Measurement of physical activity, sedentary time and continuous glucose concentrations: novel techniques for behavioural profiling

STUDY 1. INTRODUCTION. Insufficient physical activity is a major risk factor for developing type 2 diabetes. Using isotemporal substitution models, the influence of replacing modest durations of sedentary time with physical activity on diabetes risk scores can be studied. The aims of this study were to examine the relationship between diabetes risk scores, sedentary time and physical activity measured using wrist worn accelerometry, and to model the changes in risk scores by reallocating movement behaviours from lower to a higher intensity. METHODS. Data from 251 (93 males; aged 56.7 ± 8.8) participants from a mixed ethnicity cohort from Leicestershire, UK were selected for analysis. The relationship between diabetes risk (using the Leicester Diabetes Risk Assessment Score), physical activity and sedentary time was identified using multiple linear regressions and isotemporal substitution analysis. Models were calculated for main effects and also adjusted for peak oxygen uptake (VO2) and accelerometer wear time. RESULTS. Both unadjusted and adjusted models revealed that diabetes risk was inversely related to sedentary time, and positively related to light and moderate to vigorous physical activity (MVPA) (p < 0.0005). Unadjusted, the replacement of sedentary time with 10 minutes of either light or MVPA resulted in a reduction in diabetes risk score of −0.22 and −0.54, respectively. There was an eight to nine times greater reduction in risk for the same MVPA replacement models when the least active participants were compared to the pooled analysis (3.601 unadjusted). CONCLUSION. Diabetes risk is associated with sedentary time and physical activity estimated from wrist worn accelerometry. The replacement of sedentary time with MVPA is most beneficial for the least active individuals. STUDY 2. INTRODUCTION. Most associations between physical behaviours and health are assessed using intensity and duration based estimations; however, individuals accrue physical activity in differing ways and behavioural profiles have been linked with varying cardiometabolic risk factors. The frequency or regularity of behaviour may hold additional relationships with health, but have not been extensively explored. Accelerometers provide researchers with a large stream of raw data to analyse. The aim of this paper was to calculate a novel method of behavioural regularity called sample entropy from wrist worn accelerometry and to ascertain whether there are associations with cardiometabolic risk factors in adults. METHODS. Data from 290 (107 males; aged 57.0 ± 8.8) participants from a mixed ethnicity cohort from Leicestershire, UK were selected for analysis. Entropy scores were calculated using 60-second count data within MATLAB. The relationship between entropy scores, physical activity, sedentary time and cardiometabolic risk factors was identified using multiple linear regressions. Models were calculated for main effects and also adjusted for age, sex, accelerometer wear time and body mass index (BMI). RESULTS. Sample entropy scores were significantly related to high-density lipoprotein (HDL) cholesterol (b = 0.148, p = 0.042), triglycerides (b = −0.293, p = 0.042) and glycated haemoglobin (HbA1c) (b = −0.225, p = 0.006), even after adjustment for confounding variables. Traditional intensity estimates of physical activity were not associated; however, the frequency of breaks in sedentary time were significantly related to entropy scores (b = 0.004, p = 0.002). CONCLUSION. Using a novel measure of signal complexity, associations have been revealed with cardiometabolic risk factors; however further analysis in a larger, more diverse dataset is required to ascertain the utility of this technique within behavioural research and if so, what constitutes typical/average levels of entropy within a population. STUDY 3. INTRODUCTION. Acute physiological changes such as reductions in postprandial glucose excursions have been demonstrated within experimental studies that have compared being physically active to sedentary conditions. However, for this information to be truly useful, the coupling of behaviour and glucose data in a free-living environment needs to be achieved. The aim of the study was to ascertain if there is a relationship between objectively measured physical activity, sedentary time and glucose variability using glucose monitoring in an adult population. METHODS. Data from 29 participants recruited from a mixed gender sample from Leicestershire, UK were selected for analysis. Physical activity, sedentary time and interstitial glucose was measured continuously over 14 days using an accelerometer and the Freestyle Libre flash glucose monitor. Daily time (minutes) spent sedentary, and in light activity and moderate to vigorous physical activity (MVPA) were regressed against glycaemic variability indices including daily mean (average) glucose, standard deviation and mean amplitude of glycaemic excursions (MAGE). Generalised Estimating Equations were calculated between behaviour and glycaemic variability variables. Models were calculated for main effects and also adjusted for age, gender and accelerometer wear time. RESULTS. Physical activity and sedentary time were associated with measures of glucose variability, however low fitness individuals showed a stronger relationship between MVPA and MAGE (MAGE: whole sample b = −0.002, low fitness b = −0.012. Additionally, after adjustment for covariates, sedentary time was positively associated with a higher daily mean glucose (b = 0.001, p = 0.001) and MAGE (b = 0.002, p < 0.0005) for the low fitness group. MVPA was negatively associated with mean glucose (b = −0.004, p < 0.0005) and MAGE (b = −0.012, p < 0.0005); however, standard deviation of glucose was not associated with behaviour of any intensity. The magnitudes of the relationships were small, although participants were non-diabetics and exhibited relatively good glucose control i.e. minimal fluctuations in daily glucose variability. CONCLUSION. This study shows that sedentary time, physical activity and glucose variability are related. Despite supporting the previous laboratory research, it is uncertain whether any changes in glucose will reliably occur in all individuals. MVPA confers the largest reductions in glucose variability indices, yet as one of the few studies to couple behaviour and glucose data, more research is needed on larger and more diverse samples