Evidence map›Paper›PMID 42022502›Full record

ArticleFrontiers in digital health2026

IoT-based health monitoring and social welfare access for Thailand's older adults.

Chaturapron Chokphukhiao, Wonn Shweyi Thet Tun, Poomin Duankhan, Sakaowrat Masa, Patcharee Hongthong, Cholatip Pongskul, Somporn Chaiayuth, Jugsun Loeiyood, Piyathida Kuhirunyaratn, Bangonsri Jindawong and 4 more

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In one paragraph

Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Chaturapron ChokphukhiaoInformation Technology International Program, College of Computing, Khon Kaen University, Khon Kaen, Thailand.
Wonn Shweyi Thet TunDigital Health Technology Unit, Khon Kaen University National Phenome Institute (KKUNPhI), Khon Kaen University, Khon Kaen, Thailand.
Poomin DuankhanCollege of Computing, Khon Kaen University, Khon Kaen, Thailand.
Sakaowrat MasaDepartment of Integrated Science, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand.
Patcharee HongthongFaculty of Business Administration and Accountancy, Khon Kaen Business School, Khon Kaen University, Khon Kaen, Thailand.
Cholatip PongskulDepartment of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Somporn ChaiayuthDivision of Public Health and Environment Service, Office of Public Health and Environment, Khon Kaen Municipality, Khon Kaen, Thailand.
Jugsun LoeiyoodDivision of Information and Communication Technology, Khon Kaen Provincial Health Office, Khon Kaen, Thailand.
Piyathida KuhirunyaratnDepartment of Community, Family and Occupational Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Bangonsri JindawongDepartment of Community, Family and Occupational Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Nipitphon SeeooppalatDepartment of Community, Family and Occupational Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Sirapat ChiewchanwattanaCollege of Computing, Khon Kaen University, Khon Kaen, Thailand.
Rina PatramanonDigital Health Technology Unit, Khon Kaen University National Phenome Institute (KKUNPhI), Khon Kaen University, Khon Kaen, Thailand.
Khamron SunatCollege of Computing, Khon Kaen University, Khon Kaen, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Thailand's population is shifting, with 20% aged 60 and over in 2022, leading to healthcare and social welfare challenges. Digital technologies, particularly those using IoT, may improve health outcomes for older adults but face hurdles due to low digital literacy and a digital divide between urban and rural areas. This study investigated the accessibility and obstacles older adults encounter with social welfare services while assessing the effectiveness of current welfare programs. Methods: Data was gathered from 2,005 older adults through quantitative surveys regarding demographics, welfare accessibility, and the digital divide. A 6-month pilot program involving 80 users tested the daily usage of KATI smartwatches, WBP202 blood pressure monitors, and Contour Plus ELITE glucometers. All qualitative interviews, satisfaction ratings, and TAM data were directly supplied by the 80 older adult participants with the assistance of interviewers. Advanced features (alerts, SOS, predictive analytics, welfare integration) are planned for future scale-up. Results: Among the 2,005 respondents (mean age 68.9 years, 76.6% female), 64.2% had a monthly income of less than 5,000 THB, and 92.0% received the universal elderly allowance. In the pilot subgroup ( Conclusion: An IoT pilot showed good acceptability among smartphone users but risks increasing inequities without careful design for low-literacy, low-income, and homebound elders. Policies should focus on affordable devices, simple interfaces, and community training to promote inclusive digital aging.

Indexed as

digital dividedigital health technologyIoT-based healthcareolder adultssocial welfare access

Identifiers

PMID42022502
PMCPMC13096040

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LicenceCC BY
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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 OpenQuestion graph.