Evidence map›Paper›PMID 40727918›Full record

ArticleControl engineering practice2025

Dynamic Modeling and System Identification of User Engagement in mHealth Interventions using a Bayesian Approach for Missing Data Imputation.

Mohamed El Mistiri, Steven De La Torre, Benjamin M Marlin, Misha Pavel, Predrag Klasnja, Donna Spruijt-Metz, Daniel E Rivera

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Control engineering practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Mohamed El MistiriControl Systems Engineering Laboratory in the Chemical Engineering Department, School for Engineering of Matter, Transport at Arizona State University, Tempe, 85282, Arizona, USA.
Steven De La TorreHerbert Wertheim School of Public Health & Human Longevity Science, University of California San Diego, La Jolla, 92093, California, USA.
Benjamin M MarlinManning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, 01003, Massachusetts, USA.
Misha PavelKhoury College of Computer Sciences, Northeastern University, Boston, 02115, Massachusetts, USA.
Predrag KlasnjaDivision of Biomedical and Health Informatics, School of Information, University of Michigan, Ann Arbor, 48109, Michigan, USA.
Donna Spruijt-MetzDornsife Center for Economic and Social Research, University of Southern California, Los Angeles, 90089, California, USA.
Daniel E RiveraControl Systems Engineering Laboratory in the Chemical Engineering Department, School for Engineering of Matter, Transport at Arizona State University, Tempe, 85282, Arizona, USA.

Funding

TR&D3 - Rapid Translation of AI-powered Temporally Precise mHealth Interventions via Efficient and Embeddable Trustworthy Biomarker ImplementationsP41EB028242 · NIBIB · UNIVERSITY OF MEMPHIS · PI Santosh Kumar · 2020 to 2026
$9.5M
Operationalizing Behavioral Theory for mHealth: Dynamics, Context, and PersonalizationU01CA229445 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI KLASNJA, PREDRAG, MARLIN, BENJAMIN M. · 2018 to 2022
$2.1M
NCI NIH HHS U01 CA229445NIBIB NIH HHS P41 EB028242
6 · The paper itself

Abstract

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

Indexed as

Bayesian methodsControl-oriented behavioral interventionsdynamic modeling for social science applicationseHealthmissing data

Identifiers

PMID40727918
PMCPMC12290899

What OpenQuestion holds

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Registered trials

None linked

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.