Due to a lack of transparency in both algorithm and validation methodology, it is difficult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (±10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications.

Level III, comparative series.

Low amounts of physical activity (PA) and prolonged periods of sedentary activity are common in hospitalized patients. Objective PA monitoring is needed to prevent the negative effects of inactivity, but a suitable algorithm is lacking. The aim of this study is to optimize and validate a classification algorithm that discriminates between sedentary, standing, and dynamic activities, and records postural transitions in hospitalized patients under free-living conditions. Optimization and validation in comparison to video analysis were performed in orthopedic and acutely hospitalized elderly patients with an accelerometer worn on the upper leg. Data segmentation window size (WS), amount of PA threshold (PA Th) and sensor orientation threshold (SO Th) were optimized in 25 patients, validation was performed in another 25. Sensitivity, specificity, accuracy, and (absolute) percentage error were used to assess the algorithm’s performance. Optimization resulted in the best performance with parameter settings: WS 4 s, PA Th 4.3 counts per second, SO Th 0.8 g. Validation showed that all activities were classified within acceptable limits (>80% sensitivity, specificity and accuracy, ±10% error), except for the classification of standing activity. As patients need to increase their PA and interrupt sedentary behavior, the algorithm is suitable for classifying PA in hospitalized patients.

Muscle glycogen use and glucose uptake during cold exposure increases with shivering intensity. We hypothesized that cold exposure, with shivering, would subsequently increase glucose tolerance. Fifteen healthy men (age 26 ± 5 years, body mass index 23.9 ± 2.5 kg m-2) completed two experimental trials after an overnight fast. Cold exposure (10°C) was applied during the first trial, via a water-perfused suit, to induce at least 1 h of shivering in each participant. For comparison, a thermoneutral (32°C) condition was applied during the second trial, under identical conditions, for the same duration as determined during the cold exposure. After the thermal exposures, participants rested under a duvet for 90 min which was followed by a 3 h oral glucose tolerance test. Skin temperature (mean ± SE) decreased at the end of the cold exposure compared to before (26.9 ± 0.3 versus 33.7 ± 0.1°C, P < 0.001). Total energy expenditure during the 1 h of shivering was larger than during the time-matched thermoneutral condition (619 ± 23 versus 309 ± 7 kJ, P < 0.001). Cold exposure increased the areas under the glucose and insulin curves by 4.8% (P = 0.066) and 24% (P = 0.112), respectively. The Matsuda and insulin-glucose indices changed after cold exposure by −21% (P = 0.125), and 30% (P = 0.100), respectively. Cold exposure did not subsequently increase glucose tolerance. Instead, the Matsuda and insulin-glucose indices suggest insulin resistance post-shivering.

Purpose: The purpose of this study was to validate optimized algorithm parameter settings for step count and physical behavior for a pocket worn activity tracker in older adults during ADL. Secondly, for a more relevant interpretation of the results, the performance of the optimized algorithm was compared to three reference applications Methods: In a cross-sectional validation study, 20 older adults performed an activity protocol based on ADL with MOXMissActivity versus MOXAnnegarn, activPAL, and Fitbit. The protocol was video recorded and analyzed for step count and dynamic, standing, and sedentary time. Validity was assessed by percentage error (PE), absolute percentage error (APE), Bland-Altman plots and correlation coefficients. Results: For step count, the optimized algorithm had a mean APE of 9.3% and a correlation coefficient of 0.88. The mean APE values of dynamic, standing, and sedentary time were 15.9%, 19.9%, and 9.6%, respectively. The correlation coefficients were 0.55, 0.91, and 0.92, respectively. Three reference applications showed higher errors and lower correlations for all outcome variables. Conclusion: This study showed that the optimized algorithm parameter settings can more validly estimate step count and physical behavior in older adults wearing an activity tracker in the trouser pocket during ADL compared to reference applications.