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

Biofeedback and Digitalized Motivational Interviewing to Increase Daily Physical Activity: Series of Factorial N-of-1 Randomized Controlled Trials Piloting the Precious App

BACKGROUND

Insufficient physical activity is a public health concern. New technologies may improve physical activity levels and enable the identification of its predictors with high accuracy. The Precious smartphone app was developed to investigate the effect of specific modular intervention elements on physical activity and examine theory-based predictors within individuals.

OBJECTIVE

This study pilot-tested a fully automated factorial N-of-1 randomized controlled trial (RCT) with the Precious app and examined whether digitalized motivational interviewing (dMI) and heart rate variability-based biofeedback features increased objectively recorded steps. The secondary aim was to assess whether daily self-efficacy and motivation predicted within-person variability in daily steps.

METHODS

In total, 15 adults recruited from newspaper advertisements participated in a 40-day factorial N-of-1 RCT. They installed 2 study apps on their phones: one to receive intervention elements and one to collect ecological momentary assessment (EMA) data on self-efficacy, motivation, perceived barriers, pain, and illness. Steps were tracked using Xiaomi Mi Band activity bracelets. The factorial design included seven 2-day biofeedback interventions with a Firstbeat Bodyguard 2 (Firstbeat Technologies Ltd) heart rate variability sensor, seven 2-day dMI interventions, a wash-out day after each intervention, and 11 control days. EMA questions were sent twice per day. The effects of self-efficacy, motivation, and the interventions on subsequent steps were analyzed using within-person dynamic regression models and aggregated data using longitudinal multilevel modeling (level 1: daily observations; level 2: participants). The analyses were adjusted for covariates (ie, within- and between-person perceived barriers, pain or illness, time trends, and recurring events).

RESULTS

All participants completed the study, and adherence to activity bracelets and EMA measurements was high. The implementation of the factorial design was successful, with the dMI features used, on average, 5.1 (SD 1.0) times of the 7 available interventions. Biofeedback interventions were used, on average, 5.7 (SD 1.4) times out of 7, although 3 participants used this feature a day later than suggested and 1 did not use it at all. Neither within- nor between-person analyses revealed significant intervention effects on step counts. Self-efficacy predicted steps in 27% (4/15) of the participants. Motivation predicted steps in 20% (3/15) of the participants. Aggregated data showed significant group-level effects of day-level self-efficacy (B=0.462; P<.001), motivation (B=0.390; P<.001), and pain or illness (B=-1524; P<.001) on daily steps.

CONCLUSIONS

The automated factorial N-of-1 trial with the Precious app was mostly feasible and acceptable, especially the automated delivery of the dMI components, whereas self-conducted biofeedback measurements were more difficult to time correctly. The findings suggest that changes in self-efficacy and motivation may have same-day effects on physical activity, but the effects vary across individuals. This study provides recommendations based on the lessons learned on the implementation of factorial N-of-1 RCTs.

Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations

The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people's homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals' obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people's use of dynamic food environments and links to diet and health.

The NIH Science of Behavior Change Program: Looking Toward the Future

The National Institutes of Health established the Science of Behavior Change (SOBC) program to promote basic research on the initiation, personalization, and maintenance of health behavior change. The SOBC Resource and Coordinating Center now leads and supports activities to maximize the creativity, productivity, scientific rigor, and dissemination of the experimental medicine approach and experimental design resources. Here, we highlight those resources, including the Checklist for Investigating Mechanisms in Behavior-change Research (CLIMBR) guidelines introduced in this special section. We describe the ways in which SOBC can be applied across a range of domains and contexts, and end by considering ways to extend SOBC's perspective and reach, so as to best promote behavior change linked with health, quality of life, and well-being.

Use of Electronic Ecological Momentary Assessment Methodologies in Physical Activity, Sedentary Behavior, and Sleep Research in Young Adults: Systematic Review

BACKGROUND

Recent technological advances allow for the repeated sampling of real-time data in natural settings using electronic ecological momentary assessment (eEMA). These advances are particularly meaningful for investigating physical activity, sedentary behavior, and sleep in young adults who are in a critical life stage for the development of healthy lifestyle behaviors.

OBJECTIVE

This study aims to describe the use of eEMA methodologies in physical activity, sedentary behavior, and sleep research in young adults.

METHODS

The PubMed, CINAHL, PsycINFO, Embase, and Web of Science electronic databases were searched through August 2022. Inclusion criteria were use of eEMA; sample of young adults aged 18 to 25 years; at least 1 measurement of physical activity, sedentary behavior, or sleep; English language; and a peer-reviewed report of original research. Study reports were excluded if they were abstracts, protocols, or reviews. The risk of bias assessment was conducted using the National Heart, Lung, and Blood Institute's Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Screening, data extraction, and risk of bias assessments were conducted by independent authors, with discrepancies resolved by consensus. Descriptive statistics and narrative synthesis were used to identify overarching patterns within the following categories guided by the Checklist for Reporting Ecological Momentary Assessments Studies: study characteristics, outcomes and measures, eEMA procedures, and compliance.

RESULTS

The search resulted in 1221 citations with a final sample of 37 reports describing 35 unique studies. Most reports (28/37, 76%) were published in the last 5 years (2017-2022), used observational designs (35/37, 95%), consisted of samples of college students or apprentices (28/35, 80%), and were conducted in the United States (22/37, 60%). The sample sizes ranged from 14 to 1584 young adults. Physical activity was measured more frequently (28/37, 76%) than sleep (16/37, 43%) or sedentary behavior (4/37, 11%). Of the 37 studies, 11 (30%) reports included 2 movement behaviors and no reports included 3 movement behaviors. eEMA was frequently used to measure potential correlates of movement behaviors, such as emotional states or feelings (25/37, 68%), cognitive processes (7/37, 19%), and contextual factors (9/37, 24%). There was wide variability in the implementation and reporting of eEMA procedures, measures, missing data, analysis, and compliance.

CONCLUSIONS

The use of eEMA methodologies in physical activity, sedentary behavior, and sleep research in young adults has greatly increased in recent years; however, reports continue to lack standardized reporting of features unique to the eEMA methodology. Additional areas in need of future research include the use of eEMA with more diverse populations and the incorporation of all 3 movement behaviors within a 24-hour period. The findings are intended to assist investigators in the design, implementation, and reporting of physical activity, sedentary behavior, and sleep research using eEMA in young adults.

TRIAL REGISTRATION

PROSPERO CRD42021279156; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021279156.

Reducing opioid related deaths for individuals who are at high risk of death from overdose: a co-production study with people housed within prison and hostel accommodation during Covid-19

Background

A record number of Opioid-related deaths occurred in Northern Ireland in 2021 and it is acknowledged that the Covid-19 pandemic compounded drugs related deaths crisis. This co-production study set out to refine the design of a wearable device for Opioid users to detect and subsequently prevent a potential overdose situation.

Method

Purposive sampling was used to recruit people who had substance use disorders and were living in a hostel and prison during the Covid-19 pandemic. Principles of co-production influenced the study, which encompassed a focus group phase and a wearable phase. The initial phase included three focus groups with participants who inject Opioids and one focus group with workers from a street injector support service. During the wearable phase, the participant group tested the feasibility of the wearable technology in a controlled environment. This included testing the transferability of data from the device to a backend server on the cloud.

Results

All focus group participants expressed an interest in the wearable technology when it was presented to them and agreed, that in principle, such a device would be extremely beneficial to help reduce the risk of overdose within the active drug using community. Participants outlined factors which would help or hinder the design of this proposed device and their decision to wear it, if it were readily available to them. Findings from wearable phase indicated that it was feasible to use a wearable device for monitoring Opioid users' biomarkers remotely. The provision of information regarding the specific functionality of the device was considered key and could be disseminated via front line services. The data acquisition and transfer process would not be a barrier for future research.

Conclusion

Understanding the benefit and disadvantages of technologies such as a wearable device to prevent Opioid-related deaths will be critical for mitigating the risk of overdose for people who use Heroin. It was also clear that this would be particularly relevant during Covid-19 lock-down periods, when the effects of the pandemic further exacerbated the isolation and solitude experienced by people who use Heroin.

Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models

BACKGROUND

Several studies have reported on the feasibility of electronic (e-)monitoring using computers or smartphones in patients with mental disorders, including bipolar disorder (BD). While studies on e-monitoring have examined the role of demographic factors, such as age, gender, or socioeconomic status and use of health apps, to our knowledge, no study has examined clinical characteristics that might impact adherence with e-monitoring in patients with BD. We analyzed adherence to e-monitoring in patients with BD who participated in an ongoing e-monitoring study and evaluated whether demographic and clinical factors would predict adherence.

METHODS

Eighty-seven participants with BD in different phases of the illness were included. Patterns of adherence for wearable use, daily and weekly self-rating scales over 15 months were analyzed to identify adherence trajectories using growth mixture models (GMM). Multinomial logistic regression models were fitted to compute the effects of predictors on GMM classes.

RESULTS

Overall adherence rates were 79.5% for the wearable; 78.5% for weekly self-ratings; and 74.6% for daily self-ratings. GMM identified three latent class subgroups: participants with (i) perfect; (ii) good; and (iii) poor adherence. On average, 34.4% of participants showed "perfect" adherence; 37.1% showed "good" adherence; and 28.2% showed poor adherence to all three measures. Women, participants with a history of suicide attempt, and those with a history of inpatient admission were more likely to belong to the group with perfect adherence.

CONCLUSIONS

Participants with higher illness burden (e.g., history of admission to hospital, history of suicide attempts) have higher adherence rates to e-monitoring. They might see e-monitoring as a tool for better documenting symptom change and better managing their illness, thus motivating their engagement.

Desire to get drunk partially mediates effects of a combined text message-based alcohol intervention for young adults

BACKGROUND

This study aimed to test the causal effect of different text message interventions on reducing alcohol consumption indirectly by altering desire to get drunk.

METHODS

Participants were young adults randomized to interventions with different behavior change techniques: self-monitoring alone (TRACK); pre-drinking plan feedback (PLAN); post-drinking alcohol consumption feedback (USE); pre- and post-drinking goal feedback (GOAL); and a combination of techniques (COMBO) who completed at least 2 days of both pre- and post-drinking assessments over 12 weeks of intervention exposure. On the two days per week they planned to drank alcohol, participants were asked to report desire to get drunk (0 "none" to 8 "completely"). The next day, participants reported drinking quantity. Outcomes included binge drinking (defined as 4+ drinks for a woman and 5+ drinks for a man) and drinks per drinking day. Mediation was tested using path models of simultaneous between-person and within-person effects using maximum likelihood estimation.

RESULTS

At the between-person level, controlling for race and baseline AUDIT-C and within-person associations, 35.9 % of the effects of USE and 34.4 % of the effects of COMBO on reducing binge drinking were mediated through desire to get drunk. 60.8 % of the effects of COMBO on reducing drinks per drinking day were mediated through desire to get drunk. We did not find significant indirect effects for any other text-message intervention.

DISCUSSION

Findings support the hypothesized mediation model where desire to get drunk partially mediates the effects of a text message intervention using a combination of behavior change techniques on reducing alcohol consumption.

Identifying as someone who avoids virus transmission strengthens physical distancing habit-behaviour relationships: A longitudinal multi-national study during the COVID-19 pandemic

Physical distancing remains an important initiative to curb COVID-19 and virus transmission more broadly. This exploratory study investigated how physical distancing behaviour changed during the COVID-19 pandemic and whether it was associated with identity with virus transmission avoidance and physical distancing habit strength. In a longitudinal, multinational study with fortnightly repeated-assessments, associations and moderation effects were considered for both overall (person-level means) and occasion-specific deviations in habit and identity. Participants (N = 586, M age = 42, 79% female) self-reported physical distancing behavioural frequency, physical distancing habit strength, and identity with avoiding virus transmission. Physical distancing followed a cubic trajectory, with initial high engagement decreasing rapidly before increasing again near study end. Physical distancing was associated with both overall and occasion-specific virus transmission avoidant identity and physical distancing habit strength. People with strong virus transmission avoidant identity engaged in physical distancing frequently regardless of fluctuations in habit strength. However, for those with weaker virus transmission avoidant identity, physical distancing was strongly aligned with fluctuations in habit strength. To enhance engagement in physical distancing, public health messaging might fruitfully target greater or more salient virus-transmission avoidance identity and stronger physical distancing habit.

Validity and Feasibility of the Monitoring and Modeling Family Eating Dynamics System to Automatically Detect In-field Family Eating Behavior: Observational Study

Background

The field of dietary assessment has a long history, marked by both controversies and advances. Emerging technologies may be a potential solution to address the limitations of self-report dietary assessment methods. The Monitoring and Modeling Family Eating Dynamics (M2FED) study uses wrist-worn smartwatches to automatically detect real-time eating activity in the field. The ecological momentary assessment (EMA) methodology was also used to confirm whether eating occurred (ie, ground truth) and to measure other contextual information, including positive and negative affect, hunger, satiety, mindful eating, and social context.

Objective

This study aims to report on participant compliance (feasibility) to the 2 distinct EMA protocols of the M2FED study (hourly time-triggered and eating event–triggered assessments) and on the performance (validity) of the smartwatch algorithm in automatically detecting eating events in a family-based study.

Methods

In all, 20 families (58 participants) participated in the 2-week, observational, M2FED study. All participants wore a smartwatch on their dominant hand and responded to time-triggered and eating event–triggered mobile questionnaires via EMA while at home. Compliance to EMA was calculated overall, for hourly time-triggered mobile questionnaires, and for eating event–triggered mobile questionnaires. The predictors of compliance were determined using a logistic regression model. The number of true and false positive eating events was calculated, as well as the precision of the smartwatch algorithm. The Mann-Whitney U test, Kruskal-Wallis test, and Spearman rank correlation were used to determine whether there were differences in the detection of eating events by participant age, gender, family role, and height.

Results

The overall compliance rate across the 20 deployments was 89.26% (3723/4171) for all EMAs, 89.7% (3328/3710) for time-triggered EMAs, and 85.7% (395/461) for eating event–triggered EMAs. Time of day (afternoon odds ratio [OR] 0.60, 95% CI 0.42-0.85; evening OR 0.53, 95% CI 0.38-0.74) and whether other family members had also answered an EMA (OR 2.07, 95% CI 1.66-2.58) were significant predictors of compliance to time-triggered EMAs. Weekend status (OR 2.40, 95% CI 1.25-4.91) and deployment day (OR 0.92, 95% CI 0.86-0.97) were significant predictors of compliance to eating event–triggered EMAs. Participants confirmed that 76.5% (302/395) of the detected events were true eating events (ie, true positives), and the precision was 0.77. The proportion of correctly detected eating events did not significantly differ by participant age, gender, family role, or height (P>.05).

Conclusions

This study demonstrates that EMA is a feasible tool to collect ground-truth eating activity and thus evaluate the performance of wearable sensors in the field. The combination of a wrist-worn smartwatch to automatically detect eating and a mobile device to capture ground-truth eating activity offers key advantages for the user and makes mobile health technologies more accessible to nonengineering behavioral researchers.

Advancing Behavioral Intervention and Theory Development for Mobile Health: The HeartSteps II Protocol

Background: Recent advances in mobile and wearable technologies have led to new forms of interventions, called “Just-in-Time Adaptive Interventions” (JITAI). JITAIs interact with the individual at the most appropriate time and provide the most appropriate support depending on the continuously acquired Intensive Longitudinal Data (ILD) on participant physiology, behavior, and contexts. These advances raise an important question: How do we model these data to better understand and intervene on health behaviors? The HeartSteps II study, described here, is a Micro-Randomized Trial (MRT) intended to advance both intervention development and theory-building enabled by the new generation of mobile and wearable technology. Methods: The study involves a year-long deployment of HeartSteps, a JITAI for physical activity and sedentary behavior, with 96 sedentary, overweight, but otherwise healthy adults. The central purpose is twofold: (1) to support the development of modeling approaches for operationalizing dynamic, mathematically rigorous theories of health behavior; and (2) to serve as a testbed for the development of learning algorithms that JITAIs can use to individualize intervention provision in real time at multiple timescales. Discussion and Conclusions: We outline an innovative modeling paradigm to model and use ILD in real- or near-time to individually tailor JITIAs.

Intensive Longitudinal Data Collection Using Microinteraction Ecological Momentary Assessment: Pilot and Preliminary Results

Background

Ecological momentary assessment (EMA) uses mobile technology to enable in situ self-report data collection on behaviors and states. In a typical EMA study, participants are prompted several times a day to answer sets of multiple-choice questions. Although the repeated nature of EMA reduces recall bias, it may induce participation burden. There is a need to explore complementary approaches to collecting in situ self-report data that are less burdensome yet provide comprehensive information on an individual’s behaviors and states. A new approach, microinteraction EMA (μEMA), restricts EMA items to single, cognitively simple questions answered on a smartwatch with single-tap assessments using a quick, glanceable microinteraction. However, the viability of using μEMA to capture behaviors and states in a large-scale longitudinal study has not yet been demonstrated.

Objective

This paper describes the μEMA protocol currently used in the Temporal Influences on Movement & Exercise (TIME) Study conducted with young adults, the interface of the μEMA app used to gather self-report responses on a smartwatch, qualitative feedback from participants after a pilot study of the μEMA app, changes made to the main TIME Study μEMA protocol and app based on the pilot feedback, and preliminary μEMA results from a subset of active participants in the TIME Study.

Methods

The TIME Study involves data collection on behaviors and states from 246 individuals; measurements include passive sensing from a smartwatch and smartphone and intensive smartphone-based hourly EMA, with 4-day EMA bursts every 2 weeks. Every day, participants also answer a nightly EMA survey. On non–EMA burst days, participants answer μEMA questions on the smartwatch, assessing momentary states such as physical activity, sedentary behavior, and affect. At the end of the study, participants describe their experience with EMA and μEMA in a semistructured interview. A pilot study was used to test and refine the μEMA protocol before the main study.

Results

Changes made to the μEMA study protocol based on pilot feedback included adjusting the single-question selection method and smartwatch vibrotactile prompting. We also added sensor-triggered questions for physical activity and sedentary behavior. As of June 2021, a total of 81 participants had completed at least 6 months of data collection in the main study. For 662,397 μEMA questions delivered, the compliance rate was 67.6% (SD 24.4%) and the completion rate was 79% (SD 22.2%).

Conclusions

The TIME Study provides opportunities to explore a novel approach for collecting temporally dense intensive longitudinal self-report data in a sustainable manner. Data suggest that μEMA may be valuable for understanding behaviors and states at the individual level, thus possibly supporting future longitudinal interventions that require within-day, temporally dense self-report data as people go about their lives.

Perspective: A Framework for Addressing Dynamic Food Consumption Processes

The study of food consumption, diet, and related concepts is motivated by diverse goals, including understanding why food consumption impacts our health, and why we eat the foods we do. These varied motivations can make it challenging to define and measure consumption, as it can be specified across nearly infinite dimensions-from micronutrients to carbon footprint to food preparation. This challenge is amplified by the dynamic nature of food consumption processes, with the underlying phenomena of interest often based on the nature of repeated interactions with food occurring over time. This complexity underscores a need to not only improve how we measure food consumption but is also a call to support theoreticians in better specifying what, how, and why food consumption occurs as part of processes, as a prerequisite step to rigorous measurement. The purpose of this Perspective article is to offer a framework, the consumption process framework, as a tool that researchers in a theoretician role can use to support these more robust definitions of consumption processes. In doing so, the framework invites theoreticians to be a bridge between practitioners who wish to measure various aspects of food consumption and methodologists who can develop measurement protocols and technologies that can support measurement when consumption processes are clearly defined. In the paper we justify the need for such a framework, introduce the consumption process framework, illustrate the framework via a use case, and discuss existing technologies that enable the use of this framework and, by extension, more rigorous study of consumption. This consumption process framework demonstrates how theoreticians could fundamentally shift how food consumption is defined and measured towards more rigorous study of what, how, and why food is eaten as part of dynamic processes and a deeper understanding of linkages between behavior, food, and health.

Social Mobile Approaches to Reducing Weight (SMART) 2.0: protocol of a randomized controlled trial among young adults in university settings

Background

Excess weight gain in young adulthood is associated with future weight gain and increased risk of chronic disease. Although multimodal, technology-based weight-loss interventions have the potential to promote weight loss among young adults, many interventions have limited personalization, and few have been deployed and evaluated for longer than a year. We aim to assess the effects of a highly personalized, 2-year intervention that uses popular mobile and social technologies to promote weight loss among young adults.

Methods

The Social Mobile Approaches to Reducing Weight (SMART) 2.0 Study is a 24-month parallel-group randomized controlled trial that will include 642 overweight or obese participants, aged 18–35 years, from universities and community colleges in San Diego, CA. All participants receive a wearable activity tracker, connected scale, and corresponding app. Participants randomized to one intervention group receive evidence-based information about weight loss and behavior change techniques via personalized daily text messaging (i.e., SMS/MMS), posts on social media platforms, and online groups. Participants in a second intervention group receive the aforementioned elements in addition to brief, technology-mediated health coaching. Participants in the control group receive a wearable activity tracker, connected scale, and corresponding app alone. The primary outcome is objectively measured weight in kilograms over 24 months. Secondary outcomes include anthropometric measurements; physiological measures; physical activity, diet, sleep, and psychosocial measures; and engagement with intervention modalities. Outcomes are assessed at baseline and 6, 12, 18, and 24 months. Differences between the randomized groups will be analyzed using a mixed model of repeated measures and will be based on the intent-to-treat principle.

Discussion

We hypothesize that both SMART 2.0 intervention groups will significantly improve weight loss compared to the control group, and the group receiving health coaching will experience the greatest improvement. We further hypothesize that differences in secondary outcomes will favor the intervention groups. There is a critical need to advance understanding of the effectiveness of multimodal, technology-based weight-loss interventions that have the potential for long-term effects and widespread dissemination among young adults. Our findings should inform the implementation of low-cost and scalable interventions for weight loss and risk-reducing health behaviors.

Trial registration

ClinicalTrials.govNCT03907462. Registered on April 9, 2019

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.

Understanding Adolescent and Young Adult 6-Mercaptopurine Adherence and mHealth Engagement During Cancer Treatment: Protocol for Ecological Momentary Assessment

BACKGROUND

Adolescents and young adults (AYAs) with cancer demonstrate suboptimal oral chemotherapy adherence, increasing their risk of cancer relapse. It is unclear how everyday time-varying contextual factors (eg, mood) affect their adherence, stalling the development of personalized mobile health (mHealth) interventions. Poor engagement is also a challenge across mHealth trials; an effective adherence intervention must be engaging to promote uptake.

OBJECTIVE

This protocol aims to determine the temporal associations between daily contextual factors and 6-mercaptopurine (6-MP) adherence and explore the proximal impact of various engagement strategies on ecological momentary assessment survey completion.

METHODS

At the Children's Hospital of Philadelphia, AYAs with acute lymphoblastic leukemia or lymphoma who are prescribed prolonged maintenance chemotherapy that includes daily oral 6-MP are eligible, along with their matched caregivers. Participants will use an ecological momentary assessment app called ADAPTS (Adherence Assessments and Personalized Timely Support)-a version of an open-source app that was modified for AYAs with cancer through a user-centered process-and complete surveys in bursts over 6 months. Theory-informed engagement strategies will be microrandomized to estimate the causal effects on proximal survey completion.

RESULTS

With funding from the National Cancer Institute and institutional review board approval, of the proposed 30 AYA-caregiver dyads, 60% (18/30) have been enrolled; of the 18 enrolled, 15 (83%) have completed the study so far.

CONCLUSIONS

This protocol represents an important first step toward prescreening tailoring variables and engagement components for a just-in-time adaptive intervention designed to promote both 6-MP adherence and mHealth engagement.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/32789.

Physical Activity Maintenance: A Critical Narrative Review and Directions for Future Research

A clear rationale can be made for promoting long-term regular physical activity (PA), yet despite some attempts to operationalize "maintenance," no robust definition has been agreed upon, beyond arbitrary time frames of regular PA. This has likely impaired the advancement of theory and practice. The purpose of this critical narrative review was to first overview the conception of maintenance and co-requisite theoretical constructs in theories used in PA research. Our subsequent aims were to engage in a critical analysis of this literature to propose a working definition of PA maintenance followed by recommendations for future research. Relevant behavioral theories were parsed for references to maintenance or maintenance-specific constructs and constructs most likely associated with maintenance were overviewed from a recent systematic review. Based on this information, we suggest PA maintenance be operationalized as a process marked by a shift in the mechanisms of action determining behavioral performance, that engender greater perceived behavioral enactment efficiency. We suggest that maintenance should not be considered an absolute state of behavioral performance (e.g., a stage), as some constructs that were critical to behavioral performance during initiation will still be critical during PA continuation. Based on this definition, we propose a method of falsifiability hypothesis testing of theoretical constructs that may determine the maintenance process. Finally, the review concludes with suggestions for future research using this operationalization of maintenance including measure development, tests of latency to reach the peak maintenance process, validating constructs critical to determining maintenance, exploration of the contextual and individual moderators of maintenance formation, and the development of an omnibus dynamic model of initiation, continuation, and maintenance in PA behavior change.

An iterative, interdisciplinary, collaborative framework for developing and evaluating digital behavior change interventions

The rapid expansion of technology promises to transform the behavior science field by revolutionizing the ways in which individuals can monitor and improve their health behaviors. To fully live into this promise, the behavior science field must address distinct challenges, including: building interventions that are not only scientifically sound but also engaging; using evaluation methods to precisely assess intervention components for intervention optimization; and building personalized interventions that acknowledge and adapt to the dynamic ecosystem of individual and contextual variables that impact behavior change. The purpose of this paper is to provide a framework to address these challenges by leveraging behavior science, human-centered design, and data science expertise throughout the cycle of developing and evaluating digital behavior change interventions (DBCIs). To define this framework, we reviewed current models and practices for intervention development and evaluation, as well as technology industry models for product development. The framework promotes an iterative process, aiming to maximize outcomes by incorporating faster and more frequent testing cycles into the lifecycle of a DBCI. Within the framework provided, we describe each phase, from development to evaluation, to discuss the optimal practices, necessary stakeholders, and proposed evaluation methods. The proposed framework may inform practices in both academia and industry, as well as highlight the need to offer collaborative platforms to ensure successful partnerships that can lead to more effective DBCIs that reach broad and diverse populations.

Systematic review of context-aware digital behavior change interventions to improve health

Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013-2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users' health behaviors.

Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years

Background

Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions.

Objective

The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume.

Methods

We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy.

Results

Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines.

Conclusions

Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions.

Efficacy of a Just-in-Time Adaptive Intervention to Promote HIV Risk Reduction Behaviors Among Young Adults Experiencing Homelessness: Pilot Randomized Controlled Trial

BACKGROUND

People experiencing homelessness have higher rates of HIV than those who are stably housed. Mental health needs, substance use problems, and issues unique to homelessness such as lack of shelter and transiency need to be considered with regard to HIV prevention. To date, HIV prevention interventions for young adults experiencing homelessness have not specifically addressed modifiable real-time factors such as stress, sexual or drug use urge, or substance use, or been delivered at the time of heightened risk. Real-time, personalized HIV prevention messages may reduce HIV risk behaviors.

OBJECTIVE

This pilot study tested the initial efficacy of an innovative, smartphone-based, just-in-time adaptive intervention that assessed predictors of HIV risk behaviors in real time and automatically provided behavioral feedback and goal attainment information.

METHODS

A randomized attention control design was used among young adults experiencing homelessness, aged 18-25 years, recruited from shelters and drop-in centers in May 2019. Participants were randomized to either a control or an intervention group. The intervention (called MY-RID [Motivating Youth to Reduce Infection and Disconnection]) consisted of brief messages delivered via smartphone over 6 weeks in response to preidentified predictors that were assessed using ecological momentary assessments. Bayesian hierarchical regression models were used to assess intervention effects on sexual activity, drug use, alcohol use, and their corresponding urges.

RESULTS

Participants (N=97) were predominantly youth (mean age 21.2, SD 2.1 years) who identified as heterosexual (n=51, 52%), male (n=56, 57%), and African American (n=56, 57%). Reports of sexual activity, drug use, alcohol use, stress, and all urges (ie, sexual, drug, alcohol) reduced over time in both groups. Daily drug use reduced by a factor of 13.8 times over 6 weeks in the intervention group relative to the control group (Multimedia Appendix 4). Lower urges for sex were found in the intervention group relative to the control group over the duration of the study. Finally, there was a statistically significant reduction in reports of feeling stressed the day before between the intervention and control conditions (P=.03).

CONCLUSIONS

Findings indicate promising intervention effects on drug use, stress, and urges for sex in a hard-to-reach, high-risk population. The MY-RID intervention should be further tested in a larger randomized controlled trial to further investigate its efficacy and impact on sexual risk behaviors.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03911024; https://clinicaltrials.gov/ct2/show/NCT03911024.

How do you feel during the COVID-19 pandemic? A survey using psychological and linguistic self-report measures, and machine learning to investigate mental health, subjective experience, personality, and behaviour during the COVID-19 pandemic among university students

Background

The WHO has raised concerns about the psychological consequences of the current COVID-19 pandemic, negatively affecting health across societies, cultures and age-groups.

Methods

This online survey study investigated mental health, subjective experience, and behaviour (health, learning/teaching) among university students studying in Egypt or Germany shortly after the first pandemic lockdown in May 2020. Psychological assessment included stable personality traits, self-concept and state-like psychological variables related to (a) mental health (depression, anxiety), (b) pandemic threat perception (feelings during the pandemic, perceived difficulties in describing, identifying, expressing emotions), (c) health (e.g., worries about health, bodily symptoms) and behaviour including perceived difficulties in learning. Assessment methods comprised self-report questions, standardized psychological scales, psychological questionnaires, and linguistic self-report measures. Data analysis comprised descriptive analysis of mental health, linguistic analysis of self-concept, personality and feelings, as well as correlational analysis and machine learning. N = 220 (107 women, 112 men, 1 = other) studying in Egypt or Germany provided answers to all psychological questionnaires and survey items.

Results

Mean state and trait anxiety scores were significantly above the cut off scores that distinguish between high versus low anxious subjects. Depressive symptoms were reported by 51.82% of the student sample, the mean score was significantly above the screening cut off score for risk of depression. Worries about health (mental and physical health) and perceived difficulties in identifying feelings, and difficulties in learning behaviour relative to before the pandemic were also significant. No negative self-concept was found in the linguistic descriptions of the participants, whereas linguistic descriptions of feelings during the pandemic revealed a negativity bias in emotion perception. Machine learning (exploratory) predicted personality from the self-report data suggesting relations between personality and subjective experience that were not captured by descriptive or correlative data analytics alone.

Conclusion

Despite small sample sizes, this multimethod survey provides important insight into mental health of university students studying in Egypt or Germany and how they perceived the first COVID-19 pandemic lockdown in May 2020. The results should be continued with larger samples to help develop psychological interventions that support university students across countries and cultures to stay psychologically resilient during the pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40359-021-00574-x.

A Review on Strategies for Data Collection, Reflection, and Communication in Eating Disorder Apps

Eating disorders (EDs) constitute a mental illness with the highest mortality. Today, mobile health apps provide promising means to ED patients for managing their condition. Apps enable users to monitor their eating habits, thoughts, and feelings, and offer analytic insights for behavior change. However, not only have scholars critiqued the clinical validity of these apps, their underlying design principles are not well understood. Through a review of 34 ED apps, we uncovered 11 different data types ED apps collect, and 9 strategies they employ to support collection and reflection. Drawing upon personal health informatics and visualization frameworks, we found that most apps did not adhere to best practices on what and how data should be collected from and reflected to users, or how data-driven insights should be communicated. Our review offers suggestions for improving the design of ED apps such that they can be useful and meaningful in ED recovery.

Theory Integration for Lifestyle Behavior Change in the Digital Age: An Adaptive Decision-Making Framework

Despite the growing popularity of digital health interventions, limitations of traditional behavior change theories and a lack of theory integration hinder theory-driven behavior change applications. In this paper, we aim to review theories relevant to lifestyle behavior change from the broader psychology literature and then integrate these theories into a new theoretical framework called adaptive decision-making to address two specific problems. First, our framework represents lifestyle behaviors at two levels-one of individual daily decisions (action level) and one of larger behavioral episodes (reflection level)-to more closely match the temporal characteristics of lifestyle behaviors and their associated digital data. Second, the framework connects decision-making theories and learning theories to explain how behaviors and cognitive constructs dynamically influence each other, making it a suitable scaffold for building computational models. We map common digital intervention techniques onto the behavioral and cognitive processes in the framework and discuss possible contributions of the framework to both theory development and digital intervention design.

A reinforcement learning based algorithm for personalization of digital, just-in-time, adaptive interventions

Suboptimal health related behaviors and habits; and resulting chronic diseases are responsible for majority of deaths globally. Studies show that providing personalized support to patients yield improved results by preventing and/or timely treatment of these problems. Digital, just-in-time and adaptive interventions are mobile phone-based notifications that are being utilized to support people wherever and whenever necessary in coping with their health problems. In this research, we propose a reinforcement learning-based mechanism to personalize interventions in terms of timing, frequency and preferred type(s). We simultaneously employ two reinforcement learning models, namely intervention-selection and opportune-moment-identification; capturing and exploiting changes in people's long-term and momentary contexts respectively. While the intervention-selection model adapts the intervention delivery with respect to type and frequency, the opportune-moment-identification model tries to find the most opportune moments to deliver interventions throughout a day. We propose two accelerator techniques over the standard reinforcement learning algorithms to boost learning performance. First, we propose a customized version of eligibility traces for rewarding past actions throughout an agent's trajectory. Second, we utilize the transfer learning method to reuse knowledge across multiple learning environments. We validate the proposed approach in a simulated experiment where we simulate four personas differing in their daily activities, preferences on specific intervention types and attitudes towards the targeted behavior. Our experiments show that the proposed approach yields better results compared to the standard reinforcement learning algorithms and successfully capture the simulated variations associated with the personas.

Behavior science in the evolving world of digital health: considerations on anticipated opportunities and challenges

Digital health promises to increase intervention reach and effectiveness for a range of behavioral health outcomes. Behavioral scientists have a unique opportunity to infuse their expertise in all phases of a digital health intervention, from design to implementation. The aim of this study was to assess behavioral scientists' interests and needs with respect to digital health endeavors, as well as gather expert insight into the role of behavioral science in the evolution of digital health. The study used a two-phased approach: (a) a survey of behavioral scientists' current needs and interests with respect to digital health endeavors (n = 346); (b) a series of interviews with digital health stakeholders for their expert insight on the evolution of the health field (n = 15). In terms of current needs and interests, the large majority of surveyed behavioral scientists (77%) already participate in digital health projects, and from those who have not done so yet, the majority (65%) reported intending to do so in the future. In terms of the expected evolution of the digital health field, interviewed stakeholders anticipated a number of changes, from overall landscape changes through evolving models of reimbursement to more significant oversight and regulations. These findings provide a timely insight into behavioral scientists' current needs, barriers, and attitudes toward the use of technology in health care and public health. Results might also highlight the areas where behavioral scientists can leverage their expertise to both enhance digital health's potential to improve health, as well as to prevent the potential unintended consequences that can emerge from scaling the use of technology in health care.

Daily text message assessments of 6-mercaptopurine adherence and its proximal contexts in adolescents and young adults with leukemia: A pilot study

BACKGROUND

This pilot study explored the feasibility and acceptability of implementing text-based assessments of oral chemotherapy adherence in adolescents and young adults (AYA) with leukemia.

METHODS

AYA prescribed maintenance 6-mercaptopurine (6MP) received daily text message surveys and utilized an electronic pill bottle for 28 days. Text surveys assessed 6MP adherence and contextual associates (eg, mood). Feasibility was defined by recruitment/retention rates, survey completion rates, cost, and technical issues. After the 28-day period, AYA completed an acceptability survey. Secondary analyses compared text survey and electronic pill bottle adherence rates, and explored the daily associations between contextual factors and 6MP nonadherence.

RESULTS

Eighteen AYA enrolled (M age = 18, range 15-22) and completed study procedures (100% recruitment and retention rates). Adherence survey completion rates were high (M = 88.9%), the technology cost was $204.00, and there were few technical issues. AYA reported high satisfaction with the surveys and perceived them as a helpful medication reminder. While not significantly correlated, survey and electronic pill bottle adherence data converged on the majority of days (>90%). Exploratory analyses showed that AYA were more likely to miss a dose of 6MP on weekends (OR = 2.33, P = .048) and on days when their adherence motivation (OR = 0.28, P = .047) and negative affect (OR = 3.92, P = .02) worsened from their own typical functioning.

CONCLUSIONS

For AYA with leukemia, daily text-based surveys are a feasible and acceptable method for delivering medication adherence assessments, and may operate as a short-term intervention. To develop personalized mobile health interventions, findings also highlighted the need to study time-varying predictors of 6MP nonadherence.

Paradigm Shift Toward Digital Neuropsychology and High-Dimensional Neuropsychological Assessments: Review

Neuropsychologists in the digital age have increasing access to emerging technologies. The National Institutes of Health (NIH) initiatives for behavioral and social sciences have emphasized these developing scientific and technological potentials (eg, novel sensors) for augmented characterization of neurocognitive, behavioral, affective, and social processes. Perhaps these innovative technologies will lead to a paradigm shift from disintegrated and data-poor behavioral science to cohesive and data-rich science that permits improved translation from bench to bedside. The 4 main advances influencing the scientific priorities of a recent NIH Office of Behavioral and Social Sciences Research strategic plan include the following: integration of neuroscience into behavioral and social sciences, transformational advances in measurement science, digital intervention platforms, and large-scale population cohorts and data integration. This paper reviews these opportunities for novel brain-behavior characterizations. Emphasis is placed on the increasing concern of neuropsychology with these topics and the need for development in these areas to maintain relevance as a scientific discipline and advance scientific developments. Furthermore, the effects of such advancements necessitate discussion and modification of training as well as ethical and legal mandates for neuropsychological research and praxes.

A perspective on the use of ecological momentary assessment and intervention to promote stroke recovery and rehabilitation

Motivated by recent advances in technologies, ecological momentary assessment (EMA), and ecological momentary intervention (EMI) have seen a rise in behavioral medicine research that in real-time, informs the context for the behavior and prompts interventions to change that behavior in the natural setting when necessary. However, EMA and EMI have yet to be fully embraced in the field of stroke rehabilitation. Our objective is to provide a theoretically based perspective for the combined and synergistic use of EMA and EMI to promote person-centered, recovery-based durable changes in functional movement behaviors of stroke survivors. Research abounds for non-stroke populations with emerging evidence for the benefits of using real-time data capture techniques (i.e. EMA) coupled with EMI to better customize the content and timing of interventions to the inherent fluctuations in state and context that encompass the target behavior. We review existing EMA and EMI literature broadly in behavioral medicine and psychological science to identify how real-time repeated sampling technology has been used in the context of stroke rehabilitation and to delineate the pros and cons of this approach in general with non-stroke populations. We propose a coupled EMA and EMI strategy be used in conjunction with existing stroke recovery and rehabilitation practices. There is tremendous potential to effectively personalize recovery-promoting interventions to achieve durable behavior change, and importantly, shift the focus of rehabilitation practice from the health-care provider and clinical environment to the individual and their lived experience in the home and community.

Associations between effort, importance, and self-monitoring during and after a 12-week behavioural weight management program

Objective

Self‐monitoring of weight and caloric intake has been associated with improved weight loss and weight loss maintenance in behavioural weight loss programs; however, participants' adherence to self‐monitoring tends to decrease over time. To identify potential barriers to self‐monitoring adherence, the current study examined week‐to‐week associations between ratings of perceived effort, relative importance of weight loss goals, and adherence to self‐monitoring of weight and caloric intake during and after a behavioural weight loss programme.

Method

Participants were 74 adults with overweight and obesity enrolled in a 12‐week, Internet‐based weight loss programme followed by a 40‐week “maintenance” period during which no additional intervention was provided. Participants self‐reported adherence to self‐monitoring and completed ratings of effort and importance on a study website weekly throughout the study period (1 year).

Results

Longitudinal multilevel models demonstrated that higher ratings of effort were associated with fewer days of self‐monitoring of weight, β = −0.100, p < .0001, and caloric intake, β = −0.300, p < .0001. Conversely, higher ratings of importance were associated with more frequent self‐monitoring of weight, β = 0.360, p < .0001, and caloric intake, β = 0.742, p < .0001. Moreover, the magnitude of these associations were stronger during the maintenance period than during initial intervention, ps < .01.

Conclusions

Perceptions of effort and importance are both independently associated with adherence to self‐monitoring weight and caloric intake, and this effect appears to be stronger after the end of initial intervention. Future research should investigate whether tailoring intervention content based on these constructs can improve adherence to self‐monitoring.

Engineering Person-Specific Behavioral Interventions to Promote Physical Activity

Physical activity is dynamic, complex, and often regulated idiosyncratically. In this article, we review how techniques used in control systems engineering are being applied to refine physical activity theory and interventions. We hypothesize that person-specific adaptive behavioral interventions grounded in system identification and model predictive control will lead to greater physical activity than more generic, conventional intervention approaches.

Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients' dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients' unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.

Using Smartphone Survey Data and Machine Learning to Identify Situational and Contextual Risk Factors for HIV Risk Behavior Among Men Who Have Sex with Men Who Are Not on PrEP

"Just-in-time" interventions (JITs) delivered via smartphones have considerable potential for reducing HIV risk behavior by providing pivotal support at key times prior to sex. However, these programs depend on a thorough understanding of when risk behavior is likely to occur to inform the timing of JITs. It is also critical to understand the most important momentary risk factors that may precede HIV risk behavior, so that interventions can be designed to address them. Applying machine learning (ML) methods to ecological momentary assessment data on HIV risk behaviors can help answer both questions. Eighty HIV-negative men who have sex with men (MSM) who were not on PrEP completed a daily diary survey each morning and an experience sampling survey up to six times per day via a smartphone application for 30 days. Random forest models achieved the highest area under the curve (AUC) values for classifying high-risk condomless anal sex (CAS). These models achieved 80% specificity at a sensitivity value of 74%. Unsurprisingly, the most important contextual risk factors that aided in classification were participants' plans and intentions for sex, sexual arousal, and positive affective states. Findings suggest that survey data collected throughout the day can be used to correctly classify about three of every four high-risk CAS events, while incorrectly classifying one of every five non-CAS days as involving high-risk CAS. A unique set of risk factors also often emerge prior to high-risk CAS events that may be useful targets for JITs.

Microrandomized trials for promoting engagement in mobile health data collection: Adolescent/young adult oral chemotherapy adherence as an example

Long-term engagement with mobile health (mHealth) apps can provide critical data for improving empirical models for real-time health behaviors. To learn how to improve and maintain mHealth engagement, micro-randomized trials (MRTs) can be used to optimize different engagement strategies. In MRTs, participants are sequentially randomized, often hundreds or thousands of times, to different engagement strategies or treatments. The data gathered are then used to decide which treatment is optimal in which context. In this paper, we discuss an example MRT for youth with cancer, where we randomize different engagement strategies to improve self-reports on factors related to medication adherence. MRTs, moreover, can go beyond improving engagement, and we reference other MRTs to address substance abuse, sedentary behavior, and so on.

Sensing eating mimicry among family members

Family relationships influence eating behavior and health outcomes (e.g., obesity). Because eating is often habitual (i.e., automatically driven by external cues), unconscious behavioral mimicry may be a key interpersonal influence mechanism for eating within families. This pilot study extends existing literature on eating mimicry by examining whether multiple family members mimicked each other's bites during natural meals. Thirty-three participants from 10 families were videotaped while eating an unstructured family meal in a kitchen lab setting. Videotapes were coded for participants' bite occurrences and times. We tested whether the likelihood of a participant taking a bite increased when s/he was externally cued by a family eating partner who had recently taken a bite (i.e., bite mimicry). A paired-sample t-test indicated that participants had a significantly faster eating rate within the 5 s following a bite by their eating partner, compared to their bite rate at other times (t = 7.32, p < .0001). Nonparametric permutation testing identified five of 78 dyads in which there was significant evidence of eating mimicry; and 19 of 78 dyads that had p values < .1. This pilot study provides preliminary evidence that suggests eating mimicry may occur among a subset of family members, and that there may be types of family ties more prone to this type of interpersonal influence during meals.

Translational Health Disparities Research in a Data-Rich World

Background: Despite decades of research and interventions, significant health disparities persist. Seventeen years is the estimated time to translate scientific discoveries into public health action. This Narrative Review argues that the translation process could be accelerated if representative data were gathered and used in more innovative and efficient ways. Methods: The National Institute on Minority Health and Health Disparities led a multiyear visioning process to identify research opportunities designed to frame the next decade of research and actions to improve minority health and reduce health disparities. "Big data" was identified as a research opportunity and experts collaborated on a systematic vision of how to use big data both to improve the granularity of information for place-based study and to efficiently translate health disparities research into improved population health. This Narrative Review is the result of that collaboration. Results: Big data could enhance the process of translating scientific findings into reduced health disparities by contributing information at fine spatial and temporal scales suited to interventions. In addition, big data could fill pressing needs for health care system, genomic, and social determinant data to understand mechanisms. Finally, big data could lead to appropriately personalized health care for demographic groups. Rich new resources, including social media, electronic health records, sensor information from digital devices, and crowd-sourced and citizen-collected data, have the potential to complement more traditional data from health surveys, administrative data, and investigator-initiated registries or cohorts. This Narrative Review argues for a renewed focus on translational research cycles to accomplish this continual assessment. Conclusion: The promise of big data extends from etiology research to the evaluation of large-scale interventions and offers the opportunity to accelerate translation of health disparities studies. This data-rich world for health disparities research, however, will require continual assessment for efficacy, ethical rigor, and potential algorithmic or system bias.

Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support

Background

The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual's changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual's state can change rapidly, unexpectedly, and in his/her natural environment.

Purpose

Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap.

Methods

Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration.

Conclusions

As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.

Week-to-week predictors of weight loss and regain

OBJECTIVES

Despite increased interest in the development of individually tailored weight management programs, little is known about what factors proximally predict weight change.

METHOD

The current study investigated proximal (week-to-week) predictors of weight loss and regain in 74 adults during a 3-month, Internet-based behavioral weight loss program followed by a 9-month "maintenance" period (during which no additional intervention was provided). Participants were asked to self-weigh daily using scales that transmitted weight via the cellular network and to answer a brief questionnaire each week querying mood, behaviors, and cognitions hypothesized to be associated with weight loss and regain.

RESULTS

Longitudinal multilevel models demonstrated that weight loss during initial intervention was proximally predicted by (a) greater frequency of self-monitoring weight and caloric intake, consistency between eating choices and weight loss goals, and importance of "staying on track" with these goals and (b) less negative mood, boredom with weight control efforts, hunger, and temptation to eat foods "not on plan" (ps < .05). Greater weight regain after intervention was also proximally predicted by these factors (with effects in the opposite direction) and additionally by less physical activity, less positive mood, more stress, greater temptation to skip planned physical activity, and higher ratings of the amount of effort required to stay on track (ps < .05).

CONCLUSIONS

Results confirmed the importance of self-monitoring for weight loss and maintenance and identified other key week-to-week predictors of weight change. Results also supported efforts to develop intervention approaches specifically focused on weight loss maintenance. Future research should investigate whether using identified predictors to tailor intervention content and timing can improve weight outcomes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study

Background

This study examines the development of the connected health (CH) research landscape with a view to providing an overview of the existing CH research. The research field of CH has experienced rapid growth coinciding with increasing pressure on health care systems to become more proactive and patient centered.

Objective

This study aimed to assess the extent and coverage of the current body of knowledge in CH. In doing so, we sought to identify specific topics that have drawn the attention of CH researchers and to identify research gaps, in particular those offering opportunities for further interdisciplinary research.

Methods

A systematic mapping study that combined scientific contributions from research in the disciplines of medicine, business, computer science, and engineering was used. Overall, seven classification criteria were used to analyze the papers, including publication source, publication year, research type, empirical type, contribution type, research topic, and the medical condition studied.

Results

The search resulted in 208 papers that were analyzed by a multidisciplinary group of researchers. The results indicated a slow start for CH research but showed a more recent steady upswing since 2013. The majority of papers proposed health care solutions (77/208, 37.0%) or evaluated CH approaches (49/208, 23.5%). Case studies (59/208, 28.3%) and experiments (55/208, 26.4%) were the most popular forms of scientific validation used. Diabetes, cancer, multiple sclerosis, and heart conditions were among the most prevalent medical conditions studied.

Conclusions

We conclude that CH research has become an established field of research that has grown over the last five years. The results of this study indicate a focus on technology-driven research with a strong contribution from medicine, whereas the business aspects of CH have received less research attention.

Potential benefits of integrating ecological momentary assessment data into mHealth care systems

The advancement of wearable/ambulatory technologies has brought a huge change to data collection frameworks in recent decades. Mobile health (mHealth) care platforms, which utilize ambulatory devices to collect naturalistic and often intensively sampled data, produce innovative information of potential clinical relevance. For example, such data can inform clinical study design, recruitment approach, data analysis, and delivery of both "traditional" and novel (e.g., mHealth) interventions. We provide a conceptual overview of how data measured continuously or repeatedly via mobile devices (e.g., smartphone and body sensors) in daily life could be fruitfully used within a mHealth care system. We highlight the potential benefits of integrating ecological momentary assessment (EMA) into mHealth platforms for collecting, processing, and modeling data, and delivering and evaluating novel interventions in everyday life. Although the data obtained from EMA and related approaches may hold great potential benefits for mHealth care system, there are also implementation challenges; we briefly discuss the challenges to integrating EMA into mHealth care system.

Using Smartphones to Improve Treatment Retention Among Impoverished Substance-Using Appalachian Women: A Naturalistic Study

Objectives:

Longer retention in treatment is associated with positive outcomes. For women, who suffer worse drug-related problems than men, social technologies, which are more readily adopted by women, may offer promise. This naturalistic study examined whether a smartphone-based relapse-prevention system, A-CHESS (Addiction-Comprehensive Health Enhancement Support System), could improve retention for women with substance use disorders in an impoverished rural setting.

Methods:

A total of 98 women, age 18 to 40, in southeastern Kentucky and mandated to treatment, received A-CHESS with intensive outpatient treatment for 6 months. For comparison, data were obtained for a similar but non-equivalent group of 100 same-age women also mandated to treatment in the same clinics during the period. Electronic medical record data on length-of-stay and treatment service use for both groups were analyzed, with A-CHESS use data, to determine whether those using A-CHESS showed better retention than those without.

Results:

Women with A-CHESS averaged 780 service units compared with 343 for the comparison group. For those with discharge dates prior to the study’s end, A-CHESS patients stayed in treatment a mean of 410 vs 262 days for the comparison group.

Conclusions:

Given associations between retention and positive outcomes, mobile health technology such as A-CHESS may help improve outcomes among women, especially in settings where access to in-person services is difficult. The findings, based on a non-equivalent comparison, suggest the need for further exploration with rigorous experimental designs to determine whether and to what degree access to a smartphone with A-CHESS may extend and support recovery for women.

Clinical Trial Recruitment and Retention of College Students with Type 1 Diabetes via Social Media: An Implementation Case Study

BACKGROUND

We sought to quantify the efficiency and acceptability of Internet-based recruitment for engaging an especially hard-to-reach cohort (college-students with type 1 diabetes, T1D) and to describe the approach used for implementing a health-related trial entirely online using off-the-shelf tools inclusive of participant safety and validity concerns.

METHOD

We recruited youth (ages 17-25 years) with T1D via a variety of social media platforms and other outreach channels. We quantified response rate and participant characteristics across channels with engagement metrics tracked via Google Analytics and participant survey data. We developed decision rules to identify invalid (duplicative/false) records (N = 89) and compared them to valid cases (N = 138).

RESULTS

Facebook was the highest yield recruitment source; demographics differed by platform. Invalid records were prevalent; invalid records were more likely to be recruited from Twitter or Instagram and differed from valid cases across most demographics. Valid cases closely resembled characteristics obtained from Google Analytics and from prior data on platform user-base. Retention was high, with complete follow-up for 88.4%. There were no safety concerns and participants reported high acceptability for future recruitment via social media.

CONCLUSIONS

We demonstrate that recruitment of college students with T1D into a longitudinal intervention trial via social media is feasible, efficient, acceptable, and yields a sample representative of the user-base from which they were drawn. Given observed differences in characteristics across recruitment channels, recruiting across multiple platforms is recommended to optimize sample diversity. Trial implementation, engagement tracking, and retention are feasible with off-the-shelf tools using preexisting platforms.

It's Time to Think about Time in Health Psychology

This position paper addresses the need for stronger theorizing on temporal matters in health psychology. Too few theories in health psychology address temporal matters in the specified psychological phenomena, their relationships, and the explanation for these relationships. This might lead to underestimating existing relationships between constructs over time or missing out on opportunities for advancing our knowledge with regard to dynamic developments of the phenomena of interest. Examples of existing theories and approaches addressing temporal matters in health psychology are provided. Further, drawing from other disciplines, examples of methodological and conceptual approaches are outlined that offer directions on how to optimize our theories with regard to temporal matters. This might start with including time-related theoretical considerations to the definition of psychological concepts already in cross-sectional research and might reach as far as developing dynamic theories for capturing and changing phenomena of interest in real life. Challenges and opportunities of theorizing about temporal matters in health psychology are addressed.

Improving Physical Activity mHealth Interventions: Development of a Computational Model of Self-Efficacy Theory to Define Adaptive Goals for Exercise Promotion

The practice of regular physical exercise is a protective factor against noncommunicable diseases and premature mortality. In spite of that, large part of the population does not meet physical activity guidelines and many individuals live a sedentary life. Recent technological progresses and the widespread adoption of mobile technology, such as smartphone and wearables, have opened the way to the development of digital behaviour change interventions targeting physical activity promotion. Such interventions would greatly benefit from the inclusion of computational models framed on behaviour change theories and model-based reasoning. However, research on these topics is still at its infancy. The current paper presents a smartphone application and wearable device system called Muoviti! that targets physical activity promotion among adults not meeting the recommended physical activity guidelines. Specifically, we propose a computational model of behaviour change, grounded on the social cognitive theory of self-efficacy. The purpose of the computational model is to dynamically integrate information referring to individuals’ self-efficacy beliefs and physical activity behaviour in order to define exercising goals that adapt to individuals’ changes over time. The paper presents (i) the theoretical constructs that informed the development of the computational model, (ii) an overview of Muoviti! describing the system dynamics, the graphical user interface, the adopted measures and the intervention design, and (iii) the computational model based on Dynamic Decision Network. We conclude by presenting early results from an experimental study.

Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods

OBJECTIVES

The conceptual models underlying physical activity interventions have been based largely on differences between more and less active people. Yet physical activity is a dynamic behavior, and such models are not sensitive to factors that regulate behavior at a momentary level or how people respond to individual attempts at intervening. We demonstrate how a control systems engineering approach can be applied to develop personalized models of behavioral responses to an intensive text message-based intervention.

DESIGN & METHOD

To establish proof-of-concept for this approach, 10 adults wore activity monitors for 16 weeks and received five text messages daily at random times. Message content was randomly selected from three types of messages designed to target (1) social-cognitive processes associated with increasing physical activity, (2) social-cognitive processes associated with reducing sedentary behavior, or (3) general facts unrelated to either physical activity or sedentary behavior. A dynamical systems model was estimated for each participant to examine the magnitude and timing of responses to each type of text message.

RESULTS

Models revealed heterogeneous responses to different message types that varied between people and between weekdays and weekends.

CONCLUSIONS

This proof-of-concept demonstration suggests that parameters from this model can be used to develop personalized algorithms for intervention delivery. More generally, these results demonstrate the potential utility of control systems engineering models for optimizing physical activity interventions.

National Institutes of Health initiatives for advancing scientific developments in clinical neuropsychology

OBJECTIVE

The current review briefly addresses the history of neuropsychology as a context for discussion of developmental milestones that have advanced the profession, as well as areas where the progression has lagged. More recently in the digital/information age, utilization and incorporation of emerging technologies has been minimal, which has stagnated ongoing evolution of the practice of neuropsychology despite technology changing many aspects of daily living. These authors advocate for embracing National Institutes of Health (NIH) initiatives, or interchangeably referred to as transformative opportunities, for the behavioral and social sciences. These initiatives address the need for neuropsychologists to transition from fragmented and data-poor approaches to integrated and data-rich scientific approaches that ultimately improve translational applications. Specific to neuropsychology is the need for the adoption of novel means of brain-behavior characterizations.

METHOD

Narrative review Conclusions: Clinical neuropsychology has reached a developmental plateau where it is ready to embrace the measurement science and technological advances which have been readily adopted by the human neurosciences. While there are ways in which neuropsychology is making inroads into these areas, a great deal of growth is needed to maintain relevance as a scientific discipline (see Figures 1, 2, and 3) consistent with NIH initiatives to advance scientific developments. Moreover, implications of such progress require discussion and modification of training, ethical, and legal mandates of the practice of neuropsychology.