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

Background Mobile messaging is often used in behavioral weight loss interventions, yet little is known as to the extent to which they contribute to weight loss when part of a multicomponent treatment package. The multiphase optimization strategy (MOST) is a framework that researchers can use to systematically investigate interventions that achieve desirable outcomes given specified constraints. Objective This study describes the use of MOST to develop a messaging intervention as a component to test as part of a weight loss treatment package in a subsequent optimization trial. Methods On the basis of our conceptual model, a text message intervention was created to support self-regulation of weight-related behaviors. We tested the messages in the ENLIGHTEN feasibility pilot study. Adults with overweight and obesity were recruited to participate in an 8-week weight loss program. Participants received a commercially available self-monitoring smartphone app, coaching calls, and text messages. The number and frequency of text messages sent were determined by individual preferences, and weight was assessed at 8 weeks. Results Participants (n=9) in the feasibility pilot study lost 3.2% of their initial body weight over the 8-week intervention and preferred to receive 1.8 texts per day for 4.3 days per week. Researcher burden in manually sending messages was high, and the cost of receiving text messages was a concern. Therefore, a fully automated push notification system was developed to facilitate sending tailored daily messages to participants to support weight loss. Conclusions Following the completion of specifying the conceptual model and the feasibility pilot study, the message intervention went through a final iteration. Theory and feasibility pilot study results during the preparation phase informed critical decisions about automation, frequency, triggers, and content before inclusion as a treatment component in a factorial optimization trial. Trial Registration ClinicalTrials.gov NCT01814072; https://clinicaltrials.gov/ct2/show/NCT01814072

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Objectives Precision behavioral medicine techniques integrating wearable ultraviolet radiation (UVR) sensors may help individuals avoid sun exposure that places them at-risk for skin cancer. As a preliminary step in our patient-centered process of developing a just-in-time adaptive intervention, this study evaluated reactions and preferences to UVR sensors among melanoma survivors. Materials and Methods Early stage adult melanoma survivors were recruited for a focus group (n = 11) or 10-day observational study, which included daily wearing a UVR sensor and sun exposure surveys (n = 39). Both the focus group moderator guide and observational study exit interviews included questions on UVR sensing as a potential intervention strategy. These responses were transcribed and coded using an inductive strategy. Results Most observational study participants (84.6%) said they would find information provided by UVR sensors to be useful to help them learn about how specific conditions (eg, clouds, location) impact sun exposure and provide in-the-moment alerts. Focus group participants expressed enthusiasm for UVR information and identified preferred qualities of a UVR sensor, such as small size and integration with other devices. Participants in both studies indicated concern that UVR feedback may be difficult to interpret and some expressed that a UVR sensor may not be convenient or desirable to wear in daily life. Discussion Melanoma survivors believe that personalized UVR exposure information could improve their sun protection and want this information delivered in a method that is meaningful and actionable. Conclusion UVR sensing is a promising component of a precision behavioral medicine strategy to reduce skin cancer risk.

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.