Evidence map›Paper›PMID 42458444›Full record

ArticleBMC health services research2026

Barriers to AI-supported health management among older adults with chronic orthopedic conditions in China: implications for healthcare delivery and access.

Liang Xu, Jiangning Fu, Nan Jiang, Boyu Zhai, Chunpeng Li, Jing Li

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Article in BMC health services 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.

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

6 authors.

Liang XuDepartment of Orthopedic, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Jiangning FuSchool of Nursing, University of British Columbia, Vancouver, BC, Canada.
Nan JiangSchool of Nursing, Tianjin Medical University, Tianjin, China.
Boyu ZhaiCenter on Aging Psychology, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Chunpeng LiDepartment of Intelligent Engineering, Heilongjiang Agricultural Engineering Vocational College, Harbin, China.
Jing LiDepartment of Geriatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China. 15331886892@189.cn.

Funding

Medical Data Future Development Project of the Heilongjiang Medical Development Foundation 2023ZD02
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is increasingly applied in chronic disease management, yet its adoption among older adults with chronic orthopedic conditions remains limited and poorly understood in China.

methodsA qualitative study using semi-structured interviews was conducted with 13 older adults recruited from two tertiary hospitals in Beijing and Harbin. Thematic analysis was performed to identify key themes.

resultsFive themes were identified: (1) preference for traditional health management over AI; (2) limited understanding of AI-supported health management; (3) perceived constraints in technological capability and affordability; (4) lack of external support from family and community; and (5) optimistic yet unrealistic expectations of AI capabilities.

conclusionsOlder adults with chronic orthopedic conditions face structural barriers to adopting AI-supported health management. Effective implementation requires age-friendly design and stronger integration within primary healthcare systems.

Indexed as

Artificial IntelligenceHealth Services AccessibilityMusculoskeletal DiseasesAgedAged, 80 and overChinaChronic DiseaseFemaleHumansInterviews as TopicMaleMiddle AgedQualitative ResearchArtificial intelligenceChronic orthopedic conditionsHealth managementOlder adultsQualitative study

Identifiers

PMID42458444

What OpenQuestion holds

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Registered trials

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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.