Using Intervention Mapping to Develop a Decision Support System–Based Smartphone App (selfBACK) to Support Self-management of Nonspecific Low Back Pain: Development and Usability Study

Using codesign workshops to develop a conceptual framework for a mobile health app targeting chronic low back pain self-management

OBJECTIVES

The aim of this study was to gather insights from a diverse group of stakeholders, identify existing challenges and explore associated solutions to develop a conceptual framework that assists in the development of a chronic low back pain (CLBP) self-management app.

DESIGN

Codesign workshop methodology.

SETTING

The study was conducted online, allowing for the participation of a diverse group of stakeholders.

PARTICIPANTS

Participants were recruited through social media platforms and professional networks, including individuals with LBP for at least 3 months, healthcare professionals experienced in CLBP management and app developers with a background in digital health applications.

INTERVENTIONS

The study used codesign workshops focused on brainstorming, communication and reflection exercises designed to foster collaboration and gather insights from stakeholders.

OUTCOME MEASURES

Identification of key challenges and potential solutions for mobile health (mHealth) apps targeting CLBP.

RESULTS

Nine stakeholders identified four primary challenges in current back pain apps: belief and trust, motivation, safety and usability. The group also collaboratively developed and proposed practical solutions to these challenges.

CONCLUSIONS

This study illustrated the utility of the codesign workshop approach in generating critical insights for the development of a mHealth solution for CLBP. The insights gathered from these workshops can be used to inform future app development, potentially improving user engagement.

The engagement of healthcare providers in implementing the selfBACK randomised controlled trial - A mixed-methods process evaluation

Background

People with low back pain (LBP) are often recommended to self-manage their condition, but it can be challenging without support. Digital health interventions (DHIs) have shown promise in supporting self-management of LBP, but little is known about healthcare providers' (HCPs) engagement in implementing these.

Aims

We aimed to examine HCPs' engagement in patient recruitment for the selfBACK app clinical trial and explore their perceptions of the app.

Methods

In a mixed-methods design, we conducted a process evaluation alongside the selfBACK trial, triangulating quantitative data from trial recruitment logs and a vignette-based survey, and qualitative data from trial procedure documents, interviews with HCPs, and survey free-text responses. From 2019 to 2020, we recruited 57 HCPs from Norway and 39 health clinics in Denmark and collected quantitative and qualitative data in parallel. Results were integrated using displays.

Results

Overall, 825 patients were recruited by the HCPs. The vignette-based survey showed high agreement among HCPs (n = 62) with the self-management plans generated by the app (84.1-88.9%) but also highlighted concerns about tailoring and content. Interviews with HCPs (n = 19) revealed challenges with recruitment due to busy schedules, competing tasks, and varying levels of interest and engagement in the study.

Conclusions

The study identified factors that impact HCPs' engagement in recruiting patients for the selfBACK trial and highlighted overall positive views of the selfBACK app, although some concerns about the content and tailoring of the app were raised. Understanding HCP motivations and workload is crucial for the successful implementation of DHIs in clinical practice.

Effect of a smartphone self-management digital support system for low-back pain (selfBACK) among workers with high physical work demands - secondary analysis of a randomized controlled trial

OBJECTIVE

This study aimed to investigate whether physical work demands modify the effect of the selfBACK app, which is designed to support self-management of low-back pain.

METHODS

In a secondary analysis of the selfBACK trial with 346 employed participants, we stratified into low (N=165) and high physical work demands (N=181). Outcomes included the Roland-Morris Disability Questionnaire (0-24), a numeric rating scale for low-back pain intensity (0-10), the Pain Self-Efficacy Questionnaire (0-60), and work ability (0-10). Intervention effects were assessed at three- and nine-month follow-ups using a linear mixed model.

RESULTS

At three months, high physical demand workers with selfBACK showed a significant reduction in pain intensity [-0.8, 95% confidence interval (CI) -1.3- -0.2] compared to usual care. By nine months, the high physical demands workers with selfBACK reported reduced pain-related disability (-1.4, 95% CI -2.7- -0.1), improved pain self-efficacy (3.5, 95% CI 0.9-6.0), and lower pain intensity (-1.0, 95% CI -1.6- -0.4) compared to usual care. Low physical demands workers with selfBACK also improved pain self-efficacy [2.8 (95% CI 0.3-5.3)] compared to usual care. The impact of selfBACK was more noticeable among workers with high physical demands compared to their low physical demand counterparts, but no statistically significant differences were found in any outcome.

CONCLUSIONS

The selfBACK intervention had consistent effects across workers with high and low physical work demands, indicating that these demands did not modify its impact. Both groups experienced similar positive effects, highlighting the intervention's effectiveness across varying levels of physical work demands.

Developing a mobile-based brief intervention to reduce cannabis-impaired driving among youth: An intervention mapping approach

Behaviour change interventions delivered via smartphones have the potential to reduce youth cannabis use and driving under the influence of cannabis (DUIC). Countless smartphone applications (either downloadable or web-based) are available to help reduce substance use and impaired driving. However, most are developed without evidence-based content and theory, and many have poor user engagement. This study aims to: (1) describe the systematic development and theoretical foundations of a youth DUIC smartphone intervention, and (2) describe the pre-testing among a sample of youth and adult cannabis educators (prior to efficacy testing). A 6-step Intervention Mapping approach was utilized to combine theory, evidence, and user feedback to develop and implement the 'High Alert' intervention. This evidence-based and iterative process entailed: (1) conducting a needs assessment, (2) identifying intervention objectives, which map on the following DUIC determinants: knowledge, attitudes, risk perception, perceived norms, and self-efficacy, (3) selecting intervention theory and design, (4) developing of the intervention, (5) implementation, and (6) evaluation. Application of Intervention Mapping resulted in a smartphone web-based application that could support reductions in cannabis use and DUIC. The 'High Alert' intervention was created to include four modules with contents focusing on educating youth on the dangers and legal risks of DUIC, limiting risky situations, avoiding riding with an impaired driver, planning a safe ride home, and promoting safer cannabis use. Future research will test the efficacy of the intervention in reducing risky cannabis use and DUIC among youth.

Mobile App for Improving the Mental Health of Youth in Out-of-Home Care: Development Study Using an Intervention Mapping Approach

BACKGROUND

Youth in out-of-home care encounter substantial mental health challenges because of the absence of stable family and social support systems. Their vulnerability is heightened by trauma, neglect, and abuse. They struggle, especially when transitioning to independent living, coping with loneliness, anxiety, and pressure.

OBJECTIVE

This study aimed to develop a mobile app with high accessibility and long-term continuous effects to support independent living and improve mental health among youth in out-of-home care. The approach used was the systematic and step-by-step intervention mapping (IM) framework.

METHODS

The program was created using the IM framework and had 6 steps. Drawing from data from individual and focus group interviews and literature reviews, we developed a logical model of the problem. We established program outcomes and objectives, defining performance objectives and variable determinants. We identified theoretical and evidence-based methods that influence determinants. The app design integrated these methods into practical applications, allowing for the creation of self-management and emotional support tools. The development process included ongoing discussions between app designers and the research team to ensure that user needs and preferences were addressed.

RESULTS

Individual interviews and focus group discussions revealed challenges in managing daily routines and regulating emotions. The program design was based on the transtheoretical model, social cognitive theory, and elaboration likelihood model. Key features included goal setting, structured routines, emotion recognition flashcards, character models demonstrating emotion regulation strategies, verbal persuasion, and self-monitoring tools to support habit formation and emotion regulation. An implementation plan was developed to facilitate the app's adoption, execution, and maintenance, while an evaluation plan was established, including app usage analytics, user logs, and feedback surveys. A randomized controlled trial will be conducted to assess the app's impact on mental health outcomes, focusing on reducing anxiety and depressive symptoms, improving emotion regulation, and enhancing daily living skills.

CONCLUSIONS

The IM framework was beneficial in developing a mobile app to enhance the mental health of youth in out-of-home care. The study produced a program grounded in theory and evidence that caters to the needs of these individuals. Further research should aim to verify the app's effectiveness in real-world settings and refine it continuously based on user input.

Cost-effectiveness analysis of app-delivered self-management support (selfBACK) in addition to usual care for people with low back pain in Denmark

OBJECTIVES

This study aims to investigate the cost-effectiveness of individually tailored self-management support, delivered via the artificial intelligence-based selfBACK app, as an add-on to usual care for people with low back pain (LBP).

DESIGN

Secondary health-economic analysis of the selfBACK randomised controlled trial (RCT) with a 9-month follow-up conducted from a Danish national healthcare perspective (primary scenario) and a societal perspective limited to long-term productivity in the form of long-term absenteeism (secondary scenario).

SETTING

Primary care and an outpatient spine clinic in Denmark.

PARTICIPANTS

A subset of Danish participants in the selfBACK RCT, including 297 adults with LBP randomised to the intervention (n=148) or the control group (n=149).

INTERVENTIONS

App-delivered evidence-based, individually tailored self-management support as an add-on to usual care compared with usual care alone among people with LBP.

OUTCOME MEASURES

Costs of healthcare usage and productivity loss, quality-adjusted life-years (QALYs) based on the EuroQol-5L Dimension Questionnaire, meaningful changes in LBP-related disability measured by the Roland-Morris Disability Questionnaire (RMDQ) and the Pain Self-Efficacy Questionnaire (PSEQ), costs (healthcare and productivity loss measured in Euro) and incremental cost-effectiveness ratios (ICERs).

RESULTS

The incremental costs were higher for the selfBACK intervention (mean difference €230 (95% CI -136 to 595)), where ICERs showed an increase in costs of €7336 per QALY gained in the intervention group, and €1302 and €1634 for an additional person with minimal important change on the PSEQ and RMDQ score, respectively. At a cost-effectiveness threshold value of €23250, the selfBACK intervention has a 98% probability of being cost-effective. Analysis of productivity loss was very sensitive, which creates uncertainty about the results from a societal perspective limited to long-term productivity.

CONCLUSIONS

From a healthcare perspective, the selfBACK intervention is likely to represent a cost-effective treatment for people with LBP. However, including productivity loss introduces uncertainty to the results.

TRIAL REGISTRATION NUMBER

NCT03798288.

An Artificial Intelligence-Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trial

BACKGROUND

Self-management is endorsed in clinical practice guidelines for the care of musculoskeletal pain. In a randomized clinical trial, we tested the effectiveness of an artificial intelligence-based self-management app (selfBACK) as an adjunct to usual care for patients with low back and neck pain referred to specialist care.

OBJECTIVE

This study is a process evaluation aiming to explore patients' engagement and experiences with the selfBACK app and specialist health care practitioners' views on adopting digital self-management tools in their clinical practice.

METHODS

App usage analytics in the first 12 weeks were used to explore patients' engagement with the SELFBACK app. Among the 99 patients allocated to the SELFBACK interventions, a purposive sample of 11 patients (aged 27-75 years, 8 female) was selected for semistructured individual interviews based on app usage. Two focus group interviews were conducted with specialist health care practitioners (n=9). Interviews were analyzed using thematic analysis.

RESULTS

Nearly one-third of patients never accessed the app, and one-third were low users. Three themes were identified from interviews with patients and health care practitioners: (1) overall impression of the app, where patients discussed the interface and content of the app, reported on usability issues, and described their app usage; (2) perceived value of the app, where patients and health care practitioners described the primary value of the app and its potential to supplement usual care; and (3) suggestions for future use, where patients and health care practitioners addressed aspects they believed would determine acceptance.

CONCLUSIONS

Although the app's uptake was relatively low, both patients and health care practitioners had a positive opinion about adopting an app-based self-management intervention for low back and neck pain as an add-on to usual care. Both described that the app could reassure patients by providing trustworthy information, thus empowering them to take actions on their own. Factors influencing app acceptance and engagement, such as content relevance, tailoring, trust, and usability properties, were identified.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04463043; https://clinicaltrials.gov/study/NCT04463043.

Do age, gender, and education modify the effectiveness of app-delivered and tailored self-management support among adults with low back pain?-Secondary analysis of the selfBACK randomised controlled trial

selfBACK is an artificial intelligence based self-management app for low back pain (LBP) recently reported to reduce LBP-related disability. The aim of this study was to examine if age, gender, or education modify the effectiveness of the selfBACK intervention using secondary analysis of the selfBACK randomized controlled trial. Persons seeking care for LBP were recruited from primary care in Denmark and Norway and an outpatient clinic (Denmark). The intervention group (n = 232) received the selfBACK app adjunct to usual care. The control group (n = 229) received usual care only. Analyses were stratified by age (18-34, 35-64, ≥65 years), gender (male, female), and education (≤12, >12 years) to investigate differences in effect at three and nine months follow-up on LBP-related disability (Roland-Morris Disability Questionnaire [RMDQ]), LBP intensity and pain self-efficacy. Overall, there was no effect modification for any of the sociodemographic factors. However, data on LBP-related disability suggest that the effect of the intervention was somewhat more beneficial in older than in younger participants. The difference between the intervention and control group due to interaction was 2.6 (95% CI: 0.4 to 4.9) RMDQ points for those aged ≥65 years as compared to those aged 35-64 years. In conclusion, age, gender, or education did not influence the effect of the selfBACK intervention on LBP-related disability. However, older participants may have an additional long-term positive effect compared to younger participants. Trial registration: ClinicalTrials.gov Identifier: NCT03798288.

The Role of Pain Duration and Pain Intensity on the Effectiveness of App-Delivered Self-Management for Low Back Pain (selfBACK): Secondary Analysis of a Randomized Controlled Trial

Background

Clinical guidelines for nonspecific low back pain (LBP) recommend self-management tailored to individual needs and capabilities as a first-line treatment. Mobile health solutions are a promising method for delivering tailored self-management interventions to patients with nonspecific LBP. However, it is not clear if the effectiveness of such self-management interventions depends on patients' initial pain characteristics. High pain intensity and long-term symptoms of LBP have been associated with an unfavorable prognosis, and current best evidence indicates that long-term LBP (lasting more than 3 months) requires a more extensive treatment approach compared to more acute LBP. The artificial intelligence-based selfBACK app supports tailored and evidence-based self-management of nonspecific LBP. In a recent randomized controlled trial, we showed that individuals who received the selfBACK app in addition to usual care had lower LBP-related disability at the 3-month follow-up compared to those who received usual care only. This effect was sustained at 6 and 9 months.

Objective

This study aims to explore if the baseline duration and intensity of LBP influence the effectiveness of the selfBACK intervention in a secondary analysis of the selfBACK randomized controlled trial.

Methods

In the selfBACK trial, 461 adults (18 years or older) who sought care for nonspecific LBP in primary care or at an outpatient spine clinic were randomized to receive the selfBACK intervention adjunct to usual care (n=232) or usual care alone (n=229). In this secondary analysis, the participants were stratified according to the duration of the current LBP episode at baseline (≤12 weeks vs >12 weeks) or baseline LBP intensity (≤5 points vs >5 points) measured by a 0-10 numeric rating scale. The outcomes were LBP-related disability measured by the Roland-Morris Disability Questionnaire (0- to 24-point scale), average LBP intensity, pain self-efficacy, and global perceived effect. To assess whether the duration and intensity of LBP influenced the effect of selfBACK, we estimated the difference in treatment effect between the strata at the 3- and 9-month follow-ups with a 95% CI.

Results

Overall, there was no difference in effect for patients with different durations or intensities of LBP at either the 3- or 9-month follow-ups. However, there was suggestive evidence that the effect of the selfBACK intervention on LBP-related disability at the 3-month follow-up was largely confined to people with the highest versus the lowest LBP intensity (mean difference between the intervention and control group -1.8, 95% CI -3.0 to -0.7 vs 0.2, 95% CI -1.1 to 0.7), but this was not sustained at the 9-month follow-up.

Conclusions

The results suggest that the intensity and duration of LBP have negligible influence on the effectiveness of the selfBACK intervention on LBP-related disability, average LBP intensity, pain self-efficacy, and global perceived effect.

Using Intervention Mapping and Behavior Change Techniques to Develop a Digital Intervention for Self-Management in Stroke: Development Study

BACKGROUND

Digital therapeutics, such as interventions provided via smartphones or the internet, have been proposed as promising solutions to support self-management in persons with chronic conditions. However, the evidence supporting self-management interventions through technology in stroke is scarce, and the intervention development processes are often not well described, creating challenges in explaining why and how the intervention would work.

OBJECTIVE

This study describes a specific use case of using intervention mapping (IM) and the taxonomy of behavior change techniques (BCTs) in designing a digital intervention to manage chronic symptoms and support daily life participation in people after stroke. IM is an implementation science framework used to bridge the gap between theories and practice to ensure that the intervention can be implemented in real-world settings. The taxonomy of BCTs consists of a set of active ingredients designed to change self-management behaviors.

METHODS

We used the first 4 steps of the IM process to develop a technology-supported self-management intervention, interactive Self-Management Augmented by Rehabilitation Technologies (iSMART), adapted from a face-to-face stroke-focused psychoeducation program. Planning group members were involved in adapting the intervention. They also completed 3 implementation measures to assess the acceptability, appropriateness, and feasibility of iSMART.

RESULTS

In step 1, we completed a needs assessment consisting of assembling a planning group to codevelop the intervention, conducting telephone surveys of people after stroke (n=125) to identify service needs, and performing a systematic review of randomized controlled trials to examine evidence of the effectiveness of digital self-management interventions to improve patient outcomes. We identified activity scheduling, symptom management, stroke prevention, access to care resources, and cognitive enhancement training as key service needs after a stroke. The review suggested that digital self-management interventions, especially those using cognitive behavioral theory, effectively reduce depression, anxiety, and fatigue and enhance self-efficacy in neurological disorders. Step 2 identified key determinants, objectives, and strategies for self-management in iSMART, including knowledge, behavioral regulation, skills, self-efficacy, motivation, negative and positive affect, and social and environmental support. In step 3, we generated the intervention components underpinned by appropriate BCTs. In step 4, we developed iSMART with the planning group members. Especially, iSMART simplified the original psychoeducation program and added 2 new components: SMS text messaging and behavioral coaching, intending to increase the uptake by people after stroke. iSMART was found to be acceptable (mean score 4.63, SD 0.38 out of 5), appropriate (mean score 4.63, SD 0.38 out of 5), and feasible (mean score 4.58, SD 0.34 out of 5).

CONCLUSIONS

We describe a detailed example of using IM and the taxonomy of BCTs for designing and developing a digital intervention to support people after stroke in managing chronic symptoms and maintaining active participation in daily life.

Effect of an Artificial Intelligence-Based Self-Management App on Musculoskeletal Health in Patients With Neck and/or Low Back Pain Referred to Specialist Care: A Randomized Clinical Trial

Importance

Self-management is a key element in the care of persistent neck and low back pain. Individually tailored self-management support delivered via a smartphone app in a specialist care setting has not been tested.

Objective

To determine the effect of individually tailored self-management support delivered via an artificial intelligence-based app (SELFBACK) adjunct to usual care vs usual care alone or nontailored web-based self-management support (e-Help) on musculoskeletal health.

Design, Setting, and Participants

This randomized clinical trial recruited adults 18 years or older with neck and/or low back pain who had been referred to and accepted on a waiting list for specialist care at a multidisciplinary hospital outpatient clinic for back, neck, and shoulder rehabilitation. Participants were enrolled from July 9, 2020, to April 29, 2021. Of 377 patients assessed for eligibility, 76 did not complete the baseline questionnaire, and 7 did not meet the eligibility criteria (ie, did not own a smartphone, were unable to take part in exercise, or had language barriers); the remaining 294 patients were included in the study and randomized to 3 parallel groups, with follow-up of 6 months.

Interventions

Participants were randomly assigned to receive app-based individually tailored self-management support in addition to usual care (app group), web-based nontailored self-management support in addition to usual care (e-Help group), or usual care alone (usual care group).

Main Outcomes and Measures

The primary outcome was change in musculoskeletal health measured by the Musculoskeletal Health Questionnaire (MSK-HQ) at 3 months. Secondary outcomes included change in musculoskeletal health measured by the MSK-HQ at 6 weeks and 6 months and pain-related disability, pain intensity, pain-related cognition, and health-related quality of life at 6 weeks, 3 months, and 6 months.

Results

Among 294 participants (mean [SD] age, 50.6 [14.9] years; 173 women [58.8%]), 99 were randomized to the app group, 98 to the e-Help group, and 97 to the usual care group. At 3 months, 243 participants (82.7%) had complete data on the primary outcome. In the intention-to-treat analysis at 3 months, the adjusted mean difference in MSK-HQ score between the app and usual care groups was 0.62 points (95% CI, -1.66 to 2.90 points; P = .60). The adjusted mean difference between the app and e-Help groups was 1.08 points (95% CI, -1.24 to 3.41 points; P = .36).

Conclusions and Relevance

In this randomized clinical trial, individually tailored self-management support delivered via an artificial intelligence-based app adjunct to usual care was not significantly more effective in improving musculoskeletal health than usual care alone or web-based nontailored self-management support in patients with neck and/or low back pain referred to specialist care. Further research is needed to investigate the utility of implementing digitally supported self-management interventions in the specialist care setting and to identify instruments that capture changes in self-management behavior.

Trial Registration

ClinicalTrials.gov Identifier: NCT04463043.

Systematic co-development and testing of a digital behaviour change intervention for osteoarthritis and physical activity: Theoretical mapping and acceptability study

Objective

Osteoarthritis (OA) affects 8.75 million people in the UK. Physical activity (PA) is recommended as a core treatment, yet nearly half of people with OA are inactive. Accessible and user-friendly interventions are needed to motivate people with OA to be active. Digital behaviour change interventions (DBCIs) might help to support people with OA to self-manage their own levels of PA. The aim of this project was to co-develop and test a DBCI to motivate people with OA to be active.

Methods

A mixed methods design was adopted to build the theoretical foundations, develop, and test a complex DBCI. Two patient research partners with lived experience of OA were recruited onto the project team to assist with intervention development, which was guided by the intervention mapping (IM) approach. Interviews and think-aloud sessions were then used to explore attitudes, values, and perceived effectiveness of the website.

Results

The IM approach enabled the development of a prototype website to be illustrated in a clear and transparent way, showing a link between the practical materials adopted within the website and the theoretical constructs they were attempting to change. Potential users highlighted the importance of clear, easy-to-understand information, focusing on enjoyment and social connectedness.

Conclusions

DBCI development should be based on theory, adequately described, and thoroughly tested with potential users to understand how they might choose to integrate digital interventions into everyday life.

Using Intervention Mapping to Develop a Workplace Digital Health Intervention for Preconception, Pregnant, and Postpartum Women: The Health in Planning, Pregnancy and Postpartum (HiPPP) Portal

Digital health interventions that specifically target working women across the preconception, pregnancy and postpartum (PPP) life stages may address the unique barriers to engaging in healthy lifestyle behaviours and self-care during this life phase. This paper describes the development of a workplace digital health intervention to promote healthy lifestyles and wellbeing for PPP women working at a community service organization in Australia. Intervention Mapping is a framework that guides program development, implementation, and evaluation. Steps 1 to 5 of Intervention Mapping methodology (needs assessment through to program implementation) were used, including identification of determinants and change objectives across socioecological levels (i.e., individual, interpersonal, and organisational) and iterative co-design and stakeholder engagement processes. The workplace digital health intervention was successfully developed and implemented as an online portal. Content included key strategies, information, and supports to promote health and wellbeing across PPP, including supporting the return to work in the postpartum period. Examples of resource pages included a parental leave checklist, process flows, Pride resources, and Aboriginal and Torres Strait Islander resources. Findings from a pilot feasibility study indicate the portal was accessible and beneficial for women in PPP life stages. The Intervention Mapping protocol may offer a valuable roadmap for collaborative design of interventions targeting PPP women's behaviour and organisational work culture. Future work is needed to evaluate whether such interventions lead to improvements in women's health and wellbeing.

One size does not fit all: Participants' experiences of the selfBACK app to support self-management of low back pain-a qualitative interview study

BACKGROUND

Low back pain (LBP) is one of the most common reasons for disability globally. Digital interventions are a promising means of supporting people to self-manage LBP, but implementation of digital interventions has been suboptimal. An artificial intelligence-driven app, selfBACK, was developed to support self-management of LBP as an adjunct to usual care. To better understand the process of implementation from a participant perspective, we qualitatively explored factors influencing embedding, integrating, and sustaining engagement with the selfBACK app, and the self-perceived effects, acceptability, and satisfaction with the selfBACK app.

METHODS

Using a qualitative interview study and an analytic framework approach underpinned by Normalization Process Theory (NPT), we investigated the experiences of patients who participated in the selfBACK randomized controlled trial (RCT). Interviews focused on the motivation to participate in the RCT, experiences of using the selfBACK app, and views about future intended use and potential of using digital health interventions for self-management of LBP. Participants were purposively sampled to represent diversity in age, sex, and implementation reflected by a proxy measure of number of app-generated self-management plans during the first three months of RCT participation.

RESULTS

Twenty-six participants aged 21-78, eleven females and fifteen men, with two to fourteen self-management plans, were interviewed between August 2019 and April 2020. A broad range of factors influencing implementation of selfBACK within all constructs of NPT were identified. Key facilitating factors were preferences and beliefs favoring self-management, a friendly, motivational, and reassuring supporter, tailoring and personalization, convenience and ease of use, trustworthiness, perceiving benefits, and tracking achievements. Key impeding factors were preferences and beliefs not favoring self-management, functionality issues, suboptimal tailoring and personalization, insufficient time or conflicting life circumstances, not perceiving benefits, and insufficient involvement of health care practitioners. Self-perceived effects on pain and health, behavior/attitude, and gaining useful knowledge varied by participant.

CONCLUSIONS

The high prevalence of LBP globally coupled with the advantages of providing help through an app offers opportunities to help countless people. A range of factors should be considered to facilitate implementation of self-management of LBP or similar pain conditions using digital health tools.

Football Fandom as a Platform for Digital Health Promotion and Behaviour Change: A Mobile App Case Study

Background: The last decade has seen a dramatic shift toward the study of fitness surveillance, thanks in part to the emergence of mobile health (mHealth) apps that allow users to track their health through a variety of data-driven insights. This study examines the adoption trends and community mediation of the mobile fitness application ‘FanFit’, a platform aimed at promoting physical activity among sports fans by creating a fitness app branded to their favourite team for health promotion. Objective: Our study looked at the impact of a specially designed mobile app (FanFit) as a digital health intervention for initiating and maintaining physical activity as part of football club membership. Our analysis indicates that app users will adopt healthier behaviours as a result of the app’s sense of fan community and behaviour change. Methods: The findings reported here are based on an implementation of the FanFit app and, in particular, on those who participated in a more in-depth study (n = 30). These participants were Rangers FC supporters with a mix of genders (n = 19 males and n = 11 females). Focus groups and interviews were conducted with participants to ascertain users’ perspectives on the most effective methods for nudging users toward adopting and maintaining a pattern of fitness behaviours. Results: The findings show that the user community was interested in fitness and wanted to live a ‘healthy lifestyle,’ which was augmented and fuelled by the app’s competitive architecture design. Furthermore, the data reveal a new fan-health discourse about a person’s developing wants, talents, and identities as embodied beings. Conclusions: We have developed and presented valid links between the use of sports club apps and health programmes. The app could be useful for sports programmes and club providers looking for mHealth applications that provide community support through fan discourse with opportunities for both male and female fans.