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Background
Long periods of uninterrupted sitting, i.e., sedentary bouts, and their relationship with adverse health outcomes have moved into focus of public health recommendations. However, evidence on associations between sedentary bouts and adiposity markers is limited. Our aim was to investigate associations of the daily number of sedentary bouts with waist circumference (WC) and body mass index (BMI) in a sample of middle-aged to older adults.
Methods
In this cross-sectional study, data were collected from three different studies that took place in the area of Greifswald, Northern Germany, between 2012 and 2018. In total, 460 adults from the general population aged 40 to 75 years and without known cardiovascular disease wore tri-axial accelerometers (ActiGraph Model GT3X+, Pensacola, FL) on the hip for seven consecutive days. A wear time of ≥ 10 h on ≥ 4 days was required for analyses. WC (cm) and BMI (kg m− 2) were measured in a standardized way. Separate multilevel mixed-effects linear regression analyses were used to investigate associations of sedentary bouts (1 to 10 min, >10 to 30 min, and >30 min) with WC and BMI. Models were adjusted for potential confounders including sex, age, school education, employment, current smoking, season of data collection, and composition of accelerometer-based time use.
Results
Participants (66% females) were on average 57.1 (standard deviation, SD 8.5) years old and 36% had a school education >10 years. The mean number of sedentary bouts per day was 95.1 (SD 25.0) for 1-to-10-minute bouts, 13.3 (SD 3.4) for >10-to-30-minute bouts and 3.5 (SD 1.9) for >30-minute bouts. Mean WC was 91.1 cm (SD 12.3) and mean BMI was 26.9 kg m− 2 (SD 3.8). The daily number of 1-to-10-minute bouts was inversely associated with BMI (b = -0.027; p = 0.047) and the daily number of >30-minute bouts was positively associated with WC (b = 0.330; p = 0.001). All other associations were not statistically significant.
Conclusion
The findings provide some evidence on favourable associations of short sedentary bouts as well as unfavourable associations of long sedentary bouts with adiposity markers. Our results may contribute to a growing body of literature that can help to define public health recommendations for interrupting prolonged sedentary periods.
Trial registration
Study 1: German Clinical Trials Register (DRKS00010996); study 2: ClinicalTrials.gov (NCT02990039); study 3: ClinicalTrials.gov (NCT03539237).
Background
Missing data are ubiquitous in randomised controlled trials. Although sensitivity analyses for different missing data mechanisms (missing at random vs. missing not at random) are widely recommended, they are rarely conducted in practice. The aim of the present study was to demonstrate sensitivity analyses for different assumptions regarding the missing data mechanism for randomised controlled trials using latent growth modelling (LGM).
Methods
Data from a randomised controlled brief alcohol intervention trial was used. The sample included 1646 adults (56% female; mean age = 31.0 years) from the general population who had received up to three individualized alcohol feedback letters or assessment-only. Follow-up interviews were conducted after 12 and 36 months via telephone. The main outcome for the analysis was change in alcohol use over time. A three-step LGM approach was used. First, evidence about the process that generated the missing data was accumulated by analysing the extent of missing values in both study conditions, missing data patterns, and baseline variables that predicted participation in the two follow-up assessments using logistic regression. Second, growth models were calculated to analyse intervention effects over time. These models assumed that data were missing at random and applied full-information maximum likelihood estimation. Third, the findings were safeguarded by incorporating model components to account for the possibility that data were missing not at random. For that purpose, Diggle-Kenward selection, Wu-Carroll shared parameter and pattern mixture models were implemented.
Results
Although the true data generating process remained unknown, the evidence was unequivocal: both the intervention and control group reduced their alcohol use over time, but no significant group differences emerged. There was no clear evidence for intervention efficacy, neither in the growth models that assumed the missing data to be at random nor those that assumed the missing data to be not at random.
Conclusion
The illustrated approach allows the assessment of how sensitive conclusions about the efficacy of an intervention are to different assumptions regarding the missing data mechanism. For researchers familiar with LGM, it is a valuable statistical supplement to safeguard their findings against the possibility of nonignorable missingness.