Publications by authors named "Alex V Rowlands"

89 Publications

Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K.

BMC Infect Dis 2021 Sep 4;21(1):908. Epub 2021 Sep 4.

Leicester Real World Evidence Unit, Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, UK.

Background: Pre-existing comorbidities have been linked to SARS-CoV-2 infection but evidence is sparse on the importance and pattern of multimorbidity (2 or more conditions) and severity of infection indicated by hospitalisation or mortality. We aimed to use a multimorbidity index developed specifically for COVID-19 to investigate the association between multimorbidity and risk of severe SARS-CoV-2 infection.

Methods: We used data from the UK Biobank linked to laboratory confirmed test results for SARS-CoV-2 infection and mortality data from Public Health England between March 16 and July 26, 2020. By reviewing the current literature on COVID-19 we derived a multimorbidity index including: (1) angina; (2) asthma; (3) atrial fibrillation; (4) cancer; (5) chronic kidney disease; (6) chronic obstructive pulmonary disease; (7) diabetes mellitus; (8) heart failure; (9) hypertension; (10) myocardial infarction; (11) peripheral vascular disease; (12) stroke. Adjusted logistic regression models were used to assess the association between multimorbidity and risk of severe SARS-CoV-2 infection (hospitalisation/death). Potential effect modifiers of the association were assessed: age, sex, ethnicity, deprivation, smoking status, body mass index, air pollution, 25-hydroxyvitamin D, cardiorespiratory fitness, high sensitivity C-reactive protein.

Results: Among 360,283 participants, the median age was 68 [range 48-85] years, most were White (94.5%), and 1706 had severe SARS-CoV-2 infection. The prevalence of multimorbidity was more than double in those with severe SARS-CoV-2 infection (25%) compared to those without (11%), and clusters of several multimorbidities were more common in those with severe SARS-CoV-2 infection. The most common clusters with severe SARS-CoV-2 infection were stroke with hypertension (79% of those with stroke had hypertension); diabetes and hypertension (72%); and chronic kidney disease and hypertension (68%). Multimorbidity was independently associated with a greater risk of severe SARS-CoV-2 infection (adjusted odds ratio 1.91 [95% confidence interval 1.70, 2.15] compared to no multimorbidity). The risk remained consistent across potential effect modifiers, except for greater risk among older age. The highest risk of severe infection was strongly evidenced in those with CKD and diabetes (4.93 [95% CI 3.36, 7.22]).

Conclusion: The multimorbidity index may help identify individuals at higher risk for severe COVID-19 outcomes and provide guidance for tailoring effective treatment.
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http://dx.doi.org/10.1186/s12879-021-06600-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8418288PMC
September 2021

Feature selection for unsupervised machine learning of accelerometer data physical activity clusters - A systematic review.

Gait Posture 2021 Aug 13;90:120-128. Epub 2021 Aug 13.

Diabetes Research Centre, University of Leicester, Leicester General Hospital, Gwendolen Road, Leicester LE5 4PW, UK; NIHR Leicester Biomedical Research Centre, Leicester General Hospital, Gwendolen Road, Leicester, LE5 4PW, UK; Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, Division of Health Sciences, University of South Australia, Adelaide, Australia.

Background: Identifying clusters of physical activity (PA) from accelerometer data is important to identify levels of sedentary behaviour and physical activity associated with risks of serious health conditions and time spent engaging in healthy PA. Unsupervised machine learning models can capture PA in everyday free-living activity without the need for labelled data. However, there is scant research addressing the selection of features from accelerometer data. The aim of this systematic review is to summarise feature selection techniques applied in studies concerned with unsupervised machine learning of accelerometer-based device obtained physical activity, and to identify commonly used features identified through these techniques. Feature selection methods can reduce the complexity and computational burden of these models by removing less important features and assist in understanding the relative importance of feature sets and individual features in clustering.

Method: We conducted a systematic search of Pubmed, Medline, Google Scholar, Scopus, Arxiv and Web of Science databases to identify studies published before January 2021 which used feature selection methods to derive PA clusters using unsupervised machine learning models.

Results: A total of 13 studies were eligible for inclusion within the review. The most popular feature selection techniques were Principal Component Analysis (PCA) and correlation-based methods, with k-means frequently used in clustering accelerometer data. Cluster quality evaluation methods were diverse, including both external (e.g. cluster purity) or internal evaluation measures (silhouette score most frequently). Only four of the 13 studies had more than 25 participants and only four studies included two or more datasets.

Conclusion: There is a need to assess multiple feature selection methods upon large cohort data consisting of multiple (3 or more) PA datasets. The cut-off criteria e.g. number of components, pairwise correlation value, explained variance ratio for PCA, etc. should be expressly stated along with any hyperparameters used in clustering.
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http://dx.doi.org/10.1016/j.gaitpost.2021.08.007DOI Listing
August 2021

Association between accelerometer-assessed physical activity and severity of COVID-19 in UK Biobank.

Mayo Clin Proc Innov Qual Outcomes 2021 Aug 20. Epub 2021 Aug 20.

Diabetes Research Centre, Leicester General Hospital, University of Leicester, Gwendolen Rd, Leicester, LE5 4PW, UK.

Objective: To quantify the association between accelerometer-assessed physical activity and COVID-19 outcomes.

Patients And Methods: Data from 82,253 UK Biobank participants with accelerometer data (measured 2013-2015), complete covariate data, and linked COVID-19 data from 16 March 2020 to 16 March 2021 were included. Two outcomes were investigated: severe COVID-19 (positive test from in-hospital setting or COVID-19 as primary cause of death); non-severe COVID-19 (positive test from community setting). Logistic regressions were used to assess associations with moderate-to-vigorous physical activity (MVPA), total activity, and the intensity gradient. A higher intensity gradient indicates a higher proportion of vigorous activity.

Results: Average MVPA was 48.1 (32.7) minutes/day. Physical activity was associated with lower odds of severe COVID-19 (adjusted OR per SD increase: MVPA 0.75[95% CI, 0.67,0.85]; total 0.83[0.74,0.92]; intensity 0.77[0.70,0.86]), with stronger associations in women (MVPA 0.63[0.52,0.77]; total 0.76[0.64,0.90]; intensity 0.63[0.53,0.74]) than men (MVPA (0.84[0.73,0.97]; total 0.88[0.77,1.01]; intensity 0.88 [0.77,1.00]). In contrast, when mutually adjusted, total activity was associated with higher odds of a non-severe infection (1.10[1.04,1.16]), while the intensity gradient was associated with lower odds (0.91[0.86,0.97]).

Conclusion: Odds of severe-COVID-19 were ∼25% lower per SD (∼30 minutes/day) MVPA. A greater proportion of vigorous activity was associated with lower odds of severe and non-severe infections. The association between total activity and higher odds of a non-severe infection may be through greater community engagement, thus more exposure to the virus. Results support calls for public health messaging highlighting the potential of MVPA for reducing the odds of severe COVID-19.
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http://dx.doi.org/10.1016/j.mayocpiqo.2021.08.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8376658PMC
August 2021

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

Occup Environ Med 2021 Aug 19. Epub 2021 Aug 19.

School of Sport, Exercise and Health Sciences, National Centre for Sport and Exercise Medicine, Loughborough University, Loughborough, UK.

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.
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http://dx.doi.org/10.1136/oemed-2021-107643DOI Listing
August 2021

Modelling the Reallocation of Time Spent Sitting into Physical Activity: Isotemporal Substitution vs. Compositional Isotemporal Substitution.

Int J Environ Res Public Health 2021 06 8;18(12). Epub 2021 Jun 8.

Diabetes Research Centre, University of Leicester, Leicester LE5 4PW, UK.

Isotemporal substitution modelling (ISM) and compositional isotemporal modelling (CISM) are statistical approaches used in epidemiology to model the associations of replacing time in one physical behaviour with time in another. This study's aim was to use both ISM and CISM to examine and compare associations of reallocating 60 min of sitting into standing or stepping with markers of cardiometabolic health. Cross-sectional data collected during three randomised control trials (RCTs) were utilised. All participants ( = 1554) were identified as being at high risk of developing type 2 diabetes. Reallocating 60 min from sitting to standing and to stepping was associated with a lower BMI, waist circumference, and triglycerides and higher high-density lipoprotein cholesterol using both ISM and CISM ( < 0.05). The direction and magnitude of significant associations were consistent across methods. No associations were observed for hemoglobin A1c, total cholesterol, or low-density lipoprotein cholesterol for either method. Results of both ISM and CISM were broadly similar, allowing for the interpretation of previous research, and should enable future research in order to make informed methodological, data-driven decisions.
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http://dx.doi.org/10.3390/ijerph18126210DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8229040PMC
June 2021

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

Br J Sports Med 2021 Apr 12. Epub 2021 Apr 12.

PROFITH "PROmoting FITness and Health through physical activity" Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain

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.
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http://dx.doi.org/10.1136/bjsports-2020-103604DOI Listing
April 2021

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

J Nutr 2021 Jul;151(7):1844-1853

Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, United Kingdom.

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.
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http://dx.doi.org/10.1093/jn/nxab046DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8245890PMC
July 2021

The impact of COVID-19 restrictions on accelerometer-assessed physical activity and sleep in individuals with type 2 diabetes.

Diabet Med 2021 10 23;38(10):e14549. Epub 2021 Mar 23.

NIHR Leicester Biomedical Research Centre, UK and Diabetes Research Centre, College of Life Sciences, University of Leicester, Leicester, UK.

Aims: Restrictions during the COVID-19 crisis will have impacted on opportunities to be active. We aimed to (a) quantify the impact of COVID-19 restrictions on accelerometer-assessed physical activity and sleep in people with type 2 diabetes and (b) identify predictors of physical activity during COVID-19 restrictions.

Methods: Participants were from the UK Chronotype of Patients with type 2 diabetes and Effect on Glycaemic Control (CODEC) observational study. Participants wore an accelerometer on their wrist for 8 days before and during COVID-19 restrictions. Accelerometer outcomes included the following: overall physical activity, moderate-to-vigorous physical activity (MVPA), time spent inactive, days/week with ≥30-minute continuous MVPA and sleep. Predictors of change in physical activity taken pre-COVID included the following: age, sex, ethnicity, body mass index (BMI), socio-economic status and medical history.

Results: In all, 165 participants (age (mean±S.D = 64.2 ± 8.3 years, BMI=31.4 ± 5.4 kg/m , 45% women) were included. During restrictions, overall physical activity was lower by 1.7 mg (~800 steps/day) and inactive time 21.9 minutes/day higher, but time in MVPA and sleep did not statistically significantly change. In contrast, the percentage of people with ≥1 day/week with ≥30-minute continuous MVPA was higher (34% cf. 24%). Consistent predictors of lower physical activity and/or higher inactive time were higher BMI and/or being a woman. Being older and/or from ethnic minorities groups was associated with higher inactive time.

Conclusions: Overall physical activity, but not MVPA, was lower in adults with type 2 diabetes during COVID-19 restrictions. Women and individuals who were heavier, older, inactive and/or from ethnic minority groups were most at risk of lower physical activity during restrictions.
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http://dx.doi.org/10.1111/dme.14549DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995208PMC
October 2021

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

Int J Obes (Lond) 2021 05 26;45(5):1155-1159. Epub 2021 Feb 26.

Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK.

Obesity is an emerging risk factor for coronavirus disease-2019 (COVID-19). Simple measures of physical fitness, such as self-reported walking pace, may also be important risk markers. This analysis includes 412,596 UK Biobank participants with linked COVID-19 data (median age at linkage = 68 years, obese = 24%, median number of comorbidities = 1). As of August 24th 2020, there were 1001 cases of severe (in-hospital) disease and 336 COVID-19 deaths. Compared to normal weight individuals, the adjusted odds ratio (OR) of severe COVID-19 in overweight and obese individuals was 1.26 (1.07, 1.48) and 1.49 (1.25, 1.79), respectively. For COVID-19 mortality, the ORs were 1.19 (0.88, 161) and 1.82 (1.33, 2.49), respectively. Compared to those with a brisk walking pace, the OR of severe COVID-19 for steady/average and slow walkers was 1.13 (0.98, 1.31) and 1.88 (1.53, 2.31), respectively. For COVID-19 mortality, the ORs were 1.44 (1.10, 1.90) and 1.83 (1.26, 2.65), respectively. Slow walkers had the highest risk regardless of obesity status. For example, compared to normal weight brisk walkers, the OR of severe disease and COVID-19 mortality in normal weight slow walkers was 2.42 (1.53, 3.84) and 3.75 (1.61, 8.70), respectively. Self-reported slow walkers appear to be a high-risk group for severe COVID-19 outcomes independent of obesity.
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http://dx.doi.org/10.1038/s41366-021-00771-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7909370PMC
May 2021

Physical Activity after Cardiac EventS (PACES): a group education programme with subsequent text message support designed to increase physical activity in individuals with diagnosed coronary heart disease: a randomised controlled trial.

Open Heart 2021 02;8(1)

Diabetes Research Centre, College of Life Sciences, University of Leicester, Leicester, UK.

Aim: To assess the effectiveness of a low-cost pragmatic intervention (structured education and ongoing text message support) to increase daily physical activity in participants 12-48 months after a coronary heart disease cardiac event (myocardial infarction, angina or acute coronary syndrome) diagnosis.

Methods: A single-centre randomised controlled trial of 291 adults randomised to a structured education programme (n=145) or usual care (n=146). The programme consisted of two 2.5 hour sessions delivered 2 weeks apart, followed by supplementary text message support. The GENEActiv accelerometer assessed the primary outcome at 12 months (change in overall physical activity (expressed in milli gravitational (m) units) from baseline). Secondary outcomes included anthropometric, physical function, cardiovascular, biochemical and patient-reported outcome measures. Linear regression was used to compare outcome measures between groups on a modified intention-to-treat basis.

Results: Participants' mean age was 66.5±9.7 years, 84.5% males, 82.5% white British and 15.5% south Asian. At 12 months, there was no difference between the groups in terms of change in overall physical activity (-0.23 m (95% CI -1.22 to 0.75), p=0.64) and the programme was well accepted (88% attendance). Exploratory analyses showed that average moderate to vigorous physical activity (MVPA) levels increased in individuals not meeting physical activity guidelines (≥150 min per week) on enrolment compared with those who did, by 8 minutes per day (8.04 (95% CI 0.99 to 15.10), p=0.03).

Conclusion: The programme was well attended but showed no change in physical activity levels. Results show high baseline MVPA levels and suggest that Physical Activity after Cardiac EventS education may benefit cardiac patients not currently meeting activity guidelines.

Trial Registration Number: ISRCTN91163727.
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http://dx.doi.org/10.1136/openhrt-2020-001351DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7919588PMC
February 2021

Concurrent screen use and cross-sectional association with lifestyle behaviours and psychosocial health in adolescent females.

Acta Paediatr 2021 07 3;110(7):2164-2170. Epub 2021 Mar 3.

Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, USA.

Aim: To describe concurrent screen use and any relationships with lifestyle behaviours and psychosocial health.

Methods: Participants wore an accelerometer for seven days to calculate physical activity sleep and sedentary time. Screen ownership and use and psychosocial variables were self-reported. Body mass index (BMI) was measured. Relationships were explored using mixed models accounting for school clustering and confounders.

Results: In 816 adolescent females (age: 12.8 SD 0.8 years; 20.4% non-white European) use of ≥2 screens concurrently was: 59% after school, 65% in evenings, 36% in bed and 68% at weekends. Compared to no screens those using: ≥1 screens at weekends had lower physical activity; ≥2 screens at the weekend or one/two screen at bed had lower weekend moderate-to-vigorous physical activity; one screen in the evening had lower moderate-to-vigorous physical activity in the after-school and evening period; ≥1 screens after school had higher BMI; and ≥3 screens at the weekend had higher weekend sedentary time. Compared to no screens those using: 1-3 after-school screens had shorter weekday sleep; ≥1 screens after-school had lower time in bed.

Conclusion: Screen use is linked to lower physical activity, higher BMI and less sleep. These results can inform screen use guidelines.
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http://dx.doi.org/10.1111/apa.15806DOI Listing
July 2021

Physical Activity Intensity Cut-Points for Wrist-Worn GENEActiv in Older Adults.

Front Sports Act Living 2020 15;2:579278. Epub 2021 Jan 15.

Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide, SA, Australia.

This study aims to (1) establish GENEActiv intensity cutpoints in older adults and (2) compare the classification accuracy between dominant (D) or non-dominant (ND) wrist, using both laboratory and free-living data. Thirty-one older adults participated in the study. They wore a GENEActiv Original on each wrist and performed nine activities of daily living. A portable gas analyzer was used to measure energy expenditure for each task. Testing was performed on two occasions separated by at least 8 days. Some of the same participants ( = 13) also wore one device on each wrist during 3 days of free-living. Receiver operating characteristic analysis was performed to establish the optimal cutpoints. For sedentary time, both dominant and non-dominant wrist had excellent classification accuracy (sensitivity 0.99 and 0.97, respectively; specificity 0.91 and 0.86, respectively). For Moderate to Vigorous Physical Activity (MVPA), the non-dominant wrist device had better accuracy (ND sensitivity: 0.90, specificity 0.79; D sensitivity: 0.90, specificity 0.64). The corresponding cutpoints for sedentary-to-light were 255 and 375 g · min (epoch independent: 42.5 and 62.5 mg), and those for the light-to-moderate were 588 and 555 g · min (epoch-independent: 98.0 and 92.5 mg) for the non-dominant and dominant wrist, respectively. For free-living data, the dominant wrist device resulted in significantly more sedentary time and significantly less light and MVPA time compared to the non-dominant wrist.
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http://dx.doi.org/10.3389/fspor.2020.579278DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843957PMC
January 2021

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

J Sports Sci 2021 Jan 25;39(2):219-226. Epub 2020 Aug 25.

Diabetes Research Centre, University of Leicester, Leicester General Hospital , Leicester, UK.

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 m 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. : CD: Adults with ≥1 chronic disease; m: 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.
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http://dx.doi.org/10.1080/02640414.2020.1812202DOI Listing
January 2021

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

Mayo Clin Proc 2021 01 31;96(1):156-164. Epub 2020 Oct 31.

Diabetes Research Centre, Leicester Diabetes Centre, Leicester General Hospital Gwendolen Rd, Leicester, United Kingdom; National Institute for Health Research, Leicester Biomedical Research Centre, Leicester General Hospital, Leicester, United Kingdom.

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.
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http://dx.doi.org/10.1016/j.mayocp.2020.10.032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7604071PMC
January 2021

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

Appl Physiol Nutr Metab 2021 Jun 30;46(6):597-605. Epub 2020 Nov 30.

Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK.

This study examined the effect of exercise training on sleep duration and quality and bidirectional day-to-day relationships between physical activity (PA) and sleep. Fourteen inactive men with obesity (age: 49.2 ± 7.9 years, body mass index: 34.9 ± 2.8 kg/m) completed a baseline visit, 8-week aerobic exercise intervention, and 1-month post-intervention follow-up. PA and sleep were assessed continuously throughout the study duration using wrist-worn accelerometry. Generalised estimating equations were used to examine associations between PA and sleep. Sleep duration increased from 5.2 h at baseline to 6.6 h during the intervention period and 6.5 h at 1-month post-intervention follow-up ( < 0.001). Bi-directional associations showed that higher overall activity volume and moderate-to-vigorous physical activity (MVPA) were associated with earlier sleep onset time ( < 0.05). Later timing of sleep onset was associated with lower overall volume of activity, most active continuous 30 min (M30), and MVPA ( < 0.05). Higher overall activity volume, M30, and MVPA predicted more wake after sleep onset (WASO) ( < 0.001), whereas greater WASO was associated with higher overall volume of activity, M30, and MVPA ( < 0.001). An aerobic exercise intervention increased usual sleep duration. Day-to-day, more PA predicted earlier sleep onset, but worse sleep quality and vice versa. Greater levels of physical activity in the day were associated with an earlier sleep onset time that night, whereas a later timing of sleep onset was associated with lower physical activity the next day in men with obesity. Higher physical activity levels were associated with worse sleep quality, and vice versa.
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http://dx.doi.org/10.1139/apnm-2020-0361DOI Listing
June 2021

High intensity interval training does not result in short- or long-term dietary compensation in cardiac rehabilitation: Results from the FITR heart study.

Appetite 2021 03 6;158:105021. Epub 2020 Nov 6.

School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia. Electronic address:

The aim of this study was to investigate short- and long-term compensatory effects on dietary intake following high intensity interval training (HIIT) compared with usual care moderate intensity continuous training (MICT) during and following a cardiac rehabilitation program. This study investigates secondary outcomes of a clinical trial. Ninety-three participants with coronary artery disease enrolled in a 4-week cardiac rehabilitation program, were randomised to 1) 4x4-minute HIIT; or 2) 40-min of MICT (usual care). Patients were instructed to complete 3 weekly sessions (2 supervised, 1 home-based) for 4-weeks, and 3 weekly home-based sessions thereafter for another 48-weeks. Dietary intake was measured by telephone-based 24-h recall over 2 day at baseline, 4-weeks, 3-months, 6-months, and 12-months. Three-Factor Eating Questionnaire was used to measure dietary behaviour and Leeds Food Preference Questionnaire used to measure food preferences. Appetite was assessed by a visual analogue scale and appetite-regulating hormones. There was no change over the study period or differences between groups for daily energy intake at 4-weeks or 12-months. There were also no group differences for any other measures of dietary intake, fasting hunger or appetite-related hormones, dietary behaviour, or food preferences. These findings suggest that compared to moderate intensity exercise, HIIT does not result in compensatory increases of energy intake or indicators of poor diet quality. This finding appears to be the same for patients with normal weight and obesity. HIIT can therefore be included in cardiac rehabilitation programs as an adjunct or alterative to MICT, without concern for any undesirable dietary compensation.
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http://dx.doi.org/10.1016/j.appet.2020.105021DOI Listing
March 2021

Improvements in Glycemic Control After Acute Moderate-Intensity Continuous or High-Intensity Interval Exercise Are Greater in South Asians Than White Europeans With Nondiabetic Hyperglycemia: A Randomized Crossover Study.

Diabetes Care 2021 01 6;44(1):201-209. Epub 2020 Nov 6.

Diabetes Research Centre, University of Leicester, Leicester, U.K.

Objective: To examine whether circulating metabolic responses to low-volume high-intensity interval exercise (LV-HIIE) or continuous moderate-intensity aerobic exercise (CME) differ between white Europeans and South Asians with nondiabetic hyperglycemia (NDH).

Research Design And Methods: Thirteen white Europeans and 10 South Asians (combined median [interquartile range] age 67 [60-68] years, HbA 5.9% [5.8-6.1%] [41.0 (39.9-43.2) mmol ⋅ mol]) completed three 6-h conditions (sedentary control [CON], LV-HIIE, and CME) in a randomized order. Exercise conditions contained a single bout of LV-HIIE and CME, respectively (each ending at 2 h), with meals provided at 0 and 3 h. Circulating glucose (primary outcome), insulin, insulin resistance index (IRI), triglycerides, and nonesterified fatty acids were measured at 0, 0.5, 1, 2, 3, 3.5, 4, 5, and 6 h. Data were analyzed as postexercise time-averaged area under the curve (AUC) adjusted for age, sex, and preexercise AUC.

Results: Glucose was similar in each condition and with ethnicity, with no condition-by-ethnicity interaction ( ≥ 0.28). However, insulin was lower in LV-HIIE (mean [95% CI] -44.4 [-23.7, -65.1] mU ⋅ L) and CME (-33.8 [-13.7, -53.9] mU ⋅ L) compared with CON. Insulin responses were greater in South Asians (interaction = 0.03) such that values were similar in each ethnicity during exercise conditions, despite being 33% higher in South Asians during CON. IRI followed a similar pattern to insulin. Lipids were unaffected by exercise.

Conclusions: Reductions in insulin and insulin resistance after acute LV-HIIE and CME are greater in South Asians than in white Europeans with NDH. Further trials are required to examine the longer-term impact of LV-HIIE and CME on cardiometabolic health.
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http://dx.doi.org/10.2337/dc20-1393DOI Listing
January 2021

Maturational timing, physical self-perceptions and physical activity in UK adolescent females: investigation of a mediated effects model.

Ann Hum Biol 2020 Jun;47(4):384-390

SSEHS, Loughborough University, Loughborough, UK.

Background: Advanced (early) biological maturation may be a risk factor for inactivity among adolescent girls.

Aim: To test the mediational effects of body attractiveness and physical self-worth on the relationship between biological maturity and accelerometer assessed moderate-to-vigorous physical activity (MVPA) in a large multi-ethnic sample of girls from the Midlands area in the UK (11-14 years).

Subjects And Methods: Biological maturity (predicting age at peak height velocity (APHV)); self-perceptions of body attractiveness, physical self-worth, and minutes spent in MVPA were assessed in 1062 females aged 11-14 years.

Results: Structural equation modelling using maximum likelihood estimation and boot- strapping procedures supported the hypothesised model. Later maturation predicted higher perceptions of body attractiveness ( = 0.25, < .001) which, in turn, predicted higher perceptions of physical self-worth ( = 0.91, < .001) and, significantly higher MVPA ( = 0.22, < .001). Examination of the bootstrap-generated bias-corrected confidence intervals suggested that perceptions of body attractiveness and physical self-worth partially mediated a positive association between predicted APHV and MVPA ( = 0.05, < .001).

Conclusions: Greater biological maturity (i.e. early maturity) in adolescent girls is associated with less involvement in MVPA and appears to be partly explained by lower perceptions of body attractiveness and physical self-worth. Physical activity interventions should consider girls' perceptions of their pubertal related physiological changes during adolescence, particularly among early maturing girls.
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http://dx.doi.org/10.1080/03014460.2020.1784277DOI Listing
June 2020

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

J Sports Sci 2021 Feb 20;39(4):430-438. Epub 2020 Sep 20.

Department of Chemistry, University of Bergen , Bergen, Norway.

The use of high-resolution physical activity intensity spectra obtained from accelerometry can improve knowledge of associations with health and development beyond the use of traditional summary measures of intensity. The aim of the present study was to compare three different approaches for determining associations for spectrum descriptors of physical activity (the intensity gradient, principal component analysis, and multivariate pattern analysis) with relevant outcomes in children. We used two datasets including physical activity spectrum data (ActiGraph GT3X+) and 1) a cardiometabolic health outcome in 841 schoolchildren and 2) a motor skill outcome in 1081 preschool children. We compared variance explained (R) and associations with the outcomes for the intensity gradient (slope) across the physical activity spectra, a two-component principal component model describing the physical activity variables, and multivariate pattern analysis using the intensity spectra as the explanatory data matrices. Results were broadly similar for all analytical approaches. Multivariate pattern analysis explained the most variance in both datasets, likely resulting from use of more of the information available from the intensity spectra. Yet, volume and intensity dimensions of physical activity are not easily disentangled and their relative importance may be interpreted differently using different methodology.
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http://dx.doi.org/10.1080/02640414.2020.1824341DOI Listing
February 2021

Short-term and Long-term Feasibility, Safety, and Efficacy of High-Intensity Interval Training in Cardiac Rehabilitation: The FITR Heart Study Randomized Clinical Trial.

JAMA Cardiol 2020 Dec;5(12):1382-1389

Centre for Research on Exercise, Physical Activity, and Health, School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia.

Importance: High-intensity interval training (HIIT) is recognized as a potent stimulus for improving cardiorespiratory fitness (volume of oxygen consumption [VO2] peak) in patients with coronary artery disease (CAD). However, the feasibility, safety, and long-term effects of HIIT in this population are unclear.

Objective: To compare HIIT with moderate-intensity continuous training (MICT) for feasibility, safety, adherence, and efficacy of improving VO2 peak in patients with CAD.

Design, Setting, And Participants: In this single-center randomized clinical trial, participants underwent 4 weeks of supervised training in a private hospital cardiac rehabilitation program, with subsequent home-based training and follow-up over 12 months. A total of 96 participants with angiographically proven CAD aged 18 to 80 years were enrolled, and 93 participants were medically cleared for participation following a cardiopulmonary exercise test. Data were collected from May 2016 to December 2018, and data were analyzed from December 2018 to August 2019.

Interventions: A 4 × 4-minute HIIT program or a 40-minute MICT program (usual care). Patients completed 3 sessions per week (2 supervised and 1 home-based session) for 4 weeks and 3 home-based sessions per week thereafter for 48 weeks.

Main Outcomes And Measures: The primary outcome was change in VO2 peak during the cardiopulmonary exercise test from baseline to 4 weeks. Further testing occurred at 3, 6, and 12 months. Secondary outcomes were feasibility, safety, adherence, cardiovascular risk factors, and quality of life.

Results: Of 93 randomized participants, 78 (84%) were male, the mean (SD) age was 65 (8) years, and 46 were randomized to HIIT and 47 to MICT. A total of 86 participants completed testing at 4 weeks for the primary outcome, including 43 in the HIIT group and 43 in the MICT group; 69 completed testing at 12 months for VO2 peak, including 32 in the HIIT group and 37 in the MICT group. After 4 weeks, HIIT improved VO2 peak by 10% compared with 4% in the MICT group (mean [SD] oxygen uptake: HIIT, 2.9 [3.4] mL/kg/min; MICT, 1.2 [3.4] mL/kg/min; P = .02). After 12 months, there were similar improvements from baseline between groups, with a 10% improvement in the HIIT group and a 7% improvement in the MICT group (mean [SD] oxygen uptake: HIIT, 2.9 [4.5] mL/kg/min; MICT, 1.8 [4.3] mL/kg/min; P = .30). Both groups had high feasibility scores and low rates of withdrawal due to serious adverse events (3 participants in the HIIT group and 1 participant in the MICT group). One event occurred following exercise (hypotension) in the HIIT group. Over 12 months, both home-based HIIT and MICT had low rates of adherence (HIIT, 18 of 34 [53%]; MICT, 15 of 37 [41%]; P = .35) compared with the supervised stage (HIIT, 39 of 44 [91%]; MICT, 39 of 43 [91%]; P > .99).

Conclusions And Relevance: In this randomized clinical trial, a 4-week HIIT program improved VO2 peak compared with MICT in patients with CAD attending cardiac rehabilitation. However, improvements in VO2 peak at 12 months were similar for both groups. HIIT was feasible and safe, with similar adherence to MICT over 12-month follow-up. These findings support inclusion of HIIT in cardiac rehabilitation programs as an adjunct or alternative modality to moderate-intensity exercise.

Trial Registration: Australian New Zealand Clinical Trials Registry Identifier: ACTRN12615001292561.
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http://dx.doi.org/10.1001/jamacardio.2020.3511DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7489382PMC
December 2020

Physical behaviors and chronotype in people with type 2 diabetes.

BMJ Open Diabetes Res Care 2020 07;8(1)

Hanning Sleep Laboratory, University Hospitals of Leicester NHS Trust, Leicester, UK.

Introduction: Previous investigations have suggested that evening chronotypes may be more susceptible to obesity-related metabolic alterations. However, whether device-measured physical behaviors differ by chronotype in those with type 2 diabetes (T2DM) remains unknown.

Research Design And Methods: This analysis reports data from the ongoing Chronotype of Patients with Type 2 Diabetes and Effect on Glycaemic Control (CODEC) observational study. Eligible participants were recruited from both primary and secondary care settings in the Midlands area, UK. Participants were asked to wear an accelerometer (GENEActiv, ActivInsights, Kimbolton, UK) on their non-dominant wrist for 7 days to quantify different physical behaviors (sleep, sedentary, light, moderate-to-vigorous physical activity (MVPA), intensity gradient, average acceleration and the acceleration above which the most active continuous 2, 10, 30 and 60 min are accumulated). Chronotype preference (morning, intermediate or evening) was assessed using the Morningness-Eveningness Questionnaire. Multiple linear regression analyses assessed whether chronotype preference was associated with physical behaviors and their timing. Evening chronotypes were considered as the reference group.

Results: 635 participants were included (age=63.8±8.4 years, 34.6% female, body mass index=30.9±5.1 kg/m). 25% (n=159) of the cohort were morning chronotypes, 52% (n=330) intermediate and 23% (n=146) evening chronotypes. Evening chronotypes had higher sedentary time (28.7 min/day, 95% CI 8.6 to 48.3) and lower MVPA levels (-9.7 min/day, -14.9 to -4.6) compared to morning chronotypes. The intensity of the most active continuous 2-60 min of the day, average acceleration and intensity gradient were lower in evening chronotypes. The timing of physical behaviors also differed across chronotypes, with evening chronotypes displaying a later sleep onset and consistently later physical activity time.

Conclusions: People with T2DM lead a lifestyle characterized by sedentary behaviors and insufficient MVPA. This may be exacerbated in those with a preference for 'eveningness' (ie, go to bed late and get up late).
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http://dx.doi.org/10.1136/bmjdrc-2020-001375DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7368491PMC
July 2020

Effect of High-Intensity Interval Training on Visceral and Liver Fat in Cardiac Rehabilitation: A Randomized Controlled Trial.

Obesity (Silver Spring) 2020 07 31;28(7):1245-1253. Epub 2020 May 31.

Centre for Research on Exercise, Physical Activity, and Health, School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Queensland, Australia.

Objective: This study aimed to investigate the effect of exercise intensity on visceral adipose tissue (VAT) and liver fat reduction in patients with coronary artery disease (CAD) over 3 months and the maintenance of improvements over 12 months.

Methods: Forty-two participants with CAD were randomized to three sessions/week of either 4 × 4-minute high-intensity interval training (HIIT) or 40 minutes of usual care moderate-intensity continuous training (MICT) for a 4-week supervised cardiac rehabilitation program, followed by three home-based sessions/week for 11 months. Liver fat (as intrahepatic lipid) and VAT were measured via magnetic resonance techniques. Data are mean change (95% CI).

Results: HIIT and MICT significantly reduced VAT over 3 months (-350 [-548 to -153] cm vs. -456 [-634 to -278] cm ; time × group effect: P = 0.421), with further improvement over 12 months (-545 [-818 to -271] cm vs. -521 [-784 to -258] cm ; time × group effect: P = 0.577) and no differences between groups. Both groups improved liver fat over 3 months, with HIIT tending to show greater reduction than MICT (-2.8% [-4.0% to -1.6%] vs. -1.4% [-2.4% to -0.4%]; time × group effect: P = 0.077). After 12 months, improvements were maintained to a similar degree. Higher exercise intensity predicted liver fat reduction (β = -0.3 [-0.7 to 0.0]; P = 0.042).

Conclusions: HIIT and MICT reduced VAT over 3 and 12 months. For liver fat, HIIT tended to provide a slightly greater reduction compared with MICT. These findings support HIIT as a beneficial adjunct or alternative to MICT for reducing visceral and liver fat in patients with CAD.
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http://dx.doi.org/10.1002/oby.22833DOI Listing
July 2020

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

Med Sci Sports Exerc 2020 11;52(11):2331-2341

Department of Health and Human Physiology, The University of Iowa, Iowa City, IA.

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.
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http://dx.doi.org/10.1249/MSS.0000000000002380DOI Listing
November 2020

Evidence for Protein Leverage in Children and Adolescents with Obesity.

Obesity (Silver Spring) 2020 04 6;28(4):822-829. Epub 2020 Mar 6.

Murdoch Children's Research Institute, The Royal Children's Hospital, Parkville, Victoria, Australia.

Objective: The aim of this study was to test the protein leverage hypothesis in a cohort of youth with obesity.

Methods: A retrospective study was conducted in a cohort of youth with obesity attending a tertiary weight management service. Validated food questionnaires revealed total energy intake (TEI) and percentage of energy intake from carbohydrates (%EC), fats (%EF), and proteins (%EP). Individuals with a Goldberg cutoff ≥ 1.2 of the ratio of reported TEI to basal metabolic rate from fat-free mass were included. A subgroup had accelerometer data. Statistics included modeling of percentage of energy from macronutrients and TEI, compositional data analysis to predict TEI from macronutrient ratios, and mixture models for sensitivity testing.

Results: A total of 137 of 203 participants were included (mean [SD] age 11.3 [2.7] years, 68 females, BMI z score 2.47 [0.27]). Mean TEI was 10,330 (2,728) kJ, mean %EC was 50.6% (6.1%), mean %EF was 31.6% (4.9%), and mean %EP was 18.4% (3.1%). The relationship between %EP and TEI followed a power function (L coefficient -0.48; P < 0.001). TEI was inversely associated with increasing %EP. In the subgroup with < 60 min/d of moderate to vigorous physical activity (n = 48), lower BMI z scores were associated with higher %EP and moderate %EC.

Conclusions: In youth with obesity, protein dilution by either carbohydrates or fats increases TEI. Assessment of dietary protein may be useful to assist in reducing TEI and BMI in youth with obesity.
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http://dx.doi.org/10.1002/oby.22755DOI Listing
April 2020

FilterK: A new outlier detection method for k-means clustering of physical activity.

J Biomed Inform 2020 04 26;104:103397. Epub 2020 Feb 26.

School of Mathematics and Actuarial Science, University of Leicester, University Road, Leicester LE1 7RD, UK.

In this paper, a new algorithm denoted as FilterK is proposed for improving the purity of k-means derived physical activity clusters by reducing outlier influence. We applied it to physical activity data obtained with body-worn accelerometers and clustered using k-means. We compared its performance with three existing outlier detection methods: Local Outlier Factor, Isolation Forests and KNN using the ground truth (class labels), average cluster and event purity (ACEP). FilterK provided comparable gains in ACEP (0.581 → 0.596 compared to 0.580-0.617) whilst removing a lower number of outliers than the other methods (4% total dataset size vs 10% to achieve this ACEP). The main focus of our new outlier detection method is to improve the cluster purities of physical activity accelerometer data, but we also suggest it may be potentially applied to other types of dataset captured by k-means clustering. We demonstrate our method using a k-means model trained on two independent accelerometer datasets (training n = 90) and re-applied to an independent dataset (test n = 41). Labelled physical activities include lying down, sitting, standing, household chores, walking (laboratory and non-laboratory based), stairs and running. This type of clustering algorithm could be used to assist with identifying optimal physical activity patterns for health.
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http://dx.doi.org/10.1016/j.jbi.2020.103397DOI Listing
April 2020

The sedentary behaviour and physical activity patterns of survivors of a critical illness over their acute hospitalisation: An observational study.

Aust Crit Care 2020 05 6;33(3):272-280. Epub 2019 Dec 6.

University of South Australia, Adelaide, South Australia, Australia. Electronic address:

Background: Physical function is often poor in intensive care unit (ICU) survivors, yet objective descriptions of sedentary behaviour and physical activity during acute hospitalisation are lacking.

Objective: The objective of this study was to examine sedentary and activity patterns during patients' hospital-based recovery from a critical illness and associations with physical function, muscle strength, and length of stay (LOS).

Methods: This was a prospective cohort study in a tertiary ICU and acute hospital wards, which recruited 40 adults who required ≥5 days of mechanical ventilation. Data were collected at awakening (T1), ICU discharge (T2), and hospital discharge (T3), which included monitoring of body posture (sedentary behaviour) using the activPAL and activity intensity using the GENEActiv. Data were reported as time spent lying/sitting and upright, with the number of sit-to-stand transitions and upright bouts. Statistical analysis was conducted using repeated-measures analysis of variance and Spearman's rho.

Results: From awakening to hospital discharge (T1-T3, n = 23), there was a mean [95% confidence interval] decrease in % time spent lying/sitting (-3.0% [-4.6% to1.4%], p ≤ 0.001) corresponding to increased time spent upright (43.0 min [19.9, 66.1], p ≤ 0.001). Sit-to-stand transitions increased (18 [11, 28], p ≤ 0.001). The number of upright bouts ≥2 and ≥ 5 min increased (both p ≤ 0.001), but only from ICU to hospital discharge (T2-T3, 5.3 [3.1, 7.6] and 2.3 [0.9, 3.8] respectively). At ICU discharge (T2), less % of time spent lying/sitting, more minutes spent upright, and more transitions were associated with better physical function (Physical Function in Intensive Care Test-scored and de Morton Mobility Index; all rho ≥+/-0.730, p ≤ 0.001) and muscle strength (hand grip, Medical Research Council sum-score; all rho≥+/-0.505, p ≤ 0.001). There were no associations between accelerometry and hospital LOS.

Conclusions: ICU survivors' transition from highly sedentary behaviour to low intensity activity over their acute hospitalisation. Sedentary breaks may be not spread over the day such that modifying sedentary behaviour to break up prolonged lying/sitting may be a focus for future research.

Clinical Trial Registration: NCT02881801.
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http://dx.doi.org/10.1016/j.aucc.2019.10.006DOI Listing
May 2020

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

Sports Med Open 2019 Dec 5;5(1):47. Epub 2019 Dec 5.

Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, LE5 4PW, UK.

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 M/ 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.
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http://dx.doi.org/10.1186/s40798-019-0225-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6895365PMC
December 2019

Comparability of accelerometer signal aggregation metrics across placements and dominant wrist cut points for the assessment of physical activity in adults.

Sci Rep 2019 12 3;9(1):18235. Epub 2019 Dec 3.

PROFITH "PROmoting FITness and Health through physical activity" Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Ctra. Alfacar s/n, 18011, Granada, Spain.

Large epidemiological studies that use accelerometers for physical behavior and sleep assessment differ in the location of the accelerometer attachment and the signal aggregation metric chosen. This study aimed to assess the comparability of acceleration metrics between commonly-used body-attachment locations for 24 hours, waking and sleeping hours, and to test comparability of PA cut points between dominant and non-dominant wrist. Forty-five young adults (23 women, 18-41 years) were included and GT3X + accelerometers (ActiGraph, Pensacola, FL, USA) were placed on their right hip, dominant, and non-dominant wrist for 7 days. We derived Euclidean Norm Minus One g (ENMO), Low-pass filtered ENMO (LFENMO), Mean Amplitude Deviation (MAD) and ActiGraph activity counts over 5-second epochs from the raw accelerations. Metric values were compared using a correlation analysis, and by plotting the differences by time of the day. Cut points for the dominant wrist were derived using Lin's concordance correlation coefficient optimization in a grid of possible thresholds, using the non-dominant wrist estimates as reference. They were cross-validated in a separate sample (N = 36, 10 women, 22-30 years). Shared variances between pairs of acceleration metrics varied across sites and metric pairs (range in r: 0.19-0.97, all p < 0.01), suggesting that some sites and metrics are associated, and others are not. We observed higher metric values in dominant vs. non-dominant wrist, thus, we developed cut points for dominant wrist based on ENMO to classify sedentary time (<50 mg), light PA (50-110 mg), moderate PA (110-440 mg) and vigorous PA (≥440 mg). Our findings suggest differences between dominant and non-dominant wrist, and we proposed new cut points to attenuate these differences. ENMO and LFENMO were the most similar metrics, and they showed good comparability with MAD. However, counts were not comparable with ENMO, LFENMO and MAD.
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http://dx.doi.org/10.1038/s41598-019-54267-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6890686PMC
December 2019

Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data.

Sensors (Basel) 2019 Oct 17;19(20). Epub 2019 Oct 17.

Diabetes Research Centre, University of Leicester, Leicester General Hospital, Gwendolen Road, Leicester LE5 4PW, UK.

Few methods for classifying physical activity from accelerometer data have been tested using an independent dataset for cross-validation, and even fewer using multiple independent datasets. The aim of this study was to evaluate whether unsupervised machine learning was a viable approach for the development of a reusable clustering model that was generalisable to independent datasets. We used two labelled adult laboratory datasets to generate a k-means clustering model. To assess its generalised application, we applied the stored clustering model to three independent labelled datasets: two laboratory and one free-living. Based on the development labelled data, the ten clusters were collapsed into four activity categories: sedentary, standing/mixed/slow ambulatory, brisk ambulatory, and running. The percentages of each activity type contained in these categories were 89%, 83%, 78%, and 96%, respectively. In the laboratory independent datasets, the consistency of activity types within the clusters dropped, but remained above 70% for the sedentary clusters, and 85% for the running and ambulatory clusters. Acceleration features were similar within each cluster across samples. The clusters created reflected activity types known to be associated with health and were reasonably robust when applied to diverse independent datasets. This suggests that an unsupervised approach is potentially useful for analysing free-living accelerometer data.
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http://dx.doi.org/10.3390/s19204504DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832944PMC
October 2019
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