Assessing time-varying causal effect moderation in mobile health
Open accessAbstract
In mobile health interventions aimed at behavior change and maintenance, treatments are provided in real time to manage current or impending high risk situations or promote healthy behaviors in near real time. Currently there is great scientific interest in developing data analysis approaches to guide the development of mobile interventions. In particular data from mobile health studies might be used to examine effect moderators-individual characteristics, time-varying context or past treatment response that moderate the effect of current treatment on a subsequent response. This paper introduces a formal definition for moderated effects in terms of potential outcomes, a definition that is particularly suited to mobile interventions, where treatment occasions are numerous, individuals are not always available for treatment, and potential moderators might be influenced by past treatment. Methods for estimating moderated effects are developed and compared. The proposed approach is illustrated using BASICS-Mobile, a smartphone-based intervention designed to curb heavy drinking and smoking among college students.
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The weighted and centered least squares method developed by Boruvka et al [105] to evaluate data from MRT studies will be used to estimate and test the causal excursion effects of the intervention on the proximal outcomes.
“The weighted and centered least squares method developed by Boruvka et al [105] to evaluate data from MRT studies will be used to estimate and test the causal excursion effects of the intervention on the proximal outcomes.”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [105]
This method assesses parameters in treatment effect models and allows for the inclusion of covariates that can reduce the variance of the treatment effect estimates.
“This method assesses parameters in treatment effect models and allows for the inclusion of covariates that can reduce the variance of the treatment effect estimates.”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [105]
The analysis approach by Boruvka et al. produces unbiased causal treatment effects in situations where treatments are repeatedly randomized and covariates are time varying.
“we followed the analysis approach by Boruvka et al. (33) for data from microrandomized trials. This method produces unbiased causal treatment effects in situations where treatments are repeatedly randomized and covariates are time varying.”
From Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial (2020) · cites this paper as [33]
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“The weighted and centered least squares method developed by Boruvka et al [105] to evaluate data from MRT studies will be used to estimate and test the causal excursion effects of the intervention on the proximal outcomes.”
Method
- Authors
- Boruvka, A., Almirall, D., Witkiewitz, K., & Murphy, S. A.
- Affiliations
- University of Michigan (US)
- University of New Mexico (US)
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2018-07-03
- Volume
- 113
- Issue
- 523
- Pages
- 1112-1121
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- PMC
- PMC6241330
- OpenAlex
- W2226634768
- Cited by
- 152
- References
- 37
- Citations by year
- 2026: 17 · 2025: 22 · 2024: 17 · 2023: 22 · 2022: 16 · 2021: 15 · 2020: 21 · 2019: 11 · 2018: 8 · 2017: 1
- FWCI
- 7.80
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of the American Statistical Association (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institute on Alcohol Abuse and Alcoholism (R01AA023187)
- National Heart, Lung, and Blood Institute (R01HL125440)
- National Institute on Drug Abuse (P50DA039838, R01DA039901, R01DA015697)
- National Institute of Biomedical Imaging and Bioengineering (U54EB020404)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mobile health, moderated effects, potential outcomes, treatment response, behavior change, university students, heavy drinking, smoking, smartphone-based intervention
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