Moderate and Vigorous Physical Activity Intensity Cut-Points for Hip-, Wrist-, Thigh-, and Lower Back Worn...

Physical activity (PA) reduces the risk of negative mental and physical health outcomes in older adults. Traditionally, PA intensity is classified using METs, with 1 MET equal to 3.5 mL O₂·min^-1·kg^-1. However, this may underestimate moderate and vigorous intensity due to age-related changes in resting metabolic rate (RMR) and VO₂max. VO₂reserve accounts for these changes. While receiver operating characteristics (ROC) analysis is commonly used to develop PA, intensity cut-points, machine learning (ML) offers a potential alternative. This study aimed to develop ROC cut-points and ML models to classify PA intensity in older adults. Sixty-seven older adults performed activities of daily living (ADL) and two six-minute walking tests (6-MWT) while wearing six accelerometers on their hips, wrists, thigh, and lower back. Oxygen uptake was measured. ROC and ML models were developed for ENMO and Actigraph counts (AGVMC) using VO₂reserve as the criterion in two-third of the sample and validated in the remaining third. ROC-developed cut-points showed good-excellent AUC (0.84-0.93) for the hips, lower back, and thigh, but wrist cut-points failed to distinguish between moderate and vigorous intensity. The accuracy of ML models was high and consistent across all six anatomical sites (0.83-0.89). Validation of the ML models showed better results compared to ROC cut-points, with the thigh showing the highest accuracy. This study provides ML models that optimize the classification of PA intensity in very old adults for six anatomical placements hips (left/right), wrist (dominant/non-dominant), thigh and lower back increasing comparability between studies using different wear-position. Clinical Trial Registration: clinicaltrials.gov identifier: NCT04821713.