This page is alzhub.org's plain-English summary of a published study, not the paper itself. It is generated from the record we hold for this study, sourced and linked below — check the original for the full text and methods.

42/100

✓ Peer-reviewed alzhub usefulness score 42 out of 100. Digital health tools

Topic: Risk, prevention and care in population studies

Plain-language summary

Of the 99 participants, 43 were classified into the low social interaction frequency group, and 37 were classified into the high loneliness level group. The random forest model was the most suitable for exploring factors associated with low social interaction frequency (accuracy 0.849; precision 0.837; specificity 0.857; and area under the receiver operating characteristic curve 0.935). The Gradient Boosting Machine model performed best for identifying factors related to high loneliness levels (accuracy 0.838; precision 0.871; specificity 0.784; and area under the receiver operating characteristic curve 0.887). This study demonstrated the potential of machine learning-based exploratory models, using data collected from mobile ecological momentary assessment and wearable actigraphy, to detect vulnerable groups in terms of social interaction frequency and loneliness levels among older adults with subjective cognitive decline and mild cognitive impairment. Our findings highlight physical movement as a key factor associated with low social interaction frequency, and sleep quality as a key factor related to loneliness. These results suggest that social interaction frequency and loneliness may operate through distinct mechanisms. Ultimately, this approach may contribute to preventing cognitive and physical decline in older adults at high risk of dementia. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1177/20552076241269555.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Kang, B., Park, M. K., Kim, J. I., Yoon, S., Heo, S., Kang, C., et al. (2025). Exploring Factors Related to Social Isolation Among Older Adults in the Predementia Stage Using Ecological Momentary Assessments and Actigraphy: Machine Learning Approach. Journal of Medical Internet Research. https://doi.org/10.2196/69379

Vancouver

Kang B, Park MK, Kim JI, Yoon S, Heo S, Kang C, et al. Exploring Factors Related to Social Isolation Among Older Adults in the Predementia Stage Using Ecological Momentary Assessments and Actigraphy: Machine Learning Approach. Journal of Medical Internet Research. 2025. doi:10.2196/69379. PMID: 40550119.

BibTeX

@article{kang2025-40550119,
  author  = {Kang, Bada and Park, Min Kyung and Kim, Jennifer Ivy and Yoon, Seolah and Heo, Seok‐Jae and Kang, Chaeeun and Lee, SungHee and Choi, Yong-Sung and others},
  title   = {{Exploring Factors Related to Social Isolation Among Older Adults in the Predementia Stage Using Ecological Momentary Assessments and Actigraphy: Machine Learning Approach}},
  journal = {Journal of Medical Internet Research},
  year    = {2025},
  doi     = {10.2196/69379},
  pmid    = {40550119},
  url     = {https://doi.org/10.2196/69379}
}

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.

How to read this study

Design: our record does not state a study design, so check the methods section of the original before weighing the result.
Size: the record lists 99 participants. Ask whether that is enough for the question, and who they were.
Effect: the record gives no effect size, so the summary above cannot say how large any difference was.

What the evidence grade means here

This page is graded low. alzhub.org assigns the grade automatically from keywords in the abstract, not from reading the paper. Low means the abstract matched neither pattern. It is the default label, and it does not mean the work is poor; many sound lab and imaging papers land here. It can add at most 5 of the 100 points in the study score, and it is a label about design, not a verdict on whether the findings are true.

New to reading papers? Six questions to ask of any Alzheimer’s study · how we score.

Key findings

Of the 99 participants, 43 were classified into the low social interaction frequency group, and 37 were classified into the high loneliness level group.
The random forest model was the most suitable for exploring factors associated with low social interaction frequency (accuracy 0.849; precision 0.837; specificity 0.857; and area under the receiver operating characteristic curve 0.935).
The Gradient Boosting Machine model performed best for identifying factors related to high loneliness levels (accuracy 0.838; precision 0.871; specificity 0.784; and area under the receiver operating characteristic curve 0.887).
This study demonstrated the potential of machine learning-based exploratory models, using data collected from mobile ecological momentary assessment and wearable actigraphy, to detect vulnerable groups in terms of social interaction frequency and loneliness levels among older adults with subjective cognitive decline and mild cognitive impairment.
Our findings highlight physical movement as a key factor associated with low social interaction frequency, and sleep quality as a key factor related to loneliness.

Why it matters

Among 99 older adults with subjective cognitive decline and mild cognitive impairment, machine learning models identified factors linked to low social interaction frequency and high loneliness, with physical movement and sleep quality as key factors. The authors suggest this approach may contribute to preventing cognitive and physical decline in older adults at high risk of dementia, but the study itself only detects vulnerable groups.

New to this subject? Start with our plain-English guide: Digital health →

Study details

Participants99
Evidence strengthLow

How this study scored 42/100

Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomized / 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	1 / 4	99 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)
Randomized / blinded	0 / 2	No randomization or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	2 / 7	Cited 7 times (OpenAlex, 2026-10-10)
Citations per year	1 / 5	2.3 citations a year over 3 year(s) since 2025
Impact for its field and year	3 / 6	Cited 4.4× 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	1 / 4	Cited 7 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	0 / 4	Last-listed author Dahye Hong: h-index 4 (OpenAlex, whole career)
Institution's track record	1 / 3	Yonsei University: institution h-index 777 (OpenAlex)
Multicenter collaboration	1 / 3	OpenAlex lists 2 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40550119)
Free to read	2 / 2	Free to read
Total	42 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Studies in the library that share something with this one. The line above each title says what.

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

Related studies in Digital health tools from nearby years

Also tagged “palliative”

858 studies in the library carry the topic tag “palliative”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 858 tagged studies.

Studies sharing this one's topic tags

Similar studies

More on Digital health tools

See all studies in Digital health tools →