The relationship between self-reported physical frailty and sensor-based physical activity measures in older adults – a multicentric cross-sectional study

Abstract

Background The decline in everyday life physical activity reflects and contributes to the frailty syndrome. While especially self-reported frailty assessments have the advantage of reaching large groups at low costs, little is known about the relationship between the self-report and objective measured daily physical activity behavior. The main objective was to evaluate whether and to what extent a self-reported assessment of frailty is associated with daily physical activity patterns. Methods Daily activity data were obtained from 88 elderly participants (mean 80.6 ± 9.1 years) over up to 21 days. Acceleration data were collected via smartwatch. According to the results of a self-report frailty questionnaire, participants were retrospectively split up into three groups, F (frail, n = 43), P (pre-frail, n = 33), and R (robust, n = 12). Gait- and activity-related measures were derived from the built-in step detector and acceleration sensor and comprised, i.a., standard deviation of 5-s-mean amplitude deviation (MADstd), median MAD (MADmedian), and the 95th percentile of cadence (STEP95). Parameters were fed into a PCA and component scores were used to derive behavioral clusters. Results The PCA suggested two components, one describing gait and one upper limb activity. Mainly gait related parameters showed meaningful associations with the self-reported frailty score (STEP95: R2 = 0.25), while measures of upper limb activity had lower coefficients (MADmedian: R2 = 0.07). Cluster analysis revealed two clusters with low and relatively high activity in both dimensions (cluster 2 and 3). Interestingly, a third cluster (cluster 1) was characterized by high activity and low extent of ambulation. Comparisons between the clusters showed significant differences between activity, gait, age, sex, number of chronic diseases, health status, and walking aid. Particularly, cluster 1 contained a higher number of female participants, whose self-reports tended towards a low health status, the frequent use of a walking aid, and a higher score related to frailty questions. Conclusions The results demonstrate that subjective frailty assessments may be a simple first screening approach. However, especially older women using walking aids may classify themselves as frail despite still being active. Therefore, the results of self-reports may be particularly biased in older women.

Outside the Laboratory Assessment of Upper Limb Laterality in Patients With Stroke: A Cross-Sectional Study

“…The use of higher percentiles to gather information when performance is at a near capacity level has already been successfully introduced. 31 Here, we observed differences in magnitudes but not concerning laterality or influencing factors.…”

Relationship between measures provided by smartwatches and identification of frailty syndrome in older adults: a scoping review

“…The results obtained from this review generally indicate that the parameters derived from the smartwatch have been used to identify the frailty stages in different areas, with most of the studies associated with other clinical conditions (cardiovascular risk, pre-procedure of transcatheter aortic valve implantation and post-hospital discharge periods of critically ill patients) [15][16][17][18] .…”

“…The cluster analyzes showed statistically significant differences for the variables of: activity, gait, age, gender, number of chronic diseases, current health status, and use of walking aids; the chance of being female and frail increased significantly for cluster 1. A significant difference for gaitrelated parameters was found for almost all frailty criteria, suggesting that mobility may be the driving parameter related to frailty 18…”

“…In a study by Schmidle et al 18 , the older adults used the smartwatch for an average of 17.5(± 5.1) days, for a period of at least eight hours a day. The parameters derived from the smartwatch used to assess frailty were the measure of physical activity intensity based on changes in acceleration intensity, taking into account the median of all values (MAD median); and the daily step count, based on the cadence percentile parameter in steps per minute (STEP95).…”

“…The use of wearable sensors, such as the smartwatch, can be useful in combating the challenges in measuring frailty, as it is a viable, practical, accessible, reproducible and reliable instrument, without jeopardizing daily activity in the various areas in which the older adult is inserted 19 . The four studies included [15][16][17][18] used daily step count measures, and three of the four also used sleep and HR data to assess frailty in older adults [15][16][17] .…”