Observational studyFrontiers in public health2023
Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty.
Observational study in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 4 of them syntheses that pooled it.
What it found
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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
21 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The Associations Between Digital Exclusion and Physical or Cognitive Function in Middle-Aged and Older Adults: Systematic Review and Meta-Analysis.JMIR aging · 2026Pooled it
- Digital Technologies and Biomarkers for Locomotor Capacity Assessment in Older Adults: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Prediction of frailty in community older adults based on machine learning: a systematic review and meta-analysis.Frontiers in public health · 2025Pooled it
- From frailty-driven to frailty-informed care in the age of wearable AI.Communications medicine · 2026Review
- A Multidimensional Digital Health Platform to Support Intrinsic Capacity and Healthy Aging: Pilot Feasibility Study.JMIR aging · 2026Article
- Measuring Walking Stability with a Mobile Phone in Older Adults: A Validation Study.Sensors (Basel, Switzerland) · 2026Article
- Using data and artificial intelligence to improve care pathways of older people experiencing falls and frailty: Opportunities, challenges and practical considerations for clinicians.Clinical medicine (London, England) · 2026Review
- Database for Prevalence and Determinants of Frailty in the Elderly with Quantifying Functional Mobility.Scientific data · 2026Article
- Digital health technologies for the management of sarcopenia in patients receiving maintenance hemodialysis: a narrative review.Frontiers in nutrition · 2026Review
- Predictive Risk Models for Frailty Onset in Older Adults: A Scoping Review of Methodological Trends, Model Performance, and Clinical Translation Gap.Clinical interventions in aging · 2026Article
- Assessment of Frailty in Community-Dwelling Older Adults Using Smartphone-Based Digital Lifelogging: A Multi-Center, Prospective Observational Study.Sensors (Basel, Switzerland) · 2025Observational
- Retrospective Frailty Assessment in Older Adults Using Inertial Measurement Unit-Based Deep Learning on Gait Spectrograms.Sensors (Basel, Switzerland) · 2025Article
- Digital assessment of walking ability: Validity and reliability of the automated figure-of-eight walk test in older adults.PloS one · 2025Article
- Inertial sensor-based gait classification for frailty status in older adults: A cross-sectional study.Computational and structural biotechnology journal · 2025Article
- Physical Frailty Prediction Using Cane Usage Characteristics during Walking.Sensors (Basel, Switzerland) · 2024Article
- Convolutional neural network based detection of early stage Parkinson's disease using the six minute walk test.Scientific reports · 2024Article
- Artificial intelligence-enhanced patient evaluation: bridging art and science.European heart journal · 2024Review
- Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review.Journal of the American College of Cardiology · 2024Review
- How can precision health care contribute to healthy aging?Aging medicine (Milton (N.S.W)) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Frailty is a dynamic and complex geriatric condition characterized by multi-domain declines in physiological, gait and cognitive function. This study examined whether digital health technology can facilitate frailty identification and improve the efficiency of diagnosis by optimizing analytical and machine learning approaches using select factors from comprehensive geriatric assessment and gait characteristics. Methods: As part of an ongoing study on observational study of Aging, we prospectively recruited 214 individuals living independently in the community of Southern China. Clinical information and fragility were assessed using comprehensive geriatric assessment (CGA). Digital tool box consisted of wearable sensor-enabled 6-min walk test (6MWT) and five machine learning algorithms allowing feature selections and frailty classifications. Results: It was found that a model combining CGA and gait parameters was successful in predicting frailty. The combination of these features in a machine learning model performed better than using either CGA or gait parameters alone, with an area under the curve of 0.93. The performance of the machine learning models improved by 4.3-11.4% after further feature selection using a smaller subset of 16 variables. SHapley Additive exPlanation (SHAP) dependence plot analysis revealed that the most important features for predicting frailty were large-step walking speed, average step size, age, total step walking distance, and Mini Mental State Examination score. Conclusion: This study provides evidence that digital health technology can be used for predicting frailty and identifying the key gait parameters in targeted health assessments.
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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 Socratic graph.