COVID-19 Highlights the Potential for a More Dynamic Approach to Physical Activity Surveillance

Commentary</p

Physical Inactivity and COVID-19: When Pandemics Collide

In 2012, the Global Observatory for Physical Activity (GoPA!) was established to provide information that would enable countries to initiate or improve research capacity, surveillance systems, program development, and policymaking to increase physical activity levels. Findings from the first GoPA! Country Cards showed an unequal distribution of physical activity surveillance, research productivity, and policy development and implementation around the world. Regular global monitoring of these factors, especially in countries with the largest data gaps, was recommended to combat the global pandemic of physical inactivity. After 6 years and using standardized methods, GoPA! is launching the second set of Country Cards based on data up to 2019 from 217 countries. Overall results showed that periodic national surveillance of physical activity was less common in low-income countries, compared with middle- and high-income countries. Large inequities were seen with more than a 50-fold difference in publications between high- and low-income countries and 32% of the countries worldwide had no physical activity policy. GoPA! has a critical role in facilitating evidence-based physical activity promotion building on international guidelines and the World Health Organization Global Action Plan. GoPA! will continue to monitor progress as we battle the global pandemic of physical inactivity

Reply to Mekary, R.A.; Ding, E.L. Isotemporal substitution as the gold standard model for physical activity epidemiology: why it is the most appropriate for activity time research. <i>Int. J. Environ. Res. Public Health</i> 2019, 16, 797

Firstly, we would like to thank the authors for taking the time to review and comment on our paper. We believe it is invaluable to have open debates regarding current methodologies and the inference of research outputs. In the spirit of open debate and constructive discussion, we wish to respond to the observations of Mekary and Ding

Hotspots: Adherence in home foot temperature monitoring interventions for at-risk feet with diabetes - A narrative review.

Background Home foot temperature monitoring (HFTM) is recommended for those at moderate to high ulcer risk. Where a > 2.2°C difference in temperature between feet (hotspot) is detected, it is suggested that individuals (1) notify a healthcare professional (HCP); (2) reduce daily steps by 50%. We assess adherence to this and HFTM upon detecting a recurrent hotspot. Methods PubMed and Google Scholar were searched until 9 June 2023 for English-language peer-reviewed HFTM studies which reported adherence to HFTM, daily step reduction or HCP hotspot notification. The search returned 1030 results excluding duplicates of which 28 were shortlisted and 11 included. Results Typical adherence among HFTM study participants for >3 days per week was 61%–93% or >80% of study duration was 55.6%–83.1%. Monitoring foot temperatures >50% of the study duration was associated with decreased ulcer risk (Odds Ratio: 0.50, p 80% adherence. Voluntary dropout was 5.2% (Smart mats); 8.1% (sock sensor) and 4.8%–35.8% (infrared thermometers). Only 16.9%–52.5% of participants notified an HCP upon hotspot detection. Objective evidence of adherence to 50% reduction in daily steps upon hotspot detection was limited to one study where the average step reduction was a pedometer-measured 51.2%. Conclusions Ulcer risk reduction through HFTM is poorly understood given only half of the participants notify HCPs of recurrent hotspots and the number of reducing daily steps is largely unknown. HFTM adherence and dropout are variable and more research is needed to determine factors affecting adherence and those likely to adhere.</p

Feasibility of Using a GENEActiv Accelerometer with Triaxial Acceleration and Temperature Sensors to Monitor Adherence to Shoulder Sling Wear Following Surgery

Background: Self-reported adherence to sling wear is unreliable due to recall bias. We aim to assess the feasibility and accuracy of quantifying sling wear and non-wear utilising slings pre-fitted with a GENEActiv accelerometer that houses triaxial acceleration and temperature sensors. Methods: Ten participants were asked to wear slings for 480 min (8 h) incorporating 180 min of non-wear time in durations varying from 5–120 min. GENEActiv devices were fitted in sutured inner sling pockets and participants logged sling donning and doffing times. An algorithm based on variability in acceleration in three axes and temperature change was developed to identify sling wear and non-wear and compared to participants’ logs. Results: There was no significant difference between algorithm detected non-wear duration (mean ± standard deviation = 172.0 ± 6.8 min/participant) and actual non-wear (179.7 ± 1.0 min/participant). Minute-by-minute agreement of sensor-detected wear and non-wear with participant reported wear was 97.3 ± 1.5% (range = 93.9–99.0), with mean sensitivity 94.3 ± 3.5% (range = 86.1–98.3) and specificity 99.1 ± 0.8% (range = 93.7–100). Conclusion: An algorithm based on accelerometer-assessed acceleration and temperature can accurately identify shoulder sling wear/non-wear times. This method may have potential for assessing whether sling wear adherence after shoulder surgeries have any bearing on patient functional outcomes

A comparison of analytical approaches to investigate associations for accelerometry-derived physical activity spectra with health and developmental outcomes in children

The use of high-resolution physical activity intensity spectra obtained from accelerometry can improve knowledge of associations with health and development beyond the use of traditional summary measures of intensity. The aim of the present study was to compare three different approaches for determining associations for spectrum descriptors of physical activity (the intensity gradient, principal component analysis, and multivariate pattern analysis) with relevant outcomes in children. We used two datasets including physical activity spectrum data (ActiGraph GT3X+) and 1) a cardiometabolic health outcome in 841 schoolchildren and 2) a motor skill outcome in 1081 preschool children. We compared variance explained (R2) and associations with the outcomes for the intensity gradient (slope) across the physical activity spectra, a two-component principal component model describing the physical activity variables, and multivariate pattern analysis using the intensity spectra as the explanatory data matrices. Results were broadly similar for all analytical approaches. Multivariate pattern analysis explained the most variance in both datasets, likely resulting from use of more of the information available from the intensity spectra. Yet, volume and intensity dimensions of physical activity are not easily disentangled and their relative importance may be interpreted differently using different methodology

Effect of exercise on sleep and bi-directional associations with accelerometer-assessed physical activity in men with obesity

This study examined the effect of exercise training on sleep duration and quality and bidirectional day-to-day relationships between physical activity (PA) and sleep. Fourteen inactive men with obesity (age: 49.2 ± 7.9 years, body mass index: 34.9 ± 2.8 kg/m2) completed a baseline visit, 8-week aerobic exercise intervention, and 1-month post-intervention follow-up. PA and sleep were assessed continuously throughout the study duration using wrist-worn accelerometry. Generalised estimating equations were used to examine associations between PA and sleep. Sleep duration increased from 5.2 h at baseline to 6.6 h during the intervention period and 6.5 h at 1-month post-intervention follow-up (p p CONT), and MVPA (p CONT, and MVPA predicted more wake after sleep onset (WASO) (p CONT, and MVPA (p Novelty: Greater levels of physical activity in the day were associated with an earlier sleep onset time that night, whereas a later timing of sleep onset was associated with lower physical activity the next day in men with obesity. Higher physical activity levels were associated with worse sleep quality, and vice versa

Accelerometer Metrics: Healthy Adult Reference Values, Associations with Cardiorespiratory Fitness, and Clinical Implications

Purpose  Accelerometer-assessed physical activity (PA) can be summarised using cut-point-free or population-specific cut-point-based outcomes. We aimed to: 1) examine the interrelationship between cut-point-free (intensity gradient [IG] and average acceleration [AvAcc]) and cut-point-based accelerometer metrics, 2) compare the association between cardiorespiratory fitness (CRF) and cut-point-free metrics to that with cut-point-based metrics in healthy adults aged 20 to 89 years and patients with heart failure, and 3) provide age-, sex-, and CRF-related reference values for healthy adults. Methods  In the COmPLETE study, 463 healthy adults and 67 patients with heart failure wore GENEActiv accelerometers on their non-dominant wrist and underwent cardiopulmonary exercise testing. Cut-point-free (IG: distribution of intensity of activity across the day; AvAcc: proxy of volume of activity) and traditional (moderate-to-vigorous and vigorous activity) metrics were generated. The ‘interpretablePA’ R-package was developed to translate findings into clinical practice. Results  IG and AvAcc yield complementary information on PA with both IG (p = 0.009) and AvAcc (p Conclusions  IG and AvAcc are strongly associated with CRF and, thus, indirectly with the risk of non-communicable diseases and mortality, in healthy adults and patients with heart failure. However, unlike cut-point-based metrics, IG and AvAcc are comparable across populations. Our reference values provide a healthy age- and sex-specific comparison that may enhance the translation and utility of cut-point-free metrics in clinical practice.</p

Obesity, walking pace and risk of severe COVID-19 and mortality: analysis of UK Biobank.

Obesity is an emerging risk factor for coronavirus disease-2019 (COVID-19). Simple measures of physical fitness, such as self-reported walking pace, may also be important risk markers. This analysis includes 412,596 UK Biobank participants with linked COVID-19 data (median age at linkage = 68 years, obese = 24%, median number of comorbidities = 1). As of August 24th 2020, there were 1001 cases of severe (in-hospital) disease and 336 COVID-19 deaths. Compared to normal weight individuals, the adjusted odds ratio (OR) of severe COVID-19 in overweight and obese individuals was 1.26 (1.07, 1.48) and 1.49 (1.25, 1.79), respectively. For COVID-19 mortality, the ORs were 1.19 (0.88, 161) and 1.82 (1.33, 2.49), respectively. Compared to those with a brisk walking pace, the OR of severe COVID-19 for steady/average and slow walkers was 1.13 (0.98, 1.31) and 1.88 (1.53, 2.31), respectively. For COVID-19 mortality, the ORs were 1.44 (1.10, 1.90) and 1.83 (1.26, 2.65), respectively. Slow walkers had the highest risk regardless of obesity status. For example, compared to normal weight brisk walkers, the OR of severe disease and COVID-19 mortality in normal weight slow walkers was 2.42 (1.53, 3.84) and 3.75 (1.61, 8.70), respectively. Self-reported slow walkers appear to be a high-risk group for severe COVID-19 outcomes independent of obesity

Waking Up to the Importance of Sleep in Type 2 Diabetes Management: A Narrative Review

For the first time, the latest American Diabetes Association/European Association for the Study of Diabetes (ADA/EASD) consensus guidelines have incorporated a growing body of evidence linking health outcomes associated with type 2 diabetes to the movement behavior composition over the whole 24-h day. Of particular note, the importance of sleep as a key lifestyle component in the management of type 2 diabetes is promulgated and presented using three key constructs: quantity, quality, and timing (i.e., chronotype). In this narrative review we highlight some of the key evidence justifying the inclusion of sleep in the latest consensus guidelines by examining the associations of quantity, quality, and timing of sleep with measures of glycemia, cardiovascular disease risk, and mortality. We also consider potential mechanisms implicated in the association between sleep and type 2 diabetes and provide practical advice for health care professionals about initiating conversations pertaining to sleep in clinical care. In particular, we emphasize the importance of measuring sleep in a free-living environment and provide a summary of the different methodologies and targets. In summary, although the latest ADA/EASD consensus report highlights sleep as a central component in the management of type 2 diabetes, placing it, for the first time, on a level playing field with other lifestyle behaviors (e.g., physical activity and diet), the evidence base for improving sleep (beyond sleep disorders) in those living with type 2 diabetes is limited. This review should act as a timely reminder to incorporate sleep into clinical consultations, ongoing diabetes education, and future interventions.</p