Uses and abuses of real-world data in generating evidence during a pandemic.

On 11 March 2020, the World Health Organization declared COVID-19 a pandemic and called for immediate collaborative initiatives for faster access to available data, with a view to generating robust research evidence informing global and local public health policy.1 This urgency has helped a number of national bodies to secure data and their linkages and to provide safe analytical environment for researches to ask important questions, including pseudonymised data linkages, high-throughput computing environment, and access and authentication processes with clear information governance.2,3 Linkages of multiple sources of clinical data within a trusted environment are being granted at a rapid pace and there is a greater provision of access to COVID-19 studies, improved collaboration, expedited governance and ethical approval of studies.4 Some organisations have also been proactive in getting groups together to work collaboratively on relevant research questions which will rapidly benefit clinical care and public health alike

Self-directed interventions to promote weight loss: a systematic review of reviews

BACKGROUND: A wide range of self-directed weight-loss interventions are available, providing users with a variety of tools delivered through various formats to regulate weight-related behavior patterns. However, it is unclear how effective self-directed interventions are and how they promote weight loss and weight maintenance. OBJECTIVE: A systematic review of reviews was conducted to examine the effectiveness of such interventions and to identify intervention content associated with effectiveness. METHODS: MEDLINE, Embase, PsycINFO, CINAHL, and the Cochrane Library for systematic reviews were searched from 2000-2012 for reviews of the effectiveness of self-directed interventions on weight loss and weight maintenance in adults. Two reviewers used predefined inclusion criteria to select relevant reviews and assess their quality using the Overview Quality Assessment Questionnaire (OQAQ). We extracted data on effectiveness and on relationships between intervention characteristics and effectiveness. RESULTS: Twenty reviews were included and quality assessed. Findings relevant to self-directed interventions, including interactive websites, smartphone applications, and text messaging (short message service, SMS) were summarized. Findings were mixed but promising. For example, one review of Internet-based interventions found that, when used in conjunction with standard weight loss programs, these interventions resulted in a significant average increase in weight loss of 1.5 kg over evaluation periods. Unfortunately, only 7 of 20 reviews were of high methodological quality according to OQAQ scores, and only 4 employed meta-analyses. Few reviews linked intervention content to effectiveness. CONCLUSIONS: Current evidence suggests that self-directed interventions can independently promote weight loss and can augment interventions involving personal contact. Particular change techniques and delivery modes including individualized feedback, email counseling, and online social support appear to enhance effectiveness. Further reviews of the content of self-directed weight-loss intervention studies are needed to clarify which change techniques delivered through which delivery formats optimize intervention effectiveness

Comparative relevance of physical fitness and adiposity on life expectancy: A UK Biobank observational study

Objective To investigate the extent to which two measures of physical fitness, walking pace and handgrip strength, are associated with life expectancy across different levels of adiposity, as the relative importance of physical fitness and adiposity on health outcomes is still debated. Patients and Methods Usual walking pace (self-defined as slow, steady/average, brisk), dynamometer assessed handgrip strength, body mass index (BMI), waist circumference and body fat percentage were determined at baseline in the UK Biobank prospective cohort study (March 13, 2006 – January 31, 2016). Life expectancy was estimated at 45 years old. Results The median age and BMI of the 474 919 participants included in this analysis were 58.2 years and 26.7 kg/m2, respectively; over a median follow-up of 6.97 years, 12823 deaths occurred. Participants reporting brisk walking pace had longer life expectancy across all levels of BMI, ranging from 86.7 to 87.8 years in women and from 85.2 to 86.8 years in men. Conversely, subjects reporting slow walking pace had a shorter life expectancy, being the lowest observed in slow walkers with a BMI less than 20 kg/m2 (women: 72.4 years; men: 64.8 years). Smaller, less consistent differences in life expectancy were observed between participants with high and low handgrip strength, particularly in women. The same pattern of results was observed for waist circumference or body fat percentage. Conclusion Brisk walkers were found to have a longer life expectancy which was constant across different levels and indices of adiposity. These findings could help clarify the relative importance of physical fitness and adiposity on mortality

Raw Accelerometer Data Analysis with GGIR R-package: Does Accelerometer Brand Matter?

PURPOSE: To determine the agreement between outputs from contemporaneous measures of acceleration from wrist-worn GENEActiv and ActiGraph accelerometers when processed using the GGIR open source package. METHODS: Thirty-four participants wore a GENEActiv and ActiGraph GT3X+ on their non-dominant wrist continuously for two days to ensure capture of one 24 h day and one nocturnal sleep. GENEActiv.bin files and ActiGraph.csv files were analysed with R-package GGIR version 1.2-0. Key outcome variables were: wear time, average magnitude of dynamic wrist acceleration (ENMO), percentile distribution of accelerations, time spent across acceleration levels in 40 mg resolution, time in moderate-to-vigorous physical activity (MVPA: total, 10-min bouts) and duration of nocturnal sleep. RESULTS: There was high agreement between accelerometer brands for all derived outcomes (wear time, MVPA and sleep, ICC>0.96), ENMO (ICC=0.99), time spent across acceleration levels (ICC>0.93) and accelerations ≥50th percentile of the distribution (ICC>0.82). ENMO (GENEActiv = 29.9±20.7(SD)) mg, ActiGraph = 27.8±21.4 mg) and accelerations between the 5th and 75th percentile of the distribution measured by the GENEActiv were significantly higher than those measured by the ActiGraph. Correspondingly the number of minutes recorded between 0 and 40 mg was significantly greater for the ActiGraph (745 min cf. 734 min) and the number of minutes recorded between 40 and 80 mg significantly greater for the GENEActiv (110 min cf. 105 min). CONCLUSION: Derived outcomes (wear time, MVPA and sleep) were similar between brands. Brands compared well for acceleration magnitudes >50-80 mg but not lower magnitudes indicative of sedentary time. Caution is advised when comparing the magnitude of ENMO between brands, but there was high consistency between brands for the ranking of individuals for activity and sleep outcomes

Additional file 1: of Working conditions and tuberculosis mortality in England and Wales, 1890–1912: a retrospective analysis of routinely collected data

Occupations and TB mortality Supplement. (DOCX 60 kb

Type 2 Diabetes and Impaired Physical Function: A Growing Problem

The focus in diabetes care has traditionally been around the optimisation of the glycaemic control and prevention of complications. However, the prevention of frailty and improvement in physical function have now emerged as new targets of diabetes management. This is mainly driven by the significant adverse impact that early onset frailty and decline in physical function have on health outcomes, functional independence, and quality of life in people with type 2 diabetes (T2D). There is an increasing emphasis in the expert consensus and management algorithms to improve physical function in people with T2D, predominantly through lifestyle interventions, including exercise and the control of modifiable risk factors. Trials of novel glucose-lowering therapies (GLTs) also now regularly assess the impact of these novel agents on measures of physical function within their secondary outcomes to understand the impact that these agents have on physical function. However, challenges remain as there is no consensus on the best method of assessing physical function in clinical practice, and the recognition of impaired physical function remains low. In this review, we present the burden of a reduced physical function in people with T2D, outline methods of assessment used in healthcare and research settings, and discuss strategies for improvement in physical function in people with T2D

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

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

Additional file 2: of Working conditions and tuberculosis mortality in England and Wales, 1890–1912: a retrospective analysis of routinely collected data

E&W historical TB mortality data. (CSV 191 kb

Beyond Cut-points: Accelerometer Metrics that Capture the Physical Activity Profile.

PURPOSE: Commonly used physical activity metrics tell us little about the intensity distribution across the activity profile. The purpose of this paper is to introduce a metric, the intensity gradient, which can be used in combination with average acceleration (overall activity level) to fully describe the activity profile. METHODS: 1669 adolescent girls (sample 1) and 295 adults with type 2 diabetes (sample 2) wore a GENEActiv accelerometer on their non-dominant wrist for up to 7-days. Body mass index and percent body fat were assessed in both samples and physical function (grip strength, Short Physical Performance Battery, sit-to-stand repetitions) in sample 2. Physical activity metrics were: average acceleration (AccelAV); the intensity gradient (IntensityGRAD from the log-log regression line: 25 mg intensity bins (x)/time accumulated in each bin (y)); total moderate-to-vigorous physical activity (MVPA); and bouted MVPA (sample 2 only). RESULTS: Correlations between AccelAV and the IntensityGRAD (r=0.39-0.51) were similar to correlations between AccelAV and bouted MVPA (r=0.48), and substantially lower than between AccelAV and total MVPA (r>0.93). The IntensityGRAD was negatively associated with body fatness in sample 1 (p<0.05) and positively associated with physical function in sample 2 (p<0.05); associations were independent of AccelAV and potential co-variates. In contrast, MVPA was not independently associated with body fatness or physical function. CONCLUSION: AccelAV and the IntensityGRAD provide a complementary description of a person's activity profile, each explaining unique variance, and independently associated with body fatness and/or physical function. Both metrics are appropriate for reporting as standardised measures and suitable for comparison across studies using raw acceleration accelerometers. Concurrent use will facilitate investigation of the relative importance of intensity and volume of activity for a given outcome

Do doctors in dispensing practices with a financial conflict of interest prescribe more expensive drugs?

Data management and analysis from the above entitled pape