{"paper_id":"0528abee-cb0b-482e-8a86-36a8e86ce756","body_text":"OSCAR (Osteopathic Single CAse Research): A Single Case Experimental Design study evaluating osteopathic management for non-specific low back pain | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article OSCAR (Osteopathic Single CAse Research): A Single Case Experimental Design study evaluating osteopathic management for non-specific low back pain Jerry Draper-Rodi, Hilary Abbey, Kevin Brownhill, Steven Vogel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6147823/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Non-specific low back pain (NSLBP) is a major global health challenge. While osteopathic treatment shows benefit in managing NSLBP, research comparing traditional approaches with biopsychosocial-informed care is limited. This study aimed to evaluate outcomes from standard osteopathic treatment versus biopsychosocial-informed management and assess the feasibility of conducting Single Case Experimental Design (SCED) research in osteopathic practice. Methods A SCED trial with randomised treatment start times was conducted and reported in accordance with the SCRIBE checklist. Eleven patients with NSLBP were recruited before (between January and February 2022) or after (between June and July 2022) osteopaths completed a Biopsychosocial Pain Management e-learning course. Data was collected during baseline (5–15 days), treatment (4–6 weeks), and follow-up (12 weeks) periods. Primary outcomes were the Numeric Pain Rating Scale (NPRS) and Patient Specific Function Scale (PSFS). Feasibility was assessed through recruitment, retention, and protocol adherence. Results Complete data was obtained from nine osteopath-patient pairs. Linear mixed modelling showed significant daily improvements in pain (NPRS − 0.07/day, p < 0.001) and function (PSFS − 0.12/day, p < 0.001) during treatment, sustained over 12-week follow-up. These changes were clinically meaningful, with treatment-phase reductions exceeding established Minimal Important Change thresholds for both measures. Limited recruitment after the e-learning course prevented comparison of standard versus biopsychosocial-informed treatment approaches. SCED methodology proved feasible, though challenges emerged around recruitment and maintaining randomised treatment delays. Conclusions This first implementation of SCED in osteopathic practice demonstrated significant improvements in pain and function following treatment initiation. While the methodology offers a viable approach for practice-based research, future studies should address identified implementation challenges. The findings advance our understanding of osteopathic care while providing a framework for future research bridging evidence and practice. Trial registration NCT05120921 Non-specific low back pain Biopsychosocial Patient-centred Single Case Experimental Design Osteopathy Practice-based research Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Musculoskeletal disorders, particularly low back pain (LBP), represent a major global health challenge, ranking as the second highest cause of non-fatal disability and affecting over 1.63 billion individuals worldwide in 2020 ( 1 ). LBP's impact extends beyond physical symptoms, often manifesting with psychological comorbidities including anxiety, depression, and pain-related fear that can significantly affect work, physical activity, and social interaction ( 2 , 3 ). The complexity of LBP is further compounded by multiple prognostic and mediating factors across cognitive, behavioural, and social domains, which vary between patients and over time ( 4 , 5 ). Current guidelines advocate for biopsychosocial (BPS) approaches in managing complex, chronic musculoskeletal conditions, including non-specific low back pain ( 6 ). While evidence supports various non-pharmacological interventions including exercise, manipulation/mobilisation, and psychological/behavioural interventions ( 7 ), implementing BPS approaches presents unique challenges. The skills needed for BPS care can be challenging to teach and may have limited impact in changing biomedical beliefs ( 8 , 9 ). This is particularly relevant for osteopathy, which has shown benefit in managing NSLBP ( 10 , 11 ), but lacks research comparing traditional biomechanical approaches with more recent BPS-informed care. While Randomised Controlled Trials (RCTs) provide robust evidence about efficacy, their strict inclusion criteria and large sample requirements limit applicability to individual clinical cases. Single Case Experimental Designs (SCEDs) offer a methodologically rigorous alternative that enables detailed investigation of individual treatment responses while maintaining statistical validity through randomisation and systematic data collection ( 12 , 13 ). SCEDs are particularly valuable in natural clinical settings, allowing practitioners to track individual patient responses while contributing to the evidence base. This study addresses two key gaps in current knowledge: first, comparing outcomes between standard osteopathic treatment and BPS-informed management for NSLBP; and second, assessing the feasibility of implementing SCED methodology in osteopathic practice. These aims align with professional requirements for evidence-informed healthcare ( 14 ) and support the growing culture of standardised data collection in osteopathic practice ( 15 , 16 ). This study represents the first implementation of SCED methodology in osteopathic practice, combining rigorous experimental design with clinical practicality. The innovative integration of practitioner education evaluation with patient outcomes provides a new framework for practice-based research. METHODS This study employed methods specifically designed to be implementable within routine clinical practice while maintaining scientific rigour, enabling direct translation of findings to clinical practice and education. Methodological rigour was ensured through prospective registration and publication of the protocol, multiple elements of randomisation, standardised training and data collection procedures, a robust statistical analysis plan, and blinded analysis of outcomes. Reporting was in accordance with the Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) Checklist. Design This SCED trial employed a multiple-baseline design with randomised treatment start times to enhance internal validity. The study was prospectively registered (NCT05120921) and received ethical approval from the University College of Osteopathy Research Ethics Committee (ref #20210816). The protocol was published with full design details ( 17 ), and there are no planned replications. The design incorporated three key methodological features to ensure robust evaluation: Multiple baseline periods (5–15 days) with randomised start times Extended treatment phase (4–6 weeks) with daily outcomes monitoring Long-term follow-up (12 weeks) to assess maintenance of effects This design aligns with current best practice recommendations for SCED research (Kratochwill et al., 2010) while being feasible within clinical practice settings. Participants Sample size rationale was based on established SCED methodology requiring minimum data points for valid inference ( 18 ). Osteopaths A purposive sample of practising osteopaths was recruited through professional networks using the following criteria: Inclusion: registered with the General Osteopathic Council, practising in the UK, minimum 15 years clinical practice Exclusion: Recent (≤ 10 years) Osteopathic Educational Provider experience, participation in previous feasibility study ( 19 ) Patients Each participating osteopath recruited up to three patients in January-February 2022 (before completing a biopsychosocial pain management (BPM) e-learning) and in June-July 2022 (after completing the BPM e-learning), using the following criteria: Inclusion: Adults (18+) with non-specific LBP (NSLBP) > 12 weeks, Numeric Pain Rating Scale (NPRS) scores between 5 and 9, Patient Specific Function Scale (PSFS) scores between 2 and 7 Exclusion: Osteopathic treatment within previous 6 months The NPRS and PSFS eligible scores were required to minimise the risk of floor effects from low baseline symptoms or risk of harm from delaying treatment for severe symptoms. << Insert Fig. 1 around here with this title: Fig. 1 – Study design >> Study process Randomisation and allocation A blocked randomisation strategy was implemented using Qualtrics© to assign patients to treatment start dates: Early (5–8 days), Medium (9–12 days) or Late (13–15 days). Randomisation occurred separately in each osteopath's clinic before and after BPM e-learning to ensure balanced allocation across timepoints. This approach strengthened internal validity while maintaining feasibility in clinical settings ( 13 , 20 ). Data Collection Procedures Patient data were collected using Qualtrics© ( 21 ) during three periods: pre-treatment (5–15 days); treatment (4–6 weeks); and follow-up (12 weeks). Primary Outcomes: Primary patient outcome measures were the Numeric Pain Rating Scale (NPRS) and Patient Specific Function Scales (PSFS), measured daily during baseline and treatment, and weekly or fortnightly during follow-up (Fig. 3). Automated electronic reminders were sent to maximise response rate. Details about psychometric properties and minimal clinically important differences can be found in the protocol ( 17 ). Secondary Outcomes: they included the Measure Your Medical Outcome Profile 2 (MYMOP2) ( 22 ), Arthritis Research UK Musculoskeletal Health Questionnaire (MSK-HQ) ( 23 ), and the Depression, Anxiety, and Positive Outlook Scale (DAPOS) ( 24 ). A questionnaire was adapted from the Patient Enablement Index for Back Pain ( 25 ) to assess perceptions of shared decision-making, treatment outcomes, relevance of the measures, and acceptability of data collection processes. Validity and reliability are detailed in the Protocol ( 17 ). Osteopath data was collected before recruiting the first patient and after the BPM e-learning. The primary outcome measure was the modified Pain Attitudes and Beliefs Scale (PABS), a 19-item questionnaire comparing the strength of biomedical and behavioural treatment approaches ( 26 ). Osteopaths also estimated patient prognosis after initial assessments but before treatment, as this can assess therapists’ ability to estimate the risk of a poor outcome ( 27 ). Implementation procedures were established to ensure consistency and safety across all practices. Participating osteopaths completed standardised training to familiarise themselves with study protocols and data collection methods. Throughout the study, data quality was regularly monitored through automated systems with manual verification by the research team. A comprehensive adverse event protocol required osteopaths to report any treatment-related incidents to the Principal Investigator within 24 hours and complete detailed documentation, aligning with professional practice requirements ( 28 ). <<INSERT FIGURE 2 AROUND HERE WITH THIS TITLE: Fig. 2: randomisation in each group>> <<INSERT FIGURE 3 AROUND HERE WITH THIS TITLE: Fig. 3: Timing of patient outcome measures.>> Interventions Patient interventions comprised standard osteopathic treatment delivered over 6 weeks ( 29 ). Treatment followed professional standards ( 14 ) whilst allowing osteopaths to maintain their usual clinical approaches. This pragmatic design enabled practitioners to individualise care without adhering to a strict manual therapy protocol. Patients continued their existing healthcare treatments as normal. The practitioner intervention was an 8-hour BPM e-learning course, previously validated in a mixed-methods feasibility study ( 19 ). The course, updated in February 2022, incorporated ADDIE (Analyse, Design, Develop, Implement, Evaluate) and COM-B (Capability, Opportunity, Motivation – Behaviour) behaviour change models ( 30 , 31 ), offering evidence-based strategies for NSLBP management ( 32 ). Full course details are available in the protocol ( 17 ). Statistical analysis The complexity of SCED time-series data requires careful consideration of serial dependency and individual variation in treatment responses. We employed mixed effects linear modelling as our primary analytical method, enabling us to account for non-independent repeated measurements whilst accommodating individual differences in treatment trajectories. This approach was implemented using R packages LME4 ( 33 ) and lmerTest ( 34 ). Our design exceeded standard SCED power requirements, which typically specify a minimum of five data points per phase ( 18 ). Daily measurements during baseline (5–15 days) and treatment phases (28–42 days) provided robust data for detecting clinically meaningful changes. The multiple baseline design with randomised start times further strengthened statistical conclusion validity. The analysis examined changes in primary outcomes (NPRS and PSFS) across baseline, treatment and follow-up phases whilst accounting for individual patient trajectories. Secondary outcomes were planned to be analysed descriptively to contextualise primary outcome changes. To ensure analytical rigour, we implemented systematic data quality screening before analysis, maintained statistician blinding to patient stage (pre/post osteopath e-learning), and conducted separate individual and group-level analyses before synthesis. Visual analysis complemented our statistical approach, following established protocols ( 35 ) to assess points of change, linear trends and individual variability. Missing data patterns were examined and addressed using appropriate statistical methods. Full details of our analytical procedures are available in the published protocol ( 17 ). RESULTS Osteopaths 14 osteopaths took part in SCED training and 11 decided to join the study; 8 males and 3 females with a median age of 56 (range 39–61) and median of 22 years in practice (range 16–35). Five osteopaths recruited patients to the study. Only three osteopaths estimated prognosis (1 high risk; 2 medium risk), which was insufficient data for analysis. At baseline, PABS scores indicated approximately the same behavioural and biomedical attitude score: behavioural mean 32.00 (2.24); biomedical mean 31.64 (7.05). There was not enough data to compare the PABS scores before and after the e-learning course (2 participants completed the survey after the BPM course). Patients Eleven patients were recruited; 8 before the e-learning course and 3 afterwards. Sufficient SCED data points (Kratochwill et al 2010) were recorded by 9 patients; 6 male, 3 female; median age 39 (range 20–58). Four patients were randomised to Early treatment start, 3 to Medium and 2 to Late. Five patients waited < 5 days for treatment, 3 waited 5–10 days and one waited > 10 days. At baseline, the median NPRS score was 6 (range 5–9) and PSFS score 6 (range 4-7.8). Five patients identified 2–3 activities that were difficult to perform, and 6 patients listed 4–5 activities. Their ‘worst’ activities involved some degree of bending, including activities of daily living and exercise (n = 8), followed by sitting (n = 2) and walking (n = 1). Figure 4: Plots of individual time series. Green vertical bars represent start of treatment (left bar) and end of treatment (right bar, if it exists). One subject removed with only one measurement occasion. << INSERT FIGRE 4 AROUND HERE>> Modelling pain and disability scores Mixed-effects modelling of pain scores (NPRS) indicated a best-fit model incorporating a combination of gender, time, study phase and an interaction between study phase and time. Specifically, women reported a mean pain score of 4.12 (95% CI: [2.75, 5.48], p < 0.001) at baseline, whilst mean pain score was greater in men by 2.52 (95% CI: [1.35, 3.68], p < 0.001). During the treatment phase (4–6 weeks or 28–42 days), pain score decreased by a mean of 0.07 (95% CI: [0.04, 0.11], p < 0.001) per day, which translates to a total reduction of 1.96–2.94 points over the treatment period. This meets or exceeds the commonly accepted Minimal Important Change (MIC) of 2 points for NPRS in chronic low back pain. In contrast, during the pre-treatment baseline phase (5–15 days), pain actually increased by an average of 0.17 points per day, resulting in a total increase of 0.85–2.55 points before treatment began. During the post-treatment follow-up phase (12 weeks or 84 days), improvements continued but at a reduced rate of 0.04 points per day, contributing an additional reduction of approximately 3.36 points throughout follow-up. Modelling of disability scores (PSFS) indicated a best-fit model incorporating a combination of time, study phase and an interaction between study phase and time. Gender did not significantly improve the model, unlike in the NPRS model. Modelling of disability scores (PSFS) indicated a best-fit model incorporating a combination of time, study phase and an interaction between study phase and time. During the treatment phase (4–6 weeks or 28–42 days), PSFS scores decreased by 0.09 points per day (95% CI: [0.05, 0.13], p < 0.001), translating to a total reduction of 2.52–3.78 points over the treatment period. This exceeds the established Minimal Important Change (MIC) of 2 points for the PSFS in musculoskeletal conditions. In the pre-treatment baseline phase (5–15 days), PSFS scores increased by an average of 0.12 points per day, resulting in a total worsening of 0.60–1.80 points before treatment began. During the post-treatment follow-up phase (12 weeks or 84 days), improvement continued at a slower rate of 0.04 points per day, contributing an additional reduction of approximately 3.36 points throughout follow-up. Protocol deviation: it was not possible to assess the impact of BPM course on amount or rate of change, as 6 patients were recruited before osteopaths took the course but only 3 afterwards. The secondary outcome measures were not analysed or reported in this manuscript due to insufficient data collection. The raw data for these measures remains available in the data repository. Feasibility When tested, the linear mixed model was robust and data were well captured by the model, apart from 2 outlying points from 1 patient (see additional material for full model analysis). No procedural changes occurred after the study started and there were no reports of any serious adverse events related to osteopathic treatment or participation in the trial. Informal feedback from participating osteopaths revealed several challenges in recruiting patients from their own clinics. These included feelings of discomfort about asking patients who were seeking help to participate in research, time constraints, and difficulty retaining patients randomised to the delayed treatment group when earlier appointments were available at the clinic. DISCUSSION This is the first study to robustly examine how change unfolds during osteopathic management of patients with low back pain. The rigorous single-case experimental design and statistical analysis provide novel insights that can inform both postgraduate education and clinical practice. This methodological approach represents a significant advance in understanding individual patient responses to osteopathic care, moving beyond traditional group-level analyses to capture the granular detail of therapeutic change. The clinical course of low back pain is variable, and understanding this variability is important for informing treatment and patient stratification ( 36 ). While many individuals with acute low back pain experience improvement within six weeks, a significant proportion may develop persistent symptoms ( 37 ). This study addressed two key gaps in current knowledge: comparing outcomes between standard osteopathic treatment and BPS-informed management for NSLBP, and assessing the feasibility of implementing SCED methodology in osteopathic practice. These aims aligned with professional requirements for evidence-informed healthcare and supported the growing culture of standardised data collection in osteopathic practice. Treatment effectiveness and individual response patterns This study represents the first comprehensive application of SCED methodology to evaluate osteopathic treatment effectiveness for NSLBP in real clinical settings. The temporal pattern observed—initial deterioration during baseline, substantial improvement throughout treatment, and continued albeit slower gains during follow-up—provides robust evidence supporting the effectiveness of osteopathic intervention for both pain reduction and functional improvement in patients with non-specific low back pain. This temporal pattern, where improvements consistently followed treatment initiation across different randomised start times, strengthens causal inference about treatment effects. The findings align with previous research demonstrating the effectiveness of osteopathic manual treatment for NSLBP ( 10 , 11 ), while adding new insights about individual variation in treatment responses ( 5 ). The SCED approach revealed that while group-level analyses showed overall improvement, individual trajectories varied considerably, common for low back pain trajectories ( 38 , 39 ), and supporting recent calls for more personalised approaches to NSLBP management ( 40 ). The sustained improvements during follow-up align with expectations for complex interventions, where benefits should continue after treatment ends ( 31 ), though the rate of improvement typically slowed. SCED implementation in osteopathic practice This study makes a significant methodological contribution by demonstrating that SCED methodology can be successfully implemented in osteopathic practice, while also identifying important practical considerations. Nine of eleven recruited patients provided sufficient data for analysis, with good adherence to daily outcome measures and completion of follow-up assessments. This success rate compares favourably with previous SCED studies in musculoskeletal care ( 41 ). However, recruitment faced several challenges, particularly regarding randomised treatment delays. These challenges reflect broader tensions between research rigour and clinical practicality, as noted in previous studies ( 2 ). They also highlight important ethical considerations specific to private practice settings. Unlike public healthcare contexts where waiting periods are standard, private practice patients typically expect immediate access to services they are paying for. This creates tension between research requirements for randomised treatment delays and patient expectations. The weekly patient summaries provided to osteopaths represented an innovative approach to integrating research data with clinical decision-making, though the impact of these summaries on treatment choices requires further investigation. The study's systematic approach to data collection and analysis provides a model for future practice-based research. The electronic data capture system and automated reminders proved effective, though managing the resulting large dataset presented significant challenges. These experiences form the basis for specific recommendations to enhance future SCED trials in osteopathic practice. Practitioner engagement and professional development This study provides original insights into how experienced osteopaths engage with practice-based research and biopsychosocial approaches to care. The participating osteopaths showed notably strong baseline behavioural attitudes (PABS mean = 32 (2.24)), which was slightly higher than previously reported scores in similar healthcare professionals, including osteopaths (mean 29.86), chiropractors (mean 31.4) and physiotherapists (mean 31.76) ( 19 ). This unexpected finding suggests either an increasing recognition of biopsychosocial approaches within the profession since earlier studies, or potential self-selection bias in study participation. This self-selection bias aligns with recent findings from Bailey et al. ( 42 ), who found that osteopaths participating in research-focused CPD events were likely to have a more positive perception of research. Their work highlighted that while osteopaths generally recognise the need to develop the profession's evidence base, many feel they lack sufficient research skills to take an active role. Previous research has demonstrated that e-learning programmes can effectively deliver educational interventions aimed at enabling learning and improving performance in healthcare settings, particularly when developed using established pedagogical frameworks ( 43 ). The integration of practitioner education evaluation with patient outcomes represents an innovative approach to addressing the research-practice gap. Draper-Rodi et al. ( 43 ) demonstrated that using structured approaches such as the ADDIE model (Analyse, Design, Develop, Implement, Evaluate) and COM-B framework (Capability, Opportunity, Motivation – Behaviour) can effectively support behaviour change in practitioners. This kind of structured approach could help address the professional isolation commonly experienced by osteopaths ( 42 ), while simultaneously developing research capacity. The collaborative aspects of involvement in practice-based research could be particularly valuable, as Bailey et al. ( 42 ) found that osteopaths valued opportunities for networking and professional development through research engagement. The higher baseline PABS scores in our study, combined with Bailey et al.'s ( 42 ) findings about osteopaths' interest in research involvement, suggest a growing recognition within the profession of the need to engage with evidence-based approaches. However, significant barriers remain around research skills, time constraints, and concerns about practice implications that need to be addressed ( 42 ). Future initiatives should focus on providing structured support and clear pathways for practitioners to develop research competencies while maintaining clinical commitments, potentially combining e-learning approaches with practice-based research network participation. Strengths and limitations This study demonstrated several key strengths in its methodological approach. The research design prioritised rigour through multiple elements: we prospectively registered the protocol, incorporated various randomisation procedures, implemented standardised training and data collection methods, developed a robust statistical analysis plan, and ensured blinded analysis of outcomes. External validity was enhanced by conducting the study in real clinical settings with diverse patient presentations, maintaining extended follow-up periods, and collecting multiple baseline measurements. A particularly innovative aspect was the integration of practitioner education with patient outcomes, which offers a novel framework for practice-based research in osteopathy. However, several important limitations must be acknowledged. Recruitment proved challenging, resulting in a smaller sample size than originally planned. This limited our ability to compare outcomes before and after BPM training and may have introduced self-selection bias among participating osteopaths. We encountered implementation difficulties, particularly in maintaining randomised treatment delays and securing consistent completion of secondary outcome measures. Feedback about practitioners' use of weekly summaries was also more limited than anticipated. While our sample size was adequate for SCED analysis, it restricts our ability to draw broader conclusions about the comparative effectiveness of different treatment approaches. Future research directions These findings suggest several important directions for future research. Studies should investigate what constitutes acceptable waiting times for patients seeking pain treatment, particularly in the context of randomised designs. There is a need to better understand factors influencing individual treatment responses and how practitioners integrate outcome data into their clinical decision-making. Future work should also focus on developing more efficient systems for managing large-scale SCED data collection and examining how BPS training impacts practitioners with stronger biomedical orientations. Our findings demonstrate that SCED methodology offers a viable approach for practice-based research in osteopathy and could help bridge the gap between research evidence and clinical practice. However, successful implementation requires careful consideration of practical constraints and appropriate support for participating practitioners. This work contributes to the growing evidence base for osteopathic care whilst providing a framework for future research into individual treatment responses and professional development. CONCLUSIONS This study provides original contributions to osteopathic research through its first-time implementation of Single Case Experimental Design (SCED) methodology in clinical practice, revealing detailed patterns of change during osteopathic treatment for non-specific low back pain. The significance of the findings is demonstrated through clinically important improvements in pain and function that began with treatment initiation and continued through follow-up, while also establishing a feasible framework for practice-based research. Methodological rigour was assured through multiple elements including prospective registration, randomised treatment start times, standardised data collection, and robust statistical analysis. While implementation challenges were identified, particularly around recruitment and maintaining randomised treatment delays, these insights form the basis for enhancing future SCED trials in osteopathic practice. This work advances our understanding of osteopathic care for NSLBP by demonstrating clinically meaningful improvements following treatment initiation, while also providing a methodologically sound framework for future practice-based research that bridges the gap between evidence and clinical implementation. The findings demonstrate that SCEDs offer a viable approach for practice-based research in osteopathy, though successful implementation requires careful consideration of practical constraints and appropriate practitioner support. Abbreviations ADDIE: Analyse, Design, Develop, Implement, Evaluate BPM: Biopsychosocial Pain Management BPS: Biopsychosocial COM-B: Capability, Opportunity, Motivation – Behaviour CPD: Continuing Professional Development DAPOS: Depression, Anxiety, and Positive Outlook Scale LBP: Low Back Pain MSK-HQ: Arthritis Research UK Musculoskeletal Health Questionnaire MYMOP2: Measure Your Medical Outcome Profile 2 NCOR: National Council for Osteopathic Research NPRS: Numeric Pain Rating Scale NSLBP: Non-specific Low Back Pain OSCAR: Osteopathic Single CAse Research PABS: Pain Attitudes and Beliefs Scale PSFS: Patient Specific Function Scale RCT: Randomised Controlled Trial SCED: Single Case Experimental Design SOLAR: Strengthening Osteopathic Leadership and Research UCO: University College of Osteopathy Declarations Ethics approval and consent to participate: Approval was received from the University College of Osteopathy Research Ethics Committee (#16082021), and all participants (osteopaths and patients) provided written consent to participate. Consent for publication: Not applicable Availability of data and materials: The datasets generated and/or analysed during the current study are available in the OSF repository, DOI: 10.17605/OSF.IO/G89CA [https://osf.io/g89ca/] Competing interests: JDR receives fees from sales of the BPM e-learning course on the Health Sciences University CPD platform and has an interest in promoting research as Director of NCOR and Fellow of SOLAR (Strengthening Osteopathic Leadership and Research) programme in Australia. JDR and HA have received fees for teaching BPS CPD courses. Funding: The study was funded by the Osteopathic Foundation (grant number URNLG007). The funders are no role in the conceptualisation, design, data collection, analysis, decision to publish, or preparation of the manuscript, this should be declared. Author contributions: Conceptualization: JDR, HA, SV; Data curation: KB; Formal analysis: KB; Funding acquisition: JDR, HA, SV; Methodology: JDR, HA, KB, SV; Project administration: JDR; Resources: JDR; Software: KB; Validation: KB; Visualization: KB; Writing – original draft: JDR and HA; Writing – review and editing: JDR, HA, KB, SV Acknowledgments: We would like to thank Dr Kimberly J. Vannest, PhD (Chair and Professor Department of Education, College of Education and Social Services, University of Vermont) for her review of the methods for this project. References Gill TK, Mittinty MM, March LM, Steinmetz JD, Culbreth GT, Cross M, et al. Global, regional, and national burden of other musculoskeletal disorders, 1990-2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. The Lancet Rheumatology. 2023;5(11):e670–82. Caneiro JP, Smith A, Linton SJ, Moseley GL, O’Sullivan P. 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Pincus T, Williams AC de C, Vogel S, Field A. The development and testing of the depression, anxiety, and positive outlook scale (DAPOS). Pain. 2004 May;109(1–2):181–8. Molgaard Nielsen A, Hartvigsen J, Kongsted A, Öberg B, Enthoven P, Abbott A, et al. The patient enablement instrument for back pain: reliability, content validity, construct validity and responsiveness. Health Qual Life Outcomes. 2021 Apr 9;19(1):116. Bishop A, Thomas E, Foster NE. Health care practitioners’ attitudes and beliefs about low back pain: a systematic search and critical review of available measurement tools. Pain. 2007 Nov;132(1–2):91–101. Brunner E, Dankaerts W, Meichtry A, O’Sullivan K, Probst M. Physical therapists’ ability to identify psychological factors and their self-reported competence to manage chronic low back pain. Phys Ther. 2018 Jun 1;98(6):471–9. General Osteopathic Council. Continuing professional development guidance [Internet]. 2018 [cited 2024 8]. Available from: https://cpd.osteopathy.org.uk/resources/continuing-professional-development-guidance/ Ellwood J, Carnes D. An international profile of the practice of osteopaths: A systematic review of surveys. Int J Osteopath Med. 2021 Jun;40:14–21. Molenda M. In search of the elusive ADDIE model. Perform Improv. 2003 May;42(5):34–6. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011 Apr 23;6(1):42. National Institute for Health & Clinical Excellence. Low back pain and sciatica in over 16s: assessment and management. 2016 [cited 2025 Feb 6]; Available from: https://www.nice.org.uk/guidance/ng59 Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models Usinglme4. J Stat Softw [Internet]. 2015;67(1). Available from: http://dx.doi.org/10.18637/jss.v067.i01 Kuznetsova A, Brockhoff PB, Christensen RHB. LmerTest package: Tests in linear mixed effects models. J Stat Softw [Internet]. 2017;82(13). Available from: http://dx.doi.org/10.18637/jss.v082.i13 Shadish WR. Statistical analyses of single-case designs. Curr Dir Psychol Sci. 2014 Apr;23(2):139–46. Wallwork SB, Braithwaite FA, O’Keeffe M, Travers MJ, Summers SJ, Lange B, et al. The clinical course of acute, subacute and persistent low back pain: a systematic review and meta-analysis. CMAJ. 2024 Jan 21;196(2):E29–46. Foster NE, Anema JR, Cherkin D. Lancet Low Back Pain Series Working Group. Prevention and treatment of low back pain: evidence, challenges, and promising directions. Lancet. 2018;391:2368–83. Chen Y, Campbell P, Strauss VY, Foster NE, Jordan KP, Dunn KM. Trajectories and predictors of the long-term course of low back pain: cohort study with 5-year follow-up. Pain. 2018 Feb;159(2):252–60. Dunn KM, Campbell P, Jordan KP. Long-term trajectories of back pain: cohort study with 7-year follow-up. BMJ Open. 2013 Dec 11;3(12):e003838. Lim YZ, Chou L, Au RT, Seneviwickrama KMD, Cicuttini FM, Briggs AM, et al. People with low back pain want clear, consistent and personalised information on prognosis, treatment options and self-management strategies: a systematic review. J Physiother. 2019 Jul;65(3):124–35. Nikles J, Evans K, Hams A, Sterling M. A systematic review of N-of-1 trials and single case experimental designs in physiotherapy for musculoskeletal conditions. Musculoskelet Sci Pract. 2022 Dec;62(102639):102639. Bailey D, Fawkes C, Carnes D, Draper-Rodi J. The development of the National Council for Osteopathic Research - Research Network (NCOR-RN): A qualitative focus group study of osteopaths’ views. Int J Osteopath Med. 2025 Mar;55(100742):100742. Draper-Rodi J, Vogel S, Bishop A. Design and development of an e-learning programme: An illustrative commentary. Int J Osteopath Med [Internet]. 2018 Jul; Available from: http://dx.doi.org/10.1016/j.ijosm.2018.07.002 Additional Declarations Competing interest reported. JDR receives fees from sales of the BPM e-learning course on the Health Sciences University CPD platform and has an interest in promoting research as Director of NCOR and Fellow of SOLAR (Strengthening Osteopathic Leadership and Research) programme in Australia. JDR and HA have received fees for teaching BPS CPD courses. Supplementary Files Supplmaterialanalysis.docx SCRIBEchecklist.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6147823\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":455504936,\"identity\":\"a5bc0be8-17f8-4893-ad9e-9ecc4f85e04e\",\"order_by\":0,\"name\":\"Jerry Draper-Rodi\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Health Sciences University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Jerry\",\"middleName\":\"\",\"lastName\":\"Draper-Rodi\",\"suffix\":\"\"},{\"id\":455504937,\"identity\":\"02469fe5-e885-476e-8c1d-31a3a9b3b8b3\",\"order_by\":1,\"name\":\"Hilary Abbey\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Health Sciences University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hilary\",\"middleName\":\"\",\"lastName\":\"Abbey\",\"suffix\":\"\"},{\"id\":455504938,\"identity\":\"efd50b81-3f2d-42da-88cf-b8dfde474778\",\"order_by\":2,\"name\":\"Kevin Brownhill\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Health Sciences University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kevin\",\"middleName\":\"\",\"lastName\":\"Brownhill\",\"suffix\":\"\"},{\"id\":455504939,\"identity\":\"3fdba915-937a-4f45-b6ee-59c0ba1c5322\",\"order_by\":3,\"name\":\"Steven Vogel\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Health Sciences University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Steven\",\"middleName\":\"\",\"lastName\":\"Vogel\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-03-03 16:09:45\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6147823/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6147823/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":82797101,\"identity\":\"5e0fdd39-df20-429d-b669-4589e177cefd\",\"added_by\":\"auto\",\"created_at\":\"2025-05-15 10:41:13\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":71249,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eStudy design\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6147823/v1/1d952ed05a8a90975bbab93b.png\"},{\"id\":82793452,\"identity\":\"47a6b80a-d31e-4d62-9f2e-476c30158e7b\",\"added_by\":\"auto\",\"created_at\":\"2025-05-15 10:25:13\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":72700,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003erandomisation in each group\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6147823/v1/eec862e64159b6d1ce7afef1.jpg\"},{\"id\":82793456,\"identity\":\"dcc27769-4eed-4d28-a69a-3d188376cc2b\",\"added_by\":\"auto\",\"created_at\":\"2025-05-15 10:25:13\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":89504,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTiming of patient outcome measures.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6147823/v1/827736dfcd4a9c9b7d89c410.png\"},{\"id\":82795122,\"identity\":\"a6727271-5e0d-4171-8134-be87aec80035\",\"added_by\":\"auto\",\"created_at\":\"2025-05-15 10:33:13\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":801747,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePlots of individual time series. Green vertical bars represent start of treatment (left bar) and end of treatment (right bar, if it exists). One subject removed with only one measurement occasion.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6147823/v1/681da61ea8d7e61b838119fd.png\"},{\"id\":83997806,\"identity\":\"e84bb6c0-a952-4b80-ab6d-e7ca5b8326aa\",\"added_by\":\"auto\",\"created_at\":\"2025-06-05 14:02:02\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1682614,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6147823/v1/b9b7b765-4b8b-44c5-9b7d-9da4a6005fde.pdf\"},{\"id\":82795114,\"identity\":\"de012866-4bda-4d07-b46d-2674b959f73f\",\"added_by\":\"auto\",\"created_at\":\"2025-05-15 10:33:13\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1245787,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplmaterialanalysis.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6147823/v1/9f6b16744ff8bf22b6e337ec.docx\"},{\"id\":82797103,\"identity\":\"c8da50d9-f615-4a9e-ad82-8f51043a11ab\",\"added_by\":\"auto\",\"created_at\":\"2025-05-15 10:41:13\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":391505,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SCRIBEchecklist.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6147823/v1/43c18ed1323f28814177c9f4.pdf\"}],\"financialInterests\":\"Competing interest reported. JDR receives fees from sales of the BPM e-learning course on the Health Sciences University CPD platform and has an interest in promoting research as Director of NCOR and Fellow of SOLAR (Strengthening Osteopathic Leadership and Research) programme in Australia. JDR and HA have received fees for teaching BPS CPD courses.\",\"formattedTitle\":\"\\u003cp\\u003eOSCAR (Osteopathic Single CAse Research): A Single Case Experimental Design study evaluating osteopathic management for non-specific low back pain\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"BACKGROUND\",\"content\":\"\\u003cp\\u003eMusculoskeletal disorders, particularly low back pain (LBP), represent a major global health challenge, ranking as the second highest cause of non-fatal disability and affecting over 1.63\\u0026nbsp;billion individuals worldwide in 2020 (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). LBP's impact extends beyond physical symptoms, often manifesting with psychological comorbidities including anxiety, depression, and pain-related fear that can significantly affect work, physical activity, and social interaction (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). The complexity of LBP is further compounded by multiple prognostic and mediating factors across cognitive, behavioural, and social domains, which vary between patients and over time (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eCurrent guidelines advocate for biopsychosocial (BPS) approaches in managing complex, chronic musculoskeletal conditions, including non-specific low back pain (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). While evidence supports various non-pharmacological interventions including exercise, manipulation/mobilisation, and psychological/behavioural interventions (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e), implementing BPS approaches presents unique challenges. The skills needed for BPS care can be challenging to teach and may have limited impact in changing biomedical beliefs (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). This is particularly relevant for osteopathy, which has shown benefit in managing NSLBP (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e), but lacks research comparing traditional biomechanical approaches with more recent BPS-informed care.\\u003c/p\\u003e \\u003cp\\u003eWhile Randomised Controlled Trials (RCTs) provide robust evidence about efficacy, their strict inclusion criteria and large sample requirements limit applicability to individual clinical cases. Single Case Experimental Designs (SCEDs) offer a methodologically rigorous alternative that enables detailed investigation of individual treatment responses while maintaining statistical validity through randomisation and systematic data collection (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). SCEDs are particularly valuable in natural clinical settings, allowing practitioners to track individual patient responses while contributing to the evidence base.\\u003c/p\\u003e \\u003cp\\u003eThis study addresses two key gaps in current knowledge: first, comparing outcomes between standard osteopathic treatment and BPS-informed management for NSLBP; and second, assessing the feasibility of implementing SCED methodology in osteopathic practice. These aims align with professional requirements for evidence-informed healthcare (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) and support the growing culture of standardised data collection in osteopathic practice (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). This study represents the first implementation of SCED methodology in osteopathic practice, combining rigorous experimental design with clinical practicality. The innovative integration of practitioner education evaluation with patient outcomes provides a new framework for practice-based research.\\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cp\\u003eThis study employed methods specifically designed to be implementable within routine clinical practice while maintaining scientific rigour, enabling direct translation of findings to clinical practice and education. Methodological rigour was ensured through prospective registration and publication of the protocol, multiple elements of randomisation, standardised training and data collection procedures, a robust statistical analysis plan, and blinded analysis of outcomes. Reporting was in accordance with the Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) Checklist.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDesign\\u003c/h2\\u003e \\u003cp\\u003eThis SCED trial employed a multiple-baseline design with randomised treatment start times to enhance internal validity. The study was prospectively registered (NCT05120921) and received ethical approval from the University College of Osteopathy Research Ethics Committee (ref #20210816). The protocol was published with full design details (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e), and there are no planned replications.\\u003c/p\\u003e \\u003cp\\u003eThe design incorporated three key methodological features to ensure robust evaluation:\\u003c/p\\u003e \\u003cp\\u003e \\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eMultiple baseline periods (5\\u0026ndash;15 days) with randomised start times\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eExtended treatment phase (4\\u0026ndash;6 weeks) with daily outcomes monitoring\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eLong-term follow-up (12 weeks) to assess maintenance of effects\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis design aligns with current best practice recommendations for SCED research (Kratochwill et al., 2010) while being feasible within clinical practice settings.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eParticipants\\u003c/h3\\u003e\\n\\u003cp\\u003eSample size rationale was based on established SCED methodology requiring minimum data points for valid inference (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003eOsteopaths\\u003c/h3\\u003e\\n\\u003cp\\u003eA purposive sample of practising osteopaths was recruited through professional networks using the following criteria:\\u003c/p\\u003e \\u003cp\\u003e\\u003cul\\u003e\\u003cli\\u003e\\u003cp\\u003eInclusion: registered with the General Osteopathic Council, practising in the UK, minimum 15 years clinical practice\\u003c/p\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cp\\u003eExclusion: Recent (\\u0026le;\\u0026thinsp;10 years) Osteopathic Educational Provider experience, participation in previous feasibility study (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e)\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/ul\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003ePatients\\u003c/h3\\u003e\\n\\u003cp\\u003eEach participating osteopath recruited up to three patients in January-February 2022 (before completing a biopsychosocial pain management (BPM) e-learning) and in June-July 2022 (after completing the BPM e-learning), using the following criteria:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eInclusion: Adults (18+) with non-specific LBP (NSLBP)\\u0026thinsp;\\u0026gt;\\u0026thinsp;12 weeks, Numeric Pain Rating Scale (NPRS) scores between 5 and 9, Patient Specific Function Scale (PSFS) scores between 2 and 7\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eExclusion: Osteopathic treatment within previous 6 months\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe NPRS and PSFS eligible scores were required to minimise the risk of floor effects from low baseline symptoms or risk of harm from delaying treatment for severe symptoms.\\u003c/p\\u003e\\n\\n\\u003cdiv class=\\\"Heading\\\"\\u003e\\u0026lt;\\u0026lt; Insert Fig.\\u0026nbsp;1 around here with this title: Fig.\\u0026nbsp;1 \\u0026ndash; Study design \\u0026gt;\\u0026gt;\\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy process\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eRandomisation and allocation\\u003c/h2\\u003e \\u003cp\\u003eA blocked randomisation strategy was implemented using Qualtrics\\u0026copy; to assign patients to treatment start dates: Early (5\\u0026ndash;8 days), Medium (9\\u0026ndash;12 days) or Late (13\\u0026ndash;15 days). Randomisation occurred separately in each osteopath's clinic before and after BPM e-learning to ensure balanced allocation across timepoints. This approach strengthened internal validity while maintaining feasibility in clinical settings (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eData Collection Procedures\\u003c/h3\\u003e\\n\\u003cp\\u003ePatient data were collected using Qualtrics\\u0026copy; (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e) during three periods: pre-treatment (5\\u0026ndash;15 days); treatment (4\\u0026ndash;6 weeks); and follow-up (12 weeks).\\u003c/p\\u003e \\u003cp\\u003ePrimary Outcomes: Primary patient outcome measures were the Numeric Pain Rating Scale (NPRS) and Patient Specific Function Scales (PSFS), measured daily during baseline and treatment, and weekly or fortnightly during follow-up (Fig.\\u0026nbsp;3). Automated electronic reminders were sent to maximise response rate. Details about psychometric properties and minimal clinically important differences can be found in the protocol (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSecondary Outcomes: they included the Measure Your Medical Outcome Profile 2 (MYMOP2) (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e), Arthritis Research UK Musculoskeletal Health Questionnaire (MSK-HQ) (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e), and the Depression, Anxiety, and Positive Outlook Scale (DAPOS) (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). A questionnaire was adapted from the Patient Enablement Index for Back Pain (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e) to assess perceptions of shared decision-making, treatment outcomes, relevance of the measures, and acceptability of data collection processes. Validity and reliability are detailed in the Protocol (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eOsteopath data was collected before recruiting the first patient and after the BPM e-learning. The primary outcome measure was the modified Pain Attitudes and Beliefs Scale (PABS), a 19-item questionnaire comparing the strength of biomedical and behavioural treatment approaches (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). Osteopaths also estimated patient prognosis after initial assessments but before treatment, as this can assess therapists\\u0026rsquo; ability to estimate the risk of a poor outcome (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eImplementation procedures were established to ensure consistency and safety across all practices. Participating osteopaths completed standardised training to familiarise themselves with study protocols and data collection methods. Throughout the study, data quality was regularly monitored through automated systems with manual verification by the research team. A comprehensive adverse event protocol required osteopaths to report any treatment-related incidents to the Principal Investigator within 24 hours and complete detailed documentation, aligning with professional practice requirements (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026lt;INSERT FIGURE 2 AROUND HERE WITH THIS TITLE: Fig.\\u0026nbsp;2: randomisation in each group\\u0026gt;\\u0026gt;\\u003c/p\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026lt;INSERT FIGURE 3 AROUND HERE WITH THIS TITLE: Fig.\\u0026nbsp;3: Timing of patient outcome measures.\\u0026gt;\\u0026gt;\\u003c/p\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section4\\\"\\u003e \\u003ch2\\u003eInterventions\\u003c/h2\\u003e \\u003cp\\u003ePatient interventions comprised standard osteopathic treatment delivered over 6 weeks (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e). Treatment followed professional standards (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) whilst allowing osteopaths to maintain their usual clinical approaches. This pragmatic design enabled practitioners to individualise care without adhering to a strict manual therapy protocol. Patients continued their existing healthcare treatments as normal.\\u003c/p\\u003e \\u003cp\\u003eThe practitioner intervention was an 8-hour BPM e-learning course, previously validated in a mixed-methods feasibility study (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). The course, updated in February 2022, incorporated ADDIE (Analyse, Design, Develop, Implement, Evaluate) and COM-B (Capability, Opportunity, Motivation \\u0026ndash; Behaviour) behaviour change models (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e), offering evidence-based strategies for NSLBP management (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e). Full course details are available in the protocol (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eThe complexity of SCED time-series data requires careful consideration of serial dependency and individual variation in treatment responses. We employed mixed effects linear modelling as our primary analytical method, enabling us to account for non-independent repeated measurements whilst accommodating individual differences in treatment trajectories. This approach was implemented using R packages LME4 (\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e) and lmerTest (\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eOur design exceeded standard SCED power requirements, which typically specify a minimum of five data points per phase (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). Daily measurements during baseline (5\\u0026ndash;15 days) and treatment phases (28\\u0026ndash;42 days) provided robust data for detecting clinically meaningful changes. The multiple baseline design with randomised start times further strengthened statistical conclusion validity.\\u003c/p\\u003e \\u003cp\\u003eThe analysis examined changes in primary outcomes (NPRS and PSFS) across baseline, treatment and follow-up phases whilst accounting for individual patient trajectories. Secondary outcomes were planned to be analysed descriptively to contextualise primary outcome changes. To ensure analytical rigour, we implemented systematic data quality screening before analysis, maintained statistician blinding to patient stage (pre/post osteopath e-learning), and conducted separate individual and group-level analyses before synthesis.\\u003c/p\\u003e \\u003cp\\u003eVisual analysis complemented our statistical approach, following established protocols (\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e) to assess points of change, linear trends and individual variability. Missing data patterns were examined and addressed using appropriate statistical methods. Full details of our analytical procedures are available in the published protocol (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cp\\u003e \\u003cstrong\\u003eOsteopaths\\u003c/strong\\u003e \\u003cp\\u003e14 osteopaths took part in SCED training and 11 decided to join the study; 8 males and 3 females with a median age of 56 (range 39\\u0026ndash;61) and median of 22 years in practice (range 16\\u0026ndash;35). Five osteopaths recruited patients to the study. Only three osteopaths estimated prognosis (1 high risk; 2 medium risk), which was insufficient data for analysis.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003eAt baseline, PABS scores indicated approximately the same behavioural and biomedical attitude score: behavioural mean 32.00 (2.24); biomedical mean 31.64 (7.05). There was not enough data to compare the PABS scores before and after the e-learning course (2 participants completed the survey after the BPM course).\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003ePatients\\u003c/strong\\u003e \\u003cp\\u003eEleven patients were recruited; 8 before the e-learning course and 3 afterwards. Sufficient SCED data points (Kratochwill et al 2010) were recorded by 9 patients; 6 male, 3 female; median age 39 (range 20\\u0026ndash;58). Four patients were randomised to Early treatment start, 3 to Medium and 2 to Late. Five patients waited\\u0026thinsp;\\u0026lt;\\u0026thinsp;5 days for treatment, 3 waited 5\\u0026ndash;10 days and one waited\\u0026thinsp;\\u0026gt;\\u0026thinsp;10 days.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003eAt baseline, the median NPRS score was 6 (range 5\\u0026ndash;9) and PSFS score 6 (range 4-7.8). Five patients identified 2\\u0026ndash;3 activities that were difficult to perform, and 6 patients listed 4\\u0026ndash;5 activities. Their \\u0026lsquo;worst\\u0026rsquo; activities involved some degree of bending, including activities of daily living and exercise (n\\u0026thinsp;=\\u0026thinsp;8), followed by sitting (n\\u0026thinsp;=\\u0026thinsp;2) and walking (n\\u0026thinsp;=\\u0026thinsp;1).\\u003c/p\\u003e \\u003cp\\u003eFigure 4: Plots of individual time series. Green vertical bars represent start of treatment (left bar) and end of treatment (right bar, if it exists). One subject removed with only one measurement occasion.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026lt; INSERT FIGRE 4 AROUND HERE\\u0026gt;\\u0026gt;\\u003c/p\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eModelling pain and disability scores\\u003c/h2\\u003e \\u003cp\\u003eMixed-effects modelling of pain scores (NPRS) indicated a best-fit model incorporating a combination of gender, time, study phase and an interaction between study phase and time. Specifically, women reported a mean pain score of 4.12 (95% CI: [2.75, 5.48], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) at baseline, whilst mean pain score was greater in men by 2.52 (95% CI: [1.35, 3.68], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). During the treatment phase (4\\u0026ndash;6 weeks or 28\\u0026ndash;42 days), pain score decreased by a mean of 0.07 (95% CI: [0.04, 0.11], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) per day, which translates to a total reduction of 1.96\\u0026ndash;2.94 points over the treatment period. This meets or exceeds the commonly accepted Minimal Important Change (MIC) of 2 points for NPRS in chronic low back pain. In contrast, during the pre-treatment baseline phase (5\\u0026ndash;15 days), pain actually increased by an average of 0.17 points per day, resulting in a total increase of 0.85\\u0026ndash;2.55 points before treatment began. During the post-treatment follow-up phase (12 weeks or 84 days), improvements continued but at a reduced rate of 0.04 points per day, contributing an additional reduction of approximately 3.36 points throughout follow-up.\\u003c/p\\u003e \\u003cp\\u003eModelling of disability scores (PSFS) indicated a best-fit model incorporating a combination of time, study phase and an interaction between study phase and time. Gender did not significantly improve the model, unlike in the NPRS model. Modelling of disability scores (PSFS) indicated a best-fit model incorporating a combination of time, study phase and an interaction between study phase and time. During the treatment phase (4\\u0026ndash;6 weeks or 28\\u0026ndash;42 days), PSFS scores decreased by 0.09 points per day (95% CI: [0.05, 0.13], p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), translating to a total reduction of 2.52\\u0026ndash;3.78 points over the treatment period. This exceeds the established Minimal Important Change (MIC) of 2 points for the PSFS in musculoskeletal conditions. In the pre-treatment baseline phase (5\\u0026ndash;15 days), PSFS scores increased by an average of 0.12 points per day, resulting in a total worsening of 0.60\\u0026ndash;1.80 points before treatment began. During the post-treatment follow-up phase (12 weeks or 84 days), improvement continued at a slower rate of 0.04 points per day, contributing an additional reduction of approximately 3.36 points throughout follow-up.\\u003c/p\\u003e \\u003cp\\u003eProtocol deviation: it was not possible to assess the impact of BPM course on amount or rate of change, as 6 patients were recruited before osteopaths took the course but only 3 afterwards. The secondary outcome measures were not analysed or reported in this manuscript due to insufficient data collection. The raw data for these measures remains available in the data repository.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFeasibility\\u003c/h2\\u003e \\u003cp\\u003eWhen tested, the linear mixed model was robust and data were well captured by the model, apart from 2 outlying points from 1 patient (see additional material for full model analysis). No procedural changes occurred after the study started and there were no reports of any serious adverse events related to osteopathic treatment or participation in the trial.\\u003c/p\\u003e \\u003cp\\u003e Informal feedback from participating osteopaths revealed several challenges in recruiting patients from their own clinics. These included feelings of discomfort about asking patients who were seeking help to participate in research, time constraints, and difficulty retaining patients randomised to the delayed treatment group when earlier appointments were available at the clinic.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eThis is the first study to robustly examine how change unfolds during osteopathic management of patients with low back pain. The rigorous single-case experimental design and statistical analysis provide novel insights that can inform both postgraduate education and clinical practice. This methodological approach represents a significant advance in understanding individual patient responses to osteopathic care, moving beyond traditional group-level analyses to capture the granular detail of therapeutic change.\\u003c/p\\u003e \\u003cp\\u003eThe clinical course of low back pain is variable, and understanding this variability is important for informing treatment and patient stratification (\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e). While many individuals with acute low back pain experience improvement within six weeks, a significant proportion may develop persistent symptoms (\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e). This study addressed two key gaps in current knowledge: comparing outcomes between standard osteopathic treatment and BPS-informed management for NSLBP, and assessing the feasibility of implementing SCED methodology in osteopathic practice. These aims aligned with professional requirements for evidence-informed healthcare and supported the growing culture of standardised data collection in osteopathic practice.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTreatment effectiveness and individual response patterns\\u003c/h2\\u003e \\u003cp\\u003eThis study represents the first comprehensive application of SCED methodology to evaluate osteopathic treatment effectiveness for NSLBP in real clinical settings. The temporal pattern observed\\u0026mdash;initial deterioration during baseline, substantial improvement throughout treatment, and continued albeit slower gains during follow-up\\u0026mdash;provides robust evidence supporting the effectiveness of osteopathic intervention for both pain reduction and functional improvement in patients with non-specific low back pain. This temporal pattern, where improvements consistently followed treatment initiation across different randomised start times, strengthens causal inference about treatment effects.\\u003c/p\\u003e \\u003cp\\u003eThe findings align with previous research demonstrating the effectiveness of osteopathic manual treatment for NSLBP (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e), while adding new insights about individual variation in treatment responses (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). The SCED approach revealed that while group-level analyses showed overall improvement, individual trajectories varied considerably, common for low back pain trajectories (\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e), and supporting recent calls for more personalised approaches to NSLBP management (\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e). The sustained improvements during follow-up align with expectations for complex interventions, where benefits should continue after treatment ends (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e), though the rate of improvement typically slowed.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSCED implementation in osteopathic practice\\u003c/h2\\u003e \\u003cp\\u003eThis study makes a significant methodological contribution by demonstrating that SCED methodology can be successfully implemented in osteopathic practice, while also identifying important practical considerations. Nine of eleven recruited patients provided sufficient data for analysis, with good adherence to daily outcome measures and completion of follow-up assessments. This success rate compares favourably with previous SCED studies in musculoskeletal care (\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eHowever, recruitment faced several challenges, particularly regarding randomised treatment delays. These challenges reflect broader tensions between research rigour and clinical practicality, as noted in previous studies (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). They also highlight important ethical considerations specific to private practice settings. Unlike public healthcare contexts where waiting periods are standard, private practice patients typically expect immediate access to services they are paying for. This creates tension between research requirements for randomised treatment delays and patient expectations. The weekly patient summaries provided to osteopaths represented an innovative approach to integrating research data with clinical decision-making, though the impact of these summaries on treatment choices requires further investigation.\\u003c/p\\u003e \\u003cp\\u003eThe study's systematic approach to data collection and analysis provides a model for future practice-based research. The electronic data capture system and automated reminders proved effective, though managing the resulting large dataset presented significant challenges. These experiences form the basis for specific recommendations to enhance future SCED trials in osteopathic practice.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePractitioner engagement and professional development\\u003c/h2\\u003e \\u003cp\\u003eThis study provides original insights into how experienced osteopaths engage with practice-based research and biopsychosocial approaches to care. The participating osteopaths showed notably strong baseline behavioural attitudes (PABS mean\\u0026thinsp;=\\u0026thinsp;32 (2.24)), which was slightly higher than previously reported scores in similar healthcare professionals, including osteopaths (mean 29.86), chiropractors (mean 31.4) and physiotherapists (mean 31.76) (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). This unexpected finding suggests either an increasing recognition of biopsychosocial approaches within the profession since earlier studies, or potential self-selection bias in study participation.\\u003c/p\\u003e \\u003cp\\u003eThis self-selection bias aligns with recent findings from Bailey et al. (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e), who found that osteopaths participating in research-focused CPD events were likely to have a more positive perception of research. Their work highlighted that while osteopaths generally recognise the need to develop the profession's evidence base, many feel they lack sufficient research skills to take an active role. Previous research has demonstrated that e-learning programmes can effectively deliver educational interventions aimed at enabling learning and improving performance in healthcare settings, particularly when developed using established pedagogical frameworks (\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe integration of practitioner education evaluation with patient outcomes represents an innovative approach to addressing the research-practice gap. Draper-Rodi et al. (\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e) demonstrated that using structured approaches such as the ADDIE model (Analyse, Design, Develop, Implement, Evaluate) and COM-B framework (Capability, Opportunity, Motivation \\u0026ndash; Behaviour) can effectively support behaviour change in practitioners. This kind of structured approach could help address the professional isolation commonly experienced by osteopaths (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e), while simultaneously developing research capacity. The collaborative aspects of involvement in practice-based research could be particularly valuable, as Bailey et al. (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e) found that osteopaths valued opportunities for networking and professional development through research engagement.\\u003c/p\\u003e \\u003cp\\u003eThe higher baseline PABS scores in our study, combined with Bailey et al.'s (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e) findings about osteopaths' interest in research involvement, suggest a growing recognition within the profession of the need to engage with evidence-based approaches. However, significant barriers remain around research skills, time constraints, and concerns about practice implications that need to be addressed (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e). Future initiatives should focus on providing structured support and clear pathways for practitioners to develop research competencies while maintaining clinical commitments, potentially combining e-learning approaches with practice-based research network participation.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eStrengths and limitations\\u003c/h2\\u003e \\u003cp\\u003eThis study demonstrated several key strengths in its methodological approach. The research design prioritised rigour through multiple elements: we prospectively registered the protocol, incorporated various randomisation procedures, implemented standardised training and data collection methods, developed a robust statistical analysis plan, and ensured blinded analysis of outcomes. External validity was enhanced by conducting the study in real clinical settings with diverse patient presentations, maintaining extended follow-up periods, and collecting multiple baseline measurements. A particularly innovative aspect was the integration of practitioner education with patient outcomes, which offers a novel framework for practice-based research in osteopathy.\\u003c/p\\u003e \\u003cp\\u003eHowever, several important limitations must be acknowledged. Recruitment proved challenging, resulting in a smaller sample size than originally planned. This limited our ability to compare outcomes before and after BPM training and may have introduced self-selection bias among participating osteopaths. We encountered implementation difficulties, particularly in maintaining randomised treatment delays and securing consistent completion of secondary outcome measures. Feedback about practitioners' use of weekly summaries was also more limited than anticipated. While our sample size was adequate for SCED analysis, it restricts our ability to draw broader conclusions about the comparative effectiveness of different treatment approaches.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFuture research directions\\u003c/h2\\u003e \\u003cp\\u003eThese findings suggest several important directions for future research. Studies should investigate what constitutes acceptable waiting times for patients seeking pain treatment, particularly in the context of randomised designs. There is a need to better understand factors influencing individual treatment responses and how practitioners integrate outcome data into their clinical decision-making. Future work should also focus on developing more efficient systems for managing large-scale SCED data collection and examining how BPS training impacts practitioners with stronger biomedical orientations.\\u003c/p\\u003e \\u003cp\\u003eOur findings demonstrate that SCED methodology offers a viable approach for practice-based research in osteopathy and could help bridge the gap between research evidence and clinical practice. However, successful implementation requires careful consideration of practical constraints and appropriate support for participating practitioners. This work contributes to the growing evidence base for osteopathic care whilst providing a framework for future research into individual treatment responses and professional development.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"CONCLUSIONS\",\"content\":\"\\u003cp\\u003eThis study provides original contributions to osteopathic research through its first-time implementation of Single Case Experimental Design (SCED) methodology in clinical practice, revealing detailed patterns of change during osteopathic treatment for non-specific low back pain. The significance of the findings is demonstrated through clinically important improvements in pain and function that began with treatment initiation and continued through follow-up, while also establishing a feasible framework for practice-based research.\\u003c/p\\u003e \\u003cp\\u003eMethodological rigour was assured through multiple elements including prospective registration, randomised treatment start times, standardised data collection, and robust statistical analysis. While implementation challenges were identified, particularly around recruitment and maintaining randomised treatment delays, these insights form the basis for enhancing future SCED trials in osteopathic practice.\\u003c/p\\u003e \\u003cp\\u003e This work advances our understanding of osteopathic care for NSLBP by demonstrating clinically meaningful improvements following treatment initiation, while also providing a methodologically sound framework for future practice-based research that bridges the gap between evidence and clinical implementation. The findings demonstrate that SCEDs offer a viable approach for practice-based research in osteopathy, though successful implementation requires careful consideration of practical constraints and appropriate practitioner support.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cul\\u003e\\n \\u003cli\\u003eADDIE: Analyse, Design, Develop, Implement, Evaluate\\u003c/li\\u003e\\n \\u003cli\\u003eBPM: Biopsychosocial Pain Management\\u003c/li\\u003e\\n \\u003cli\\u003eBPS: Biopsychosocial\\u003c/li\\u003e\\n \\u003cli\\u003eCOM-B: Capability, Opportunity, Motivation \\u0026ndash; Behaviour\\u003c/li\\u003e\\n \\u003cli\\u003eCPD: Continuing Professional Development\\u003c/li\\u003e\\n \\u003cli\\u003eDAPOS: Depression, Anxiety, and Positive Outlook Scale\\u003c/li\\u003e\\n \\u003cli\\u003eLBP: Low Back Pain\\u003c/li\\u003e\\n \\u003cli\\u003eMSK-HQ: Arthritis Research UK Musculoskeletal Health Questionnaire\\u003c/li\\u003e\\n \\u003cli\\u003eMYMOP2: Measure Your Medical Outcome Profile 2\\u003c/li\\u003e\\n \\u003cli\\u003eNCOR: National Council for Osteopathic Research\\u003c/li\\u003e\\n \\u003cli\\u003eNPRS: Numeric Pain Rating Scale\\u003c/li\\u003e\\n \\u003cli\\u003eNSLBP: Non-specific Low Back Pain\\u003c/li\\u003e\\n \\u003cli\\u003eOSCAR: Osteopathic Single CAse Research\\u003c/li\\u003e\\n \\u003cli\\u003ePABS: Pain Attitudes and Beliefs Scale\\u003c/li\\u003e\\n \\u003cli\\u003ePSFS: Patient Specific Function Scale\\u003c/li\\u003e\\n \\u003cli\\u003eRCT: Randomised Controlled Trial\\u003c/li\\u003e\\n \\u003cli\\u003eSCED: Single Case Experimental Design\\u003c/li\\u003e\\n \\u003cli\\u003eSOLAR: Strengthening Osteopathic Leadership and Research\\u003c/li\\u003e\\n \\u003cli\\u003eUCO: University College of Osteopathy\\u003c/li\\u003e\\n\\u003c/ul\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate:\\u0026nbsp;\\u003c/strong\\u003eApproval was received from the University College of Osteopathy Research Ethics Committee (#16082021), and all participants (osteopaths and patients) provided written consent to participate.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication:\\u003c/strong\\u003e Not applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials:\\u0026nbsp;\\u003c/strong\\u003eThe datasets generated and/or analysed during the current study are available in the OSF repository, DOI:\\u0026nbsp;10.17605/OSF.IO/G89CA [https://osf.io/g89ca/]\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests:\\u003c/strong\\u003e JDR receives fees from sales of the BPM e-learning course on the Health Sciences University CPD platform and has an interest in promoting research as Director of NCOR and Fellow of SOLAR (Strengthening Osteopathic Leadership and Research) programme in Australia. JDR and HA have received fees for teaching BPS CPD courses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u0026nbsp;\\u003c/strong\\u003eThe study was funded by the Osteopathic Foundation (grant number URNLG007). The funders are no role in the conceptualisation, design, data collection, analysis, decision to publish, or preparation of the manuscript, this should be declared.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions:\\u0026nbsp;\\u003c/strong\\u003eConceptualization: JDR, HA, SV; Data curation: KB; Formal analysis: KB; Funding acquisition: JDR, HA, SV; Methodology: JDR, HA, KB, SV; Project administration: JDR; Resources: JDR; Software: KB; Validation: KB; Visualization: KB; Writing \\u0026ndash; original draft: JDR and HA; Writing \\u0026ndash; review and editing: JDR, HA, KB, SV\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments:\\u0026nbsp;\\u003c/strong\\u003eWe would like to thank Dr Kimberly J. Vannest, PhD (Chair and Professor Department of Education, College of Education and Social Services, University of Vermont) for her review of the methods for this project.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eGill TK, Mittinty MM, March LM, Steinmetz JD, Culbreth GT, Cross M, et al. Global, regional, and national burden of other musculoskeletal disorders, 1990-2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. The Lancet Rheumatology. 2023;5(11):e670\\u0026ndash;82.\\u003c/li\\u003e\\n\\u003cli\\u003eCaneiro JP, Smith A, Linton SJ, Moseley GL, O\\u0026rsquo;Sullivan P. How does change unfold? an evaluation of the process of change in four people with chronic low back pain and high pain-related fear managed with Cognitive Functional Therapy: A replicated single-case experimental design study. Behav Res Ther. 2019 Jun;117:28\\u0026ndash;39.\\u003c/li\\u003e\\n\\u003cli\\u003eJones S, Hurrell E. A single case experimental design: how do different psychological outcome measures capture the experience of a client undergoing CBT for chronic pain. Br J Pain. 2019 Feb;13(1):6\\u0026ndash;12.\\u003c/li\\u003e\\n\\u003cli\\u003eO\\u0026rsquo;Sullivan P, Caneiro JP, O\\u0026rsquo;Keeffe M, O\\u0026rsquo;Sullivan K. Unraveling the complexity of low back pain. J Orthop Sports Phys Ther. 2016 Nov;46(11):932\\u0026ndash;7.\\u003c/li\\u003e\\n\\u003cli\\u003eDraper-Rodi J, Vogel S, Bishop A. Identification of prognostic factors and assessment methods on the evaluation of non-specific low back pain in a biopsychosocial environment: A scoping review. Int J Osteopath Med. 2018 Dec;30:25\\u0026ndash;34.\\u003c/li\\u003e\\n\\u003cli\\u003eWorld Health Organization. WHO guideline for non-surgical management of chronic primary low back pain in adults in primary and community care settings. Gen\\u0026egrave;ve, Switzerland: World Health Organization; 2023. 274 p.\\u003c/li\\u003e\\n\\u003cli\\u003eJenkins HJ, Ferreira G, Downie A, Maher C, Buchbinder R, Hancock MJ. The available evidence on the effectiveness of 10 common approaches to the management of non-specific low back pain: An evidence map. Eur J Pain. 2022 Aug;26(7):1399\\u0026ndash;411.\\u003c/li\\u003e\\n\\u003cli\\u003eVibe Fersum K, O\\u0026rsquo;Sullivan P, Skouen JS, Smith A, Kv\\u0026aring;le A. Efficacy of classification-based cognitive functional therapy in patients with non-specific chronic low back pain: a randomized controlled trial. Eur J Pain. 2013 Jul;17(6):916\\u0026ndash;28.\\u003c/li\\u003e\\n\\u003cli\\u003eBeneciuk JM, George SZ. Pragmatic implementation of a stratified primary care model for low back pain management in outpatient physical therapy settings: Two-phase, sequential preliminary study. Phys Ther. 2015 Aug;95(8):1120\\u0026ndash;34.\\u003c/li\\u003e\\n\\u003cli\\u003eFranke H, Franke J-D, Fryer G. Osteopathic manipulative treatment for nonspecific low back pain: a systematic review and meta-analysis. BMC Musculoskelet Disord. 2014 Aug 30;15(1):286.\\u003c/li\\u003e\\n\\u003cli\\u003eVerhaeghe N, Schepers J, van Dun P, Annemans L. Osteopathic care for spinal complaints: A systematic literature review. PLoS One. 2018 Nov 2;13(11):e0206284.\\u003c/li\\u003e\\n\\u003cli\\u003eVlaeyen JWS, Wicksell RK, Simons LE, Gentili C, De TK, Tate RL, et al. From boulder to Stockholm in 70 years: Single case experimental designs in clinical research. Psychol Rec. 2020 Dec;70(4):659\\u0026ndash;70.\\u003c/li\\u003e\\n\\u003cli\\u003eTanious R, Manolov R, Onghena P, Vlaeyen JW. Single-case experimental designs: the importance of randomization and replication. Nature Reviews Methods Primers. 2024;4(1).\\u003c/li\\u003e\\n\\u003cli\\u003eGeneral Osteopathic Council. Osteopathic practice standards [Internet]. 2018 [cited 2024 8]. Available from: https://www.osteopathy.org.uk/news-and-resources/document-library/osteopathic-practice-standards/updated-osteopathic-practice-standards/\\u003c/li\\u003e\\n\\u003cli\\u003eFawkes, CL. Mathias, J. And Moore, A. Standardised data collection within osteopathic practice in the UK: development and first use of a tool to profile osteopathic care in 2009 [Internet]. 2010 [cited 2025 Feb 6]. Available from: https://www.osteopathy.org.uk/news-and-resources/document-library/research-and-surveys/standardised-data-collection-project/\\u003c/li\\u003e\\n\\u003cli\\u003eNational Council for Osteopathic Research. Collecting PROM data in your practice [Internet]. 2020 [cited 2025 Feb 6]. Available from: https://www.ncor.org.uk/practitioners/patient-reported-outcomes/prom-app-collecting-prom-data-in-practice/\\u003c/li\\u003e\\n\\u003cli\\u003eDraper-Rodi J, Abbey H, Brownhill K, Vogel S. OSCAR (Osteopathic Single CAse Research) \\u0026ndash; Assessing the effect of standard and biopsychosocial osteopathic management for patients with non-specific low back pain: Protocol for a Single Case Experimental Design (SCED). Int J Osteopath Med. 2023 Jun;48(100660):100660.\\u003c/li\\u003e\\n\\u003cli\\u003eKratochwill TR, Hitchcock J, Horner RH, Levin JR, Odom SL, Rindskopf DM and Shadish WR. Single case designs technical documentation [Internet]. What Works Clearinghouse website. 2010 [cited 2025 Feb 6]. Available from: http://ies.ed.gov/ncee/wwc/pdf/wwc_scd.pdf.\\u003c/li\\u003e\\n\\u003cli\\u003eDraper-Rodi J, Vogel S, Bishop A. Effects of an e-learning programme on osteopaths\\u0026rsquo; back pain attitudes: a mixed methods feasibility study. Pilot Feasibility Stud. 2021 Sep 13;7(1):174.\\u003c/li\\u003e\\n\\u003cli\\u003eKrasny-Pacini A, Evans J. Single-case experimental designs to assess intervention effectiveness in rehabilitation: A practical guide. Ann Phys Rehabil Med. 2018 May;61(3):164\\u0026ndash;79.\\u003c/li\\u003e\\n\\u003cli\\u003eQualtrics. Qualtrics XM - experience management software [Internet]. Qualtrics. 2015 [cited 2025 Feb 6]. Available from: https://www.qualtrics.com/en-gb/\\u003c/li\\u003e\\n\\u003cli\\u003ePolus BI, Kimpton AJ, Walsh MJ. Use of the measure your medical outcome profile (MYMOP2) and W-BQ12 (Well-Being) outcomes measures to evaluate chiropractic treatment: an observational study. Chiropr Man Therap. 2011 Mar 20;19(1):7.\\u003c/li\\u003e\\n\\u003cli\\u003eHill JC, Kang S, Benedetto E, Myers H, Blackburn S, Smith S, et al. Development and initial cohort validation of the Arthritis Research UK Musculoskeletal Health Questionnaire (MSK-HQ) for use across musculoskeletal care pathways. BMJ Open. 2016 Aug 5;6(8):e012331.\\u003c/li\\u003e\\n\\u003cli\\u003ePincus T, Williams AC de C, Vogel S, Field A. The development and testing of the depression, anxiety, and positive outlook scale (DAPOS). Pain. 2004 May;109(1\\u0026ndash;2):181\\u0026ndash;8.\\u003c/li\\u003e\\n\\u003cli\\u003eMolgaard Nielsen A, Hartvigsen J, Kongsted A, \\u0026Ouml;berg B, Enthoven P, Abbott A, et al. The patient enablement instrument for back pain: reliability, content validity, construct validity and responsiveness. Health Qual Life Outcomes. 2021 Apr 9;19(1):116.\\u003c/li\\u003e\\n\\u003cli\\u003eBishop A, Thomas E, Foster NE. Health care practitioners\\u0026rsquo; attitudes and beliefs about low back pain: a systematic search and critical review of available measurement tools. Pain. 2007 Nov;132(1\\u0026ndash;2):91\\u0026ndash;101.\\u003c/li\\u003e\\n\\u003cli\\u003eBrunner E, Dankaerts W, Meichtry A, O\\u0026rsquo;Sullivan K, Probst M. Physical therapists\\u0026rsquo; ability to identify psychological factors and their self-reported competence to manage chronic low back pain. Phys Ther. 2018 Jun 1;98(6):471\\u0026ndash;9.\\u003c/li\\u003e\\n\\u003cli\\u003eGeneral Osteopathic Council. Continuing professional development guidance [Internet]. 2018 [cited 2024 8]. Available from: https://cpd.osteopathy.org.uk/resources/continuing-professional-development-guidance/\\u003c/li\\u003e\\n\\u003cli\\u003eEllwood J, Carnes D. An international profile of the practice of osteopaths: A systematic review of surveys. Int J Osteopath Med. 2021 Jun;40:14\\u0026ndash;21.\\u003c/li\\u003e\\n\\u003cli\\u003eMolenda M. In search of the elusive ADDIE model. Perform Improv. 2003 May;42(5):34\\u0026ndash;6.\\u003c/li\\u003e\\n\\u003cli\\u003eMichie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011 Apr 23;6(1):42.\\u003c/li\\u003e\\n\\u003cli\\u003eNational Institute for Health \\u0026amp; Clinical Excellence. Low back pain and sciatica in over 16s: assessment and management. 2016 [cited 2025 Feb 6]; Available from: https://www.nice.org.uk/guidance/ng59\\u003c/li\\u003e\\n\\u003cli\\u003eBates D, M\\u0026auml;chler M, Bolker B, Walker S. Fitting linear mixed-effects models Usinglme4. J Stat Softw [Internet]. 2015;67(1). Available from: http://dx.doi.org/10.18637/jss.v067.i01\\u003c/li\\u003e\\n\\u003cli\\u003eKuznetsova A, Brockhoff PB, Christensen RHB. LmerTest package: Tests in linear mixed effects models. J Stat Softw [Internet]. 2017;82(13). Available from: http://dx.doi.org/10.18637/jss.v082.i13\\u003c/li\\u003e\\n\\u003cli\\u003eShadish WR. Statistical analyses of single-case designs. Curr Dir Psychol Sci. 2014 Apr;23(2):139\\u0026ndash;46.\\u003c/li\\u003e\\n\\u003cli\\u003eWallwork SB, Braithwaite FA, O\\u0026rsquo;Keeffe M, Travers MJ, Summers SJ, Lange B, et al. The clinical course of acute, subacute and persistent low back pain: a systematic review and meta-analysis. CMAJ. 2024 Jan 21;196(2):E29\\u0026ndash;46.\\u003c/li\\u003e\\n\\u003cli\\u003eFoster NE, Anema JR, Cherkin D. Lancet Low Back Pain Series Working Group. Prevention and treatment of low back pain: evidence, challenges, and promising directions. Lancet. 2018;391:2368\\u0026ndash;83.\\u003c/li\\u003e\\n\\u003cli\\u003eChen Y, Campbell P, Strauss VY, Foster NE, Jordan KP, Dunn KM. Trajectories and predictors of the long-term course of low back pain: cohort study with 5-year follow-up. Pain. 2018 Feb;159(2):252\\u0026ndash;60.\\u003c/li\\u003e\\n\\u003cli\\u003eDunn KM, Campbell P, Jordan KP. Long-term trajectories of back pain: cohort study with 7-year follow-up. BMJ Open. 2013 Dec 11;3(12):e003838.\\u003c/li\\u003e\\n\\u003cli\\u003eLim YZ, Chou L, Au RT, Seneviwickrama KMD, Cicuttini FM, Briggs AM, et al. People with low back pain want clear, consistent and personalised information on prognosis, treatment options and self-management strategies: a systematic review. J Physiother. 2019 Jul;65(3):124\\u0026ndash;35.\\u003c/li\\u003e\\n\\u003cli\\u003eNikles J, Evans K, Hams A, Sterling M. A systematic review of N-of-1 trials and single case experimental designs in physiotherapy for musculoskeletal conditions. Musculoskelet Sci Pract. 2022 Dec;62(102639):102639.\\u003c/li\\u003e\\n\\u003cli\\u003eBailey D, Fawkes C, Carnes D, Draper-Rodi J. The development of the National Council for Osteopathic Research - Research Network (NCOR-RN): A qualitative focus group study of osteopaths\\u0026rsquo; views. Int J Osteopath Med. 2025 Mar;55(100742):100742.\\u003c/li\\u003e\\n\\u003cli\\u003eDraper-Rodi J, Vogel S, Bishop A. Design and development of an e-learning programme: An illustrative commentary. Int J Osteopath Med [Internet]. 2018 Jul; Available from: http://dx.doi.org/10.1016/j.ijosm.2018.07.002\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Non-specific low back pain, Biopsychosocial, Patient-centred, Single Case Experimental Design, Osteopathy, Practice-based research\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6147823/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6147823/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eNon-specific low back pain (NSLBP) is a major global health challenge. While osteopathic treatment shows benefit in managing NSLBP, research comparing traditional approaches with biopsychosocial-informed care is limited. This study aimed to evaluate outcomes from standard osteopathic treatment versus biopsychosocial-informed management and assess the feasibility of conducting Single Case Experimental Design (SCED) research in osteopathic practice.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eA SCED trial with randomised treatment start times was conducted and reported in accordance with the SCRIBE checklist. Eleven patients with NSLBP were recruited before (between January and February 2022) or after (between June and July 2022) osteopaths completed a Biopsychosocial Pain Management e-learning course. Data was collected during baseline (5\\u0026ndash;15 days), treatment (4\\u0026ndash;6 weeks), and follow-up (12 weeks) periods. Primary outcomes were the Numeric Pain Rating Scale (NPRS) and Patient Specific Function Scale (PSFS). Feasibility was assessed through recruitment, retention, and protocol adherence.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eComplete data was obtained from nine osteopath-patient pairs. Linear mixed modelling showed significant daily improvements in pain (NPRS \\u0026minus;\\u0026thinsp;0.07/day, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and function (PSFS \\u0026minus;\\u0026thinsp;0.12/day, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) during treatment, sustained over 12-week follow-up. These changes were clinically meaningful, with treatment-phase reductions exceeding established Minimal Important Change thresholds for both measures. Limited recruitment after the e-learning course prevented comparison of standard versus biopsychosocial-informed treatment approaches. SCED methodology proved feasible, though challenges emerged around recruitment and maintaining randomised treatment delays.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eThis first implementation of SCED in osteopathic practice demonstrated significant improvements in pain and function following treatment initiation. While the methodology offers a viable approach for practice-based research, future studies should address identified implementation challenges. The findings advance our understanding of osteopathic care while providing a framework for future research bridging evidence and practice.\\u003c/p\\u003e\\u003ch2\\u003eTrial registration\\u003c/h2\\u003e \\u003cp\\u003eNCT05120921\\u003c/p\\u003e\",\"manuscriptTitle\":\"OSCAR (Osteopathic Single CAse Research): A Single Case Experimental Design study evaluating osteopathic management for non-specific low back pain\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-15 10:25:08\",\"doi\":\"10.21203/rs.3.rs-6147823/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"e3391b7a-33ed-4afd-9d52-d745df4d0240\",\"owner\":[],\"postedDate\":\"May 15th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-06-05T13:53:46+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-15 10:25:08\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6147823\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6147823\",\"identity\":\"rs-6147823\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}