Can Smartphone-Derived Step Data Predict Laboratory-Induced Real-Life Like Fall-Risk in Community- Dwelling Older Adults? - Wang, Yiru; Gangwani, Rachana; Kannan, Lakshmi; Schenone, Alison; Wang, Edward; Bhatt, Tanvi (Frontiers in Sports and Active Living, 2020)
Background: As age progresses, decline in physical function predisposes older adults to high fall-risk, especially on exposure to environmental perturbations such as slips and trips. However, there is limited evidence of association between daily community ambulation, an easily modifiable factor of physical activity (PA), and fall-risk. Smartphones, equipped with accelerometers, can quantify and display daily ambulation-related PA simplistically in terms of number of steps. If any association between daily steps and fall-risks is established, smartphones due to its convenience and prevalence could provide health professionals with a meaningful outcome measure, in addition to existing clinical measurements, to identify older adults at high fall-risk. Objective: This study aimed to explore whether smartphone-derived step data during older adults’ community ambulation alone or together with commonly used clinical fall-risk measurements could predict falls following laboratory-induced real-life like slips or trips. Relationship between step data and PA questionnaire and clinical fall-risk assessments were examined as well. Methods: Forty-nine community-dwelling older adults completed Berg Balance Scale [BBS], Activities-specific Balance Confidence scale [ABC], Timed Up-and-Go [TUG] and Physical Activity Scale for the Elderly [PASE]. One-week and one-month smartphone steps data were retrieved. Participants’ one-year fall history was noted. All participants’ fall outcomes to laboratory-induced slip-and-trip perturbations were recorded. Logistic regression was performed to identify a model that best predict laboratory falls. Pearson correlations examined relationships between study variables. Results: A model including age, TUG, and fall history significantly predicted laboratory falls with a sensitivity of 94.3%, specificity of 58.3% and an overall accuracy of 85.1%. Neither one-week nor one-month steps data could predict laboratory falls. One-month steps data significantly positively correlated with BBS (r=0.386, p=0.006) and ABC (r=0.369, p=0.012) and negatively correlated with fall history (rp=-0.293, p=0.041). Conclusion: Older participants with fall history and higher TUG scores were more likely to fall in the laboratory. No association between smartphone steps data and laboratory fall-risk was established in our study population of healthy community-dwelling older adults which calls for further studies on varied population. Although modest, results do reveal a relationship between steps data and functional balance deficits and fear of falls.