research-article

Just-in-Time but Not Too Much: Determining Treatment Timing in Mobile Health

Authors:

Walter Dempsey,

Syed Monowar Hossain,

Mustafa al'Absi,

Predrag Klasnja,

Susan MurphyAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 4

Article No.: 179, Pages 1 - 21

https://doi.org/10.1145/3287057

Published: 27 December 2018 Publication History

Abstract

There is a growing scientific interest in the use and development of just-in-time adaptive interventions in mobile health. These mobile interventions typically involve treatments, such as reminders, activity suggestions and motivational messages, delivered via notifications on a smartphone or a wearable to help users make healthy decisions in the moment. To be effective in influencing health, the combination of the right treatment and right delivery time is likely critical. A variety of prediction/detection algorithms have been developed with the goal of pinpointing the best delivery times. The best delivery times might be times of greatest risk and/or times at which the user might be most receptive to the treatment notifications. In addition, to avoid over burdening users, there is of ten a constraint on the number of treatments that should be provided per time interval (e.g., day or week). Yet there may be many more times at which the user is predicted or detected to be at risk and/or receptive. The goal then is to spread treatment uniformly across all of these times. In this paper, we introduce a method that spreads the treatment uniformly across the delivery times. This method can also be used to provide data for learning whether the treatments are effective at the delivery times. This work is motivated by our work on two mobile health studies, a smoking cessation study and a physical activity study.

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Cited By

Suh JHowe ELewis RHernandez JSaha KAlthoff TCzerwinski M(2024)Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention ImplementationJMIR Mental Health10.2196/4897411(e48974)Online publication date: 12-Sep-2024
https://doi.org/10.2196/48974
Hurley LNezami BValle CTate D(2024)Motivated information avoidance in an mHealth weight loss intervention: Associations between unmet behavioral goals and likelihood of viewing program messagesDIGITAL HEALTH10.1177/2055207624128736510Online publication date: 21-Oct-2024
https://doi.org/10.1177/20552076241287365
Sefidgar YJörke MSuh JSaha KIqbal SRamos GCzerwinski M(2024)Improving Work-Nonwork Balance with Data-Driven Implementation Intention and Mental ContrastingProceedings of the ACM on Human-Computer Interaction10.1145/36373518:CSCW1(1-29)Online publication date: 26-Apr-2024
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Index Terms

Just-in-Time but Not Too Much: Determining Treatment Timing in Mobile Health
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic algorithms

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 4

December 2018

1169 pages

EISSN:2474-9567

DOI:10.1145/3301777

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Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 27 December 2018

Accepted: 01 October 2018

Revised: 01 August 2018

Received: 01 May 2018

Published in IMWUT Volume 2, Issue 4

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Suh JHowe ELewis RHernandez JSaha KAlthoff TCzerwinski M(2024)Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention ImplementationJMIR Mental Health10.2196/4897411(e48974)Online publication date: 12-Sep-2024
https://doi.org/10.2196/48974
Hurley LNezami BValle CTate D(2024)Motivated information avoidance in an mHealth weight loss intervention: Associations between unmet behavioral goals and likelihood of viewing program messagesDIGITAL HEALTH10.1177/2055207624128736510Online publication date: 21-Oct-2024
https://doi.org/10.1177/20552076241287365
Sefidgar YJörke MSuh JSaha KIqbal SRamos GCzerwinski M(2024)Improving Work-Nonwork Balance with Data-Driven Implementation Intention and Mental ContrastingProceedings of the ACM on Human-Computer Interaction10.1145/36373518:CSCW1(1-29)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637351
Alikhanov JZhang PNoh YKim H(2024)Design of Contextual Filtered Features for Better Smartphone-User Receptivity PredictionIEEE Internet of Things Journal10.1109/JIOT.2023.333171511:7(11707-11722)Online publication date: 1-Apr-2024
https://doi.org/10.1109/JIOT.2023.3331715
Breitwieser JNeubauer ASchmiedek FBrod G(2024)Realizing the potential of mobile interventions for educationnpj Science of Learning10.1038/s41539-024-00289-99:1Online publication date: 12-Dec-2024
https://doi.org/10.1038/s41539-024-00289-9
Bell LGarnett CBao YCheng ZQian TPerski OPotts HWilliamson E(2023)How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized TrialJMIR mHealth and uHealth10.2196/3834211(e38342)Online publication date: 9-Jun-2023
https://doi.org/10.2196/38342
Liu XDeliu NChakraborty B(2023)Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public HealthAmerican Journal of Public Health10.2105/AJPH.2022.307150113:1(60-69)Online publication date: Jan-2023
https://doi.org/10.2105/AJPH.2022.307150
Wang JQi ZWong R(2023)Projected state-action balancing weights for offline reinforcement learningThe Annals of Statistics10.1214/23-AOS230251:4Online publication date: 1-Aug-2023
https://doi.org/10.1214/23-AOS2302
Xu JYan XFigueroa CWilliams JChakraborty B(2023)A flexible micro-randomized trial design and sample size considerationsStatistical Methods in Medical Research10.1177/0962280223118851332:9(1766-1783)Online publication date: 25-Jul-2023
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Rawassizadeh RRong Y(2023)ODSearchProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694886:4(1-25)Online publication date: 11-Jan-2023
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