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Accurate assessment of the type, duration, and intensity of physical activity (PA) in daily life is considered very important because of the close relationship between PA level, health, and well-being. Therefore, the assessment of PA using lightweight wearable sensors has gained interest in recent years. In particular, the use of activity monitors could help to measure the health-related effects of specific PA interventions. Our study, named as Run4Vit, focuses on evaluating the acute and longterm effects of an eight-week running intervention on PA behaviour and vitality. To achieve this goal, we developed an algorithm to detect running and estimate instantaneous cadence using a single trunk-fixed accelerometer. Cadence was computed using time and frequency domain approaches. Validation was performed over a wide range of locomotion speeds using an open-source gait database. Across all subjects, the cadence estimation algorithms achieved a mean bias and precision of - 0.01 &#x00B1; 0.69 steps/min for the temporal method and 0.02 &#x00B1; 1.33 steps/min for the frequency method. The running detection algorithm demonstrated very good performance, with an accuracy of 98&#x0025; and a precision superior to 99&#x0025;. These algorithms could be used to extract metrics related to the multiple dimensions of PA, and provide reliable outcome measures for the Run4Vit longitudinal running intervention program. Clinical Relevance&#x2014; This work aims at validating a multi-dimensional physical activity (PA) classification algorithm for assessing the acute and long-term effects of eight weeks running intervention on PA behaviours and vitality

Walking and running cadence estimation using a single trunk-fixed accelerometer for daily physical activities assessment