Abstract Introduction Adherence to continuous positive airway pressure (CPAP) therapy is difficult for many patients with obstructive sleep apnea. Clinical outcomes are improved with CPAP adherence ≥6 hr/night, yet scalable approaches to personalized support are limited. We developed a theory-based, clinic experience–driven conceptual dynamical model for optimizing CPAP use and applied it to a ‘just-in-time’ adaptive version of SleepWell24, a smartphone application integrating near–real-time wearable data, CPAP use, and patient-reported symptoms and CPAP-related problems with evidence-based behavior change interventions. In this feasibility optimization trial, we examined patient acceptability and initial outcomes of the adaptive SleepWell24 platform, and guided by the conceptual model, evaluated a control systems engineering approach to demonstrate, in simulation, improved CPAP adherence. Methods Aim 1: Naïve CPAP users (N=10; M age=61.7, 60% female) participated in this 60-day ongoing trial. Patients were encouraged to interact daily with SleepWell24 to report symptoms, receive personalized troubleshooting recommendations, set goals, and receive feedback on CPAP use (via Wi-Fi-enabled smart plug assessing CPAP ‘on/off state’) and sleep/activity (Fitbit). We assessed SleepWell24 use, post-intervention acceptability, and consistent CPAP use defined as ≥6hr, for ≥6nights/week for ≥2 consecutive weeks. Aim 2: The conceptual model of CPAP adherence produced a dynamical system, informed by Model Predictive Control (MPC) as a decision-making algorithm to optimize CPAP use to ≥6hr/night by tailoring actionable behavioral recommendations using patient data integrated in real-time. Results Aim 1: Across the 60-day trial, average daily SleepWell24 engagement was 72.7%. Median CPAP adherence of ≥6hr/night was 65.8%. All participants (100%) rated SleepWell24 as moderately-to-totally acceptable and would recommend it; perceived helpfulness averaged 7.0 (of 10). Eighty percent achieved consistent CPAP use. Aim 2: In simulation, the MPC framework managed CPAP-associated symptoms (e.g., nasal, anxiety, leak) and adjusted CPAP use goals, improving predicted CPAP adherence and sleep duration and quality. Conclusion SleepWell24 was engaging, acceptable, and helpful, with most users reaching optimal CPAP use. We will next estimate dynamical models using data from trial patients, a necessary step towards full automation of SleepWell24. Support (if any) AASMF #285-SR-22

BACKGROUND:Entry to higher education coincides with a period of accelerated psychosocial and brain development. Student need for acceptable and accessible well-being and mental health support is straining university resources. AIMS:To evaluate the acceptability and impact of a digital mental health literacy course tailored for undergraduates and delivered as an accredited interdisciplinary elective. METHOD:Analyses included pre-post course survey data from enrolled students and longitudinal U-Flourish Well-Being Survey data from a comparison sample of non-course takers over the same period (2021-2024). Linear mixed-effects models examined associations between course participation and 12-week changes in mental health literacy, psychosocial risk factors, well-being and common mental health concerns. RESULTS:Pre-post course survey data (N = 2884) supported high acceptability, improvements in resilience (+0.06; 95% CI 0.03-0.08, p < 0.001) and self-compassion (+0.65; 95% CI 0.46-0.84, p < 0.001), and a reduction in brooding (-0.31; 95% CI -0.44 to-0.18, p < 0.001). Taking the course was associated with a reduction in anxiety (β = -0.41; 95% CI -0.55 to -0.27, p < 0.001) and cannabis use (proportional odds ratio 0.82; 95% CI 0.75-0.90, p < 0.001), improvement in sleep quality (β = 0.79; 95% CI 0.61-0.97, p < 0.001) and evidence of a protective effect on well-being (β = 0.24; 95% CI 0.11-0.36, p < 0.001) and depressive symptoms (β = -0.37; 95% CI -0.52 to -0.21, p < 0.001), compared with non-course takers. Effects differed by gender, with women benefitting most, but were comparable across minoritised student subgroups. CONCLUSIONS:Mental health literacy delivered as an accredited undergraduate interdisciplinary course is highly acceptable and associated with improvement in psychological coping and positive effects on student mental health and well-being. Future research should focus on more diverse student samples, underlying mechanisms and sustained effects.

BackgroundRegulating gestational weight gain (GWG) in pregnant women with overweight or obesity is difficult, particularly because of the narrow range of recommended GWG for optimal health outcomes. Given that many pregnant women show excessive GWG and considering the lack of a “gold standard” intervention to manage GWG, there is a timely need for effective and efficient approaches to regulate GWG. We have enhanced the Healthy Mom Zone (HMZ) 2.0 intervention with a novel digital platform, automated dosage changes, and personalized strategies to regulate GWG, and our pilot study demonstrated successful recruitment, compliance, and utility of our new control system and digital platform. ObjectiveThe goal of this paper is to describe the study protocol for a randomized controlled optimization trial to examine the efficacy of the enhanced HMZ 2.0 intervention with the new automated control system and digital platform to regulate GWG and influence secondary maternal and infant outcomes while collecting implementation data to inform future scalability. MethodsThis is an efficacy study using a randomized controlled trial design. HMZ 2.0 is a multidosage, theoretically based, and individually tailored adaptive intervention that is delivered through a novel digital platform with an automated link of participant data to a new model-based predictive control algorithm to predict GWG. Our new control system computes individual dosage changes and produces personalized physical activity (PA) and energy intake (EI) strategies to deliver just-in-time dosage change recommendations to regulate GWG. Participants are 144 pregnant women with overweight or obesity randomized to an intervention (n=72) or attention control (n=72) group, stratified by prepregnancy BMI (<29.9 vs ≥30 kg/m2), and they will participate from approximately 8 to 36 weeks of gestation. The sample size is based on GWG (primary outcome) and informed by our feasibility trial showing a 21% reduction in GWG in the intervention group compared to the control group, with 3% dropout. Secondary outcomes include PA, EI, sedentary and sleep behaviors, social cognitive determinants, adverse pregnancy and delivery outcomes, infant birth weight, and implementation outcomes. Analyses will include descriptive statistics, time series and fixed effects meta-analytic approaches, and mixed effects models. ResultsRecruitment started in April 2024, and enrollment will continue through May 2027. The primary (GWG) and secondary (eg, maternal and infant health) outcome results will be analyzed, posted on ClinicalTrials.gov, and published after January 2028. ConclusionsExamining the efficacy of the novel HMZ 2.0 intervention in terms of GWG and secondary outcomes expands the boundaries of current GWG interventions and has high clinical and public health impact. There is excellent potential to further refine HMZ 2.0 to scale-up use of the novel digital platform by clinicians as an adjunct treatment in prenatal care to regulate GWG in all pregnant women. International Registered Report Identifier (IRRID)DERR1-10.2196/66637

This paper describes a systematic approach for epidemic control using control-relevant identification coupled with a multi-input, multi-output 3-Degree-of-Freedom Kalman filter-based Hybrid Model Predictive Control (MIMO 3DoF-KF HMPC) featuring online controller reconfiguration. The combined data-driven modeling and control strategy is evaluated on a Susceptible-Infected-Recovered (SIR) model involving vaccination and loss of immunity (i.e., reinfection). "Zippered" multisine input signals and ARX estimation are applied to obtain a multivariable dynamic model that is the basis for an HMPC algorithm featuring both continuous and categorical (i.e., discrete level) actions through a Mixed Integer Quadratic Programming (MIQP) formulation. The goal is to reduce the infected population while balancing societal impacts. The hybrid formulation with reconfiguration dynamically adjusts health intervention policies, such as categorical lockdown levels and continuous vaccination rates. This approach enables operational goals such as reducing the infected population to a desired interval or relaxing lockdown to a designated setpoint; furthermore, the 3DoF formulation enables independent tuning for setpoint tracking and measured and unmeasured disturbance rejection, allowing scalable solutions across diverse epidemiological settings. The framework is demonstrated through two demanding case studies involving 90% infection reduction under time-varying recovery and loss of immunity. The resulting closed-loop model provides a practical tool for guiding government policy and public decisions during pandemics.

Obstructive sleep apnea (OSA) is a widespread sleep disorder that significantly impacts public health. Continuous positive airway pressure (CPAP) therapy is the gold standard for treating OSA; however, adherence to treatment remains a major challenge, and many patients discontinue CPAP use shortly after initiation. To address this challenge, this article presents a three-degree-of-freedom Kalman filter-based hybrid model predictive control (3DoFKF HMPC) framework aimed at improving CPAP adherence and quality of life among patients with OSA. A hypothetical dynamic model (developed with OSA domain experts) and patterned after the SleepWell24 intervention is presented; this model serves as both the simulation and internal model for the 3DoF-KF HMPC, which provides “ambitious yet attainable” CPAP goals while coordinating actions to mitigate reported symptoms. By integrating behavioral strategies with hybrid model predictive control techniques, the framework personalizes CPAP recommendations, aiming to enhance participant engagement and adherence. The results presented in this article show promise for the effectiveness of a future “just-in-time” control engineering-based adaptive intervention with the goal of optimizing CPAP use and improving healthrelated outcomes for newly diagnosed OSA patients.

Digital behavior change interventions (DBCIs) have been found to positively impact health behaviors and are becoming increasingly important as an emerging topic for control systems applications. However, their effectiveness is heavily dependent upon user engagement with both the digital tool (e.g., mHealth app, wearable activity tracker) and the behavior change intervention (e.g., exercise activity planning). In this paper, engagement refers to the unique interactions of a participant with either of these components resulting in digital traces (e.g., app page views). Furthermore, engagement in DBCIs will change over the course of the intervention in response to an individual’s environment, context, and psychological state. Intensive data collection enables modeling engagement in DBCIs as a dynamical system using fluid analogies, and applying prediction-error methods from system identification to estimate models. Missingness represents both a fundamental and practical concern in this application domain. This work addresses missingness using a novel Bayesian imputation method applied to data from the HeartSteps II physical activity intervention study. The benefits of this approach include the ability to impute missing data points more accurately than traditional methods and quantify uncertainty resulting from imputation and data scarcity; the latter is essential to the implementation of robust closed-loop interventions. The methods presented in this work provide insights into critical factors that impact engagement behavior over time and in context, ultimately benefiting the development of digital behavior change interventions relying on control engineering approaches.

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.

This paper presents a Model-on-Demand (MoD) approach to system identification and its integration with a three-degree-of-freedom Kalman filter-based Model Predictive Control (3DoF-KF MPC) framework. MoD estimation represents a hybrid of local and global modeling techniques, judiciously formulated to take advantage of both while not being computationally demanding. The 3DoF-KF MPC algorithm enables responses to set point changes and measured and unmeasured disturbances to be tuned intuitively and independently, thereby providing superior performance and ease of use over tuning with move suppression and error weights as done with conventional MPC algorithms. The algorithm proposed in this paper involves estimating MoD-based predictive models that are seamlessly integrated into 3DoF-KF MPC to generate control actions that vary with operating conditions. This results in notable performance enhancements in the context of both SISO and MIMO control compared to conventional ARX models. Performance and robustness of the 3DoF-KF MoD MPC framework are demonstrated in this paper through two case studies involving (i) epidemic control of a variant of the widely used SISO Susceptible-Infected-Removed (SIR) model and (ii) a nonlinear, highly interactive MIMO Continuous Stirred Tank Reactor (CSTR) model. The second case study further provides guidelines for designing informative databases for effective MoD-based MIMO identification and implementing 3DoF-KF MPC-based control for a demanding class of systems. Overall, this paper demonstrates technological and practical improvements in system identification and control of nonlinear SISO and MIMO systems through the synergistic integration of MoD estimation and 3DoF-KF MPC, providing an effective approach for operating complex nonlinear process systems.

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).

This paper describes an optimized behavioral intervention Healthy Mom Zone (HMZ) for managing gestational weight gain featuring sequential decision-making using Hybrid Model Predictive Control (HMPC). Dynamical models incorporating both behavioral and physiological aspects of the problem are presented and estimated from HMZ participant data via constrained semi-physical modeling. Daily measurements are provided to a controller that ultimately makes judicious (though infrequent) augmentations on categorical dosages of healthy eating and physical activity intervention components. Consequently, an HMPC algorithm is required which must follow a logical sequence of control actions conforming to practical requirements. A case study shows the benefits relative to a conventional "IF-THEN" approach. The computational framework (both modeling and control) serves as the basis for the Healthy Mom Zone 2.0 intervention currently being evaluated in a randomized clinical trial (NIH R01DK134863, NCT05807594) at Penn State University.

Strong evidence indicates physical activity (PA) reduces risk of various cancers, yet only a third of adults in the US meet guidelines for PA. While effective 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 (DHIs) 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 coupled with the development of personalized knowledge, skills, and practices in 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 via a self-experimentation approach to develop personalized knowledge, skills, and practices. The primary aim is to evaluate differences in minutes of moderate to vigorous physical activity (MVPA) per week at 12-month, 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. The YourMove Study is a 12-month randomized controlled trial (RCT) that includes 386 inactive adults aged 25-80 years. All participants receive, 1) a Fitbit Versa smartwatch and corresponding smartphone application, 2) weekly PA goal suggestions and feedback, behavioral change strategies, and reminders via text messaging, and 3) up to $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 by “Control Optimization Trial” (COT) approach, and 2) a “knowledge, skills, and practices 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, assessed via Actigraph. Recruitment began in October 2022 and concluded in August 2024. Data collection will conclude in August 2025 with results expected by the early 2026. 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, it 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 successful, results will provide justification to explore both the COT approach and self-experimentation approach for other complex, idiosyncratic, and dynamic behaviors such as weight management, smoking, or substance abuse. ClinicalTrials.gov NCT05598996

This scoping review aimed to synthesise the methodological steps taken by researchers in the development of formal, dynamical systems models of health psychology theories pertaining to health behaviours unfolding at the within-person level. We searched Ovid MEDLINE, PsycINFO, the ACM Digital Library and IEEE in July 2023. We included studies of any design providing that they reported on the development, refinement and/or testing of a formal, dynamical systems model, with no restrictions on population or setting. A narrative synthesis with frequency analyses was conducted, which informed the development of an initial set of ‘best practice’ recommendations. A total of 17 modelling projects reported across 29 studies were included. We found that current health psychology modelling efforts have largely been concentrated to a small number of interdisciplinary teams in the United States (79.3%). The models aimed to better understand dynamic processes (69.0%) or inform the development of adaptive interventions (31.0%). Models typically aimed to formalise the Social Cognitive Theory (31.0%) or the Self-Regulation Theory (17.2%) and varied in complexity (range: 3-30 model variables and free parameters). Only 3.4% of studies reported involving stakeholders in the modelling process and 10.3% drew on Open Science practices. Formal, dynamical systems modelling is poised to help health psychologists develop and refine theories, ultimately enabling the development of more potent interventions.

The COVID-19 pandemic has given rise to many significant research activities, among these a resurgence of the use of control-oriented approaches for modeling and controlling epidemics. An examination of a SIR (Susceptible-Infectious-Recovered) dynamic model under endemic conditions using Internal Model Control (IMC) shows that a two-degree-of-freedom (2DoF) PID with filter structure is a natural solution for understanding how to manage a pandemic, with model-based IMC-PID tuning being extremely effective when evaluated on a first-principles, nonlinear plant model. Dynamic modeling (nonlinear and linearized), PID controller design, and closed-loop evaluation (under conditions that include vaccination and the loss of immunity/potential for re-infection) are presented, with the results demonstrating the deep insights that can be gained from simple models and control policies. Computational models as presented in this work could be used to inform the actions of governments and individuals.

OBJECTIVE:To examine a potential synergistic effect of history of childhood adversity and COVID-19 pandemic exposure on the association with mental health concerns in undergraduate students. Participants: We used U-Flourish Survey data from 2019 (pre-pandemic) and 2020 (during-pandemic) first-year cohorts (n = 3,149) identified at entry to a major Canadian University. METHODS:Interactions between childhood adversity (physical and sexual abuse, and peer bullying) and COVID-19 pandemic exposure regarding mental health concern (depressive and anxiety symptoms, suicidality, and non-suicidal self-harm) were examined on an additive scale. RESULTS:We found a positive additive interaction between physical abuse and pandemic exposure in relation to suicidality (combined effect was greater than additive effect (risk difference 0.54 vs. 0.36)). Conversely, less than additive interactions between peer bullying and pandemic regarding depression and anxiety were observed. CONCLUSIONS:Childhood adversities have diverse reactions to adult stressor depending on the nature of the childhood adversity and the mental health outcomes.

Objective: Digital behavior change interventions (DBCIs) are feasibly effective tools for addressing physical activity. However, in-depth understanding of participants' long-term engagement with DBCIs remains sparse. Since the effectiveness of DBCIs to impact behavior change depends, in part, upon participant engagement, there is a need to better understand engagement as a dynamic process in response to an individual's ever-changing biological, psychological, social, and environmental context. Methods: The year-long micro-randomized trial (MRT) HeartSteps II provides an unprecedented opportunity to investigate DBCI engagement among ethnically diverse participants. We combined data streams from wearable sensors (Fitbit Versa, i.e., walking behavior), the HeartSteps II app (i.e. page views), and ecological momentary assessments (EMAs, i.e. perceived intrinsic and extrinsic motivation) to build the idiographic models. A system identification approach and a fluid analogy model were used to conduct autoregressive with exogenous input (ARX) analyses that tested hypothesized relationships between these variables inspired by Self-Determination Theory (SDT) with DBCI engagement through time. Results: Data from 11 HeartSteps II participants was used to test aspects of the hypothesized SDT dynamic model. The average age was 46.33 (SD=7.4) years, and the average steps per day at baseline was 5,507 steps (SD=6,239). The hypothesized 5-input SDT-inspired ARX model for app engagement resulted in a 31.75 % weighted RMSEA (31.50 % on validation and 31.91 % on estimation), indicating that the model predicted app page views almost 32% better relative to the mean of the data. Among Hispanic/Latino participants, the average overall model fit across inventories of the SDT fluid analogy was 34.22 % (SD=10.53) compared to 22.39 % (SD=6.36) among non-Hispanic/Latino Whites, a difference of 11.83 %. Across individuals, the number of daily notification prompts received by the participant was positively associated with increased app page views. The weekend/weekday indicator and perceived daily busyness were also found to be key predictors of the number of daily application page views. Conclusions: This novel approach has significant implications for both personalized and adaptive DBCIs by identifying factors that foster or undermine engagement in an individual's respective context. Once identified, these factors can be tailored to promote engagement and support sustained behavior change over time.

This paper presents a Three-Degree-of-Freedom Model Predictive Control (3DoF MPC) framework based on Multi-Input-Multi-Output (MIMO) "Model-on-Demand" (MoD) estimation. MoD is a data-centric weighted regression algorithm that generates local models over adaptively varying neighborhoods of changing operating conditions. The 3DoF formulation enables individualized tuning of parameters relating to setpoint tracking and measured and unmeasured disturbance rejection. Online estimation of system dynamics using MIMO MoD and augmentation with the 3DoF MPC structure allows the generation of control laws based on efficient locally linear approximations of system nonlinearities. This paper evaluates the framework through a case study involving a nonlinear MIMO Continuous Stirred Tank Reactor (CSTR) model. The MIMO CSTR system is highly interactive, making data-driven estimation and control notably more challenging than its SISO counterpart. The generation of an informative database using modified "zippered" multisines is presented. The paper concludes with a case study demonstrating the effectiveness of 3DoF MoD MPC in achieving constrained MIMO control of reactor concentration and temperature in the presence of disturbances through a flexible and intuitive approach. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licneses/by-nc-nd/4.0/)

Behavioral interventions (such as those developed to increase physical activity, achieve smoking cessation, or weight loss) can be represented as dynamic process systems incorporating a multitude of factors, ranging from cognitive (internal) to environmental (external) influences. This facilitates the application of system identification and control engineering methods to address questions such as: what drives individuals to improve health behaviors (such as engaging in physical activity)? In this paper, the goal is to efficiently estimate personalized, dynamic models which in turn will lead to control systems that can optimize this behavior. This problem is examined in system identification applied to the Just Walk study that aimed to increase walking behavior in sedentary adults. The paper presents a Discrete Simultaneous Perturbation Stochastic Approximation (DSPSA)-based modeling of the Goal Attainment construct estimated using AutoRegressive with eXogenous inputs (ARX) models. Feature selection of participants and ARX order selection is achieved through the DSPSA algorithm, which efficiently handles computationally expensive calculations. DSPSA can search over large sets of features as well as regressor structures in an informed, principled manner to model behavioral data within reasonable computational time. DSPSA estimation highlights the large individual variability in motivating factors among participants in Just Walk, thus emphasizing the importance of a personalized approach for optimized behavioral interventions.