ArticleSensors (Basel, Switzerland)2021
Unsupervised Assessment of Balance and Falls Risk Using a Smartphone and Machine Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- The Use of Wearable Sensors and Machine Learning Methods to Estimate Biomechanical Characteristics During Standing Posture or Locomotion: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Artificial intelligence for detecting fall events, assessing fall risks, and preventing fall occurrences in the elderly: A narrative review.Clinical medicine (London, England) · 2026Review
- Healthcare applications for elderly: a scoping review and a practical roadmap for developing health apps.BMC medical informatics and decision making · 2026Article
- Automatic multi-IMU-based deep learning evaluation of intensity during static standing balance training exercises.Journal of neuroengineering and rehabilitation · 2025Article
- The Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment.Journal of medical Internet research · 2025Article
- Article
- Balance Assessment Using a Handheld Smartphone with Principal Component Analysis for Anatomical Calibration.Sensors (Basel, Switzerland) · 2024Article
- The Dual Task Ball Balancing Test and Its Association With Cognitive Function: Algorithm Development and Validation.Journal of medical Internet research · 2024Article
- Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review.Online journal of public health informatics · 2024Article
- Enhancing public health in developing nations through smartphone-based motor assessment.Frontiers in digital health · 2024Article
- Mobile Technology for Falls Prevention in Older Adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Review
- Balance Assessment Using a Smartwatch Inertial Measurement Unit with Principal Component Analysis for Anatomical Calibration.Sensors (Basel, Switzerland) · 2023Article
- Patient experiences of virtual care across specialist neuroscience and psychiatry clinics related to the second wave of the COVID-19 pandemic in Calgary, Alberta.Health and technology · 2023Article
- Telemedicine in neurology: advances and possibilities.Arquivos de neuro-psiquiatria · 2022Review
- The Developments and Iterations of a Mobile Technology-Based Fall Risk Health Application.Frontiers in digital health · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessment of health and physical function using smartphones (mHealth) has enormous potential due to the ubiquity of smartphones and their potential to provide low cost, scalable access to care as well as frequent, objective measurements, outside of clinical environments. Validation of the algorithms and outcome measures used by mHealth apps is of paramount importance, as poorly validated apps have been found to be harmful to patients. Falls are a complex, common and costly problem in the older adult population. Deficits in balance and postural control are strongly associated with falls risk. Assessment of balance and falls risk using a validated smartphone app may lessen the need for clinical assessments which can be expensive, requiring non-portable equipment and specialist expertise. This study reports results for the real-world deployment of a smartphone app for self-directed, unsupervised assessment of balance and falls risk. The app relies on a previously validated algorithm for assessment of balance and falls risk; the outcome measures employed were trained prior to deployment on an independent data set. Results for a sample of 594 smartphone assessments from 147 unique phones show a strong association between self-reported falls history and the falls risk and balance impairment scores produced by the app, suggesting they may be clinically useful outcome measures. In addition, analysis of the quantitative balance features produced seems to suggest that unsupervised, self-directed assessment of balance in the home is feasible.
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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.