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Introduction: Interrupted Time Series (ITS) studies may be used to assess the impact of an interruption, such as an intervention or exposure. The data from such studies are particularly amenable to visual display and, when clearly depicted, can readily show the short-and long-term impact of an interruption. Further, wellconstructed graphs allow data to be extracted using digitizing software, which can facilitate their inclusion in systematic reviews and meta-analyses. Aim: We provide recommendations for graphing ITS data, examine the properties of plots presented in ITS studies, and provide examples employing our recommendations. Methods and results: Graphing recommendations from seminal data visualization resources were adapted for use with ITS studies. The adapted recommendations cover plotting of data points, trend lines, interruptions, additional lines and general graph components. We assessed whether 217 graphs from recently published (2013-2017) ITS studies met our recommendations and found that 130 graphs (60%) had clearly distinct data points, 100 (46%) had trend lines, and 161 (74%) had a clearly defined interruption. Accurate data extraction (requiring distinct points that align with axis tick marks and labels that allow the points to be interpreted) was possible in only 72 (33%) graphs. Conclusion: We found that many ITS graphs did not meet our recommendations and could be improved with simple changes. Our proposed recommendations aim to achieve greater standardization and improvement in the display of ITS data, and facilitate re-use of the data in systematic reviews and metaanalyses.

Background: Sample size calculations for longitudinal cluster randomised trials, such as crossover and stepped-wedge trials, require estimates of the assumed correlation structure. This includes both within-period intra-cluster correlations, which importantly differ from conventional intra-cluster correlations by their dependence on period, and also cluster autocorrelation coefficients to model correlation decay. There are limited resources to inform these estimates. In this article, we provide a repository of correlation estimates from a bank of real-world clustered datasets. These are provided under several assumed correlation structures, namely exchangeable, block-exchangeable and discrete-time decay correlation structures. Methods: Longitudinal studies with clustered outcomes were collected to form the CLustered OUtcome Dataset bank. Forty-four available continuous outcomes from 29 datasets were obtained and analysed using each correlation structure. Patterns of within-period intra-cluster correlation coefficient and cluster autocorrelation coefficients were explored by study characteristics. Results: The median within-period intra-cluster correlation coefficient for the discrete-time decay model was 0.05 (interquartile range: 0.02–0.09) with a median cluster autocorrelation of 0.73 (interquartile range: 0.19–0.91). The within-period intra-cluster correlation coefficients were similar for the exchangeable, block-exchangeable and discrete-time decay correlation structures. Within-period intra-cluster correlation coefficients and cluster autocorrelations were found to vary with the number of participants per cluster-period, the period-length, type of cluster (primary care, secondary care, community or school) and country income status (high-income country or low- and middle-income country). The within-period intra-cluster correlation coefficients tended to decrease with increasing period-length and slightly decrease with increasing cluster-period sizes, while the cluster autocorrelations tended to move closer to 1 with increasing cluster-period size. Using the CLustered OUtcome Dataset bank, an RShiny app has been developed for determining plausible values of correlation coefficients for use in sample size calculations. Discussion: This study provides a repository of intra-cluster correlations and cluster autocorrelations for longitudinal cluster trials. This can help inform sample size calculations for future longitudinal cluster randomised trials.

Background:Most research on walking for transport has focused on the walkability of residential neighborhoods, overlooking the contribution of places of work/study and the ease with which destinations outside the immediate neighborhood can be accessed, referred to as regional accessibility.Objectives:We aimed to examine if local accessibility/walkability around place of work/study and regional accessibility are independently and interactively associated with walking.Methods:A sample of 4,913 adult commuters was derived from a household travel survey in Melbourne, Australia (2012–2014). Local accessibility was measured as the availability of destinations within an 800-m pedestrian network from homes and places of work/education using a local living index [LLI; 0–3 (low), 4–6, 7–9, and 10–12 (high) destinations]. Regional accessibility was estimated using employment opportunity, commute travel time by mode, and public transport accessibility. Every individual’s potential minutes of walking for each level of exposure (observed and counter to fact) were predicted using multivariable regression models including confounders and interaction terms. For each contrast of exposure levels of interest, the corresponding within-individual differences in predicted walking were averaged across individuals to estimate marginal effects.Results:High LLI at home and work/education was associated with more minutes walking than low LLI by 3.9 [95% confidence interval (CI): 2.3, 5.5] and 8.3 (95% CI: 7.3, 9.3) min, respectively, in mutually adjusted models. Across regional accessibility measures, an independent association with walking and an interactive association with LLI at work/education was observed. To take one example, the regional accessibility measure of “Jobs within 30 min by public transport” was associated with 4.3 (95% CI: 2.9, 5.7) more mins walking for high (≥30,000  jobs) compared with low (<4,000  jobs) accessibility in adjusted models. The estimated difference for high vs. low LLI (work/education) (among those with low regional accessibility) was 3.6 min (95% CI: 2.3, 4.8), while the difference for high vs. low regional accessibility (among those with low LLI) was negligible (−0.01; 95% CI: −1.2, 1.2). However, the combined effect estimate for high LLI and high regional accessibility, compared with low on both, was 12.8 min (95% CI: 11.1, 14.5), or 9.3 (95% CI: 6.7, 11.8) min/d walking more than expected based on the separate effect estimates.Conclusions:High local living (work/education) and regional accessibility, regardless of the regional accessibility measure used, are positively associated with physical activity. High exposure to both is associated with greater benefit than exposure to one or the other alone. https://doi.org/10.1289/EHP3395

Background: Systematic reviews are used to inform healthcare decision making. In reviews that aim to examine the effects of organisational, policy change or public health interventions, or exposures, evidence from interrupted time series (ITS) studies may be included. A core component of many systematic reviews is meta-analysis, which is the statistical synthesis of results across studies. There is currently a lack of guidance informing the choice of meta-analysis methods for combining results from ITS studies, and there have been no studies examining the meta-analysis methods used in practice. This study therefore aims to describe current meta-analysis methods used in a cohort of reviews of ITS studies. Methods: We will identify the 100 most recent reviews (published between 1 January 2000 and 11 October 2019) that include meta-analyses of ITS studies from a search of eight electronic databases covering several disciplines (public health, psychology, education, economics). Study selection will be undertaken independently by two authors. Data extraction will be undertaken by one author, and for a random sample of the reviews, two authors. From eligible reviews we will extract details at the review level including discipline, type of interruption and any tools used to assess the risk of bias / methodological quality of included ITS studies; at the meta-analytic level we will extract type of outcome, effect measure(s), meta-analytic methods, and any methods used to re-analyse the individual ITS studies. Descriptive statistics will be used to summarise the data. Conclusions: This review will describe the methods used to meta-analyse results from ITS studies. Results from this review will inform future methods research examining how different meta-analysis methods perform, and ultimately, the development of guidance.