Evidence map›Paper›PMID 41050625›Full record

ArticleDigital health

Promoting digital resilience and healthy aging: Older adults' experiences with intelligent exercise.

Hui-Chen Tsai, Ching-Wen Wei, Fong-Ping Tang, Kuan-Yu Peng, Heng-Hsin Tung

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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

5 authors.

Hui-Chen TsaiDepartment of Nursing, College of Nursing, National Yang Ming Chiao Tung University, Taipei.
Ching-Wen WeiInstitute of Clinical Nursing, National Chung Hsing University, Taichung.
Fong-Ping TangInstitute of Clinical Nursing, National Yang Ming Chiao Tung University, Taipei.
Kuan-Yu PengTaiwan Semiconductor Manufacturing Company Charity Foundation, Hsinchu.
Heng-Hsin TungDepartment of Nursing, College of Nursing, National Yang Ming Chiao Tung University, Taipei.ORCID https://orcid.org/0000-0001-9843-9924

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: With the rise of the global aging population, innovative solutions are needed to support healthy aging. Intelligent exercise systems using IoT, big data, and AI offer real-time monitoring, improved safety, interactive engagement, and remote supervision marking a shift from traditional models and promoting self-managed health in older adults. Purpose: To investigate how Intelligent exercise influence older adults' digital resilience and adaptation, by integrating quantitative outcome measures with qualitative insights into their real-world experiences. Methods: This mixed-methods study recruited participants via purposive sampling from a senior fitness club. Participants completed a quantitative questionnaire and a one-to-one qualitative interview to discuss their experiences and adjustment processes when engaging in intelligent exercise. Results: A total of 69 participants were randomly assigned to either the intervention or control group. The intervention group completed at least six months of an introductory smart sports equipment course. Data were collected via questionnaire, and 16 participants took part in interviews. Resilience scores in the intervention group increased significantly from 21.35 ± 4.37 at baseline to 23.35 ± 5.4 after six months ( Conclusion: Intelligent exercise program can strengthen older adults' digital resilience and promote healthy aging. Future development should enhance usability and accessibility to support sustained adoption.

Indexed as

artificial intelligencedigital resilienceelderly caremixed-methods approachSmart sports equipment

Identifiers

PMID41050625
PMCPMC12489239

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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.