2 min read

ID: 1199142

Short Link: https://gregory-ms.com/articles/1199142/

Discovery Date: 25 January 2023, 03:44:27 UTC

Published Date: 2023-01-24 00:00:00

Source: BioMedCentral

Link: https://bmcgeriatr.biomedcentral.com/articles/10.1186/s12877-022-03711-2

Manual Selection: none

Machine Learning Gaussian Naive Bayes Model: false


jats:titleAbstract</jats:title>jats:sec jats:titleBackground</jats:title> jats:pThe 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.</jats:p> </jats:sec>jats:sec jats:titleMethods</jats:title> jats:pDaily 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, jats:italicn</jats:italic> = 43), P (pre-frail, jats:italicn</jats:italic> = 33), and R (robust, jats:italicn</jats:italic> = 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.</jats:p> </jats:sec>jats:sec jats:titleResults</jats:title> jats:pThe 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: Rjats:sup2</jats:sup> = 0.25), while measures of upper limb activity had lower coefficients (MADmedian: Rjats:sup2</jats:sup> = 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.</jats:p> </jats:sec>jats:sec jats:titleConclusions</jats:title> jats:pThe 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.</jats:p> </jats:sec>

Noun Phrases in Title

  • The relationship
  • self-reported physical frailty
  • physical activity measures
  • older adults
  • a multicentric cross-sectional study
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