Moderate and Vigorous Physical Activity Intensity Cut-Points for Hip-, Wrist-, Thigh-, and Lower Back Worn Accelerometer in Very Old Adults

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 O-2<middle dot>min(-1)<middle dot>kg(-1). However, this may underestimate moderate and vigorous intensity due to age-related changes in resting metabolic rate (RMR) and VO(2)max. VO(2)reserve 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 VO(2)reserve 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: identifier: NCT04821713