Purpose: Researchers have found numerous interventions effective in increasing physical activity levels, yet little is known about their broader dissemination. This ‘scale-up’ of effective physical activity interventions is increasingly recognized as imperative for population wide health improvements and reduced burden of disease. The purpose of this study was to systematically review physical activity interventions scaled-up in community settings by exploring adaptations made as part of the scale-up process and any impact scale-up may have had on effect size.

Methods: We performed a search of six electronic databases, reference lists of reviews, and contacted experts within the field. An intervention was considered ‘scaled-up’ if it had been delivered to a greater number of participants than a preceding randomised control trial (‘pre-scale’) in which a significant intervention effect (p≤0.05) was found for any measure of physical activity. Two authors independently screened studies, extracted data and assessed risk of bias using the Cochrane risk-of-bias tool. Adaptations made to scale interventions were categorised using the Adaptome model and differences in effect size from pre-scale to scaled-up trials were quantified ([effect size reported in the scaled-up trial/effect size reported in the pre-scale trial]×100).

Results: Eleven studies were identified as eligible for inclusion. Of these, three targeted solely physical activity and the remaining eight studies focused broadly on obesity prevention or healthy lifestyles. A variety of adaptations were made for scale-up, with the mode of delivery being the most common adaptation (e.g., qualified facilitator vs member of the research team). Seven studies included a measure of physical activity common to the pre-scale trial that enabled calculation of an effect size difference. Majority of scaled-up studies retained a proportion of the pre-scale trial effect size (16-71%), one study experienced a scale-up penalty (<0%), and three studies showed improvements (>100%).

Conclusion: Adaptations may impact the effectiveness of interventions delivered at scale. Interventions adapted specifically for scale-up retained at least half the intervention effect. This review provides valuable insight for researchers and public health practitioners interested in the design and scale-up of physical activity interventions, and contributes to the growing evidence-base for delivering health promotion interventions at-scale.

Purpose: Exercise is medicine’ (E=M) currently has no position in routine clinical care. To support E=M referral in clinical care, implementation strategies were developed, based on a new blueprint of an E=M decision aid. This study aimed to test the feasibility of implementing E=M in routine clinical care by conducting implementation strategies and an E=M-tool that provides patient tailored E=M-prescriptions.  

Methods: A pilot study was performed in four departments of two different Dutch university hospitals (UMC Groningen and Amsterdam UMC, location VUmc), including an extensive learning process evaluation based on the RE-AIM framework. A mixed method approach was used, with  questionnaires for clinicians, semi-structured  stakeholder interviews involved in the pilot (clinicians, patients, hospital managers and lifestyle coaches) as well as not involved in the pilot, tracking the usage of the newly developed tool and field notes of researchers.  

Results: Since local needs were different between the two university hospitals it was necessary to develop two slightly different E=M tools. Clinicians, patients, managers, as well as lifestyle coaches were positive about the applied implementation strategies. An efficient tool that is user-friendly with solid referral options turned out to be necessary for a good implementation of E = M. Clinicians experienced they were more effectively able to discuss E=M with their patients. The time investment was considered small (less than 5 minutes) and referral to a lifestyle coach was efficient. Patients found E=M of added value to the usual treatment.   

Conclusions: This study illustrates that implementing E=M in routine clinical care can be feasible. Outcomes gave detailed insight in important factors regarding the implementation of E=M in clinical care. With this research we provide an E=M blueprint of a new E=M decision aid in routine clinical care and serve a continuation of research on the implementation of E=M.  

Purpose: Several barriers hinder clinicians to prescribe exercise to their patients, such as lack of time, knowledge or support. As a result, ‘exercise is medicine’ (E=M) is not systematically implemented in general routine hospital care. Therefore, the aim of this study was to link evidence-based implementation strategies to barriers identified by clinicians in order to develop an implementation blueprint for E=M prescription.

Methods: Guided by the systematic Implementation Mapping protocol (Fernandez et al. 2019), we applied the five steps using strong stakeholders participation to match implementation strategies to barriers identified during interviews with clinicians working at two university hospitals in the Netherlands. We used available theory and evidence-informed strategies from the Taxonomy of Behaviour Change Methods from Kok et al. (2016) and the Effective Practice and Organisation of Care taxonomy from Powell et al. (2015). For each barrier we defined what needed to be changed (e.g. knowledge, beliefs, structures, policy agendas). Next, we identified strategies on how to change these barriers, such as training for clinicians, insight into possible exercise options within the area, and role models for clinicians. Next, we matched the implementation strategies to the practical activities and developed a blueprint for implementation as well as an evaluation plan.

Results: The blueprint for implementation of the E=M consists of bundled implementation strategies to support clinicians, department managers and stakeholders in the broader context through the adoption, co-creation, implementation and sustainability stages. Each stage is supported by implementation tools, practical applications and materials bundled in an implementation blueprint allowing tailoring to the specific clinical context. 

Conclusions: Operationalization of strategies into activities, tools, practical applications and materials led to the development of an implementation blueprint tailored to the specific clinical context. The implementation blueprint will be used to support implementation of E=M during a pilot study in four departments of university hospitals in the Netherlands (departments of Rehabilitation and Orthopaedics of UMC Groningen, and Rehabilitation and Oncology of Amsterdam UMC).

Purpose: Little is known about the long-term maintenance of implementation and patient adherence rates of Exercise Referral Scheme (ERS) after their evaluation. Drawing on a national, standardised ERS delivered 10-years after a randomised controlled trial, this mixed-methods study set out to i) examine uptake and adherence levels over a 10-year period ii) uncover the predictive characteristics associated with scheme adherence and iii) explore stakeholder perceptions of the key factors and emerging local practices for supporting uptake and adherence.

Methods: Scheme data were provided on all generic pathway patients (referred for CHD risk factors and/or mental health reasons) between 2007 and 2017. Patients were categorised as adherers (completing all 16-weeks of scheme), non-adherers (withdrawing before 16 weeks), non-starters (referred but not up taking the scheme) and waiting group (on a waiting list). Qualitative data were gathered during a one-to-one interview with scheme coordinators (N=22), scheme delivers (N=8) and leisure managers (N=5). Thematic analysis was conducted. Multi-nominal regression was used to analyse the four-category adherence grouping with ‘completed’ as the reference category. Associations were examined between adherence grouping and the following scheme entry predictors; age, gender, socioeconomic status and referral reason. Relative risk ratios (RRRs) were reported alongside 95% confidence intervals (95%CI).

Results: In total 73,401 study participants comprised; 25,249 (34.4%) adherers, 15,968 (21.8%) non-adherers, 24,697 (33.6%) non-starters and 7487 (10.2%) waiting group. Compared with adherers, non-starters (RRR:1.10, 95%CI:1.06 to 1.14) and waiting group (RRR:1.06, 95%CI:1.00 to 1.12) were more likely to be male. Across all three groups, patients were more likely to be younger and more deprived compared to adherers, with a consistent increasing risk gradient for both predictors. Clear patterning was found among scheme referral reason, with mental health referrals less likely to be in the adherers group. Stakeholder accounts offered insights into the patient data findings highlighting both supportive (comfortable environment, nudge tactics, personalised approach, role modelling and socialisation) and unsupportive (low patient motivation on referral, pricing of post-scheme exercise and area-level capacity) factors to patient adherence.

Conclusions: Findings enhance our understanding of the variation in uptake and adherence and can inform future practices of ERS referrers and deliverers.

Purpose

Optimal antenatal nutrition, physical activity and gestational weight gain (GWG) confer positive health outcomes for both mother and child. While there are an increasing number of research-based antenatal lifestyle interventions, few have been translated into clinical care and hence the barriers and facilitators to implementation are poorly understood. This report documents the facilitators and areas for negotiation in implementing a mHealth lifestyle intervention into routine clinical care.  

Methods

txt4two is a multi-modality mHealth intervention aiming to promote healthy nutrition, physical activity and GWG in pregnant women. A pilot RCT (n=91) demonstrated a significantly lower GWG (7.8kg + 4.7 versus 9.7 kg + 3.9; p= 0.041) and smaller reductions in physical activity (p=0.001).  Following the pilot, a tertiary antenatal hospital is pragmatically implementing and evaluating a tailored version of txt4two, using the RE-AIM model. Modifications include a comparison of three modes of introduction and embedding other txt4two modalities into current digital platforms. Stakeholder group discussion and an open-ended email delivered question elicited facilitators and areas negotiated in implementing txt4two in an antenatal service.

Results 

Facilitators

• Implementation focus of txt4two and robust implementation plan

• Early inclusion of key stakeholders 

• Embedded research dietitian within service

• Health services redesign priority embracing digital disruption 

Negotiations 

• Intellectual property ownership and transactions 

• Legal agreements across institutions

• Technology infrastructure 

• Additional ethics concerns/requirements for digital delivery

• Refocusing intervention towards unique needs of women

• Health professional behavior change and digital intervention views

Conclusion

This paper reports the unique challenges and negotiated areas required to pragmatically implement an mHealth intervention into routine clinical care.   As we move beyond pilots and embed research programs within services, this data furthers the dialogue for implementation science in the digital space. 

Purpose: Implementation science is proposed as a possible solution to the evidence-practice gap that exists across a range of healthcare settings, including behavioural health interventions such as physical activity.  Little is known about the current state of implementation research as it applies to physical activity interventions that are integrated within clinical settings.  The purpose of this research is to: 1) reflect the current status of implementation research as it applies to clinical physical activity interventions; and 2) provide recommendations for future research in physical activity that draws explicitly from priorities identified by implementation scientists to enhance translational efforts. 

Methods: A theory-informed mapping process was undertaken to identify the current state of implementation research.  This work was based on the Consolidated Framework for Implementation Research (CFIR).  The CFIR synthesises the factors that influence implementation across 5 domains: Characteristics of the: 1) intervention, 2) individual, 3) inner setting (i.e. organisation), 4) outer setting (i.e. socio-political environment) and 5) process.  Experts in implementation science, public health and physical activity informed the final composition of implementation mapping. 

Results: A comprehensive picture of the current state of clinical physical activity implementation research, as guided by the CFIR, is produced.  We found many examples of studies that describe individual influences on implementation, including healthcare providers lack of knowledge, perceived lack of time and self-efficacy.  Far less attention appears to focus on understanding inner or outer setting factors.  For example, we identified specific policy levers in Australia and the UK as conducive to implementing clinical physical activity interventions.  Despite this, our mapping did not identify studies that have explored the contribution of these factors to the implementation potential of clinical physical activity interventions.   

Conclusions:  To the best of our knowledge this is one of the first efforts to map implementation of physical activity interventions, used in healthcare settings, against one of the most widely cited implementation frameworks.  The findings from this work bridge multiple research disciplines (implementation science, physical activity, public health) to advance the implementation potential of clinical physical activity interventions in healthcare settings. 

Purpose: Physical inactivity is a major public health issue in Australia, contributing to the massive burden of chronic disease. There are many examples of novel, technology-based physical activity programs, however, to date, few attempts have been made to implement RCT-tested programs and deliver them at scale. This study aimed to evaluate the effectiveness of an advertising campaign to disseminate an evidence-based physical activity smartphone app.  

Methods: The advertising experiment was a 3x3x3x2 factorial design, testing 54 unique advertisements scheduled over three weekly waves. The budget was AUD$10,000. Three advertising parameters were explored: platforms (Facebook; Messenger; Instagram), selling-techniques (soft-sell, sending viewers from an ad to a landing-page, then to an app store, and hard-sell, sending viewers directly to an app store: Apple Store and Google Play) and themes (Health and Wellbeing; Body and Self-Confidence; Social Enjoyment). Advertisement efficacy was operationalised in terms of viewer engagement, assessing clickthrough per AUD$1. A three-way ANOVA was conducted to explore the effects of advertising factors on clickthrough.

Results: Overall, advertisements reached 1,373,273 people, achieved 2,989 clickthroughs and 667 app installs. Facebook advertisements yielded a total of 320 app installs, followed by Messenger at 262 app installs and Instagram at 98 app installs. Hard-sell ads collectively attracted a total of 667 app installs (Apple store app installs=366; Google Play app installs=314) while soft-sell landing page ads attracted 0 app installs.

Clickthrough differed on the basis of platform, whereby Facebook (clicks=1,245) and Messenger (clicks=1248) were superior to Instagram (clicks=496; F=8.98; p=.001). Hard-sell advertisements encouraging viewers to ‘download now’ (Apple: clicks=1,256; Google: clicks=1,285), produced greater clickthrough than the landing-page (clicks=448; F=10.77; p=.000). Advertisements with a Health and Wellbeing theme (clicks=1,264) attracted greater clickthrough than Social-Enjoyment (clicks=609; F= 5.71; p=.009), yet no significant differences in clickthrough were found amongst other themes. No significant interaction effects were found.

Conclusions: Social media advertisements for an evidence-based physical activity app differed on the basis of social media platform, selling technique, and advertising theme. The most effective social media advertisements were those placed on Facebook and Messenger, using a “hard-sell” approach (i.e. direct to app store), with themes relating to Health and Wellbeing.