Evaluating the Mental Health of Physician-Trainees Using an SMS Text Message-Based Assessment Tool: Longitudinal Pilot Study.

Background: Physician burnout is a multibillion-dollar issue in the United States. Despite its prevalence, burnout is difficult to accurately measure. Institutions generally rely on periodic surveys that are subject to recall bias.

Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis.

Background: Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge.

Objective: The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits.

Methods: We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps.

Lessons Learned: Beta-Testing the Digital Health Checklist for Researchers Prompts a Call to Action by Behavioral Scientists.

Digital technologies offer unique opportunities for health research. For example, Twitter posts can support public health surveillance to identify outbreaks (eg, influenza and COVID-19), and a wearable fitness tracker can provide real-time data collection to assess the effectiveness of a behavior change intervention. With these opportunities, it is necessary to consider the potential risks and benefits to research participants when using digital tools or strategies.

Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: a systematic review.

Background And Aims: Lapse risk when trying to stop or reduce harmful substance use is idiosyncratic, dynamic and multi-factorial. Just-in-time adaptive interventions (JITAIs) aim to deliver tailored support at moments of need or opportunity. We aimed to synthesize evidence on decision points, tailoring variables, intervention options, decision rules, study designs, user engagement and effectiveness of technology-mediated JITAIs for reducing harmful substance use.

Development of a Control-Oriented Model of Social Cognitive Theory for Optimized mHealth Behavioral Interventions.

Mobile health (mHealth) technologies are contributing to the increasing relevance of control engineering principles in understanding and improving health behaviors, such as physical activity. Social Cognitive Theory (SCT), one of the most influential theories of health behavior, has been used as the conceptual basis for behavioral interventions for smoking cessation, weight management, and other health-related outcomes. This paper presents a control-oriented dynamical systems model of SCT based on fluid analogies that can be used in system identification and control design problems relevant to the design and analysis of .

Evaluation of intervention components to maximize outcomes of behavioral obesity treatment delivered online: A factorial experiment following the multiphase optimization strategy framework.

Background: Behavioral lifestyle intervention (BLI) is recommended as a first-line treatment for obesity. While BLI has been adapted for online delivery to improve potential for dissemination while reducing costs and barriers to access, weight losses are typically inferior to gold standard treatment delivered in-person. It is therefore important to refine and optimize online BLI in order to improve the proportion of individuals who achieve a minimum clinically significant weight loss and mean weight loss.

Citizen science to further precision medicine: from vision to implementation.

The active involvement of citizen scientists in setting research agendas, partnering with academic investigators to conduct research, analyzing and disseminating results, and implementing learnings from research can improve both processes and outcomes. Adopting a citizen science approach to the practice of precision medicine in clinical care and research will require healthcare providers, researchers, and institutions to address a number of technical, organizational, and citizen scientist collaboration issues. Some changes can be made with relative ease, while others will necessitate cultural shifts, redistribution of power, recommitment to shared goals, and improved communication.

Innovative methods for observing and changing complex health behaviors: four propositions.

Precision health initiatives aim to progressively move from traditional, group-level approaches to health diagnostics and treatments toward ones that are individualized, contextualized, and timely. This article aims to provide an overview of key methods and approaches that can help facilitate this transition in the health behavior change domain. This article is a narrative review of the methods used to observe and change complex health behaviors.

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.

Modelling multiple health behavior change with network analyses: results from a one-year study conducted among overweight and obese adults.

This study examined the between-person associations of seven health behaviors in adults with obesity participating in a weight loss intervention, as well as the covariations between these behaviors within-individuals across the intervention. The present study included data from a 12-month weight loss trial (N = 278). Seven health behaviors (physical activity, sedentary behavior, sleep duration, and consumption of fruits, vegetables, total fat and added sugar) were measured at baseline, 6- and 12-months.

In search of a daily physical activity "sweet spot": Piloting a digital tracking intervention for people with multiple sclerosis.

Objective: This pilot study tested a course-based intervention to help people with multiple sclerosis (MS) match their daily activity to symptom severity ("sweet spot") using wearable activity trackers.

Methods: This two-phase study recruited online research network members reporting MS and who were utilizing Fitbit One™ activity trackers. In the first phase, participant interviews assessed demand based on physical activity and the use of behavior-change techniques.

Why we need a small data paradigm.

Background: There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various 'big data' efforts. While these methods are necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary 'small data' paradigm that can function both autonomously from and in collaboration with big data is also needed.

The history and future of digital health in the field of behavioral medicine.

Since its earliest days, the field of behavioral medicine has leveraged technology to increase the reach and effectiveness of its interventions. Here, we highlight key areas of opportunity and recommend next steps to further advance intervention development, evaluation, and commercialization with a focus on three technologies: mobile applications (apps), social media, and wearable devices. Ultimately, we argue that future of digital health behavioral science research lies in finding ways to advance more robust academic-industry partnerships.

Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps.

Background: HeartSteps is an mHealth intervention that encourages regular walking via activity suggestions tailored to the individuals' current context.

Purpose: We conducted a micro-randomized trial (MRT) to evaluate the efficacy of HeartSteps' activity suggestions to optimize the intervention.

Methods: We conducted a 6-week MRT with 44 adults.

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.

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.

Fresh Fruit and Vegetable Program and Requests for Fruits and Vegetables Outside School Settings.

Background: Consumption of fruits and vegetables (F/V) among elementary school-aged children remains inadequate, especially among low-income children. The US Department of Agriculture's Fresh Fruit and Vegetable Program (FFVP) provides F/V as snacks to children during the school day, outside of school meals. School-based initiatives are successful in changing behaviors in school settings; however, their influence on behaviors outside of schools needs investigation.

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.

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.

Development of a dynamic computational model of social cognitive theory.

Social cognitive theory (SCT) is among the most influential theories of behavior change and has been used as the conceptual basis of health behavior interventions for smoking cessation, weight management, and other health behaviors. SCT and other behavior theories were developed primarily to explain differences between individuals, but explanatory theories of within-person behavioral variability are increasingly needed as new technologies allow for intensive longitudinal measures and interventions adapted from these inputs. These within-person explanatory theoretical applications can be modeled as dynamical systems.

Combining Motivational and Physical Intervention Components to Promote Fall-Reducing Physical Activity Among Community-Dwelling Older Adults: A Feasibility Study.

Purpose: To assess the feasibility of a new intervention, Ready~Steady, in terms of demand, acceptability, implementation, and limited efficacy.

Design: Randomized controlled trial; repeated measures.

Setting: Two rural communities in Itasca County, Minnesota.