ORCID Profile
0000-0002-6374-7908
Current Organisation
Leeds Trinity University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Informa UK Limited
Date: 02-10-2022
DOI: 10.1080/02640414.2022.2147134
Abstract: 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%).
Publisher: Oxford University Press (OUP)
Date: 27-10-2022
DOI: 10.1093/EURHEARTJ/EHAC613
Abstract: 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. 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. 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 in iduals.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 22-05-2020
DOI: 10.1249/MSS.0000000000002380
Abstract: 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. 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). There were additive associations of volume and intensity with hip aBMD and total body BMC (low-intensity/low-volume cf. high-intensity/high-volume = ∆0.082 g·cm −2 and ∆169.8 g, respectively). For males only, spine aBMD intensity was associated independently of volume (low-intensity cf. high-intensity = ∆0.049 g·cm −2 ). For hip structural geometry, volume was associated independently of intensity (low-volume cf. high-volume = ∆4.8–6.6%). 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.
Publisher: Springer Science and Business Media LLC
Date: 12-2019
DOI: 10.1186/S40798-019-0225-9
Abstract: The lack of consensus on meaningful and interpretable physical activity outcomes from accelerometer data h ers 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 M 1 / 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 in idual activity profiles relative to percentiles and compare activity profiles between domains and/or time periods.
Publisher: Informa UK Limited
Date: 25-08-2020
Publisher: Informa UK Limited
Date: 20-06-2021
Publisher: MDPI AG
Date: 23-08-2023
DOI: 10.3390/S23177353
Abstract: 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 s le 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 in idual 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 in idual 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.
Publisher: Cold Spring Harbor Laboratory
Date: 24-02-2022
DOI: 10.1101/2022.02.23.22271386
Abstract: Although the cardiovascular disease (CVD) benefits of both overall volume and intensity of physical activity (PA) are known, the role of PA intensity, over and above volume, is poorly understood. We aimed to investigate the interplay between PA volume and intensity in relation to incident CVD. Data were from 88,412 UK Biobank participants without prevalent CVD (58% women) who wore an accelerometer on their dominant wrist for 7 days, from which we estimated total physical activity 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, adjusted for potential confounders. There were 4,068 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 17% (95%CI: 8-26%) lower when MVPA accounted for 20% rather than 10% of 15 kJ/kg/d PAEE equivalent to the difference between a 12-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 combination of higher PAEE and %MVPA was associated with lower CVD rates. Rates were 24% (10-35%) lower for 20 kJ/kg/d PAEE with 20% from MVPA, and 49% (23-66%) lower for 30 kJ/kg/d with 40% from MVPA (compared to 15 kJ/kg/d PAEE with 10% MVPA). Reductions in CVD risk may be achievable through higher levels of PA volume and intensity, with the role of moderately intense PA appearing particularly important for future CVD risk. Our findings support multiple approaches or strategies to PA participation, some of which may be more practical or appealing to different in iduals.
Publisher: Elsevier BV
Date: 10-2020
Publisher: Elsevier BV
Date: 2021
Publisher: Informa UK Limited
Date: 20-09-2021
DOI: 10.1080/02640414.2021.1976491
Abstract: 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-s le (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 m
Publisher: Elsevier BV
Date: 03-2021
Publisher: MDPI AG
Date: 07-06-2023
DOI: 10.3390/S23125382
Abstract: 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 in idualized 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 in idualized wake windows. Sleep disruption was assessed via the average acceleration over standardized (least active 8 continuous hours (L8h)) and in idualized 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 in idualized wake/sleep windows. In conclusion, standardized wake and sleep windows may have stronger associations with health due to capturing variations in sleep durations across in iduals, while in idualized windows represent a purer measure of wake/sleep behaviors.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Nathan Dawkins.