Mechanisms in orthodox and complementary alternative medicine (CAM) management of back pain

Mechanisms in Orthodox and Complementary Alternative Medicine (CAM) management of back pain: an observational study

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN60867554

Enrollment

1743

Registered

2015-02-17

Start date

2015-03-01

Completion date

Unknown

Last updated

2021-05-17

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Back pain management in orthodox (physiotherapy) and CAM (acupuncture & osteopathy) settings in both the NHS and private settings. Musculoskeletal Diseases

Interventions

1. Prospective Longitudinal Questionnaire Study: addresses issues that are best suited to quantitative methods, concerning effect sizes, outcome predictors and mediators. This study uses practitioner

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Practitioners: 1. Registered with the relevant professional body (Osteopaths: General Osteopathic Council, GOsC; Physiotherapists: the Chartered Society of Physiotherapy, CSP; Acupuncturists: British Acupuncture Council, BAcC) 2. Working in either the NHS or private sector 3. At least 3 years relatively recent clinical experience of musculoskeletal problems 4. Treat at least one low back pain patient per week Patients: 1. Adult (at least 18 years) 2. Seeking treatment from a participating practitioner 3. Score at least 4 on the RMDQ (our primary outcome, described below)

Exclusion criteria

Exclusion criteria: Practitioners: 1. Not registered with the relevant professional body 2. Less than 3 years relevant and recent clinical experience Patients: 1. Inability to complete questionnaires in English (waived for Welsh participants to comply with legal and research governance requirements) 2. Serious underlying pathology (inflammatory arthritis, malignancy) 3. Practitioner-identified conditions that would prevent the sought treatment being applied (e.g. pregnancy when seeking acupuncture)

Design outcomes

Primary

Measure	Time frame
Longitudinal Questionnaire Study; the primary outcome measure will be the Roland and Morris Disability Questionnaire at T1 = baseline (after 1st treatment for new episode of LBP); T2 = during the course of treatment (2 weeks post-baseline); T3 = short-term outcome (3 months post-baseline). The main analysis will be performed by multilevel methods (e.g. REML) using appropriate statistical software (e.g. MLWin) to construct a multi-level regression model taking into account the clustering of individual patients within practitioners. This can be described as a 2-level model in which level 1 = individual patient and level 2 = practitioner. As a secondary aim, the patient outcomes can be modelled as time-varying repeated measures while the non-specific factors remain time-invariant predictors.	—

Measure

Time frame

Longitudinal Questionnaire Study; the primary outcome measure will be the Roland and Morris Disability Questionnaire at T1 = baseline (after 1st treatment for new episode of LBP); T2 = during the course of treatment (2 weeks post-baseline); T3 = short-term outcome (3 months post-baseline). The main analysis will be performed by multilevel methods (e.g. REML) using appropriate statistical software (e.g. MLWin) to construct a multi-level regression model taking into account the clustering of individual patients within practitioners. This can be described as a 2-level model in which level 1 = individual patient and level 2 = practitioner. As a secondary aim, the patient outcomes can be modelled as time-varying repeated measures while the non-specific factors remain time-invariant predictors.

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Secondary

Measure	Time frame
1. Social Role, Disability, Work Disability, Pain, Wellbeing, Satisfaction at T1, T2 and T3 2. Therapeutic alliance (WAIS) at T2 3. Organisational appointments at T1 4. Access 5. Facilities (APS-mp, PSQ) 6. Modalities Duration 7. Treatment beliefs 8. Attitudes to back pain (ABS-mp) 9. Outcome expectations 10. Mediators/prognostic indicators 10.1. Risk complexity for recovery (STarT Back) 10.2. Self-efficacy for pain management 10.3. Adherence to lifestyle advice 10.4. Illness perceptions (Brief IPQ) These will be measured at the same time points as the primary outcome. We will test for main effects of the predictors (whether each factor predicts outcome and determine relative effect sizes), interaction effects (interactions between non-specific factors and therapy), and mediation effects. Multilevel modelling provides an ideal framework for examining such a complex dataset and testing our hypotheses which involve not only main effects but also complex interactions between the variables. Nested Qualitative study: The Roter Interactional Analysis System (RAIS) will be used to generate frequency counts for different categories of utterances and we will combine frequency counts and calculate a ration of patient-centred to doctor-centred talk to produce a patient centered index for each taped consultation T2.1. We will: 1. Quantitatively compare patient-practitioner interactions on the index of patient centeredness. Assuming scores on the patient-centeredness index are normally distributed, a 3x2 ANOVA will test for effects of therapy (osteopathy, physiotherapy, acupuncture) and healthcare sector (NHS, private) on patient-centeredness. 2. We will integrate the data from	—

Measure

Time frame

1. Social Role, Disability, Work Disability, Pain, Wellbeing, Satisfaction at T1, T2 and T3 2. Therapeutic alliance (WAIS) at T2 3. Organisational appointments at T1 4. Access 5. Facilities (APS-mp, PSQ) 6. Modalities Duration 7. Treatment beliefs 8. Attitudes to back pain (ABS-mp) 9. Outcome expectations 10. Mediators/prognostic indicators 10.1. Risk complexity for recovery (STarT Back) 10.2. Self-efficacy for pain management 10.3. Adherence to lifestyle advice 10.4. Illness perceptions (Brief IPQ) These will be measured at the same time points as the primary outcome. We will test for main effects of the predictors (whether each factor predicts outcome and determine relative effect sizes), interaction effects (interactions between non-specific factors and therapy), and mediation effects. Multilevel modelling provides an ideal framework for examining such a complex dataset and testing our hypotheses which involve not only main effects but also complex interactions between the variables. Nested Qualitative study: The Roter Interactional Analysis System (RAIS) will be used to generate frequency counts for different categories of utterances and we will combine frequency counts and calculate a ration of patient-centred to doctor-centred talk to produce a patient centered index for each taped consultation T2.1. We will: 1. Quantitatively compare patient-practitioner interactions on the index of patient centeredness. Assuming scores on the patient-centeredness index are normally distributed, a 3x2 ANOVA will test for effects of therapy (osteopathy, physiotherapy, acupuncture) and healthcare sector (NHS, private) on patient-centeredness. 2. We will integrate the data from

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Countries

United Kingdom

Contacts

Public ContactSusan Eardley

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 4, 2026