How explicable are differences between reviews that appear to address a similar research question? A review of reviews of physical activity interventions

Background Systematic reviews are promoted as being important to inform decision-making. However, when presented with a set of reviews in a complex area, how easy is it to understand how and why they may differ from one another? Methods An analysis of eight reviews reporting evidence on effectiveness of community interventions to promote physical activity. We assessed review quality and investigated overlap of included studies, citation of relevant reviews, consistency in reporting, and reasons why specific studies may be excluded. Results There were 28 included studies. The majority (n = 22; 79%) were included only in one review. There was little cross-citation between reviews (n = 4/28 possible citations; 14%). Where studies appeared in multiple reviews, results were consistently reported except for complex studies with multiple publications. Review conclusions were similar. For most reviews (n = 6/8; 75%), we could explain why primary data were not included; this was usually due to the scope of the reviews. Most reviews tended to be narrow in focus, making it difficult to gain an understanding of the field as a whole. Conclusions In areas where evaluating impact is known to be difficult, review findings often relate to uncertainty of data and methodologies, rather than providing substantive findings for policy and practice. Systematic ?maps? of research can help identify where existing research is robust enough for multiple in-depth syntheses and also show where new reviews are needed. To ensure quality and fidelity, review authors should systematically search for all publications from complex studies. Other relevant reviews should be searched for and cited to facilitate knowledge-building

The proportion of adolescents with individual clinical indicators of a sleep problem (all estimate are from sleep diary reports on school nights).

<p>The proportion of adolescents with individual clinical indicators of a sleep problem (all estimate are from sleep diary reports on school nights).</p

Correlations between variables entered into the logistic regression.

***<p><i>p</i><.001, **<i>p</i><.01, *<i>p</i><.05.</p>†<p>Sleep problems, depressed mood and unrefreshing sleep are dichotomous categorical variables that are scored such that a higher score indicates the presence of that variable.</p

The main reasons provided by adolescents and parents for reporting that the adolescent has a sleep problem.

<p>The main reasons provided by adolescents and parents for reporting that the adolescent has a sleep problem.</p

Ethnic differences in the relationship between step cadence and physical function in older adults.

This study investigated associations between step cadence and physical function in healthy South Asian (SA) and White European (WE) older adults, aged ≥60. Participants completed the 60-s Sit-to-Stand (STS-60) test of physical function. Free-living stepping was measured using the activPAL3™. Seventy-one WEs (age = 72 ± 5, 53% male) and 33 SAs (age = 71 ± 5, 55% male) were included. WEs scored higher than SAs in the STS-60 (23 vs 20 repetitions, p = 0.045). Compared to WEs, SAs had significantly lower total and brisk (≥100 steps/min) steps (total: 8971 vs 7780 steps/day, p = 0.041; brisk: 5515 vs 3723 steps/day, p = 0.001). In WEs, 1000 brisk steps and each decile higher proportion of steps spent brisk stepping were associated with STS-60 (β = 0.72 95% CI 0.05, 1.38 and β = 1.01 95% CI 0.19, 1.82, respectively), with associations persisting across mean peak 1 min (β = 1.42 95% CI 0.12, 2.71), 30 min (β = 1.71 95% CI 0.22, 3.20), and 60 min (β = 2.16 95% CI 0.62, 3.71) stepping periods. Associations were not observed in SAs. Ethnic differences in associations between ambulation and physical function may exist in older adults which warrant further investigation.</p

Additional file 1: of Associations of discretionary screen time with mortality, cardiovascular disease and cancer are attenuated by strength, fitness and physical activity: findings from the UK Biobank study

Table S1. Number of participants with missing data for covariates. Table S2. Cut-off points for age- and sex-specific physical activity tertiles. Table S3. Cut-off points for age- and sex-specific grip strength tertiles. Table S4. Cut-off points for age- and sex-specific fitness tertiles. Table S5. Cohort characteristics by categories of TV viewing. Table S6. Cohort characteristics by categories of PC screen time. Table S7. Cohort characteristics by age- and sex-specific tertiles of total physical activity. Table S8. Cohort characteristics by age- and sex-specific tertiles of cardiorespiratory fitness. Table S9. Cohort characteristics by age- and sex-specific tertiles of handgrip strength. Table S10. Correlation between TV viewing, total physical activity and grip strength. Figure S1. Cox proportional hazard model of the association of 1-h increments in screen time, TV viewing and PC screen time with CVD and cancer mortality. Figure S2. Cox proportional hazard models of the association of overall discretionary screen time with CVD and cancer mortality by physical activity, fitness and handgrip strength strata. Figure S3. Cox proportional hazard models of the association of overall discretionary TV viewing with CVD and cancer mortality by physical activity, fitness and handgrip strength strata. Figure S4. Cox proportional hazard models of the association of overall discretionary PC screen time with CVD and cancer mortality by physical activity, fitness and handgrip strength strata. Table S11. Cox proportional hazard estimates of the association of overall discretionary screen time with all-cause mortality, CVD and cancer incidence and mortality by physical activity, fitness and handgrip strength strata. Table S12. Cox proportional hazard estimates of the association of discretionary TV viewing with all-cause mortality, CVD and cancer incidence and mortality by physical activity, fitness and handgrip strength strata. Table S13. Cox proportional hazard estimates of the association of discretionary PC screen time with all-cause mortality, CVD and cancer incidence and mortality by physical activity, fitness and handgrip strength strata. (DOCX 1552 kb

Additional file 1: Figure S1. of Associations of mutually exclusive categories of physical activity and sedentary time with markers of cardiometabolic health in English adults: a cross-sectional analysis of the Health Survey for England

Categorical associations with markers of cardiometabolic health (beta coefficients (99 % CIs)). Table S1 - Sensitivity analyses showing the weighted mutually exclusive behavioural category prevalence [n; %]. Table S2 - Sensitivity analyses showing the categorical associations with markers of cardiometabolic health (beta coefficients (99 % CIs)). (DOCX 47 kb

Recommendations from Diabetes UK’s 2022 diabetes and physical activity workshop

Aims To describe the process and outputs of a workshop convened to identify key priorities for future research in the area of diabetes and physical activity and provide recommendations to researchers and research funders on how best to address them. Methods A one-day research workshop was conducted, bringing together researchers, people living with diabetes, healthcare professionals, and members of staff from Diabetes UK to identify and prioritise recommendations for future research into physical activity and diabetes. Results Workshop attendees prioritised four key themes for further research: (i) Better understanding of the physiology of exercise in all groups of people: in particular, what patient metabolic characteristics influence or predict the physiological response to physical activity, and the potential role of physical activity in beta cell preservation; (ii) Designing physical activity interventions for maximum impact; (iii) Promoting sustained physical activity across the life course ; (iv) Designing physical activity studies for groups with multiple long-term conditions. Conclusions This paper outlines recommendations to address the current gaps in knowledge related to diabetes and physical activity and calls on the research community to develop applications in these areas and funders to consider how to stimulate research in these areas.</p