Abstract
Guidelines for low back pain (LBP) often recommend the use of self-management such as unsupervised exercise, booklets, and online education. Another potentially useful way for patients to self-manage LBP is by using smartphone applications (apps). However, to date, there has been no rigorous evaluation of LBP apps and no guidance for consumers on how to select high-quality, evidence-based apps. This chapter reviews smartphone apps for the self-management of LBP and evaluates their content quality and whether they recommend evidence-based interventions.
This chapter shows that generally app developers are selecting interventions that are endorsed by guidelines, although their quality is low. There are many apps available for the self-management of LBP, but their effectiveness in improving patient outcomes has not been rigorously assessed. App developers need to work closely with healthcare professionals, researchers, and patients to ensure app content is accurate, evidence based, and engaging.
Introduction
Low back pain (LBP) is a major global public health issue and the leading cause of disability in most countries according to the Global Burden of Disease Study 2015 . Guidelines for LBP often recommend the use of self-management , which is broadly described as patients being proactive in employing strategies to manage and monitor their own health and well-being . Examples of self-management strategies for LBP include unsupervised exercise, booklets, and online education (e.g., websites) . A potentially useful way for patients to self-manage their health condition is by using smartphone applications (“apps”). To date, there has been no rigorous evaluation of apps for the self-management of LBP and no guidance for consumers on how to select high-quality, evidence-based LBP apps.
There are over 165,000 health and wellness apps available from the iTunes and Google Play stores, and nearly a quarter of these focus on disease and treatment management . Given the minimum regulatory control over their content , consumers and clinicians should question whether the content in these apps is based on current best practice guidelines . Consumers may rely on in-app or online user ratings and reviews to select an app, but this information is subject to bias, which means consumers may struggle to make informed decisions. One method of assessing the quality and “fitness-for-purpose” of apps is by using validated scales and benchmarking app content against current best practice guidelines . A number of systematic reviews have evaluated the quality and content of apps that help patients manage various health conditions, such as diabetes, concussion, bipolar disorders, and depression . However, to date, there have been no reviews focusing on apps for managing LBP. Given the large number of publicly available apps for the self-management of LBP, it is prudent and timely to evaluate their quality and ascertain whether they reflect current best practice guideline recommendations.
The purpose of this review was to identify apps for the self-management of LBP and assess their quality (e.g., functionality, design) and content (compliance with best practice guidelines) to help consumers make informed decisions.
Methods
This systematic review follows standard recommendations for traditional reviews outlined in the PRISMA statement . First, we constructed a search strategy using prespecified eligibility criteria and performed an initial screening of all apps and a full review of relevant apps. Following this, we extracted the data and assessed the quality of the included apps using a reliable tool (see below) specifically designed for mobile health apps. The review protocol was prospectively registered on the PROSPERO Register of Systematic Reviews: CRD42016048420. As no personal data were collected as part of this review, ethical approval was not required for this study.
Search strategy
The Australian iTunes and Google Play stores were searched for apps related to the self-management of LBP in November 2016. Together, these two online app stores have more than 4.8 million apps available for download and account for 97% of the Australian smartphone market . We used three keywords recommended by the Cochrane Back and Neck Group used in traditional systematic reviews of LBP interventions: “low back pain,” “back pain,” and “lumbago”. Two reviewers (GCM and MBP) performed the initial screening independently on the basis of the name and description of apps. Apps that met the inclusion criteria were then downloaded onto their devices (iPhone 6 iOS 10.0.2 and ASUS ZenFone 2 Android 6.0) for full review. Disagreements regarding inclusion were resolved by consensus.
Inclusion criteria
Apps were included if they were in English, were available to the general public, and were a self-contained product (i.e., did not require add-ons or an external device). No limitations on the costs of apps were applied. Only apps created or updated in 2015–2017 were included because a recent update ensures software functionality and ongoing technical support. The focus of the study was to include apps specifically developed for the self-management of LBP. Although self-management is considered a broad construct , we only included apps that clearly offered at least one treatment option that encouraged patients to be actively involved in the management of their condition such as unsupervised exercise programs. We also included apps that taught patients skills to be used during their daily management through advice or educational interventions, which are important components of self-management of LBP . However, apps providing only general information about LBP (e.g., common risk factors, lower back anatomy) were excluded because these apps do not provide a specific treatment plan to be followed. Additionally, we excluded apps aimed at identifying risk factors, or prevention or those focused on diagnostic tests of LBP. We also excluded apps that were developed for healthcare practitioners and those that offered treatments for pregnancy-related LBP, sciatica, other health conditions, or general health and well-being. Where the same app was available on different platforms (iOS or Android), the iOS version of the app was kept for inclusion and analysis. When both paid and free versions of an app were available, we reviewed only the paid version. If the free app offered in-app purchases, we evaluated the full content of the app.
Data extraction and analysis
Two independent reviewers (GCM and MBP) used an electronic spreadsheet (Microsoft Excel 2010, Redmond, WA, USA) to extract relevant information from the included apps. The information extracted included name of the app, version, developer, update date, cost, presence of in-app purchases, and platform availability. When available, we extracted the number of consumer reviews and rating (5-star rating system). We also extracted information on the type of intervention offered in the included apps. Disagreements relating to the categories assigned to each app were resolved by consensus.
Content and quality assessment
We used the recently published 2016 National Institute for Health and Care Excellence (NICE) guidelines for LBP to identify whether the included apps provided evidence-based interventions (categorized as “yes/no”) . For this, we mapped app interventions to recommendations listed in the NICE guidelines. This guideline provides the most recent best practice recommendations for the assessment and management of LBP and sciatica in people aged 16 or older. The NICE guidelines reviewed the evidence for a broad range of interventions, used individually or in combination, ranging from advice and noninvasive interventions to injections and surgery. NICE guideline recommendations are based on the quality of the underpinning evidence and a trade-off between the benefits and harms of an intervention .
A trained reviewer (GCM or MBP) assessed the quality of apps that provided evidence-based interventions using the mobile application rating scale (MARS) . MARS is a 23-item questionnaire, each question containing a five-point response scale (1-inadequate, 2-poor, 3-acceptable, 4-good, and 5-excellent). The items are categorized into five domains: engagement (fun, interesting, customizable, interactive, and well-targeted to audience), functionality (functioning, easy to learn, navigation, logic flow, and gestural design of app), aesthetics (graphic design, overall visual appeal, color scheme, and stylistic consistency), information quality (quality and quantity of information, credibility of developer), and a general, overall quality scale. MARS has shown excellent internal consistency (alpha = 0.90) and inter-rater reliability (intraclass correlation coefficient, ICC = 0.79) .
As a reliability check for the MARS ratings, 20 randomly selected apps were independently assessed by a second reviewer (GCM, MBP, or HL) . We then calculated the inter-rater reliability (ICC 2,1 ) of the MARS total score, and if ICC values were greater than 0.85, we considered the agreement between reviewers as excellent, and no further consensus was performed. In addition to the MARS total score, we used MARS item 15 specifically (“is app content correct, well written, and relevant to the goal/topic of the app?”) to assess the quality of the information provided and whether the app content was appropriate for LBP. MARS item 18 was used to evaluate the credibility and trustworthiness of the app developer. Finally, MARS item 19 was used to assess whether the app had been tested in randomized controlled trials, and we scored this item by searching the name of the app on Google Scholar. The three highest-scoring apps using the MARS scale are described in more detail in the Results section.
Classification of exercise interventions
Given that most of the apps for the self-management of LBP focused on exercise interventions, we classified them according to the categories proposed in the NICE guidelines:
- •
Biomechanical exercises: exercise interventions primarily directed at altering or improving spinal mechanics (e.g., muscle strengthening, stretching, range of motion exercises, motor control exercises, Pilates, or the McKenzie method).
- •
Aerobic exercises: exercise interventions directed at improving cardiovascular fitness and endurance (e.g., running, walking).
- •
Mind–body exercises: exercise interventions that combine physical, mental, and spiritual focus (e.g., Yoga, Tai Chi, and mindfulness).
- •
Mixed modality exercises: exercise interventions that incorporate any combination of the previous three categories.
Data analysis
The characteristics of the included apps were summarized as means or medians for continuous data and as frequencies and proportions for categorical data. We used multivariable regression analysis to investigate whether the quality of apps (MARS total score) was associated with in-app customer rating (5-star system) and the price of apps. We ranked the apps providing evidence-based interventions using the MARS scale total score. We used STATA v14 (StataCorp, College Station, TX) for all analyses.
Methods
This systematic review follows standard recommendations for traditional reviews outlined in the PRISMA statement . First, we constructed a search strategy using prespecified eligibility criteria and performed an initial screening of all apps and a full review of relevant apps. Following this, we extracted the data and assessed the quality of the included apps using a reliable tool (see below) specifically designed for mobile health apps. The review protocol was prospectively registered on the PROSPERO Register of Systematic Reviews: CRD42016048420. As no personal data were collected as part of this review, ethical approval was not required for this study.
Search strategy
The Australian iTunes and Google Play stores were searched for apps related to the self-management of LBP in November 2016. Together, these two online app stores have more than 4.8 million apps available for download and account for 97% of the Australian smartphone market . We used three keywords recommended by the Cochrane Back and Neck Group used in traditional systematic reviews of LBP interventions: “low back pain,” “back pain,” and “lumbago”. Two reviewers (GCM and MBP) performed the initial screening independently on the basis of the name and description of apps. Apps that met the inclusion criteria were then downloaded onto their devices (iPhone 6 iOS 10.0.2 and ASUS ZenFone 2 Android 6.0) for full review. Disagreements regarding inclusion were resolved by consensus.
Inclusion criteria
Apps were included if they were in English, were available to the general public, and were a self-contained product (i.e., did not require add-ons or an external device). No limitations on the costs of apps were applied. Only apps created or updated in 2015–2017 were included because a recent update ensures software functionality and ongoing technical support. The focus of the study was to include apps specifically developed for the self-management of LBP. Although self-management is considered a broad construct , we only included apps that clearly offered at least one treatment option that encouraged patients to be actively involved in the management of their condition such as unsupervised exercise programs. We also included apps that taught patients skills to be used during their daily management through advice or educational interventions, which are important components of self-management of LBP . However, apps providing only general information about LBP (e.g., common risk factors, lower back anatomy) were excluded because these apps do not provide a specific treatment plan to be followed. Additionally, we excluded apps aimed at identifying risk factors, or prevention or those focused on diagnostic tests of LBP. We also excluded apps that were developed for healthcare practitioners and those that offered treatments for pregnancy-related LBP, sciatica, other health conditions, or general health and well-being. Where the same app was available on different platforms (iOS or Android), the iOS version of the app was kept for inclusion and analysis. When both paid and free versions of an app were available, we reviewed only the paid version. If the free app offered in-app purchases, we evaluated the full content of the app.
Data extraction and analysis
Two independent reviewers (GCM and MBP) used an electronic spreadsheet (Microsoft Excel 2010, Redmond, WA, USA) to extract relevant information from the included apps. The information extracted included name of the app, version, developer, update date, cost, presence of in-app purchases, and platform availability. When available, we extracted the number of consumer reviews and rating (5-star rating system). We also extracted information on the type of intervention offered in the included apps. Disagreements relating to the categories assigned to each app were resolved by consensus.
Content and quality assessment
We used the recently published 2016 National Institute for Health and Care Excellence (NICE) guidelines for LBP to identify whether the included apps provided evidence-based interventions (categorized as “yes/no”) . For this, we mapped app interventions to recommendations listed in the NICE guidelines. This guideline provides the most recent best practice recommendations for the assessment and management of LBP and sciatica in people aged 16 or older. The NICE guidelines reviewed the evidence for a broad range of interventions, used individually or in combination, ranging from advice and noninvasive interventions to injections and surgery. NICE guideline recommendations are based on the quality of the underpinning evidence and a trade-off between the benefits and harms of an intervention .
A trained reviewer (GCM or MBP) assessed the quality of apps that provided evidence-based interventions using the mobile application rating scale (MARS) . MARS is a 23-item questionnaire, each question containing a five-point response scale (1-inadequate, 2-poor, 3-acceptable, 4-good, and 5-excellent). The items are categorized into five domains: engagement (fun, interesting, customizable, interactive, and well-targeted to audience), functionality (functioning, easy to learn, navigation, logic flow, and gestural design of app), aesthetics (graphic design, overall visual appeal, color scheme, and stylistic consistency), information quality (quality and quantity of information, credibility of developer), and a general, overall quality scale. MARS has shown excellent internal consistency (alpha = 0.90) and inter-rater reliability (intraclass correlation coefficient, ICC = 0.79) .
As a reliability check for the MARS ratings, 20 randomly selected apps were independently assessed by a second reviewer (GCM, MBP, or HL) . We then calculated the inter-rater reliability (ICC 2,1 ) of the MARS total score, and if ICC values were greater than 0.85, we considered the agreement between reviewers as excellent, and no further consensus was performed. In addition to the MARS total score, we used MARS item 15 specifically (“is app content correct, well written, and relevant to the goal/topic of the app?”) to assess the quality of the information provided and whether the app content was appropriate for LBP. MARS item 18 was used to evaluate the credibility and trustworthiness of the app developer. Finally, MARS item 19 was used to assess whether the app had been tested in randomized controlled trials, and we scored this item by searching the name of the app on Google Scholar. The three highest-scoring apps using the MARS scale are described in more detail in the Results section.
Classification of exercise interventions
Given that most of the apps for the self-management of LBP focused on exercise interventions, we classified them according to the categories proposed in the NICE guidelines:
- •
Biomechanical exercises: exercise interventions primarily directed at altering or improving spinal mechanics (e.g., muscle strengthening, stretching, range of motion exercises, motor control exercises, Pilates, or the McKenzie method).
- •
Aerobic exercises: exercise interventions directed at improving cardiovascular fitness and endurance (e.g., running, walking).
- •
Mind–body exercises: exercise interventions that combine physical, mental, and spiritual focus (e.g., Yoga, Tai Chi, and mindfulness).
- •
Mixed modality exercises: exercise interventions that incorporate any combination of the previous three categories.
Data analysis
The characteristics of the included apps were summarized as means or medians for continuous data and as frequencies and proportions for categorical data. We used multivariable regression analysis to investigate whether the quality of apps (MARS total score) was associated with in-app customer rating (5-star system) and the price of apps. We ranked the apps providing evidence-based interventions using the MARS scale total score. We used STATA v14 (StataCorp, College Station, TX) for all analyses.
Results
Search results
Our search on the iTunes and Google Play stores yielded 723 apps. After the initial screening based on the name and the app description, 612 apps were excluded. The primary reasons for exclusion at this stage were as follows: apps were targeted for healthcare providers and apps were not targeting patients with LBP. We downloaded 110 apps for a full evaluation based on our inclusion criteria, and further 49 apps were excluded. Of these, over one-third (19/49, 39%) were excluded because they were not updated since 2015, and another 16/49 (33%) were excluded because they only provided general information, which was not considered a self-management intervention for LBP. Finally, 61 apps were included in this review ( Fig. 1 ).
Characteristics of included apps
Of the 61 apps included in this review, 24 (39%) were found on iTunes exclusively, 33 (54%) on Google Play exclusively, and 4 (7%) were found on both app stores. Six apps had two versions available for download: a paid (or “pro”) version and a free (or “lite”) version; in these cases, the paid versions of these apps were included in the review. There were 22 (36%) paid apps, ranging in price from AUD $0.99 to AUD $14.99 (median AUD $1.99). Of the 39 (64%) free apps, 6 offered in-app purchases with prices ranging from AUD $0.99 to AUD $17.99. Only 25 (41%) apps were reviewed by customers, on a 5-star rating system; the median customer rating was 3.8 stars (range, 1–5). The number of customer reviews per app ranged from 0 reviews for 35 apps to 374 reviews for 1 app (Back Pain Relief Yoga Poses–17.0). The characteristics of each app are presented in the online appendix.
Interventions for LBP
The included apps recommended a range of interventions ( Table 1 ). Only three apps recommended interventions not endorsed by the NICE guidelines: Brainwave Entrainment (Backache Relief–1.0), Qigong exercises (Qigong for Back Pain Relief–1.0.1), and Graded Motor Imagery (Recognise Back–1.1). Of the 31 apps recommending biomechanical exercises, 2 (3%) offered strengthening exercises alone, 14 (23%) offered strengthening exercises in combination with stretching (23%), and the remaining 15 (25%) recommended a combination of interventions, such as core stability, Pilates, and McKenzie exercises. There were 17 (28%) apps offering mind–body exercises (Yoga) as a self-management strategy for LBP. Finally, four (7%) apps prescribed combinations of aerobic, biomechanical, and mind–body exercises.
| Interventions | No. | % |
|---|---|---|
| Education + Biomechanical exercises | 6 | 9.8 |
| Biomechanical exercises only | ||
| Strengthening | 2 | 3.3 |
| Strengthening/Stretching | 14 | 23.0 |
| Combination of biomechanical exercises | 15 | 24.6 |
| Mind–body exercises only | ||
| Yoga | 17 | 27.9 |
| Mixed modality | 4 | 6.6 |
| Other a | ||
| Graded motor imagery | 1 | 1.6 |
| Qigong exercises | 1 | 1.6 |
| Brainwave entrainment | 1 | 1.6 |
Related posts:
Evidence and methods in back pain research
Clinical guidelines for low back pain: A critical review of consensus and inconsistencies across three major guidelines
Behaviour change and self-management interventions in persistent low back pain
The prevalence, risk factors, prognosis and treatment for back pain in children and adolescents: An overview of systematic reviews
Causal mechanisms in the clinical course and treatment of back pain
Developing implementation science to improve the translation of research to address low back pain: A critical review
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