Evidence map›Paper›PMID 34076580›Full record

Trial reportJMIR mHealth and uHealth2021

Motivating Adherence to Exercise Plans Through a Personalized Mobile Health App: Enhanced Action Design Research Approach.

Ruo-Ting Sun, Wencui Han, Hsin-Lu Chang, Michael J Shaw

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
9.3field-weighted citation impact, top 2% of its field
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

17 citing papers in PubMed, 45 citations in OpenAlex.

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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

4 authors at 2 institutions in 2 countries.

Ruo-Ting SunDepartment of Business Administration, University of Illinois, Urbana-Champaign, Champaign, IL, United States.ORCID 0000-0003-1606-3391
Wencui HanDepartment of Business Administration, University of Illinois, Urbana-Champaign, Champaign, IL, United States.ORCID 0000-0001-6932-9945
Hsin-Lu ChangDepartment of Management Information Systems, National Chengchi University, Taipei, Taiwan.ORCID 0000-0003-4843-1259
Michael J ShawDepartment of Business Administration, University of Illinois, Urbana-Champaign, Champaign, IL, United States.ORCID 0000-0001-9772-6972
University of Illinois Urbana-Champaign · USNational Chengchi University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysical inactivity is a global issue that affects people's health and productivity. With the advancement of mobile technologies, many apps have been developed to facilitate health self-management. However, few studies have examined the effectiveness of these mobile health (mHealth) apps in motivating exercise adherence.

objectiveThis study aims to demonstrate the enhanced action design research (ADR) process and improve the design of mHealth apps for exercise self-management. Specifically, we investigate whether sending motivational messages improves adherence to exercise plans, whether the motivational effect is affected by personality, the impact of message type and repetition, and the process of involving a field experiment in the design process and learning new design principles from the results.

methodsThis formative research was conducted by proposing an enhanced ADR process. We incorporated a field experiment into the process to iteratively refine and evaluate the design until it converges into a final mHealth app. We used the Apple ResearchKit to develop the mHealth app and promoted it via trainers at their gyms. We targeted users who used the app for at least two months. Participants were randomly assigned to 1 of the 12 groups in a 2×3×2 factorial design and remained blinded to the assigned intervention. The groups were defined based on personality type (thinking or feeling), message type (emotional, logical, or none), and repetition (none or once). Participants with different personality types received tailored and repeated messages. Finally, we used the self-reported completion rate to measure participants' adherence level to exercise plans. By analyzing users' usage patterns, we could verify, correct, and enhance the mHealth app design principles.

resultsIn total, 160 users downloaded the app, and 89 active participants remained during the 2-month period. The results suggest a significant main effect of personality type and repetition and a significant interaction effect between personality type and repetition. The adherence rate of people with feeling personality types was 18.15% higher than that of people with thinking types. Emotional messages were more effective than logical messages in motivating exercise adherence. Although people received repeated messages, they were more likely to adhere to exercise plans. With repeated reminders, the adherence rates of people with thinking personality types were significantly improved by 27.34% (P<.001).

conclusionsThis study contributes to the literature on mHealth apps. By incorporating a field experiment into the ADR process, we demonstrate the benefit of combining design science and field experiments. This study also contributes to the research on mHealth apps. The principles learned from this study can be applied to improve the effectiveness of mHealth apps. The app design can be considered a foundation for the development of more advanced apps for specific diseases, such as diabetes and asthma, in future research.

Indexed as

Mobile ApplicationsSelf-ManagementTelemedicineExerciseHumansMotivationaction design researchadherenceMBTImobile healthmobile phonemotivationpersonality

Identifiers

PMID34076580
PMCPMC8209532
OpenAlexW3139954185

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.