Multi-Method Formative Evaluation of a Digital Online Grocery Shopping Assistant Among Special Supplemental Nutrition Program for Women, Infants, and Children Participants.

OBJECTIVE: Assess the acceptability of a digital grocery shopping assistant among rural women with low income. DESIGN: Simulated shopping experience, semistructured interviews, and a choice experiment. SETTING: Rural central North Carolina Special Supplemental Nutrition Program for Women, Infants, and Children clinic. PARTICIPANTS: Thirty adults (aged ≥18 years) recruited from a Special Supplemental Nutrition Program for Women, Infants, and Children clinic. PHENOMENON OF INTEREST: A simulated grocery shopping experience with the Retail Online Shopping Assistant (ROSA) and mixed-methods feedback on the experience. ANALYSIS: Deductive and inductive qualitative content analysis to independently code and identify themes and patterns among interview responses and quantitative analysis of simulated shopping experience and choice experiment. RESULTS: Most participants liked ROSA (28/30, 93%) and found it helpful and likely to change their purchase across various food categories and at checkout. Retail Online Shopping Assistant's reminders and suggestions could reduce less healthy shopping habits and diversify food options. Participants desired dynamic suggestions and help with various health conditions. Participants preferred a racially inclusive, approachable, cartoon-like, and clinically dressed character. CONCLUSIONS AND IMPLICATIONS: This formative study suggests ROSA could be a beneficial tool for facilitating healthy online grocery shopping among rural shoppers. Future research should investigate the impact of ROSA on dietary behaviors further.

An Adaptive Behavioral Intervention for Weight Loss Management: A Randomized Clinical Trial.

Importance: Lifestyle interventions for weight loss are difficult to implement in clinical practice. Self-managed mobile health implementations without or with added support after unsuccessful weight loss attempts could offer effective population-level obesity management. Objective: To test whether a wireless feedback system (WFS) yields noninferior weight loss vs WFS plus telephone coaching and whether participants who do not respond to initial treatment achieve greater weight loss with more vs less vigorous step-up interventions. Design, Setting, and Participants: In this noninferiority randomized trial, 400 adults aged 18 to 60 years with a body mass index of 27 to 45 were randomized in a 1:1 ratio to undergo 3 months of treatment initially with WFS or WFS plus coaching at a US academic medical center between June 2017 and March 2021. Participants attaining suboptimal weight loss were rerandomized to undergo modest or vigorous step-up intervention. Interventions: The WFS included a Wi-Fi activity tracker and scale transmitting data to a smartphone app to provide daily feedback on progress in lifestyle change and weight loss, and WFS plus coaching added 12 weekly 10- to 15-minute supportive coaching calls delivered by bachelor's degree-level health promotionists viewing participants' self-monitoring data on a dashboard; step-up interventions included supportive messaging via mobile device screen notifications (app-based screen alerts) without or with coaching or powdered meal replacement. Participants and staff were unblinded and outcome assessors were blinded to treatment randomization. Main Outcomes and Measures: The primary outcome was the between-group difference in 6-month weight change, with the noninferiority margin defined as a difference in weight change of -2.5 kg; secondary outcomes included between-group differences for all participants in weight change at 3 and 12 months and between-group 6-month weight change difference among nonresponders exposed to modest vs vigorous step-up interventions. Results: Among 400 participants (mean [SD] age, 40.5 [11.2] years; 305 [76.3%] women; 81 participants were Black and 266 were White; mean [SD] body mass index, 34.4 [4.3]) randomized to undergo WFS (n = 199) vs WFS plus coaching (n = 201), outcome data were available for 342 participants (85.5%) at 6 months. Six-month weight loss was -2.8 kg (95% CI, -3.5 to -2.0) for the WFS group and -4.8 kg (95% CI, -5.5 to -4.1) for participants in the WFS plus coaching group (difference in weight change, -2.0 kg [90% CI, -2.9 to -1.1]; P < .001); the 90% CI included the noninferiority margin of -2.5 kg. Weight change differences were comparable at 3 and 12 months and, among nonresponders, at 6 months, with no difference by step-up therapy. Conclusions and Relevance: A wireless feedback system (Wi-Fi activity tracker and scale with smartphone app to provide daily feedback) was not noninferior to the same system with added coaching. Continued efforts are needed to identify strategies for weight loss management and to accurately select interventions for different individuals to achieve weight loss goals. Trial Registration: ClinicalTrials.gov Identifier: NCT02997943.

Design of experiments with sequential randomizations on multiple timescales: the hybrid experimental design.

Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (e.g., coaching sessions adapted monthly based on clinical progress, combined with motivational messages from a mobile device adapted daily based on the person's daily emotional state). The hybrid experimental design (HED) is a new experimental approach that enables researchers to answer scientific questions about the construction of psychological interventions in which components are delivered and adapted on different timescales. These designs involve sequential randomizations of study participants to intervention components, each at an appropriate timescale (e.g., monthly randomization to different intensities of coaching sessions and daily randomization to different forms of motivational messages). The goal of the current manuscript is twofold. The first is to highlight the flexibility of the HED by conceptualizing this experimental approach as a special form of a factorial design in which different factors are introduced at multiple timescales. We also discuss how the structure of the HED can vary depending on the scientific question(s) motivating the study. The second goal is to explain how data from various types of HEDs can be analyzed to answer a variety of scientific questions about the development of multicomponent psychological interventions. For illustration, we use a completed HED to inform the development of a technology-based weight loss intervention that integrates components that are delivered and adapted on multiple timescales.

An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study.

BACKGROUND: Predicting the likelihood of success of weight loss interventions using machine learning (ML) models may enhance intervention effectiveness by enabling timely and dynamic modification of intervention components for nonresponders to treatment. However, a lack of understanding and trust in these ML models impacts adoption among weight management experts. Recent advances in the field of explainable artificial intelligence enable the interpretation of ML models, yet it is unknown whether they enhance model understanding, trust, and adoption among weight management experts. OBJECTIVE: This study aimed to build and evaluate an ML model that can predict 6-month weight loss success (ie, ≥7% weight loss) from 5 engagement and diet-related features collected over the initial 2 weeks of an intervention, to assess whether providing ML-based explanations increases weight management experts' agreement with ML model predictions, and to inform factors that influence the understanding and trust of ML models to advance explainability in early prediction of weight loss among weight management experts. METHODS: We trained an ML model using the random forest (RF) algorithm and data from a 6-month weight loss intervention (N=419). We leveraged findings from existing explainability metrics to develop Prime Implicant Maintenance of Outcome (PRIMO), an interactive tool to understand predictions made by the RF model. We asked 14 weight management experts to predict hypothetical participants' weight loss success before and after using PRIMO. We compared PRIMO with 2 other explainability methods, one based on feature ranking and the other based on conditional probability. We used generalized linear mixed-effects models to evaluate participants' agreement with ML predictions and conducted likelihood ratio tests to examine the relationship between explainability methods and outcomes for nested models. We conducted guided interviews and thematic analysis to study the impact of our tool on experts' understanding and trust in the model. RESULTS: Our RF model had 81% accuracy in the early prediction of weight loss success. Weight management experts were significantly more likely to agree with the model when using PRIMO (χ2=7.9; P=.02) compared with the other 2 methods with odds ratios of 2.52 (95% CI 0.91-7.69) and 3.95 (95% CI 1.50-11.76). From our study, we inferred that our software not only influenced experts' understanding and trust but also impacted decision-making. Several themes were identified through interviews: preference for multiple explanation types, need to visualize uncertainty in explanations provided by PRIMO, and need for model performance metrics on similar participant test instances. CONCLUSIONS: Our results show the potential for weight management experts to agree with the ML-based early prediction of success in weight loss treatment programs, enabling timely and dynamic modification of intervention components to enhance intervention effectiveness. Our findings provide methods for advancing the understandability and trust of ML models among weight management experts.

Optimizing adaptive stepped-care interventions to change adults' health behaviors: A systematic review.

Chronic diseases are ubiquitous and costly in American populations. Interventions targeting health behavior change to manage chronic diseases are needed, but previous efforts have fallen short of producing meaningful change on average. Adaptive stepped-care interventions, that tailor treatment based on the needs of the individual over time, are a promising new area in health behavior change. We therefore conducted a systematic review of tests of adaptive stepped-care interventions targeting health behavior changes for adults with chronic diseases. We identified 9 completed studies and 13 research protocols testing adaptive stepped-care interventions for health behavior change. The most common health behaviors targeted were substance use, weight management, and smoking cessation. All identified studies test intermediary tailoring for treatment non-responders via sequential multiple assignment randomized trials (SMARTs) or singly randomized trials (SRTs); none test baseline tailoring. From completed studies, there were few differences between embedded adaptive interventions and minimal differences between those classified as treatment responders and non-responders. In conclusion, updates to this work will be needed as protocols identified here publish results. Future research could explore baseline tailoring variables, apply methods to additional health behaviors and target populations, test tapering interventions for treatment responders, and consider adults' context when adapting interventions.

Guidance on selecting a translational framework for intervention development: Optimizing interventions for impact.

Intervention development frameworks offer the behavioral sciences a systematic and rigorous empirical process to guide the translation of basic science into practice in pursuit of desirable public health and clinical outcomes. The multiple frameworks that have emerged share a goal of optimization during intervention development and can increase the likelihood of arriving at an effective and disseminable intervention. Yet, the process of optimizing an intervention differs functionally and conceptually across frameworks, creating confusion and conflicting guidance on when and how to optimize. This paper seeks to facilitate the use of translational intervention development frameworks by providing a blueprint for selecting and using a framework by considering the process of optimization as conceptualized by each. First, we operationalize optimization and contextualize its role in intervention development. Next, we provide brief overviews of three translational intervention development frameworks (ORBIT, MRC, and MOST), identifying areas of overlap and divergence thereby aligning core concepts across the frameworks to improve translation. We offer considerations and concrete use cases for investigators seeking to identify and use a framework in their intervention development research. We push forward an agenda of a norm to use and specify frameworks in behavioral science to support a more rapid translational pipeline.

Validation and results of a novel survey assessing decisional balance for a whole food plant-based diet among US adults.

Importance: Consuming a whole food plant-based diet (WFPBD) is a promising, low-risk strategy for reducing risk of prevalent chronic disease and certain cancers, with synergistic benefits for climate and environment. However, few US adults report consuming a WFPBD. Understanding the reasons for this inconsistency is important for developing and implementing interventions for promoting a WFPBD. However, no research to elucidate decisional balance driving current consumption patterns in the US exists. Objective: This research aims to validate an online survey to assess decisional balance for the consumption of a WFPBD, describe attitudes and beliefs toward adopting a WFPBD, and evaluate socio-demographic differences in decisional balance for consuming a WFPBD among a convenience sample of US adults. Design: Online cross-sectional data collection followed by confirmatory factor analysis (CFA), validation of internal consistency, and examination of invariance across socio-demographic variables. Sensitivity analysis of full vs. truncated survey to predict self-reported dietary patterns and consumption behaviors were evaluated. Results of the survey and significant differences by socio-demographics were assessed. Setting: Online survey based on previous research, created via Qualtrics, and administered through MTurk. Participants: A total of 412 US adults, majority female (66%), White (75%), 30-60 years old (54%), ≥ Bachelor's degree (85%), and earning ≥ $45K (68%). Main outcomes and measures: Factor loadings, covariance of survey items, associations with self-reported dietary pattern and consumption measures, and differences in pros, cons, and decisional balance across socio-demographic variables. Results: CFA reduced the survey from 49 to 12 items and demonstrated invariance across socio-demographic variables. Pros and cons varied inversely and significantly (cov = -0.59), as expected. Cronbach's α 's for subscales in the final, reduced model were high (>0.80). Pros, cons, and decisional balance in both the full and the reduced model were significantly (p < 0.05) associated with self-reported dietary pattern and consumption. Conclusion and relevance: Our analyses indicate the WFPBD Survey is a parsimonious and psychometrically sound instrument for evaluation of decisional balance to consume a WFPBD diet among our sample of US adults. These results may be instrumental for development and deployment of interventions intended to promote consumption of a WFPBD in the US.

Mobile Health Tobacco Cessation Interventions to Promote Health Equity: Current Perspectives.

Although US tobacco use trends show overall improvement, social disadvantage continues to drive significant disparities. Traditional tobacco cessation interventions and public policy initiatives have failed to equitably benefit socially-disadvantaged populations. Advancements in mobile digital technologies have created new opportunities to develop resource-efficient mobile health (mHealth) interventions that, relative to traditional approaches, have greater reach while still maintaining comparable or greater efficacy. Their potential for affordability, scalability, and efficiency gives mHealth tobacco cessation interventions potential as tools to help redress tobacco use disparities. We discuss our perspectives on the state of the science surrounding mHealth tobacco cessation interventions for use by socially-disadvantaged populations. In doing so, we outline existing models of health disparities and social determinants of health (SDOH) and discuss potential ways that mHealth interventions might be optimized to offset or address the impact of social determinants of tobacco use. Because smokers from socially-disadvantaged backgrounds face multi-level barriers that can dynamically heighten the risks of tobacco use, we discuss cutting-edge mHealth interventions that adapt dynamically based on context. We also consider complications and pitfalls that could emerge when designing, evaluating, and implementing mHealth tobacco cessation interventions for socially-disadvantaged populations. Altogether, this perspective article provides a conceptual foundation for optimizing mHealth tobacco cessation interventions for the socially-disadvantaged populations in greatest need.

Fostering interdisciplinary collaboration: A longitudinal social network analysis of the NIH mHealth Training Institutes.

BACKGROUND/OBJECTIVE: Growing recognition that collaboration among scientists from diverse disciplines fosters the emergence of solutions to complex scientific problems has spurred initiatives to train researchers to collaborate in interdisciplinary teams. Evaluations of collaboration patterns in these initiatives have tended to be cross-sectional, rather than clarifying temporal changes in collaborative dynamics. Mobile health (mHealth), the science of using mobile, wireless devices to improve health outcomes, is a field whose advancement needs interdisciplinary collaboration. The NIH-supported annual mHealth Training Institute (mHTI) was developed to meet that need and provides a unique testbed. METHODS: In this study, we applied a longitudinal social network analysis technique to evaluate how well the program fostered communication among the disciplinarily diverse scholars participating in the 2017-2019 mHTIs. By applying separable temporal exponential random graph models, we investigated the formation and persistence of project-based and fun conversations during the mHTIs. RESULTS: We found that conversations between scholars of different disciplines were just as likely as conversations within disciplines to form or persist in the 2018 and 2019 mHTI, suggesting that the mHTI achieved its goal of fostering interdisciplinary conversations and could be a model for other team science initiatives; this finding is also true for scholars from different career stages. The presence of team and gender homophily effects in certain years suggested that scholars tended to communicate within the same team or gender. CONCLUSION: Our results demonstrate the usefulness of longitudinal network models in evaluating team science initiatives while clarifying the processes driving interdisciplinary communications during the mHTIs.

A mHealth intervention to preserve and promote ideal cardiovascular health in college students: Design and protocol of a cluster randomized controlled trial.

BACKGROUND: Cardiovascular disease (CVD) remains the leading cause of death globally. Seven health factors are associated with ideal cardiovascular health: being a non-smoker; not overweight; physically active; having a healthy diet; and normal blood pressure; fasting plasma glucose and cholesterol. Whereas approximately half of U.S. youth have ideal levels in at least 5 of the 7 components of cardiovascular health, this proportion falls to 16% by adulthood. OBJECTIVE: We will evaluate whether the NUYou cardiovascular mHealth intervention is more effective than an active comparator to promote cardiovascular health during the transition to young adulthood. METHODS: 302 incoming freshmen at a midwest university will be cluster randomized by dormitory into one of two mHealth intervention groups: 1) Cardiovascular Health (CVH), addressing behaviors related to CVD risk; or 2) Whole Health (WH), addressing behaviors unrelated to CVD. Both groups will receive smartphone applications, co-designed with students to help them manage time, interact with other participants via social media, and report health behaviors weekly. The CVH group will also have self-monitoring features to track their risk behaviors. Cardiovascular health will be assessed at the beginning of freshman year and the end of freshman and sophomore years. Linear mixed models will be used to compare groups on a composite of the seven cardiovascular-related health factors. SIGNIFICANCE: This is the first entirely technology-mediated multiple health behavior change intervention delivered to college students to promote cardiovascular health. Findings will inform the potential for primordial prevention in young adulthood. TRIAL REGISTRATION NUMBER: clinicaltrials.gov #NCT02496728.

A Factorial Experiment to Optimize Remotely Delivered Behavioral Treatment for Obesity: Results of the Opt-IN Study.

OBJECTIVE: Intensive behavioral obesity treatments face scalability challenges, but evidence is lacking about which treatment components could be cut back without reducing weight loss. The Optimization of Remotely Delivered Intensive Lifestyle Treatment for Obesity (Opt-IN) study applied the Multiphase Optimization Strategy to develop an entirely remotely delivered, technology-supported weight-loss package to maximize the amount of weight loss attainable for ≤$500. METHODS: Six-month weight loss was examined among adults (N = 562) with BMI ≥ 25 who were randomly assigned to conditions in a factorial experiment crossing five dichotomous treatment components set to either low/high (12 vs. 24 coaching calls) or off/on (primary care provider reports, text messaging, meal replacements, and buddy training). RESULTS: About 84.3% of participants completed the final assessment. The treatment package yielding maximum weight loss for ≤$500 included 12 coaching calls, buddy training, and primary care provider progress reports; produced average weight loss of 6.1 kg, with 57.1% losing ≥5% and 51.8% losing ≥7%; and cost $427 per person. The most expensive candidate-treatment component (24 vs. 12 coaching calls) was screened out of the optimized treatment package because it did not increase weight loss. CONCLUSIONS: Systematically testing each treatment component's effect on weight loss made it possible to eliminate more expensive but less impactful components, yielding an optimized, resource-efficient obesity treatment for evaluation in a randomized controlled trial.

Lifestyle intervention effects on the frequency and duration of daily moderate-vigorous physical activity and leisure screen time.

OBJECTIVE: How a healthy lifestyle intervention changes the frequency and duration of daily moderate-vigorous physical activity and sedentary behavior has not been well characterized. Secondary analyses of data from the Make Better Choices randomized controlled trial were conducted to evaluate how interventions to increase physical activity or reduce leisure screen time affected the frequency and duration of these behaviors during treatment initiation and follow-up. METHOD: Participants were 202 adults who exhibited insufficient physical activity, excessive screen time and poor diet during a 14-day baseline screening period. The design was a randomized controlled trial with a 3-week intervention period followed by eight 3- to 7-day bursts of data collection over the 6-month follow-up period after intervention termination. Participants self-reported on their physical activity and screen time at the end of each day. RESULTS: A 2-part multilevel model indicated that, relative to baseline levels, the physical activity intervention increased the odds of daily moderate-vigorous intensity physical activity (frequency) but not the duration of activity during the intervention period and these effects persisted (albeit somewhat more weakly) during the follow-up period. The screen time intervention reduced both the frequency and duration of daily screen time from the beginning of the intervention through the follow-up period. CONCLUSIONS: A 3-week intervention increased daily physical activity frequency but not duration, and reduced both the frequency and duration of daily leisure screen time. These effects were maintained over 20 weeks following the end of the intervention. (PsycINFO Database Record

Smartphone applications to support weight loss: current perspectives.

Lower cost alternatives are needed for the traditional in-person behavioral weight loss programs to overcome challenges of lowering the worldwide prevalence of overweight and obesity. Smartphones have become ubiquitous and provide a unique platform to aid in the delivery of a behavioral weight loss program. The technological capabilities of a smartphone may address certain limitations of a traditional weight loss program, while also reducing the cost and burden on participants, interventionists, and health care providers. Awareness of the advantages smartphones offer for weight loss has led to the rapid development and proliferation of weight loss applications (apps). The built-in features and the mechanisms by which they work vary across apps. Although there are an extraordinary number of a weight loss apps available, most lack the same magnitude of evidence-based behavior change strategies typically used in traditional programs. As features develop and new capabilities are identified, we propose a conceptual model as a framework to guide the inclusion of features that can facilitate behavior change and lead to reductions in weight. Whereas the conventional wisdom about behavior change asserts that more is better (with respect to the number of behavior change techniques involved), this model suggests that less may be more because extra techniques may add burden and adversely impact engagement. Current evidence is promising and continues to emerge on the potential of smartphone use within weight loss programs; yet research is unable to keep up with the rapidly improving smartphone technology. Future studies are needed to refine the conceptual model's utility in the use of technology for weight loss, determine the effectiveness of intervention components utilizing smartphone technology, and identify novel and faster ways to evaluate the ever-changing technology.