Assessing adherence to physical activity guidelines and correlates among older Korean adults with a focus on 10-minute bout duration using subjective and objective measures

TL;DR

This study compares self-reported and accelerometer-measured physical activity in older Korean adults, finding significant differences in adherence rates and influencing factors.

Contribution

The study introduces a focus on 10-minute bout duration with tolerance settings and compares subjective and objective PA measures in older adults.

Findings

Self-reported adherence was 34%, while accelerometer-based rates ranged from 16% to 62.8%.

Bland-Altman analyses showed notable differences between subjective and objective PA measurements.

Accelerometer data highlighted sex, age, and cholesterol levels as key correlates, while self-report emphasized education and income.

Abstract

Understanding physical activity (PA) patterns in older adults is crucial for effectively promoting adherence to PA guidelines. However, measuring PA can be challenging because it involves a balance between ease of administration and accuracy of data collection. The primary objective of this study was to analyze PA levels in older Korean adults using both self-reported and objective accelerometer measures, and the secondary objective was to investigate the factors associated with adherence to PA guidelines.

Using data from the Korea National Health and Nutrition Examination Survey (KNHANES VII-2) 2017, we assessed 425 older adults who provided both self-reported and accelerometer data. Adherence to PA guidelines was evaluated using two different thresholds for the accelerometer data, each applied both strictly and with a tolerance of one to two minutes when defining a 10-minute bout.

Self-reported data indicated a 34% adherence rate, whereas accelerometer-based rates ranged from 16% to 62.8%, depending on the cutoff values and tolerance settings. Bland-Altman analyses demonstrated notable differences between subjective and objective measurements. In terms of correlates, the questionnaire data highlighted factors such as education and household income, whereas the accelerometer-based findings emphasized sex, age, hypertriglyceridemia, and low high-density lipoprotein cholesterol.

This study revealed substantial discrepancies in both moderate-to-vigorous PA adherence estimates and significant predictors when comparing self-reported surveys to accelerometer data among older adults. For estimating population-level adherence, in the absence of accelerometer cutoff values established by a specialized lab, both self-reported data and accelerometer measurements offer unique insights. Meanwhile, when analyzing the factors influencing PA adherence, accelerometer data may be preferable, as subjective biases in self-report can affect the observed correlates in statistical results.

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