Dataset Information

Estimating physical activity from self-reported behaviours in large-scale population studies using network harmonisation: findings from UK Biobank and associations with disease outcomes.

ABSTRACT:

Background

UK Biobank is a large prospective cohort study containing accelerometer-based physical activity data with strong validity collected from 100,000 participants approximately 5 years after baseline. In contrast, the main cohort has multiple self-reported physical behaviours from > 500,000 participants with longer follow-up time, offering several epidemiological advantages. However, questionnaire methods typically suffer from greater measurement error, and at present there is no tested method for combining these diverse self-reported data to more comprehensively assess the overall dose of physical activity. This study aimed to use the accelerometry sub-cohort to calibrate the self-reported behavioural variables to produce a harmonised estimate of physical activity energy expenditure, and subsequently examine its reliability, validity, and associations with disease outcomes.

Methods

We calibrated 14 self-reported behavioural variables from the UK Biobank main cohort using the wrist accelerometry sub-cohort (n = 93,425), and used published equations to estimate physical activity energy expenditure (PAEE_SR). For comparison, we estimated physical activity based on the scoring criteria of the International Physical Activity Questionnaire, and by summing variables for occupational and leisure-time physical activity with no calibration. Test-retest reliability was assessed using data from the UK Biobank repeat assessment (n = 18,905) collected a mean of 4.3 years after baseline. Validity was assessed in an independent validation study (n = 98) with estimates based on doubly labelled water (PAEE_DLW). In the main UK Biobank cohort (n = 374,352), Cox regression was used to estimate associations between PAEE_SR and fatal and non-fatal outcomes including all-cause, cardiovascular diseases, respiratory diseases, and cancers.

Results

PAEE_SR explained 27% variance in gold-standard PAEE_DLW estimates, with no mean bias. However, error was strongly correlated with PAEE_DLW (r = -.98; p < 0.001), and PAEE_SR had narrower range than the criterion. Test-retest reliability (Λ = .67) and relative validity (Spearman = .52) of PAEE_SR outperformed two common approaches for processing self-report data with no calibration. Predictive validity was demonstrated by associations with morbidity and mortality, e.g. 14% (95%CI: 11-17%) lower mortality for individuals meeting lower physical activity guidelines.

Conclusions

The PAEE_SR variable has good reliability and validity for ranking individuals, with no mean bias but correlated error at individual-level. PAEE_SR outperformed uncalibrated estimates and showed stronger inverse associations with disease outcomes.

SUBMITTER: Pearce M

PROVIDER: S-EPMC7074990 | biostudies-literature |

REPOSITORIES: biostudies-literature

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