Settler colonialism is increasingly recognized as a structural force shaping Indigenous health, yet analytic frameworks in the health sciences have limitations in representing its causal dynamics. While Indigenous communities have long identified settler colonialism as foundational in shaping health and wellbeing, conventional epidemiologic approaches rely on causal assumptions—linearity, discrete exposures, and stable pathways—that are misaligned with its theorized properties as a dynamic, transtemporal, and adaptive social process. This misalignment contributes to epistemic exclusion, whereby insights from Indigenous epistemologies and settler colonial studies cannot be represented, remaining analytically unobservable within dominant methodological paradigms. We argue that addressing this gap requires achieving epistemic fit across three interdependent layers: teleological—orienting inquiry toward relationality rather than dominance; ontological—mapping settler colonialism’s higher-order functions onto context-specific forms; and epistemological—selecting causal representations adequate to the phenomenon’s complexity. Drawing on theory construction methodology, we develop a provisional framework for the settler colonial determination of health and outline how its causal architecture may be represented through multi-causal, feedback-informed approaches. We further identify concrete implications for empirical research, including the use of configurational causal logics and dynamic modeling strategies to capture interaction, emergence, and historical dependence. By positioning settler colonialism as a conditioning causal structure rather than a discrete determinant or indecipherable past episode, this framework extends existing public health approaches and provides a foundation for developing empirically tractable models that better align with Indigenous epistemologies and lived experience. Advancing such approaches is essential for generating explanations—and ultimately interventions—adequate to the complexity of health inequities in settler colonial contexts.

BackgroundMobile device–enabled interventions known as digital therapeutics (DTx) are increasingly used to prevent chronic disease by targeting psychological and behavioral processes. Individuals’ unique experiences while receiving DTx comprise real-world evidence (RWE) for evaluating DTx performance. An emerging strategy for early-stage DTx formative work uses small sample sizes, which facilitate efficient iteration and agile learning, while evaluating performance against descriptive benchmarks defined a priori, therefore minimizing the risk for confirmation bias. This study test benchmarks from the DTx RWE framework to formatively evaluate a novel DTx (the eMOTION study) to enhance affective response (ie, how people feel) during physical activity (PA). ObjectiveThis study aimed to determine whether the eMOTION DTx met a priori benchmarks for safety (<1% of participants report an adverse event), plausibility (≥51% of participants experience increased enjoyment in PA), usability (eg, ≥51% of participants report adequate usability), sustainability, feasibility (eg, <70% of participants report dissatisfaction), and equity (equity and accessibility are approximately equal across subgroups). MethodsParticipants (N=36; mean age 46, SD 14 years; 20/37, 54% female) underwent stratified random assignment to test one of four DTx versions for 14 days (n=9 each): (1) intensity PA goals, (2) affect PA goals with type and context recommendations, (3) affect PA goals with savoring exercises, and (4) affect PA goals with type, context, and savoring. Participants completed daily intervention sessions, asking them to focus on achieving a target heart rate (intensity) or feeling good (affect) during PA. Smartwatches were used to track PA and answer ecological momentary assessment (EMA) questions about how they felt during PA. Performance toward benchmarks was primarily assessed via official Institutional Review Board reporting channels (safety), interviews (plausibility, accessibility, and usability), and questionnaires (System Usability Scale [usability], Delighted-Terrible Scale [sustainability and feasibility], and equity). ResultsThe eMOTION DTx versions exceeded all a priori safety, plausibility, accessibility, usability, sustainability, feasibility, and equity thresholds. For safety, no adverse events were reported. Regarding plausibility, more than half of the participants who received affect goals reported increased PA enjoyment at the end of the study. Moreover, 64%-72% (23-26 out of 36) of participants rated the DTx at or above the standard System Usability Scale cutoff point for acceptable usability. More than 60% (22/36) of participants reported satisfaction with all DTx components, supporting DTx sustainability and feasibility. Finally, there was evidence for equity, with plausibility and accessibility comparable across sex, race, ethnicity, income, age, BMI, mobility, and physical constraint subgroups. ConclusionsSince DTx RWE Framework benchmarks for safety, plausibility, accessibility, usability, sustainability, feasibility, and equity were largely met, the eMOTION Study DTx is ready for a full-scale efficacy trial to refine the DTx and optimize efficiency and feasibility. Our approach incorporated transparent decision-making to generate results that are more readily translatable, easily replicable, and reflect current best practices in the field of DTx. Trial RegistrationClinicalTrials.gov NCT06125964; https://clinicaltrials.gov/study/NCT06125964

Community-academic partnerships are increasingly recognized as essential for advancing equitable public health outcomes. Yet many partnerships struggle to move beyond short-term, project-based collaboration toward sustained, trust-based engagement with communities. This commentary draws on the experience of the University of California San Diego Center for Community Health (CCH) and its long-standing partnerships with immigrant, refugee, and other underserved communities in San Diego County. Over more than two decades of practice, CCH and its community partners developed the Community-Led Transformation (CLT) approach to guide authentic community-academic collaboration. We describe three interdependent pillars of CLT: valuing community expertise, fostering trust-based partnerships, and ensuring fair access to resources and power-sharing. Examples from CCH programs, coalitions, and research collaborations illustrate how these principles are operationalized in practice. We also reflect on structural challenges within academic institutions, including funding instability, administrative barriers, and limitations in partnership infrastructure, and strategies used to navigate these constraints while sustaining community partnerships. We provide specific recommendations for academic partners, community partners, and funders to facilitate community-academic partnerships via increased capacity building, infrastructural supports, and greater relationship and trust building. The CLT framework has therefore been a success within CCH, and can provide practical insights for a variety of partners and institutions seeking to build authentic, durable partnerships that meaningfully advance public health and health equity.

Popular relapse prevention theories are represented using natural language descriptions and lack temporal information about how phenomena of interest (i.e., ‘relapse’, ‘prolapse’, ‘abstinence’) are dynamically caused over time and within individuals. We drew on the Theory Construction Methodology to develop a formal and computational model of relapse in smoking cessation. We used a participatory, iterative, multi-method approach involving an informal theory and computational model review, stakeholder interviews with researchers, people with lived experience, stop smoking practitioners, and policymakers (N = 15) and in silico simulations. We propose an initial within-person system dynamics model of relapse (‘COMPLAPSE’) in which biopsychosocial factors (e.g., stressors, cigarette cues, cravings, self-efficacy) are represented as time-varying inputs and state variables. These factors jointly determine the momentary preference for each behavioural option (i.e., smoke a cigarette, use a regulatory strategy, do nothing), with the probability of selecting each option (i.e., the output) generated by a softmax function. The simulations highlight the model’s ability to generate representational patterns of relapse, prolapse and abstinence, thus providing an early sense-check of its explanatory adequacy. In addition, local sensitivity analyses demonstrate that systematic variation of selected model parameters leads to expected qualitative shifts from, for example, prolapse to relapse. We discuss the implications of our work for relapse prevention theories and real-world applications, including the development and optimisation of technology-mediated just-in-time adaptive interventions for relapse prevention in smoking cessation.

Background: Existing behavior change theories often treat physical activity (PA) engagement as a linear, stage-like process driven by stable factors (e.g., self-efficacy, intention). Yet real-world PA emerges from fluctuating configurations of internal and external conditions, requiring models that better capture this complexity. Method: Using participatory systems science and Theory Construction Methodology, we developed an Integrative Model of PA as a prototheory. We conducted participatory systems-modeling interviews with 26 adults to elicit idiographic maps of how PA emerged in everyday life; integrated these maps with established behavioral frameworks via abductive synthesis and ontological mapping; and, through expert consensus, formalized causal propositions using configurational logic, activation regions, and adaptive feedback. Results: Co-modeling showed that PA engagement arose when multiple situational and personal features aligned in configurations that created “moments to act” and gated transitions across PA states. These configurations were evaluated via state-dependent value integration, with short-term feedback tuning situational weights and longer-term feedback reshaping identity, expectations, and readiness. These patterns align with Falk’s self-relevant value integration framework, suggesting a bridge between behavioral and neural accounts. Conclusion: The model conceptualizes PA as a nonlinear, adaptive valuation process shaped by configurational patterns and multi-timescale feedback, yielding testable hypotheses and guiding adaptive, person-centered interventions.

While Digital Therapeutics (DTx) are widely considered a key strategy to reach certain populations with unmet healthcare needs, a range of differences in the impact and adoption of DTx still exists. These differences are not just rooted in access, but also in gaps in knowledge about how to produce community-relevant DTx, primarily stemming from the implicit or explicit exclusion of those with both relevant trained expertise (gained through formal education or professional experience) and lived expertise (gained through personal and direct experience). This paper expands the traditional conceptualization of the digital divide beyond access to encompass four interconnected domains: the Digital Knowledge Divide, Digital Evidence Generation Divide, Digital Production Divide, and Digital Adoption Divide. Drawing on Ridgeway's cultural schema theory of status, we demonstrate how conventional team hierarchies in DTx development systematically allocate status and decision-making authority through automatic cultural defaults, credentials, professional roles, demographic characteristics, rather than through contextual assessment of who possesses the most relevant expertise for specific decisions. To address this challenge, we propose a theoretical framework for dynamic expertise integration that deliberately disrupts rapid-stabilizing hierarchies by creating explicit relational spaces where teams can recognize and value both lived and trained expertise contextually. We operationalize this framework through the DTx Team Building Worksheet, a practical tool that integrates team science approaches with Community-Led Transformation principles and Culturally and Community Responsive Design. The Worksheet provides structured processes for assessing diverse forms of expertise, defining roles dynamically, and identifying decision-making priorities that shift appropriately across the DTx lifecycle. This integrated approach including problem analysis, theoretical framework, and practical tool, offers a pathway toward more equitable DTx development by enabling teams to make status dynamics explicit, expand what counts as expertise, and establish new consensual norms about contextually-appropriate status allocation. We invite stakeholders across sectors to test and refine these tools in diverse contexts, recognizing that creating equitable DTx requires sustained commitment to partnerships that genuinely honor multiple forms of expertise and willingness to disrupt comfortable hierarchies in service of producing interventions truly designed for and with the communities they aim to serve.

Self Determination Theory posits that individuals may be more likely to initiate and maintain behaviors tied to intrinsic (vs. extrinsic) motivations and may provide a useful framework for understanding youth participation in novel sports. Using the Intrinsic Motivation Inventory (IMI) and Patient-Centered Assessment and Counseling for Exercise Plus Nutrition (PACE+) surveys, motivation and physical activity habits were explored in 27 children/adolescents (ages 7–16) enrolled in Parkour, an individual, non-competitive youth sport. Fifteen Parkour participants were also interviewed to gain an understanding of their motivations for participating. Study participants had high median IMI subscale scores related to interest/enjoyment (6.71/7) and perceived choice (6.40/7) compared to the whole scale. Similarly median sub-scale Pros and Self-Efficacy scores for physical activity from the PACE+ were high (4.25/5 and 3.91/5, respectively). The themes of autonomy and enjoyment were consistently reported in the qualitative interviews, expanding on the quantitative results. Other themes included appreciation for camaraderie, creativity, and a drive for improvement. These results provide early evidence that Parkour, and similar lifestyle sports, may be tied more to intrinsic than extrinsic motivations and could have potential for adoption and maintenance by youth with low motivation to engage in physical activity to promote healthy behaviors.

This report describes the transformation of a San Diego County collective impact initiative to center community voices through the co-creation of a community council to address childhood obesity. We present seven suggested recommendations for others interested in forming a community council within a collective impact effort and highlight challenges and future directions.

A major problem in global health is insufficient physical activity (PA) by individuals, despite its proven benefits. In this paper, Model Predictive Control (MPC) is evaluated as the basis for delivering personalised optimal adaptive behavioural interventions aimed at improving PA (in terms of the number of steps walked per day). Utilising the behavioural framework of Social Cognitive Theory (SCT) expressed as a fluid analogy computational model, a series of diverse control strategies are proposed under different circumstances that provide insights into how MPC can serve as a broad-based framework for delivering PA behavioural interventions. The complexities of measurement and information availability, physical and budgetary constraints, and plant limitations and their impact on decision-making are explored, with the results obtained demonstrating MPC's potential to deliver feasible, personalised, and user-friendly behavioural interventions under conditions involving limited measurements, nonlinearity, and plant-model mismatch.

Mobile device-enabled interventions known as digital therapeutics (DTx) are increasingly used to prevent chronic disease by targeting psychological and behavioral processes. Individuals’ unique experiences while receiving DTx comprise real-world evidence (RWE) for evaluating DTx performance. The current study applied the DTx RWE Framework to formatively evaluate a novel DTx (the eMOTION Study) to enhance affective response (i.e., how people feel) during physical activity (PA). We sought to determine whether the eMOTION DTx met a priori benchmarks for safety, efficacy, accessibility/usability, sustainability/feasibility, and equity. Participants (N=36, Mage=46 [SD=14], 53% female) underwent stratified random assignment to test one of four DTx versions for 14-days (n=9 each): (1) intensity PA goals; (2) affect PA goals with type/context recommendations; (3) affect PA goals with savoring exercises; and (4) affect PA goals with type/context and savoring. Participants completed daily intervention sessions asking them to focus on achieving a target heart rate (intensity) or feeling good (affect) during PA. Smartwatches were used to track PA and answer ecological momentary assessment (EMA) about how they felt during PA. Performance toward benchmarks was primarily assessed with questionnaires, interviews, and EMA. The eMOTION DTx versions exceeded all a priori safety, efficacy, accessibility/usability, sustainability/feasibility, and equity thresholds. For safety, no adverse events were reported. Regarding efficacy, over half of participants who received affect goals reported increased PA enjoyment at the end of the study, and post-hoc multilevel models of EMA data revealed that the affect (vs. intensity) groups had significantly greater “positive” affective responses during PA (Ps= .02 to .04; Cohen’s ds= 0.63 to 0.67). Moreover, 64-72% of participants rated the DTx at or above the standard System Usability Scale cut-point for acceptable accessibility/usability. More than 60% of participants reported satisfaction with all DTx components, supporting DTx sustainability/feasibility. Finally, there was evidence for equity, with efficacy and accessibility comparable across sex, race, ethnicity, income, age, BMI, mobility, and physical constraint sub-groups. DTx RWE Framework benchmarks indicated that the eMOTION Study DTx is ready for a full-scale effectiveness trial to refine the DTx to optimize efficiency and feasibility. Our approach incorporated transparent decision-making to generate results that are more readily-translatable, easily replicable, and reflect current best practices in the field of DTx. This project was registered on ClinicalTrials.gov (ID: NCT06125964) and Open Science Framework (DOI: 10.17605/OSF.IO/QTF79).

The Healing Experiences of Adversity Among Latinos (HEALthy4You; H4Y) study was a multi-sector partnership between an academic research institution, a Federally Qualified Health Center (FQHC), and a multi-sector collective impact coalition focused on childhood obesity prevention. The goal of HEALthy4You was to develop community-centered and culturally appropriate precision interventions within FQHCs for Latino families to address predictors of adverse child experiences and treat childhood obesity. A multidisciplinary and multi-sector research, clinical, and community team (N = 29) was formed in September 2020 to co-design the study, which launched in June 2022. The team utilized a co-creation approach combined with the Exploration, Preparation, Implementation, and Sustainment framework to facilitate a collaborative design process. We conducted an internal and retrospective process evaluation in March 2023 to identify antecedents and situational factors associated with project formation, with a focus on understanding tensions and challenges with a broad partnership structure. We outline the team's co-creation process and describe internal challenges and pitfalls that emerged when developing the project. We sought to better understand the impact of differing perspectives, priorities, and goals between disciplines, sectors, and roles; differing approaches to evidence and evidence production; and team strategies to mitigate and manage competing pressures and priorities. This case report describes lessons learned, intending to share insights to support future development of best practices in project, partner, and team formation between researchers, clinicians, and community members. More specifically, these lessons could help inform community-led research endeavors between academic institutions, FQHCs, and community-based organizations (CBOs).

Control systems engineering has contributed to a paradigm shift in behavioral science and medicine. Among the applications of control systems engineering in behavioral medicine includes understanding, on an individual level, the dynamics of behavior change and leveraging this knowledge to deliver optimized, personalized interventions. These principles are the foundation of the control optimization trial (COT) framework that aims to facilitate the dissemination of datadriven, control-oriented behavioral interventions and consequently improve individual and public health. YourMove (ClinicalTrials.gov ID NCT05598996), a first-of-its-kind COT study, is an intervention to increase physical activity in sedentary adults and the culmination of years of research into the effectiveness of system identification and model predictive control (MPC) design in behavior change interventions. This paper summarizes the methods utilized in YourMove and provides promising preliminary results for illustrative participants in the ongoing study. The results presented are consistent with scenarios simulated in prior work and validate the COT framework as an effective tool for delivering personalized closed-loop interventions. In particular, results from this study demonstrate the performance and robustness of a three-degree-of-freedom Kalman filter-based hybrid model predictive control (3DoF-KF HMPC) algorithm in real-world settings.

Background While effective physical activity (PA) interventions exist, interventions often work only for some individuals or only for a limited time. Thus, there is a need for digital health interventions that account for dynamic, idiosyncratic PA determinants to support each person’s PA. We hypothesize that supporting individuals with their personal PA goals requires a personalized intervention that both supports each person in forming daily habits of walking more and develops personalized knowledge, skills, and practices regarding engaging in exercise routines. We operationalized these adaptive features via a digital health intervention called YourMove that uses a control systems approach to support personalized habit formation and a self-experimentation approach to develop personalized knowledge, skills, and practices. Objective The primary aim is to evaluate differences in minutes of moderate to vigorous PA (MVPA) per week at 12 months comparing our personalized intervention, called YourMove, with an active control that is similar but without personalization of the intervention components and mimics best-in-class digital health worksite wellness programs. Methods The YourMove study is a 12-month randomized controlled trial that involves 386 inactive adults aged 25 to 80 years. All participants receive (1) a Fitbit Versa smartwatch and corresponding smartphone app; (2) weekly PA goal suggestions and feedback, behavior change strategies, and reminders via SMS text messaging; and (3) up to US $50 in incentives for reaching daily step goals. Participants randomized to the active control group, modeled after worksite wellness programs, receive all the elements described in addition to a static daily step goal and static point rewards. Participants randomized to the intervention group receive (1) a habit formation element with daily personalized step goals and personalized point rewards generated through a control optimization trial approach and (2) a knowledge, skill, and practice development element featuring a self-guided self-experimentation tool that helps individuals find strategies to improve MVPA. The primary outcome is objectively assessed weekly minutes of MVPA via an ActiGraph monitor. Results Recruitment began in October 2022 and concluded in August 2024. Data collection will conclude in August 2025, with results expected by early 2026. Conclusions We hypothesize that the intervention group will show greater improvement in MVPA than the active control group at 12 months. If the hypothesis is supported, this will provide compelling evidence to suggest that personalized and perpetually adaptive support can enhance PA more effectively than intervention elements commonly used in digital health worksite wellness programs. If the trial is successful, the results will provide justification to explore both the control optimization trial approach and self-experimentation approach for other complex, idiosyncratic, and dynamic behaviors such as weight management, smoking, or substance abuse. Trial Registration ClinicalTrials.gov NCT05598996; https://clinicaltrials.gov/study/NCT05598996 International Registered Report Identifier (IRRID) DERR1-10.2196/70599

BACKGROUND:Traditional dietary assessment methods used in nutrition research and practice are self-reported, burdensome, and prone to error, limiting utility. In recent years, sensor-based devices and machine learning approaches have emerged as promising tools for automating eating behavior detection and initiating different approaches to assessing intake. These technologies have potential to enhance dietary assessment and its accuracy, support personalized dietary interventions through real time, context-aware feedback, and reduce burden on respondents and practitioners. A prior 2021 review by the authors concluded that existing devices are not yet feasible for dietetic practice. OBJECTIVES:This study aims to conduct a scoping review of sensor-based devices capable of detecting eating and drinking and to evaluate whether recent advancements have improved their feasibility for use in real-world nutrition applications. METHODS:A scoping review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Scoping Reviews framework. Studies published between January 2022 and September 2025 that evaluated the performance of sensor-based devices in identifying food and/or beverage intake were included. Devices were evaluated against 6 feasibility criteria to assess real-world applicability: ≥80% accuracy, freedom in food and beverage selection; social acceptability and comfort; long battery life; real-time detection; and ability to detect both eating and drinking. RESULTS:Fifty studies (52 devices) were included: 19 wrist-worn, 8 neck-worn, 7 ear-worn, 7 glasses-type, 6 in the "other" category, and 5 multiposition devices. None met all 6 feasibility criteria. The most common unmet criterion was adequate battery life (n = 43), followed by real-time processing (n = 37), variety of foods or behaviors in testing (n = 31), detection of both eating and drinking (n = 31), social acceptability and comfort (n = 15), and accuracy (n = 10). CONCLUSIONS:Although no sensor-based devices met all criteria for real-world feasibility, recent advancements suggest meaningful progress in areas of social acceptability and computational efficiency. These improvements signal a shift toward more practical, user-friendly designs that may soon be capable of supporting automated dietary assessment and individualized nutrition care.

Objective:A key concept in health psychology is behavioral maintenance. However, previous research has struggled to establish shared conceptualizations and operational definitions. This study aimed to contribute to this debate by examining whether a simple conceptual proposition of physical activity maintenance as 'the performance of physical activity according to an intended target threshold over a specific period of observation' can be empirically supported, and under which boundary conditions. Specifically, we explored different formulations of two boundary conditions: activity threshold and timescale of change. Methods:We analyzed 350 time series (length = 182 days) of moderate-to-vigorous physical activity (MVPA) collected daily with Fitbit from participants in a weight loss intervention. All participants reported an intention to engage in at least 150 min of MVPA per week over the following six months. Activity thresholds were defined based on each participant's baseline MVPA. Generalized Additive Models were used to model individual trajectories across varying timescales (7, 14, 28, and 56 days). Results:At short timescales (7-14 days) trajectories crossed the threshold frequently, indicating high variability. At longer timescales (28-56 days) trajectories were more stable, with participants tending to stay either above or below their threshold, aligning with our target conceptualization of maintenance. Relaxing the threshold by 10-20% relatively increased the proportion of participants classified as maintainers, though maintenance remained uncommon for participants with higher thresholds. Conclusions:Our findings provide initial evidence on which boundary conditions support detecting physical activity maintenance as conceptually defined. These results underscore the importance of systematically testing boundary conditions to advance understanding of behavioral maintenance.Trial registration: ClinicalTrials.gov identifier: NCT03907462.

Background California adopted universal screening of adverse childhood experiences (ACEs) in January 2020 and dedicated significant financial and human resources to "ACES Aware," a statewide campaign to scale ACEs screening throughout the state. Provider perspectives after the roll-out of ACEs Aware have been understudied. The aim of this study was to understand provider perspectives on universal ACEs screening in primary care. We explored indicators of acceptability, utility, and barriers and facilitators of screening for ACEs. We also investigated treatments offered for disclosed ACEs.Methods A cross-sectional survey with quantitative and qualitative components was distributed via Facebook, Twitter, and electronic listservs between March and April 2022, 2 years after the launch of ACEs Aware. The survey included the validated and reliable "Acceptability of Implementation Measure" and "Feasibility of Implementation Measure" as well as multiple choice, ranking, and free-text items to understand determinants of screening and treatment approaches.Results Eighty two primary care providers in California, working primarily in pediatrics (84%), completed the survey. The majority (78%) received training on assessing ACEs and 60% reported using the Pediatric ACEs and Related Life-events Screener (PEARLS). About 22% "strongly agree" that PEARLS is acceptable and 32% "strongly agree" that PEARLS is feasible. Only 17% "strongly agree" that they like PEARLS. The top barriers were: (1) insufficient time; (2) unclear treatment pathway for detected ACEs; and (3) inadequate staffing to perform screening. The top facilitators for screening were: (1) financial incentives for providers to screen; (2) financial incentives for organizational leadership to implement screening; and (3) leadership support of screeners. The top approaches for addressing ACEs were: (1) behavioral therapy; (2) case navigation; and (3) trauma-informed care.Conclusion This study provided a first look at provider perspectives on ACEs screening and treatment in a sample of California providers. Most responding providers report currently screening for ACEs and using PEARLS. Perceptions of feasibility were slightly higher than for acceptability. Facilitators were largely top-down and organizational in nature, such as financial incentives and leadership support. Future directions could include an exploration into why some providers may find ACEs unappealing and research to identify effective and accessible treatment approaches for ACEs.

The integration of control systems principles in behavioral medicine involves developing interventions that can be personalized to foster healthy behaviors, such as meaningful and consistent engagement in physical activity. In this paper, system identification and hybrid model predictive control are applied to design individualized behavioral interventions using the control optimization trial (COT) framework. The paper details the multiple stages of a COT, from experimental design in system identification to controller implementation, and demonstrates its efficacy using participant data from Just Walk, an intervention that promotes walking behavior in sedentary adults. Mixed partitioning of estimation and validation data is applied to estimate ARX models for an illustrative participant, selecting the model with the best performance over a weighted norm balancing predictive ability with overall data fit. This model serves as the internal model in a three-degree-of-freedom Kalman filter-based Hybrid Model Predictive Controller (3DoF-KF HMPC) that provides “ambitious but doable” goals for initiation and maintenance phases of the physical activity intervention. Performance and robustness in a closed-loop setting are evaluated via both nominal and Monte Carlo simulation; the latter confirms the inherent robustness properties of the controller under plant-model mismatch. These results serve as proof of concept for the COT approach, which is currently being evaluated with human participants in the clinical trial YourMove (R01CA244777, NCT05598996).