ArticleNature communications2025
Wearable AI for on-device frailty assessment.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- A biomimetic, ultralow-power edge-AI-empowered and self-sustaining gait analysis system.Science advances · 2026Article
- From frailty-driven to frailty-informed care in the age of wearable AI.Communications medicine · 2026Review
- From Biosignals to Bedside: A Review of Real-Time Edge Machine Learning for Wearable Health Monitoring.Bioengineering (Basel, Switzerland) · 2026Review
- Decision-centered wearable biosensors for personalized rehabilitation: integrating multimodal monitoring, artificial intelligence, and closed-loop intervention.Frontiers in bioengineering and biotechnology · 2026Review
- Mapping the global landscape of wearable devices in healthcare: A dual-database bibliometric analysis (2016-2026).Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Continuously operating wearables offer detailed insight into chronic health conditions and have the potential to reshape diagnostic and screening tools. However, the energy demands and large datasets created by constant monitoring over weeks to months are difficult or impossible to integrate into existing clinical practice, limiting the utility of this device class. Machine learning offers the opportunity to condense these large datasets into streamlined, digestible trends with the potential for significant clinical impact, although off-device inference requires advanced network infrastructure and substantial power availability for radios. Here, we introduce a device framework that integrates artificial intelligence with clinical grade biosignal acquisition at the edge, performing on-device inference with clinical grade fidelity over extended durations with no interaction required by the wearer. We utilize this framework to perform gait-based frailty assessment during in vivo trials (N
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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.