The Role of Executive Function in the Effectiveness of Multi-Component Interventions Targeting Physical Activity Behavior in Office Workers

A knowledge gap remains in understanding how to improve the intervention effectiveness in office workers targeting physically active (PA) behavior. We aim to identify the modifying effect of executive function (EF) on the intervention effectiveness targeting PA-behaviors, and to verify whether the observed effect varies by Job Demand Control (JDC) categories. This workplace-based intervention study included 245 participants who were randomized into a control group and two intervention arms—promoting physical activity (iPA) group or reducing sedentary behavior (iSED) group. The interventions were conducted through counselling-based cognitive behavioral therapy and team activities over 6 months. PA-behaviors were measured by an accelerometer. EF was assessed by the Trail Making Test-B, Stroop, and n-back test. The JDC categories were measured by the demand control questionnaire. Higher EF level at baseline was significantly associated with the intervention effect on increased sleep time (β-coefficient: 3.33, p = 0.003) and decreased sedentary time (−2.76, p = 0.049) in the iSED-group. Participants with active jobs (high job demands, high control) presented significantly increased light-intensity PA in the iSED-group in comparison to the control group. Among participants with a high level of EF and active jobs, relative to the control group, the iPA-group showed a substantial increase in light-intensity PA (1.58, p = 0.036) and the iSED-group showed a tendency of reducing sedentary behavior (−5.35, p = 0.054). The findings suggest that office workers with a high EF and active jobs may benefit most from an intervention study targeting PA-behaviors.

Process Evaluation of MAPS: A Highly Tailored Digital Intervention to Support Medication Adherence in Primary Care Setting

Background: Medication adherence can prevent health risks, but many patients do not adhere to their prescribed treatment. Our recent trial found that a digital intervention was effective at improving medication adherence in non-adherent patients with Hypertension or Type 2 Diabetes; but we do not know how it brought about behavioural changes. This research is a post-trial process evaluation of the mechanism by which the intervention achieved its intended effects.Methods: A mixed methods design with quantitative and qualitative evidence synthesis was employed. Data was generated by two studies. Study 1 used questionnaires to measure the underlying mechanisms of and the medication adherence behaviour, and digital logfiles to objectively capture intervention effects on the process of behaviour change. Multilevel regression analysis on 57 complete intervention group cases tested the effects of the intervention at modifying the mechanism of behaviour change and in turn at improving medication adherence. Study 2 used in depth interviews with a subsample of 20 intervention patients, and eight practise nurses. Thematic analysis provided evidence about the overarching intervention functions and recommendations to improve intervention reach and impact in primary care.Results: Study 1 found that intervention effectiveness was significantly associated with positive changes in the underlying mechanisms of behaviour change (R2 = 0.26, SE = 0.98, P = 0.00); and this effect was heightened twofold when the tailored intervention content and reporting on medication taking (R2 = 0.59, SE = 0.74, P = 0.00) was interested into the regression model. Study 2 suggested that the intervention supported motivation and ability to adherence, although clinically meaningful effects would require very brief medication adherence risk appraisal and signposting to ongoing digitally delivered behavioural support during clinical consultations.Conclusion: This post trial process evaluation used objective methods to capture the intervention effect on the mechanisms of behaviour change to explain intervention effectiveness, and subjective accounts to explore the circumstances under which these effects were achieved. The results of this process evaluation will inform a large scale randomised controlled trial in primary care.

Identifying effective intervention strategies to reduce children’s screen time: a systematic review and meta-analysis

Background Excessive screen time ($$\ge$$ ≥ 2 h per day) is associated with childhood overweight and obesity, physical inactivity, increased sedentary time, unfavorable dietary behaviors, and disrupted sleep. Previous reviews suggest intervening on screen time is associated with reductions in screen time and improvements in other obesogenic behaviors. However, it is unclear what study characteristics and behavior change techniques are potential mechanisms underlying the effectiveness of behavioral interventions. The purpose of this meta-analysis was to identify the behavior change techniques and study characteristics associated with effectiveness in behavioral interventions to reduce children’s (0–18 years) screen time. Methods A literature search of four databases (Ebscohost, Web of Science, EMBASE, and PubMed) was executed between January and February 2020 and updated during July 2021. Behavioral interventions targeting reductions in children’s (0–18 years) screen time were included. Information on study characteristics (e.g., sample size, duration) and behavior change techniques (e.g., information, goal-setting) were extracted. Data on randomization, allocation concealment, and blinding was extracted and used to assess risk of bias. Meta-regressions were used to explore whether intervention effectiveness was associated with the presence of behavior change techniques and study characteristics. Results The search identified 15,529 articles, of which 10,714 were screened for relevancy and 680 were retained for full-text screening. Of these, 204 studies provided quantitative data in the meta-analysis. The overall summary of random effects showed a small, beneficial impact of screen time interventions compared to controls (SDM = 0.116, 95CI 0.08 to 0.15). Inclusion of the Goals, Feedback, and Planning behavioral techniques were associated with a positive impact on intervention effectiveness (SDM = 0.145, 95CI 0.11 to 0.18). Interventions with smaller sample sizes (n < 95) delivered over short durations (< 52 weeks) were associated with larger effects compared to studies with larger sample sizes delivered over longer durations. In the presence of the Goals, Feedback, and Planning behavioral techniques, intervention effectiveness diminished as sample size increased. Conclusions Both intervention content and context are important to consider when designing interventions to reduce children’s screen time. As interventions are scaled, determining the active ingredients to optimize interventions along the translational continuum will be crucial to maximize reductions in children’s screen time.

Analysis of contamination in cluster randomized trials of malaria interventions

Background In cluster randomized trials (CRTs) of interventions against malaria, mosquito movement between households ultimately leads to contamination between intervention and control arms, unless they are separated by wide buffer zones. Methods This paper proposes a method for adjusting estimates of intervention effectiveness for contamination and for estimating a contamination range between intervention arms, the distance over which contamination measurably biases the estimate of effectiveness. A sigmoid function is fitted to malaria prevalence or incidence data as a function of the distance of households to the intervention boundary, stratified by intervention status and including a random effect for the clustering. The method is evaluated in a simulation study, corresponding to a range of rural settings with varying intervention effectiveness and contamination range, and applied to a CRT of insecticide treated nets in Ghana. Results The simulations indicate that the method leads to approximately unbiased estimates of effectiveness. Precision decreases with increasing mosquito movement, but the contamination range is much smaller than the maximum distance traveled by mosquitoes. For the method to provide precise and approximately unbiased estimates, at least 50% of the households should be at distances greater than the estimated contamination range from the discordant intervention arm. Conclusions A sigmoid approach provides an appropriate analysis for a CRT in the presence of contamination. Outcome data from boundary zones should not be discarded but used to provide estimates of the contamination range. This gives an alternative to “fried egg” designs, which use large clusters (increasing costs) and exclude buffer zones to avoid bias.