Evaluation of Concurrent Validity between a Smartphone Self-Test Prototype and Clinical Instruments for Balance and Leg Strength

Abstract

The evolving use of sensors to objectively assess movements is a potentially valuable addition to clinical assessments. We have developed a new self-test application prototype, MyBalance, in the context of fall prevention aimed for use by older adults in order to independently assess balance and functional leg strength. The objective of this study was to investigate the new self-test application for concurrent validity between clinical instruments and variables collected with a smartphone. The prototype has two test procedures: static standing balance test in two positions, and leg strength test performed as a sit-to-stand test. Thirty-one older adults were assessed for balance and functional leg strength, in an outpatient physiotherapy setting, using seven different clinical assessments and three sensor-tests. The results show that clinical instruments and sensor measurements correlate to a higher degree for the smartphone leg strength test. For balance tests, only a few moderate correlations were seen in the Feet Together position and no significant correlations for the Semi Tandem Stance. This study served as a first step to develop a smartphone self-test application for older adults to assess functional balance at home. Further research is needed to test validity, reliability, and user-experience of this new self-test application.

Feasibility of performance-based and self-reported outcomes in self-managed falls prevention exercise interventions for independent older adults living in the community

“…In the near future we may have reliable and valid measurement tools based on digital technology with built-in sensors in smartphones that are easy to use at a low cost [ 54 ]. Such sensor-tests may also provide opportunities for self-assessments [ 9 – 11 ], which could be used in self-management of exercise interventions.…”

“…Digital technology seems to provide more support in self-management of exercise than a paper booklet [ 8 ] and can also support adherence, provide feedback, facilitate documentation and registration of adherence. In the future, smartphone technology may also enable self-assessed outcome measurements [ 9 – 11 ] to motivate self-management of exercise and falls, and objectively monitor change in function.…”

The Developments and Iterations of a Mobile Technology-Based Fall Risk Health Application

“…Greene et al ( 26 ) used machine learning methods to develop an algorithm for predicting falls using mobile technology but did not test their app with older adult users. Mansson et al ( 27 , 28 ) developed an app to measure leg strength and balance and tested its usability with older adults but did not measure other fall risk factors. Taheri-Kharameh et al ( 29 ) included educational recommendations in their mHealth app for users based on their fall risk.…”

Validity of muscular fitness self-assessments in the ecofit smartphone application: A correlation study

“…Spearman’s rank correlation coefficient was used to examine the validity between participants’ performance on the research-assessed and self-assessed fitness tests. The strength of the correlation was considered negligible (<0.3), weak (0.31–0.50), moderate (0.5–0.7), strong (0.7–0.9), or very strong 0.91–1.0 [ 18 , 19 ]. Intraclass correlation coefficients (ICC) were also calculated to determine the reliability between the researcher- and self-assessed fitness tests.…”