ArticleBMC geriatrics2026
Machine learning in mental health promotion for older adults: a scoping review.
Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectivesOwing to the rapidly aging global population, an increasing number of older adults are experiencing mental health problems. Although machine learning has shown a lot of potential for promoting mental health in this group, there are no scoping reviews in this field. This study aimed to provide an overview of the applications of machine learning in promoting the mental health of older adults and identify associated trends and challenges.
methodsA scoping review was conducted based on the framework by Arksey and O’Malley. Three electronic databases, including Web of Science, PubMed, and IEEE Xplore, were systematically searched from database inception to March 15, 2026. We included English-language studies on the use of machine learning to promote mental health among older adults. Relevant information was extracted, summarized, and analyzed.
resultsA total of 144 articles were included in this review. Our review showed that the current research reveals diverse data and algorithms, with machine learning applications concentrated in two directions. One is the prediction of the risk of mental health problems, and the other is the detection and identification of mental health status. There are still challenges in methodology and application directions, although machine learning effectively helps address some limitations of existing research.
conclusionsFuture research may consider improving data quality, implementing longitudinal designs more extensively, enhancing model interpretability, and broadening research on various mental health problems and intervention–effect prediction. These initiatives may strengthen the empirical basis for clinical judgment and public health policy.
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Registered trials
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.