Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry.

Background: Accurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods.

Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children.

Background: The ActiPASS software was developed from the open-source Acti4 activity classification algorithm for thigh-worn accelerometry. However, the original algorithm has not been validated in children or compared with a child-specific set of algorithm thresholds. This study aims to evaluate the accuracy of ActiPASS in classifying activity types in children using 2 published sets of Acti4 thresholds.

Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types.

Background: The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method.

Home office shift and sedentary behaviour in Germany during the COVID-19 pandemic: descriptives and related socioecological correlates.

COVID-19 working restrictions resulted in a location shift of white-collar workers into their home office (HO). Little is known about how the proportion of HO affects sitting and physical activity (PA) behaviour during working hours, and potential correlates are not well understood. A cross-sectional sample of currently working adults in HO ( = 575) completed questionnaires regarding HO proportion before and during the pandemic, work-related sitting and PA, and socioecological factors regarding HO sitting time.

A pragmatic randomised controlled trial referring to a Personalised Self-management SUPport Programme (P-SUP) for persons enrolled in a disease management programme for type 2 diabetes mellitus and/or for coronary heart disease.

Background: Type 2 diabetes mellitus (T2DM) and coronary heart disease (CHD) are two chronic diseases that cause a tremendous burden. To reduce this burden, several programmes for optimising the care for these diseases have been developed. In Germany, so-called disease management programmes (DMPs), which combine components of Disease Management and the Chronic Care Model, are applied.