Evidence map›Paper›PMID 29959109›Full record

ArticleJMIR mHealth and uHealth2018

Self-Management of Chronic Diseases Among Older Korean Adults: An mHealth Training, Protocol, and Feasibility Study.

Heejung Kim, Eunhee Park, Sangeun Lee, Mijung Kim, Eun Jeong Park, Soyun Hong

Registry-linked trialAbstract 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. It is linked to trial NCT05883930 (The Effect of Mobile Software "Let's Move" Program Developed Based on Transtheoretical Model on Sedentary Life Behaviors of University Students), which is not on this map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 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.

NCT05883930 naunknown statusnot on this mapstarted 2023, after this paper: background citation

The Effect of Mobile Software "Let's Move" Program Developed Based on Transtheoretical Model on Sedentary Life Behaviors of University Students

TypeinterventionalSponsorMarmara UniversityRan2023 to 2024Enrolled194ConditionsPublic Health NursingArms'Let's Move' program, no intervention
3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  6. Observational
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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

6 authors.

Heejung KimCollege of Nursing, Yonsei University, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0003-3719-0111
Eunhee ParkSchool of Nursing, University at Buffalo, Buffalo, NY, United States.ORCID http://orcid.org/0000-0002-1577-2244
Sangeun LeeCollege of Nursing, Yonsei University, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0001-6936-592X
Mijung KimMapo Senior Welfare Center, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0001-9107-8812
Eun Jeong ParkSeodaemun Senior Welfare Center, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0002-9693-7097
Soyun HongCollege of Nursing, Yonsei University, Seoul, Republic Of Korea.ORCID http://orcid.org/0000-0003-4847-3619

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMost training programs for self-management of chronic diseases in Korea currently involve face-to-face interactions primarily in a health care setting. Therefore, older Koreans living in the community continue to seek other training opportunities for the management of chronic diseases. This has led to the development of new training methods, such as mobile health (mHealth) care, which are valuable in community centers and homes.

objectiveThis feasibility study (1) developed an mHealth training protocol to empower community-dwelling elderly individuals to manage their chronic diseases; (2) examined the feasibility of delivering this mHealth training protocol to elderly individuals through mobile tablets and applications (apps); and (3) discussed the contextual and methodological challenges associated with the development of this protocol.

methodsThe mHealth training protocol was developed based on the eHealth Enhanced Chronic Care Model and comprised of four phases. Phase 1 included standardized technology (mobile tablets) training using guidebooks, demonstrations, and guided practice. Phase 2 included provision of standardized information about disease management that was obtained from governmental and professional health care organizations. Phase 3 included provision of training on the use of high-quality mHealth apps that were selected based on individual diagnoses. Phase 4 included encouraging the patients to practice using self-selected mHealth apps based on their individual needs. Quantitative descriptive statistics and qualitative content analyses of user evaluations were used to assess the feasibility and user acceptance of this protocol.

resultsOf the 27 older adults included in this study, 25 completed all 4 weeks of the mHealth training. The attrition rate was 7% (2/27), and the reasons included time conflicts, emotional distress, and/or family discouragement. The men required little or no training for Phase 1, and in comparison with men, women seemed to depend more on the mHealth trainers in Phase 3. Gender, level of education, and previous experience of using smartphones were associated with the speed of learning, level of confidence, and overall competence.

conclusionsA tailored and personalized approach is required to develop mHealth training protocols for older adults. Self-management of chronic diseases via mHealth training requires careful consideration of the complex nature of human behavior, emotional responses, and familial influences. Therefore, integration of a theoretical, clinical, and technical approach is necessary for the successful development and implementation of an mHealth training program that targets older adults with chronic diseases in a community setting.

Indexed as

chronic diseasecommunity health serviceeHealth Enhanced Chronic Care Modelelderlyfeasibility studymobile health

Identifiers

PMID29959109
PMCPMC6045790

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Registered trials

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.