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Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in Long-Term Care for Older People: Comprehensive Analysis Through Bibliometric, Google Trends, and Content Analysis
Shuo-Chen Chien, Chia-Ming Yen, Yu‐Hung Chang, Ying‐Erh Chen, Chia-Chun Liu, Yu-Ping Hsiao, Ping-Yen Yang, Hong-Ming Lin et al.. Journal of Medical Internet Research, 2025-03-04
Topic: Risk, prevention and care in population studies
A total of 6378 papers published between 2014 and 2023 were analyzed. The bibliometric analysis revealed that the United States, China, and Canada are leading contributors, with strong thematic overlaps in areas such as dementia care, machine learning, and wearable health monitoring technologies. High correlations were found between academic and public interest, in key topics such as "long-term care" (τ=0.89, P<.001) and "caregiver" (τ=0.72, P=.004). The content analysis demonstrated that social robots, particularly PARO, significantly improved mood and reduced agitation in patients with dementia. However, limitations, including small sample sizes, short study durations, and a narrow focus on dementia care, were noted. AI, IoT, and EI collectively form a powerful ecosystem in LTC settings, addressing different aspects of care for older adults. Our study suggests that increased international collaboration and the integration of emerging themes such as "rehabilitation," "stroke," and "mHealth" are necessary to meet the evolving care needs of this population. Additionally, incorporating high-interest keywords such as "machine learning," "smart home," and "caregiver" can enhance discoverability and relevance for both academic and public audiences. Future research should focus on expanding sample sizes, conducting long-term multicenter trials, and exploring broader health conditions beyond dementia, such as frailty and depression.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Chien, S.-C., Yen, C.-M., Chang, Y., Chen, Y., Liu, C.-C., Hsiao, Y.-P., et al. (2025). Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in Long-Term Care for Older People: Comprehensive Analysis Through Bibliometric, Google Trends, and Content Analysis. Journal of Medical Internet Research. https://doi.org/10.2196/56692
Vancouver
Chien S.-C, Yen C.-M, Chang Y, Chen Y, Liu C.-C, Hsiao Y.-P, et al. Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in Long-Term Care for Older People: Comprehensive Analysis Through Bibliometric, Google Trends, and Content Analysis. Journal of Medical Internet Research. 2025. doi:10.2196/56692. PMID: 40053718.
BibTeX
@article{chien2025-40053718,
author = {Chien, Shuo-Chen and Yen, Chia-Ming and Chang, Yu‐Hung and Chen, Ying‐Erh and Liu, Chia-Chun and Hsiao, Yu-Ping and Yang, Ping-Yen and Lin, Hong-Ming and others},
title = {{Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in Long-Term Care for Older People: Comprehensive Analysis Through Bibliometric, Google Trends, and Content Analysis}},
journal = {Journal of Medical Internet Research},
year = {2025},
doi = {10.2196/56692},
pmid = {40053718},
url = {https://doi.org/10.2196/56692}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
Key findings
- A total of 6378 papers published between 2014 and 2023 were analyzed.
- The bibliometric analysis revealed that the United States, China, and Canada are leading contributors, with strong thematic overlaps in areas such as dementia care, machine learning, and wearable health monitoring technologies.
- High correlations were found between academic and public interest, in key topics such as "long-term care" (τ=0.89, P<.001) and "caregiver" (τ=0.72, P=.004).
- The content analysis demonstrated that social robots, particularly PARO, significantly improved mood and reduced agitation in patients with dementia.
- However, limitations, including small sample sizes, short study durations, and a narrow focus on dementia care, were noted.
Why it matters
This analysis of published research found that social robots such as PARO improved mood and reduced agitation in patients with dementia, but the studies had small sample sizes and short durations. It does not show that AI, IoT, or edge intelligence can prevent, treat, or cure Alzheimer's disease.
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Study details
- Participants6,378
- Evidence strengthLow
How this study scored 52/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of Medical Internet Research) |
| Journal's citation record | 3 / 4 | Journal of Medical Internet Research: journal h-index 286 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 3 / 4 | 6,378 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 3 / 7 | Cited 26 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 5 / 6 | Cited 31.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 26 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Ren‐Hua Chung: h-index 25 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | China Medical University: institution h-index 382 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40053718) |
| Free to read | 2 / 2 | Free to read |
| Total | 52 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.2196/56692
- PubMed: 40053718
- Original source →
- Free full text: Journal of Medical Internet Research →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
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