Factors related to the implementation and scale-up of physical activity interventions in Ireland: a qualitative study with policy makers, funders, researchers and practitioners.

BACKGROUND: Current literature reports a gap between development of effective interventions to promote physical activity and the systematic uptake into real-world settings. Factors relating to implementation and scale-up of physical activity interventions have been examined, however the perspectives of multiple stakeholders from different domains are not well researched. The purpose of this study was to examine the perceived factors related to physical activity intervention implementation and scale-up in different domains from different stakeholders on the island of Ireland. METHODS: Practitioners, researchers, funders and policy makers in Ireland were invited to take part in a semi-structured interview exploring factors related to the implementation and scale-up of eleven different physical activity interventions. A thematic analysis was conducted to identify factors related to the implementation and scale-up of the included interventions. The data collection and analysis were guided by the Consolidated Framework for Implementation Research. RESULTS: Thirty-eight participants took part in the interviews which identified factors related to 1) intervention planning and practical considerations; 2) organisational structures, staffing and resources related to delivery; 3) reflection, evaluation and updating of the intervention; and 4) practical consideration related to scale-up. Furthermore, participants referred to the ongoing commitment, engagement, and support needed throughout the implementation process. CONCLUSIONS: Future research and practice needs to consider how different factors are experienced at different implementation stages and by the different stakeholder groups involved. The findings highlight multiple inter-related factors that influence the implementation and scale-up of physical activity interventions, but also identifies many strategies that can be utilised to aid future successes.

Differential Associations of Total and Context-Specific Sedentary Time with Depressive Symptoms Among Adolescents: Results from Ireland's CSPPA Study.

BACKGROUND: Higher levels of sedentary behaviour (SB) and screen-time are associated with greater symptoms of depression in adolescents, but the effect of the type and context of SB and screen-time remains underexplored. As part of a nationally-representative observational study, the current cross-sectional study examined associations between SB, screen-time and depressive symptoms among 422 adolescents (13.5 ± 0.92 years; 125 female) in the Republic of Ireland. METHOD: Participants completed the Quick Inventory of Depressive Symptomatology and self-reported weekly SB, categorised into mentally-active screen-time (e.g., computer use for fun), mentally-passive screen-time (e.g., television viewing) and mentally-active non-screen-based SB (e.g., reading). Mann-Whitney U tests and Kruskal-Wallis H tests examined differences in screen-time and depressive symptoms by relevant covariates. Linear regression quantified crude and adjusted associations between total SB and mentally-active and mentally-passive screen-time and SB, and depressive symptoms. RESULTS: Crude and adjusted linear regressions showed total SB was significantly, positively associated with depressive symptoms (unadjusted: ß = 0.27, p = 0.002, adjusted: ß = 0.27, p = 0.002). When type and context were examined in the same model, only mentally-active screen-time was positively associated with depressive symptoms (unadjusted: ß = 0.37, p = 0.009, adjusted: ß = 0.39, p = 0.007). CONCLUSION: Differential associations between total SB and mentally-active screen-time and SB, versus mentally-passive screen-time, and depressive symptoms among Irish adolescents were observed. Findings highlight the importance of investigating the context and type of SB and screen-time in adolescents.

Assessing the implementation of physical activity-promoting public policies in the Republic of Ireland: a study using the Physical Activity Environment Policy Index (PA-EPI).

BACKGROUND: Government policy can promote physical activity (PA) as part of a multilevel systems-based approach. The Physical Activity Environment Policy Index (PA-EPI) is a monitoring framework which assesses the implementation of government policy by drawing on the experience of national stakeholders. This study is the first to assess the extent of policy implementation in the Republic of Ireland using the PA-EPI tool, and to provide information on how policy implementation can be improved, with the intention of maximizing its impact on population levels of PA. METHODS: This mixed-methods research study, comprising eight steps, was carried out in 2022. Information documenting the evidence for implementation of PA policy, across all 45 PA-EPI indicators, was collected via systematic document analysis, and validated via survey and interview with government officials. Thirty-two nongovernment stakeholders rated this evidence on a five-point Likert scale. Aggregated scores were reviewed by stakeholders who collectively identified and prioritized critical implementation gaps. RESULTS: Of the 45 PA-EPI indicators, one received an implementation rating of 'none/very little', 25 received a rating of 'low' and 19 received a 'medium' rating. No indicator was rated as fully implemented. The indicators that received the highest level of implementation related to sustained mass media campaigns promoting PA and PA monitoring. Ten priority recommendations were developed. CONCLUSIONS: This study reveals substantial implementation gaps for PA policy in the Republic of Ireland. It provides recommendations for policy action to address these gaps. In time, studies utilizing the PA-EPI will enable cross-country comparison and benchmarking of PA policy implementation, incentivizing improved PA policy creation and implementation.

Associations between screen-time, physical activity and depressive symptoms differ based on gender and screen-time mode.

Higher levels of physical activity (PA) and screen time (ST) are associated with positive and negative mental health outcomes among adolescents, respectively. Research is needed to determine the interrelationship between ST and PA with depressive symptoms and the influence of ST modes. This study examines the associations between ST and PA level with depressive symptoms among 1756 adolescents (15.2 ± 1.6y; 995 female) in Ireland. Participants completed the Quick Inventory of Depressive Symptomatology and self-reported weekly ST (TV, computer, and phone use) and PA level (low, moderate, and high). Linear regressions examined associations between ST mode, PA level and relevant covariates with depressive symptoms in the full sample by gender, and in a sample stratified by PA level. Chi-square tests and one-way ANOVA examined gender differences in hours of ST, PA levels, and depressive symptoms. Differential associations were observed depending on the gender and ST mode. Higher levels of computer (ß = 0.106, p ≤ 0.000) and phone use (ß = 0.138, p ≤ 0.000) showed the strongest associations with depressive symptoms. PA level was inversely associated with depressive symptoms (ß = - 0.175, p ≤ 0.000). When the sample was stratified by PA level, only associations between phone use and depressive symptoms were moderated by PA level for males and females. These findings highlight the complex interrelationships between ST, PA, and depressive symptoms, and that associations may vary based on gender and ST mode. This may have implications for future interventions. Increasing PA and reducing ST should be targeted concurrently with consideration given to different media and genders. Future research should explore longitudinal and prospective associations.

What we know about the actual implementation process of public physical activity policies: results from a scoping review.

BACKGROUND: Physical inactivity rates have remained high worldwide since 2001. Public policies are an essential upstream lever to target individual physical activity (PA) behaviour. However, implementers have different strategies and face implementation challenges that are poorly understood. The present study analyzes the implementation processes of public policies to promote PA in terms of: (i) the policies covered and their legal quality, (ii) the actors and stakeholders involved in the implementation process and (iii) the used implementation strategies (vertical, horizontal or a mix). METHODS: A scoping review was systematically conducted (registered Open Science Framework: osf.io/7w84q/), searching 10 databases and grey literature until March 2022. Of the 7741 titles and abstracts identified initially, 10 studies were included. RESULTS: The current evidence includes high-income countries (USA, n = 7; UK, New Zealand and Oman, n = 1 each). Policy areas covered are education (school sector) and PA promotion in general (national PA plans or city-wide approaches). The legal classification ranges from laws (school sector) to coordination and budgeting to non-legally binding recommendations. The jurisdictions covered were federal (n = 4), state (n = 1), county (n = 1), school district (n = 1) and city (n = 3). Implementation strategies for city-wide approaches are characterized by a coordinated approach with vertical and horizontal integration; federal PA policies by a mix of implementation strategies; and the school sector by a strict horizontal top-down integration without the involvement of other actors. CONCLUSION: Implementation strategies differ by policy field. Therefore, continuous evaluation of the implementation process is necessary to align policy implementation with policy goals to promote individual PA behaviour.

Reaching consensus on definitions for food and physical activity policies: experience from the Policy Evaluation Network.

BACKGROUND: An upsurge in policy evaluation research within public health sciences has led to multi-disciplinary research networks like the 'Policy Evaluation Network' (PEN). This multi-disciplinary collaboration highlighted the need for consensus on clear, common terminology and definitions to facilitate the multi-disciplinary research. This article outlines the development process of the PEN definitions glossary tool, with a focus on the key domains of policy design, implementation and outcomes as they apply to physical activity, sedentary behaviour and dietary behaviours. METHODS: A project specific participatory process was undertaken, involving PEN researchers (n = 48) from seven European countries across various disciplinary backgrounds. All involved researchers were invited to identify and collate definitions that were commonly used in their research field. Terms and definitions were discussed and debated during three online workshops. Subsequently, the definitions were discussed and refined until consensus was reached. RESULTS: Consensus definitions for 93 terms related to the evaluation of policy design, implementation and outcomes are provided. Consensus was reached on a range of terms where the terms were understood and used differently across represented disciplines (e.g. 'Outcome' and 'Impact'). A conceptual 'Inter-relations in policy-related concepts' diagram was developed to enable navigation through an online database with key terms. CONCLUSIONS: The definitions resulting from this participatory process has supported PEN researchers and practitioners across disciplines to reach a shared understanding of different terms related to policy evaluation. Thus, providing a platform for avoiding conflicting use of the same terms in differing contexts over the course of the PEN work programme, facilitating clear and consistent communication, and allowing for clarity within collaborative multi-disciplinary projects and in public-facing messages.

The impact of mass-media campaigns on physical activity: a review of reviews through a policy lens.

BACKGROUND: This review of reviews aimed to: (1) summarize the evidence from published reviews on the effectiveness of mass-media campaigns to promote physical activity (PA) or PA-related determinants (intermediate psychological and proximal outcomes) and (2) to identify policy-relevant recommendations related to successful PA campaigns. METHODS: An extensive literature search was performed on 1 March 2021. Reviews that evaluated the impact of campaigns on distal (e.g. PA) and/or proximal outcomes of PA (awareness, knowledge, etc.) and that targeted the general population or subsets were included. Quality of reviews was assessed using the AMSTAR-2 tool. Policy-relevant recommendations were systematically derived and synthesized and formulated as good practice statements. A protocol was registered beforehand (ID: CRD42021249184). RESULTS: A total of 1915 studies were identified, of which 22 reviews were included. The most consistent evidence was found for the effectiveness of mass-media campaigns on proximal outcomes, while the evidence for distal outcomes was mixed. Good practice statements were derived: (1) to achieve behaviour change, mass-media is an important component of larger, multilevel and multicomponent strategies; (2) mass-media strategies should be coordinated and aligned at local- and national-level and be sustained, monitored and resourced at these levels and (3) media should be tailored to reduce socioeconomic inequalities. CONCLUSIONS: Mass-media can play an important role in the promotion of PA. In general, evidence was more inconsistent for effectiveness on distal outcomes than for proximal outcomes. Policy-relevant recommendations include that mass-media strategies should be resourced, coordinated, aligned, sustained, monitored and evaluated on the local and national level.

The Physical Activity Environment Policy Index for monitoring government policies and actions to improve physical activity.

BACKGROUND: A multifaceted response, including government action, is essential to improve population levels of physical activity (PA). This article describes the development process of the 'Physical Activity Environment Policy Index' (PA-EPI) monitoring framework, a tool to assess government policies and actions for creating a healthy PA environment. METHODS: An iterative process was undertaken. This involved a review of policy documents from authoritative organizations, a PA policy audit of four European countries, and a systematic review of scientific literature. This was followed by an online consultation with academic experts (N = 101; 20 countries, 72% response rate), and policymakers (N = 40, 4 EU countries). During this process, consensus workshops were conducted, where quantitative and qualitative data, alongside theoretical and pragmatic considerations, were used to inform PA-EPI development. RESULTS: The PA-EPI is conceptualized as a two-component 'policy' and 'infrastructure support' framework. The two-components comprise eight policy and seven infrastructure support domains. The policy domains are education, transport, urban design, healthcare, public education (including mass media), sport-for-all, workplaces and community. The infrastructure support domains are leadership, governance, monitoring and intelligence, funding and resources, platforms for interaction, workforce development and health-in-all-policies. Forty-five 'good practice statements' or indicators of ideal good practice within each domain conclude the PA-EPI. A potential eight-step process for conducting the PA-EPI is described. CONCLUSIONS: Once pre-tested and piloted in several countries of various sizes and income levels, the PA-EPI good practice statements will evolve into benchmarks established by governments at the forefront of creating and implementing policies to address inactivity.

Dietary behaviour and physical activity policies in Europe: learnings from the Policy Evaluation Network (PEN).

The European Policy Evaluation Network (PEN), initiated in autumn 2018, aimed at advancing the evidence base for public policies impacting dietary behaviour, physical activity and sedentary behaviours in Europe. This is needed because non-communicable diseases-the leading cause of global mortality-are substantially caused by physical inactivity and unhealthy dietary behaviours, which in turn are driven by upstream factors that have not yet been addressed effectively by prevention approaches. Thus, successful policy interventions are required that target entire populations and tackle the 'causes of the causes'. To advance our knowledge on the effective implementation of policies and their impact in terms of improving health behaviours, PEN focused on five research tasks: (i) Adaptation and implementation of a Food Environment Policy Index (Food-EPI) and development of a Physical Activity Environment Policy Index (PA-EPI); (ii) Mapping of health-related indicators needed for policy evaluation and facilitating a harmonized pan-European approach for surveillance to assess the impact of policy interventions; (iii) Refining quantitative methods to evaluate the impact of public policies; (iv) Identifying key barriers and facilitators of implementation of policies; and (v) Advance understanding the equity impact of the development, implementation and evaluation of policies aimed at promoting physical activity and a healthy diet. Finally, and in order to provide concrete evidence for policymaking, existing exemplary policies, namely sugar-sweetened beverages taxation, active transport policies and school policies on nutrition and physical activity were assessed in consideration of these five tasks. At the end of the PEN project's formal runtime, considerable advancements have been made. Here, we present an overview of the most important learnings and outputs.

Acceptability of policies targeting dietary behaviours and physical activity: a systematic review of tools and outcomes.

BACKGROUND: Successful implementation of health policies require acceptance from the public and policy-makers. This review aimed to identify tools used to assess the acceptability of policies targeting physical activity and dietary behaviour, and examine if acceptability differs depending on characteristics of the policy and of the respondents. METHODS: A systematic review (PROSPERO: CRD42021232326) was conducted using three databases (Science Direct, PubMed and Web of Science). RESULTS: Of the initial 7780 hits, we included 48 eligible studies (n = 32 on dietary behaviour, n = 11 on physical activity and n = 5 on both), using qualitative and quantitative designs (n = 25 cross-sectional, quantitative; n = 15 qualitative; n = 5 randomized controlled trials; n = 3 mixed-methods design). Acceptability was analysed through online surveys (n = 24), interviews (n = 10), focus groups (n = 10), retrospective textual analysis (n = 3) and a taste-test experiment (n = 1). Notably, only 3 (out of 48) studies applied a theoretical foundation for their assessment. Less intrusive policies such as food labels and policies in a later stage of the implementation process received higher levels of acceptability. Women, older participants and respondents who rated policies as appropriate and effective showed the highest levels of acceptability. CONCLUSION: Highly intrusive policies such as taxations or restrictions are the least accepted when first implemented, but respondents' confidence in the relevance and effectiveness of the policy may boost acceptability over the course of implementation. Studies using validated tools and a theoretical foundation are needed to further examine opportunities to increase acceptability.

Selection of key indicators for European policy monitoring and surveillance for dietary behaviour, physical activity and sedentary behaviour.

BACKGROUND: A pan-European approach to evaluate policy impact on health behaviour requires the employment of a consensus set of established and relevant indicators. METHODS: As part of the Joint Programming Initiative on a Healthy Diet for a Healthy Life, the Policy Evaluation Network PEN identified key indicators of health behaviours and their determinants. These key indicators are already, or have the potential to be, adopted by large European Union surveillance systems for the assessment of policy impact. The iterative selection process included consultations in two rounds via email prior to a 2-days expert workshop. The experts collated a list of dietary behaviour, physical activity and sedentary behaviour indicators for European policy monitoring in young and adult populations based on existing frameworks and literature reviews. The expert panel was composed of researchers, policy makers and representatives of major European surveillance systems and related initiatives, as well as, representatives of organisations providing monitoring data, such as the European Commission and Eurostat. RESULTS: The process provided two lists of key indicators including 37 diet 'policy' indicators and 35 indicators for dietary behaviour and their 'determinants'; as well as 32 physical activity 'policy' indicators and 35 indicators for physical activity, sedentary behaviour and their 'determinants'. CONCLUSION: A total of 139 key indicators related to the individual, the setting and the population level, and suitable for the assessment of dietary behaviour, physical activity and sedentary behaviour were prioritised by policy makers and researchers with the ultimate aim to embed policy evaluation measures in existing surveillance systems across the European Union. In a next step, data sources and suitable instruments will be identified to assess these key indicators.

Identification of health-related behavioural clusters and their association with demographic characteristics in Irish university students.

BACKGROUND: Students engage in risky health-related behaviours that influence their current and future health status. Health-related behaviours cluster among adults and differently based on sub-populations characteristics but research is lacking for university populations. Examining the clustering of health- related behaviours can inform our initiatives and strategies, while examining cluster members' characteristics can help target those who can prosper most from health promotion efforts. This study examines the clustering of health-related behaviours in Irish university students, and investigates the relationship with students' sex, age, field of study and accommodation type. METHODS: An online survey was completed by 5672 Irish university students (51.3% male; 21.60 ± 5.65 years) during 2014. Two-step cluster analysis was used to understand how health-related behaviours (physical activity, smoking, alcohol intake, drug use and dietary habits) cluster among male and female students. Binary logistic regressions were conducted to examine the likelihood of students falling into certain clusters based on their characteristics. RESULTS: Five cluster groups were identified in males and four in females. A quarter of males were categorised as ideal healthy with older students and those from certain fields of study having a higher likelihood of being classified in a low physical activity and poor diet (OR = 1.06-2.89), alcohol consumption (OR = 1.03-3.04), or smoking and drug use (OR = 1.06-2.73) cluster. Forty-five percent of females were categorised as ideal healthy with older females more likely to be in a low active and smoking cluster (OR = 1.03), and less likely to be in a convenience food cluster (OR = 0.96). Females from certain fields of study were also more likely to be classified in these clusters (OR = 1.59-1.76). Students living away from their family home had in increased likelihood of being in a cluster related to a higher frequency of alcohol consumption (OR = 1.72-3.05). CONCLUSION: Health-related behaviours cluster among this population and need to be taken into account when designing multi-health interventions and policies. These findings can be used to target student groups at risk, leading to more efficient and successful health promotion efforts. The addition of modules providing information regarding health-related behaviours are advised in all fields of study.

Physical Activity, Sedentary Behavior, and Diet-Related eHealth and mHealth Research: Bibliometric Analysis.

BACKGROUND: Electronic health (eHealth) and mobile health (mHealth) approaches to address low physical activity levels, sedentary behavior, and unhealthy diets have received significant research attention. However, attempts to systematically map the entirety of the research field are lacking. This gap can be filled with a bibliometric study, where publication-specific data such as citations, journals, authors, and keywords are used to provide a systematic overview of a specific field. Such analyses will help researchers better position their work. OBJECTIVE: The objective of this review was to use bibliometric data to provide an overview of the eHealth and mHealth research field related to physical activity, sedentary behavior, and diet. METHODS: The Web of Science (WoS) Core Collection was searched to retrieve all existing and highly cited (as defined by WoS) physical activity, sedentary behavior, and diet related eHealth and mHealth research papers published in English between January 1, 2000 and December 31, 2016. Retrieved titles were screened for eligibility, using the abstract and full-text where needed. We described publication trends over time, which included journals, authors, and countries of eligible papers, as well as their keywords and subject categories. Citations of eligible papers were compared with those expected based on published data. Additionally, we described highly-cited papers of the field (ie, top ranked 1%). RESULTS: The search identified 4805 hits, of which 1712 (including 42 highly-cited papers) were included in the analyses. Publication output increased on an average of 26% per year since 2000, with 49.00% (839/1712) of papers being published between 2014 and 2016. Overall and throughout the years, eHealth and mHealth papers related to physical activity, sedentary behavior, and diet received more citations than expected compared with papers in the same WoS subject categories. The Journal of Medical Internet Research published most papers in the field (9.58%, 164/1712). Most papers originated from high-income countries (96.90%, 1659/1717), in particular the United States (48.83%, 836/1712). Most papers were trials and studied physical activity. Beginning in 2013, research on Generation 2 technologies (eg, smartphones, wearables) sharply increased, while research on Generation 1 (eg, text messages) technologies increased at a reduced pace. Reviews accounted for 20 of the 42 highly-cited papers (n=19 systematic reviews). Social media, smartphone apps, and wearable activity trackers used to encourage physical activity, less sedentary behavior, and/or healthy eating were the focus of 14 highly-cited papers. CONCLUSIONS: This study highlighted the rapid growth of the eHealth and mHealth physical activity, sedentary behavior, and diet research field, emphasized the sizeable contribution of research from high-income countries, and pointed to the increased research interest in Generation 2 technologies. It is expected that the field will grow and diversify further and that reviews and research on most recent technologies will continue to strongly impact the field.

Behavior Change Techniques in Physical Activity eHealth Interventions for People With Cardiovascular Disease: Systematic Review.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of premature death and disability in Europe, accounting for 4 million deaths per year and costing the European Union economy almost €196 billion annually. There is strong evidence to suggest that exercise-based secondary rehabilitation programs can decrease the mortality risk and improve health among patients with CVD. Theory-informed use of behavior change techniques (BCTs) is important in the design of cardiac rehabilitation programs aimed at changing cardiovascular risk factors. Electronic health (eHealth) is the use of information and communication technologies (ICTs) for health. This emerging area of health care has the ability to enhance self-management of chronic disease by making health care more accessible, affordable, and available to the public. However, evidence-based information on the use of BCTs in eHealth interventions is limited, and particularly so, for individuals living with CVD. OBJECTIVE: The aim of this systematic review was to assess the application of BCTs in eHealth interventions designed to increase physical activity (PA) in CVD populations. METHODS: A total of 7 electronic databases, including EBSCOhost (MEDLINE, PsycINFO, Academic Search Complete, SPORTDiscus with Full Text, and CINAHL Complete), Scopus, and Web of Science (Core Collection) were searched. Two authors independently reviewed references using the software package Covidence (Veritas Health Innovation). The reviewers met to resolve any discrepancies, with a third independent reviewer acting as an arbitrator when required. Following this, data were extracted from the papers that met the inclusion criteria. Bias assessment of the studies was carried out using the Cochrane Collaboration's tool for assessing the risk of bias within Covidence; this was followed by a narrative synthesis. RESULTS: Out of the 987 studies that were identified, 14 were included in the review. An additional 9 studies were added following a hand search of review paper references. The average number of BCTs used across the 23 studies was 7.2 (range 1-19). The top three most frequently used BCTs included information about health consequences (78%, 18/23), goal setting (behavior; 74%, 17/23), and joint third, self-monitoring of behavior and social support (practical) were included in 11 studies (48%, 11/23) each. CONCLUSIONS: This systematic review is the first to investigate the use of BCTs in PA eHealth interventions specifically designed for people with CVD. This research will have clear implications for health care policy and research by outlining the BCTs used in eHealth interventions for chronic illnesses, in particular CVD, thereby providing clear foundations for further research and developments in the area.

Impact of physical activity domains on subsequent physical activity in youth: a 5-year longitudinal study.

This study evaluates how domains of physical activity (PA) in youth predict later PA and assesses factors influencing changes in sports participation. Young people from the Children's Sport Participation and Physical Activity study (n = 873; baseline age 10-18 years; 30.4% male) completed self-report surveys in 2009 and 2014. In a multiple linear regression analysis, participation frequency in club sport (ß = 0.18) and extracurricular sport (ß = 0.13) significantly predicted PA 5 years later, adjusted for age, sex and urban/rural classification (P < 0.01). Overall, rates of regular (at least once per week) youth sports participation were high (males 79.3-85.5%; females 74.8-83.2%). Uptake and dropout of specific sports varied widely. Despite high levels of migration into and out of Gaelic games, they remained popular at follow-up. Weight training was the only sport that increased in both sexes (P < 0.05). Fitness, friends and enjoyment were top motivations for taking up a new sport. Other commitments, a lack of interest and time were important factors leading to sports dropout. PA promotion strategies should include youth sport, take into consideration what sports are attractive to young people and address reasons for uptake and dropout.

Validity of a two-item physical activity questionnaire for assessing attainment of physical activity guidelines in youth.

BACKGROUND: As physical activity is important for health and well-being, it is essential to monitor population prevalence of physical activity. Surveillance is dependent on the use of valid and reliable measurement tools. The PACE+ questionnaire is used globally in youth and has acceptable reliability; however it has not been validated in a European sample. The purpose of this study is to validate this instrument in a sample of 10-18 year old Irish youth. METHODS: Participants (n = 419, 45.7 % male) completed the PACE+ two-item questionnaire and were asked to wear an Actigraph accelerometer for eight consecutive days. Freedson cut-points were used to estimate moderate to vigorous physical activity from accelerometer counts. Analyses compared self-report and accelerometry data in participants with (1) ≥5 and (2) seven valid accelerometer days. Calculations were performed for the whole sample, and were stratified by sex and school level (primary; post-primary). RESULTS: Spearman correlations between self-reported physical activity levels and accelerometry derived minutes of moderate-to-vigorous physical activity per day were small (r = 0.27; seven valid days) to moderate (r = 0.34; ≥5 valid days). Higher correlations were found in older participants (post-primary r = 0.39; primary r = 0.24) and females (r = 0.39; males r = 0.27) using ≥5 valid days. The agreement level was high (68-96 %). The accuracy of classifying those not meeting the guidelines (specificity) was moderate to high (59-100 %). CONCLUSIONS: The PACE+ self-report instrument has acceptable validity for assessing non-achievement of the adolescent physical activity recommendations. The validity is higher in females and increases with age. The continued use of the tool is recommended and will allow for comparability between studies, tracking of physical activity over time including trends in youth population prevalence.

How Does the University Environment Relate to Student's Physical Activity Patterns in Ireland.

BACKGROUND: Identifying factors related to physical activity in university students can aid the development of health promotion interventions, but there is limited research regarding the influence of university environments. This study examined the relationship between level of provision for university environments that aim to promote physical activity and self-reported physical activity patterns of students. METHODS: An environmental audit tool was completed by universities (n = 28) on the island of Ireland to acquire information about physical activity opportunities, resources, and supports offered. Students (N = 6951; 50.7% male; 21.51 [5.55] y) completed an online survey, providing responses about their active transport and recreational physical activity behaviors. Binary logistic regressions were used to examine the associations between environmental factors that support physical activity and clustered physical activity patterns, while controlling for gender, age, and university size. RESULTS: Universities with a high provision for organizational structures and internal partnerships, indoor facilities, and sport clubs increase the odds of their students having more active physical activity patterns. Increased provision of investment and personnel was seen to have a mixed relationship with students' physical activity engagement, highlighting the need to understand where resources are needed and not just increase them. CONCLUSIONS: It is important for universities to have adequate organizational structures with internal partnerships to understand how resources can be maximized to support physical activity engagement across the whole student population. University campuses hold the potential for increasing student engagement in physical activity, and these findings can help inform campus-wide initiatives that foster active student populations for improving overall long-term health.

Move for Life an intervention for inactive adults aged 50 years and older: a cluster randomised feasibility trial.

Background: Move for Life (MFL) is a theory-informed intervention that was developed to augment established physical activity (PA) programmes and enable inactive adults aged 50 years and older to be more active. This study examined the feasibility of MFL and sought to provide evidence of its potential for improving PA and associated health outcomes. Methods: A 3-arm cluster randomised feasibility trial compared MFL intervention, usual provision (UP) and control (CON) groups at baseline (T0), post-intervention (T1, at 8, 10 or 12- weeks) and 6-month follow up (T2). We used purposive sampling strategies to recruit participants according to characteristics of interest. Feasibility outcomes assessed recruitment, fidelity, adherence, retention and data completion rates based on pre-set criteria. Primary outcomes were accelerometer-based moderate-to-vigorous intensity PA (MVPA) and self-reported compliance with physical activity guidelines (PAGL). Secondary outcomes included light intensity PA (LiPA), standing time, sedentary time, body composition (adiposity), physical function and psychological well-being. We used linear mixed models (continuous outcomes) or generalized estimated equations (categorical outcomes) to estimate group differences over time in the study outcomes. Results: Progression criteria for feasibility outcomes were met, and 733 individuals were recruited. Considering a 6-month period (T0-T2), while self-reported compliance with PAGL increased in MFL relative to UP and CON and in UP relative to CON, standing time decreased in MFL relative to CON and sedentary time increased in the latter compared to UP. Waist circumference decreased in MFL relative to UP and CON. MFL outperformed UP in the Timed Up and Go Test while MFL and UP increased the distance covered in the Six-Minute Walk Test compared to CON. Psychological well-being increased in MFL relative to CON (all p < 0.05). Conclusion: Findings show that MFL is feasible, while data are promising with regards to the potential of improving community PA programmes for adults aged 50 or more years. Clinical trial registration: https://www.isrctn.com/Registration#ISRCTN11235176.

Findings from a pragmatic cluster randomised controlled feasibility trial of a music and dance programme for community dwelling older adults.

INTRODUCTION: Functional decline, chronic illness, reduced quality of life and increased healthcare utilisation are common in older adults. Evidence suggests music and dance can support healthy ageing in older adults. This study explored the feasibility, potential for effect and cost effectiveness of the Music and Movement for Health (MMH) programme among community-dwelling older adults using a pragmatic cluster-randomised, controlled feasibility trial design. METHODS: Community-dwelling adults aged 65 years or older were recruited to seven clusters in the Mid-West region of Ireland. Clusters were block randomised to either the MMH intervention or control. Primary feasibility outcomes included recruitment, retention, adherence, fidelity, and safety. Secondary outcomes measured physical activity, physical and cognitive performance, and psychosocial well-being, along with healthcare utilisation were assessed at baseline and after 12 weeks. RESULTS: The study successfully met feasibility targets, with recruitment (n = 100), retention (91 %), adherence (71 %), data completeness (92 %) and intervention fidelity (21 out of 24) all meeting predetermined criteria. Both groups exhibited an increase in self-reported physical activity and improved physical function. Participants in the intervention group scored consistently better in psychosocial measures compared to the control group at follow-up. The health economic analysis confirmed the feasibility of the methodology employed and points to the potential cost-effectiveness of the MMH relative to the control or no organised programme. DISCUSSION AND IMPLICATIONS: The MMH intervention and study design were found to be feasible and acceptable with important findings to inform future evaluation of the clinical and cost-effectiveness of a definitive randomised controlled trial.