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

Resistance to data loss from the Freestyle Libre:Impact on glucose variability indices and recommendations for data analysis

Like many wearables, flash glucose monitoring relies on user compliance and is subject to missing data. As recent research is beginning to utilise glucose technologies as behaviour change tools, it is important to understand whether missing data is tolerable. Complete Freestyle Libre data files were amputed to remove 1-6 hours of data both at random and over mealtimes (breakfast, lunch and dinner). Absolute percent errors (MAPE) and intraclass correlation coefficients (ICC) were calculated to evaluate agreement and reliability. Thirty-two (91%) participants provided at least one complete day (24-hours) of data (age: 44.8±8.6 years, female: 18 (56%); mean fasting glucose: 5.0±0.6 mmol/L). Mean and CONGA (60 minutes) were robust to data loss (MAPE ≤3%). Larger errors were calculated for standard deviation, coefficient of variation (CV) and MAGE at increasing missingness (MAPE 2-10%, 2-9% and 4-18%, respectively). ICC decreased as missing data increased, with most indicating excellent reliability (>0.9) apart from certain MAGE ICC, which indicated good reliability (0.84-0.9). Researchers and clinicians should be aware of the potential for larger errors when reporting standard deviation, CV and MAGE at higher rates of data loss in nondiabetic populations. But where mean and CONGA are of interest, data loss is less of a concern. Novelty: As research now utilises flash glucose monitoring as behavioural change tools in nondiabetic populations, it is important to consider the influence of missing data. Glycaemic variability indices of mean and CONGA are robust to data loss, but standard deviation, CV and MAGE are influenced at higher rates of missingness

The influence of muscle mass in the assessment of lower limb strength in COPD [Abstract]

The influence of muscle mass in the assessment of lower limb strength in COPD [Abstract

Comparing Glucose Outcomes Following Face-to-Face and Remote Initiation of Flash Glucose Monitoring in People Living With Diabetes.

Background: When launched, FreeStyle Libre (FSL; a flash glucose monitor) onboarding was mainly conducted face-to-face. The COVID-19 pandemic accelerated a change to online starts with patients directed to online videos such as Diabetes Technology Network UK for education. We conducted an audit to evaluate glycemic outcomes in people who were onboarded face-to-face versus those who were onboarded remotely and to determine the impact of ethnicity and deprivation on those outcomes. Methods: People living with diabetes who started using FSL between January 2019 and April 2022, had their mode of onboarding recorded and had at least 90 days of data in LibreView with >70% data completion were included in the audit. Glucose metrics (percent time in ranges) and engagement statistics (previous 90-day averages) were obtained from LibreView. Differences between glucose variables and onboarding methods were compared using linear models, adjusting for ethnicity, deprivation, sex, age, percent active (where appropriate), and duration of FSL use. Results: In total, 935 participants (face-to-face 44% [n = 413]; online 56% [n = 522]) were included. There were no significant differences in glycemic or engagement indices between onboarding methods and ethnicities, but the most deprived quintile had significantly lower percent active time (b = −9.20, P = .002) than the least deprived quintile. Conclusions: Online videos as an onboarding method can be used without significant differences in glucose and engagement metrics. The most deprived group within the audit population had lower engagement metrics, but this did not translate into differences in glucose metrics.</p

Sedentary behaviour is associated with heightened cardiovascular, inflammatory and cortisol reactivity to acute psychological stress

Background Sedentary behaviour is a risk factor for cardiovascular disease (CVD), but the underlying mechanisms remain unclear. Exaggerated psychobiological responses to acute psychological stress increase CVD risk. Sedentary behaviour is associated with characteristics that can predict large psychobiological stress response patterns (e.g., elevated resting blood pressure and systemic inflammation), but it is currently unknown whether sedentary behaviour and stress reactivity are directly linked. The aim of this study was to examine associations between device-assessed sedentary behaviour and measures of stress reactivity. Methods Sixty-one healthy adults wore an activPAL (thigh) and ActiGraph (wrist) for seven days to measure habitual levels of sedentary behaviour (mean ± SD = 9.96 ± 1.48 hours/day) and moderate-to-vigorous physical activity (mean ± SD = 101.82 ± 42.92 minutes/day). Participants then underwent stress reactivity testing, where beat-to-beat cardiovascular (e.g., blood pressure, total peripheral resistance), inflammatory (plasma interleukin-6, leukocytes) and salivary cortisol measurements were taken in response to an 8-minute socially evaluative Paced Auditory Serial Addition Test. Results Higher volumes of daily sedentary behaviour were associated with larger stress responses for diastolic blood pressure (Β=1.264, 95%CI=0.537—1.990, p=.005), total peripheral resistance (Β=40.563, 95%CI=19.310—61.812, p<.001), interleukin-6 (Β=0.219, 95%CI=0.109—0.329, p<.001) and cortisol (Β=1.844, 95%CI=1.139—2.549, p<.001). These findings emerged independent of a priori determined covariates, including daily levels of moderate-to-vigorous physical activity and adiposity. Discussion Exaggerated stress reactivity is characteristic of high sedentary behaviour and could be a novel mechanism linking sedentary behaviour with CVD. Future work should examine the impact of reducing sedentary behaviour on measures of stress reactivity, as this may have clinical relevance for preventing CVD

Sensing interstitial glucose to nudge active lifestyles (SIGNAL): feasibility of combining novel self-monitoring technologies for persuasive behaviour change

Introduction Increasing physical activity (PA) reduces the risk of developing diabetes, highlighting the role of preventive medicine approaches. Changing lifestyle behaviours is difficult and is often predicated on the assumption that individuals are willing to change their lifestyles today to reduce the risk of developing disease years or even decades later. The self-monitoring technologies tested in this study will present PA feedback in real time, parallel with acute physiological data. Presenting the immediate health benefits of being more physically active may help enact change by observing the immediate consequences of that behaviour. The present study aims to assess user engagement with the self-monitoring technologies in individuals at moderate-to-high risk of developing type 2 diabetes. Methods and analysis 45 individuals with a moderate-to-high risk, aged ≥40 years old and using a compatible smartphone, will be invited to take part in a 7-week protocol. Following 1 week of baseline measurements, participants will be randomised into one of three groups: group 1 -glucose feedback followed by biobehavioural feedback (glucose plus PA); group 2 - PA feedback followed by biobehavioural feedback; group 3 - biobehavioural feedback. A PA monitor and a flash glucose monitor will be deployed during the intervention. Participants will wear both devices throughout the intervention but blinded to feedback depending on group allocation. The primary outcome is the level of participant engagement and will be assessed by device use and smartphone usage. Feasibility will be assessed by the practicality of the technology and screening for diabetes risk. Semistructured interviews will be conducted to explore participant experiences using the technologies. Trial registration number ISRCTN17545949. Registered on 15/05/2017

Investigating the association between child television viewing and measured child adiposity outcomes in a large nationally representative sample of New Zealanders: a cross-sectional study

Background: This study investigates whether parental characteristics moderate the association between child television (TV) viewing and obesity. Methods: Cross-sectional data from the New Zealand Health Survey (NZHS) were pooled for the years 2013/14–2016/17 (n=9,022). Parents from adult surveys were related to child data in the child NZHS. Child TV viewing was estimated using self-reported time for each weekday and weekend. Childhood body mass index (BMI) and obesity was defined using the International Obesity Task Force (IOTF) cut-off values. Effect modification was assessed by interaction and by stratifying binary logistic regression (Adjusted Odds Ratio (AOR) [95% Confidence Intervals (CI]) analyses by parent education (low, moderate, high) and ethnicity (Asian, European/other, Māori, Pacific). Results: Watching ≥2 hours TV on average per day in the past week, relative to <2 hours TV viewing, was associated with a higher odds of childhood obesity (AOR=1.26 [1.06, 1.51]). Stratification showed that the association between ≥2 hours TV and obesity was most pronounced for children with parents of European/other parent ethnicity (AOR=1.85 [1.36, 2.52]), and low education (AOR=1.36 [1.01, 1.85]) and high education (AOR=1.50 [1.03, 2.20]). Conclusion: We found support for a moderating role of parent ethnicity and some evidence of parent education suggesting a more complex relationship exists between child TV viewing and obesity than is sometimes suggested

Physical Activity and Respiratory Health (PhARaoH): Data from a Cross-Sectional Study

The dataset consists of a densely phenotyped sample of adults collected from March to August 2014. The dataset captures behavioural, physical, physiological and psychosocial characteristics of individuals with and without a General Practitioner diagnosis of chronic obstructive pulmonary disease (COPD). Data were collected at Glenfield Hospital on 436 individuals (139 COPD patients and 297 apparently healthy adults) aged 40–75 years, residing in Leicestershire and Rutland, United Kingdom. The dataset includes seven days of raw wrist-worn accelerometry, venous blood biomarkers, non-invasive point-of-care cardio-metabolic risk profiles, physical measures and questionnaire data

Examining the Use of Glucose and Physical Activity Self-Monitoring Technologies in Individuals at Moderate to High Risk of Developing Type 2 Diabetes: Randomized Trial

Background: Self-monitoring of behavior (namely, diet and physical activity) and physiology (namely, glucose) has been shown to be effective in type 2 diabetes (T2D) and prediabetes prevention. By combining self-monitoring technologies, the acute physiological consequences of behaviors could be shown, prompting greater consideration to physical activity levels today, which impact the risk of developing diabetes years or decades later. However, until recently, commercially available technologies have not been able to show individuals the health benefits of being physically active. Objective: The objective of this study was to examine the usage, feasibility, and acceptability of behavioral and physiological self-monitoring technologies in individuals at risk of developing T2D. Methods: A total of 45 adults aged .40 years and at moderate to high risk of T2D were recruited to take part in a 3-arm feasibility trial. Each participant was provided with a behavioral (Fitbit Charge 2) and physiological (FreeStyle Libre flash glucose monitor) monitor for 6 weeks, masked according to group allocation. Participants were allocated to glucose feedback (4 weeks) followed by glucose and physical activity (biobehavioral) feedback (2 weeks; group 1), physical activity feedback (4 weeks) followed by biobehavioral feedback (2 weeks; group 2), or biobehavioral feedback (6 weeks; group 3). Participant usage (including time spent on the apps and number of glucose scans) was the primary outcome. Secondary outcomes were the feasibility (including recruitment and number of sensor displacements) and acceptability (including monitor wear time) of the intervention. Semistructured qualitative interviews were conducted at the 6-week follow-up appointment. Results: For usage, time spent on the Fitbit and FreeStyle Libre apps declined over the 6 weeks for all groups. Of the FreeStyle Libre sensor scans conducted by participants, 17% (1798/10,582) recorded rising or falling trends in glucose, and 24% (13/45) of participants changed .1 of the physical activity goals. For feasibility, 49% (22/45) of participants completed the study using the minimum number of FreeStyle Libre sensors, and a total of 41 sensors were declared faulty or displaced. For acceptability, participants wore the Fitbit for 40.1 (SD 3.2) days, and 20% (9/45) of participants and 53% (24/45) of participants were prompted by email to charge or sync the Fitbit, respectively. Interviews unearthed participant perceptions on the study design by suggesting refinements to the eligibility criteria and highlighting important issues about the usability, wearability, and features of the technologies. Conclusions: Individuals at risk of developing T2D engaged with wearable digital health technologies providing behavioral and physiological feedback. Modifications are required to both the study and to commercially available technologies to maximize the chances of sustained usage and behavior change. The study and intervention were feasible to conduct and acceptable to most participants. Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN) 17545949; isrctn.com/ISRCTN17545949

The impact of socioeconomic factors, social determinants, and ethnicity on the utilization of glucose sensor technology among persons with diabetes mellitus: a narrative review

Continuous glucose monitoring (CGM) usage has been shown to improve disease outcomes in people living with diabetes by facilitating better glycemic management. However, previous research has suggested that access to these devices can be influenced by nonmedical factors such as socioeconomic status and ethnicity. It is critical that equitable access to CGM devices is ensured as people from those groups experience poorer diabetes-related health outcomes. In this narrative review, we provide an overview of the various healthcare systems worldwide and how socioeconomic status, social context, and ethnicity shape device usage and the associated health outcomes. In general, we found that having a lower socioeconomic status and belonging to an ethnic minority group negatively impact CGM usage. While financial means proved to be an important mediator in this process, it was not the sole driver as disparities persisted even after adjustment for factors such as income and insurance status. Recommendations to increase CGM usage for people of a lower socioeconomic status and ethnic minorities include increasing the availability of financial, administrative, and educational support, for both patients and healthcare providers. However, recommendations will vary due to local country-specific circumstances, such as reimbursement criteria and healthcare ecosystems.</p