Evidence mapPaperPMID 42490534Full record

ArticleJournal of medical Internet research2026

Long-Term Associations of a Mobile-Based Chronic Disease Management Program With Employee Health: Four-Year Retrospective Cohort Study.

Yujin Park, Yoon Ji Kim, Boram Choi, Chang-Bo Noh, Su Hwan Kim, Jiseon Bang, Ji-Eun Lee, Jae-Heon Kang, Ye Seul Bae

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In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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3 · Its place in the literature

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4 · The record

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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

9 authors.

Yujin Park *Healthcare Data Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.ORCID http://orcid.org/0000-0002-7936-9307
Yoon Ji Kim *Big Data Research Center, Division of Healthcare Planning, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.ORCID http://orcid.org/0009-0005-7876-9332
Boram ChoiDivision of Healthcare Planning, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.ORCID http://orcid.org/0009-0006-8300-880X
Chang-Bo NohDivision of Healthcare Planning, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.ORCID http://orcid.org/0009-0000-4465-5736
Su Hwan KimDepartment of Information Statistics, Gyeongsang National University, Jinju-si, Gyeongsangnam-do, Republic of Korea.ORCID http://orcid.org/0000-0002-8465-3564
Jiseon BangSafety & Health Team, Global Manufacturing & Infra Technology, Samsung Electronics Co.Ltd., Hwaseong-si, Gyeonggi-do, Republic of Korea.ORCID http://orcid.org/0009-0006-2881-8682
Ji-Eun LeeSafety & Health Team, Global Manufacturing & Infra Technology, Samsung Electronics Co.Ltd., Hwaseong-si, Gyeonggi-do, Republic of Korea.ORCID http://orcid.org/0009-0000-1288-9642
Jae-Heon KangDivision of Healthcare Planning, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.ORCID http://orcid.org/0000-0002-5209-0824
Ye Seul BaeBig Data Research Center, Division of Healthcare Planning, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.ORCID http://orcid.org/0000-0003-0763-5458

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The burden of hypertension (HTN), type 2 diabetes mellitus (DM), and obesity is increasing among employees, driven by workplace-related factors such as sedentary behavior and unhealthy lifestyles, while follow-up management after health checkups remains inadequate. Mobile health (mHealth) interventions have emerged as promising tools to support self-management and sustain lifestyle changes; however, evidence regarding their long-term associations in real-world occupational settings remains limited. Objective: This study aimed to investigate whether a short-term mobile-based chronic disease management program was associated with sustained improvements in metabolic health outcomes among employees with metabolic risks over a 4-year period. Methods: The authors evaluated a 12-week mobile-based chronic disease management program delivered as part of an employee assistance program targeting workers with metabolic risk factors. Using propensity score-matched cohorts from the Kangbuk Samsung Health Study (HTN: N=90; DM: N=78; obesity: N=132), we analyzed longitudinal health examination data from 2019 (baseline) and 2023 (follow-up). Primary outcomes included blood pressure (BP), BMI, fasting glucose, glycated hemoglobin, triglycerides, and lipid profiles. Adjusted longitudinal changes were assessed using linear mixed effects models. Results: Among propensity score-matched cohorts, the intervention group showed more favorable changes compared to the controls. In the HTN cohort, systolic BP and diastolic BP showed no significant group × time interactions, although diastolic BP was lower in the intervention group at follow-up (P=.045). In the DM cohort, diastolic BP showed a significant group × time interaction (P=.02), with a decrease over time observed in the intervention group, while glycemic indicators did not differ significantly between groups. In the obesity cohort, triglycerides and high-density lipoprotein cholesterol showed significant group × time interactions (both P<.001), indicating more favorable longitudinal changes in the intervention group. Overall, the intervention groups demonstrated improved or stable cardiometabolic profiles compared to the control groups over the follow-up period. Conclusions: A short-term, personalized mHealth intervention was associated with favorable long-term changes over a 4-year follow-up period among employees with chronic disease risk. This real-world evidence supports integrating digital health programs into routine workplace prevention strategies to bridge the postcheckup care gap.

Indexed as

Diabetes Mellitus, Type 2Disease ManagementOccupational HealthTelemedicineAdultChronic DiseaseDigital HealthFemaleHumansHypertensionMaleMiddle AgedObesityRetrospective Studieschronic disease managementdigital health coachingdigital interventionEAPemployee assistance programmHealthmobile health

Identifiers

PMID42490534
PMCPMC13394854

What Socratic holds

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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 Socratic graph.