ArticleJournal of medical Internet research2018
Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions.
Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05273437 (Control Systems Engineering for Counteracting Notification Fatigue), which is not on this map. Cited by 47 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Control Systems Engineering for Counteracting Notification Fatigue: An Examination of Health Behavior Change.
Who cites it
47 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.Frontiers in digital health · 2025Pooled it
- Within-person associations between psychological and contextual factors and lapse incidence in smokers attempting to quit: A systematic review and meta-analysis of ecological momentary assessment studies.Addiction (Abingdon, England) · 2023Pooled it
- Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: a systematic review.Addiction (Abingdon, England) · 2022Pooled it
- Adaptive Goals and Reinforcement Timing to Increase Physical Activity in Adults: A Factorial Randomized Trial.American journal of preventive medicine · 2022Trial
- Adaptive, behavioral intervention impact on weight gain, physical activity, energy intake, and motivational determinants: results of a feasibility trial in pregnant women with overweight/obesity.Journal of behavioral medicine · 2021Trial
- Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior.PloS one · 2021Trial
- Self-efficacy and Physical Activity in Overweight and Obese Adults Participating in a Worksite Weight Loss Intervention: Multistate Modeling of Wearable Device Data.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Trial
- Key Features of Engagement Strategies in Nutrition Apps for Adults: Scoping Review.JMIR mHealth and uHealth · 2026Article
- Digital Twins for Just-in-Time Adaptive Interventions (JITAIs): Framework for Optimizing and Continually Improving JITAIs.Journal of medical Internet research · 2026Article
- Scientific Impact on Socially Beneficial Behaviors: Impact and Efficiency Evidence From Behavior Change Interventions.Social and personality psychology compass · 2025Article
- Dynamic Modeling and System Identification of User Engagement in mHealth Interventions using a Bayesian Approach for Missing Data Imputation.Control engineering practice · 2025Article
- Optimizing and Testing an Individualized and Adaptive Physical Activity Digital Health Intervention: Protocol for a Control Optimization Trial Embedded Within a Randomized Controlled Trial.JMIR research protocols · 2025Article
- Model Predictive Control in mHealth: A Decision Framework for Optimised Personalised Physical Activity Interventions.International journal of control · 2025Article
- Healthy Mom Zone Adaptive Intervention With a Novel Control System and Digital Platform to Manage Gestational Weight Gain in Pregnant Women With Overweight or Obesity: Study Design and Protocol for a Randomized Controlled Trial.JMIR research protocols · 2025Article
- Data-Driven Mobile Health: System Identification and Hybrid Model Predictive Control to Deliver Personalized Physical Activity Interventions.IEEE open journal of control systems · 2025Article
- Achieving clinically meaningful outcomes in digital health: a six-step, cyclical precision engagement framework (ENGAGE).Frontiers in digital health · 2025Article
- 3DoF-KF HMPC: A Kalman filter-based Hybrid Model Predictive Control Algorithm for Mixed Logical Dynamical Systems.Control engineering practice · 2025Article
- Supervised machine learning to predict smoking lapses from Ecological Momentary Assessments and sensor data: Implications for just-in-time adaptive intervention development.PLOS digital health · 2024Article
- Precision feedback: A conceptual model.Learning health systems · 2024Article
- Predicting Goal Attainment in Process-Oriented Behavioral Interventions Using a Data-Driven System Identification Approach.Journal of process control · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. OVERVIEW: We 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. IMPLICATIONS: Control 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.