Assessment of the Quality of Mobile Applications (Apps) for Management of Low Back Pain Using the Mobile App Rating Scale (MARS)

Digital health interventions may improve different behaviours. However, the rapid proliferation of technological solutions often does not allow for a correct assessment of the quality of the tools. This study aims to review and assess the quality of the available mobile applications (apps) related to interventions for low back pain. Two reviewers search the official stores of Android (Play Store) and iOS (App Store) for localisation in Spain and the United Kingdom, in September 2019, searching for apps related to interventions for low back pain. Seventeen apps finally are included. The quality of the apps is measured using the Mobile App Rating Scale (MARS). The scores of each section and the final score of the apps are retrieved and the mean and standard deviation obtained. The average quality ranges between 2.83 and 4.57 (mean 3.82) on a scale from 1 (inadequate) to 5 (excellent). The best scores are found in functionality (4.7), followed by aesthetic content (mean 4.1). Information (2.93) and engagement (3.58) are the worst rated items. Apps generally have good overall quality, especially in terms of functionality and aesthetics. Engagement and information should be improved in most of the apps. Moreover, scientific evidence is necessary to support the use of applied health tools.

[1]  Arianne P Verhagen,et al.  Exercise therapy for chronic nonspecific low-back pain. , 2010, Best practice & research. Clinical rheumatology.

[2]  Rachelle Buchbinder,et al.  Care for low back pain: can health systems deliver? , 2019, Bulletin of the World Health Organization.

[3]  Amy P Abernethy,et al.  Rapid, responsive, relevant (R3) research: a call for a rapid learning health research enterprise , 2013, Clinical and Translational Medicine.

[4]  Beth D Darnall,et al.  Effects of a Pain Catastrophizing Induction on Sensory Testing in Women with Chronic Low Back Pain: A Pilot Study , 2017, Pain research & management.

[5]  R. Chou,et al.  Nonpharmacologic Therapies for Acute and Chronic Low Back Pain: A Review of the Evidence for an American Pain Society/American College of Physicians Clinical Practice Guideline , 2007, Annals of Internal Medicine.

[6]  Timothy J Wilt,et al.  Noninvasive Treatments for Acute, Subacute, and Chronic Low Back Pain: A Clinical Practice Guideline From the American College of Physicians. , 2017, Annals of internal medicine.

[7]  Emmanuel Stamatakis,et al.  The association between physical activity and low back pain: a systematic review and meta-analysis of observational studies , 2019, Scientific Reports.

[8]  Annemiek J. Linn,et al.  Taking online computer-tailoring forward: The potential of tailoring the message frame and delivery mode of online health behaviour change interventions , 2015 .

[9]  Oksana Zelenko,et al.  Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps , 2015, JMIR mHealth and uHealth.

[10]  Christina Cheng,et al.  Evaluating mobile phone applications for health behaviour change: A systematic review , 2018, Journal of telemedicine and telecare.

[11]  P. Salkovskis,et al.  Understanding the link between feelings of mental defeat, self-efficacy and the experience of chronic pain , 2018, British journal of pain.

[12]  Ali Sunyaev,et al.  Availability and quality of mobile health app privacy policies , 2015, J. Am. Medical Informatics Assoc..

[13]  T. Vos,et al.  Global, regional, and national incidence and prevalence, and years lived with disability for 328 diseases and injuries in 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016 , 2017 .

[14]  Ying Li,et al.  Assessing the quality of mobile applications targeting postpartum depression in China. , 2020, International journal of mental health nursing.

[15]  J. Booth,et al.  Aerobic vs. resistance exercise for chronic non-specific low back pain: A systematic review and meta-analysis. , 2018, Journal of back and musculoskeletal rehabilitation.

[16]  Alejandro Carrasco,et al.  Privacy Assessment in Mobile Health Apps: Scoping Review , 2020, JMIR mHealth and uHealth.

[17]  Stephen Hardy,et al.  Quality of Deaf and Hard-of-Hearing Mobile Apps: Evaluation Using the Mobile App Rating Scale (MARS) With Additional Criteria From a Content Expert , 2019, JMIR mHealth and uHealth.

[18]  Elizabeth M. Borycki,et al.  Issues and Considerations for Healthcare Consumers Using Mobile Applications , 2013, ITCH.

[19]  C. Short,et al.  TaylorActive – Examining the effectiveness of web-based personally-tailored videos to increase physical activity: a randomised controlled trial protocol , 2015, BMC Public Health.

[20]  Simon Hunter,et al.  Randomized controlled trial of a 12-week digital care program in improving low back pain , 2019, npj Digital Medicine.

[21]  Zahra Mirbolook Jalali,et al.  Effect of Cognitive-Behavioral Training on Pain Self-Efficacy, Self-Discovery, and Perception in Patients with Chronic Low-Back Pain: A Quasi-Experimental Study , 2019, Anesthesiology and pain medicine.

[22]  Afaf Girgis,et al.  Efficacy of tailored-print interventions to promote physical activity: a systematic review of randomised trials , 2011, The international journal of behavioral nutrition and physical activity.

[23]  Aidan Cashin,et al.  Exercise for chronic musculoskeletal pain: A biopsychosocial approach. , 2017, Musculoskeletal care.

[24]  George Demiris,et al.  Smartphone Applications to Support Sleep Self-Management: Review and Evaluation. , 2018, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[25]  C. Baird,et al.  The pilot study. , 2000, Orthopedic nursing.

[26]  Mette Jensen Stochkendahl,et al.  National Clinical Guidelines for non-surgical treatment of patients with recent onset low back pain or lumbar radiculopathy , 2017, European Spine Journal.

[27]  Jo Nijs,et al.  Patient-centeredness in physiotherapy: What does it entail? A systematic review of qualitative studies , 2016 .

[28]  Shobhit Kumar,et al.  A systematic review of the smartphone applications available for coronavirus disease 2019 (COVID19) and their assessment using the mobile app rating scale (MARS) , 2020, medRxiv.

[29]  Stoyan R. Stoyanov,et al.  Review and Evaluation of Mindfulness-Based iPhone Apps , 2015, JMIR mHealth and uHealth.

[30]  Tobias Dehling,et al.  Exploring the Far Side of Mobile Health: Information Security and Privacy of Mobile Health Apps on iOS and Android , 2015, JMIR mHealth and uHealth.

[31]  Carola Berking,et al.  Skin Cancer Smartphone Applications for German-speaking Patients: Review and Content Analysis Using the Mobile App Rating Scale. , 2019, Acta dermato-venereologica.

[32]  Elisabetta Ceretti,et al.  Does evidence support the use of mobile phone apps as a driver for promoting healthy lifestyles from a public health perspective? A systematic review of Randomized Control Trials. , 2017, Patient education and counseling.

[33]  Eva-Maria Messner,et al.  German Mobile Apps in Rheumatology: Review and Analysis Using the Mobile Application Rating Scale (MARS) , 2019, JMIR mHealth and uHealth.

[34]  R B Perez,et al.  Intensive interdisciplinary treatment for a patient with coexisting pain and obesity: A case study. , 2018, Obesity research & clinical practice.

[35]  Alejandro Salazar,et al.  Measuring the Quality of Mobile Apps for the Management of Pain: Systematic Search and Evaluation Using the Mobile App Rating Scale , 2018, JMIR mHealth and uHealth.

[36]  C. Cook,et al.  Clinical guidelines for low back pain: A critical review of consensus and inconsistencies across three major guidelines. , 2016, Best practice & research. Clinical rheumatology.

[37]  M. O. Sharif,et al.  Patient focused oral hygiene apps: an assessment of quality (using MARS) and knowledge content , 2019, British Dental Journal.

[38]  C. Vandelanotte,et al.  Can you elaborate on that? Addressing participants’ need for cognition in computer-tailored health behavior interventions , 2018, Health psychology review.

[39]  F. Cicuttini,et al.  People with low back pain want clear, consistent and personalised information on prognosis, treatment options and self-management strategies: a systematic review. , 2019, Journal of physiotherapy.

[40]  Floor M. Kroese,et al.  Tailor made for health psychology: issues in the design and effectiveness of internet interventions , 2015 .

[41]  C. Abraham,et al.  The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the Reporting of Behavior Change Interventions , 2013, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[42]  David Kotz,et al.  Privacy in mobile technology for personal healthcare , 2012, CSUR.

[43]  J. Chenot,et al.  Non-Specific Low Back Pain. , 2017, Deutsches Arzteblatt international.

[44]  Sharareh R. Niakan Kalhori,et al.  Quality Evaluation of English Mobile Applications for Gestational Diabetes: App Review using Mobile Application Rating Scale (MARS). , 2020, Current diabetes reviews.

[45]  John Burns,et al.  Literacy-Adapted Cognitive Behavioral Therapy Versus Education for Chronic Pain at Low-Income Clinics , 2018, Annals of Internal Medicine.

[46]  Laura R. Olevitch,et al.  Tailoring Health Messages: Customizing Communication With Computer Technology , 1999 .

[47]  Agusti Solanas,et al.  Security and Privacy Analysis of Mobile Health Applications: The Alarming State of Practice , 2018, IEEE Access.

[48]  Joon Lee,et al.  Consumer Mobile Apps for Potential Drug-Drug Interaction Check: Systematic Review and Content Analysis Using the Mobile App Rating Scale (MARS) , 2018, JMIR mHealth and uHealth.

[49]  Jordan Rivera,et al.  Commercially Available Smartphone Apps to Support Postoperative Pain Self-Management: Scoping Review , 2017, JMIR mHealth and uHealth.

[50]  Daniel J. O'Keefe,et al.  The Relative Persuasiveness of Gain-Framed and Loss-Framed Messages for Encouraging Disease Detection Behaviors: A Meta-Analytic Review , 2009 .

[51]  G. Andersson.  Epidemiological features of chronic low-back pain , 1999, The Lancet.

[52]  Charles Abraham,et al.  A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management , 2016, International Journal of Behavioral Nutrition and Physical Activity.

[53]  Stoyan R. Stoyanov,et al.  Development and Validation of the User Version of the Mobile Application Rating Scale (uMARS) , 2016, JMIR mHealth and uHealth.

[54]  A. Bandura.  Health Promotion by Social Cognitive Means , 2004, Health education & behavior : the official publication of the Society for Public Health Education.

[55]  K. Ickmans,et al.  Best Evidence Rehabilitation for Chronic Pain Part 3: Low Back Pain , 2019, Journal of clinical medicine.