Self-management Of Low Back Pain Research Articles

MHealth Apps for the Self-Management of Low Back Pain: Systematic Search in App Stores and Content Analysis.

With the rapid development of mobile health (mHealth) technology, many health apps have been introduced to the commercial market for people with back pain conditions. However, little is known about their content, quality, approaches to care for low back pain (LBP), and associated risks of use. The aims of this research were to (1) identify apps for the self-management of LBP currently on the market and (2) assess their quality, intervention content, theoretical approaches, and risk-related approaches. The UK iTunes and Google Play stores were initially searched for apps related to the self-management of LBP in May 2022. A repeat search in June 2023 was conducted to ensure that any relevant new apps developed in the last year were incorporated into the review. A total of 3 keywords recommended by the Cochrane Back and Neck Group were used to search apps "low back pain," "back pain," and "lumbago." The quality of the apps was assessed by using the 5-point Mobile App Rating Scale (MARS). A total of 69 apps (25 iOS and 44 Android) met the inclusion criteria. These LBP self-management apps mainly provide recommendations on muscle stretching (n=51, 73.9%), muscle strengthening (n=42, 60.9%), core stability exercises (n=32, 46.4%), yoga (n=19, 27.5%), and information about LBP mechanisms (n=17, 24.6%). Most interventions (n=14, 78%) are consistent with the recommendations in the National Institute for Health and Care Excellence (NICE) guidelines. The mean (SD) MARS overall score of included apps was 2.4 (0.44) out of a possible 5 points. The functionality dimension was associated with the highest score (3.0), whereas the engagement and information dimension resulted in the lowest score (2.1). Regarding theoretical and risk-related approaches, 18 (26.1%) of the 69 apps reported the rate of intervention progression, 11 (15.9%) reported safety checks, only 1 (1.4%) reported personalization of care, and none reported the theoretical care model or the age group targeted. mHealth apps are potentially promising alternatives to help people manage their LBP; however, most of the LBP self-management apps were of poor quality and did not report the theoretical approaches to care and their associated risks. Although nearly all apps reviewed included a component of care listed in the NICE guidelines, the model of care delivery or embracement of care principles such as the application of a biopsychosocial model was unclear.

Efficacy of mHealth Interventions for Improving the Pain and Disability of Individuals With Chronic Low Back Pain: Systematic Review and Meta-Analysis.

Low back pain is one of the main causes of disability worldwide. Individuals with chronic conditions have been widely affected by the COVID-19 pandemic. In this context, mobile health (mHealth) has become popular, mostly due to the widespread use of smartphones. Despite the considerable number of apps for low back pain available in app stores, the effectiveness of these technologies is not established, and there is a lack of evidence regarding the effectiveness of the isolated use of mobile apps in the self-management of low back pain. We summarized the evidence on the effectiveness of mHealth interventions on pain and disability for individuals with chronic low back pain. We conducted a systematic review and meta-analysis comparing mHealth to usual care or no intervention. The search terms used were related to low back pain and mHealth. Only randomized controlled trials were included. The primary outcomes were pain intensity and disability, and the secondary outcome was quality of life. Searches were carried out in the following databases, without date or language restriction: PubMed, Scopus, Embase, Physiotherapy Evidence Database (PEDro), the Cochrane Library, and OpenGrey, in addition to article references. The risk of bias was analyzed using the PEDro scale. Data were summarized descriptively and through meta-analysis (pain intensity and disability). In the meta-analysis, eligible studies were combined while considering clinical and methodological homogeneity. The certainty of evidence was assessed using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) criteria. A total of 5 randomized controlled trials were included, totaling 894 participants (447 allocated to the mHealth group and 445 to the usual care group), and they had similar methodological structure and interventions. Follow-up ranged from 6 weeks to 12 months. The studies did not demonstrate significant differences for pain intensity (mean difference -0.86, 95% CI -2.29 to 0.58; P=.15) and disability (standardized mean difference -0.24, 95% CI -0.69 to 0.20; P=.14) when comparing mHealth and usual care. All studies showed biases, with emphasis on nonconcealed allocation and nonblinding of the outcome evaluator. The certainty of evidence was rated as low for the analyzed outcomes. mHealth alone was no more effective than usual care or no treatment in improving pain intensity and disability in individuals with low back pain. Due to the biases found and the low certainty of evidence, the evidence remains inconclusive, and future quality clinical trials are needed.

Software-based interventions for low back pain management: A systematic review and meta-analysis.

Using software for self-management interventions can improve health outcomes for individuals with low back pain, but there is a dearth of research to confirm its effectiveness. Additionally, no known research has evaluated the effective elements of software-based interventions for low back pain self-management components. This study aimed to synthesize the effectiveness of software-based interventions to promote self-management health outcomes among individuals with low back pain. A systematic review and meta-analysis was conducted. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement, relevant studies up to July 2022 were searched via four electronic databases: PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, and Web of Science. 4908 adults with low back pain who participated in 23 studies were included. Software-based interventions were effective in reducing fear avoidance (mean difference [MD] = -0.95, 95% CI: -1.45 to -0.44), pain catastrophizing (MD = -1.31, 95% CI: -1.84 to -0.78), disability (MD = -8.21, 95% CI: -13.02 to -3.39), and pain intensity (MD = -0.86, 95% CI: -1.17 to -0.55). Specifically, interventions that included an exercise component were more effective in reducing pain and disability. Additionally, cognitive behavioral therapy (CBT) intervention significantly reduced fear avoidance and pain catastrophizing but had no noticeable impact on disability and pain compared to standard treatment. The certainty of the evidence in this review varied from very low to high across outcomes. The heterogeneity of the study results was significant, suggesting that future studies in this area could optimize the design, time points, measures, and outcomes to strengthen the evidence. Low back pain self-management interventions delivered through software-based programs effectively reduce pain intensity, disability, fear avoidance, and pain catastrophizing. Low back pain is among the most common reasons for seeking healthcare visits. Combining exercise and counseling through soft-based programs may effectively address this issue and its associated suffering and disability.

Development of a booster intervention for graded sensorimotor retraining (RESOLVE) in people with persistent low back pain: A nested, randomised, feasibility trial.

Low back pain contributes to an increasing global health burden exacerbated by unsustained improvements from current treatments. There is a need to develop, and test interventions to maintain initial improvements from low back pain treatments. One option is to implement a booster intervention. This study aimed to develop and test the feasibility of implementing a booster intervention delivered remotely to supplement the benefits from a complex intervention for chronic low back pain. This study was nested in the RESOLVE trial. The booster intervention was developed by an expert group, including a clinical psychologist, exercise physiologist and physiotherapists, and based on a motivational interviewing framework. We developed a conversational flow chart to support the clinician to guide participants towards achieving their pre-specified personal goals and future low back pain self-management. Participants with chronic low back pain who were aged over 18years and fluent in English were recruited. The booster intervention was delivered in one session, remotely, by telephone. The intervention was considered feasible if: participants were able to be contacted or <3 contacts were necessary to arrange the booster session; there were sufficient willing participants (<15% of sample unwilling to participate); and participants and research clinicians reported a perceived benefit of >7/10. Fifty participants with chronic non-specific low back pain were recruited to test the feasibility of implementing the booster intervention. Less than three contact attempts were necessary to arrange the booster session, only one participant declined to participate. Participants perceived the session to be beneficial; on a 0 to 10 scale of perceived benefit, the average score recorded was 8.3 (SD 2.0). Clinicians also reported a moderate perceived benefit to the participant; the average score recorded by clinicians was 6.3 (SD 1.6). We developed a step by step, simple booster intervention that was perceived to be beneficial to participants with chronic low back pain. The booster can feasibly be delivered remotely following a complex intervention.

An Evaluation of a Mobile App for Chronic Low Back Pain Management: Prospective Pilot Study.

BackgroundChronic low back pain is challenging to manage due to multidisciplinary considerations. It has substantial socioeconomic impacts and cannot be simply treated with pharmacotherapy, nonsurgical intervention, or spine surgery. Medical consensus recommends optimizing conservative self-management therapies (eg, home exercise, wellness strategies, yoga, etc) as first-line treatment options for chronic low back pain. However, access to these modalities is often limited and secondary to cost, convenience, and ease of use. Mobile health apps have emerged as a cost-effective and accessible option for chronic low back pain self-management. Established in-person pain programs can provide the structure for an optimal mobile app adaptation. PainNavigator (PainNavigator, Inc) is an example of a mobile app that is based on an Ascension-Illinois group–based pain program—Pain Rehabilitation Outpatient-Camp.ObjectiveThis was a prospective pilot clinical trial that evaluated the PainNavigator platform’s utility in low back pain management to inform future trial development.MethodsA total of 75 participants who used PainNavigator were studied. Pain, Enjoyment, and General Activity (PEG-3) scale scores and scores from a brief anxiety and depression scale based on the Patient Health Questionnaire-4 (PHQ-4) were obtained at baseline and following program completion. The PEG-3 total score was used, in addition to individual items—Average Pain, Pain Effect on Enjoyment, and Pain Effect on Activity. The PHQ-4 total score was also used, in addition to other individual items, including Felt Depressed, Loss of Interest, Felt Anxious, and Difficult to Control Worry. Paired sample t tests (2-tailed) compared mean differences in scores from before and after participants received the intervention.ResultsThe analysis found that PEG-3 (n=27) and PHQ-4 (n=27) total scores were significantly lower upon the completion of PainNavigator (P<.001 and P=.001, respectively). The findings showed a 36% reduction in PEG-3 total scores, a 40% reduction in pain intensity, and a 40% reduction in PHQ-4 total scores. Scores for individual PEG-3 scale and PHQ-4 items also significantly decreased. All PEG-3 measures had large effect sizes. The PHQ-4 total score and Difficult to Control Worry item had large effect sizes, while the other three measures had medium effect sizes.ConclusionsThese findings show that PainNavigator has clinical significance in managing chronic low back pain and can be easily utilized to improve patient care. All PEG-3 scale and PHQ-4 measures significantly improved following the use of the platform, supporting the multidimensional, biopsychosocial approach to low back pain management. Differences in effect sizes may inform quality improvement investigations, such as optimizing features that impact measures with only medium effect sizes. This feasibility study demonstrates an effective protocol, and it will inform future, more extensive randomized controlled trials.

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

BackgroundLow 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.MethodsUsing 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.ResultsTwenty-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.ConclusionsThe 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.

MHealth Apps for Low Back Pain Self-management: Scoping Review.

BackgroundThe role of self-management in health promotion, as well as prevention and rehabilitation, is increasing through the use of mobile health (mHealth) apps. Such mHealth apps are also increasingly being used for self-management of low back pain (LBP), but their effectiveness has not been sufficiently explored.ObjectiveThe aim of this scoping review was to provide an overview of the literature on self-management mHealth apps and their effects on the levels of pain and disability in people with LBP.MethodsWe applied the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) methodology, including a priori research questions. A literature search was conducted in 2 databases (PubMed and PEDro) for studies published between January 1, 2015, and June 17, 2021. Interventional, cohort, or case series studies with an interventional period were included if the mHealth app included built-in self-management content, the app was used for self-management for people with LBP, and the study reported outcomes regarding pain and disability in people with LBP.ResultsIn total, 7 studies were selected for the review with overall 2307 persons with LBP, of whom 1328 (57.56%) were women. Among the studies (5/7, 71%) that reported the type of pain, 85% (390/459) of the participants were experiencing chronic LBP. A total of 5 different mHealth apps were identified, of which 4 contributed to a statistically significant reduction in LBP and clinically meaningful changes. Of the 7 studies, 4 (57%) used 4 different assessments for disability, of which 3 (75%) showed statistically significant improvements in the level of functional ability of participants in the experimental groups using an mHealth app with built-in self-management content for LBP.ConclusionsThis scoping review supports the conclusion that people with LBP may benefit from mHealth apps that provide self-management content. However, the generalizability of the findings is limited because of heterogeneity in the pain characterization of the included participants and the intervention durations. More high-quality studies with longer follow-up periods to investigate personalized mHealth approaches are recommended for LBP self-management.

Pain cognitions and impact of low back pain after participation in a self-management program: a qualitative study

BackgroundBenefits from low back pain (LBP) treatments seem to be related to patients changing their pain cognitions and developing an increased sense of control. Still, little is known about how these changes occur. The objective of this study was to gain insights into possible shifts in the understanding of LBP and the sense of being able to manage pain among patients participating in a LBP self-management intervention.MethodsUsing a qualitative study and a content analytic framework, we investigated the experiences of patients with LBP who participated in ‘GLA:D® Back’, a group-based structured patient education and exercise program. Data were generated through qualitative semi-structured interviews conducted between January 2019 and October 2019. Interviews focused on experiences with pain and were analysed using a thematic analytical approach. The Common Sense Model and self-efficacy theory formed the theoretical framework for the interpretations. Participants were sampled to represent people who were either dissatisfied or satisfied with their participation in GLA:D® Back. Fifteen participants aged 26–62, eight women and seven men, were interviewed from February to April 2020.ResultsFour main themes, corresponding to the characterisation of four patient groups, were identified: ‘Feeling miscast, ‘Maintaining reservations', ‘Struggling with habits’ and ‘Handling it’. The participants within each group differed in how they understood, managed, and communicated about their LBP. Some retained the perception of LBP as a threatening disease, some expressed a changed understanding that did not translate into new behaviors, while others had changed their understanding of pain and their reaction to pain.ConclusionsThe same intervention was experienced very differently by different people dependent on how messages and communication resonated with the individual patient's experiences and prior understanding of LBP. Awareness of the ways that individuals’ understanding of LBP interact with behaviour and physical activities appear central for providing adaptive professional support and meeting the needs of individual patients.

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

BackgroundInternational guidelines consistently endorse the promotion of self-management for people with low back pain (LBP); however, implementation of these guidelines remains a challenge. Digital health interventions, such as those that can be provided by smartphone apps, have been proposed as a promising mode of supporting self-management in people with chronic conditions, including LBP. However, the evidence base for digital health interventions to support self-management of LBP is weak, and detailed descriptions and documentation of the interventions are lacking. Structured intervention mapping (IM) constitutes a 6-step process that can be used to guide the development of complex interventions.ObjectiveThe aim of this paper is to describe the IM process for designing and creating an app-based intervention designed to support self-management of nonspecific LBP to reduce pain-related disability.MethodsThe first 5 steps of the IM process were systematically applied. The core processes included literature reviews, brainstorming and group discussions, and the inclusion of stakeholders and representatives from the target population. Over a period of >2 years, the intervention content and the technical features of delivery were created, tested, and revised through user tests, feasibility studies, and a pilot study.ResultsA behavioral outcome was identified as a proxy for reaching the overall program goal, that is, increased use of evidence-based self-management strategies. Physical exercises, education, and physical activity were the main components of the self-management intervention and were designed and produced to be delivered via a smartphone app. All intervention content was theoretically underpinned by the behavior change theory and the normalization process theory.ConclusionsWe describe a detailed example of the application of the IM approach for the development of a theory-driven, complex, and digital intervention designed to support self-management of LBP. This description provides transparency in the developmental process of the intervention and can be a possible blueprint for designing and creating future digital health interventions for self-management.

Evidencing the behaviour change model underpinning a personalised and tailored app for low back pain

Purpose: Self-management of low back pain is supported by national guidance (Savigny, 2016; Foster et al., 2018) . Digital modalities have gained traction as a potentially effective way of delivering information and education. It is vital to evaluate how individual components of an intervention work, and how this influences the outcome and effectiveness of an intervention (Aalbers et al., 2011), in order to understand what makes people interact and act on health information delivered. The aim of this project was to use the Behaviour Change Wheel (BCW), a widely used model of Behaviour Change (Michie et al., 2011), to illustrate how content and functionality of an app was aligned with behavioural theory and behaviour change techniques.

Development and feasibility testing of web-based intervention for self-management of low back pain in nurses: A mixed-method study

Purpose: Nurses have a higher prevalence of low back pain (LBP) than other occupations globally; this is associated with decreased job productivity, greater work absence, disability and functional limitation. Studies in the Kingdom of Saudi Arabia (KSA) report high rates of LBP in nursing consistent with findings from other countries. Digital interventions have demonstrated efficacy in supporting self-management of LBP and improvements in self-reported outcome measures of pain, functionality, quality of life.

Outcomes of participatory ergonomics and self-management in commercial clam farmers with chronic low back pain: a feasibility study

ABSTRACT Purpose : Participatory ergonomics engages workers in the development of strategies to reduce to workplace-related pain, offering a flexible and practical option to create individualized context-specific strategies. This paper describes the outcomes of a feasibility study using a participatory approach for self-management of low back pain in clam farmers. Methods : A within-subject time-control design with repeated baseline and post-intervention assessment was used. After refining individual and team-based strategies, stakeholder interviews, and rapid prototyping, workers selected three strategies to use for 8 weeks. Frequency and ease of use for strategies are described. Pre-post paired t-tests were used for analysis of pain-related disability, difficulty and pain with work tasks, pain-related-fear, self-efficacy, and coping. Analysis of improvements exceeding published and individual variability was calculated. Results : Participants chose both team and individual strategies, most using strategies 5 days a week >50% of the time. Significant improvements for pain-related disability, pain during specific tasks, pain-related anxiety, and coping were seen after 8 weeks of implementing strategies. No changes in task difficulty, fear, self-efficacy and average resting pain were reported. Pain improvements > MDC95 were reported by 74% with 56-64% > personalized MDC95 for lifting tasks. Conclusions : Pain-related disability, work activity pain ratings and related pain anxiety and coping improved beyond individual variability in this feasibility study. Multiple strategies allowed workers to choose relevant self-management options. Introduction of work-related changes in the workplace, visual demonstration, review of team videos and reminders were helpful. Further studies of this approach are needed.

Availability, content and quality of commercially available smartphone applications for the self-management of low back pain: a systematic assessment

Purpose Explore smartphone apps that may be recommended by clinicians for the self-management of low back pain. Methods Prospectively registered systematic assessment of self-contained apps for self-management of low back pain on the Google Play and Apple App stores (Oceania), including ≥1 NICE low back pain and sciatica clinical guideline-recommended component and functioning without health professional input. Outcomes were quality (Mobile App Rating Scale; MARS), and self-management (Self-Management Support Checklist; SMS-14) and behaviour change potential (App Behaviour Change Scale; ABACUS). Results 25 apps were included. The average quality of included apps was acceptable (Mean MARS score of 3.9 out of a maximum possible 5). The self-management support and behaviour change potential of included apps appeared low (mean SMS-14 score was 3.4/14; mean ABACUS score was 5.4/21). The apps showed no significant correlation between app consumer ratings and MARS scores. App quality was significantly correlated with app price (p = 0.049) but not consumer ratings, however, these findings were based on a small number of studies and the overall model was not significant. Conclusions Smartphone apps for the self-management of low back pain are of average to good quality, with questionable potential for self-management and behaviour change. Clinicians should consider that few apps were designed to specifically incorporate self-management support and behaviour change potential when recommending apps to clients. Further development in these areas of app design would be of benefit. Implications for Rehabilitation Smartphone apps have the potential to improve LBP self-management outcomes, however, apps are not well regulated and the quality of information and advice provided is often low quality. The findings from this systematic assessment indicate that LBP self-management apps have limited self-management support and behaviour change potential features. Recommendation of current smartphone apps for LBP should take into consideration that although apps are of acceptable quality they are not specifically designed with self-management support and behaviour change principles. App quality does not appear to be associated with consumer ratings, however, there may be a positive association between app price and quality which requires further investigation.

App-Delivered Self-Management Support System for Adults with Low Back Pain

SELFBACK is a decision support system that is being developed by the European Union for self-management of low back pain. Delivered via a smartphone

Effectiveness of digital support intervention for self-management of low back pain among obese women

Effectiveness of digital support intervention for self-management of low back pain among obese women

Machine Learning Applied to Low Back Pain Rehabilitation – A Systematic Review

<strong>Background:</strong> Low back pain (LBP) is the leading cause of disability worldwide and an important cause of work absenteeism in the active population. As a recurrent condition, prevention is crucial. Home exercises are effective, but adherence and accurate performance of the exercises are difficult to monitor by doctors and therapists. Machine learning (ML) applied to rehabilitation systems could be a solution to address telerehabilitation for people with chronic LBP if it holds sufficient accuracy in monitoring adherence performance while providing patient guidance. The aim was to search and review studies that have used ML techniques for rehabilitation of people with LBP. To develop an understanding on the outcomes measured, the clinical setting (face-to-face rehabilitation or remote rehabilitation) where interventions took place, and the clinical research methodology that has been used. <strong>Materials and Methods:</strong> a systematic review was performed based on research material obtained from literature indexed on MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, Web of Science and IEEE Xplore databases to locate papers focused on the use of ML applied to rehabilitation of LBP. <strong>Results:</strong> after revision of the inclusion and exclusion criteria using the PRISMA methodology, only 14 studies remained for the analysis that is presented as a qualitative synthesis. <strong>Conclusions:</strong> ML approaches applied to rehabilitation could help health professionals and LBP patients to manage this condition that affects a significant amount of the active population. ML could be applied to support clinical decisions and to guide patients self-manage their LBP remotely, which makes it a potential telerehabilitation solution. More and better studies, with more participants and following guidelines for best research practice are needed to strengthen the clinical evidence. <strong>HIGHLIGHTS</strong> <ul><li>LBP is the leading cause of disability worldwide</li><li>LBP is a recurrent condition in active population and one of the most common causes of workforce absenteeism, being a significant socio-economic problem</li><li>Home exercises are effective for prevention of LBP</li><li>It is difficult to measure the adherence and the performance accuracy of home exercises</li><li>ML techniques can detect trunk range of motion, muscle recruitment and monitor posture and pain and other LBP-related symptoms that support monitoring and guidance of patient performance in a personalized way</li><li>ML applied to reliable non-invasive monitoring procedures is a promising area to promote self-management of LBP in telerehabilitation scenarios</li></ul><p align="left">

Barriers and facilitators to patient uptake and utilisation of digital interventions for the self-management of low back pain: a systematic review of qualitative studies

ObjectivesLow back pain (LBP) is a leading contributor to disability globally. Self-management is a core component of LBP management. We aimed to synthesise published qualitative literature concerning digital health interventions...

Plausibility of Using a Checklist With YouTube to Facilitate the Discovery of Acute Low Back Pain Self-Management Content: Exploratory Study.

BackgroundAccess to guideline-consistent effective care for acute low back pain (ALBP) is generally regarded as limited. Researchers have recognized the potential of YouTube as a clinical and patient education resource that may improve access to appropriate care. However, the heterogeneity of evaluation approaches and variable quality of health information have generally limited the potential of YouTube as a self-management intervention.ObjectiveThis study aims to increase the understanding of ALBP content available on YouTube in 2020 and to establish the plausibility of using a simple checklist to facilitate the discovery of YouTube content consistent with current guidelines. We examined the following 4 research questions: how was the data set defined, what are the metadata characteristics of the videos in the data set, what is the information quality of ALBP YouTube videos, and what are the characteristics of the YouTube data set based on an ALBP self-management checklist?MethodsThis was an exploratory, qualitative infodemiology study. We identified videos in our data set through YouTube search based on popular ALBP-relevant search terms identified through Google Trends for YouTube. We accessed YouTube metadata using the YouTube data tools developed by the University of Amsterdam. We used a modified Brief DISCERN checklist to examine the information quality. We developed a checklist based on the 2018 Lancet Low Back Pain guidelines to examine self-management content.ResultsWe analyzed a data set of 202 YouTube videos authored by chiropractors, physicians, physiotherapists, and instructors of yoga and other disciplines. We identified clear differences in the ALBP videos in our data set based on the authors’ disciplines. We found that the videos authored by each discipline strongly featured a specific intervention domain, that is, education, treatment, or exercise. We also found that videos authored by physicians were consistently coded with the highest ALBP self-management content scores than all other disciplines.ConclusionsThe results returned by YouTube in response to a search for back pain–related content were highly variable. We suggest that a simple checklist may facilitate the discovery of guideline-concordant ALBP self-management content on YouTube. Further research may identify the clinical contexts in which the use of an ALBP checklist with YouTube is feasible.

App-Delivered Self-Management Intervention Trial selfBACK for People With Low Back Pain: Protocol for Implementation and Process Evaluation.

BackgroundImplementation and process evaluation is vital for understanding how interventions function in different settings, including if and why interventions have different effects or do not work at all.ObjectiveThis paper presents the protocol for an implementation and process evaluation embedded in a multicenter randomized controlled trial conducted in Denmark and Norway (the selfBACK project). selfBACK is a data-driven decision support system that provides participants with weekly self-management plans for low back pain. These plans are delivered through a smartphone app and tailored to individual participants by using case-based reasoning methodology. In the trial, we compare selfBACK in addition to usual care with usual care alone.MethodsThe aim of this study is to conduct a convergent mixed-methods implementation and process evaluation of the selfBACK app by following the reach, effectiveness, adoption, implementation, and maintenance framework. We will evaluate the process of implementing selfBACK and investigate how participants use the intervention in daily life. The evaluation will also cover the reach of the intervention, health care provider willingness to adopt it, and participant satisfaction with the intervention. We will gather quantitative measures by questionnaires and measures of data analytics on app use and perform a qualitative exploration of the implementation using semistructured interviews theoretically informed by normalization process theory. Data collection will be conducted between March 2019 and October 2020.ResultsThe trial opened for recruitment in February 2019. This mixed-methods implementation and evaluation study is embedded in the randomized controlled trial and will be collecting data from March 2019 to October 2020; dissemination of trial results is planned thereafter. The results from the process evaluation are expected 2021-2022.ConclusionsThis study will provide a detailed understanding of how self-management of low back pain can be improved and how a digital health intervention can be used as an add-on to usual care to support patients to self-manage their low back pain. We will provide knowledge that can be used to explore the possibilities of extending the generic components of the selfBACK system and key drivers that could be of use in other conditions and diseases where self-management is an essential prevention or treatment strategy.Trial RegistrationClinicalTrials.gov NCT03798288; https://www.clinicaltrials.gov/ct2/show/NCT03798288International Registered Report Identifier (IRRID)DERR1-10.2196/20308

Usability and Acceptability of an App (SELFBACK) to Support Self-Management of Low Back Pain: Mixed Methods Study.

BackgroundSelf-management is the key recommendation for managing nonspecific low back pain (LBP). However, there are well-documented barriers to self-management; therefore, methods of facilitating adherence are required. Smartphone apps are increasingly being used to support self-management of long-term conditions such as LBP.ObjectiveThe aim of this study was to assess the usability and acceptability of the SELFBACK smartphone app, designed to support and facilitate self-management of non-specific LBP. The app provides weekly self-management plans, comprising physical activity, strength and flexibility exercises, and patient education. The plans are tailored to the patient’s characteristics and symptom progress by using case-based reasoning methodology.MethodsThe study was carried out in 2 stages using a mixed-methods approach. All participants undertook surveys, and semistructured telephone interviews were conducted with a subgroup of participants. Stage 1 assessed an app version with only the physical activity component and a web questionnaire that collects information necessary for tailoring the self-management plans. The physical activity component included monitoring of steps recorded by a wristband, goal setting, and a scheme for sending personalized, timely, and motivational notifications to the user’s smartphone. Findings from Stage 1 were used to refine the app and inform further development. Stage 2 investigated an app version that incorporated 3 self-management components (physical activity, exercises, and education). A total of 16 participants (age range 23-71 years) with ongoing or chronic nonspecific LBP were included in Stage 1, and 11 participants (age range 32-56 years) were included in Stage 2.ResultsIn Stage 1, 15 of 16 participants reported that the baseline questionnaire was easy to answer, and 84% (13/16) found the completion time to be acceptable. Overall, participants were positive about the usability of the physical activity component but only 31% (5/16) found the app functions to be well integrated. Of the participants, 90% (14/16) were satisfied with the notifications, and they were perceived as being personalized (12/16, 80%). In Stage 2, all participants reported that the web questionnaire was easy to answer and the completion time acceptable. The physical activity and exercise components were rated useful by 80% (8/10), while 60% (6/10) rated the educational component useful. Overall, participants were satisfied with the usability of the app; however, only 50% (5/10) found the functions to be well integrated, and 20% (2/10) found them to be inconsistent. Overall, 80% (8/10) of participants reported it to be useful for self-management. The interviews largely reinforced the survey findings in both stages.ConclusionsThis study has demonstrated that participants considered the SELFBACK app to be acceptable and usable and that they thought it would be useful for supporting self-management of LBP. However, we identified some limitations and suggestions useful to guide further development of the SELFBACK app and other mobile health interventions.