Evidence map›Paper›PMID 30048546›Full record

ArticlePloS one2018

Effect of self-monitoring on long-term patient engagement with mobile health applications.

Kyunghee Lee, Hyeyon Kwon, Byungtae Lee, Guna Lee, Jae Ho Lee, Yu Rang Park, Soo-Yong Shin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 3 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

73 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  16. Predicting measurement continuity in home blood pressure monitoring using machine learning.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
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13 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

7 authors.

Kyunghee LeeMike Ilitch School of Business, Wayne State University, Detroit, Michigan, United States of America.
Hyeyon KwonDepartment of Biomedical Informatics, Asan Medical Center, Seoul, Korea.
Byungtae LeeKAIST College of Business, Seoul, Korea.
Guna LeeDivision of Nursing Science, College of Health Science, Ewha Womans University, Seoul, Korea.
Jae Ho LeeDepartment of Biomedical Informatics, Asan Medical Center, Seoul, Korea.
Yu Rang ParkDepartment of Biomedical Systems Informatics Yonsei University College of Medicine, Seoul, Korea.
Soo-Yong ShinDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the growing adoption of the mobile health (mHealth) applications (apps), few studies address concerns with low retention rates. This study aimed to investigate how the usage patterns of mHealth app functions affect user retention. We collected individual usage logs for 1,439 users of single tethered personal health record app, which spanned an 18-months period from August 2011 to January 2013. The user logs contained timestamps whenever an individual uses each function, which enables us to identify the usage patterns based on the intensity of using a particular function in the app. We then estimated how these patterns were related to 1) the app usage over time (using the random effect model) and 2) the probability of stopping the use of the application (using the Cox proportional hazard model). The analyses suggested that the users utilize the app most at the time of the adoption and gradually reduce their usage over time. The average duration of use after starting the app was 25.62 weeks (SD: 18.41). The degree of the usage reduction, however, decreases as the self-monitoring function is more frequently used (coefficient = 0.002, P = 0.013); none of the other functions has this effect. Moreover, engaging with the self-monitoring function frequently (coefficient = -0.18, P = 0.003) and regularly (coefficient = 0.10, P = 0.001) significantly also reduces the probability of abandoning the application. Specifically, the estimated survival rate indicates that, after 40 weeks since the adoption, the probability of the regular users of self-monitoring to stay in use was about 80% while that of non-user was about 60%. This study provides the empirical evidence that sustained use of mHealth app is closely linked to the regular usage on self-monitoring function. The implications can be extended to the education of users and physicians to produce better outcomes as well as application development for effective user interfaces.

Indexed as

Medical Records Systems, ComputerizedMobile ApplicationsPatient Generated Health DataPatient ParticipationTelemedicineAdultAgedFemaleHumansMaleMiddle AgedPatient DropoutsTime Factors

Identifiers

PMID30048546
PMCPMC6062090

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.