Digital support interventions for the self-management of low back pain: a systematic review

Background: Low back pain (LBP) is a common cause of disability and is ranked as the most burdensome health condition globally. Self-management, including components on increased knowledge, monitoring of symptoms, and physical activity, are consistently recommended in clinical guidelines as cost-effective strategies for LBP management and there is increasing interest in the potential role of digital health. Objective: The study aimed to synthesize and critically appraise published evidence concerning the use of interactive digital interventions to support self-management of LBP. The following specific questions were examined: (1) What are the key components of digital self-management interventions for LBP, including theoretical underpinnings? (2) What outcome measures have been used in randomized trials of digital self-management interventions in LBP and what effect, if any, did the intervention have on these? and (3) What specific characteristics or components, if any, of interventions appear to be associated with beneficial outcomes? Methods: Bibliographic databases searched from 2000 to March 2016 included Medline, Embase, CINAHL, PsycINFO, Cochrane Library, DoPHER and TRoPHI, Social Science Citation Index, and Science Citation Index. Reference and citation searching was also undertaken. Search strategy combined the following concepts: (1) back pain, (2) digital intervention, and (3) self-management. Only randomized controlled trial (RCT) protocols or completed RCTs involving adults with LBP published in peer-reviewed journals were included. Two reviewers independently screened titles and abstracts, full-text articles, extracted data, and assessed risk of bias using Cochrane risk of bias tool. An independent third reviewer adjudicated on disagreements. Data were synthesized narratively. Results: Of the total 7014 references identified, 11 were included, describing 9 studies: 6 completed RCTs and 3 protocols for future RCTs. The completed RCTs included a total of 2706 participants (range of 114-1343 participants per study) and varied considerably in the nature and delivery of the interventions, the duration/definition of LBP, the outcomes measured, and the effectiveness of the interventions. Participants were generally white, middle aged, and in 5 of 6 RCT reports, the majority were female and most reported educational level as time at college or higher. Only one study reported between-group differences in favor of the digital intervention. There was considerable variation in the extent of reporting the characteristics, components, and theories underpinning each intervention. None of the studies showed evidence of harm. Conclusions: The literature is extremely heterogeneous, making it difficult to understand what might work best, for whom, and in what circumstances. Participants were predominantly female, white, well educated, and middle aged, and thus the wider applicability of digital self-management interventions remains uncertain. No information on cost-effectiveness was reported. The evidence base for interactive digital interventions to support patient self-management of LBP remains weak

PET/CT imaging of spinal inflammation and microcalcification in patients with low back pain: A pilot study on the quantification by artificial intelligence-based segmentation

Background: Current imaging modalities are often incapable of identifying nociceptive sources of low back pain (LBP). We aimed to characterize these by means of positron emission tomography/computed tomography (PET/CT) of the lumbar spine region applying tracers 18F-fluorodeoxyglucose (FDG) and 18F-sodium fluoride (NaF) targeting inflammation and active microcalcification, respectively. Methods: Using artificial intelligence (AI)-based quantification, we compared PET findings in two sex- and age-matched groups, a case group of seven males and five females, mean age 45 \ub1 14 years, with ongoing LBP and a similar control group of 12 pain-free individuals. PET/CT scans were segmented into three distinct volumes of interest (VOIs): lumbar vertebral bodies, facet joints and intervertebral discs. Maximum, mean and total standardized uptake values (SUVmax, SUVmean and SUVtotal) for FDG and NaF uptake in the 3 VOIs were measured and compared between groups. Holm–Bonferroni correction was applied to adjust for multiple testing. Results: FDG uptake was slightly higher in most locations of the LBP group including higher SUVmean in the intervertebral discs (0.96 \ub1 0.34 vs. 0.69 \ub1 0.15). All NaF uptake values were higher in cases, including higher SUVmax in the intervertebral discs (11.63 \ub1 3.29 vs. 9.45 \ub1 1.32) and facet joints (14.98 \ub1 6.55 vs. 10.60 \ub1 2.97). Conclusion: Observed intergroup differences suggest acute inflammation and microcalcification as possible nociceptive causes of LBP. AI-based quantification of relevant lumbar VOIs in PET/CT scans of LBP patients and controls appears to be feasible. These promising, early findings warrant further investigation and confirmation

Could the clinical interpretability of subgroups detected using clustering methods be improved by using a novel two-stage approach?

Background: Recognition of homogeneous subgroups of patients can usefully improve prediction of their outcomes and the targeting of treatment. There are a number of research approaches that have been used to recognise homogeneity in such subgroups and to test their implications. One approach is to use statistical clustering techniques, such as Cluster Analysis or Latent Class Analysis, to detect latent relationships between patient characteristics. Influential patient characteristics can come from diverse domains of health, such as pain, activity limitation, physical impairment, social role participation, psychological factors, biomarkers and imaging. However, such 'whole person' research may result in data-driven subgroups that are complex, difficult to interpret and challenging to recognise clinically. This paper describes a novel approach to applying statistical clustering techniques that may improve the clinical interpretability of derived subgroups and reduce sample size requirements. Methods: This approach involves clustering in two sequential stages. The first stage involves clustering within health domains and therefore requires creating as many clustering models as there are health domains in the available data. This first stage produces scoring patterns within each domain. The second stage involves clustering using the scoring patterns from each health domain (from the first stage) to identify subgroups across all domains. We illustrate this using chest pain data from the baseline presentation of 580 patients. Results: The new two-stage clustering resulted in two subgroups that approximated the classic textbook descriptions of musculoskeletal chest pain and atypical angina chest pain. The traditional single-stage clustering resulted in five clusters that were also clinically recognisable but displayed less distinct differences. Conclusions: In this paper, a new approach to using clustering techniques to identify clinically useful subgroups of patients is suggested. Research designs, statistical methods and outcome metrics suitable for performing that testing are also described. This approach has potential benefits but requires broad testing, in multiple patient samples, to determine its clinical value. The usefulness of the approach is likely to be context-specific, depending on the characteristics of the available data and the research question being asked of it

App-delivered self-management intervention trial selfBACK for people with low back pain: protocol for implementation and process evaluation

Background: Implementation 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. Objective: This 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. Methods: The 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. Results: The 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. Conclusions: This 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 Registration: ClinicalTrials.gov NCT03798288; https://www.clinicaltrials.gov/ct2/show/NCT03798288 International Registered Report Identifier (IRRID): DERR1-10.2196/20308

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

An app-delivered self-management program for people with low back pain: protocol for the selfBACK randomized controlled trial.

Background: Low back pain (LBP) is prevalent across all social classes, in all age groups, and across industrialized and developing countries. From a global perspective, LBP is considered the leading cause of disability and negatively impacts everyday life and well-being. Self-management is a recommended first-line treatment, and mobile apps are a promising platform to support self-management of conditions like LBP. In the selfBACK project, we have developed a digital decision support system made available for the user via an app intended to support tailored self-management of nonspecific LBP. Objective: The trial aims to evaluate the effectiveness of using the selfBACK app to support self-management in addition to usual care (intervention group) versus usual care only (control group) in people with nonspecific LBP. Methods: This is a single-blinded, randomized controlled trial (RCT) with two parallel arms. The selfBACK app provides tailored self-management plans consisting of advice on physical activity, physical exercises, and educational content. Tailoring of plans is achieved by using case-based reasoning (CBR) methodology, which is a branch of artificial intelligence. The core of the CBR methodology is to use data about the current case (participant) along with knowledge about previous and similar cases to tailor the self-management plan to the current case. This enables a person-centered intervention based on what has and has not been successful in previous cases. Participants in the RCT are people with LBP who consulted a health care professional in primary care within the preceding 8 weeks. Participants are randomized to using the selfBACK app in addition to usual care versus usual care only. We aim to include a total of 350 participants (175 participants in each arm). Outcomes are collected at baseline, 6 weeks, and 3, 6, and 9 months. The primary end point is difference in pain-related disability between the intervention group and the control group assessed by the Roland-Morris Disability Questionnaire at 3 months. Results: The trial opened for recruitment in February 2019. Data collection is expected to be complete by fall 2020, and the results for the primary outcome are expected to be published in fall 2020. Conclusions: This RCT will provide insights regarding the benefits of supporting tailored self-management of LBP through an app available at times convenient for the user. If successful, the intervention has the potential to become a model for the provision of tailored self-management support to people with nonspecific LBP and inform future interventions for other painful musculoskeletal conditions

Diagnosis and treatment of musculoskeletal chest pain: design of a multi-purpose trial

<p>Abstract</p> <p>Background</p> <p>Acute chest pain is a major health problem all over the western world. Active approaches are directed towards diagnosis and treatment of potentially life threatening conditions, especially acute coronary syndrome/ischemic heart disease. However, according to the literature, chest pain may also be due to a variety of extra-cardiac disorders including dysfunction of muscles and joints of the chest wall or the cervical and thoracic part of the spine. The diagnostic approaches and treatment options for this group of patients are scarce and formal clinical studies addressing the effect of various treatments are lacking.</p> <p>Methods/Design</p> <p>We present an ongoing trial on the potential usefulness of chiropractic diagnosis and treatment in patients dismissed from an acute chest pain clinic without a diagnosis of acute coronary syndrome. The aims are to determine the proportion of patients in whom chest pain may be of musculoskeletal rather than cardiac origin and to investigate the decision process of a chiropractor in diagnosing these patients; further, to examine whether chiropractic treatment can reduce pain and improve physical function when compared to advice directed towards promoting self-management, and, finally, to estimate the cost-effectiveness of these procedures. This study will include 300 patients discharged from a university hospital acute chest pain clinic without a diagnosis of acute coronary syndrome or any other obvious cardiac or non-cardiac disease. After completion of the clinic's standard cardiovascular diagnostic procedures, trial patients will be examined according to a standardized protocol including a) a self-report questionnaire; b) a semi-structured interview; c) a general health examination; and d) a specific manual examination of the muscles and joints of the neck, thoracic spine, and thorax in order to determine whether the pain is likely to be of musculoskeletal origin. To describe the patients status with regards to ischemic heart disease, and to compare and indirectly validate the musculoskeletal diagnosis, myocardial perfusion scintigraphy is performed in all patients 2–4 weeks following discharge. Descriptive statistics including parametric and non-parametric methods will be applied in order to compare patients with and without musculoskeletal chest pain in relation to their scintigraphic findings. The decision making process of the chiropractor will be elucidated and reconstructed using the CART method. Out of the 300 patients 120 intended patients with suspected musculoskeletal chest pain will be randomized into one of two groups: a) a course of chiropractic treatment (therapy group) of up to ten treatment sessions focusing on high velocity, low amplitude manipulation of the cervical and thoracic spine, mobilisation, and soft tissue techniques. b) Advice promoting self-management and individual instructions focusing on posture and muscle stretch (advice group). Outcome measures are pain, physical function, overall health, self-perceived treatment effect, and cost-effectiveness.</p> <p>Discussion</p> <p>This study may potentially demonstrate that a chiropractor is able to identify a subset of patients suffering from chest pain predominantly of musculoskeletal origin among patients discharged from an acute chest pain clinic with no apparent cardiac condition. Furthermore knowledge about the benefits of manual treatment of patients with musculoskeletal chest pain will inform clinical decision and policy development in relation to clinical practice.</p> <p>Trial registration</p> <p>NCT00462241 and NCT00373828</p

Lack of consensus across clinical guidelines regarding the role of psychosocial factors within low back pain care: a systematic review

It is widely accepted that psychosocial prognostic factors should be addressed by clinicians in their assessment and management of patient suffering from low back pain (LBP). On the other hand, an overview is missing how these factors are addressed in clinical LBP guidelines. Therefore, our objective was to summarize and compare recommendations regarding the assessment and management of psychosocial prognostic factors for LBP chronicity, as reported in clinical LBP guidelines. We performed a systematic search of clinical LBP guidelines (PROSPERO registration number 154730). This search consisted of a combination of previously published systematic review articles and a new systematic search in medical or guideline-related databases. From the included guidelines, we extracted recommendations regarding the assessment and management of LBP which addressed psychosocial prognostic factors (ie, psychological factors ["yellow flags"], perceptions about the relationship between work and health, ["blue flags"], system or contextual obstacles ["black flags") and psychiatric symptoms ["orange flags"]). In addition, we evaluated the level or quality of evidence of these recommendations. In total, we included 15 guidelines. Psychosocial prognostic factors were addressed in 13 of 15 guidelines regarding their assessment and in 14 of 15 guidelines regarding their management. Recommendations addressing psychosocial factors almost exclusively concerned "yellow" or "black flags," and varied widely across guidelines. The supporting evidence was generally of very low quality. We conclude that in general, clinical LBP guidelines do not provide clinicians with clear instructions about how to incorporate psychosocial factors in LBP care and should be optimized in this respect. More specifically, clinical guidelines vary widely in whether and how they address psychosocial factors, and recommendations regarding these factors generally require better evidence support. This emphasizes a need for a stronger evidence-base underlying the role of psychosocial risk factors within LBP care, and a need for uniformity in methodology and terminology across guidelines. PERSPECTIVE: This systematic review summarized clinical guidelines on low back pain (LBP) on how they addressed the identification and management of psychosocial factors. This review revealed a large amount of variety across guidelines in whether and how psychosocial factors were addressed. Moreover, recommendations generally lacked details and were based on low quality evidence

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

Background: International 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. Objective: The 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. Methods: The 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 &gt;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. Results: A 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. Conclusions: We 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