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

The past, present, and future of behavioral obesity treatment

Over the last century, hundreds of evaluations have been conducted to examine weight-management interventions related to diet, physical activity, and behavior therapy. These investigations have contributed to a growing body of knowledge that has consistently advanced the field of obesity treatment, while also revealing some persistent challenges. This narrative review summarizes key findings from randomized controlled trials conducted in adults that have combined diet, physical activity, and behavior therapy, an approach variously referred to as behavioral treatment, comprehensive lifestyle modification, or intensive lifestyle intervention. The review shows that current behavioral approaches induce average reductions in baseline body weight of 5 to 10% at 6 to 12 months. Such losses have proven effective in reducing the risk of type 2 diabetes in persons with impaired glucose tolerance and in improving other obesity-related complications. These benefits have also been associated with reductions in healthcare costs. Despite these advances, behavioral treatment is challenged by the need for larger losses to achieve optimal improvements in health, by difficulties associated with maintaining weight loss, and by barriers limiting access to treatment. New anti-obesity medications, when combined with behavioral obesity treatment, hold promise of addressing the first two issues.

Addressing rural health disparities by optimizing "high-touch" intervention components in digital obesity treatment: The iREACH Rural study

BACKGROUND

Rural residents are more impacted by obesity and related comorbidities than their urban counterparts. Digital weight management interventions may produce meaningful weight loss among rural residents.

OBJECTIVES

The iREACH Rural Study aims to identify "high-touch" component(s) that contribute to meaningful weight loss (≥1.5 kg) at 6-months, over and above what the 24-week core online program produces. Three treatment components are assessed: group video sessions (yes/no); self-monitoring feedback (counselor-crafted/pre-scripted, modular); and individual coaching calls (yes/no).

DESIGN

The iREACH Rural Study is a factorial experiment (n = 616).

METHODS

Participants receive up to 3 "high-touch" components (weekly synchronous facilitated group video sessions, weekly counselor-crafted self-monitoring feedback, and individual coaching calls) to determine which contribute meaningfully to 6-month weight loss. Participants complete assessments at baseline, 2 months, 6 months, and 12 months. Weight loss at 6 months (primary outcome) and 12 months (secondary outcome) is measured by Bluetooth-enabled scales. The study seeks to identify the weight loss approach for underserved rural residents which optimizes weight change outcomes and also examines costs associated with delivering different treatment constellations.

SUMMARY

The iREACH Rural Study is the first of its kind to isolate digital weight loss intervention components to determine which meaningfully contribute to long-term weight loss among rural residing individuals. The results may be used to refine digital weight loss programs by enhancing their effectiveness to allow broad dissemination.

Results of a pilot sequential multiple assignment randomized trial using counseling to augment a digital weight loss program

Objective

Adaptive interventions may improve the potency and scalability of behavioral weight loss interventions, but the treatments-or treatment combinations-that should be offered are unknown. A two-stage pilot sequential multiple assignment randomized trial was used to test the timing and dose of human support added to a core digital weight loss program.

Methods

In stage 1, 99 adults with overweight/obesity were randomized at baseline to a kick-off with or without additional human support. In stage 2, "early non-responders" who had not achieved a 2% weight loss were re-randomized after 4 weeks to either biweekly counseling (120 min over 8 weeks) or a one-time check-in (30 min) with a dietitian. "Early responders" continued with the mHealth program alone. Feasibility and acceptability were assessed against pre-specified criteria. Preliminary outcomes (weight loss, self-monitoring and behavioral goal adherence) were explored.

Results

The study met all feasibility and acceptability criteria. The rate of early response was 52.5%. Mean (SE) 3-month percent weight losses were significantly greater in early responders (-6.63% (0.72)) than non-responders (-1.70% (0.43), p < 0.001). Outcomes were similar by first- and second-line treatment though more counseling (27.3%) than check-in (12.5%) participants achieved a 5% weight loss.

Conclusions

Identifying early responders may help optimize weight loss interventions, but more research is needed on rescue treatments for early non-responders.

Trial Registration

ClinicalTrial.gov, NCT05929469.

Optimization of implementation strategies using the Multiphase Optimization STratgey (MOST) framework: Practical guidance using the factorial design

The Multiphase Optimization STrategy (MOST) is a framework that uses three phases-preparation, optimization, and evaluation-to develop multicomponent interventions that achieve intervention EASE by strategically balancing Effectiveness, Affordability, Scalability, and Efficiency. In implementation science, optimization of the intervention requires focus on the implementation strategies-things that we do to deliver the intervention-and implementation outcomes. MOST has been primarily used to optimize the components of the intervention related to behavioral or health outcomes. However, innovative opportunities to optimize discrete (i.e. single strategy) and multifaceted (i.e. multiple strategies) implementation strategies exist and can be done independently, or in conjunction with, intervention optimization. This article details four scenarios where the MOST framework and the factorial design can be used in the optimization of implementation strategies: (i) the development of new multifaceted implementation strategies; (ii) evaluating interactions between program components and a discrete or multifaceted implementation strategies; (iii) evaluating the independent effects of several discrete strategies that have been previously evaluated as a multifaceted implementation strategy; and (iv) modification of a discrete or multifaceted implementation strategy for the local context. We supply hypothetical school-based physical activity examples to illustrate these four scenarios, and we provide hypothetical data that can help readers make informed decisions derived from their trial data. This manuscript offers a blueprint for implementation scientists such that not only is the field using MOST to optimize the effectiveness of an intervention on a behavioral or health outcome, but also that the implementation of that intervention is optimized.

Decision-making in the multiphase optimization strategy: Applying decision analysis for intervention value efficiency to optimize an information leaflet to promote key antecedents of medication adherence

Advances in the multiphase optimization strategy (MOST) have suggested a new approach, decision analysis for intervention value efficiency (DAIVE), for selecting an optimized intervention based on the results of a factorial optimization trial. The new approach opens possibilities to select optimized interventions based on multiple valued outcomes. We applied DAIVE to identify an optimized information leaflet intended to support eventual adherence to adjuvant endocrine therapy for women with breast cancer. We used empirical performance data for five candidate leaflet components on three hypothesized antecedents of adherence: beliefs about the medication, objective knowledge about AET, and satisfaction with medication information. Using data from a 25 factorial trial (n = 1603), we applied the following steps: (i) We used Bayesian factorial analysis of variance to estimate main and interaction effects for the five factors on the three outcomes. (ii) We used posterior distributions for main and interaction effects to estimate expected outcomes for each leaflet version (32 total). (iii) We scaled and combined outcomes using a linear value function with predetermined weights indicating the relative importance of outcomes. (iv) We identified the leaflet that maximized the value function as the optimized leaflet, and we systematically varied outcome weights to explore robustness. The optimized leaflet included two candidate components, side-effects, and patient input, set to their higher levels. Selection was generally robust to weight variations consistent with the initial preferences for three outcomes. DAIVE enables selection of optimized interventions with the best-expected performance on multiple outcomes.

Understanding the selection of support partners in a behavioral weight loss program

OBJECTIVE

Engaging a support partner during behavioral weight loss (BWL) programs can improve outcomes. However, little information is available about those selected as support partners. The study aimed to (1) characterize support partners and qualities of the relationship, (2) assess differences in relationship dynamics across relationship types, and (3) assess differences in supportive relationships by participant gender.

METHODS AND MEASURES

Upon entering a 24-month BWL program, participants (N = 323) nominated a support person from their existing social networks. Participants completed self-report measures at baseline, including the Working Alliance Inventory and study-specific measures assessing characteristics of the support person and comfort in communicating with them.

RESULTS

Spouse/partners were chosen by 43.3% of all participants. Among married participants, 80.9% of men and 53.8% of women chose their spouse/partner. Working alliance was lower when the support partner was a spouse/partner versus a friend (p < 0.05, r = -0.19). Comfort talking about exercise with a support partner was higher than talking about eating or weight (p < 0.001, η2p= 0.05).

CONCLUSION

A variety of friends and family can be selected as support partners during BWL. Programs should attend to the unique needs of men versus women when identifying sources of support.

Optimising a digitally delivered behavioural weight loss programme: study protocol for a factorial cluster randomised controlled trial

BACKGROUND

Digitally delivered weight loss programmes can provide a convenient, potentially cheaper, and scalable treatment option for people who may need to lose weight. However, outcomes are often inferior to in-person interventions in the long-term. This trial will use principles from the Multiphase Optimisation Strategy (MOST) framework to test whether it can enhance the effectiveness of a commercial digital behavioural weight loss programme. This trial aims to identify an optimised combination of four intervention components to enhance weight loss over a 24-week period. We will also explore which components contribute to improvements in participant retention and engagement with the programme.

METHODS

Approximately 1400 adults with a BMI > 21 kg/m2 will be enrolled and randomised to one of 16 experimental conditions in a 24 factorial cluster design. The trial will test four intervention components: an introductory video call with the health coach, drop-in webchat sessions with the health coach, goal setting statements, and food diary review and feedback. All participants will receive the core digital behavioural weight loss programme and up to four new intervention components. Participation in the trial will last for 24 weeks. The primary outcome will be weight change at 16 weeks. Other outcomes, measured at 4, 16, and 24 weeks, include programme drop-out and engagement (number of interactions with the three main app functions). Fidelity and acceptability will be assessed using data on component adherence and self-report questionnaires. Decision-making for the enhanced programme will be based on components that contribute to at least a minimal improvement in weight loss, defined as ≥ 0.75kg, alone or in combination with other components.

DISCUSSION

The factorial design is an efficient way to test the efficacy of behavioural components alone, or in combination, to improve the effectiveness of digital weight loss programmes. This trial will test the implementation of the MOST framework in an industry setting, using routinely collected data, which may provide a better way to refine and evaluate these types of interventions in a model of continuous service improvement.

TRIAL REGISTRATION

Trial registration: ISRCTN, ISRCTN14407868. Registered 5 January 2024, 10.1186/ISRCTN14407868.

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.

Empirical Development of a Behavioral Intervention for African American/Black and Latino Persons with Unsuppressed HIV Viral Load Levels: An Application of the Multiphase Optimization Strategy (MOST) Using Cost-Effectiveness as an Optimization Objective

We used results from an optimization randomized controlled trial which tested five behavioral intervention components to support HIV antiretroviral adherence/HIV viral suppression, grounded in the multiphase optimization strategy and using a fractional factorial design to identify intervention components with cost-effectiveness sufficiently favorable for scalability. Results were incorporated into a validated HIV computer simulation to simulate longer-term effects of combinations of components on health and costs. We simulated the 32 corresponding long-term trajectories for viral load suppression, health related quality of life (HRQoL), and costs. The components were designed to be culturally and structurally salient. They were: motivational interviewing counseling sessions (MI), pre-adherence skill building (SB), peer mentorship (PM), focused support groups (SG), and patient navigation (short version [NS], long version [NL]. All participants also received health education on HIV treatment. We examined four scenarios: one-time intervention with and without discounting and continuous interventions with and without discounting. In all four scenarios, interventions that comprise or include SB and NL (and including health education) were cost effective (< $100,000/quality-adjusted life year). Further, with consideration of HRQoL impact, maximal intervention became cost-effective enough to be scalable. Thus, a fractional factorial experiment coupled with cost-effectiveness analysis is a promising approach to optimize multi-component interventions for scalability. The present study can guide service planning efforts for HIV care settings and health departments.

Scalable Telehealth Cancer Care: integrated healthy lifestyle program to live well after cancer treatment

Northwestern University's Center for Scalable Telehealth Cancer Care (STELLAR) is 1 of 4 Cancer Moonshot Telehealth Research Centers of Excellence programs funded by the National Cancer Institute to establish an evidence base for telehealth in cancer care. STELLAR is grounded in the Institute of Medicine's vision that quality cancer care includes not only disease treatment but also promotion of long-term health and quality of life (QOL). Cigarette smoking, insufficient physical activity, and overweight and obesity often co-occur and are associated with poorer treatment response, heightened recurrence risk, decreased longevity, diminished QOL, and increased treatment cost for many cancers. These risk behaviors are prevalent in cancer survivors, but their treatment is not routinely integrated into oncology care. STELLAR aims to foster patients' long-term health and QOL by designing, implementing, and sustaining a novel telehealth treatment program for multiple risk behaviors to be integrated into standard cancer care. Telehealth delivery is evidence-based for health behavior change treatment and is well suited to overcome access and workflow barriers that can otherwise impede treatment receipt. This paper describes STELLAR's 2-arm randomized parallel group pragmatic clinical trial comparing telehealth-delivered, coach-facilitated multiple risk behavior treatment vs self-guided usual care for the outcomes of reach, effectiveness, and cost among 3000 cancer survivors who have completed curative intent treatment. This paper also discusses several challenges encountered by the STELLAR investigative team and the adaptations developed to move the research forward.

Multi-stage optimization strategy based on contextual analysis to create M-health components for case management model in breast cancer transitional care: the CMBM study as an example

BACKGROUND

None of the early M-Health applications are designed for case management care services. This study aims to describe the process of developing a M-health component for the case management model in breast cancer transitional care and to highlight methods for solving the common obstacles faced during the application of M-health nursing service.

METHODS

We followed a four-step process: (a) Forming a cross-functional interdisciplinary development team containing two sub-teams, one for content development and the other for software development. (b) Applying self-management theory as the theoretical framework to develop the M-health application, using contextual analysis to gain a comprehensive understanding of the case management needs of oncology nursing specialists and the supportive care needs of out-of-hospital breast cancer patients. We validated the preliminary concepts of the framework and functionality of the M-health application through multiple interdisciplinary team discussions. (c) Adopting a multi-stage optimization strategy consisting of three progressive stages: screening, refining, and confirmation to develop and continually improve the WeChat mini-programs. (d) Following the user-centered principle throughout the development process and involving oncology nursing specialists and breast cancer patients at every stage.

RESULTS

Through a continuous, iterative development process and rigorous testing, we have developed patient-end and nurse-end program for breast cancer case management. The patient-end program contains four functional modules: "Information", "Interaction", "Management", and "My", while the nurse-end program includes three functional modules: "Consultation", "Management", and "My". The patient-end program scored 78.75 on the System Usability Scale and showed a 100% task passing rate, indicating that the programs were easy to use.

CONCLUSIONS

Based on the contextual analysis, multi-stage optimization strategy, and interdisciplinary team work, a WeChat mini-program has been developed tailored to the requirements of the nurses and patients. This approach leverages the expertise of professionals from multiple disciplines to create effective and evidence-based solutions that can improve patient outcomes and quality of care.

Innovation in the Delivery of Behavioral Health Services

Several factors motivate the need for innovation to improve the delivery of behavioral health services, including increased rates of mental health and substance use disorders, limited access to services, inconsistent use of evidence-based practices, and persistent racial and ethnic disparities. This narrative review identifies promising innovations that address these challenges, assesses empirical evidence for the effectiveness of these innovations and the extent to which they have been adopted and implemented, and suggests next steps for research. We review five categories of innovations: organizational models, including a range of novel locations for providing services and new ways of organizing services within and across sites; information and communication technologies; workforce; treatment technologies; and policy and regulatory changes. We conclude by discussing the need to strengthen and accelerate the contributions of implementation science to close the gap between the launch of innovative behavioral health services and their widespread use.

The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring

Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.

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.

A Digital Intervention to Promote Self-Management Self-Efficacy Among Community-Dwelling Individuals With Stroke: Pilot Randomized Controlled Trial

BACKGROUND

Digital interventions provided through smartphones or the internet that are guided by a coach have been proposed as promising solutions to support the self-management of chronic conditions. However, digital intervention for poststroke self-management is limited; we developed the interactive Self-Management Augmented by Rehabilitation Technologies (iSMART) intervention to address this gap.

OBJECTIVE

This study aimed to examine the feasibility and initial effects of the iSMART intervention to improve self-management self-efficacy in people with stroke.

METHODS

A parallel, 2-arm, nonblinded, randomized controlled trial of 12-week duration was conducted. A total of 24 participants with mild-to-moderate chronic stroke were randomized to receive either the iSMART intervention or a manual of stroke rehabilitation (attention control). iSMART was a coach-guided, technology-supported self-management intervention designed to support people managing chronic conditions and maintaining active participation in daily life after stroke. Feasibility measures included retention and engagement rates in the iSMART group. For both the iSMART intervention and active control groups, we used the Feasibility of Intervention Measure, Acceptability of Intervention Measure, and Intervention Appropriateness Measure to assess the feasibility, acceptability, and appropriateness, respectively. Health measures included the Participation Strategies Self-Efficacy Scale and the Patient-Reported Outcomes Measurement Information System's Self-Efficacy for Managing Chronic Conditions.

RESULTS

The retention rate was 82% (9/11), and the engagement (SMS text message response) rate was 78% for the iSMART group. Mean scores of the Feasibility of Intervention Measure, Acceptability of Intervention Measure, and Intervention Appropriateness Measure were 4.11 (SD 0.61), 4.44 (SD 0.73), and 4.36 (SD 0.70), respectively, which exceeded our benchmark (4 out of 5), suggesting high feasibility, acceptability, and appropriateness of iSMART. The iSMART group showed moderate-to-large effects in improving self-efficacy in managing emotions (r=0.494), symptoms (r=0.514), daily activities (r=0.593), and treatments and medications (r=0.870), but the control group showed negligible-to-small effects in decreasing self-efficacy in managing emotions (r=0.252), symptoms (r=0.262), daily activities (r=0.136), and treatments and medications (r=0.049). In addition, the iSMART group showed moderate-to-large effects of increasing the use of participation strategies for management in the home (r=0.554), work (r=0.633), community (r=0.673), and communication activities (r=0.476). In contrast, the control group showed small-to-large effects of decreasing the use of participation strategies for management in the home (r=0.567), work (r=0.342, community (r=0.215), and communication activities (r=0.379).

CONCLUSIONS

Our findings support the idea that iSMART was feasible to improve poststroke self-management self-efficacy. Our results also support using a low-cost solution, such as SMS text messaging, to supplement traditional therapeutic patient education interventions. Further evaluation with a larger sample of participants is still needed.

TRIAL REGISTRATION

ClinicalTrials.gov 202004137; https://clinicaltrials.gov/study/NCT04743037?id=202004137&rank=1.

Optimization of a mHealth Physical Activity Promotion Intervention With Mindful Awareness for Young Adult Cancer Survivors: Design and Methods of Opt2Move Full Factorial Trial

Background

Opt2Move is a theory-guided moderate and vigorous physical activity (MVPA) promotion trial that uses multiphase optimization strategy (MOST) methodology to evaluate the individual and combined effects of four intervention components in a full factorial experiment among young adult cancer survivors (YACS; N = 304). All participants will receive the core mHealth MVPA intervention, which includes a Fitbit and standard self-monitoring Opt2Move smartphone application. YACS will be randomized to one of 16 conditions to receive between zero and four additional components each with two levels (yes v. no): E-Coach, buddy, general mindfulness, and MVPA-specific mindfulness.

Objective

The primary aim is to determine the individual and combined effects of the components on MVPA post-intervention (12-weeks) and at 24-week follow-up. The secondary aim is to examine how changes in MVPA are associated with patient-reported outcomes, light-intensity activity, sedentary time, and sleep duration and quality. Potential mediators and moderators of component effects will also be examined.

Results

Results will support the selection of a package of intervention components optimized to maximize MVPA to be tested in a randomized controlled trial.

Conclusion

Opt2Move represents the first systematic effort to use MOST to design an optimized, scalable mHealth MVPA intervention for YACS and will lead to an improved understanding of how to effectively change YACS' MVPA and ultimately, improve health and disease outcomes.

Harnessing technology and gamification to increase adult physical activity: a cluster randomized controlled trial of the Columbia Moves pilot

BACKGROUND

The use of health technologies and gamification to promote physical activity has increasingly been examined, representing an opportunistic method for harnessing social support inherent within existing social ties. However, these prior studies have yielded mixed findings and lacked long-term follow-up periods. Thus, a pilot cluster randomized controlled trial was conducted to gauge the feasibility and preliminary efficacy of a digital gamification-based physical activity promotion approach among teams of insufficiently active adults with existing social ties.

METHODS

Teams (N = 24; 116 total participants) were randomized to either a 12-week intervention (Fitbit, step goals, app, feedback; TECH) or the same program plus gamification (TECH + Gamification). Mixed effects models were used to compare group differences in treatment adherence, and changes in social support, steps, and moderate-to-vigorous physical activity at 12 weeks and 52 weeks from baseline, adjusted for sociodemographic characteristics and team size.

RESULTS

TECH had a lower mean number of days of Fitbit self-monitoring versus TECH + Gamification during the intervention (adjusted difference: -.30; 95% CI, -.54 to -.07; P = .01). Post-intervention, TECH had 47% lower odds of self-monitoring 7 days per week versus TECH + Gamification (.53; 95% CI, .31 to .89; P = .02). No differences were observed between TECH + Gamification and TECH in increases in social support (0.04; 95% CI, -.21 to .29; P = .76), ActiGraph-measured daily steps (-425; 95% CI, -1065 to 215; P = .19), or moderate-to-vigorous physical activity minutes (-3.36; 95% CI, -8.62 to 1.91; P = .21) from baseline to 12 weeks or in the regression of these improvements by 1 year (Ps > .05). Although not significant in the adjusted models (Ps > .05), clinically meaningful differences in Fitbit-measured daily steps (TECH, 7041 ± 2520; TECH + Gamification, 7988 ± 2707) and active minutes (TECH, 29.90 ± 29.76; TECH + Gamification, 36.38 ± 29.83) were found during the intervention.

CONCLUSIONS

A gamified physical activity intervention targeting teams of adults with existing social ties was feasible and facilitated favorable, clinically meaningful additive physical activity effects while in place but did not drive enhanced, long-term physical activity participation. Future investigations should explore optimal team dynamics and more direct ways of leveraging social support (training teams; gamifying social support).

TRIAL REGISTRATION

Clinicaltrials.gov ( NCT03509129 , April 26, 2018).

Early and slow weight loss responders' perceptions of an adaptive diabetes prevention trial: A qualitative perspective

Objective

Behavioural weight loss interventions demonstrate success on average, yet participants who respond more slowly may benefit from an augmented, tailored approach. Thus, an augmented intervention for early, slow weight loss responders was implemented. This qualitative analysis explored participants' perceptions of and experiences of the 12-month weight loss programme to inform future intervention development and implementation.

Design

Individual interviews were conducted post-treatment. In-person, in-depth interviews were offered prior to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic; videoconferencing interviews were offered following the pandemic onset.

Setting

A Midwestern US university workplace.

Method

Adults ⩾21 years old with overweight or obesity and prediabetes (n = 39) were recruited and volunteered to participate in a semi-structured interview.

Results

Content analysis identified recurring themes among early and early slow weight loss responders. Social support received during weekly group sessions was a critical intervention component. Some early slow responders expressed difficulty with self-regulatory strategies such as setting specific yet attainable goals, and requested additional contact prior to the maintenance study phase - treatment randomisation at month 5 disrupted group dynamics. Early slow responders who received the intervention via videoconferencing believed the format interfered with their ability to establish friendships with other participants and gain insight and support from others' lived experiences.

Conclusion

Weight loss interventions may be more efficacious when considering participant heterogeneity during implementation of behavioural strategies. Findings suggest early slow weight loss responders may benefit from greater emphasis on goal setting and striving, agentic thinking, and social support. Future research is needed to examine the implications of these insights on intervention outcomes.

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.

Study Design, Rationale, and Methodology for Promote Weight Loss in Patients With Peripheral Artery Disease Who Also Have Obesity: The PROVE Trial

Background Overweight and obesity are associated with adverse functional outcomes in people with peripheral artery disease (PAD). The effects of weight loss in people with overweight/obesity and PAD are unknown. Methods The PROVE (Promote Weight Loss in Obese PAD Patients to Prevent Mobility Loss) Trial is a multicentered randomized clinical trial with the primary aim of testing whether a behavioral intervention designed to help participants with PAD lose weight and walk for exercise improves 6-minute walk distance at 12-month follow-up, compared with walking exercise alone. A total of 212 participants with PAD and body mass index ≥25 kg/m2 will be randomized. Interventions are delivered using a Group Mediated Cognitive Behavioral intervention model, a smartphone application, and individual telephone coaching. The primary outcome is 12-month change in 6-minute walk distance. Secondary outcomes include total minutes of walking exercise/wk at 12-month follow-up and 12-month change in accelerometer-measured physical activity, the Walking Impairment Questionnaire distance score, and the Patient-Reported Outcomes Measurement Information System mobility questionnaire. Tertiary outcomes include 12-month changes in perceived exertional effort at the end of the 6-minute walk, diet quality, and the Short Physical Performance Battery. Exploratory outcomes include changes in gastrocnemius muscle biopsy measures of mitochondrial cytochrome C oxidase activity, mitochondrial biogenesis, capillary density, and inflammatory markers. Conclusions The PROVE randomized clinical trial will evaluate the effects of exercise with an intervention of coaching and a smartphone application designed to achieve weight loss, compared with exercise alone, on walking performance in people with PAD and overweight/obesity. Results will inform optimal treatment for the growing number of patients with PAD who have overweight/obesity. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT04228978.

Investigation of Active Ingredients Within Internet-Delivered Cognitive Behavioral Therapy for Depression: A Randomized Optimization Trial

Importance

There is limited understanding of how complex evidence-based psychological interventions such as cognitive behavioral therapy (CBT) for depression work. Identifying active ingredients may help to make therapy more potent, brief, and scalable.

Objective

To test the individual main effects and interactions of 7 treatment components within internet-delivered CBT for depression to investigate its active ingredients.

Design, Setting, and Participants

This randomized optimization trial using a 32-condition, balanced, fractional factorial optimization experiment (IMPROVE-2) recruited adults with depression (Patient Health Questionnaire-9 [PHQ-9] score ≥10) from internet advertising and the UK National Health Service Improving Access to Psychological Therapies service. Participants were randomized from July 7, 2015, to March 29, 2017, with follow-up for 6 months after treatment until December 29, 2017. Data were analyzed from July 2018 to April 2023.

Interventions

Participants were randomized with equal probability to 7 experimental factors within the internet CBT platform, each reflecting the presence vs absence of specific treatment components (activity scheduling, functional analysis, thought challenging, relaxation, concreteness training, absorption, and self-compassion training).

Main Outcomes and Measures

The primary outcome was depression symptoms (PHQ-9 score). Secondary outcomes include anxiety symptoms and work, home, and social functioning.

Results

Among 767 participants (mean age [SD] age, 38.5 [11.62] years; range, 18-76 years; 635 women [82.8%]), 506 (66%) completed the 6-month posttreatment follow-up. On average, participants receiving internet-delivered CBT had reduced depression (pre-to-posttreatment difference in PHQ-9 score, -7.79 [90% CI, -8.21 to -7.37]; 6-month follow-up difference in PHQ-9 score, -8.63 [90% CI, -9.04 to -8.22]). A baseline score-adjusted analysis of covariance model using effect-coded intervention variables (-1 or +1) found no main effect on depression symptoms for the presence vs absence of activity scheduling, functional analysis, thought challenging, relaxation, concreteness training, or self-compassion training (posttreatment: largest difference in PHQ-9 score [functional analysis], -0.09 [90% CI, -0.56 to 0.39]; 6-month follow-up: largest difference in PHQ-9 score [relaxation], -0.18 [90% CI, -0.61 to 0.25]). Only absorption training had a significant main effect on depressive symptoms at 6-month follow-up (posttreatment difference in PHQ-9 score, 0.21 [90% CI, -0.27 to 0.68]; 6-month follow-up difference in PHQ-9 score, -0.54, [90% CI, -0.97 to -0.11]).

Conclusions and Relevance

In this randomized optimization trial, all components of internet-delivered CBT except absorption training did not significantly reduce depression symptoms relative to their absence despite an overall average reduction in symptoms. The findings suggest that treatment benefit from internet-delivered CBT probably accrues from spontaneous remission, factors common to all CBT components (eg, structure, making active plans), and nonspecific therapy factors (eg, positive expectancy), with the possible exception of absorption focused on enhancing direct contact with positive reinforcers.

Trial Registration

isrctn.org Identifier: ISRCTN24117387.

Evaluation of a combined, online intervention for binge-type eating disorders and high body weight in young adults

BACKGROUND

Binge-type eating disorders (EDs; i.e., bulimia nervosa, binge eating disorder) are common among young adults with high body weight, yet few interventions target both conditions. This study tested an online guided self-help intervention that provided cognitive behavioral therapy (CBT) tools for EDs and behavioral weight loss (BWL) content to young adults with binge-type EDs and high body weight.

METHOD

60 adults aged 18-39 with clinical/subclinical binge-type EDs and high body weight were randomized to a combined condition or a CBT-only condition. Participants received self-help content for 8 weeks and self-reported ED attitudes, frequency of binge eating and compensatory behaviors, and weight at baseline, 4-weeks, and 8-weeks. Linear mixed models and negative binomial models compared changes between conditions in ED attitudes, ED behaviors, and weight at each timepoint. Chi-square test and independent samples t-test compared program completion and session engagement between conditions.

RESULTS

No significant differences in weight change or ED symptom change emerged between the conditions. Both conditions achieved significant reductions in ED attitudes, binge episodes, and compensatory behaviors from baseline to 8-weeks (ps < .05). Neither condition demonstrated significant weight loss from baseline to 8-weeks. Program completion (47 %) and session engagement (57 %) were equally high across conditions.

DISCUSSION

Both conditions achieved ED symptom change; however, neither condition was associated with weight change. Research is needed to identify the types of strategies and doses of BWL that promote clinically significant weight and ED symptom change in young adults.

Psychological Support Strategies for Adults With Type 2 Diabetes in a Very Low-Carbohydrate Web-Based Program: Randomized Controlled Trial

BACKGROUND

A very low-carbohydrate (VLC) nutritional strategy may improve glycemic control and weight loss in adults with type 2 diabetes (T2D). However, the supplementary behavioral strategies that might be able to improve outcomes using this nutritional strategy are uncertain.

OBJECTIVE

This study aims to compare the impact of adding 3 different supplementary behavioral strategies to a web-based VLC diet intervention. To our knowledge, this is the first trial to randomize participants to different frequencies of dietary self-monitoring.

METHODS

The study included 112 overweight adults with T2D (hemoglobin A1c ≥6.5%) taking no antiglycemic medications or only metformin. They received a remotely delivered 12-month VLC diet intervention. Participants were randomly assigned through a full factorial 2×2×2 design to supplementary strategies: either daily or monthly dietary self-monitoring, either mindful eating training or not, and either positive affect skills training or not. Our research goal was to determine whether 3 different supplemental strategies had at least a medium effect size (Cohen d=0.5).

RESULTS

Overall, the VLC intervention led to statistically significant improvements in glycemic control (-0.70%, 95% CI -1.04% to -0.35%; P<.001), weight loss (-6.82%, 95% CI -8.57% to -5.08%; P<.001), and depressive symptom severity (Cohen d -0.67, 95% CI -0.92 to -0.41; P<.001). Furthermore, 30% (25/83) of the participants taking metformin at baseline reduced or discontinued their metformin. Only 1 Cohen d point estimate reached 0.5; daily (vs monthly) dietary self-monitoring had a worse impact on depressive symptoms severity (Cohen d=0.47, 95% CI -0.02 to 0.95; P=.06). None of the strategies had a statistically significant effect on outcomes. For changes in our primary outcome, hemoglobin A1c, the daily (vs monthly) dietary self-monitoring impact was 0.42% (95% CI -0.28% to 1.12%); for mindful eating, it was -0.47% (95% CI -1.15% to 0.22%); and for positive affect, it was 0.12% (95% CI -0.57% to 0.82%). Other results for daily (vs monthly) dietary self-monitoring were mixed, suggesting an increase in weight (0.98%) and depressive symptoms (Cohen d=0.47), less intervention satisfaction (Cohen d=-0.20), more sessions viewed (3.02), and greater dietary adherence (Cohen d=0.24). For mindful eating, the results suggested a benefit for dietary adherence (Cohen d=0.24) and intervention satisfaction (Cohen d=0.30). For positive affect, the results suggested a benefit for depressive symptoms (Cohen d=-0.32), the number of sessions viewed (3.68), dietary adherence (Cohen d=0.16), and intervention satisfaction (Cohen d=0.25).

CONCLUSIONS

Overall, our results support the use of a VLC diet intervention in adults with T2D. The addition of monthly (not daily) dietary self-monitoring, mindful eating, and positive affect skills training did not show a definitive benefit, but it is worth further testing.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03037528; https://clinicaltrials.gov/ct2/show/NCT03037528.

Multiphase optimization strategy: How to build more effective, affordable, scalable and efficient social and behavioural oral health interventions

This commentary introduces the field of social behavioural oral health interventions to the multiphase optimization strategy (MOST). MOST is a principled framework for the development, optimization and evaluation of multicomponent interventions. Drawing from the fields of engineering, behavioural science, economics, decision science and public health, intervention optimization requires a strategic balance of effectiveness with affordability, scalability and efficiency. We argue that interventions developed using MOST are more likely to maximize the public health impact of social behavioural oral health interventions.

Parent-based interventions to improve multiple lifestyle risk behaviors among adolescents: A systematic review and meta-analysis

Lifestyle risk behaviors often co-occur and are prevalent among adolescents. Parent-based interventions addressing risk behaviors concurrently have the potential to improve youth and parent outcomes. This systematic review evaluated the efficacy of parent-based interventions targeting multiple lifestyle risk behaviors among adolescents and parents. MEDLINE (Ovid), Embase (Ovid), PsycInfo (Ovid), Scopus, CINAHL, the Cochrane Database of Systematic Reviews (CDSR) and Cochrane Central Register of Controlled Trials (CENTRAL) were searched from 2010-May 2021. Eligible studies were randomised controlled trials (RCTs) of parent-based interventions addressing 2+ risk behaviors: alcohol use, smoking, poor diet, physical inactivity, sedentary behaviors, and poor sleep. Studies directly targeting parents, and that assessed adolescent outcomes (11-18 years) were eligible. Where possible, random-effects meta-analysis was conducted. From 11,975 identified records, 46 publications of 36 RCTs (n = 28,322 youth, n = 7385 parents) were eligible. Parent-based interventions were associated with improved adolescent moderate-to-vigorous physical activity (MVPA) [Odds Ratio (OR) = 1.82, 95% CI = 1.18, 2.81; p = 0.007], and reduced screen time (SMD = -0.39, 95% CI = -0.62, -0.16, p = 0.0009) and discretionary food intake (SMD = -0.18; 95% CI = -0.30, -0.06; p = 0.002) compared to controls. However, there was some evidence that interventions increased the odds of ever using tobacco in the medium-term (OR = 1.47, 95% CI = 0.99, 2.18, p = 0.06) and of past month tobacco use in the long-term (OR = 1.46, 95% CI = 1.12, 1.90; p = 0.005). Overall, the quality of evidence was moderate. Parent-based interventions targeting multiple risk behaviors improved adolescent MVPA, and reduced screen time discretionary food intake. Further research is needed to address sleep problems and increase intervention efficacy, particularly for alcohol and tobacco use.

The EVO study protocol for a randomized controlled evaluation trial of an optimized weight management intervention

BACKGROUND

Obesity is a substantial public health concern; however, gold-standard behavioral treatments for obesity are costly and burdensome. Existing adaptations to the efficacious Diabetes Prevention Program (DPP) demonstrate mixed results. Our prior research applying the Multiphase Optimization Strategy (MOST) to DPP identifies a more parsimonious, less costly intervention (EVO) resulting in significant weight loss.

OBJECTIVE

The aim of the remotely conducted EVO trial is to test the non-inferiority of EVO against DPP. We will conduct economic evaluations alongside the trial to estimate delivery and patient costs, cost-effectiveness, and lifetime healthcare costs of EVO as compared to DPP. Exploratory analyses will examine maintenance, moderators, and mediators of the treatment effect.

STUDY DESIGN

The EVO trial will recruit nationally to randomize 524 participants with obesity. Participants will receive either EVO or DPP over a 6 month period. EVO participants will be provided online lessons, a smartphone application to self-monitor diet, physical activity, and weight, and attend 12 brief calls with a Health Promotionist. DPP participants will receive the first 6 months of the Center for Disease Control's T2D materials and attend 16 one-hour video call sessions with staff certified in DPP delivery. Weight will be measured at baseline, 3-, 6-, and 12-months. Itemized delivery cost will be collected. Staff and participants will also provide information to estimate costs for intervention-related activities.

SIGNIFICANCE

The EVO trial could establish evidence supporting dissemination of a scalable, cost-effective behavioral treatment with potential to shift clinical practice guidelines, inform policy, and reduce the prevalence of obesity.

Evaluation Methods Applied to Digital Health Interventions: What Is Being Used beyond Randomised Controlled Trials?-A Scoping Review

Despite the potential of digital health interventions (DHIs), evaluations of their effectiveness face challenges. DHIs are complex interventions and currently established evaluation methods, e.g., the randomised controlled trial (RCT), are limited in their application. This study aimed at identifying alternatives to RCTs as potentially more appropriate evaluation approaches. A scoping review was conducted to provide an overview of existing evaluation methods of DHIs beyond the RCT. Cochrane Central Register of Controlled Trials, MEDLINE, Web of Science, and EMBASE were screened in May 2021 to identify relevant publications, while using defined inclusion and exclusion criteria. Eight studies were extracted for a synthesis comprising four alternative evaluation designs. Factorial designs were mostly used to evaluate DHIs followed by stepped-wedge designs, sequential multiple assignment randomised trials (SMARTs), and micro randomised trials (MRTs). Some of these methods allow for the adaptation of interventions (e.g., SMART or MRT) and the evaluation of specific components of interventions (e.g., factorial designs). Thus, they are appropriate for addressing some specific needs in the evaluation of DHIs. However, it remains unsolved how to establish these alternative evaluation designs in research practice and how to deal with the limitations of the designs.

The benefit of telemedicine in obesity care

It has been estimated that, by 2030, nearly 80% of adults in the United States will have pre-obesity or obesity. Despite the continued rise in obesity prevalence and the difficulty for many affected patients to lose weight and maintain lost weight, the use of guideline-supported treatments, including pharmacotherapy, intensive behavioral counseling, and bariatric surgery, remains low. There are many potential barriers to effective use of antiobesity treatments, including limited access to guideline-supported obesity care (often driven by practical challenges, geographic barriers, limited insurance coverage, and high cost of care) and a dearth of specialists and comprehensive treatment teams. Driven in part by the COVID-19 pandemic, the recent expansion of telemedicine offers unique opportunities to mitigate these factors. This review discusses the use of telemedicine to facilitate obesity treatment. Continued growth and utility of telemedicine for obesity care require further formative and experimental research to determine best practices, assess challenges for implementation, and evaluate long-term outcomes, as well as proactive policy changes to promote ongoing use of telemedicine beyond the COVID-19 pandemic.

Beyond Calorie Tracking: A Pilot Trial of a Remotely Delivered Behavioral Weight Loss Intervention Using an Ad Libitum Plant-Based Diet (Preprint)

Background

Many traditional lifestyle interventions use calorie prescriptions, but most individuals have difficulty sustaining calorie tracking and thus weight loss. In contrast, whole food plant-based diets (WFPBDs) have previously shown significant weight loss without this issue. However, most WFPBD interventions are face-to-face and time-intensive, and do not leverage gold standard behavioral strategies for health behavior change.

Objective

This open pilot trial was the first to evaluate the feasibility of a fully featured, remotely delivered behavioral weight loss intervention using an ad libitum WFPBD.

Methods

Over 12 weeks, participants (N=15) with overweight or obesity received a newly designed program that integrated behavioral weight loss and a WFPBD prescription via weekly web-based modules and brief phone coaching calls. Assessments were performed at baseline, midtreatment (6 weeks), and after treatment (12 weeks).

Results

The intervention was rated as highly acceptable (mean 4.40 out of 5, SE 0.18), and attrition was low (6.7%). In all, intention-to-treat analyses revealed that 69% (10.4/15) of the participants lost 5% of their weight (mean –5.89, SE 0.68 kg). Predefined benchmarks for quality of life were met.

Conclusions

A pilot digital behavioral weight loss intervention with a non–energy-restricted WFPBD was feasible, and the mean acceptability was high. Minimal contact time (80-150 minutes of study interventionist time per participant over 12 weeks) led to clinically relevant weight loss and dietary adherence for most participants (10.4/15, 69% and 11.8/15, 79%, respectively), and quality of life improvements (reliable change indices >1.53). We hope that this work will serve as a springboard for future larger scale randomized controlled studies evaluating the efficacy of such programs for weight loss, dietary change, and quality of life.

Trial Registration

ClinicalTrials.gov NCT04892030; https://clinicaltrials.gov/ct2/show/NCT04892030

Using factorial mediation analysis to better understand the effects of interventions

To improve understanding of how interventions work or why they do not work, there is need for methods of testing hypotheses about the causal mechanisms underlying the individual and combined effects of the components that make up interventions. Factorial mediation analysis, i.e., mediation analysis applied to data from a factorial optimization trial, enables testing such hypotheses. In this commentary, we demonstrate how factorial mediation analysis can contribute detailed information about an intervention's causal mechanisms. We briefly review the multiphase optimization strategy (MOST) and the factorial experiment. We use an empirical example from a 25 factorial optimization trial to demonstrate how factorial mediation analysis opens possibilities for better understanding the individual and combined effects of intervention components. Factorial mediation analysis has important potential to advance theory about interventions and to inform intervention improvements.

Human-centered design methods to achieve preparation phase goals in the multiphase optimization strategy framework

Background

The public health impact of behavioral and biobehavioral interventions to prevent and treat mental health and substance use problems hinges on developing methods to strategically maximize their effectiveness, affordability, scalability, and efficiency.

Methods

The multiphase optimization strategy (MOST) is an innovative, principled framework that guides the development of multicomponent interventions. Each phase of MOST (Preparation, Optimization, Evaluation) has explicit goals and a range of appropriate research methods to achieve them. Methods for attaining Optimization and Evaluation phase goals are well-developed. However, methods used in the Preparation phase are often highly researcher-specific, and concrete ways to achieve Preparation phase goals are a priority area for further development.

Results

We propose that the discover, design, build, and test (DDBT) framework provides a theory-driven and methods-rich roadmap for achieving the goals of the Preparation phase of MOST, including specifying the conceptual model, identifying and testing candidate intervention components, and defining the optimization objective. The DDBT framework capitalizes on strategies from the field of human-centered design and implementation science to drive its data collection methods.

Conclusions

MOST and DDBT share many conceptual features, including an explicit focus on implementation determinants, being iterative and flexible, and designing interventions for the greatest public health impact. The proposed synthesized DDBT/MOST approach integrates DDBT into the Preparation phase of MOST thereby providing a framework for rigorous and efficient intervention development research to bolster the success of intervention optimization.

Plain Language Summary

1. What is already known about the topic? Optimizing behavioral interventions to balance effectiveness with affordability, scalability, and efficiency requires a significant investment in intervention development.2. What does this paper add? This paper provides a structured approach to integrating human-centered design principles into the Preparation phase of the multiphase optimization strategy (MOST).3. What are the implications for practice, research, or policy? The proposed synthesized model provides a framework for rigorous and efficient intervention development research in the Preparation phase of MOST that will ensure the success of intervention optimization and contribute to improving public health impact of mental health and substance use interventions.

Digitally characterizing the dynamics of multiple health behavior change

OBJECTIVE

We applied the ORBIT model to digitally define dynamic treatment pathways whereby intervention improves multiple risk behaviors. We hypothesized that effective intervention improves the frequency and consistency of targeted health behaviors and that both correlate with automaticity (habit) and self-efficacy (self-regulation).

METHOD

Study 1: Via location scale mixed modeling we compared effects when hybrid mobile intervention did versus did not target each behavior in the Make Better Choices 1 (MBC1) trial (n = 204). Participants had all of four risk behaviors: low moderate-vigorous physical activity (MVPA) and fruit and vegetable consumption (FV), and high saturated fat (FAT) and sedentary leisure screen time (SED). Models estimated the mean (location), between-subjects variance, and within-subject variance (scale).

RESULTS

Treatment by time interactions showed that location increased for MVPA and FV (Bs = 1.68, .61; ps < .001) and decreased for SED and FAT (Bs = -2.01, -.07; ps < .05) more when treatments targeted the behavior. Within-subject variance modeling revealed group by time interactions for scale (taus = -.19, -.75, -.17, -.11; ps < .001), indicating that all behaviors grew more consistent when targeted.

METHOD

Study 2: In the MBC2 trial (n = 212) we examined correlations between location, scale, self-efficacy, and automaticity for the three targeted behaviors.

RESULTS

For SED, higher scale (less consistency) but not location correlated with lower self-efficacy (r = -.22, p = .014) and automaticity (r = -.23, p = .013). For FV and MVPA, higher location, but not scale, correlated with higher self-efficacy (rs = .38, .34, ps < .001) and greater automaticity (rs = .46, .42, ps < .001).

CONCLUSIONS

Location scale mixed modeling suggests that both habit and self-regulation changes probably accompany acquisition of complex diet and activity behaviors. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

One view of the next decade of research on behavioral and biobehavioral approaches to cancer prevention and control: intervention optimization

As a new decade begins, we propose that the time is right to reexamine current methods and procedures and look for opportunities to accelerate progress in cancer prevention and control. In this article we offer our view of the next decade of research on behavioral and biobehavioral interventions for cancer prevention and control. We begin by discussing and questioning several implicit conventions. We then briefly introduce an alternative research framework: the multiphase optimization strategy (MOST). MOST, a principled framework for intervention development, optimization, and evaluation, stresses not only intervention effectiveness, but also intervention affordability, scalability, and efficiency. We review some current limitations of MOST along with future directions for methodological work in this area, and suggest some changes in the scientific environment we believe would permit wider adoption of intervention optimization. We propose that wider adoption of intervention optimization would have a positive impact on development and successful implementation of interventions for cancer prevention and control and on intervention science more broadly, including accumulation of a coherent base of knowledge about what works and what does not; establishment of an empirical basis for adaptation of interventions to different settings with different levels and types of resources; and, in the long run, acceleration of progress from Stage 0 to Stage V in the National Institutes of Health Model of Stages of Intervention Development.

The preparation phase in the multiphase optimization strategy (MOST): a systematic review and introduction of a reporting checklist

Multicomponent behavioral interventions developed using the multiphase optimization strategy (MOST) framework offer important advantages over alternative intervention development models by focusing on outcomes within constraints relevant for effective dissemination. MOST consists of three phases: preparation, optimization, and evaluation. The preparation phase is critical to establishing the foundation for the optimization and evaluation phases; thus, detailed reporting is critical to enhancing rigor and reproducibility. A systematic review of published research using the MOST framework was conducted. A structured framework was used to describe and summarize the use of MOST terminology (i.e., preparation phase and optimization objective) and the presentation of preparation work, the conceptual model, and the optimization. Fifty-eight articles were reviewed and the majority focused on either describing the methodology or presenting results of an optimization trial (n = 38, 66%). Although almost all articles identified intervention components (96%), there was considerable variability in the degree to which authors fully described other elements of MOST. In particular, there was less consistency in use of MOST terminology. Reporting on the MOST preparation phase is varied, and there is a need for increased focus on explicit articulation of key design elements and rationale of the preparation phase. The proposed checklist for reporting MOST studies would significantly advance the use of this emerging methodology and improve implementation and dissemination of MOST. Accurate reporting is essential to reproducibility and rigor of scientific trials as it ensures future research fully understands not only the methodology, but the rationale for intervention and optimization decisions.

Cost-Effectiveness of Lipid-Lowering Treatments in Young Adults

BACKGROUND

Raised low-density lipoprotein cholesterol (LDL-C) in young adulthood (aged 18-39 years) is associated with atherosclerotic cardiovascular disease (ASCVD) later in life. Most young adults with elevated LDL-C do not currently receive lipid-lowering treatment.

OBJECTIVES

This study aimed to estimate the prevalence of elevated LDL-C in ASCVD-free U.S. young adults and the cost-effectiveness of lipid-lowering strategies for raised LDL-C in young adulthood compared with standard care.

METHODS

The prevalence of raised LDL-C was examined in the U.S. National Health and Nutrition Examination Survey. The CVD Policy Model projected lifetime quality-adjusted life years (QALYs), health care costs, and incremental cost-effectiveness ratios (ICERs) for lipid-lowering strategies. Standard care was statin treatment for adults aged ≥40 years based on LDL-C, ASCVD risk, or diabetes plus young adults with LDL-C ≥190 mg/dL. Lipid lowering incremental to standard care with moderate-intensity statins or intensive lifestyle interventions was simulated starting when young adult LDL-C was either ≥160 mg/dL or ≥130 mg/dL.

RESULTS

Approximately 27% of ASCVD-free young adults have LDL-C of ≥130 mg/dL, and 9% have LDL-C of ≥160 mg/dL. The model projected that young adult lipid lowering with statins or lifestyle interventions would prevent lifetime ASCVD events and increase QALYs compared with standard care. ICERs were US$31,000/QALY for statins in young adult men with LDL-C of ≥130 mg/dL and US$106,000/QALY for statins in young adult women with LDL-C of ≥130 mg/dL. Intensive lifestyle intervention was more costly and less effective than statin therapy.

CONCLUSIONS

Statin treatment for LDL-C of ≥130 mg/dL is highly cost-effective in young adult men and intermediately cost-effective in young adult women.

Methods-Motivational Interviewing Approach for Enhanced Retention and Attendance

INTRODUCTION

Suboptimal and differential participant engagement in randomized trials-including retention at primary outcome assessments and attendance at intervention sessions-undermines rigor, internal validity, and trial conclusions.

METHODS

First, this study describes Methods-Motivational Interviewing approach and strategies for implementation. This approach engages potential participants before randomization through interactive, prerequisite orientation sessions that illustrate the scientific rationale behind trial methods in accessible language and use motivational interviewing to diffuse ambivalence about participation. Then, this study examines the potential improvements in retention (proportion of participants assessed at follow-up visits) and attendance (e.g., mean percentage of intervention sessions attended, percentage of participants who attended 0 sessions) in 3 randomized weight-management trials that quickly added prerequisite orientations to their protocols following early signs of suboptimal or differential participant engagement (Supporting Health by Integrating Nutrition and Exercise [2009-2013, n=194]; Get Social [2016-2020, n=217]; GestationaL Weight Gain and Optimal Wellness [2014-2018, n=389]). Using a pre-post analytical design, adjusted estimates from regression models controlling for condition and assessment timepoint (analyses from 2020) are reported.

RESULTS

After adding prerequisite orientations, all 3 trials attained higher participant engagement. Retention at assessments was 11.4% and 17.3% higher (Get Social and Supporting Health by Integrating Nutrition and Exercise, respectively). Mean percentage of attendance at intervention sessions was 8.8% higher (GestationaL Weight Gain and Optimal Wellness), and 10.1% fewer participants attended 0 intervention sessions (Get Social). Descriptively, all the remaining retention and attendance outcomes were consistently higher but were nonsignificant. Across the trials, adding prerequisite orientations did not impact the proportion of eligible participants enrolled or the baseline demographics.

CONCLUSIONS

The Methods-Motivational Interviewing approach shows promise for increasing the rigor of randomized trials and is readily adaptable to in-person, webinar, and conference call formats.

TRIAL REGISTRATION

All 3 trials are registered at www.clinicaltrials.gov (Supporting Health by Integrating Nutrition and Exercise: NCT00960414; Get Social: NCT02646618; and GestationaL Weight Gain and Optimal Wellness: NCT02130232).

The multiphase optimization strategy (MOST) in child maltreatment prevention research

Each year hundreds of thousands of children and families receive behavioral interventions designed to prevent child maltreatment; yet rates of maltreatment have not declined in over a decade. To reduce the prevalence and prevent the life-long negative consequences of child maltreatment, behavioral interventions must not only be effective, but also affordable, scalable, and efficient to meet the demand for these services. An innovative approach to intervention science is needed. The purpose of this article is to introduce the multiphase optimization strategy (MOST) to the field of child maltreatment prevention. MOST is an engineering-inspired framework for developing, optimizing, and evaluating multicomponent behavioral interventions. MOST enables intervention scientists to empirically examine the performance of each intervention component, independently and in combination. Using a hypothetical example of a home visiting intervention and artificial data, this article demonstrates how MOST may be used to optimize the content of a parent-focused in-home intervention and the engagement strategies of an intervention to increase completion rate to identify an intervention that is effective, efficient, economical, and scalable. We suggest that MOST will ultimately improve prevention science and hasten the progress of translational science to prevent child maltreatment.

Applying the Multiphase Optimization Strategy for the Development of Optimized Interventions in Palliative Care

Recent systematic reviews and meta-analyses have reported positive benefit of multicomponent "bundled" palliative care interventions for patients and family caregivers while highlighting limitations in determining key elements and mechanisms of improvement. Traditional research approaches, such as the randomized controlled trial (RCT), typically treat interventions as "bundled" treatment packages, making it difficult to assess definitively which aspects of an intervention can be reduced or replaced or whether there are synergistic or antagonistic interactions between intervention components. Progressing toward palliative care interventions that are effective, efficient, and scalable will require new strategies and novel approaches. One such approach is the Multiphase Optimization Strategy (MOST), a framework informed by engineering principles, that uses a systematic process to empirically identify an intervention comprised of components that positively contribute to desired outcomes under real-life constraints. This article provides a brief overview and application of MOST and factorial trial design in palliative care research, including our insights from conducting a pilot factorial trial of an early palliative care intervention to enhance the decision support skills of advanced cancer family caregivers (Project CASCADE).

A Hybrid Evaluation-Optimization Trial to Evaluate an Intervention Targeting the Intersection of Alcohol and Sex in College Students and Simultaneously Test an Additional Component Aimed at Preventing Sexual Violence

BACKGROUND

Using the multiphase optimization strategy (MOST), we previously developed and optimized an online behavioral intervention, itMatters, aimed at reducing the risk of sexually transmitted infections (STI) among first-year college students by targeting the intersection of alcohol use and sexual behaviors.

PURPOSE

We had two goals: (a) to evaluate the optimized itMatters intervention and (b) to determine whether the candidate sexual violence prevention (SVP) component (included at the request of participating universities) had a detectable effect and therefore should be added to create a new version of itMatters. We also describe the hybrid evaluation-optimization trial we conducted to accomplish these two goals in a single experiment.

METHODS

First year college students (N = 3,098) at four universities in the USA were individually randomized in a hybrid evaluation-optimization 2 × 2 factorial trial. Data were analyzed using regression models, with pre-test outcome variables included as covariates in the models. Analyses were conducted separately with (a) immediate post-test scores and (b) 60-day follow-up scores as outcome variables.

RESULTS

Experimental results indicated a significant effect of itMatters on targeted proximal outcomes (norms) and on one distal behavioral outcome (binge drinking). There were no significant effects on other behavioral outcomes, including the intersection of alcohol and sexual behaviors. In addition, there were mixed results (positive short-term effect; no effect at 60-day follow-up) of the SVP component on targeted proximal outcomes (students' self-efficacy to reduce/prevent sexual violence and perceived effectiveness of protective behavioral strategies).

CONCLUSIONS

The hybrid evaluation-optimization trial enabled us to evaluate the individual and combined effectiveness of the optimized itMatters intervention and the SVP component in a single experiment, conserving resources and providing greatly improved efficiency.

TRIAL REGISTRATION

NCT04095065.

Exploring Beliefs and Preferences for Reducing Sedentary Behavior Among Adults With Symptomatic Knee Osteoarthritis or Knee Replacement

Objective

Physical activity has numerous benefits for those with symptomatic knee osteoarthritis (KOA) or knee replacement, yet many individuals engage in insufficient activity. The purpose of this study was to explore beliefs about sedentary behavior, barriers to standing, and program preferences for adults with symptomatic KOA or knee replacement.

Methods

Forty‐two individuals ≥50 years with symptomatic KOA or knee replacement completed an online survey assessing current knee pain and function, sitting time, physical activity participation, beliefs about sedentary behavior, and preferences for a sedentary reduction program.

Results

Participants indicated barriers to standing were pain, discomfort, or working on a computer. Most participants shared interest to participate in a program to reduce sitting time. Participants chose education, self‐monitoring, and activity tracking as most preferable components for an intervention design.

Conclusion

Future interventions to reduce sedentary time may utilize these results to tailor programs for those with symptomatic KOA or knee replacement.

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.

STUDY DESIGN

Five experimental intervention components will be tested as adjuncts to an established 12-month online BLI: virtual reality for BLI skills training, interactive video feedback, tailored intervention to promote physical activity, skills for dysregulated eating, and social support combined with friendly competition. Following the Multiphase Optimization Strategy (MOST) framework, the components will first be refined and finalized during Preparation Phase pilot testing and then evaluated in a factorial experiment with 384 adults with overweight or obesity. A priori optimization criteria that balance efficacy and efficiency will be used to create a finalized treatment package that produces the best weight loss outcomes with the fewest intervention components. Mediation analysis will be conducted to test hypothesized mechanisms of action and a moderator analysis will be conducted to understand for whom and under what circumstances the interventions are effective.

CONCLUSION

This study will provide important information about intervention strategies that are useful for improving outcomes of online BLI. The finalized treatment package will be suitable for testing in a future randomized trial in the MOST Evaluation Phase.

Challenges and Opportunities for the Prevention and Treatment of Cardiovascular Disease Among Young Adults: Report From a National Heart, Lung, and Blood Institute Working Group

Improvements in cardiovascular disease (CVD) rates among young adults in the past 2 decades have been offset by increasing racial/ethnic and gender disparities, persistence of unhealthy lifestyle habits, overweight and obesity, and other CVD risk factors. To enhance the promotion of cardiovascular health among young adults 18 to 39 years old, the medical and broader public health community must understand the biological, interpersonal, and behavioral features of this life stage. Therefore, the National Heart, Lung, and Blood Institute, with support from the Office of Behavioral and Social Science Research, convened a 2-day workshop in Bethesda, Maryland, in September 2017 to identify research challenges and opportunities related to the cardiovascular health of young adults. The current generation of young adults live in an environment undergoing substantial economic, social, and technological transformations, differentiating them from prior research cohorts of young adults. Although the accumulation of clinical and behavioral risk factors for CVD begins early in life, and research suggests early risk is an important determinant of future events, few trials have studied prevention and treatment of CVD in participants <40 years old. Building an evidence base for CVD prevention in this population will require the engagement of young adults, who are often disconnected from the healthcare system and may not prioritize long-term health. These changes demand a repositioning of existing evidence-based treatments to accommodate new sociotechnical contexts. In this article, the authors review the recent literature and current research opportunities to advance the cardiovascular health of today's young adults.