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.

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.

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.

The potential blunting effect of metformin and/or statin therapy on physical activity-induced associations with HbA1c in type 2 diabetes.

Highlights Our analysis indicates a potential blunting effect of metformin and/or statin therapy on physical activity-induced associations with HbA1c. The benefit of daily physical activity on glycemic control in people with type 2 diabetes is potentially more apparent in those prescribed neither metformin nor statin therapy. As physical activity is rarely prescribed in isolation of other background medications used to manage type 2 diabetes, the results of this analysis may help to maximize interventions delivered through routine clinical care, while allowing for personalization in prescribed physical activity and pharmacotherapy.

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

PURPOSE: Accelerometer-assessed physical activity (PA) can be summarized 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 yr 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 nondominant 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 < 0.001) independently associated with CRF in healthy individuals (adjusted R2 = 73.9%). Only IG was independently associated with CRF in patients with heart failure ( P = 0.043, adjusted R2 = 38.4%). The best cut-point-free and cut-point-based model had similar predictive value for CRF in both cohorts. We produced age- and sex-specific reference values and percentile curves for IG, AvAcc, moderate-to-vigorous PA, and vigorous PA for healthy adults. CONCLUSIONS: IG and AvAcc are strongly associated with CRF and thus indirectly with the risk of noncommunicable 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.

Walking pace and the time between the onset of noncommunicable diseases and mortality: a UK Biobank prospective cohort study.

PURPOSE: To estimate time spent in various cardiovascular disease (CVD) and cancer states, according to self-reported walking pace. METHODS: In total, 391,744 UK Biobank participants were included (median age = 57 years; 54.7% women). Data were collected 2006-2010, with follow-up collected in 2021. Usual walking pace was self-defined as slow, steady, average, or brisk. Multistate modeling determined the transition rate and mean sojourn time in and across three different states (healthy, CVD or cancer, and death) upon a time horizon of 10 years. RESULTS: The mean sojourn time in the healthy state was longer, while that in the CVD or cancer state was shorter in individuals reporting an average or brisk walking pace (vs. slow). A 75-year-old woman reporting a brisk walking pace spent, on average, 8.4 years of the next 10 years in a healthy state; an additional 8.0 (95% CI: 7.3, 8.7) months longer than a 75-year-old woman reporting a slow walking pace. This corresponded to 4.3 (3.7, 4.9) fewer months living with CVD or cancer. Similar results were seen in men. CONCLUSIONS: Adults reporting an average or brisk walking pace at baseline displayed a lower transition to disease development and a greater proportion of life lived without CVD or cancer. AVAILABILITY OF DATA AND MATERIALS: Research was conducted using the UK Biobank resource under Application #33266. The UK Biobank resource can be accessed by researchers on application. Variables derived for this study have been returned to the UK Biobank for future applicants to request. No additional data are available.

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.

Differences in Dietary Intake, Eating Occasion Timings and Eating Windows between Chronotypes in Adults Living with Type 2 Diabetes Mellitus.

Chronotype studies investigating dietary intake, eating occasions (EO) and eating windows (EW) are sparse in people with type 2 Diabetes mellitus (T2DM). This analysis reports data from the CODEC study. The Morningness-Eveningness questionnaire (MEQ) assessed chronotype preference. Diet diaries assessed dietary intake and temporal distribution. Regression analysis assessed whether dietary intake, EW, or EO differed by chronotype. 411 participants were included in this analysis. There were no differences in energy, macronutrient intake or EW between chronotypes. Compared to evening chronotypes, morning and intermediate chronotypes consumed 36.8 (95% CI: 11.1, 62.5) and 20.9 (95% CI: -2.1, 44.1) fewer milligrams of caffeine per day, respectively. Evening chronotypes woke up over an hour and a half later than morning (01:36 95% CI: 01:09, 02:03) and over half an hour later than intermediate chronotypes (00:45 95% CI: 00:21; 01:09. Evening chronotypes went to sleep over an hour and a half later than morning (01:48 95% CI: 01:23; 02:13) and an hour later than intermediate chronotypes (01:07 95% CI: 00:45; 01:30). Evening chronotypes' EOs and last caffeine intake occurred later but relative to their sleep timings. Future research should investigate the impact of chronotype and dietary temporal distribution on glucose control to optimise T2DM interventions.

Efficacy of a personalised activity plan for BREAKing UP sitting time in patients with intermittent claudication (the BREAK UP study).

INTRODUCTION: The aim of this study was to investigate the concept of an 8-week personalised activity plan, using short periods of physical activity to break up sitting time in people with Intermittent Claudication (IC), to improve walking ability, and reduce time spent sitting. METHODS: The study was designed as a single centre, single arm, before and after study and is registered with clinicaltrials.gov (NCT04572737). The co-primary outcomes are time spent sitting and walking ability measured via the walking impairment questionnaire. Normally distributed data was analysed using paired samples T-tests; non-normally distributed data was analysed using related-samples Wilcoxon signed rank tests. RESULTS: There was a significant improvement in both co-primary outcomes: walking ability and time spent sitting, as well as the following secondary outcomes: total bouts and time spent in prolonged sitting, time spent standing and stepping, anxiety, depression, and activity levels reported on the vascular quality of life questionnaire. CONCLUSION: An 8-week personalised activity plan to break up sitting time shows promise as a treatment for people with IC, improving walking ability and reducing time spent sitting. This study supports the use of large randomised controlled trials to further develop this treatment in people with IC.

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.

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.

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.

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.

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.

Device-measured physical activity behaviours, and physical function, in people with type 2 diabetes mellitus and peripheral artery disease: A cross-sectional study.

AIM: To quantify differences in device-measured physical activity (PA) behaviours, and physical function (PF), in people with type 2 diabetes mellitus (T2DM) with and without peripheral artery disease (PAD). MATERIALS AND METHODS: Participants from the Chronotype of Patients with T2DM and Effect on Glycaemic Control cross-sectional study wore accelerometers on their non-dominant wrist for up to 8-days to quantify: volume and intensity distribution of PA, time spent inactive, time in light PA, moderate-to-vigorous PA in at least 1-minute bouts (MVPA1min), and the average intensity achieved during the most active continuous 2, 5, 10, 30, and 60-minute periods of the 24-h day. PF was assessed using the short physical performance battery (SPPB), the Duke Activity Status Index (DASI), sit-to-stand repetitions in 60 s (STS-60); hand-grip strength was also assessed. Differences between subjects with and without PAD were estimated using regressions adjusted for possible confounders. RESULTS: 736 participants with T2DM (without diabetic foot ulcers) were included in the analysis, 689 had no PAD. People with T2DM and PAD undertake less PA (MVPA1min: -9.2 min [95 % CI: -15.3 to -3.0; p = 0.004]) (light intensity PA: -18.7 min [-36.4 to -1.0; p = 0.039]), spend more time inactive (49.2 min [12.1 to 86.2; p = 0.009]), and have reduced PF (SPPB score: -1.6 [-2.5 to -0.8; p = 0.001]) (DASI score: -14.8 [-19.8 to -9.8; p = 0.001]) (STS-60 repetitions: -7.1 [-10.5 to -3.8; p = 0.001]) compared to people without; some differences in PA were attenuated by confounders. Reduced intensity of activity for the most active continuous 2-30 min in the 24-h day, and reduced PF, persisted after accounting for confounders. There were no significant differences in hand-grip strength. CONCLUSIONS: Findings from this cross-sectional study suggest that, the presence of PAD in T2DM may have been associated with lower PA levels and PF.

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.

Association between Chronotype and Physical Behaviours in Adolescent Girls.

The aim of this study was to (1) describe accelerometer-assessed physical behaviours by chronotype, and (2) examine the association between chronotype and accelerometer-assessed physical behaviours in a cohort of adolescent girls. Chronotype (single question) and physical behaviours (GENEActiv accelerometer on the non-dominant wrist) were assessed in 965 adolescent girls (13.9 ± 0.8 years). Linear mixed-effects models examined the relationships among chronotype and physical behaviours (time in bed, total sleep time, sleep efficiency, sedentary time, overall, light and moderate-to-vigorous physical activity) on weekdays and weekend days. Over the 24 h day, participants spent 46% sedentary, 20% in light activity, 3% in moderate-to-vigorous physical activity, and 31% in 'time in bed'. Seventy percent of participants identified as 'evening' chronotypes. Compared to evening chronotypes, morning chronotypes engaged in less sedentary time (10 min/day) and had higher overall physical activity (1.3 mg/day, ~30 min of slow walking) on weekdays. Most girls identified as evening chronotypes with a large proportion of their day spent sedentary and a small amount in physical activities which may be exacerbated in evening chronotypes on weekdays. The results maybe be important for programmes aiming to promote physical activity in adolescent girls.

Four-Year Increase in Step Cadence Is Associated with Improved Cardiometabolic Health in People with a History of Prediabetes.

PURPOSE: To investigate associations between 4-yr change in step cadence and markers of cardiometabolic health in people with a history of prediabetes and to explore whether these associations are modified by demographic factors. METHODS: In this prospective cohort study, adults, with a history of prediabetes, were assessed for markers of cardiometabolic health (body mass index, waist circumference, high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C], triglycerides, and glycated hemoglobin A1c [HbA1c]), and free-living stepping activity (activPAL3™) at baseline, 1 yr, and 4 yr. Brisk steps per day were defined as the number of steps accumulated at ≥100 steps per minute and slow steps per day as those accumulated at <100 steps per minute; the mean peak stepping cadence during the most active 10 minutes of the day was also derived. Generalized estimating equations examined associations between 4-yr change in step cadence and change in cardiometabolic risk factors, with interactions by sex and ethnicity. RESULTS: Seven hundred ninety-four participants were included (age, 59.8 ± 8.9 yr; 48.7% women; 27.1% ethnic minority; total steps per day, 8445 ± 3364; brisk steps per day, 4794 ± 2865; peak 10-min step cadence, 128 ± 10 steps per minute). Beneficial associations were observed between change in brisk steps per day and change in body mass index, waist circumference, HDL-C, and HbA1c. Similar associations were found between peak 10-min step cadence and HDL-C and waist circumference. Interactions by ethnicity revealed change in brisk steps per day and change in peak 10-min step cadence had a stronger association with HbA1c in White Europeans, whereas associations between change in 10-min peak step cadence with measures of adiposity were stronger in South Asians. CONCLUSIONS: Change in the number of daily steps accumulated at a brisk pace was associated with beneficial change in adiposity, HDL-C, and HbA1c; however, potential benefits may be dependent on ethnicity for outcomes related to HbA1c and adiposity.

Effects of sleep disturbance on dyspnoea and impaired lung function following hospital admission due to COVID-19 in the UK: a prospective multicentre cohort study.

BACKGROUND: Sleep disturbance is common following hospital admission both for COVID-19 and other causes. The clinical associations of this for recovery after hospital admission are poorly understood despite sleep disturbance contributing to morbidity in other scenarios. We aimed to investigate the prevalence and nature of sleep disturbance after discharge following hospital admission for COVID-19 and to assess whether this was associated with dyspnoea. METHODS: CircCOVID was a prospective multicentre cohort substudy designed to investigate the effects of circadian disruption and sleep disturbance on recovery after COVID-19 in a cohort of participants aged 18 years or older, admitted to hospital for COVID-19 in the UK, and discharged between March, 2020, and October, 2021. Participants were recruited from the Post-hospitalisation COVID-19 study (PHOSP-COVID). Follow-up data were collected at two timepoints: an early time point 2-7 months after hospital discharge and a later time point 10-14 months after hospital discharge. Sleep quality was assessed subjectively using the Pittsburgh Sleep Quality Index questionnaire and a numerical rating scale. Sleep quality was also assessed with an accelerometer worn on the wrist (actigraphy) for 14 days. Participants were also clinically phenotyped, including assessment of symptoms (ie, anxiety [Generalised Anxiety Disorder 7-item scale questionnaire], muscle function [SARC-F questionnaire], dyspnoea [Dyspnoea-12 questionnaire] and measurement of lung function), at the early timepoint after discharge. Actigraphy results were also compared to a matched UK Biobank cohort (non-hospitalised individuals and recently hospitalised individuals). Multivariable linear regression was used to define associations of sleep disturbance with the primary outcome of breathlessness and the other clinical symptoms. PHOSP-COVID is registered on the ISRCTN Registry (ISRCTN10980107). FINDINGS: 2320 of 2468 participants in the PHOSP-COVID study attended an early timepoint research visit a median of 5 months (IQR 4-6) following discharge from 83 hospitals in the UK. Data for sleep quality were assessed by subjective measures (the Pittsburgh Sleep Quality Index questionnaire and the numerical rating scale) for 638 participants at the early time point. Sleep quality was also assessed using device-based measures (actigraphy) a median of 7 months (IQR 5-8 months) after discharge from hospital for 729 participants. After discharge from hospital, the majority (396 [62%] of 638) of participants who had been admitted to hospital for COVID-19 reported poor sleep quality in response to the Pittsburgh Sleep Quality Index questionnaire. A comparable proportion (338 [53%] of 638) of participants felt their sleep quality had deteriorated following discharge after COVID-19 admission, as assessed by the numerical rating scale. Device-based measurements were compared to an age-matched, sex-matched, BMI-matched, and time from discharge-matched UK Biobank cohort who had recently been admitted to hospital. Compared to the recently hospitalised matched UK Biobank cohort, participants in our study slept on average 65 min (95% CI 59 to 71) longer, had a lower sleep regularity index (-19%; 95% CI -20 to -16), and a lower sleep efficiency (3·83 percentage points; 95% CI 3·40 to 4·26). Similar results were obtained when comparisons were made with the non-hospitalised UK Biobank cohort. Overall sleep quality (unadjusted effect estimate 3·94; 95% CI 2·78 to 5·10), deterioration in sleep quality following hospital admission (3·00; 1·82 to 4·28), and sleep regularity (4·38; 2·10 to 6·65) were associated with higher dyspnoea scores. Poor sleep quality, deterioration in sleep quality, and sleep regularity were also associated with impaired lung function, as assessed by forced vital capacity. Depending on the sleep metric, anxiety mediated 18-39% of the effect of sleep disturbance on dyspnoea, while muscle weakness mediated 27-41% of this effect. INTERPRETATION: Sleep disturbance following hospital admission for COVID-19 is associated with dyspnoea, anxiety, and muscle weakness. Due to the association with multiple symptoms, targeting sleep disturbance might be beneficial in treating the post-COVID-19 condition. FUNDING: UK Research and Innovation, National Institute for Health Research, and Engineering and Physical Sciences Research Council.

Sedentary Time Is Independently Related to Adipose Tissue Insulin Resistance in Adults With or at Risk of Type 2 Diabetes.

INTRODUCTION: This cross-sectional study examined associations of device-measured sedentary time and moderate-to-vigorous physical activity (MVPA) with adipose tissue insulin resistance in people with or at high risk of type 2 diabetes (T2DM). METHOD: Data were combined from six previous experimental studies (within our group) involving patients with T2DM or primary risk factors (median (interquartile range) age, 66.2 (66.0-70.8) yr; body mass index (BMI), 31.1 (28.0-34.4) kg·m -2 ; 62% male; n = 179). Adipose tissue insulin resistance was calculated as the product of fasted circulating insulin and nonesterified fatty acids (ADIPO-IR), whereas sedentary time and MVPA were determined from wrist-worn accelerometery. Generalized linear models examined associations of sedentary time and MVPA with ADIPO-IR with interaction terms added to explore the moderating influence of ethnicity (White European vs South Asian), BMI, age, and sex. RESULTS: In finally adjusted models, sedentary time was positively associated with ADIPO-IR, with every 30 min of sedentary time associated with a 1.80-unit (95% confidence interval, 0.51-3.06; P = 0.006) higher ADIPO-IR. This relationship strengthened as BMI increased ( ß = 3.48 (95% confidence interval, 1.50-5.46), P = 0.005 in the upper BMI tertile (≥33.2 kg·m -2 )). MVPA was unrelated to ADIPO-IR. These results were consistent in sensitivity analyses that excluded participants taking statins and/or metformin ( n = 126) and when separated into the participants with T2DM ( n = 32) and those at high risk ( n = 147). CONCLUSIONS: Sedentary time is positively related to adipose tissue insulin sensitivity in people with or at high risk of T2DM. This relationship strengthens as BMI increases and may help explain established relationships between greater sedentary time, ectopic lipid, and hyperglycemia.