Relationship of device measured physical activity type and posture with cardiometabolic health markers: pooled dose-response associations from the Prospective Physical Activity, Sitting and Sleep Consortium.

AIMS/HYPOTHESIS: The aim of this study was to examine the dose-response associations of device-measured physical activity types and postures (sitting and standing time) with cardiometabolic health. METHODS: We conducted an individual participant harmonised meta-analysis of 12,095 adults (mean ± SD age 54.5±9.6 years; female participants 54.8%) from six cohorts with thigh-worn accelerometry data from the Prospective Physical Activity, Sitting and Sleep (ProPASS) Consortium. Associations of daily walking, stair climbing, running, standing and sitting time with a composite cardiometabolic health score (based on standardised z scores) and individual cardiometabolic markers (BMI, waist circumference, triglycerides, HDL-cholesterol, HbA1c and total cholesterol) were examined cross-sectionally using generalised linear modelling and cubic splines. RESULTS: We observed more favourable composite cardiometabolic health (i.e. z score <0) with approximately 64 min/day walking (z score [95% CI] -0.14 [-0.25, -0.02]) and 5 min/day stair climbing (-0.14 [-0.24, -0.03]). We observed an equivalent magnitude of association at 2.6 h/day standing. Any amount of running was associated with better composite cardiometabolic health. We did not observe an upper limit to the magnitude of the dose-response associations for any activity type or standing. There was an inverse dose-response association between sitting time and composite cardiometabolic health that became markedly less favourable when daily durations exceeded 12.1 h/day. Associations for sitting time were no longer significant after excluding participants with prevalent CVD or medication use. The dose-response pattern was generally consistent between activity and posture types and individual cardiometabolic health markers. CONCLUSIONS/INTERPRETATION: In this first activity type-specific analysis of device-based physical activity, ~64 min/day of walking and ~5.0 min/day of stair climbing were associated with a favourable cardiometabolic risk profile. The deleterious associations of sitting time were fully attenuated after exclusion of participants with prevalent CVD and medication use. Our findings on cardiometabolic health and durations of different activities of daily living and posture may guide future interventions involving lifestyle modification.

Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection.

This paper aims to examine the role of global positioning system (GPS) sensor data in real-life physical activity (PA) type detection. Thirty-three young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semi-structured and real-life. One general random forest (RF) model integrating data from all sensors and five individual RF models using data from each sensor position were trained using semi-structured (Scenario 1) and combined (semi-structured + real-life) data (Scenario 2). The results showed that in general, adding GPS features (speed and elevation difference) to accelerometer data improves classification performance particularly for detecting non-level and level walking. Assessing the transferability of the models on real-life data showed that models from Scenario 2 are strongly transferable, particularly when adding GPS data to the training data. Comparing individual models indicated that knee-models provide comparable classification performance (above 80%) to general models in both scenarios. In conclusion, adding GPS data improves real-life PA type classification performance if combined data are used for training the model. Moreover, the knee-model provides the minimal device configuration with reliable accuracy for detecting real-life PA types.

The Key Factors in Physical Activity Type Detection Using Real-Life Data: A Systematic Review.

Background: Physical activity (PA) is paramount for human health and well-being. However, there is a lack of information regarding the types of PA and the way they can exert an influence on functional and mental health as well as quality of life. Studies have measured and classified PA type in controlled conditions, but only provided limited insight into the validity of classifiers under real-life conditions. The advantage of utilizing the type dimension and the significance of real-life study designs for PA monitoring brought us to conduct a systematic literature review on PA type detection (PATD) under real-life conditions focused on three main criteria: methods for detecting PA types, using accelerometer data collected by portable devices, and real-life settings. Method: The search of the databases, Web of Science, Scopus, PsycINFO, and PubMed, identified 1,170 publications. After screening of titles, abstracts and full texts using the above selection criteria, 21 publications were included in this review. Results: This review is organized according to the three key elements constituting the PATD process using real-life datasets, including data collection, preprocessing, and PATD methods. Recommendations regarding these key elements are proposed, particularly regarding two important PA classes, i.e., posture and motion activities. Existing studies generally reported high to near-perfect classification accuracies. However, the data collection protocols and performance reporting schemes used varied significantly between studies, hindering a transparent performance comparison across methods. Conclusion: Generally, considerably less studies focused on PA types, compared to other measures of PA assessment, such as PA intensity, and even less focused on real-life settings. To reliably differentiate the basic postures and motion activities in real life, two 3D accelerometers (thigh and hip) sampling at 20 Hz were found to provide the minimal sensor configuration. Decision trees are the most common classifier used in practical applications with real-life data. Despite the significant progress made over the past year in assessing PA in real-life settings, it remains difficult, if not impossible, to compare the performance of the various proposed methods. Thus, there is an urgent need for labeled, fully documented, and openly available reference datasets including a common evaluation framework.

The Influence of Acute Physical Activity on Working Memory.

The effect of three types of physical activity on two types of working memory were investigated. Participants were 20 adult males who trained twice a week in volleyball two hours per session. Procedures included two pre and post intervention tests of working memory: the Digit span and Visual Memory Span subtests of the Wechsler Memory Scale-Revised. Interventions included tactical volleyball formation, body-weight resistance exercises, 15 minutes of running, and sub-maximal aerobic activity. Volleyball activity improved memory performance to a greater extent than the other two activities. Results indicate that immediately after acute exercise there is an increase in working memory function, more evident after physical activity in which cognitive functioning is inherent.

Strain energy in the femoral neck during exercise.

Physical activity is recommended to mitigate the incidence of hip osteoporotic fractures by improving femoral neck strength. However, results from clinical studies are highly variable and unclear about the effects of physical activity on femoral neck strength. We ranked physical activities recommended for promoting bone health based on calculations of strain energy in the femoral neck. According to adaptive bone-remodeling theory, bone formation occurs when the strain energy (S) exceeds its homeostatic value by 75%. The potential effectiveness of activity type was assessed by normalizing strain energy by the applied external load. Tensile strain provided an indication of bone fracture. External force and joint motion data for 15 low- and high-load weight-bearing and resistance-based activities were used. High-load activities included weight-bearing activities generating a ground force above 1 body-weight and maximal resistance exercises about the hip and the knee. Calculations of femoral loads were based on musculoskeletal and finite-element models. Eight of the fifteen activities were likely to trigger bone formation, with isokinetic hip extension (ΔS=722%), one-legged long jump (ΔS=572%), and isokinetic knee flexion (ΔS=418%) inducing the highest strain energy increase. Knee flexion induced approximately ten times the normalized strain energy induced by hip adduction. Strain and strain energy were strongly correlated with the hip-joint reaction force (R(2)=0.90-0.99; p<0.05) for all activities, though the peak load location was activity-dependent. None of the exercises was likely to cause fracture. Femoral neck mechanics is activity-dependent and maximum isokinetic hip-extension and knee-flexion exercises are possible alternative solutions to impact activities for improving femoral neck strength.