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BackgroundSince desk-dominated work environments facilitate sedentary behavior, office workers sit for 66% of their working days and only 8% succeed in interrupting their prolonged periods of sitting within the first 55 minutes. Yet stretches of long and uninterrupted sitting increase the likelihood of several chronic metabolic and cardiovascular diseases. ObjectiveWe therefore developed a computer-based app designed to interrupt periods of prolonged sitting among office employees. MethodsWhen developing the intervention, we applied the intervention mapping protocol. This approach for the systematic design of theory and evidence-based behavior change programs consists of 6 steps: creation of a logic model of the problem, creation of a logic model of change, program design, program production, design of an implementation plan, and development of an evaluation plan. ResultsWorking through all 6 steps has resulted in an individually adaptable intervention to reduce sedentary behavior at work. The intervention, UPcomplish, consists of tailored, half-automatized motivational components delivered by a coach. To register sedentary behavior, the VitaBit (VitaBit Software International BV) toolkit, a wearable accelerometry-based monitoring device, is used. Among others, UPcomplish includes personalized goal setting, tailored suggestions to overcome hurdles, and weekly challenges. The VitaBit toolkit supports the participants to monitor their behavior in relation to self-set goals. ConclusionsIntervention mapping is a useful protocol not only for the systematic development of a comprehensive intervention to reduce sedentary behavior but also for planning program adherence, program implementation, and program maintenance. It facilitates obtaining the participation of relevant stakeholders at different ecological levels in the development process of the intervention and anticipating facilitators to and barriers of program implementation and maintenance. Trial RegistrationNetherlands Trial Register NL7503; https://www.trialregister.nl/trial/7503

Harassment and abuse represent a pervasive and critical problem in sport with far-reaching consequences. Survivors' testimonials underscore the profound and enduring impact of these experiences at individual, interpersonal, organizational and community level. Many of their stories reveal painful inaction from responsible adults in the sport organization, aggravating the harm. Other contributing factors to the harm inflicted include a culture of silence, lack of knowledge and understanding of what constitutes abuse, unawareness of reporting and supporting mechanisms, and fear of potential consequences. While effective bystander interventions have been developed outside the sport context, particularly targeting students in higher education, such initiatives have yet to be extensively adapted and assessed within the sport context. To address this gap, the Safe Sport Allies Erasmus+ collaborative partnership relied on the intervention mapping approach as a guiding framework to systematically develop a bystander training program (i.e., Safe Sport Allies) to train youth sport participants and youth sport coaches to act as effective bystanders. The current paper describes the comprehensive development process and provides an overview of implementation and evaluation possibilities. Throughout the paper, it is explained how each step of the Intervention Mapping approach shaped the Safe Sport Allies bystander training program. The program development, and the developed plans for implementation and evaluation are presented, shedding light on challenges encountered. The bystander training program developed in this paper and the implementation and evaluation plans can serve as an outline to build future interventions within this critical domain of safeguarding in sport. [ABSTRACT FROM AUTHOR]

Regression analyses are commonly used for selecting determinants to target in behavior change interventions, but the aim of this article is to explain why regression analyses are not suitable for this purpose (i.e. the regression trap). This aim is achieved by providing (1) a theoretical rationale based on overlap among determinants; (2) a mathematical rationale based on the formulas that are used to calculate regression coefficients; and (3) examples based on real-world data. First, the meaning of regression coefficients is commonly explained as expressing the association between a determinant and a target behavior 'holding all other predictors constant.' We explain that this often boils down to 'neglecting a part of the psyche.' Second, we demonstrate that the interpretation of regression coefficients is distorted by correlations between determinants. Third, the examples provided demonstrate the impact this has in practice. This results in interventions targeting determinants that are less relevant and, thereby, have less impact on behavior change. There are theoretical, mathematical, and practical reasons why regression analyses, and by extension multivariate analyses relying on correlations, are not suitable to select determinants to target in behavior change interventions. Instead, intervention developers should consider univariate distributions and bivariate association estimates simultaneously and there are freely accessible tools available to do so. [ABSTRACT FROM AUTHOR]

When developing behavior change interventions in a systematic way, it is important to select determinants relevant to the target behavior. Data is needed to gain insight into the determinant structures (the relative strengths of associations between determinants and behavior) and their univariate distributions. This insight is crucial to select the most relevant determinants, but at the same time institutions tasked with behavior change (e.g. prevention organizations, municipal health services) often operate under prohibitive resource constraints, which also extend to how easily they can collect data from a sample. This paper introduces CIBERlite – an approach that furnishes the intervention developer with an idea of the relevance of a limited number of determinants using short measurements informed by theory. The first study (N = 401) in a series of three explores the convergent validity of short and full measurements of determinants derived from the Reasoned Action Approach. The short measurements are used in the main study (N = 415) that serves as a proof-of-concept for the CIBERlite plot, an efficient visualization combining data of determinant structures and their univariate distributions for eight behaviors. The unexpected patterns detected in the main study led to an expert estimation study (N = 45), which shows that individual experts have difficulty in predicting how people score on determinants. This stresses the importance of conducting determinant studies and CIBERlite is a valuable alternative to do so if resources are limited. [ABSTRACT FROM AUTHOR]