Physical Activity Assessed by Wrist and Thigh Worn Accelerometry and Associations with Cardiometabolic Health.

Physical activity is increasingly being captured by accelerometers worn on different body locations. The aim of this study was to examine the associations between physical activity volume (average acceleration), intensity (intensity gradient) and cardiometabolic health when assessed by a thigh-worn and wrist-worn accelerometer. A sample of 659 office workers wore an Axivity AX3 on the non-dominant wrist and an activPAL3 micro on the right thigh concurrently for 24 h a day for 8 days. An average acceleration (proxy for physical activity volume) and intensity gradient (intensity distribution) were calculated from both devices using the open-source raw accelerometer processing software GGIR. Clustered cardiometabolic risk (CMR) was calculated using markers of cardiometabolic health, including waist circumference, triglycerides, HDL-cholesterol, mean arterial pressure and fasting glucose. Linear regression analysis assessed the associations between physical activity volume and intensity gradient with cardiometabolic health. Physical activity volume derived from the thigh-worn activPAL and the wrist-worn Axivity were beneficially associated with CMR and the majority of individual health markers, but associations only remained significant after adjusting for physical activity intensity in the thigh-worn activPAL. Physical activity intensity was associated with CMR score and individual health markers when derived from the wrist-worn Axivity, and these associations were independent of volume. Associations between cardiometabolic health and physical activity volume were similarly captured by the thigh-worn activPAL and the wrist-worn Axivity. However, only the wrist-worn Axivity captured aspects of the intensity distribution associated with cardiometabolic health. This may relate to the reduced range of accelerations detected by the thigh-worn activPAL.

Accelerometer-Assessed Physical Activity in People with Type 2 Diabetes: Accounting for Sleep when Determining Associations with Markers of Health.

High physical activity levels during wake are beneficial for health, while high movement levels during sleep are detrimental to health. Our aim was to compare the associations of accelerometer-assessed physical activity and sleep disruption with adiposity and fitness using standardized and individualized wake and sleep windows. People (N = 609) with type 2 diabetes wore an accelerometer for up to 8 days. Waist circumference, body fat percentage, Short Physical Performance Battery (SPPB) test score, sit-to-stands, and resting heart rate were assessed. Physical activity was assessed via the average acceleration and intensity distribution (intensity gradient) over standardized (most active 16 continuous hours (M16h)) and individualized wake windows. Sleep disruption was assessed via the average acceleration over standardized (least active 8 continuous hours (L8h)) and individualized sleep windows. Average acceleration and intensity distribution during the wake window were beneficially associated with adiposity and fitness, while average acceleration during the sleep window was detrimentally associated with adiposity and fitness. Point estimates for the associations were slightly stronger for the standardized than for individualized wake/sleep windows. In conclusion, standardized wake and sleep windows may have stronger associations with health due to capturing variations in sleep durations across individuals, while individualized windows represent a purer measure of wake/sleep behaviors.

Stepping up with GGIR: Validity of step cadence derived from wrist-worn research-grade accelerometers using the verisense step count algorithm.

The Verisense Step Count Algorithm facilitates generation of steps from wrist-worn accelerometers. Based on preliminary evidence suggesting a proportional bias with overestimation at low steps/day, but underestimation at high steps/day, the algorithm parameters have been revised. We aimed to establish validity of the original and revised algorithms relative to waist-worn ActiGraph step cadence. We also assessed whether step cadence was similar across accelerometer brand and wrist. Ninety-eight participants (age: 58.6±11.1 y) undertook six walks (~500 m hard path) at different speeds (cadence: 92.9±9.5-127.9±8.7 steps/min) while wearing three accelerometers on each wrist (Axivity, GENEActiv, ActiGraph) and an ActiGraph on the waist. Of these, 24 participants also undertook one run (~1000 m). Mean bias for the original algorithm was -21 to -26.1 steps/min (95% limits of agreement (LoA) ~±65 steps/min) and mean absolute percentage error (MAPE) 17-22%. This was unevenly distributed with increasing error as speed increased. Mean bias and 95%LoA were halved with the revised algorithm parameters (~-10 to -12 steps/min, 95%LoA ~30 steps/min, MAPE ~10-12%). Performance was similar across brand and wrist. The revised step algorithm provides a more valid measure of step cadence than the original, with MAPE similar to recently reported wrist-wear summary MAPE (7-11%).

Physical activity volume, intensity, and incident cardiovascular disease.

AIMS: The interplay between physical activity (PA) volume and intensity is poorly understood in relation to cardiovascular disease (CVD) risk. This study aimed to investigate the role of PA intensity, over and above volume, in relation to incident CVD. METHODS AND RESULTS: Data were from 88 412 UK Biobank middle-aged adults (58% women) without prevalent CVD who wore accelerometers on their dominant wrist for 7 days, from which we estimated total PA energy expenditure (PAEE) using population-specific validation. Cox proportional hazards regressions modelled associations between PAEE (kJ/kg/day) and PA intensity (%MVPA; the fraction of PAEE accumulated from moderate-to-vigorous-intensity PA) with incident CVD (ischaemic heart disease or cerebrovascular disease), adjusted for potential confounders. There were 4068 CVD events during 584 568 person-years of follow-up (median 6.8 years). Higher PAEE and higher %MVPA (adjusted for PAEE) were associated with lower rates of incident CVD. In interaction analyses, CVD rates were 14% (95% confidence interval: 5-23%) lower when MVPA accounted for 20% rather than 10% of 15 kJ/kg/d PAEE; equivalent to converting a 14 min stroll into a brisk 7 min walk. CVD rates did not differ significantly between values of PAEE when the %MVPA was fixed at 10%. However, the lowest CVD rates were observed for combinations of both higher PAEE and %MVPA. CONCLUSION: Reductions in CVD risk may be achievable through higher PA volume and intensity, with the role of moderately intense PA appearing particularly important. This supports multiple approaches or strategies to PA participation, some of which may be more practical or appealing to different individuals.

Importance of Overall Activity and Intensity of Activity for Cardiometabolic Risk in Those with and Without a Chronic Disease.

INTRODUCTION: Higher levels of physical activity are associated with lower cardiometabolic risk. However, the relative contribution of overall activity and the intensity of activity are unclear. Our aim was to determine the relative contribution of overall activity and intensity distribution of activity to cardiometabolic risk in a cross-sectional analysis of apparently healthy office workers and in people with one or more chronic disease. METHODS: Clustered cardiometabolic risk score was calculated from mean arterial pressure, high-density lipoprotein cholesterol, triglycerides and HbA1c. Open-source software (GGIR) was used to generate average acceleration and intensity gradient from wrist-worn accelerometer data for two data sets: office-workers who did not have a self-reported medical condition ( n = 399, 70% women) and adults with one or more chronic disease ( n = 1137, 34% women). Multiple linear regression analyses were used to assess the relative contribution of overall activity and intensity of activity to cardiometabolic risk. RESULTS: When mutually adjusted, both overall activity and intensity of activity were independently associated with cardiometabolic risk in the healthy group ( P < 0.05). However, for the CD group, although mutually adjusted associations for average acceleration were significantly associated with cardiometabolic risk ( P < 0.001), intensity was not. In healthy individuals, cardiometabolic risk was lower in those with high overall activity and/or intensity of activity, and who also undertook at least 10 min brisk walking. In those with a chronic disease, risk was lower in those who undertook at least 60 min slow walking. CONCLUSIONS: These findings suggest interventions aiming to optimize cardiometabolic health in healthy adults could focus on increasing both intensity and amount of physical activity. However, in those with chronic disease, increasing the amount of activity undertaken, regardless of intensity, may be more appropriate.

Normative wrist-worn accelerometer values for self-paced walking and running: a walk in the park.

This study aimed to a) determine whether wrist acceleration varies by accelerometer brand, wear location, and age for self-paced "slow", "normal" and "brisk" walking; b) develop normative acceleration values for self-paced walking and running for adults. One-hundred-and-three adults (40-79 years) completed self-paced "slow", "normal" and "brisk" walks, while wearing three accelerometers (GENEActiv, Axivity, ActiGraph) on each wrist. A sub-sample (n = 22) completed a self-paced run. Generalized estimating equations established differences by accelerometer brand, wrist, and age-group (walking only, 40-49, 50-59, 60-69, 70-79 years) for self-paced walking and running. Brand*wrist interactions showed ActiGraph dominant wrist values were ~10% lower than GENEActiv/Axivity values for walking and running, and non-dominant ActiGraph values were ~5% lower for running only (p < 0.001). Acceleration during brisk walking was lower in those aged 70-79 (p < 0.05). Normative acceleration values (non-dominant wrist, all brands; dominant wrist GENEActiv/Axivity) for slow and normal walking were 140 mg and 210 mg. Brisk walking, values were 350 mg in those aged 40-69 years, but 270 mg in those aged 70-79 years. Accelerations >600 mg approximated running. These values facilitate user-friendly interpretation of accelerometer-determined physical activity in large cohort and epidemiological datasets.

Differences in Accelerometer-Measured Patterns of Physical Activity and Sleep/Rest Between Ethnic Groups and Age: An Analysis of UK Biobank.

BACKGROUND: Physical activity and sleep are important for health; whether device-measured physical activity and sleep differ by ethnicity is unclear. This study aimed to compare physical activity and sleep/rest in white, South Asian (SA), and black adults by age. METHODS: Physical activity and sleep/rest quality were assessed using accelerometer data from UK Biobank. Linear regressions, stratified by sex, were used to analyze differences in activity and sleep/rest. An ethnicity × age group interaction term was used to assess whether ethnic differences were consistent across age groups. RESULTS: Data from 95,914 participants, aged 45-79 years, were included. Overall activity was 7% higher in black, and 5% lower in SA individuals compared with white individuals. Minority ethnic groups had poorer sleep/rest quality. Lower physical activity and poorer sleep quality occurred at a later age in black and SA adults (>65 y), than white adults (>55 y). CONCLUSIONS: While black adults are more active, and SA adults less active, than white adults, the age-related reduction appears to be delayed in black and SA adults. Sleep/rest quality is poorer in black and SA adults than in white adults. Understanding ethnic differences in physical activity and rest differ may provide insight into chronic conditions with differing prevalence across ethnicities.

Evaluation of an 8-Week Vegan Diet on Plasma Trimethylamine-N-Oxide and Postchallenge Glucose in Adults with Dysglycemia or Obesity.

BACKGROUND: Trimethylamine N-oxide (TMAO), a metabolite generated by the gut in response (in part) to meat consumption, is linked to poor cardiometabolic health. OBJECTIVES: We investigate the effect of an 8-week vegan diet, followed by a 4-week period of unrestricted diet, on glucose tolerance and plasma TMAO in human omnivores with obesity or dysglycemia. METHODS: This interventional single-group prospective trial involved 23 regular meat eaters with dysglycemia [glycated hemoglobin ≥ 5.7% and ≤8% (39-64 mmol/mol)], or obesity (ΒΜΙ ≥ 30 kg/m2) aged 57.8 ± 10.0 years. Participants [14 men (60.9%) and 9 women (39.1%)] were supported in following a vegan diet for 8 weeks, followed by 4 weeks of unrestricted diet. The primary outcomes (plasma TMAO and glucose) were assessed at baseline, during the vegan diet (weeks 1 and 8), and after the unrestricted diet period (week 12). TMAO was assessed after fasting and glucose was measured as a time-averaged total AUC using a 180-minute oral-glucose-tolerance test. Generalized estimating equation models with an exchangeable correlation structure were used to assess changes from baseline, adjusting for age, sex, ethnicity, and weight. RESULTS: TMAO levels (marginal mean) were reduced after weeks 1 and 8 of a vegan diet compared to baseline, from 10.7 (97.5% CI, 6.61-17.3) µmol/L to 5.66 (97.5% CI, 4.56-7.02) µmol/L and 6.38 (97.5% CI, 5.25-7.74) µmol/L, respectively; however, levels rebounded at week 12 after resumption of an unrestricted diet (17.5 µmol/L; 97.5% CI, 7.98-38.4). Postprandial glucose levels (marginal means) were reduced after weeks 1 and 8 compared to baseline, from 8.07 (97.5% CI, 7.24-8.90) mmol/L to 7.14 (97.5% CI, 6.30-7.98) mmol/L and 7.34 (97.5% CI, 6.63-8.04) mmol/L, respectively. Results for glucose and TMAO were independent of weight loss. Improvements in the lipid profile and markers of renal function were observed at week 8. CONCLUSIONS: These findings suggest that a vegan diet is an effective strategy for improving glucose tolerance and reducing plasma TMAO in individuals with dysglycemia or obesity. This study was registered at clinicaltrials.gov as NCT03315988.

Comparing 24 h physical activity profiles: Office workers, women with a history of gestational diabetes and people with chronic disease condition(s).

This study demonstrates a novel data-driven method of summarising accelerometer data to profile physical activity in three diverse groups, compared with cut-point determined moderate-to-vigorous physical activity (MVPA). GGIR was used to generate average daily acceleration, intensity gradient, time in MVPA and MX metrics (acceleration above which the most active X-minutes accumulate) from wrist-worn accelerometer data from three datasets: office-workers (OW, N = 697), women with a history of post-gestational diabetes (PGD, N = 267) and adults with ≥1 chronic disease (CD, N = 1,325). Average acceleration and MVPA were lower in CD, but not PGD, relative to OW (-5.2 mg and -30.7 minutes, respectively, P < 0.001). Both PGD and CD had poorer intensity distributions than OW (P < 0.001). Application of a cut-point to the M30 showed 7%, 17% and 28%, of OW, PGD and CD, respectively, accumulated 30 minutes of brisk walking per day. Radar plots showed OW had higher overall activity than CD. The relatively poor intensity distribution of PGD, despite similar overall activity to OW, was due to accumulation of more light and less higher intensity activity. These data-driven methods identify aspects of activity that differ between groups, which may be missed by cut-point methods alone. Abbreviations: CD: Adults with ≥1 chronic disease; mg: Milli-gravitational unit; MVPA: Moderate-to-vigorous physical activity; OW: Office workers; PGD: Women with a history of post-gestational diabetes; VPA: Vigorous physical activity.

Association of Timing and Balance of Physical Activity and Rest/Sleep With Risk of COVID-19: A UK Biobank Study.

Behavioral lifestyle factors are associated with cardiometabolic disease and obesity, which are risk factors for coronavirus disease 2019 (COVID-19). We aimed to investigate whether physical activity, and the timing and balance of physical activity and sleep/rest, were associated with SARS-CoV-2 positivity and COVID-19 severity. Data from 91,248 UK Biobank participants with accelerometer data and complete covariate and linked COVID-19 data to July 19, 2020, were included. The risk of SARS-CoV-2 positivity and COVID-19 severity-in relation to overall physical activity, moderate-to-vigorous physical activity (MVPA), balance between activity and sleep/rest, and variability in timing of sleep/rest-was assessed with adjusted logistic regression. Of 207 individuals with a positive test result, 124 were classified as having a severe infection. Overall physical activity and MVPA were not associated with severe COVID-19, whereas a poor balance between activity and sleep/rest was (odds ratio [OR] per standard deviation: 0.71; 95% confidence interval [CI], 0.62 to 0.81]). This finding was related to higher daytime activity being associated with lower risk (OR, 0.75; 95% CI, 0.61 to 0.93) but higher movement during sleep/rest being associated with higher risk (OR, 1.26; 95% CI, 1.12 to 1.42) of severe infection. Greater variability in timing of sleep/rest was also associated with increased risk (OR, 1.21; 95% CI, 1.08 to 1.35). Results for testing positive were broadly consistent. In conclusion, these results highlight the importance of not just physical activity, but also quality sleep/rest and regular sleep/rest patterns, on risk of COVID-19. Our findings indicate the risk of COVID-19 was consistently approximately 1.2-fold greater per approximately 40-minute increase in variability in timing of proxy measures of sleep, indicative of irregular sleeping patterns.

Management of fatigue with physical activity and behavioural change support in vasculitis: a feasibility study.

OBJECTIVE: Patients with ANCA-associated vasculitis (AAV) experience high levels of fatigue, despite disease remission. This study assessed the feasibility and acceptability of a definitive randomized controlled trial of a behavioural-based physical activity intervention to support fatigue self-management in AAV patients. METHODS: AAV patients in disease remission with fatigue (Multidimensional Fatigue Inventory-20 general fatigue domain ≥14) were randomly allocated to intervention or standard care in this single-centre open-label randomized controlled feasibility study. The intervention lasted 12 weeks and comprised eight face-to-face physical activity sessions with a facilitator and 12 weekly telephone calls. Participants were encouraged to monitor their physical activity using a tracker device (Fitbit). Standard care involved sign-posting to fatigue websites. The primary outcome was feasibility of a phase III trial assessed against three stop/go traffic light criteria, (recruitment, intervention adherence and study withdrawal). A qualitative study assessed participant views about the intervention. RESULTS: A total of 248 patients were screened and 134 were eligible to participate (54%). Stop/go criteria were amber for recruitment; 43/134 (32%, 95% CI: 24, 40) eligible participants randomized, amber for adherence; 73% of participants attended all eight physical activity sessions, but only 11/22 (50%, 95% CI: 29, 71%) completed the intervention as per the intended schedule, and green for study withdrawal; 2/43 participants withdrew before 24 weeks (5%, 95% CI: 0, 11). Qualitative results suggested the intervention was acceptable. CONCLUSION: This study suggests a behavioural-based physical activity intervention targeting fatigue self-management was acceptable to patients with AAV, although recruitment and protocol adherence will need modification prior to a definitive trial. CLINICAL TRIAL REGISTRATION NUMBER: ISRCTN11929227.

Physical Activity for Bone Health: How Much and/or How Hard?

PURPOSE: High-impact physical activity is associated with bone health, but higher volumes of lower-intensity activity may also be important. The aims of this study were to: 1) investigate the relative importance of volume and intensity of physical activity accumulated during late adolescence for bone health at age 23 yr; and 2) illustrate interpretation of the results. METHODS: This is a secondary analysis of data from the Iowa Bone Development Study, a longitudinal study of bone health from childhood through to young adulthood. The volume (average acceleration) and intensity distribution (intensity gradient) of activity at age 17, 19, 21, and 23 yr were calculated from raw acceleration ActiGraph data and averaged across ages. Hip areal bone mineral density (aBMD), total body bone mineral content (BMC), spine aBMD, and hip structural geometry (dual-energy X-ray absorptiometry, Hologic QDR4500A) were assessed at age 23 yr. Valid data, available for 220 participants (124 girls), were analyzed with multiple regression. To elucidate significant effects, we predicted bone outcomes when activity volume and intensity were high (+1SD), medium (mean), and low (-1SD). RESULTS: There were additive associations of volume and intensity with hip aBMD and total body BMC (low-intensity/low-volume cf. high-intensity/high-volume = [INCREMENT]0.082 g·cm and [INCREMENT]169.8 g, respectively). For males only, spine aBMD intensity was associated independently of volume (low-intensity cf. high-intensity = [INCREMENT]0.049 g·cm). For hip structural geometry, volume was associated independently of intensity (low-volume cf. high-volume = [INCREMENT]4.8-6.6%). CONCLUSIONS: The activity profile associated with optimal bone outcomes was high in intensity and volume. The variation in bone health across the activity volume and intensity distribution suggests intensity is key for aBMD and BMC, whereas high volumes of lower intensity activity may be beneficial for hip structural geometry.

Enhancing the value of accelerometer-assessed physical activity: meaningful visual comparisons of data-driven translational accelerometer metrics.

The lack of consensus on meaningful and interpretable physical activity outcomes from accelerometer data hampers comparison across studies. Cut-point analyses are simple to apply and easy to interpret but can lead to results that are not comparable. We propose that the optimal accelerometer metrics for data analysis are not the same as the optimal metrics for translation. Ideally, analytical metrics are precise continuous variables that cover the intensity spectrum, while translational metrics facilitate meaningful, public-health messages and can be described in terms of activities (e.g. brisk walking) or intensity (e.g. moderate-to-vigorous physical activity). Two analytical metrics that capture the volume and intensity of the 24-h activity profile are average acceleration (volume) and intensity gradient (intensity distribution). These allow investigation of independent, additive and interactive associations of volume and intensity of activity with health; however, they are not immediately interpretable. The MX metrics, the acceleration above which the most active X minutes are accumulated, are translational metrics that can be interpreted in terms of indicative activities. Using a range of MX metrics illustrates the intensity gradient and average acceleration (i.e. 24-h activity profile). The M120, M60, M30, M15 and M5 illustrate the most active accumulated minutes of the day, the M1/3DAY the most active accumulated 8 h of the day. We demonstrate how radar plots of MX metrics can be used to interpret and translate results from between- and within-group comparisons, provide information on meeting guidelines, assess individual activity profiles relative to percentiles and compare activity profiles between domains and/or time periods.