufficiently-active young adults (18-29 years; N = 22) were recruited from November 2019 to January 2020 and wore a Fitbit smartwatch for 6 months. They received 0-6 messages/day via smartphone app notifications, timed and selected at random from three content libraries (Move More, Sit Less, and Inspirational Quotes). System identification techniques from control systems engineering were used to identify person-specific dynamical models of physical activity in response to messages before and after the pandemic declaration on March 13, 2020.

RESULTS

Daily step counts decreased significantly following the pandemic declaration on weekdays (Cohen's d = -1.40) but not on weekends (d = -0.26). The mean overall speed of the response describing physical activity (dominant pole magnitude) did not change significantly on either weekdays (d = -0.18) or weekends (d = -0.21). In contrast, there was limited rank-order consistency in specific features of intervention responses from before to after the pandemic declaration.

CONCLUSIONS

Generalizing models of behavioral dynamics across dramatically different environmental contexts (and participants) may lead to flawed decision rules for just-in-time physical activity interventions. Periodic model-based adaptations to person-specific decision rules (i.e., continuous tuning interventions) for digital messages are recommended when contexts change.

Sleep quality, valence, energetic arousal, and calmness as predictors of device-based measured physical activity during a three-week mHealth intervention

Physical inactivity is known to be a risk factor for several noncommunicable diseases and has a high prevalence in today’s society. Therefore, it is crucial to understand the psychological factors associated with physical activity (PA). Recent developments in the field of ambulatory assessment and technological advances are promising to enhance our understanding of this relationship by analyzing longitudinal data within- and between-persons. These analyses can reveal important factors to design behavior change interventions to enhance PA. Therefore, this study used an ecological momentary assessment during the 3‑week intervention period in the SMARTFAMILY2.0 trial and aimed to investigate whether valence, calmness, energetic arousal, and sleep quality predict daily steps and moderate to vigorous PA. Overall, 49 adults (35–60 years) and 40 children (5–19 years) were included in this analysis and self-rated their mental state within our smartphone application while also wearing a hip-worn accelerometer for 21 consecutive days (996 days included) during the intervention period. Multilevel analyses were conducted to predict daily PA while considering covariables (e.g., child/adult and non-wear time) both within- and between-persons. The results indicated that higher than average ratings of a person’s valence and energetic arousal on one day predicted increased PA while higher than average calmness predicted decreased PA at the same day within this person. Sleep quality and between-person effects of the affective states showed no clear associations to PA. Overall, these results showed that within-person associations of valence, calmness, and energetic arousal should be considered when designing PA interventions for both children and adults. The influence of sleep quality, as well as between-person effects, should be further explored by future studies.

Optimizing behavioral interventions to regulate gestational weight gain with sequential decision policies using hybrid model predictive control

Excessive gestational weight gain is a significant public health concern that has been the recent focus of control systems-based interventions. Healthy Mom Zone (HMZ) is an intervention study that aims to develop and validate an individually-tailored and "intensively adaptive" intervention to manage weight gain for pregnant women with overweight or obesity using control engineering approaches. This paper presents how Hybrid Model Predictive Control (HMPC) can be used to assign intervention dosages and consequently generate a prescribed intervention with dosages unique to each individuals needs. A Mixed Logical Dynamical (MLD) model enforces the requirements for categorical (discrete-level) doses of intervention components and their sequential assignment into mixed-integer linear constraints. A comprehensive system model that integrates energy balance and behavior change theory, using data from one HMZ participant, is used to illustrate the workings of the HMPC-based control system for the HMZ intervention. Simulations demonstrate the utility of HMPC as a means for enabling optimized complex interventions in behavioral medicine, and the benefits of a HMPC framework in contrast to conventional interventions relying on "IF-THEN" decision rules.

Dynamic models of stress-smoking responses based on high-frequency sensor data

Self-reports indicate that stress increases the risk for smoking; however, intensive data from sensors can provide a more nuanced understanding of stress in the moments leading up to and following smoking events. Identifying personalized dynamical models of stress-smoking responses can improve characterizations of smoking responses following stress, but techniques used to identify these models require intensive longitudinal data. This study leveraged advances in wearable sensing technology and digital markers of stress and smoking to identify person-specific models of stress and smoking system dynamics by considering stress immediately before, during, and after smoking events. Adult smokers (n = 45) wore the AutoSense chestband (respiration-inductive plethysmograph, electrocardiogram, accelerometer) with MotionSense (accelerometers, gyroscopes) on each wrist for three days prior to a quit attempt. The odds of minute-level smoking events were regressed on minute-level stress probabilities to identify person-specific dynamic models of smoking responses to stress. Simulated pulse responses to a continuous stress episode revealed a consistent pattern of increased odds of smoking either shortly after the beginning of the simulated stress episode or with a delay, for all participants. This pattern is followed by a dramatic reduction in the probability of smoking thereafter, for about half of the participants (49%). Sensor-detected stress probabilities indicate a vulnerability for smoking that may be used as a tailoring variable for just-in-time interventions to support quit attempts.

Person-specific dose-finding for a digital messaging intervention to promote physical activity

OBJECTIVE

Digital messaging is an established method for promoting physical activity. Systematic approaches for dose-finding have not been widely used in behavioral intervention development. We apply system identification tools from control systems engineering to estimate dynamical models and inform decision rules for digital messaging intervention to promote physical activity.

METHOD

Insufficiently active emerging and young adults (n = 45) wore an activity monitor that recorded minute-level step counts and heart rate and received 0-6 digital messages daily on their smartphone for 6 months. Messages were drawn from 3 content libraries (move more, sit less, inspirational quotes). Location recordings via location services in the user's smartphone were used to lookup weather indices at the time and place of message delivery. Following system identification, responses to each message type were simulated under different conditions. Response features were extracted to summarize dynamic processes.

RESULTS

A generic model based on composite data was conservative and did not capture the heterogeneous responses evident in person-specific models. No messages were uniformly ineffective but responses to specific message content in different contexts varied between people. Exterior temperature at the time of message receipt moderated the size of some message effects.

CONCLUSIONS

A generic model of message effects on physical activity can provide the initial evidence for context-sensitive decision rules in a just-in-time adaptive intervention, but it is likely to be error-prone and inefficient. As individual data accumulates, person-specific models should be estimated to optimize treatment and evolve as people are exposed to new environments and accumulate new experiences. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Applying P-Technique Factor Analysis to Explore Person-Specific Models of Readiness-to-Exercise

Recent research in exercise prescription and periodization has emphasized the importance of subjective experience, both in medium- and long-term monitoring, but also in the acute experience. Emerging evidence also highlights an important role of subjective readiness (pre-exercise mental and physical states) in determining how exercise is experienced, and in acutely modifying the prescribed exercise intensity. The concept of "readiness-to-exercise" shows promise in enabling and informing this acute decision-making to optimize the experiences and outcomes of exercise. While subjective experiences can be effectively assessed using psychometric scales and instruments, these are often developed and deployed using cross-sectional samples, with resulting structures that reflect a normative pattern (nomothetic). These patterns may fail to reflect individual differences in sensitivity, experience and saliency (idiographic). We conducted this research with the primary aim of comparing the nomothetical and idiographic approaches to modeling the relatively novel concept of readiness-to-exercise. Study 1 (nomothetic) therefore analyzed data collected from 572 participants who completed a one-time survey using R-technique factor analysis. Results indicated a four-factor structure that explained 60% of the variance: "health and fitness;" "fatigue;" "vitality" and "physical discomfort." Study 2 (idiographic) included a sample of 29 participants who completed the scale multiple times, between 42 and 56 times: permitting intra-individual analysis using separate P-technique factor analyses. Our analyses suggested that many individuals displayed personal signature, or "profiles" of readiness-to-exercise that differed in structure from the nomothetic form: only two participants' personal signatures contained four structures as modeled in Study 1, whereas the majority demonstrated either two or three factors. These findings raise important questions about how experiential data should be collected and modeled, for use in research (conceptual development and measurement) and applied practice (prescribing, monitoring)-as well as in more applied research (implementation, effectiveness).

Identifying behavioral types of dietary lapse from a mobile weight loss program: Preliminary investigation from a secondary data analysis

Success in behavioral weight loss (BWL) programs depends on adherence to the recommended diet to reduce caloric intake. Dietary lapses (i.e., deviations from the BWL diet) occur frequently and can adversely affect weight loss outcomes. Research indicates that lapse behavior is heterogenous; there are many eating behaviors that could constitute a dietary lapse, but they are rarely studied as distinct contributors to weight outcomes. This secondary analysis aims to evaluate six behavioral lapse types during a 10-week mobile BWL program (eating a large portion, eating when not intended, eating an off-plan food, planned lapse, being unaware of caloric content, and endorsing multiple types of lapse). Associations between weekly behavioral lapse type frequency and weekly weight loss were investigated, and predictive contextual characteristics (psychological, behavioral, and environmental triggers for lapse) and individual difference (e.g., age, gender) factors were examined across lapse types. Participants (N = 121) with overweight/obesity (M_BMI = 34.51; 84.3% female; 69.4% White) used a mobile BWL program for 10 weeks, self-weighed weekly using Bluetooth scales, completed daily ecological momentary assessment of lapse behavior and contextual characteristics, and completed a baseline demographics questionnaire. Linear mixed models revealed significant negative associations between unplanned lapses and percent weight loss. Unplanned lapses from eating a large portion, eating when not intended, and having multiple "types" were significantly negatively associated with weekly percent weight loss. A lasso regression showed that behavioral lapse types share many similar stable factors, with other factors being unique to specific lapse types. Results add to the prior literature on lapses and weight loss in BWL and provide preliminary evidence that behavioral lapse types could aid in understanding adherence behavior and developing precision medicine tools to improve dietary adherence.

Seasons, weather, and device-measured movement behaviors: a scoping review from 2006 to 2020

BACKGROUND

This scoping review summarized research on (a) seasonal differences in physical activity and sedentary behavior, and (b) specific weather indices associated with those behaviors.

METHODS

PubMed, CINAHL, and SPORTDiscus were searched to identify relevant studies. After identifying and screening 1459 articles, data were extracted from 110 articles with 118,189 participants from 30 countries (almost exclusively high-income countries) on five continents.

RESULTS

Both physical activity volume and moderate-to-vigorous physical activity (MVPA) were greater in summer than winter. Sedentary behavior was greater in winter than either spring or summer, and insufficient evidence existed to draw conclusions about seasonal differences in light physical activity. Physical activity volume and MVPA duration were positively associated with both the photoperiod and temperature, and negatively associated with precipitation. Sedentary behavior was negatively associated with photoperiod and positively associated with precipitation. Insufficient evidence existed to draw conclusions about light physical activity and specific weather indices. Many weather indices have been neglected in this literature (e.g., air quality, barometric pressure, cloud coverage, humidity, snow, visibility, windchill).

CONCLUSIONS

The natural environment can influence health by facilitating or inhibiting physical activity. Behavioral interventions should be sensitive to potential weather impacts. Extreme weather conditions brought about by climate change may compromise health-enhancing physical activity in the short term and, over longer periods of time, stimulate human migration in search of more suitable environmental niches.