David Salman

Objectives The aim of this study was to gather insights from a diverse group of stakeholders, identify existing challenges and explore associated solutions to develop a conceptual framework that assists in the development of a chronic low back pain (CLBP) self-management app. Design Codesign workshop methodology. Setting The study was conducted online, allowing for the participation of a diverse group of stakeholders. Participants Participants were recruited through social media platforms and professional networks, including individuals with LBP for at least 3 months, healthcare professionals experienced in CLBP management and app developers with a background in digital health applications. Interventions The study used codesign workshops focused on brainstorming, communication and reflection exercises designed to foster collaboration and gather insights from stakeholders. Outcome measures Identification of key challenges and potential solutions for mobile health (mHealth) apps targeting CLBP. Results Nine stakeholders identified four primary challenges in current back pain apps: belief and trust, motivation, safety and usability. The group also collaboratively developed and proposed practical solutions to these challenges. Conclusions This study illustrated the utility of the codesign workshop approach in generating critical insights for the development of a mHealth solution for CLBP. The insights gathered from these workshops can be used to inform future app development, potentially improving user engagement.

Background: Identifying clusters of diseases may aid understanding of shared aetiology, management of co-morbidities, and the discovery of new disease associations. Our study aims to identify disease clusters using a large set of long-term conditions and comparing methods that use the co-occurrence of diseases versus methods that use the sequence of disease development in a person over time. Methods: We use electronic health records from over ten million people with multimorbidity registered to primary care in England. First, we extract data-driven representations of 212 diseases from patient records employing (i) co-occurrence-based methods and (ii) sequence-based natural language processing methods. Second, we apply the graph-based Markov Multiscale Community Detection (MMCD) to identify clusters based on disease similarity at multiple resolutions. We evaluate the representations and clusters using a clinically curated set of 253 known disease association pairs, and qualitatively assess the interpretability of the clusters. Results: Both co-occurrence and sequence-based algorithms generate interpretable disease representations, with the best performance from the skip-gram algorithm. MMCD outperforms k-means and hierarchical clustering in explaining known disease associations. We find that diseases display an almost-hierarchical structure across resolutions from closely to more loosely similar co-occurrence patterns and identify interpretable clusters corresponding to both established and novel patterns. Conclusions: Our method provides a tool for clustering diseases at different levels of resolution from co-occurrence patterns in high-dimensional electronic health records, which could be used to facilitate discovery of associations between diseases in the future.

Background: Low back pain (LBP) is a significant health problem worldwide, with a lifetime prevalence of 84% in the general adult population. To rationalise the management of LBP, clinical practice guidelines (CPGs) have been issued in various countries around the world. This study aims to identify and compare the recommendations of recent CPGs for the management of LBP across the world. Methods: MEDLINE, EMBASE, CINAHL, PEDro, and major guideline databases were searched from 2017 to 2022 to identify CPGs. CPGs focusing on information regarding the management and/or treatment of non-specific LBP were considered eligible. The quality of included guidelines was evaluated using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument. Results: Our analysis identified a total of 22 CPGs that met the inclusion criteria, and were of middle and high methodological quality as assessed by the AGREE II tool. The guidelines exhibited heterogeneity in their recommendations, particularly in the approach to different stages of LBP. For acute LBP, the guidelines recommended the use of non-steroidal anti-inflammatory drugs (NSAIDs), therapeutic exercise, staying active, and spinal manipulation. For subacute LBP, the guidelines recommended the use of NSAIDs, therapeutic exercise, staying active, and spinal manipulation. For chronic LBP, the guidelines recommended therapeutic exercise, the use of NSAIDs, spinal manipulation, and acupuncture. Conclusions: Current CPGs provide recommendations for almost all major aspects of the management of LBP, but there is marked heterogeneity between them. Some recommendations lack clarity and overlap with other treatments within the guidelines.

Background: 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. Objective: 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. Methods: 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). Results: 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. Conclusions: 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.

BACKGROUND 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. OBJECTIVE 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. METHODS 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). RESULTS 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. CONCLUSIONS 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.

SRC is common in high impact and collision sports,³ and is a condition many GPs will have seen. Over 1% of all emergency department attendances in England and Wales in 2016 were due to concussion, with many more potentially undiagnosed or coded incorrectly.⁴ SRCs account for a significant proportion (up to 60% in children and adolescents) of these cases,⁵ and the UK brain injury association, Headway, reports increasing rates of head injuries and acquired brain injuries over the past two decades.⁶ This may also reflect increased recognition and media coverage of the condition.

Objectives To determine whether the frequency of diagnostic codes for long-term conditions (LTCs) in primary care electronic healthcare records (EHRs) is associated with (1) disease coding incentives, (2) General Practice (GP), (3) patient sociodemographic characteristics and (4) calendar year of diagnosis. Design Retrospective cohort study. Setting GPs in England from 2015 to 2022 contributing to the Clinical Practice Research Datalink Aurum dataset. Participants All patients registered to a GP with at least one incident LTC diagnosed between 1 January 2015 and 31 December 2019. Primary and secondary outcome measures The number of diagnostic codes for an LTC in (1) the first and (2) the second year following diagnosis, stratified by inclusion in the Quality and Outcomes Framework (QOF) financial incentive programme. Results 3 113 724 patients were included, with 7 723 365 incident LTCs. Conditions included in QOF had higher rates of annual coding than conditions not included in QOF (1.03 vs 0.32 per year, pConclusions The frequency of diagnostic codes for newly diagnosed LTCs is influenced by factors including patient sociodemographics, disease inclusion in QOF, GP practice and the impact of the COVID-19 pandemic. Natural language processing or other methods using temporally ordered code sequences should account for these factors to minimise potential bias.

Objectives: To quantify the associations between shielding status and loneliness at the start of the COVID-19 pandemic, and physical activity (PA) levels throughout the pandemic. Methods: Demographic, health and lifestyle characteristics of 7748 cognitively healthy adults aged >50, and living in London, were surveyed from April 2020 to March 2021. The International Physical Activity Questionnaire (IPAQ) short-form assessed PA before COVID-19 restrictions, and up to 6 times over 11 months. Linear mixed models investigated associations between shielding status and loneliness at the onset of the pandemic, with PA over time. Results: Participants who felt ‘often lonely’ at the outset of the pandemic completed an average of 522 and 547 fewer Metabolic Equivalent of Task (MET) minutes/week during the pandemic (95% CI: -809, -236, p<0.001) (95% CI: -818, -275, p<0.001) than those who felt ‘never lonely’ in univariable and multivariable models adjusted for demographic factors respectively. Those who felt ‘sometimes lonely’ completed 112 fewer MET minutes/week (95% CI: -219, -5, p = 0.041) than those who felt ‘never lonely’ following adjustment for demographic factors. Participants who were shielding at the outset of the pandemic completed an average of 352 fewer MET minutes/week during the pandemic than those who were not (95% CI: -432, -273; p<0.001) in univariable models and 228 fewer MET minutes/week (95% CI: -307, -150, p<0.001) following adjustment for demographic factors. No significant associations were found after further adjustment for health and lifestyle factors. Conclusions: Those shielding or lonely at pandemic onset were likely to have completed low levels of PA during the pandemic. These associations are influenced by co-morbidities and health status.

Background: Chronic low back pain (CLBP) is a highly prevalent musculoskeletal condition affecting 60–80% of the general population within their lifetime. Given the large numbers of people affected, self-management approaches have been introduced as a way to manage this condition with endorsement by the national institute for health and care excellence. Interventions are often termed self-management without defining either content or goals. Our study sought to determine the content, characteristics, and evidence for self-management of CLBP. Methods: This narrative review was conducted using a systematic approach to search journal articles in English that focused on CLBP self-management. MEDLINE, EMBASE, CINAHL, and PsycINFO databases were used to identify publications with terms relating to back pain and self-management from January 2016 until January 2022. Results: In total, 15 studies were found suitable for inclusion in the review. Core components of self-management strategies include exercise, education, and psychological interventions, but there was a lack of consistency with respect to content. Intervention characteristics were either under-reported or varied. Furthermore, outcome measures used to assess these self-management programmes were diverse, mainly focusing on functional disability and pain intensity. Conclusions: Inconsistencies in the content of self-management interventions, intervention characteristics, and outcome measures used for assessing self-management programmes were found across the literature. Current self-management approaches do not consider the complex biopsychosocial nature of CLBP. A consensus on the key components of self-management interventions, and how they should be evaluated, will pave the way for research to determine whether self-management can effectively manage CLBP.

Identifying clusters of co-occurring diseases can aid understanding of shared aetiology, management of co-morbidities, and the discovery of new disease associations. Here, we use data from a population of over ten million people with multimorbidity registered to primary care in England to identify disease clusters through a two-stage process. First, we extract data-driven representations of 212 diseases from patient records employing i) co-occurrence-based methods and ii) sequence-based natural language processing methods. Second, we apply multiscale graph-based clustering to identify clusters based on disease similarity at multiple resolutions, which outperforms k-means and hierarchical clustering in explaining known disease associations. We find that diseases display an almost-hierarchical structure across resolutions from closely to more loosely similar co-occurrence patterns and identify interpretable clusters corresponding to both established and novel patterns. Our method provides a tool for clustering diseases at different levels of resolution from co-occurrence patterns in high-dimensional electronic healthcare record data.