Linda M. Collins

Background: Self-monitoring is a vital component of behavioral obesity treatment. It often involves tracking dietary intake, physical activity, and body weight. However, the optimal combination of self-monitoring strategies that maximizes weight loss is unknown. To address this gap, we leverage a framework called the multiphase optimization strategy, which facilitates the identification of an intervention’s “active ingredients” that promote weight loss and its “inactive ingredients” that have little impact, thus adding unnecessary patient effort and time demands. Objective: This study aims to examine the unique and combined weight loss effects of 3 popular self-monitoring strategies (tracking dietary intake, steps, and body weight). Methods: Spark was an optimization-randomized clinical trial that used a 2 × 2 × 2 full factorial design with 8 experimental conditions. Participants, US adults with overweight or obesity (N=176), were randomized to receive 0-3 self-monitoring strategies in a 6-month fully digital weight loss intervention. For each assigned strategy, participants were instructed to self-monitor daily via commercially available digital tools (a mobile app, wearable activity tracker, and smart scale) and received a corresponding goal (eg, a daily calorie goal) and weekly automated feedback. All participants received core intervention components, including weekly lessons and action plans informed by Social Cognitive Theory, to promote healthy eating and physical activity. Assessments occurred at baseline and at 1, 3, and 6 months. Weight was assessed objectively via a smart scale. The primary aim is to test the main effects of the 3 self-monitoring components and their interactions on weight change from baseline to 6 months. Secondary outcomes include change in BMI, caloric intake, diet quality, physical activity, and health-related quality of life, as well as 1- and 3-month weight change and the relation between self-monitoring engagement and weight change. Patterns of engagement will be operationalized as the percentage of days of self-monitoring during the 6-month intervention. Moderators of weight loss success will be explored to understand whether certain subgroups of individuals benefit more from specific self-monitoring strategies. We also conducted a separate embedded experiment to test the impact of a self-directed web-based orientation session on 6-month trial retention. After the intervention, semistructured qualitative interviews were conducted with a subset of participants to elucidate factors that impact engagement and its link to weight loss. Results: Recruitment occurred from September 2023 to November 2024. Data collection was completed in June 2025. Data analysis is ongoing. Conclusions: This trial will provide evidence as to which self-monitoring strategies are the “active ingredients” in a fully digital weight loss intervention and begin to explore which subgroups may do best with which strategies. These results have potential for public health impact by maximizing weight loss while minimizing patient burden. Trial Registration: ClinicalTrials.gov NCT05249465, https://clinicaltrials.gov/study/NCT05249465 International Registered Report Identifier (IRRID): DERR1-10.2196/75629

Background: Young adults from racial and ethnic minority (REM) groups are at greater risk of disengaging from vital mental health services than their majority group peers. Emerging research suggests developmentally tailored interventions that enable personalized exploration of cultural and structural contexts; encourage trust in relationships with service providers; enhance hope for recovery; and increase self-efficacy to adhere to treatment can improve engagement among underserved REM young adults with mental health disorders. Objective: Just Do You is a brief young adult treatment engagement intervention. Although Just Do You showed evidence of efficacy with medium effect sizes in a previous clinical trial, not all participants benefited, and some of the proposed mediators were not changed. Grounded in the multiphase optimization strategy (MOST), we propose to improve the provision and effects of Just Do You by testing a set of new candidate intervention components that complement the original Just Do You program. This can potentially enhance its effects on engagement and mental health service use for REM young adults. Methods: A total of 3 new candidate behavioral intervention components and their putative mediators were designed in collaboration with REM young adults and other key informants. The new candidate components and their mechanisms of action are (1) cultural identity (perceived cultural competency), (2) future self (hope), and (3) understanding environments (self-efficacy). A pilot optimization randomized controlled trial will be conducted in an outpatient mental health setting with REM participants aged 18-34 years (N=80). The candidate components will be tested in a 23-factorial design where all participants will receive Just Do You and be randomly assigned to one of 8 experimental conditions, each composed of a unique combination of candidate components. We will assess the new components’ acceptability and feasibility, and explore preliminary evidence of their effects on the primary outcome (engagement in mental health care) and mediators at a 3-month follow-up assessment. Results: The study began recruitment in December 2024 and is planned to continue through December 2026. Final study completion is planned for March 2027. The results will be disseminated via professional and scientific publications, presentations, and social media, as well as to members of the participant community. Conclusions: Findings from this study will supply critical deployment-focused evidence to inform a future randomized controlled trial as the next step in this program of research. We ultimately aim to create an effective, efficient, and scalable intervention to improve engagement and mental health outcomes among REM young adults. Trial Registration: ClinicalTrials.gov NCT06508450; https://clinicaltrials.gov/study/NCT06508450 International Registered Report Identifier (IRRID): DERR1-10.2196/68885

BACKGROUND Self-monitoring is a vital component of behavioral obesity treatment. It often involves tracking dietary intake, physical activity, and body weight. However, the optimal combination of self-monitoring strategies that maximizes weight loss is unknown. To address this gap, we leverage a framework called the multiphase optimization strategy, which facilitates the identification of an intervention’s “active ingredients” that promote weight loss and its “inactive ingredients” that have little impact, thus adding unnecessary patient effort and time demands. OBJECTIVE This study aims to examine the unique and combined weight loss effects of 3 popular self-monitoring strategies (tracking dietary intake, steps, and body weight). METHODS Spark was an optimization-randomized clinical trial that used a 2 × 2 × 2 full factorial design with 8 experimental conditions. Participants, US adults with overweight or obesity (N=176), were randomized to receive 0-3 self-monitoring strategies in a 6-month fully digital weight loss intervention. For each assigned strategy, participants were instructed to self-monitor daily via commercially available digital tools (a mobile app, wearable activity tracker, and smart scale) and received a corresponding goal (eg, a daily calorie goal) and weekly automated feedback. All participants received core intervention components, including weekly lessons and action plans informed by Social Cognitive Theory, to promote healthy eating and physical activity. Assessments occurred at baseline and at 1, 3, and 6 months. Weight was assessed objectively via a smart scale. The primary aim is to test the main effects of the 3 self-monitoring components and their interactions on weight change from baseline to 6 months. Secondary outcomes include change in BMI, caloric intake, diet quality, physical activity, and health-related quality of life, as well as 1- and 3-month weight change and the relation between self-monitoring engagement and weight change. Patterns of engagement will be operationalized as the percentage of days of self-monitoring during the 6-month intervention. Moderators of weight loss success will be explored to understand whether certain subgroups of individuals benefit more from specific self-monitoring strategies. We also conducted a separate embedded experiment to test the impact of a self-directed web-based orientation session on 6-month trial retention. After the intervention, semistructured qualitative interviews were conducted with a subset of participants to elucidate factors that impact engagement and its link to weight loss. RESULTS Recruitment occurred from September 2023 to November 2024. Data collection was completed in June 2025. Data analysis is ongoing. CONCLUSIONS This trial will provide evidence as to which self-monitoring strategies are the “active ingredients” in a fully digital weight loss intervention and begin to explore which subgroups may do best with which strategies. These results have potential for public health impact by maximizing weight loss while minimizing patient burden. CLINICALTRIAL ClinicalTrials.gov NCT05249465, https://clinicaltrials.gov/study/NCT05249465 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/75629

In the classical paradigm for intervention research, the components that are to make up an intervention are identified, pilot tested, and then immediately assembled into a treatment package and subjected to an evaluation randomized controlled trial (RCT) to assess the performance of the entire package. Intervention optimization, which adapts ideas from technological fields to intervention science in order to hasten scientific progress, is an alternative to the classical paradigm. The first article introducing intervention optimization via the multiphase optimization strategy (MOST) was published in Annals of Behavioral Medicine in 2005. In this commentary, I reflect on the evolution of intervention optimization from that first publication to today, and on what the future could hold if the intervention science field continues to adopt the optimization paradigm. I propose that if intervention optimization became standard operating procedure, the field would accumulate a coherent base of knowledge about what specific intervention strategies work, for whom, under which circumstances, and why; every intervention produced would contain only components that contribute enough to justify their resource requirements; interventions would be readily implementable; and as the knowledge base grew, interventions would be improved continually.

BACKGROUND Young adults from racial and ethnic minority (REM) groups are at greater risk of disengaging from vital mental health services than their majority group peers. Emerging research suggests developmentally tailored interventions that enable personalized exploration of cultural and structural contexts; encourage trust in relationships with service providers; enhance hope for recovery; and increase self-efficacy to adhere to treatment can improve engagement among underserved REM young adults with mental health disorders. OBJECTIVE Just Do You is a brief young adult treatment engagement intervention. Although Just Do You showed evidence of efficacy with medium effect sizes in a previous clinical trial, not all participants benefited, and some of the proposed mediators were not changed. Grounded in the multiphase optimization strategy (MOST), we propose to improve the provision and effects of Just Do You by testing a set of new candidate intervention components that complement the original Just Do You program. This can potentially enhance its effects on engagement and mental health service use for REM young adults. METHODS A total of 3 new candidate behavioral intervention components and their putative mediators were designed in collaboration with REM young adults and other key informants. The new candidate components and their mechanisms of action are (1) cultural identity (perceived cultural competency), (2) future self (hope), and (3) understanding environments (self-efficacy). A pilot optimization randomized controlled trial will be conducted in an outpatient mental health setting with REM participants aged 18-34 years (N=80). The candidate components will be tested in a 23-factorial design where all participants will receive Just Do You and be randomly assigned to one of 8 experimental conditions, each composed of a unique combination of candidate components. We will assess the new components’ acceptability and feasibility, and explore preliminary evidence of their effects on the primary outcome (engagement in mental health care) and mediators at a 3-month follow-up assessment. RESULTS The study began recruitment in December 2024 and is planned to continue through December 2026. Final study completion is planned for March 2027. The results will be disseminated via professional and scientific publications, presentations, and social media, as well as to members of the participant community. CONCLUSIONS Findings from this study will supply critical deployment-focused evidence to inform a future randomized controlled trial as the next step in this program of research. We ultimately aim to create an effective, efficient, and scalable intervention to improve engagement and mental health outcomes among REM young adults. CLINICALTRIAL ClinicalTrials.gov NCT06508450; https://clinicaltrials.gov/study/NCT06508450 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/68885

In current practice, intervention scientists applying the multiphase optimization strategy (MOST) with a 2^k factorial optimization trial use a component screening approach (CSA) to select intervention components for inclusion in an optimized intervention. In this approach, scientists review all estimated main effects and interactions to identify the important ones based on a fixed threshold, and then base decisions about component selection on these important effects. We propose an alternative posterior expected value approach based on Bayesian decision theory. This new approach aims to be easier to apply and more readily extensible to a variety of intervention optimization problems. We used Monte Carlo simulation to evaluate the performance of a posterior expected value approach and CSA (automated for simulation purposes) relative to two benchmarks: random component selection, and the classical treatment package approach. We found that both the posterior expected value approach and CSA yielded substantial performance gains relative to the benchmarks. We also found that the posterior expected value approach outperformed CSA modestly but consistently in terms of overall accuracy, sensitivity, and specificity, across a wide range of realistic variations in simulated factorial optimization trials. We discuss implications for intervention optimization and promising future directions in the use of posterior expected value to make decisions in MOST. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Objective: Few individuals with eating disorders (EDs) receive treatment. Innovations are needed to identify individuals with EDs and address care barriers. We developed a chatbot for promoting services uptake that could be paired with online screening. However, it is not yet known which components drive effects. This study estimated individual and combined contributions of four chatbot components on mental health services use (primary), chatbot helpfulness, and attitudes toward changing eating/shape/weight concerns (“change attitudes,” with higher scores indicating greater importance/readiness).Methods: Two hundred five individuals screening with an ED but not in treatment were randomized in an optimization randomized controlled trial to receive up to four chatbot components: psychoeducation, motivational interviewing, personalized service recommendations, and repeated administration (follow‐up check‐ins/reminders). Assessments were at baseline and 2, 6, and 14 weeks.Results: Participants who received repeated administration were more likely to report mental health services use, with no significant effects of other components on services use. Repeated administration slowed the decline in change attitudes participants experienced over time. Participants who received motivational interviewing found the chatbot more helpful, but this component was also associated with larger declines in change attitudes. Participants who received personalized recommendations found the chatbot more helpful, and receiving this component on its own was associated with the most favorable change attitude time trend. Psychoeducation showed no effects.Discussion: Results indicated important effects of components on outcomes; findings will be used to finalize decision making about the optimized intervention package. The chatbot shows high potential for addressing the treatment gap for EDs.

Optimizing multicomponent behavioral and biobehavioral interventions presents a complex decision problem. To arrive at an intervention that is both effective and readily implementable, it may be necessary to weigh effectiveness against implementability when deciding which components to select for inclusion. Different components may have differential effectiveness on an array of outcome variables. Moreover, different decision-makers will approach this problem with different objectives and preferences. Recent advances in decision-making methodology in the multiphase optimization strategy (MOST) have opened new possibilities for intervention scientists to optimize interventions based on a wide variety of decision-maker preferences, including those that involve multiple outcome variables. In this study, we introduce decision analysis for intervention value efficiency (DAIVE), a decision-making framework for use in MOST that incorporates these new decision-making methods. We apply DAIVE to select optimized interventions based on empirical data from a factorial optimization trial. We define various sets of hypothetical decision-maker preferences, and we apply DAIVE to identify optimized interventions appropriate to each case. We demonstrate how DAIVE can be used to make decisions about the composition of optimized interventions and how the choice of optimized intervention can differ according to decision-maker preferences and objectives. We offer recommendations for intervention scientists who want to apply DAIVE to select optimized interventions based on data from their own factorial optimization trials. (PsycInfo Database Record (c) 2024 APA, all rights reserved).