Adaptive Behavioral Interventions Research Articles

An Adaptive Behavioral Intervention for Weight Loss Management

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. 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. 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. 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. 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. 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. 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. ClinicalTrials.gov Identifier: NCT02997943.

Digital Adaptive Behavioral Interventions to Improve HIV Prevention and Care: Innovations in Intervention Approach and Experimental Design.

Recent advances in digital technologies can be leveraged to adapt HIV prevention and treatment services to the rapidly changing needs of individuals in everyday life. However, to fully take advantage of these technologies, it is critical to effectively integrate them with human-delivered components. Here, we introduce a new experimental approach for optimizing the integration and adaptation of digital and human-delivered behavioral intervention components for HIV prevention and treatment. Typically, human-delivered components can be adapted on a relatively slow timescale (e.g., every few months or weeks), while digital components can be adapted much faster (e.g., every few days or hours). Thus, the systematic integration of these components requires an experimental approach that involves sequential randomizations on multiple timescales. Selecting an experimental approach should be motivated by the type of adaptive intervention investigators would like to develop, and the scientific questions they have about its construction.

A High Protein Diet Is Associated with Improved Glycemic Control Following Exercise among Adolescents with Type 1 Diabetes.

Nutritional strategies are needed to aid people with type 1 diabetes (T1D) in managing glycemia following exercise. Secondary analyses were conducted from a randomized trial of an adaptive behavioral intervention to assess the relationship between post-exercise and daily protein (g/kg) intake on glycemia following moderate-to-vigorous physical activity (MVPA) among adolescents with T1D. Adolescents (n = 112) with T1D, 14.5 (13.8, 15.7) years of age, and 36.6% overweight or obese, provided measures of glycemia using continuous glucose monitoring (percent time above range [TAR, >180 mg/dL], time-in-range [TIR, 70-180 mg/dL], time-below-range [TBR, <70 mg/dL]), self-reported physical activity (previous day physical activity recalls), and 24 h dietary recall data at baseline and 6 months post-intervention. Mixed effects regression models adjusted for design (randomization assignment, study site), demographic, clinical, anthropometric, dietary, physical activity, and timing covariates estimated the association between post-exercise and daily protein intake on TAR, TIR, and TBR from the cessation of MVPA bouts until the following morning. Daily protein intakes of ≥1.2 g/kg/day were associated with 6.9% (p = 0.03) greater TIR and -8.0% (p = 0.02) less TAR following exercise, however, no association was observed between post-exercise protein intake and post-exercise glycemia. Following current sports nutrition guidelines for daily protein intake may promote improved glycemia following exercise among adolescents with T1D.

Design of the Advancing Care for Type 1 Diabetes and Obesity Network energy metabolism and sequential multiple assignment randomized trial nutrition pilot studies: An integrated approach to develop weight management solutions for individuals with type 1 diabetes

Young adults with type 1 diabetes (T1D) often have difficulty co-managing weight and glycemia. The prevalence of overweight and obesity among individuals with T1D now parallels that of the general population and contributes to dyslipidemia, insulin resistance, and risk for cardiovascular disease. There is a compelling need to develop a program of research designed to optimize two key outcomes—weight management and glycemia—and to address the underlying metabolic processes and behavioral challenges unique to people with T1D. For an intervention addressing these dual outcomes to be effective, it must be appropriate to the unique metabolic phenotype of T1D, and to biological and behavioral responses to glycemia (including hypoglycemia) that relate to weight management. The intervention must also be safe, feasible, and accepted by young adults with T1D. In 2015, we established a consortium called ACT1ON: Advancing Care for Type 1 Diabetes and Obesity Network, a transdisciplinary team of scientists at multiple institutions. The ACT1ON consortium designed a multi-phase study which, in parallel, evaluated the mechanistic aspects of the unique metabolism and energy requirements of individuals with T1D, alongside a rigorous adaptive behavioral intervention to simultaneously facilitate weight management while optimizing glycemia. This manuscript describes the design of our integrative study—comprised of an inpatient mechanistic phase and an outpatient behavioral phase—to generate metabolic, behavioral, feasibility, and acceptability data to support a future, fully powered sequential, multiple assignment, randomized trial to evaluate the best approaches to prevent and treat obesity while co-managing glycemia in people with T1D.Clinicaltrials.gov identifiers: NCT03651622 and NCT03379792. The present study references can be found here:https://clinicaltrials.gov/ct2/show/NCT03651622https://clinicaltrials.gov/ct2/show/NCT03379792?term=NCT03379792&draw=2&rank=1Submission Category: “Study Design, Statistical Design, Study Protocols”.

Preliminary findings in a platform‐based MCI trial designed to promote adaptive behaviors

AbstractBackgroundReceiving a diagnosis of mild cognitive impairment (MCI) may lead to experiences of emotional turmoil stemming from a perceived lack of clarity around diagnosis and prognosis. Previous research has identified several maladaptive behaviors resulting from the diagnosis of MCI that may be targeted for intervention in order to help those with a new diagnosis of MCI best adapt behaviors to maintain functional abilities.MethodA platform‐based MCI trial cohort of individuals, newly diagnosed with MCI from a U.S. Alzheimer’s Disease Research Center, was used to pilot a multicomponent interdisciplinary intervention targeting adaptive behavioral responses to the diagnosis of MCI. Thematic analysis was employed with fifteen focus groups across three study groups of 14 participants. After engaging with each of the platform‐based MCI trial components, participants’ and their study partner’s perceptions of each intervention’s acceptability, satisfaction, and compliance across the multicomponent interdisciplinary intervention were assessed.ResultsThemes that emerged from the data included a preference towards education, learning new information, and skill development. Both persons with MCI and study partners favored intervention components that were novel to them. They also reported higher satisfaction with activities facilitated by a professional (e.g., social worker, physical therapist, or pharmacist) as opposed to intervention activities that were self‐directed. Participants with MCI and their study partners reported those with impairment’s challenges utilizing technology independently, requiring assistance in setting up online medical portals, using wearable exercise trackers and automated pillboxes that represented technology‐dependent components of the intervention.ConclusionThese data suggest that persons, newly diagnosed with MCI, are interested in adaptive, biopsychosocial interventions for persons with MCI. Acceptability, satisfaction, and compliance may be increased when individuals are engaging in learning novel information and skills that are led and supported by professionals. Assistance with and or training in the use of technology‐based interventions may be needed to maximize the impact of such approaches. Further studies exploring the impact on the cognitive and functional decline that may be mediated by adaptive behavioral interventions in persons newly diagnosed with MCI are warranted.

On the Identification of Social Cognitive Theory Models and Closed-loop Intervention Simulations Using Hybrid Model Predictive Control

Abstract This paper presents closed-loop intervention simulations for an identified, data-validated model of Social Cognitive Theory (SCT). A reduced-complexity SCT model structure consisting of dynamic operant conditioning and self-efficacy loops is considered for the prediction of physical activity behavior. Consistent with real-world requirements, including the need for hybrid decision rules policies, the proposed closed-loop intervention design follows a Hybrid Model Predictive Control formulation. The prime goal of this paper is to reinforce the viability of the system identification and control engineering frameworks in the design of optimized and perpetually adaptive behavioral health interventions.

Engineering Person-Specific Behavioral Interventions to Promote Physical Activity.

Physical activity is dynamic, complex, and often regulated idiosyncratically. In this article, we review how techniques used in control systems engineering are being applied to refine physical activity theory and interventions. We hypothesize that person-specific adaptive behavioral interventions grounded in system identification and model predictive control will lead to greater physical activity than more generic, conventional intervention approaches.

Adaptive Intervention Designs to Promote Behavioral Change in Adults: What Is the Evidence?

Adaptive behavioral interventions tailor the type or dose of intervention strategies to individuals over time to improve saliency and intervention efficacy. This review describes the unique characteristics of adaptive intervention designs, summarizes recent diabetes-related prevention studies, which used adaptive designs, and offers recommendations for future research. Eight adaptive intervention studies were reported since 2013 to reduce sedentary behavior or improve weight management in overweight or obese adults. Primarily, feasibility studies were conducted. Preliminary results suggest that just-in-time adaptive interventions can reduce sedentary behavior or increase minutes of physical activity through repeated prompts. A stepped-down weight management intervention did not increase weight loss compared to a fixed intervention. Other adaptive interventions to promote weight management are underway and require further evaluation. Additional research is needed to target a broader range of health-related behaviors, identify optimal decision points and dose for intervention, develop effective engagement strategies, and evaluate outcomes using randomized trials.

Two-step recruitment process optimizes retention in FLEX clinical trial.

IntroductionThe Flexible Lifestyle Empowering Change Study (FLEX) is a multi-site randomized controlled trial to test the efficacy of an adaptive behavioral intervention to promote self-management and improve glycemic control for adolescents with type 1 diabetes mellitus. A two-step recruitment process was used to optimize study retention by facilitating informed decision-making regarding participation.MethodsThose who expressed interest at first contact were given more detailed study information followed by telephone calls to the adolescents and their parents to answer questions and explore potential barriers to participation before making a decision regarding study enrollment.ResultsOf 694 eligible adolescents who were invited to participate, 397 (57.2%) expressed interest when initially contacted (Step 1). Upon completion of the follow-up telephone calls (Step 2), 276 (39.8%) still agreed to participate; and 258 (37.2%) enrolled and completed a baseline visit with a parent/guardian. Completion rates for measurement visits remained high throughout the study, with an end-of-study retention rate of 93.4%; and only 12 (4.7%) families withdrew from the study.ConclusionThe two-step recruitment process encourages potential participants to thoughtfully evaluate their willingness to participate, as well as their ability to make a commitment to the full completion of study requirements. When demonstrating the efficacy of a randomized controlled trial, it may be preferable to accept lower recruitment rates in order to optimize retention rates. The additional time and effort required to implement this two-step process is worthwhile. With a high retention rate, we can be more confident that the outcomes of the randomized controlled trial actually reflect the impact of the intervention.

Efficacy of the Flexible Lifestyles Empowering Change intervention on metabolic and psychosocial outcomes in adolescents with type 1 diabetes (FLEX): a randomised controlled trial

Adolescents with type 1 diabetes commonly have poor glycaemic control. We aimed to test the efficacy of a newly developed adaptive behavioral intervention (Flexible Lifestyles Empowering Change; FLEX) on metabolic and psychosocial outcomes in adolescents with type 1 diabetes. Young people (13-16 years, type 1 diabetes duration >1 year, HbA1c of 64-119 mmol/mol [8·0-13·0%], and without other serious medical conditions or pregnancy) from two clinical sites (Colorado and Ohio, USA) were eligible for enrolment. One caregiver was required to participate actively in the study. Adolescent participants were randomly assigned to the FLEX intervention, which used motivational interviewing and problem-solving skills training to enhance patients' self-management, or usual care control. Intervention fidelity was assessed by a behavioral psychologist with specific expertise in motivational interviewing and who was not otherwise involved in the study via audiotaped sessions. The primary outcome was measurement of glycated haemoglobin A1c (HbA1c) at 18 months. Secondary outcomes included motivation and intention, problem solving skills, self-management behaviors, symptoms of depression, health related quality of life, fear of hypoglycemia, diabetes family conflict, risk factors for T1D complications (BMI, blood pressure, and plasma lipids), and hypoglycemia derived from continuous glucose monitoring (percent time below 3·0 and 3·9 mmol/l [54 and 70 mg/dl]). Intention-to-treat analyses used mixed effects models, with fixed effects including site, timepoint, intervention group, intervention by timepoint, and baseline level of primary (HbA1c) or secondary outcomes (α=0·05). FLEX is registered on clinicaltrials.gov, number NCT01286350. Young people recruited from May 1, 2014 to April 4, 2016 were randomly assigned to FLEX (n=130) or usual care control (n=128). Mean diabetes duration was 6·4 (SD 3·8) years, and 71% (181 out of 256) of patients used insulin pump therapy. Retention was 93%, with 241 out of 258 completing the 18-month assessment. The intervention fidelity score was 4·40 of 5·00 for motivational interviewing and 97% for session content. At 18 months, HbA1c was not significantly different between intervention (83 [13] mmol/mol at baseline; 84 [19] mmol/mol at follow-up); and control (80 [14] mmol/mol at baseline; 82 [17] mmol/mol at follow-up); change in intervention versus control was -0·7 mmol/mol (95% CI -4·7 to 3·4, p=0·75). The intervention was associated with improved scores for motivation (p=0·011), problem solving (p=0·024), diabetes self-management profile (p=0·013), youth report of overall quality of life (p=0·0089), selected domains related to fear of hypoglycaemia (p=0·036 for youth's helplessness or worry; p=0·0051 for parent's efforts to maintain high blood glucose), parent report of diabetes family conflict (p=0·0001), total cholesterol (p=0·038), and diastolic blood pressure (p=0·015). A total of 54 serious adverse events were identified; 34 of these were diabetes-related, including low blood glucose requiring assistance (n=3) and high blood glucose with diabetic ketoacidosis and emergency response (n=25). The FLEX intervention did not significantly change HbA1c among these adolescents with elevated HbA1c, but did positively affect several psychosocial outcomes over 18 months. Further analyses will provide information regarding drivers of positive response to the intervention and will point to future directions for improvement in the approach. National Institutes of Health and National Institute of Diabetes Digestive Diseases and Kidney and the Helmsley Charitable Trust.

Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions.

BackgroundAdaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions.ObjectiveThe purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions.OverviewWe start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step.ImplicationsControl engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities.

The Flexible Lifestyles Empowering Change (FLEX) Intervention Trial for Adolescents with Type 1 Diabetes—Primary and Secondary Outcomes

Youth with T1D often have poor glycemic control and compromised health and well-being. Our aim was to test the efficacy of the FLEX adaptive behavioral intervention on the primary (HbA1c) and secondary (psychosocial and metabolic) outcomes at 18 months. Youth (n=258) aged 13-16 years, diagnosed &amp;gt;1 year with T1D with HbA1c from 8.0-13.0%, were randomized at two sites to FLEX or usual care control. Mean (sd) duration of diabetes was 6.4 (3.8) years, 50% were girls, 78% non-Hispanic white, mean HbA1c was 9.6% (1.2) and 71% used insulin pump therapy. FLEX used motivational interviewing and problem-solving skills training to enhance self-management. Intervention fidelity was assessed via random selection of audiotaped sessions for counseling strategy and session content. Intent-to-treat analyses used mixed effects models, with fixed effects of site, timepoint, intervention group, intervention by timepoint and baseline level of primary or secondary outcomes, with a random intercept (alpha=0.05). Retention was 97%, with intervention fidelity 4.6 out of 5 for counseling strategy and 97.4% complete for session content. HbA1c was not statistically significantly different between intervention and control at 18 months. Among secondary outcomes at 18 months, the intervention was associated with improved scores for symptoms of depression (p=0.04), quality of life (p=0.01), motivation (p=0.009), problem-solving (p=0.04), and diabetes self-management profile (p=0.01), as well as better total cholesterol (p=0.04), and better diastolic blood pressure (p=0.01). The FLEX intervention did not change the primary outcome, but did positively effect psychosocial outcomes over the 18 months of the trial. Further analyses will reveal information regarding drivers of positive response to the intervention and will point to directions for improvement in the approach, perhaps incorporating more specific direction towards achievement of glycemic targets. Disclosure E.J. Mayer-Davis: None. F.K. Bishop: None. K.A. Driscoll: None. C.M. Hunter: None. D.M. Maahs: Advisory Panel; Self; Insulet Corporation. Consultant; Self; Abbott. Research Support; Self; Medtronic, Bigfoot Biomedical, Dexcom, Inc., Insulet Corporation, Roche Diabetes Care Health and Digital Solutions. M. Seid: None. D. Standiford: None. J. Thomas: None.

Computational model for behavior shaping as an adaptive health intervention strategy.

Adaptive behavioral interventions that automatically adjust in real-time to participants' changing behavior, environmental contexts, and individual history are becoming more feasible as the use of real-time sensing technology expands. This development is expected to improve shortcomings associated with traditional behavioral interventions, such as the reliance on imprecise intervention procedures and limited/short-lived effects. JITAI adaptation strategies often lack a theoretical foundation. Increasing the theoretical fidelity of a trial has been shown to increase effectiveness. This research explores the use of shaping, a well-known process from behavioral theory for engendering or maintaining a target behavior, as a JITAI adaptation strategy. A computational model of behavior dynamics and operant conditioning was modified to incorporate the construct of behavior shaping by adding the ability to vary, over time, the range of behaviors that were reinforced when emitted. Digital experiments were performed with this updated model for a range of parameters in order to identify the behavior shaping features that optimally generated target behavior. Narrowing the range of reinforced behaviors continuously in time led to better outcomes compared with a discrete narrowing of the reinforcement window. Rapid narrowing followed by more moderate decreases in window size was more effective in generating target behavior than the inverse scenario. The computational shaping model represents an effective tool for investigating JITAI adaptation strategies. Model parameters must now be translated from the digital domain to real-world experiments so that model findings can be validated.

Modeling individual differences: A case study of the application of system identification for personalizing a physical activity intervention

BackgroundControl systems engineering methods, particularly, system identification (system ID), offer an idiographic (i.e., person-specific) approach to develop dynamic models of physical activity (PA) that can be used to personalize interventions in a systematic, scalable way. The purpose of this work is to: (1) apply system ID to develop individual dynamical models of PA (steps/day measured using Fitbit Zip) in the context of a goal setting and positive reinforcement intervention informed by Social Cognitive Theory; and (2) compare insights on potential tailoring variables (i.e., predictors expected to influence steps and thus moderate the suggested step goal and points for goal achievement) selected using the idiographic models to those selected via a nomothetic (i.e., aggregated across individuals) approach. MethodA personalized goal setting and positive reinforcement intervention was deployed for 14 weeks. Baseline PA measured in weeks 1–2 was used to inform personalized daily step goals delivered in weeks 3–14. Goals and expected reward points (granted upon goal achievement) were pseudo-randomly assigned using techniques from system ID, with goals ranging from their baseline median steps/day up to 2.5× baseline median steps/day, and points ranging from 100 to 500 (i.e., $0.20–$1.00). Participants completed a series of daily self-report measures. Auto Regressive with eXogenous Input (ARX) modeling and multilevel modeling (MLM) were used as the idiographic and nomothetic approaches, respectively. ResultsParticipants (N = 20, mean age = 47.25 ± 6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ± 6.82 kg/m2) adults. Results from ARX modeling suggest that individuals differ in the factors (e.g., perceived stress, weekday/weekend) that influence their observed steps/day. In contrast, the nomothetic model from MLM suggested that goals and weekday/weekend were the key variables that were predictive of steps. Assuming the ARX models are more personalized, the obtained nomothetic model would have led to the identification of the same predictors for 5 of the 20 participants, suggesting a mismatch of plausible tailoring variables to use for 75% of the sample. ConclusionThe idiographic approach revealed person-specific predictors beyond traditional MLM analyses and unpacked the inherent complexity of PA; namely that people are different and context matters. System ID provides a feasible approach to develop personalized dynamical models of PA and inform person-specific tailoring variable selection for use in adaptive behavioral interventions.

The Flexible Lifestyle Empowering Change (FLEX) intervention for self-management in adolescents with type 1 diabetes: Trial design and baseline characteristics

The Flexible Lifestyle Empowering Change (FLEX) Intervention Study is a multi-site randomized controlled trial to test the efficacy of an adaptive behavioral intervention to promote self-management for youth with type 1 diabetes mellitus (T1D). This paper details FLEX design, demographic characteristics of the sample, and outcome variables at baseline. Participants were randomized to either an intervention or control arm after their baseline standardized measurement visit. Baseline data for the primary (glycemic levels) and secondary outcome variables (e.g., motivation and problem-solving, health-related quality of life, risk factors associated with T1D complications) as well as the potential mediator variables (e.g., self-management behavior, family conflict and responsibility) suggest that the study sample was representative of the general population of adolescents with T1D and their parents. The FLEX adaptive intervention is an innovative application of a tailored treatment intervention designed to be readily adopted in real-world practice to meet each adolescent's individualized T1D self-management goals.

Treatment Sequencing for Childhood ADHD: A Multiple-Randomization Study of Adaptive Medication and Behavioral Interventions

Behavioral and pharmacological treatments for children with attention deficit/hyperactivity disorder (ADHD) were evaluated to address whether endpoint outcomes are better depending on which treatment is initiated first and, in case of insufficient response to initial treatment, whether increasing dose of initial treatment or adding the other treatment modality is superior. Children with ADHD (ages 5–12, N = 146, 76% male) were treated for 1 school year. Children were randomized to initiate treatment with low doses of either (a) behavioral parent training (8 group sessions) and brief teacher consultation to establish a Daily Report Card or (b) extended-release methylphenidate (equivalent to .15 mg/kg/dose bid). After 8 weeks or at later monthly intervals as necessary, insufficient responders were rerandomized to secondary interventions that either increased the dose/intensity of the initial treatment or added the other treatment modality, with adaptive adjustments monthly as needed to these secondary treatments. The group beginning with behavioral treatment displayed significantly lower rates of observed classroom rule violations (the primary outcome) at study endpoint and tended to have fewer out-of-class disciplinary events. Further, adding medication secondary to initial behavior modification resulted in better outcomes on the primary outcomes and parent/teacher ratings of oppositional behavior than adding behavior modification to initial medication. Normalization rates on teacher and parent ratings were generally high. Parents who began treatment with behavioral parent training had substantially better attendance than those assigned to receive training following medication. Beginning treatment with behavioral intervention produced better outcomes overall than beginning treatment with medication.

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention.

The term adaptive intervention has been used in behavioral medicine to describe operationalized and individually tailored strategies for prevention and treatment of chronic, relapsing disorders. Control systems engineering offers an attractive means for designing and implementing adaptive behavioral interventions that feature intensive measurement and frequent decision-making over time. This is illustrated in this paper for the case of a low-dose naltrexone treatment intervention for fibromyalgia. System identification methods from engineering are used to estimate dynamical models from daily diary reports completed by participants. These dynamical models then form part of a model predictive control algorithm which systematically decides on treatment dosages based on measurements obtained under real-life conditions involving noise, disturbances, and uncertainty. The effectiveness and implications of this approach for behavioral interventions (in general) and pain treatment (in particular) are demonstrated using informative simulations.

Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions.

Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or "just-in-time" behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.

A Risk-Based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

This paper examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based Model Predictive Control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this paper can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm.

Hybrid Model Predictive Control Applied to Production-Inventory Systems

AbstractHybrid production-inventory systems are characterized by discrete decisions on production levels and/or capacity. These systems have broad applicability to important, emerging applications of process control concepts, among them time-varying adaptive behavioral interventions and supply chain management. This paper examines the usefulness of hybrid model predictive control (HMPC) in these two novel application settings. In a hypothetical adaptive behavioral intervention inspired by Fast Track (a preventive intervention for reducing conduct disorder in at-risk children), HMPC is presented as a means to improve the assignment of frequency of home-based counseling visits to families with low parental function. In supply chain management, the usefulness of HMPC for assigning production capacity in an inventory control problem under conditions of varying customer demand is presented. These problems are modeled as mixed logical dynamical (MLD) systems, with HMPC consisting of a Mixed Integer Quadratic Program (MIQP) that employs a three-degree-of-freedom parametrization for achieving ease of tuning and facilitating robust performance under uncertainty.