BackgroundNo school-based physical activity (PA) interventions among older adolescents have demonstrated long-term effectiveness, and few of them so far have addressed sedentary behaviour (SB). Based on behavioural theories and evidence, we designed a multi-level intervention to increase PA and decrease SB among vocational school students. This study investigates feasibility and acceptability of two main intervention components and research procedures. We also examine uptake of behaviour change techniques (BCTs) by the participants.MethodsDesign was an outcome assessor blinded, cluster-randomised controlled trial. Four classes of students (matched pairs) were randomised into one intervention and one control arm. The intervention consisted of (1) a 6-h group-based intervention for students, (2) two 2-h training workshops to reduce their students’ sitting in class for teachers, and (3) provision of light PA equipment in classrooms. At baseline (T1), mid-intervention (T2) at 3 weeks, post-intervention (T3) and 6 months after baseline (T4) we measured hypothesised psychosocial mediators and self-reported PA and sitting. Objective assessment of PA and SB (7-day accelerometry) was conducted at T1, T3 and T4. Body composition (bioimpedance) was measured at T1 and T4. Students and teachers in the intervention arm filled in acceptability questionnaires at T3.ResultsRecruitment rate was 64% (students) and 88.9% (teachers), and at T3, all post-intervention measurements were completed by 33 students (retention 76.7%) and 15 teachers (retention 93.8%). Acceptability ratings of sessions were high (students M = 6.29, scale 1–7), and data collection procedures were feasible. Intervention arm students reported increased use of BCTs, but uptake of some key BCTs was suboptimal. BCT use correlated highly with objective measures of PA. Based on both self-report and student evaluation, teachers in the intervention arm increased the use of sitting reduction strategies at post-intervention and T4 follow-up (p < .05).ConclusionsWe detected willingness of the target groups to participate, good response rates to questionnaires, adequate retention, as well as acceptability of the trial protocol. Investigation of BCT use among students helped further enhance intervention procedures to promote BCT use. After making necessary modifications identified, intervention effectiveness can next be tested in a definitive trial.Trial registration ISRCTN34534846. Registered 23 May 2014. Retrospectively registered.Electronic supplementary materialThe online version of this article (doi:10.1186/s12966-017-0484-0) contains supplementary material, which is available to authorized users.

BackgroundPhysical activity (PA) has been shown to decline during adolescence, and those with lower education have lower levels of activity already at this age, calling for targeted efforts for them. No previous study has demonstrated lasting effects of school-based PA interventions among older adolescents. Furthermore, these interventions have rarely targeted sedentary behaviour (SB) despite its relevance to health. The Let’s Move It trial aims to evaluate the effectiveness and the cost-effectiveness of a school-based, multi-level intervention, on PA and SB, among vocational school students. We hypothesise that the intervention is effective in increasing moderate-to-vigorous-intensity physical activity (MVPA), particularly among those with low or moderate baseline levels, and decreasing SB among all students.MethodsThe design is a cluster-randomised parallel group trial with an internal pilot study. The trial is conducted in six vocational schools in the Helsinki Metropolitan area, Finland. The intervention is carried out in 30 intervention classes, and 27 control classes retain the standard curriculum. The randomisation occurs at school-level to avoid contamination and to aid delivery.Three of the six schools, randomly allocated, receive the ‘Let’s Move It’ intervention which consists of 1) group sessions and poster campaign targeting students’ autonomous PA motivation and self-regulation skills, 2) sitting reduction in classrooms via alterations in choice architecture and teacher behaviour, and 3) enhancement of PA opportunities in school, home and community environments. At baseline, student participants are blind to group allocation. The trial is carried out in six batches in 2015–2017, with main measurements at pre-intervention baseline, and 2-month and 14-month follow-ups. Primary outcomes are for PA, MVPA measured by accelerometry and self-report, and for SB, sedentary time and breaks in sedentary time (accelerometry).Key secondary outcomes include measured body composition, self-reported well-being, and psychological variables. Process variables include measures of psychosocial determinants of PA (e.g. autonomous motivation) and use of behaviour change techniques. Process evaluation also includes qualitative interviews. Intervention fidelity is monitored.DiscussionThe study will establish whether the Let’s Move It intervention is effective in increasing PA and reducing SB in vocational school students, and identify key processes explaining the results.Trial registrationISRCTN10979479. Registered: 31.12.2015Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-3094-x) contains supplementary material, which is available to authorized users.

The compendium of self-enactable techniques to change and self-manage motivation and behaviour (

BackgroundDesigning evidence-based interventions to address socioeconomic disparities in health and health behaviours requires a better understanding of the specific explanatory mechanisms. We aimed to investigate a comprehensive range of potential theoretical mediators of physical activity (PA) and screen time in different socioeconomic status (SES) groups: a high SES group of high school students, and a low SES group of vocational school students. The COM-B system, including the Theoretical Domains Framework (TDF), was used as a heuristic framework to synthesise different theoretical determinants in this exploratory study.MethodsFinnish vocational and high school students (N = 659) aged 16–19, responded to a survey assessing psychological, social and environmental determinants of activity (PA and screen time). These determinants are mappable into the COM-B domains: capability, opportunity and motivation. The outcome measures were validated self-report measures for PA and screen time. The statistical analyses included a bootstrapping-based mediation procedure.ResultsRegarding PA, there were SES differences in all of the COM-B domains. For example, vocational school students reported using less self-monitoring of PA, weaker injunctive norms to engage in regular PA, and fewer intentions than high school students. Mediation analyses identified potential mediators of the SES-PA relationship in all of three domains: The most important candidates included self-monitoring (CI95 for b: 0.19–0.47), identity (0.04–0.25) and material resources available (0.01–0.16). However, SES was not related to most determinants of screentime, where there were mainly gender differences. Most determinants were similarly related with both behaviours in both SES groups, indicating no major moderation effect of SES on these relationships.ConclusionsThis study revealed that already in the first years of educational differentiation, levels of key PA determinants differ, contributing to socioeconomic differences in PA. The analyses identified the strongest mediators of the SES-PA association, but additional investigation utilising longitudinal and experimental designs are needed. This study demonstrates the usefulness of combining constructs from various theoretical approaches to better understand the role of distinct mechanisms that underpin socioeconomic health behaviour disparities.

Understanding the mechanisms underlying the effects of behaviour change interventions is vital for accumulating valid scientific evidence, and useful to informing practice and policy-making across multiple domains. Traditional approaches to such evaluations have applied study designs and statistical models, which implicitly assume that change is linear, constant and caused by independent influences on behaviour (such as behaviour change techniques). This article illustrates limitations of these standard tools, and considers the benefits of adopting a complex adaptive systems approach to behaviour change research. It (1) outlines the complexity of behaviours and behaviour change interventions; (2) introduces readers to some key features of complex systems and how these relate to human behaviour change; and (3) provides suggestions for how researchers can better account for implications of complexity in analysing change mechanisms. We focus on three common features of complex systems (i.e., interconnectedness, non-ergodicity and non-linearity), and introduce Recurrence Analysis, a method for non-linear time series analysis which is able to quantify complex dynamics. The supplemental website provides exemplifying code and data for practical analysis applications. The complex adaptive systems approach can complement traditional investigations by opening up novel avenues for understanding and theorising about the dynamics of behaviour change.

Understanding the mechanisms underlying the effects of behaviour change interventions is vital for accumulating valid scientific evidence, and useful to informing practice and policy-making across multiple domains. Traditional approaches to such evaluations have applied study designs and statistical models, which implicitly assume that change is linear, constant and caused by independent influences on behaviour (such as behaviour change techniques). This article illustrates limitations of these standard tools, and considers the benefits of adopting a complex adaptive systems approach to behaviour change research. It 1) outlines the complexity of behaviours and behaviour change interventions, 2) introduces readers to some key features of complex systems and how these relate to human behaviour change, and 3) provides suggestions for how researchers can better account for implications of complexity in analysing change mechanisms. We focus on three common features of complex systems (i.e. interconnectedness, non-ergodicity and non-linearity), and introduce Recurrence Analysis, a method for nonlinear time series analysis which is able to quantify complex dynamics. The supplemental website (https://git.io/Jffrm) provides exemplifying code and data for practical analysis applications. The complex adaptive systems approach opens up novel avenues for understanding and theorising about the dynamics of behaviour change.

Despite the positive health effect of physical activity, one third of the world’s population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at “high-resolution” and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (~25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants’ median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant’s individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of “just-in-time adaptive” behavioral interventions based on the detection of early warning signals for sudden behavioral losses.

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