Twenty-four-hour physical behaviour profiles across type 2 diabetes mellitus subtypes.

AIM: To investigate how 24-h physical behaviours differ across type 2 diabetes (T2DM) subtypes. MATERIALS AND METHODS: We included participants living with T2DM, enrolled as part of an ongoing observational study. Participants wore an accelerometer for 7 days to quantify physical behaviours across 24 h. We used routinely collected clinical data (age at onset of diabetes, glycated haemoglobin level, homeostatic model assessment index of beta-cell function, homeostatic model assessment index of insulin resistance, body mass index) to replicate four previously identified subtypes (insulin-deficient diabetes [INS-D], insulin-resistant diabetes [INS-R], obesity-related diabetes [OB] and age-related diabetes [AGE]), via k-means clustering. Differences in physical behaviours across the diabetes subtypes were assessed using generalized linear models, with the AGE cluster as the reference. RESULTS: A total of 564 participants were included in this analysis (mean age 63.6 ± 8.4 years, 37.6% female, mean age at diagnosis 53.1 ± 10.0 years). The proportions in each cluster were as follows: INS-D: n = 35, 6.2%; INS-R: n = 88, 15.6%; OB: n = 166, 29.4%; and AGE: n = 275, 48.8%. Compared to the AGE cluster, the OB cluster had a shorter sleep duration (-0.3 h; 95% confidence interval [CI] -0.5, -0.1), lower sleep efficiency (-2%; 95% CI -3, -1), lower total physical activity (-2.9 mg; 95% CI -4.3, -1.6) and less time in moderate-to-vigorous physical activity (-6.6 min; 95% CI -11.4, -1.7), alongside greater sleep variability (17.9 min; 95% CI 8.2, 27.7) and longer sedentary time (31.9 min; 95% CI 10.5, 53.2). Movement intensity during the most active continuous 10 and 30 min of the day was also lower in the OB cluster. CONCLUSIONS: In individuals living with T2DM, the OB subtype had the lowest levels of physical activity and least favourable sleep profiles. Such behaviours may be suitable targets for personalized therapeutic lifestyle interventions.

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.

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 < 0.001) in one study (n = 173), but no additional risk reduction was found for >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.

Validation of an automated sleep detection algorithm using data from multiple accelerometer brands.

To evaluate the criterion validity of an automated sleep detection algorithm applied to data from three research-grade accelerometers worn on each wrist with concurrent laboratory-based polysomnography (PSG). A total of 30 healthy volunteers (mean [SD] age 31.5 [7.2] years, body mass index 25.5 [3.7] kg/m2 ) wore an Axivity, GENEActiv and ActiGraph accelerometer on each wrist during a 1-night PSG assessment. Sleep estimates (sleep period time window [SPT-window], sleep duration, sleep onset and waking time, sleep efficiency, and wake after sleep onset [WASO]) were generated using the automated sleep detection algorithm within the open-source GGIR package. Agreement of sleep estimates from accelerometer data with PSG was determined using pairwise 95% equivalence tests (±10% equivalence zone), intraclass correlation coefficients (ICCs) with 95% confidence intervals and limits of agreement (LoA). Accelerometer-derived sleep estimates except for WASO were within the 10% equivalence zone of the PSG. Reliability between data from the accelerometers worn on either wrist and PSG was moderate for SPT-window duration (ICCs ≥ 0.65), sleep duration (ICCs ≥ 0.54), and sleep onset (ICCs ≥ 0.61), mostly good for waking time (ICCs ≥ 0.80), but poor for sleep efficiency (ICCs ≥ 0.08) and WASO (ICCs ≥ 0.08). The mean bias between all accelerometer-derived sleep estimates worn on either wrist and PSG were low; however, wide 95% LoA were observed for all sleep estimates, apart from waking time. The automated sleep detection algorithm applied to data from Axivity, GENEActiv and ActiGraph accelerometers, worn on either wrist, provides comparable measures to PSG for SPT-window and sleep duration, sleep onset and waking time, but a poor measure of wake during the sleep period.

Reference values for wrist-worn accelerometer physical activity metrics in England children and adolescents.

BACKGROUND: Over the last decade use of raw acceleration metrics to assess physical activity has increased. Metrics such as Euclidean Norm Minus One (ENMO), and Mean Amplitude Deviation (MAD) can be used to generate metrics which describe physical activity volume (average acceleration), intensity distribution (intensity gradient), and intensity of the most active periods (MX metrics) of the day. Presently, relatively little comparative data for these metrics exists in youth. To address this need, this study presents age- and sex-specific reference percentile values in England youth and compares physical activity volume and intensity profiles by age and sex. METHODS: Wrist-worn accelerometer data from 10 studies involving youth aged 5 to 15 y were pooled. Weekday and weekend waking hours were first calculated for youth in school Years (Y) 1&2, Y4&5, Y6&7, and Y8&9 to determine waking hours durations by age-groups and day types. A valid waking hours day was defined as accelerometer wear for ≥ 600 min·d-1 and participants with ≥ 3 valid weekdays and ≥ 1 valid weekend day were included. Mean ENMO- and MAD-generated average acceleration, intensity gradient, and MX metrics were calculated and summarised as weighted week averages. Sex-specific smoothed percentile curves were generated for each metric using Generalized Additive Models for Location Scale and Shape. Linear mixed models examined age and sex differences. RESULTS: The analytical sample included 1250 participants. Physical activity peaked between ages 6.5-10.5 y, depending on metric. For all metrics the highest activity levels occurred in less active participants (3rd-50th percentile) and girls, 0.5 to 1.5 y earlier than more active peers, and boys, respectively. Irrespective of metric, boys were more active than girls (p < .001) and physical activity was lowest in the Y8&9 group, particularly when compared to the Y1&2 group (p < .001). CONCLUSIONS: Percentile reference values for average acceleration, intensity gradient, and MX metrics have utility in describing age- and sex-specific values for physical activity volume and intensity in youth. There is a need to generate nationally-representative wrist-acceleration population-referenced norms for these metrics to further facilitate health-related physical activity research and promotion.

Life expectancy following a cardiovascular event in individuals with and without type 2 diabetes: A UK multi-ethnic population-based observational study.

BACKGROUND AND AIMS: We aimed to evaluate the life expectancy following the first cardiovascular disease (CVD) event by type 2 diabetes (T2D) status and ethnicity. METHODS AND RESULTS: We used the Clinical Practice Research Datalink database in England (UK), linked to the Hospital Episode Statistics information, to identify individuals with and without T2D who survived a first CVD event between 1st Jan 2007 and 31st Dec 2017; subsequent death events were extracted from the Office for National Statistics database. Ethnicity was categorised as White, South Asian (SA), Black, or other. Flexible parametric survival models were used to estimate survival and predict life expectancy. 59,939 individuals with first CVD event were included: 7596 (12.7%) with T2D (60.9% men; mean age at event: 69.7 years [63.2 years in SA, 65.9 in Black, 70.2 in White]) and 52,343 without T2D (56.7% men; 65.9 years [54.7 in Black, 58.2 in SA, 66.3 in White]). Accounting for potential confounders (sex, deprivation, lipid-lowering medication, current smoking, and pre-existing hypertension), comparing individuals with vs without T2D the mortality rate was 53% higher in White (hazard ratio [HR]: 1.53 [95% CI: 1.44, 1.62]), corresponding to a potential loss of 3.87 (3.30, 4.44) life years at the age of 50 years in individuals with T2D. No evidence of a difference in life expectancy was observed in individuals of SA (HR: 0.82 [0.52, 1.29]; -1.36 [-4.58, 1.86] life years), Black (HR: 1.26 [0.59, 2.70]; 1.21 [-2.99, 5.41] life years); and other (HR: 1.64 [0.80, 3.39]; 3.89 [-2.28, 9.99] life years) ethnic group. CONCLUSION: Following a CVD event, T2D is associated with a different prognosis and life years lost among ethnic groups.

Absolute intensity thresholds for tri-axial wrist and waist accelerometer-measured movement behaviors in adults.

AIM: This study was aimed to: (1) compare raw triaxial acceleration data from GENEActiv (GA) and ActiGraph GT3X+ (AG) placed on the non-dominant wrist; (2) compare AG placed on the non-dominant and dominant wrist, and waist; (3) derive brand- and placement-specific absolute intensity thresholds for inactive and sedentary time, and physical activity intensity in adults. METHODS: Eighty-six adults (44 men; 34.6 ± 10.8 years) performed nine activities while simultaneously wearing GA and AG on wrist and waist. Acceleration (in gravitational equivalent units; mg) was compared with oxygen uptake (measured with indirect calorimetry). RESULTS: Increases in acceleration mirrored increases in intensity of activities, regardless of device brand and placement. Differences in acceleration between GA and AG worn at the non-dominant wrist were small but tended to be high at lower intensity activities. Thresholds for differentiating inactivity (<1.5 MET) from activity (≥1.5 MET) ranged from 25 mg (AG non-dominant wrist; sensitivity 93%, specificity 95%) to 40 mg (AG waist; sensitivity 78%, specificity 100%). For moderate intensity (≥3 METs), thresholds ranged from 65 mg (AG waist; sensitivity 96%, specificity 94%) to 92 mg (GA non-dominant; sensitivity 93%, specificity 98%); vigorous intensity (≥6 METs) thresholds ranged from 190 mg (AG waist; sensitivity 82%, specificity 92%) to 283 mg (GA non-dominant; sensitivity 93%, specificity 98%). CONCLUSION: Raw triaxial acceleration outputs from two widely used accelerometer brands may have limited comparability in low intensity activities. Thresholds derived in this study can be utilized in adults to reasonably classify movement behaviors into categories of intensity.

Physical activity volume and intensity distribution in relation to bone, lean and fat mass in children.

Considering physical activity (PA) volume and intensity may provide novel insights into the relationships of PA with bone, lean, and fat mass. This study aimed to assess the associations of PA volume, PA intensity distribution, including moderate-to-vigorous PA (MVPA) with total-body-less-head bone mineral content (BMC), lean, and fat mass in children. A population sample of 290 Finnish children (158 females) aged 9-11 years from the Physical Activity and Nutrition in Children (PANIC) Study was studied. PA, including MVPA, was assessed with a combined heart rate and movement sensor, and the uniaxial acceleration was used to calculate average-acceleration (a proxy metric for PA volume) and intensity-gradient (reflective of PA intensity distribution). Linear regression analyzed the associations of PA volume, PA intensity and MVPA with BMC, lean mass, and fat mass assessed by dual-energy X-ray absorptiometry. PA volume was positively associated with BMC in females (unstandardised regression coefficient [ß] = 0.26) and males (ß = 0.47), and positively associated with lean (ß = 7.33) and negatively associated with fat mass in males (ß = -20.62). PA intensity was negatively associated with BMC in males (ß = -0.13). MVPA was positively associated with lean mass in females and males (ß = 0.007 to 0.012), and negatively associated with fat mass in females and males (ß = -0.030 to -0.029). PA volume may be important for improving BMC in females and males, and increasing lean and reducing fat mass in males, whereas MVPA may be important for favorable lean and fat outcomes in both sexes.

Can quantifying the relative intensity of a person's free-living physical activity predict how they respond to a physical activity intervention? Findings from the PACES RCT.

OBJECTIVES: To determine whether quantifying both the absolute and relative intensity of accelerometer-assessed physical activity (PA) can inform PA interventions. We hypothesised that individuals whose free-living PA is at a low relative intensity are more likely to increase PA in response to an intervention, as they have spare physical capacity. METHOD: We conducted a secondary data analysis of a 12-month randomised controlled trial, Physical Activity after Cardiac EventS, which was designed to increase PA but showed no improvement. Participants (N=239, 86% male; age 66.4 (9.7); control N=126, intervention N=113) wore accelerometers for 7 days and performed the incremental shuttle walk test (ISWT) at baseline and 12 months. PA intensity was expressed in absolute terms (intensity gradient) and relative to acceleration at maximal physical capacity (predicted from an individual's maximal ISWT walking speed). PA outcomes were volume and absolute intensity gradient. RESULTS: At baseline, ISWT performance was positively correlated with PA volume (r=0.50, p<0.001) and absolute intensity (r=0.50, p<0.001), but negatively correlated with relative intensity (r=-0.13, p=0.025). Relative intensity of PA at baseline moderated the change in absolute intensity (p=0.017), but not volume, of PA postintervention. Low relative intensity at baseline was associated with increased absolute intensity gradient (+0.5 SD), while high relative intensity at baseline was associated with decreased absolute intensity gradient (-0.5 SD). CONCLUSION: Those with low relative intensity of PA were more likely to increase their absolute PA intensity gradient in response to an intervention. Quantifying absolute and relative PA intensity of PA could improve enables personalisation of interventions.

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.

Self-reported walking pace: A simple screening tool with lowest risk of all-cause mortality in those that 'walk the talk'.

To determine whether the association between self-reported walking pace and all-cause mortality (ACM) persists across categories of accelerometer-assessed physical activity status. Data from 93,709 UK Biobank participants were included. Physical activity was assessed using wrist-worn accelerometers for 7-days. Participants accumulating <150 min/week moderate-to-vigorous- activity were classed as "inactive", ≥150 min/week moderate (≥3 METs) activity as "somewhat active" excluding those with ≥150 min/week upper-moderate-to-vigorous activity (≥4.3 METs), who were classed as "high-active". Over a 6.3 y (median) follow-up, 2,173 deaths occurred. More than half of slow walkers were "inactive", but only 26% of steady and 12% of brisk walkers. Associations between walking pace and ACM were consistent with those for activity. "High active" brisk walkers had the lowest risk of ACM (Hazard Ratio (HR) 0.22; 95% CI: 0.17,0.28), relative to "inactive" slow walkers. Within those classed as "inactive", steady (HR 0.54; 0.46,0.64) and brisk walkers (HR 0.42; 0.34,0.52) had lower risk than slow walkers. In conclusion, self-reported walking pace was associated with accelerometer-assessed physical activity with both exposures having similar associations with ACM. "inactive", steady, and brisk walkers had lower ACM risk than slow walkers. The pattern was similar for "High active" participants. Overall, "High active" brisk walkers had lowest risk.

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.

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.

The Influence of Accelerometer Epoch Length on Associations of Physical Activity Intensity and Volume with Bone Outcomes.

Two accelerometer metrics (intensity-gradient and average-acceleration) can be used to determine the relative contributions of physical activity (PA) volume and intensity for health, but it is unknown whether epoch length influences the associations detected. This is important when considering bone health, as bone is particularly responsive to high intensity PA, which may be underestimated by longer epochs. This study aimed to assess the associations between average-acceleration, a proxy measure of PA volume, and intensity-gradient, reflective of PA intensity distribution, from PA data from 1-s to 60-s epochs at age 17 to 23 years with bone outcomes at age 23 years. This is a secondary analysis of 220 participants (124 females) from the Iowa Bone Development Study, a longitudinal study of bone health from childhood to early adulthood. Accelerometer-assessed PA data, captured at age 17 to 23 years, were summarised over 1-s, 5-s, 15-s, 30-s, and 60-s epochs, to generate average-acceleration and intensity-gradient from each epoch length, averaged across ages. Regression analysed associations between mutually adjusted average-acceleration and intensity-gradient with dual-energy X-ray absorptiometry assessed total-body-less-head (TBLH) bone mineral content (BMC), spine areal bone mineral density (aBMD), hip aBMD, and femoral neck cross-sectional area and section modulus at age 23 years. Intensity-gradient was positively associated with TBLH BMC in females, with spine aBMD in males, and with hip aBMD and geometry in both sexes, when a 1 to 5-s epoch was used. Average-acceleration was positively associated with TBLH BMC, spine aBMD and hip aBMD in males, generally when the adjustment for intensity-gradient was from > 1-s epochs. Intensity and volume were important for bone outcomes in both sexes and males, respectively. A 1 to 5-s epoch length was most appropriate to assess the mutually adjusted associations of intensity-gradient and average-acceleration with bone outcomes in young adults.

Relative protein intake and associations with markers of physical function in those with type 2 diabetes.

AIMS: To examine the independent associations between relative protein intake (g kg-1  day 1 ) and markers of physical function in those with type 2 diabetes, while also comparing with current guidelines for protein intake. METHODS: This analysis reports data from the ongoing Chronotype of Patients with Type 2 Diabetes and Effect on Glycaemic Control (CODEC) study. Functional assessments included: Short Physical Performance Battery (SPPB), 60 s sit-to-stand (STS-60), 4-m gait speed, time to rise from a chair (×5) and handgrip strength. Participants also completed a self-reported 4 day diet diary. Regression analyses assessed whether relative protein intake was associated with markers of physical function. Interaction terms assessed whether the associations were modified by sex, age, HbA1c or body mass index (BMI). RESULTS: 413 participants were included (mean ± SD:age = 65.0 ± 7.7 years, 33% female, BMI = 30.6 ± 5.1 kg/m2 ). The average total protein intake was 0.88 ± 0.31 g kg-1  day-1 . 33% of individuals failed to meet the reference nutrient intake for the United Kingdom (≥0.75 g kg-1  day-1 ), and 87% for European recommendations (≥1.2 g kg-1  day-1 ). After adjustment, each 0.5 g/kg of protein intake was associated with an 18.9% (95% CI: 2.3, 35.5) higher SPPB score, 22.7% (1.1, 44.3) more repetitions in STS-60, 21.1% (4.5, 37.7) faster gait speed and 33.2% (16.9, 49.5) lower chair rise time. There were no associations with handgrip strength or any interactions. CONCLUSIONS: Relative protein intake was positively associated with physical function outcomes, even after consideration of total energy intake. As a number of individuals were below the current guidelines, protein intake may be a modifiable factor of importance for people with type 2 diabetes.

Device-assessed sleep and physical activity in individuals recovering from a hospital admission for COVID-19: a multicentre study.

BACKGROUND: The number of individuals recovering from severe COVID-19 is increasing rapidly. However, little is known about physical behaviours that make up the 24-h cycle within these individuals. This study aimed to describe physical behaviours following hospital admission for COVID-19 at eight months post-discharge including associations with acute illness severity and ongoing symptoms. METHODS: One thousand seventy-seven patients with COVID-19 discharged from hospital between March and November 2020 were recruited. Using a 14-day wear protocol, wrist-worn accelerometers were sent to participants after a five-month follow-up assessment. Acute illness severity was assessed by the WHO clinical progression scale, and the severity of ongoing symptoms was assessed using four previously reported data-driven clinical recovery clusters. Two existing control populations of office workers and individuals with type 2 diabetes were comparators. RESULTS: Valid accelerometer data from 253 women and 462 men were included. Women engaged in a mean ± SD of 14.9 ± 14.7 min/day of moderate-to-vigorous physical activity (MVPA), with 12.1 ± 1.7 h/day spent inactive and 7.2 ± 1.1 h/day asleep. The values for men were 21.0 ± 22.3 and 12.6 ± 1.7 h /day and 6.9 ± 1.1 h/day, respectively. Over 60% of women and men did not have any days containing a 30-min bout of MVPA. Variability in sleep timing was approximately 2 h in men and women. More severe acute illness was associated with lower total activity and MVPA in recovery. The very severe recovery cluster was associated with fewer days/week containing continuous bouts of MVPA, longer total sleep time, and higher variability in sleep timing. Patients post-hospitalisation with COVID-19 had lower levels of physical activity, greater sleep variability, and lower sleep efficiency than a similarly aged cohort of office workers or those with type 2 diabetes. CONCLUSIONS: Those recovering from a hospital admission for COVID-19 have low levels of physical activity and disrupted patterns of sleep several months after discharge. Our comparative cohorts indicate that the long-term impact of COVID-19 on physical behaviours is significant.

Sleep duration and sleep efficiency in UK long-distance heavy goods vehicle drivers.

OBJECTIVES: To profile sleep duration and sleep efficiency in UK long-distance heavy goods vehicle (HGV) drivers and explore demographic, occupational and lifestyle predictors of sleep. METHODS: Cross-sectional analyses were carried out on 329 HGV drivers (98.5% men) recruited across an international logistics company within the midland's region, UK. Sleep duration and efficiency were assessed via wrist-worn accelerometry (GENEActiv) over 8 days. Proportions of drivers with short sleep duration (<6 hour/24 hours and <7 hour/24 hours) and inadequate sleep efficiency (<85%) were calculated. Demographic, occupational and lifestyle data were collected via questionnaires and device-based measures. Logistic regression assessed predictors of short sleep duration and inadequate sleep efficiency. RESULTS: 58% of drivers had a mean sleep duration of <6 hour/24 hours, 91% demonstrated <7-hour sleep/24 hours and 72% achieved <85% sleep efficiency. Sleeping <6 hour/24 hours was less likely in morning (OR 0.45, 95% CI 0.21 to 0.94) and afternoon (OR 0.24, CI 0.10 to 0.60) shift workers (vs night) and if never smoked (vs current smokers) (OR 0.45, CI -0.22 to 0.92). The likelihood of sleeping <7 hour/24 hours reduced with age (OR 0.92, CI 0.87 to 0.98). The likelihood of presenting inadequate sleep efficiency reduced with age (OR 0.96, CI 0.93 to 0.99) and overweight body mass index category (vs obese) (OR 0.47, CI 0.27 to 0.82). CONCLUSIONS: The high prevalence of short sleep duration and insufficient sleep quality (efficiency) rate suggest that many HGV drivers have increased risk of excessive daytime sleepiness, road traffic accidents and chronic disease. Future sleep research in UK HGV cohorts is warranted given the road safety and public health implications.

GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies.

The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers' decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.

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%).

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.