Exploring behavioral markers of long-term physical activity maintenance: a case study of system identification modeling within a behavioral intervention

Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions

BACKGROUND

Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept.

OBJECTIVE

The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d).

METHODS

We recruited physically inactive English-speaking adults aged ≥25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person's steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person's baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI.

RESULTS

As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023.

CONCLUSIONS

This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52161.

Complex lifestyle intervention among inactive older adults with elevated cardiovascular disease risk and obesity: a mixed-method, single-arm feasibility study for RESTART-a randomized controlled trial

Background

Physical inactivity and obesity are global public health challenges. Older adults are important to target for prevention and management of disease and chronic conditions. However, many individuals struggle with maintaining increased physical activity (PA) and improved diet. This feasibility study provides the foundation for the RESTART trial, a randomized controlled trial (RCT) to test a complex intervention to facilitate favourable lifestyle changes older adults can sustain. The primary objective of this study was to investigate study feasibility (recruitment, adherence, side-effects, and logistics) using an interdisciplinary approach.

Methods

This 1-year prospective mixed-method single-arm feasibility study was conducted in Tromsø, Norway, from September 2017. We invited by mail randomly selected participants from the seventh survey of the Tromsø Study (2015–2016) aged 55–75 years with sedentary lifestyle, obesity, and elevated cardiovascular risk. Participants attended a 6-month complex lifestyle intervention program, comprising instructor-led high-intensive exercise and nutritionist- and psychologist-led counselling, followed by a 6-month follow-up. All participants used a Polar activity tracker for daily activity monitoring during the intervention. Participants were interviewed three times throughout the study. Primary outcome was study feasibility measures.

Results

We invited potential participants (n=75) by mail of which 27 % (n=20) agreed to participate. Telephone screening excluded four participants, and altogether 16 participants completed baseline screening. The intervention and test procedures of primary and secondary outcomes were feasible and acceptable for the participants. There were no exercise-induced injuries, indicating that the intervention program is safe. Participants experienced that the dietary and psychological counselling were delivered too early in the intervention and in too close proximity to the start of the exercise program. Minor logistic improvements were implemented throughout the intervention period.

Conclusion

This study indicates that it is feasible to conduct a full-scale RCT of a multi-component randomized intervention trial, based on the model of the present study. No dropouts due to exercise-induced injury indicates that the exercises were safe. While minor improvements in logistics were implemented during the intervention, we will improve recruitment and adherence strategies, rearrange schedule of intervention contents (exercise, diet, and psychology), as well as improve the content of the dietary and behavioural counselling to maximize outcome effects in the RESTART protocol.

Trial registration

ClinicalTrials.gov Identifier: NCT03807323 Registered 16 January 2019 – retrospectively registered.

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).

Experiment in a Box (XB): An Interactive Technology Framework for Sustainable Health Practices

This paper presents the Experiment in a Box (XB) framework to support interactive technology design for building health skills. The XB provides a suite of experiments—time-limited, loosely structured evaluations of health heuristics for a user-as-experimenter to select from and then test in order to determine that heuristic’s efficacy, and to explore how it might be incorporated into the person’s life and when necessary, to support their health and wellbeing. The approach leverages self-determination theory to support user autonomy and competence to build actionable, personal health knowledge skills and practice (KSP). In the three studies of XB presented, we show that with even the short engagement of an XB experiment, participants develop health practices from the interventions that are still in use long after the intervention is finished. To situate the XB approach relative to other work around health practices in HCI in particular, we contribute two design continua for this design space: insourcing to outsourcing and habits to heuristics. From this analysis, we demonstrate that XB is situated in a largely under-explored area for interactive health interventions: the insourcing and heuristic oriented area of the design space. Overall, the work offers a new scaffolding, the XB Framework, to instantiate time-limited interactive technology interventions to support building KSP that can thrive in that person, significantly both post-interventions, and independent of that technology.

Barriers to and Facilitators of Technology in Cardiac Rehabilitation and Self-Management: Systematic Qualitative Grounded Theory Review

Background

Dealing with cardiovascular disease is challenging, and people often struggle to follow rehabilitation and self-management programs. Several systematic reviews have explored quantitative evidence on the potential of digital interventions to support cardiac rehabilitation (CR) and self-management. However, although promising, evidence regarding the effectiveness and uptake of existing interventions is mixed. This paper takes a different but complementary approach, focusing on qualitative data related to people’s experiences of technology in this space.

Objective

Through a qualitative approach, this review aims to engage more directly with people’s experiences of technology that supports CR and self-management. The primary objective of this paper is to provide answers to the following research question: What are the primary barriers to and facilitators and trends of digital interventions to support CR and self-management? This question is addressed by synthesizing evidence from both medical and computer science literature. Given the strong evidence from the field of human-computer interaction that user-centered and iterative design methods increase the success of digital health interventions, we also assess the degree to which user-centered and iterative methods have been applied in previous work.

Methods

A grounded theory literature review of articles from the following major electronic databases was conducted: ACM Digital Library, PsycINFO, Scopus, and PubMed. Papers published in the last 10 years, 2009 to 2019, were considered, and a systematic search with predefined keywords was conducted. Papers were screened against predefined inclusion and exclusion criteria. Comparative and in-depth analysis of the extracted qualitative data was carried out through 3 levels of iterative coding and concept development.

Results

A total of 4282 articles were identified in the initial search. After screening, 61 articles remained, which were both qualitative and quantitative studies and met our inclusion criteria for technology use and health condition. Of the 61 articles, 16 qualitative articles were included in the final analysis. Key factors that acted as barriers and facilitators were background knowledge and in-the-moment understanding, personal responsibility and social connectedness, and the need to support engagement while avoiding overburdening people. Although some studies applied user-centered methods, only 6 involved users throughout the design process. There was limited evidence of studies applying iterative approaches.

Conclusions

The use of technology is acceptable to many people undergoing CR and self-management. Although background knowledge is an important facilitator, technology should also support greater ongoing and in-the-moment understanding. Connectedness is valuable, but to avoid becoming a barrier, technology must also respect and enable individual responsibility. Personalization and gamification can also act as facilitators of engagement, but care must be taken to avoid overburdening people. Further application of user-centered and iterative methods represents a significant opportunity in this space.

Rationale, design, and development of SleepWell24: A smartphone application to promote adherence to positive airway pressure therapy among patients with obstructive sleep apnea

BACKGROUND

Positive airway pressure (PAP) therapy is the gold standard treatment for obstructive sleep apnea (OSA), a chronic disorder that affects 6-13% of the adult population. However, adherence to PAP therapy is challenging, and current approaches to improve adherence have limited efficacy and scalability.

METHODS/DESIGN

To promote PAP adherence, we developed SleepWell24, a multicomponent, evidence-based smartphone application that delivers objective biofeedback concerning PAP use and sleep/physical activity patterns via cloud-based PAP machine and wearable sensor data, and behavior change strategies and troubleshooting of PAP therapy interface use. This randomized controlled trial will evaluate the feasibility, acceptability, and initial efficacy of SleepWell24 compared to a usual care control condition during the first 60 days of PAP therapy among patients newly diagnosed with OSA.

DISCUSSION

SleepWell24 is an innovative, multi-component behavior change intervention, designed as a self-management approach to addressing the psychosocial determinants of adherence to PAP therapy among new users. The results will guide lengthier future trials that assess numerous patient-centered and clinical outcomes.

Integrating Behavioral Science With Mobile (mHealth) Technology to Optimize Health Behavior Change Interventions

Abstract. Recent rapid advances in technology have provided us with a golden opportunity to effect change in health-related outcomes for chronic disease by employing digital technologies to encourage and support behavior change to promote and maintain health. Behavior change theories are the bedrock to developing evidence-based mHealth interventions. Digital technologies enable researchers to empirically test behavioral theories in “real-world” contexts using behavior change techniques ( Hekler, Michie, et al., 2016 ). According to the European Commission (2014) among the world’s population of 7 billion, there are over 5 billion mobile devices and over 90% of the users have their mobile device near them 24 hr a day. This provides a huge opportunity for behavior change and one that health psychologists have already begun to address. However, while a novel and exciting area of research, many early studies have been criticized for lacking a strong evidence base in both design and implementation. The European Commission conducted a public consultation in 2016 on the issues surrounding the use of mHealth tools (e.g., apps) and found a lack of global standards was a significant barrier. Recently, the World Health Organization (WHO) mHealth Technical Evidence Review Group developed the mHealth evidence reporting and assessment (mERA) checklist for specifying the content of mHealth interventions. Health psychologists play a key role in developing mHealth interventions, particularly in the management of chronic disease. This article discusses current challenges facing widespread integration of mobile technology into self-management of chronic disease including issues around security and regulation, as well as investigating mechanisms to overcoming these barriers.

The impact of weather on summer and winter exercise behaviors

BACKGROUND

Outdoor exercise is an enjoyable way for individuals to improve fitness, but it is dependent on weather conditions. This study examines the association between weather conditions and outdoor exercise after adjustment for age, sex, race, and socioeconomic status.

METHODS

We used data representative of American adults from the University of Michigan/Thomson Reuters June 2013 surveys of consumers (core and supplement) to investigate self-reported exercise behavior in summer and winter. Multivariate multinomial logistic regression models estimated the odds of delayed or indoor exercise compared with outdoor exercise.

RESULTS

Of the 502 respondents, 16.3% did not regularly exercise outdoors (i.e., at least once a week), and many would delay exercise both in summer (51.8%) and winter (43.9%). Individuals listing rain as the predominant adverse weather condition had 3.33 times higher odds of exercising indoors (95% confidence interval (CI): 1.34-8.28) and 3.49 times higher odds of delaying exercise (95%CI: 1.69-7.21) compared with those mentioning heat as the predominant adverse condition. Individuals for whom ice or snow was an adverse winter weather condition were more likely to delay exercise (odds ratio (OR) = 3.34; 95%CI: 1.19-9.36), compared with those concerned with cold.

CONCLUSION

This study found that race, age, and education exacerbate the negative effects of adverse weather conditions on the decision to exercise outdoors. Accordingly, any recommendation for an individual to exercise outdoors should be combined with an evaluation of the individual's outdoor environment along with strategies for the individual to continue exercising, indoors or outdoors, when adverse weather is present.

Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions

BACKGROUND

Adaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions.

OBJECTIVE

The purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions.

OVERVIEW

We start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step.

IMPLICATIONS

Control engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities.

Attending to Timely Contingencies: Promoting Physical Activity Uptake Among Adults with Serious Mental Illness with an Exercise-For-Mood vs. an Exercise-For-Fitness Prescription

Despite evidence for both physical and mental health benefits achieved through regular exercise, most Americans fail to meet minimum recommendations. Altering the behavioral contingency from a focus on long-term health benefits to immediate mood benefits represents a novel method for exercise promotion. The current study examined a single-session exercise-for-mood intervention against two time-matched comparison conditions in 152 patients with serious mental illness attending a partial hospital program, a population marked by significant health disparities. This intervention was compared to a standard exercise-for-fitness intervention and a time-matched no-exercise control. Among patients with high levels of exercise prior to the partial hospital program, the exercise-for-mood intervention yielded significant increases in exercise. Implications for exercise promotion interventions among psychiatrically ill patients are discussed.

Modeling individual differences: A case study of the application of system identification for personalizing a physical activity intervention

BACKGROUND

Control systems engineering methods, particularly, system identification (system ID), offer an idiographic (i.e., person-specific) approach to develop dynamic models of physical activity (PA) that can be used to personalize interventions in a systematic, scalable way. The purpose of this work is to: (1) apply system ID to develop individual dynamical models of PA (steps/day measured using Fitbit Zip) in the context of a goal setting and positive reinforcement intervention informed by Social Cognitive Theory; and (2) compare insights on potential tailoring variables (i.e., predictors expected to influence steps and thus moderate the suggested step goal and points for goal achievement) selected using the idiographic models to those selected via a nomothetic (i.e., aggregated across individuals) approach.

METHOD

A personalized goal setting and positive reinforcement intervention was deployed for 14 weeks. Baseline PA measured in weeks 1-2 was used to inform personalized daily step goals delivered in weeks 3-14. Goals and expected reward points (granted upon goal achievement) were pseudo-randomly assigned using techniques from system ID, with goals ranging from their baseline median steps/day up to 2.5× baseline median steps/day, and points ranging from 100 to 500 (i.e., $0.20-$1.00). Participants completed a series of daily self-report measures. Auto Regressive with eXogenous Input (ARX) modeling and multilevel modeling (MLM) were used as the idiographic and nomothetic approaches, respectively.

RESULTS

Participants (N = 20, mean age = 47.25 ± 6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ± 6.82 kg/m²) adults. Results from ARX modeling suggest that individuals differ in the factors (e.g., perceived stress, weekday/weekend) that influence their observed steps/day. In contrast, the nomothetic model from MLM suggested that goals and weekday/weekend were the key variables that were predictive of steps. Assuming the ARX models are more personalized, the obtained nomothetic model would have led to the identification of the same predictors for 5 of the 20 participants, suggesting a mismatch of plausible tailoring variables to use for 75% of the sample.

CONCLUSION

The idiographic approach revealed person-specific predictors beyond traditional MLM analyses and unpacked the inherent complexity of PA; namely that people are different and context matters. System ID provides a feasible approach to develop personalized dynamical models of PA and inform person-specific tailoring variable selection for use in adaptive behavioral interventions.

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs

Objective

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies.

Methods

The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience.

Results

The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research.

Conclusions

By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

Evaluating the effect of smoking cessation treatment on a complex dynamical system

OBJECTIVE

To understand the dynamic relations among tobacco withdrawal symptoms to inform the development of effective smoking cessation treatments. Dynamical system models from control engineering are introduced and utilized to evaluate complex treatment effects. We demonstrate how dynamical models can be used to examine how distinct withdrawal-related processes are related over time and how treatment influences these relations.

METHOD

Intensive longitudinal data from a randomized placebo-controlled smoking cessation trial (N=1504) are used to estimate a dynamical model of withdrawal-related processes including momentary craving, negative affect, quitting self-efficacy, and cessation fatigue for each of six treatment conditions (nicotine patch, nicotine lozenge, bupropion, patch + lozenge, bupropion + lozenge, and placebo).

RESULTS

Estimation and simulation results show that (1) withdrawal measurements are interrelated over time, (2) nicotine patch + nicotine lozenge showed reduced cessation fatigue and enhanced self-efficacy in the long-term while bupropion + nicotine lozenge was more effective at reducing negative affect and craving, and (3) although nicotine patch + nicotine lozenge had a better initial effect on cessation fatigue and self-efficacy, nicotine lozenge had a stronger effect on negative affect and nicotine patch had a stronger impact on craving.

CONCLUSIONS

This approach can be used to provide new evidence illustrating (a) the total impact of treatment conditions (via steady state values) and (b) the total initial impact (via rate of initial change values) on smoking-related outcomes for separate treatment conditions, noting that the conditions that produce the largest change may be different than the conditions that produce the fastest change.

Agile science: creating useful products for behavior change in the real world

Evidence-based practice is important for behavioral interventions but there is debate on how best to support real-world behavior change. The purpose of this paper is to define products and a preliminary process for efficiently and adaptively creating and curating a knowledge base for behavior change for real-world implementation. We look to evidence-based practice suggestions and draw parallels to software development. We argue to target three products: (1) the smallest, meaningful, self-contained, and repurposable behavior change modules of an intervention; (2) “computational models” that define the interaction between modules, individuals, and context; and (3) “personalization” algorithms, which are decision rules for intervention adaptation. The “agile science” process includes a generation phase whereby contender operational definitions and constructs of the three products are created and assessed for feasibility and an evaluation phase, whereby effect size estimates/casual inferences are created. The process emphasizes early-and-often sharing. If correct, agile science could enable a more robust knowledge base for behavior change.

Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention

Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person's evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m²), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant's median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t² = -247, t = -5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.

Toward Usable Evidence: Optimizing Knowledge Accumulation in HCI Research on Health Behavior Change

Over the last ten years, HCI researchers have introduced a range of novel ways to support health behavior change, from glanceable displays to sophisticated game dynamics. Yet, this research has not had as much impact as its originality warrants. A key reason for this is that common forms of evaluation used in HCI make it difficult to effectively accumulate-and use-knowledge across research projects. This paper proposes a strategy for HCI research on behavior change that retains the field's focus on novel technical contributions while enabling accumulation of evidence that can increase impact of individual research projects both in HCI and the broader behavior-change science. The core of this strategy is an emphasis on the discovery of causal effects of individual components of behavior-change technologies and the precise ways in which those effects vary with individual differences, design choices, and contexts in which those technologies are used.

A smartphone "app"-delivered randomized factorial trial targeting physical activity in adults

Rapid technological development has challenged researchers developing mobile moderate-to-vigorous physical activity (MVPA) interventions. This 12-week randomized factorial intervention aimed to determine the individual and combined impact of a self-monitoring smartphone-app (tracking, feedback, education) and two theory-based modules (goal-setting, points-based feedback) on MVPA, key psychosocial outcomes, and application usage. Adults (N = 116; M _age  = 41.38 ± 7.57) received (1) a basic self-monitoring app, (2) the basic app plus goal setting, (3) the basic app plus points-based feedback, or (4) the basic app plus both modules. All individuals increased MVPA by more than 11 daily minutes. Those with points-based feedback demonstrated still higher levels of MVPA and more favorable psychosocial and app usage outcomes across the intervention. Those with access to in-app goal setting had higher levels of app usage relative to those without the component. It is imperative that effective digital intervention "ingredients" are identified, and these findings provide early evidence to this effect. Trial Registration clinicaltrials.gov identifier NCT02592590.

Advancing Models and Theories for Digital Behavior Change Interventions

To be suitable for informing digital behavior change interventions, theories and models of behavior change need to capture individual variation and changes over time. The aim of this paper is to provide recommendations for development of models and theories that are informed by, and can inform, digital behavior change interventions based on discussions by international experts, including behavioral, computer, and health scientists and engineers. The proposed framework stipulates the use of a state-space representation to define when, where, for whom, and in what state for that person, an intervention will produce a targeted effect. The "state" is that of the individual based on multiple variables that define the "space" when a mechanism of action may produce the effect. A state-space representation can be used to help guide theorizing and identify crossdisciplinary methodologic strategies for improving measurement, experimental design, and analysis that can feasibly match the complexity of real-world behavior change via digital behavior change interventions.

Cardiorespiratory fitness moderates the effect of an affect-guided physical activity prescription: a pilot randomized controlled trial

Physical activity (PA) interventions have a clear role in promoting mental health. Current PA guidelines directed toward specific PA intensities may have negative effects on affective response to exercise, and affective response is an important determinant of PA adherence. In this randomized trial of 67 previously inactive adults, we compared the effects of a PA prescription emphasizing the maintenance of positive affect to one emphasizing a target heart rate, and tested the extent to which the effect of the affect-guided prescription on PA is moderated by cardiorespiratory fitness (CRF). We found the effect of an affect-guided prescription was significantly moderated by CRF. At one week, for participants with lower CRF (i.e. poor conditioning), the affect-guided prescription resulted in significantly greater change in PA minutes (M = 240.8) than the heart rate-guided prescription (M = 165.7), reflecting a moderate-sized effect (d = .55). For those with higher CRF (i.e. good conditioning), the means were in the opposite direction but not significantly different. At one month, the same pattern emerged but the interaction was not significant. We discuss the implications of these findings for the type of PA prescriptions offered to individuals in need.

Increasing walking among older people: A test of behaviour change techniques using factorial randomised N-of-1 trials

Objective: Evaluations of techniques to promote physical activity usually adopt a randomised controlled trial (RCT). Such designs inform how a technique performs on average but cannot be used for treatment of individuals. Our objective was to conduct the first N-of-1 RCTs of behaviour change techniques with older people and test the effectiveness of the techniques for increasing walking within individuals.

Design: Eight adults aged 60–87 were randomised to a 2 (goal-setting vs. active control) × 2 (self-monitoring vs. active control) factorial RCT over 62 days. The time series data were analysed for each single case using linear regressions.

Main outcome measures: Walking was objectively measured using pedometers.

Results: Compared to control days, goal-setting increased walking in 4 out of 8 individuals and self-monitoring increased walking in 7 out of 8 individuals. While the probability for self-monitoring to be effective in 7 out of 8 participants was beyond chance (p = .03), no intervention effect was significant for individual participants. Two participants had a significant but small linear decrease in walking over time.

Conclusion: We demonstrate the utility of N-of-1 trials for advancing scientific enquiry of behaviour change and in practice for increasing older people’s physical activity.

Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research

Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

Dynamic Factor Analysis for Multivariate Time Series: An Application to Cognitive Trajectories

We propose a dynamic factor model appropriate for large epidemiological studies and develop an estimation algorithm which can handle datasets with large number of subjects and short temporal information. The algorithm uses a two cycle iterative approach for parameter estimation in such a large dataset. Each iteration consists of two distinct cycles, both following an EM algorithm approach. This iterative process will continue until convergence is achieved. We utilized a dataset from the National Alzheimer Coordinating Center (NACC) to estimate underlying measures of cognition based on a battery of observed neuropsychological tests. We assess the goodness of fit and the precision of the dynamic factor model estimators and compare it with a non-dynamic version in which temporal information is not used. The dynamic factor model is superior to a non-dynamic version with respect to fit statistics shown in simulation experiments. Moreover, it has increased power to detect differences in the rate of decline for a given sample size.

Time regained: when people stop a physical activity program, how does their time use change? A randomised controlled trial

The aim of this study was to investigate how previously inactive adults who had participated in a structured, partly supervised 6-week exercise program restructured their time budgets when the program ended. Using a randomised controlled trial design, 129 previously inactive adults were recruited and randomly allocated to one of three groups: a Moderate or Extensive six-week physical activity intervention (150 and 300 additional minutes of exercise per week, respectively) or a Control group. Additional physical activity was accumulated through both group and individual exercise sessions with a wide range of activities. Use of time and time spent in energy expenditure zones was measured using a computerised 24-h self-report recall instrument, the Multimedia Activity Recall for Children and Adults, and accelerometry at baseline, mid- and end-program and at 3- and 6-months follow up. At final follow up, all significant changes in time use domains had returned to within 20 minutes of baseline levels (Physical Activity 1-2 min/d, Active Transport 3-9 min/d, Self-Care 0-2 min/d, Television/Videogames 13-18 min/d in the Moderate and Extensive group, relative to Controls, respectively, p>0.05). Similarly, all significant changes in time spent in the moderate energy expenditure zone had returned to within 1-3 min/d baseline levels (p>0.05), however time spent in vigorous physical activity according to accelerometry estimates remained elevated, although the changes were small in magnitude (1 min/d in the Moderate and Extensive groups, relative to Controls, p=0.01). The results of this study demonstrate strong recidivist patterns in physical activity, but also in other aspects of time use. In designing and determining the effectiveness of exercise interventions, future studies would benefit from considering the whole profile of time use, rather than focusing on individual activities.

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention

The term adaptive intervention has been used in behavioral medicine to describe operationalized and individually tailored strategies for prevention and treatment of chronic, relapsing disorders. Control systems engineering offers an attractive means for designing and implementing adaptive behavioral interventions that feature intensive measurement and frequent decision-making over time. This is illustrated in this paper for the case of a low-dose naltrexone treatment intervention for fibromyalgia. System identification methods from engineering are used to estimate dynamical models from daily diary reports completed by participants. These dynamical models then form part of a model predictive control algorithm which systematically decides on treatment dosages based on measurements obtained under real-life conditions involving noise, disturbances, and uncertainty. The effectiveness and implications of this approach for behavioral interventions (in general) and pain treatment (in particular) are demonstrated using informative simulations.

Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions

Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or "just-in-time" behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.

A personalized and control systems engineering conceptual approach to target childhood anxiety in the contexts of cultural diversity

In the child and adolescent anxiety area, some progress has been made to develop evidence-based prevention protocols, but less is known about how to best target these problems in children and families of color. In general, data show differential program effects with some minority children benefiting significantly less. Our preliminary data, however, show promise and suggest cultural parameters to consider in the tailoring process beyond language and cultural symbols. It appears that a more focused approach to culture might help activate intervention components and its intended effects by focusing, for example, on the various facets of familismo when working with some Mexican parents. However, testing the effects and nuances of cultural adaption vis-à-vis a focused personalized approach is methodologically challenging. For this reason, we identify control systems engineering design methods and provide example scenarios relevant to our data and recent intervention work.