Evidence map›Paper›PMID 29631989›Full record

ArticleJMIR mHealth and uHealth2018

Managing Patient-Generated Health Data Through Mobile Personal Health Records: Analysis of Usage Data.

Yu Rang Park, Yura Lee, Ji Young Kim, Jeonghoon Kim, Hae Reong Kim, Young-Hak Kim, Woo Sung Kim, Jae-Ho Lee

Open access · goldAbstract 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 33 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 2 pooled it
13.7field-weighted citation impact, top 1% 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

33 citing papers in PubMed, 2 syntheses or guidelines pooled it, 64 citations in OpenAlex.

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  19. Promoting Exercise Training Remotely.Life (Basel, Switzerland) · 2022
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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

8 authors at 2 institutions in 1 country.

Yu Rang Park *Department of Biomedical Informatics, Asan Medical Center, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0002-4210-2094
Yura Lee *Department of Biomedical Informatics, Asan Medical Center, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0003-2048-3727
Ji Young KimMedical Information Office, Asan Medical Center, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0002-0548-6738
Jeonghoon KimMedical Information Office, Asan Medical Center, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0002-2338-9827
Hae Reong KimDepartment of Biomedical Informatics, Asan Medical Center, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0002-2079-0498
Young-Hak KimDepartment of Biomedical Informatics, Asan Medical Center, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0002-3610-486X
Woo Sung KimDepartment of Biomedical Informatics, Asan Medical Center, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0002-4396-4403
Jae-Ho LeeDepartment of Biomedical Informatics, Asan Medical Center, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0003-2619-1231
Asan Medical Center · KRUlsan College · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPersonal health records (PHRs) and mHealth apps are considered essential tools for patient engagement. Mobile PHRs (mPHRs) can be a platform to integrate patient-generated health data (PGHD) and patients' medical information. However, in previous studies, actual usage data and PGHD from mPHRs have not been able to adequately represent patient engagement.

objectiveBy analyzing 5 years' PGHD from an mPHR system developed by a tertiary hospital in South Korea, we aimed to evaluate how PGHD were managed and identify issues in PGHD management based on actual usage data. Additionally, we analyzed how to improve patient engagement with mPHRs by analyzing the actively used services and long-term usage patterns.

methodsWe gathered 5 years (December 2010 to December 2015) of log data from both hospital patients and general users of the app. We gathered data from users who entered PGHD on body weight, blood pressure (BP), blood glucose levels, 10-year cardiovascular disease (CVD) risk, metabolic syndrome risk, medication schedule, insulin, and allergy. We classified users according to whether they were patients or general users based on factors related to continuous use (≥28 days for weight, BP, and blood glucose, and ≥180 days for CVD and metabolic syndrome), and analyzed the patients' characteristics. We compared PGHD entry counts and the proportion of continuous users for each PGHD by user type.

resultsThe total number of mPHR users was 18,265 (patients: n=16,729, 91.59%) with 3620 users having entered weight, followed by BP (n=1625), blood glucose (n=1374), CVD (n=764), metabolic syndrome (n=685), medication (n=252), insulin (n=72), and allergy (n=61). Of those 18,256 users, 3812 users had at least one PGHD measurement, of whom 175 used the PGHD functions continuously (patients: n=142, 81.14%); less than 1% of the users had used it for more than 4 years. Except for weight, BP, blood glucose, CVD, and metabolic syndrome, the number of PGHD records declined. General users' continuous use of PGHD was significantly higher than that of patients in the blood glucose (P<.001) and BP (P=.03) functions. Continuous use of PGHD in health management (BP, blood glucose, and weight) was significantly greater among older users (P<.001) and men (P<.001). In health management (BP, weight, and blood glucose), overall chronic disease and continuous use of PGHD were not statistically related (P=.08), but diabetes (P<.001) and cerebrovascular diseases (P=.03) were significant.

conclusionsAlthough a small portion of users managed PGHD continuously, PGHD has the potential to be useful in monitoring patient health. To realize the potential, specific groups of continuous users must be identified, and the PGHD service must target them. Further evaluations for the clinical application of PGHD, feedback regarding user interfaces, and connections with wearable devices are needed.

Indexed as

health records, personalmobile healthpatient engagementpatient-generated health datapatient participationpersonal health recordtelemedicine

Identifiers

PMID29631989
PMCPMC5913571
OpenAlexW2796817098

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.