Unsupervised Assessment of Balance and Falls Risk Using a Smartphone and Machine Learning.

Barry R Greene,Lilian Genaro Motti Ader,Brian Caulfield,Killian Mcmanus

doi:10.3390/s21144770

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.

Highlights

An estimated 30% of adults over the age of 65 fall each year, with 1 in 5 dying within 12 months. 1 in 3 of those experiencing a fall never return home after a fall-related hip fracture [1]
We report an observational study of the performance of a smartphone application for assessment, management and prevention of falls risk in older adults, tested on a large statistically independent sample
This study reports an analysis of anonymised data obtained from users of the Kinesis BalanceTM fall prevention app (Kinesis Health Technologies, Dublin, Ireland), a Class I medical device, between September 2020 and May 2021

Summary

Introduction

An estimated 30% of adults over the age of 65 fall each year, with 1 in 5 dying within 12 months. 1 in 3 of those experiencing a fall never return home after a fall-related hip fracture [1]. An estimated 30% of adults over the age of 65 fall each year, with 1 in 5 dying within 12 months. The direct healthcare cost of falls has been estimated to be $50 Bn in the US [2] and €25 Bn per annum in the EU [3]; an ageing population means that these costs are increasing steadily each year. Effective early intervention is dependent on accurate screening and identification of those people at risk of experiencing a fall, before the first fall has occurred, ensuring appropriate referral for patients at higher risk [6]. Self-directed, remote assessment of falls risk prior to the first fall, would allow timely intervention and ease the burden on overstretched healthcare systems [6]. There was a compromise between relying on either clinic-based observational measures that are subjective in nature or laboratory based measurements that are complex, expensive and available to a very small minority of specialists

Methods

Results

Conclusion