Evidence map›Paper›PMID 30497999›Full record

ArticleJMIR mHealth and uHealth2018

To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health App.

Niranjan Bidargaddi, Daniel Almirall, Susan Murphy, Inbal Nahum-Shani, Michael Kovalcik, Timothy Pituch, Haitham Maaieh, Victor Strecher

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
110citing papers in PubMed, 8 pooled it
–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

110 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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  17. Self-relevant appeals to engage in self-monitoring of alcohol use: A microrandomized trial.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2023
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  20. Translating strategies for promoting engagement in mobile health: A proof-of-concept microrandomized trial.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2021
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50 more citing papers are in PubMed but not listed here.

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

8 authors.

Niranjan BidargaddiPersonal Health Informatics, College of Medicine & Public Health, Adelaide, Australia.ORCID http://orcid.org/0000-0003-2868-9260
Daniel AlmirallInsitute for Social Research, Michigan University, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0002-9697-6600
Susan MurphyDepartment of Statistics, Harvard University, Boston, MA, United States.ORCID http://orcid.org/0000-0002-2032-4286
Inbal Nahum-ShaniInsitute for Social Research, Michigan University, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0001-6138-9089
Michael KovalcikInsitute for Social Research, Michigan University, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0003-4232-2473
Timothy PituchJool Health, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0003-4468-2720
Haitham Maaieh *Jool Health, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0001-8227-2402
Victor StrecherJool Health, Ann Arbor, MI, United States.ORCID http://orcid.org/0000-0002-0587-2305

Funding

Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LI, RUNZE · 2015 to 2019
$13.9M
Novel Methods for Intensive Longitudinal Data in SMART Studies of Drug Abuse and HIVR01DA039901 · NIDA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALMIRALL, DANIEL, NAHUM-SHANI, INBAL BILLIE · 2015 to 2024
$5.4M
Novel use of mHealth data to identify states of vulnerability and receptivity to JITAIs SupplementU01CA229437 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NAHUM-SHANI, INBAL BILLIE, WETTER, DAVID W · 2018 to 2022
$2.8M
Data-Based Methods for Just-In-Time Adaptive Interventions in Alcohol UseR01AA023187 · NIAAA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MURPHY, SUSAN A · 2015 to 2019
$2.6M
NIAAA NIH HHS R01 AA023187NIDA NIH HHS P50 DA039838NIDA NIH HHS R01 DA039901
6 · The paper itself

Abstract

backgroundMobile health (mHealth) apps provide an opportunity for easy, just-in-time access to health promotion and self-management support. However, poor user engagement with these apps remains a significant unresolved challenge.

objectiveThis study aimed to assess the effect of sending versus not sending a push notification containing a contextually tailored health message on proximal engagement, measured here as self-monitoring via the app. Secondary aims were to examine whether this effect varies by the number of weeks enrolled in the program or by weekday versus weekend. An exploratory aim was to describe how the effect on proximal engagement differs between weekday versus weekend by the time of day.

methodsThe study analyzes the causal effects of push notifications on proximal engagement in 1255 users of a commercial workplace well-being intervention app over 89 days. The app employs a microrandomized trial (MRT) design to send push notifications. At 1 of 6 times per day (8:30 am, 12:30 pm, 5:30 pm, 6:30 pm, 7:30 pm, and 8:30 pm; selected randomly), available users were randomized with equal probability to be sent or not sent a push notification containing a tailored health message. The primary outcome of interest was whether the user self-monitored behaviors and feelings at some time during the next 24 hours via the app. A generalization of log-linear regression analysis, adapted for use with data arising from an MRT, was used to examine the effect of sending a push notification versus not sending a push notification on the probability of engagement over the next 24 hours.

resultsUsers were estimated to be 3.9% more likely to engage with the app in the next 24 hours when a tailored health message was sent versus when it was not sent (risk ratio 1.039; 95% CI 1.01 to 1.08; P<.05). The effect of sending the message attenuated over the course of the study, but this effect was not statistically significant (P=.84). The effect of sending the message was greater on weekends than on weekdays, but the difference between these effects was not statistically significant (P=.18). When sent a tailored health message on weekends, the users were 8.7% more likely to engage with the app (95% CI 1.01 to 1.17), whereas on weekdays, the users were 2.5% more likely to engage with the app (95% CI 0.98 to 1.07). The effect of sending a tailored health message was greatest at 12:30 pm on weekends, when the users were 11.8% more likely to engage (90% CI 1.02 to 1.13).

conclusionsSending a push notification containing a tailored health message was associated with greater engagement in an mHealth app. Results suggested that users are more likely to engage with the app within 24 hours when push notifications are sent at mid-day on weekends.

Indexed as

health promotionlifestylemobile applicationspush notificationself reportsmartphoneubiquitous computing

Identifiers

PMID30497999
PMCPMC6293241

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.