Objectives
This is a protocol for a Cochrane Review (intervention). The objectives are as follows:
To assess the benefits and harms of graded activity compared to placebo, sham, or no treatment for pain and function in adults with chronic non‐specific low back pain.
Background
Low back pain has been one of the leading causes of disability worldwide for more than 30 years (Chen 2022; Vos 2020), and occurs in all age groups (Hartvigsen 2003; Hoy 2012). Low back pain is associated with decreased function, societal participation, and personal financial prosperity (Hartvigsen 2018). The economic, social, and societal impact of low back pain is comparable to other higher‐profile conditions, like heart disease, cancer, and mental health (Maniadakis 2000). According to this cost‐of‐illness study, back pain places a larger economic burden than any other disease that has been analysed economically in the United Kingdom. The total direct cost of low back pain was estimated to be £1632 million, with the indirect cost exceeding £12,000 million, surpassing the indirect cost of coronary heart disease in the United Kingdom in 1998 (Maniadakis 2000). People who suffer from chronic (persistent or recurrent) low back pain are primarily responsible for most of the social and economic expenses connected with low back pain (Hartvigsen 2018).
Despite the high prevalence and significant economic and societal burden of low back pain worldwide, and the growing number of studies in this area, the most effective low back pain treatment remains uncertain. Clinical guidelines and recent research have recommended both exercise and psychological therapies for the management of low back pain (Almeida 2018; Foster 2018; Hayden 2021b). Graded activity is one treatment option that combines these two components. Therefore, it is important to investigate its effectiveness in managing this burdensome condition.
Description of the condition
Low back pain is considered to be a multifactorial condition that has been associated with biological, psychological, and social mechanisms (Hartvigsen 2018). Low back pain refers to discomfort and pain between the buttock creases and the lower limb margins, which may or may not be accompanied by referred pain or neurological symptoms (Dionne 2008). The majority of individuals with low back pain are not diagnosed with a recognisable, specific source of pain (e.g. fracture, cancer, infection, ankylosing spondylitis, or spondyloarthritis), but instead, are labelled as having 'non‐specific low back pain' (Airaksinen 2006; Van Tulder 2006).
According to the duration of symptoms, low back pain can be classified into acute (i.e. pain or discomfort lasts for up to six weeks), subacute (i.e. symptoms last between six and 12 weeks), and chronic (i.e. symptoms last for 12 weeks or longer (Airaksinen 2006; Costa 2009)). This duration of symptoms classification is used by health professionals to determine the likely low back pain prognosis. Individuals with chronic non‐specific low back pain have less chance of recovery than those with acute symptoms. Thirty‐five percent of those with acute low back pain present with a full recovery by nine months, and 41% by one year after the onset of the initial symptoms (Costa 2009). Physical activity levels and psychological factors may contribute to the recovery of people with low back pain. At the same time, low levels of physical activity are associated with a high prevalence of low back pain (Alzahrani 2019). A recently published systematic review found that psychosocial factors (e.g. fear of movement, self‐efficacy, catastrophising, and depression) may predict disability outcomes in individuals with chronic low back pain (Alhowimel 2021). This review focusses on adults with chronic low back pain. Thus, it is important to investigate interventions targetting both physical activity and psychosocial factors.
Description of the intervention
Since graded activity incorporates exercise with the biopsychosocial model of pain, using principles of cognitive behavioural therapy, this intervention has received increasing attention over the last decades. Graded activity aims to improve physical function through functionally focused exercises, including muscle strength, endurance, and balance (Leeuw 2008), while at the same time, addressing poor self‐efficacy, pain‐related fear, kinesiophobia, and unhelpful beliefs and behaviours (Asmundson 1997). The programme starts with the person identifying specific functional goals and setting a baseline capacity. The graded activity programme then progresses towards these functional goals using cognitive behavioural strategies, such as quotas, pacing, and self‐reinforcement to progress activities in a time‐contingent manner, regardless of pain (rather than a traditional pain‐contingent manner (López‐de‐Uralde‐Villanueva 2016)). By gradually increasing the level of physical activity based on these cognitive behavioural strategies, participants can build their capacity to perform exercises and experience a sense of achievement. The first graded activity protocol developed by Lindström and colleagues comprised functional assessments, and cognitive and behavioural approaches to improve activity tolerance through an individualised, submaximal, gradually increased exercise programme (Lindström 1992). Due to the combination of physical activity and psychological components, graded activity has been an endorsed treatment option for managing low back pain, especially for people with persistent symptoms (Foster 2018; Macedo 2012).
How the intervention might work
The theory is that graded activity is associated with both mechanical and psychological or cognitive mechanisms. Graded activity targets specific muscle groups related to the purposeful movement associated with fear, and promotes engagement in activities that strengthen the postural musculature, stabilisation, and body coordination (Fordyce 1976), but mainly deals with increased endurance and decreasing deconditioning. The psychological/cognitive mechanisms are directly linked to 'operant conditioning', where a person's negative behaviour toward pain while exercising (i.e. avoiding physical activity) can be changed by modifying the immediate consequence of the behaviour (Fordyce 1976; Fordyce 1973). An essential principle of the operant conditioning approach is to develop an individually‐graded activity programme to teach the person that exercise is safe, regardless of pain (Fordyce 1973; Fordyce 1976). Based on the results obtained from the individual functional capacity testing at baseline, the therapist sets quotas of exercises for participants and advises them to engage in these exercises on a daily basis for the first week. Subsequently, exercises are systematically increased in a time‐contingent manner, until the person's functional goals are attained. Positive reinforcement is provided throughout the programme as the person increases activity level and gains function (Fordyce 1976).
Why it is important to do this review
The number of studies on graded activity for low back pain has increased in recent years. The effectiveness of this intervention has been evaluated in randomised controlled trials (RCTs (Bello 2015; Heymans 2006; Idowu 2020; Leeuw 2008; Linton 2008; Macedo 2008; Magalhães 2015; Magalhães 2018; Smeets 2006; Staal 2004; Steenstra 2007; Van der Roer 2008)). Although three systematic reviews have been conducted, they are outdated, do not include the most recently published RCTs on this topic, and do not provide the certainty of available evidence (López‐de‐Uralde‐Villanueva 2016; Macedo 2010; Schaafsma 2013). Therefore, we will conduct a Cochrane review to provide accurate and robust information on the effectiveness of graded activity for chronic non‐specific low back pain.
This proposed review will be conducted as part of an overarching Network Review, which will update the Cochrane review, ‘Exercise therapy for chronic low back pain’ (Hayden 2021b). This collaborative review model aims to foster greater collaboration among those who synthesise the available evidence in the low back pain research field while adhering to robust, standard systematic review methods. To avoid duplication of effort and boost efficiency, sub‐review teams will collaborate to update this Cochrane review, and conduct corresponding sub‐reviews (e.g. graded activity for acute and sub‐acute low back pain), using co‐ordinated review processes, and sharing resources, tasks, and tools. Our sub‐review on graded activity for chronic low back pain will employ the same comprehensive methods and criteria as the other focused reviews, and contribute to the overarching Network Review comparing the effectiveness of various exercise treatment approaches.
Objectives
To assess the benefits and harms of graded activity compared to placebo, sham, or no treatment for pain and function in adults with chronic non‐specific low back pain.
Methods
Criteria for considering studies for this review
Types of studies
We will include published reports of completed randomised controlled trials (RCTs), cross‐over RCTs, and cluster‐RCTs. There will be no restrictions on the date or language of publication. We will exclude conference proceedings, theses, opinion pieces, correspondence, and stand‐alone abstracts.
Types of participants
We will include adults (i.e. minimum age 16 years, with the majority of participants 18 years and older) of any sex, with chronic symptoms of non‐specific low back pain (i.e. mean back pain duration of the study group was at least 12 weeks (Airaksinen 2006)). We will define non‐specific low back pain as pain or discomfort not attributed to a specific source or recognisable pathology (Airaksinen 2006; Van Tulder 2006).
We will exclude studies that enroled people with low back pain related to specific conditions, such as pregnancy, disc herniation, spinal stenosis, piriformis syndrome, fracture, ankylosing spondylitis, spondyloarthritis, infection, neoplasm, or metastasis. We will also exclude studies of post‐surgical populations or those exclusively focused on acute exacerbations of chronic low back pain (i.e. flares (Costa 2019)). If studies include a mixture of individual and symptom characteristics (e.g. adults with leg pain), we will include them if non‐specific low back pain is the main complaint for most participants.
We will not restrict the setting or context of the studies.
Types of interventions
We will include studies that evaluated a graded activity intervention if they described the tested treatment as graded activity, if the study used relevant handbooks to guide their interventions (Fordyce 1973; Sanders 1996), or when the intervention included these three features:
the treatment goals were functional activities;
the treatment included cognitive and behavioural principles (e.g. operant conditioning principles, exercise quotas, pacing, and self‐reinforcement); and
the exercise programme had a baseline and progressive exercise and activity tolerance using a time‐contingent system (instead of pain reduction) using quotas and pacing.
The principles of operant conditioning are designed to modify a person's immediate responses to negative behaviours associated with pain, such as avoiding physical activity. This is accomplished by demonstrating that exercise can be done safely even when experiencing pain. Pacing is another proactive self‐management technique aimed at balancing the time spent on activity and rest, in order to enhance overall function and engagement in meaningful activities.
We will exclude any studies that refer to the intervention as a graded activity but do not provide relevant handbooks or the three core features mentioned above. We will also exclude studies on cognitive behavioural treatment or other interventions that do not include an explicit exercise programme using quotas and pacing.
In this review, we will include studies that compared graded activity to:
placebo, sham, or attention control;
no trial treatment (i.e. when no specific treatment provided by the trial is described, including wait‐listing control, no intervention; or when trial authors stated that participants could receive usual, normal, or standardised care, but this was not controlled by the trial and may be offered to all groups; or when exercise and comparison groups are offered, or receive, the same co‐interventions, allowing the effect of the exercise treatment to be isolated).
Types of outcome measures
Major outcomes
Pain intensity: measured on a continuous self‐reported scale (e.g. Visual Analogue Scale (VAS); Numeric Rating Scale (NRS), or other validated measures). If studies present more than one measure for pain intensity, we will give preference to NRS, VAS, and other validated measures, in this order of priority.
Functional limitation (i.e. disability): measured on a continuous, self‐report scale (e.g. Roland‐Morris Disability Questionnaire (RMDQ (Roland 2000)), Oswestry Disability Index (ODI (Fairbank 1980)), or other validated measures. If studies present more than one measure for disability, we will give preference to RMDQ, ODI, and other validated measures, in this order of priority.
Health‐related quality of life: measured by any validated measure, in the following order of priority SF‐36 or SF‐12 (36/12‐Item Short Form Health Survey (Ware 1992)); PROMIS‐GH‐10 (10‐item Patient‐Reported Outcomes Measurement Information System Global Health short form (Hays 2009)); EQ‐5D (EuroQoL 5 domains (EuroQol 2019)).
Psychological functioning: measured by any of the following measures, such as the Beck Depression Inventory (Beck 1987; Beck 1988); Zung Depression Index (Zung 1986); Patient Health Questionnaire‐9 (PHQ‐9 (Kroenke 2001)); Montgomery‐Åsberg Depression Rating Scale (MADRS (Davidson 1986)); Hamilton Rating Scale for Depression (HRSD (Hamilton 1986)); Center for Epidemiologic Studies Depression Scale (CES‐D (Radloff 1977)); Hospital Anxiety and Depression Scale (HADS (Zigmond 1983)); Hopkins Symptoms Checklist for anxiety and depression (HSCL (Derogatis 1974)). No specific order of preference for measurement tools will be applied.
Participant‐reported treatment success
Adverse events (e.g. exacerbation of back symptoms, muscle soreness, etc.)
Withdrawals due to adverse events
Minor outcomes
Return to work/absenteeism: any objective or subjective evaluation of sick leave or absence from work
Self‐efficacy: any measure of self‐reported confidence to perform activities and achieve goals despite low back pain symptoms
Cost: any measurement of healthcare expenditure reported in the trial
All eligible studies will be included in the review, regardless of whether they report our major outcomes.
Timing of outcome assessments
We will extract and report outcome data grouped into three time periods: (1) end of treatment (i.e. follow‐up measured within one week before or after the end of treatment), (2) moderate‐term (i.e. follow‐up measured between 14 weeks and 47 weeks after the end of treatment, closest to 6 months), and (3) long‐term follow‐up (i.e. follow‐up measured at least 48 weeks after the end of treatment, closest to 12 months).
For primary analyses, we will use the post‐treatment time period closest to the end of treatment.
For studies that included boosters, we will consider the last session prior to the booster as the end of treatment, and ultimately, we will conduct a subgroup analysis with these studies to see if there are any differences in effectiveness between studies that offered the booster sessions and those that did not.
To accommodate variations in reporting, and allow the largest number of sufficiently similar eligible studies, we will define closest to end of treatment as the available follow‐up period that is at least half‐way through the treatment programme, and up to 14 weeks post‐treatment. In applying this definition, we will prioritise time periods available for a trial in this order: (1) follow‐up measured zero to six weeks post‐treatment, (2) follow‐up measured more than half‐way through the treatment to the end of treatment, then (3) follow‐up measured between 6 weeks and 14 weeks post‐treatment.
Primary outcomes
.
Secondary outcomes
.
Search methods for identification of studies
Electronic searches
Our overarching network review team will run updated and optimised electronic searches to ensure adequate retrieval for the broad overarching review, and the embedded focused Cochrane reviews (referred to as sub‐reviews, including this review). See Appendix 1 for the MEDLINE electronic strategy. The search optimisation will be conducted with the inclusion of additional exercise‐type terms and fewer databases searched (see search optimisation report; Appendix 2). A second independent health librarian will review the search strategy, using the PRESS Checklist (McGowan 2016).
Our overarching review team will run the full electronic search strategy and reconcile the results against the search return of the previous Cochrane review (Hayden 2021b).
We will conduct the search on the following databases with no date or language restrictions:
Cochrane Central Register of Controlled Trials (CENTRAL; current issue) in the Cochrane Library;
MEDLINE OvidSP (1946 to current);
Embase (Embase.com; 1974 to current)
We will manage citations using EndNote X8 software (Clarivate 2017). Our sub‐review will use the same search strategy used for the broad overarching review and the Cochrane Musculoskeletal review of exercise for acute low back pain.
Searching other resources
We will review the list of included studies for other potentially relevant trials.
Our research team will review trial protocols and registrations identified by the Cochrane CENTRAL search. CENTRAL includes ClinicalTrials.gov (www.ClinicalTrials.gov) and the World Health Organization International Clinical Trials Registry Platform (ICTRP) Search Portal (apps.who.int/trialsearch/). We will contact the authors of all ongoing trials by e‐mailing them a link to a REDCap survey to ask if the trials are complete, published, or both (Harris 2019).
Before we complete analyses and reporting, our research team will search for retractions and publication corrections within our set of eligible studies using the software Zotero, which is integrated with the Retraction Watch database (Retraction Watch 2018; Zotero 2023).
Data collection and analysis
Selection of studies
We will conduct study selection and data extraction collaboratively with the Network Review team members. Pairs of review authors from our collaborative review pool of 35 review authors will independently screen citations based on titles and abstracts, and subsequently full text, for inclusion in the review. They will consult with a third review author to resolve any disagreements, if necessary. The citations that remain unclear after review of the title and abstract will proceed to full‐text assessment. When an inclusion criterion remains unclear at the full‐text assessment level, we will contact the study author to ask for further information.
We will follow structured procedures for citation management, study selection, and data extraction, using evidence synthesis tools and online software, i.e. Cochrane Screen4Me resources (Noel‐Storr 2021), including a machine learning algorithm (Thomas 2021), and Cochrane Crowd RCT classification (Noel‐Storr 2021), to pre‐screen results from the new search, as these strategies are accurate.
We will record the selection process in sufficient detail to complete a characteristics of excluded studies table, and a PRISMA flow diagram (Page 2020).
Data extraction and management
Two review authors (LA and NB) will independently extract study characteristics and outcome data from included studies using a data collection form that has been piloted on at least one study in the review. They will consult with a third review author (LM) to resolve any disagreements, if necessary. We will extract the following study characteristics.
Methods: study design, number of study centres and location, study setting, and date of study
Participants: number, mean age, sex, disease duration, severity of condition, diagnostic criteria, important chronic low back pain baseline data, inclusion criteria, and exclusion criteria
Interventions: intervention, comparison, concomitant interventions, and excluded interventions. We will describe the exercise dose by the number of hours of exercise per session, and by the number of sessions. In addition, we will indicate all non‐exercise co‐interventions included in the exercise treatment. We will provide a detailed description of the interventions using the CERT checklist for exercise interventions, as required by Cochrane Musculoskeletal (Slade 2016).
Outcomes: major and minor outcomes specified and collected, and time points reported
Characteristics of trial design, as outlined in the assessment of risk of bias in the included studies section
Notes: trial funding and notable declarations of interest of trial authors
Information needed for the GRADE assessment (e.g. baseline risk in the control group for key outcomes)
We will extract the number of events and number of participants per treatment group for dichotomous outcomes, and means and standard deviations and number of participants per treatment group for continuous outcomes. We will note in the characteristics of included studies table if outcome data were not reported in a usable way, and when data were transformed or estimated from a graph. We will resolve disagreements by consensus or by involving a third review author (JY). One review author (GB) will transfer data to the Review Manager file (RevMan 2024). We will double‐check that data are entered correctly by comparing the data presented in the systematic review with the study reports.
We will extract all data into DistillerSR, using pre‐developed and tested forms (DistillerSR 2020).
For continuous and dichotomous outcomes, if studies report between‐group differences that are adjusted for baseline scores (e.g. analysis of covariance adjusted for baseline score) with final values, or change from baseline values for the same continuous outcome, we will extract adjusted final values over change scores, as necessary.
We will extract data analysed by intention‐to‐treat (ITT) wherever possible. If we identify cross‐over RCTs, we will extract data from the first time point only.
When required, our research team will contact trial authors to request any missing study information, data points, or risk of bias information. We will send the trial authors an e‐mail with a link to a REDCap data capture form, in which extracted data and missing fields are clearly displayed (Harris 2019). Trial authors will be asked to add the missing information, and will have the opportunity to correct any incorrect extracted data and then submit the survey through REDCap. The previously updated exercise review for low back pain successfully used this data‐checking survey strategy (Hayden 2021b). To find and fix inaccurate data, including before and after electronic data transfer to the RevMan file, we will adhere to predefined data validation and cleaning guidelines. This will involve inspection, verification, cleaning, re‐verification, and reporting (Appendix 3; RevMan 2024).
Assessment of risk of bias in included studies
We will assess the risk of bias for randomised trials using a modified version of the RoB 1 criteria recommended by Cochrane (Higgins 2011), and informed by Cochrane Back and Neck (Furlan 2015). We will classify individual items as high risk, low risk, or unclear risk. We will assess these bias domains.
Selection bias (method of randomisation, treatment allocation concealment, similarity of baseline characteristics)
Performance bias (blinding of participants and care provider, intention‐to‐treat)
Attrition bias (missing outcome data/dropouts)
Detection bias (blinding of outcome assessors, similar timing of outcome assessment)
Reporting bias (selective outcome reporting)
Other biases (avoidance of co‐interventions, compliance)
Two review authors (LA and LM) will independently conduct risk of bias assessments, and reach consensus. Any disagreements that cannot be resolved through discussion will be referred to a third review author. We will calculate inter‐rater reliability, related to risk of bias, as overall agreement and Kappa scores, based on assessments before consensus judgements are reached.
For any cluster‐randomised trials, we will assess an additional domain, bias arising from the timing and recruitment of participants. We will report judgements for each bias domain in a risk of bias table.
We will assess studies as having an overall high risk of bias if any domain is judged to have a high risk of bias. We will assess the risk of bias for all major self‐reported outcomes combined. If judgements differ according to major outcome, we will assess the items separately, and base our domain and overall assessment on the outcome judged to have the highest risk of bias.
Assessment of bias in conducting the systematic review
We will conduct the review according to this published protocol, and report any deviations from it in the Differences between protocol and review section of the completed review.
Assessment of research integrity
In addition to the risk of bias assessment, we will conduct a comprehensive evaluation of several characteristics of research integrity for the included studies, such as:
prospective trial registration. When a trial was registered before or within a month of the trial start date (i.e. date that the first participant was enroled), we will classify it as prospectively registered. Otherwise, we will classify the trial as retrospectively registered.
publication in a presumed predatory journal. Two review authors will independently use the decision algorithm of Boulos and colleagues to assess if a trial was published in a predatory journal (Boulos 2022).
inadequate reporting of minimal basic CONSORT items (Hayden 2020; Hayden 2021a).
If we judge that studies were not prospectively registered, were published in a presumed predatory journal, and have inadequate reporting, we will exclude them from this review.
Measures of treatment effect
We will analyse continuous outcomes as a mean difference (MD) with a 95% confidence interval (CI), when a sufficiently similar scale is used to measure an outcome. We will enter data presented on a scale with a consistent direction of effect across studies. When continuous outcomes are measured on different scales, we will calculate the standardised mean difference (SMD), with corresponding 95% CIs. SMDs will be multiplied by a typical among‐person standard deviation, which is the baseline standard deviation of the control groups from the most representative trials, to be back‐translated to a scale of 0 to 100 (Higgins 2024b).
We will synthesise the functional limitation outcome using the Roland‐Morris Disability Questionnaire, when reported. A systematic review and meta‐analysis found that the Roland‐Morris Disability Questionnaire and the Oswestry Disability Index are highly correlated and similarly responsive enough for meta‐analysis (Chiarotto 2016). We will re‐scale the results to a continuous scale (ranging from 0 to 100) and analyse them as mean differences with 95% CIs.
Dichotomous outcomes (e.g. adverse events) will be expressed as risk ratios with 95% CIs.
Minimally important differences
We will consider a difference of 10 points on a scale of 0 to 100 points for pain intensity or functional disability for a comparison of exercise treatment to no treatment. This difference represents the smallest worthwhile effect, based on a 20% reduction, which is the estimated median for a patient‐reported, smallest worthwhile effect from the average baseline pain and functional disability (Ferreira 2013; Hayden 2021b).
Unit of analysis issues
If a single trial reports multiple arms, we will only consider the arms that are relevant to our analysis. If a meta‐analysis combines two comparisons (such as exercise A versus placebo and exercise B versus placebo), we will divide the control group in half to avoid counting it twice.
For any eligible cross‐over trials, we will only extract and analyse data from the first phase of the trial, preceding the cross‐over, to prevent carry‐over effects.
If we identify any cluster‐RCTs, or studies that incorporate more than one joint in the analysis, we will use the methods reported in Chapter 23 of the Cochrane Handbook for Systematic Reviews of Interventions, and multiply the standard error of the effect estimate (from an analysis ignoring clustering) by the square root of the design effect (Higgins 2024a). We will conduct the meta‐analysis using the inflated variances using the generic inverse‐variance method.
Dealing with missing data
We will first try to contact and request the missing data from the study authors. If we cannot reach them, or they cannot provide us with the required data, we will use the mean variance from studies that have similar populations with low back pain and are not at high risk of bias.
For dichotomous outcomes, such as the number of withdrawals due to adverse events, we will calculate the withdrawal rate using the number of participants randomised to the group as the denominator.
For continuous outcomes, such as the mean change in pain score, we will calculate the MD or SMD based on the number of participants analysed at that time point. If the number of participants analysed is not presented for each time point, we will use the number of participants randomised to each group.
If standard deviations are missing, we will calculate them from other statistics, such as standard errors, CIs, or P values, following the recommended methods in the Cochrane Handbook (Higgins 2024a). If we cannot calculate standard deviations, we will impute them (e.g. from other studies in the meta‐analysis).
Assessment of heterogeneity
We will evaluate the clinical and methodological diversity among the included studies, and pool data if the studies are considered clinically homogeneous in the study population, intervention, and outcomes. We will evaluate statistical heterogeneity by visually inspecting the forest plots, which includes examining the direction and magnitude of effects, and the degree of overlap between confidence intervals.
We will use the I2 and Chi2 statistical tests to quantify inconsistency among the trials in each analysis, as recommended in the Cochrane Handbook (Deeks 2024).
0% to 40% might not be important;
30% to 60% may represent moderate heterogeneity;
50% to 90% may represent substantial heterogeneity;
75% to 100% represents considerable heterogeneity.
We will keep in mind that the observed value of I² depends on the magnitude and direction of effects and the strength of evidence for heterogeneity (e.g. P value from the Chi² test, or a CI for the I² statistic, noting that the uncertainty in the value of the I² statistic is substantial when the number of studies is small).
Assessment of reporting biases
If we include 10 or more studies in any meta‐analysis, we will create and examine funnel plots to explore possible small study biases, and undertake formal statistical tests to investigate funnel plot asymmetry (Egger 1997). When interpreting funnel plots, we will examine the different possible reasons for funnel plot asymmetry, as outlined in Chapter 13 of the Cochrane Handbook (Page 2024).
To assess outcome reporting bias, we will check trial protocols against published reports. For studies published after 1 July 2005, we will screen the trial registries, ClinicalTrials.gov and WHO ICTRP, for the trial protocol and compare the information reported in the protocols with those reported in the published manuscripts. We will evaluate whether selective reporting of outcomes is present.
Data synthesis
Our primary comparison will be graded activity versus placebo, sham, or attention control. Our second comparison will be graded activity versus no treatment.
We will conduct meta‐analyses only if the treatments, participants, and underlying clinical questions are similar enough for pooling to make sense. We will use a random‐effects model for our meta‐analyses.
Subgroup analysis and investigation of heterogeneity
We plan to carry out the following subgroup analyses for functional disability:
Studies that offered booster sessions versus those that did not, to test whether booster sessions are an effective way to prolong positive treatment effects or improve symptoms of long‐term low back pain after a graded activity intervention.
Studies that included participants with or without leg pain or neurological symptoms, to test whether the effectiveness of the graded activity interventions varies between these two populations.
We will use the formal test for subgroup interactions in Review Manager, and will use caution in the interpretation of subgroup analyses, as advised in section 10 of the Cochrane Handbook (Deeks 2024; RevMan 2024). The magnitude of the effects will be compared between the subgroups by assessing the overlap of the CIs of the summary estimate.
Sensitivity analysis
We plan to conduct sensitivity analyses by:
excluding studies judged as high or unclear risk of selection and detection bias, to investigate the robustness of our results for pain intensity and functional disability.
excluding studies with potential research integrity concerns, if we judge that they suffer from multiple sufficient concerns. If a study reports less than 75% of the required CONSORT items (i.e. 17 or fewer of the 24 items), we will judge it to be inadequately reported.
Summary of findings and assessment of the certainty of the evidence
We will follow the guidelines in Chapters 14 and 15 of the Cochrane Handbook, to interpret results, and will take care that we distinguish a lack of evidence of effect from a lack of effect (Schünemann 2024a; Schünemann 2024b). We will base our conclusions only on the findings from the quantitative or narrative synthesis of the studies included in this review. We will avoid making practice recommendations. Our implications for research will suggest future research priorities and outline the remaining uncertainties in the area.
We will create a summary of findings (SoF) table for the major outcomes: pain, function, quality of life, psychological functioning, treatment success, adverse events, and withdrawals due to adverse events at short‐term follow‐up (post‐treatment assessment closest to three months). The comparisons will be placebo or sham in the first SoF table, and no treatment in the second SoF table.
Two review authors (LA and LM) will independently assess the certainty of the evidence. We will use the five GRADE considerations (i.e. study limitations; inconsistency; imprecision; indirectness; and publication bias) to assess the certainty of the body of evidence, based on the studies that contributed data to the meta‐analyses for the prespecified outcomes, and report the certainty of evidence as high, moderate, low, or very low. We will use GRADEpro GDT software to prepare the SoF tables (GRADEpro GDT). We will justify all decisions to downgrade the certainty of evidence in footnotes, and make comments to aid the reader's understanding of the review where necessary.
See Appendix 4 for details on the five GRADE considerations, and definitions for high‐, moderate‐, low‐, and very low‐certainty evidence.
Acknowledgements
The Canadian Institutes of Health Research provided funding for the ‘Exercise for the treatment of chronic low back pain’ project that supported the overarching Collaborative Review (Project Grant Competition, PJT‐173478). We thank Collaborative Review Central Team members: Rachel Ogilvie (project co‐ordinator), and Shazia Kashif (analyst); and we acknowledge Leah Boulos and Kristy Hancock, Evidence Synthesis Coordinators at the Maritime SPOR Support Unit for assistance with the literature search for the collaborative review. We acknowledge the contributions of Collaborative Review Leads: Lisandra Almeida de Oliveira, Geronimo Bejarano, Kasper Bülow, Carol Cancelliere, Annemarie de Zoete, Jill Hayden, Fabianna Jesus‐Moraleida, Tiê Parma Yamato, Bruno Saragiotto, Lisa Susan Wieland, and additional central and sub‐review team members contributing to screening and data extraction: Nora Bakaa, Jennifer Cartwright, Gaelan Connell, Cristiano Costa, Ben Csiernik, Stephanie Di Pelino, Junior Vitorino Fandim, Shireen Harbin, Wilhelmina IJzelenberg, Carsten Bogh Juhl, Mariana Leite, Alanna MacDonald, Luciana Macedo, Devin Manning, Diego Roger‐Silva, Pedro Isaac Santos Chaves, Heather Shearer, Daniele Sirineu Pereira, Danielle Southerst, Maria N Wilson, Jessica Wong, Leslie Verville, Hainan Yu.
We thank Somayyeh Mohammadi for support with Persian language trials. We would like to thank our patient advisor, Heather Taylor. We appreciate advice and guidance from our Collaborative Review Working Group members: Rachelle Buchbinder, Manuela Ferreira, Andrea Furlan, Jan Hartvigsen, Toby Lasserson, Chris Maher, Amir Qaseem, Peter Tugwell, and Maurits van Tulder.
We thank Victoria Pennick, Cochrane Central Production Service, for her meticulous copy‐editing of this Cochrane review protocol. We also thank McMaster University for supporting this work.
Appendices
Appendix 1. Preliminary MEDLINE Ovid search strategy
MEDLINE All Ovid
Developed by Leah Boulos, Maritime SPOR SUPPORT Unit, Nova Scotia, Canada and the BACK Program team, Dalhousie University, Halifax, NS, Canada.
Published in: Hayden JA, Ogilvie R, Kashif S, Singh S, Boulos L, Stewart SA, Wieland LS, Jesus‐Moraleida FR, Saragiotto BT, Yamato TP, de Zoete A, Bülow K, Almeida de Oliveira L, Bejarano G, Cancelliere C. Exercise treatments for chronic low back pain: a network meta‐analysis. Cochrane Database of Systematic Reviews 2023, Issue 6. Art. No.: CD015608. DOI: 10.1002/14651858.CD015608.
| 1 | randomized controlled trial [pt] |
| 2 | controlled clinical trial [pt] |
| 3 | randomized [tiab] |
| 4 | placebo [tiab] |
| 5 | drug therapy [sh] |
| 6 | randomly [tiab] |
| 7 | trial [tiab] |
| 8 | groups [tiab] |
| 9 | or/1‐8 |
| 10 | animals [mh] NOT humans [mh] |
| 11 | 9 not 10 |
| 12 | exp Back Pain/ |
| 13 | Intervertebral Disc Displacement/ |
| 14 | exp Sciatic Neuropathy/ |
| 15 | exp Spondylosis/ |
| 16 | (back ache* or backache* or back disorder* or back pain*).tw,kw,kf. |
| 17 | coccydynia.tw,kw,kf. |
| 18 | ((disc? or disk?) adj1 (degenerat* or displace* or hernia* or prolapse* or slipped)).tw,kw,kf. |
| 19 | dorsalgia.tw,kw,kf. |
| 20 | (lumb* adj4 pain).tw,kw,kf. |
| 21 | lumbago.tw,kw,kf. |
| 22 | (sciatic neuropathy or sciatica or ischialgia).tw,kw,kf. |
| 23 | (spondylosis or spondylolysis or spondylolisthesis).tw,kw,kf. |
| 24 | or/12‐23 |
| 25 | exp Exercise/ |
| 26 | exp Exercise Therapy/ |
| 27 | exp Exercise Movement Techniques/ |
| 28 | Physical Therapy Modalities/ |
| 29 | exp Recreation/ |
| 30 | Recreation Therapy/ |
| 31 | exp Physical Fitness/ |
| 32 | exercis*.tw,kw,kf. |
| 33 | (kinesiotherapy or recreation*).tw,kw,kf. |
| 34 | McKenzie.tw,kw,kf. |
| 35 | Alexander.tw,kw,kf. |
| 36 | William.tw,kw,kf. |
| 37 | Feldenkrais.tw,kw,kf. |
| 38 | (McGill adj5 (method or technique)).tw,kw,kf. |
| 39 | (training adj2 (strength* or physical or fitness or core or ergonomic* or musc* or spine or spinal or balance or stabil*)).tw,kw,kf. |
| 41 | ((core or musc*) adj2 (strengthen* or stabiliz* or stabilis* or stability or endurance or condition*)).tw,kw,kf. |
| 41 | functional restoration.tw,kw,kf. |
| 42 | pilates*.tw,kw,kf. |
| 43 | (yoga or hatha or ashtanga or bikram or iyengar or kripalu or kundalini or sivananda or vinyasa or raja or radja or bhakti or jnana or kriya or karma or yama or niyama or asana or pranayama or pratyahara or dharana or dhyana or samadhi or bandha or mudra or yin).tw,kw,kf. |
| 44 | aerobic*.tw,kw,kf. |
| 45 | (high intensity interval training or hiit).tw,kw,kf. |
| 46 | walk* or run or running or jog or jogging or sport* or cycling or biking or swim* or dance or dancing or gymnastic* or boxing or kickboxing or stretch*).tw,kw,kf. |
| 47 | (aquacise or aquacize or aquasize or aquafit* or zumba or barre).tw,kw,kf. |
| 48 | (tai chi or tai ji or taiji or taijiquan or taijizhang).tw,kw,kf. |
| 49 | eldoa.tw,kw,kf. |
| 50 | (glad adj5 (hip? or knee? or osteoarthritis)).tw,kw,kf. |
| 51 | (otago adj5 (program* or balance or strength or training)).tw,kw,kf. |
| 52 | (bone fit or bonefit).tw,kw,kf. |
| 53 | walk tall.tw,kw,kf. |
| 54 | (dynamic neuromuscular stabili?ation or dns).tw,kw,kf. |
| 55 | 55 active rehabilitation.tw,kw,kf. |
| 56 | or/25‐55 |
| 57 | Alexander Disease/ |
| 58 | Williams Syndrome/ |
| 59 | or/57‐58 |
| 60 | 56 not 59 |
| 61 | 11 and 24 and 60 |
Appendix 2. Search optimisation report: Exercise for low back pain review
Literature search optimisation report
Prepared by Leah Boulos, Maritime SPOR SUPPORT Unit, Nova Scotia, Canada.
Published in: Hayden JA, Ogilvie R, Kashif S, Singh S, Boulos L, Stewart SA, Wieland LS, Jesus‐Moraleida FR, Saragiotto BT, Yamato TP, de Zoete A, Bülow K, Almeida de Oliveira L, Bejarano G, Cancelliere C. Exercise treatments for chronic low back pain: a network meta‐analysis. Cochrane Database of Systematic Reviews 2023, Issue 6. Art. No.: CD015608. DOI: 10.1002/14651858.CD015608.
Gold standard
The gold standard for this optimisation experiment was derived from studies included in the Exercise for Low Back Pain Cochrane review, as well as a list of potentially relevant studies not included in the review. The total number of studies in the gold standard was 305.
Database coverage
98% of the studies in the gold standard (n = 298) were found in CENTRAL, MEDLINE, or Embase. 1% of the studies (n = 4) were found in CINAHL, SPORTDiscus, or PEDro. There were no unique studies found in PsycINFO. 1% of the studies (n = 3) were not found in any of the seven databases searched for this review. This data supports searching only CENTRAL, MEDLINE, and Embase for this review.
Proposed revisions to exercise portion of search
Based on a keyword analysis of studies not retrieved by the MEDLINE search, the following changes are proposed.
| Original search | Revised search | ||
| 1 | exp Exercise/ | 1 | exp Exercise/ |
| 2 | exercis*.tw,kf. | 2 | exp Exercise Therapy/ |
| 3 | exp Exercise Therapy/ | 3 | exp Exercise Movement Techniques/ |
| 4 | exp Exercise Movement Techniques/ | 4 | Physical Therapy Modalities/ |
| 5 | exp Physical Therapy Modalities/ | 5 | exp Recreation/ |
| 6 | McKenzie.tw,kf. | 6 | Recreation Therapy/ |
| 7 | Alexander.tw,kf. | 7 | exp Physical Fitness/ |
| 8 | William.tw,kf. | 8 | exercis*.tw,kf. |
| 9 | Feldenkrais.tw,kf. | 9 | recreation*.tw,kf. |
| 10 | exp Yoga/ | 10 | McKenzie.tw,kf. |
| 11 | exp Recreation/ | 11 | Alexander.tw,kf. |
| 12 | exp Physical Fitness/ | 12 | William.tw,kf. |
| 13 | (yoga or pilates).tw,kf. | 13 | Feldenkrais.tw,kf. |
| 14 | (Tai Chi or Tai Ji or Taiji or Taijiquan).tw,kf. | 14 | (training adj2 (strength* or physical or fitness or core or ergonomic* or musc* or spine or spinal or balance or stabil*)).tw,kf. |
| 15 | or/1‐14 | 15 | (core adj2 (strengthen* or stabiliz* or stabilis* or stability)).tw,kf. |
| 16 | exp Alexander Disease/ | 16 | functional restoration.tw,kf. |
| 17 | exp Williams Syndrome/ | 17 | (pilates* or yoga).tw,kf. |
| 18 | 16 or 17 | 18 | aerobic*.tw,kf. |
| 19 | 15 not 18 | 19 | (walk* or run or running or jog or jogging or sport* or cycling or swim* or dance or dancing or gymnastic* or boxing or kickboxing).tw,kf. |
| 20 | (aquacise or aquacize or aquasize or aquafitness or zumba or barre).tw,kf. | ||
| 21 | (tai chi or tai ji or taiji or taijiquan).tw,kf. | ||
| 22 | active rehabilitation.tw,kf. | ||
| 23 | or/1‐22 | ||
| 24 | Alexander Disease/ | ||
| 25 | Williams Syndrome/ | ||
| 26 | or/24‐25 | ||
| 27 | 23 not 26 | ||
Search characteristics
Below is a comparison of characteristics for the original vs revised searches. The search characteristics in CENTRAL remain essentially unchanged. In MEDLINE, sensitivity increases from 95.8% to 97.7%, while the number needed to read (NNR) decreases from 18 to 17. In Embase, sensitivity increases from 90.2% to 95.7%, but the NNR increases from 25 to 35.
| Search Strategy | Comprehensiveness/ sensitivity/ recall ‐ a/(a+c) | Efficiency/ precision ‐ a/(a+b) | NNR ‐ 1/(a/(a+b)) | Specificity ‐ d/(b+d) | Accuracy ‐ (a+d)/(a+b+c+d) |
| CENTRAL | |||||
| Original | 95.5% | 4.6% | 22 | 80.9% | 81.1% |
| Revised | 95.2% | 4.3% | 23 | 79.9% | 80.1% |
| MEDLINE | |||||
| Original | 95.8% | 5.4% | 18 | 80.2% | 80.4% |
| Revised | 97.7% | 5.8% | 17 | 81.2% | 81.4% |
| Embase | |||||
| Original | 90.2% | 4.0% | 25 | 90.3% | 90.3% |
| Revised | 95.7% | 2.9% | 35 | 85.4% | 85.4% |
Records for screening
If a search update were run today (1 September 2021) for new results published in 2021 only, the results would be as follows:
| Original search | Revised search | ||
| CENTRAL | 275 | CENTRAL | 313 |
| MEDLINE | 232 | MEDLINE | 248 |
| Embase | 311 | Embase | 461 |
| CINAHL | 262 | ||
| PsycINFO | 13 | ||
| SPORTDiscus | 55 | ||
| PEDro | 25 | ||
| Total results | 1173 | Total results | 1022 |
| Duplicates removed | 400 | Duplicates removed | 289 |
| Total to screen | 773 | Total to screen | 733 |
Conclusions
Running the revised version of the exercise portion of the search in fewer databases would result in an increase in sensitivity while slightly reducing the number of records to screen.
Appendix 3. Data cleaning and validation guidelines: Exercise treatment for chronic low back pain (Collaborative Review)
Developed by the BACK Program team, Dalhousie University. Halifax, NS, Canada. Please cite as: Kashif S, Hayden JA. Data cleaning and validation guidelines: Exercise treatment for chronic low back pain (Collaborative Review). 2022; version June 28, 2022
Published in: Hayden JA, Ogilvie R, Kashif S, Singh S, Boulos L, Stewart SA, Wieland LS, Jesus‐Moraleida FR, Saragiotto BT, Yamato TP, de Zoete A, Bülow K, Almeida de Oliveira L, Bejarano G, Cancelliere C. Exercise treatments for chronic low back pain: a network meta‐analysis. Cochrane Database of Systematic Reviews 2023, Issue 6. Art. No.: CD015608. DOI: 10.1002/14651858.CD015608.
There are always some sources of errors in the systematic data collection process, irrespective of the procedures and error‐preventing measures, which can cause inconsistencies in data. These errors include, but are not limited to, measurement errors, entry errors, context errors, and processing errors. These inconsistencies can inflict erroneous data analysis. The major concern before data analysis should be to ensure the accuracy and consistency of the data, which necessitates a systematic and planned way for identifying and treating errors (ACAPS 2016; Van den Broeck 2005).
Limited guidance is available in the literature for the methodologies and standards for optimum data cleaning for systematic reviews. We present our proposed approach for this collaborative systematic review below.
Data generation process for systematic review
The robust methods, including clear data collection forms and guidance documentation, will be used for systematic review data collection. Also, all reviewers conducting data extraction will complete formal training and a calibration exercise. DistillerSR systematic review management software will be used to track data extraction and facilitate consensus. The process for data extraction will include one review team member extracting study data, including population, intervention(s), comparison(s), outcome information (measure and timing), and completing the Cochrane RoB 1 tool for each study, onto pre‐tested standardised DistillerSR forms (DistillerSR 2020; Higgins 2011). A second reviewer will conduct a careful quality check of the full data extraction, with discussion and consensus decisions for any discrepancies. Data collected in past updates of this review will be checked.
Sources of anomalies in systematic review data
The common sources of erroneous data points and anomalies in systematic review data that we will consider are:
Errors in data extraction (e.g. measurement errors, syntax errors, recording error);
Misunderstanding by reviewers;
Misreporting by trialists;
Integrity issues (e.g. data fabrication or falsification).
Objectives of data cleaning
The main objectives for using this data cleaning guideline are to:
Discover and correct data extraction errors;
Detect and note any study data extracted correctly but flagged for potential integrity concerns (for sensitivity analyses);
Construct a standardised data format to be used in different research projects;
Structure more understandable and reproducible data in terms of transparency and accountability.
Standards for systematic review quality data
Quality data are crucial for a reliable analysis, and hence, for concerned inferences. The decisions based on poor data may be misleading, and hence, have less confidence. The broad standards of quality data include: accessibility, accuracy, comparability, completeness, consistency, coherence, credibility, reliability, flexibility, plausibility, relevance, usefulness, timeliness, uniqueness, and validity (ACAPS 2016; Britan 2009). For this systematic review database, we will focus on the five quality standards of validity, accuracy, completeness, uniformity, and integrity.
Data validity
Data validity refers to the degree to which data points essentially represent what is anticipated to be measured (Britan 2009), which implies that the data should conform to defined restrictions for different variables and values in the data. For systematic review data, we will focus on the restrictions for variable types, variable range, mandatory variables restriction, unique variable restriction, set membership restriction, regular expression patterns restriction, cross‐field validation restriction, and randomness restriction (Table below).
Data accuracy
Data accuracy implies the extent to which the data points are expected to be close to the true prespecified values. Applying all data validity constraints does not mean that all valid values are accurate and also precise. So, further checks, such as cross‐tab evaluation, association or correlation should be applied for data accuracy.
Data completeness
Data completeness defines the level to which all required data are known and complete. There are various reasons for missing data, and we will improve and lessen the problem by contacting the original source (trial authors, using REDCap survey procedure), if possible (Harris 2019).
Data uniformity
Data uniformity represents the level to which the data are quantified, using the same defined unit of measure for different variables. And so, to obtain data uniformity, some data will be converted to a single measure unit for different variables. For example, outcome data converted to a common scale as appropriate, exercise frequency expressed as a common time measure.
Data integrity
Data integrity indicates the correctness of the collected data without intended manipulation. This implies that all data from all studies should be free from data exploitation for any reason. Data integrity problems may arise during transcription, which can be checked and corrected; or more seriously, due to deliberate manipulation, which we will examine using randomness tests or graphical methods, and addressed accordingly.
Table: Data validity restrictions
| Restrictions type | Description |
| Variable type restriction | The variables in the data should be of a particular data type, e.g. character, numeric, date, etc. |
| Range restriction | Values in a particular variable (numeric) should fall within a certain specified range. |
| Mandatory restriction | Certain variables in data cannot be empty. |
| Unique restriction | A variable, or a combination of variables, must be unique across data. |
| Set‐membership restriction | The values of a categorical variable come from a set of specified values. |
| Regular expression patterns restriction | The character/text variables should be in a certain defined pattern. |
| Cross‐field validation restriction | The different variables in the data set should meet the certain conditions that span across other variables. |
| Randomness restriction | The certain numeric variable values or a group of values should follow the random pattern for accuracy and unbiasedness. |
Former data cleaning measures
After data extraction and importing, the first measure will be to save the original data file with a specified format, and make a copy of the original data for further processing. The second measure will be to consider the two main pieces of information as:
Unit of observation of the data;
Unique and fully identified ID variables of the data.
Unit of observation of the data will involve two check points.
What does each row represent in the data?
Which variable in the data will be the main unique ID identifying each row?
After this, the first check will be identifying duplicated IDs in the data; corrections will be made after consensus.
Steps in data cleaning
Data cleaning is defined as the recurrent cycle of various stages, which consists of screening, diagnosing, and editing with the procedures and methods to deal with data problems and errors (Van den Broeck 2005).
We will use the R package for all data scrubbing measures (De Jonge 2013). For this systematic review database, data cleaning will be an iterative process of a sequence of five phases, to produce high‐quality data while considering data quality standards at different phases.
1. Data inspection
The data inspection will be the detection of structural errors by diagnosing the unexpected, incorrect, and inconsistent data points. The data inspection will be a time‐consuming procedure, which involves many methods for exploring the raw data for error detection, and we will focus on and define a priori the most important variables for checking (and types of checks for these select variables) to be feasible. The Exploratory Data Analysis is the most used method for data inspections and rectification of data errors. This involves data profiling and data visualisation to describe the possible structure and values in the data using statistical properties of the data (Dasu 2003; Hellerstein 2008; Tukey 1977).
Data profiling
The data reporting/profiling using summary/descriptive statistics will be beneficial to overview the structure of data points. Using statistical methods, such as measures of central tendencies and dispersions to investigate data will underline the unexpected and thus erroneous data points. Similarly, data tabulation will be applied to confirm the unique values and logical consistency between different variables. For example, these approaches will be useful to:
Check types of recorded variables as character or numeric;
Test the range of data variables lying within the specific values;
Identify the unique values in different variables;
Confirm a given standard of certain variables;
Confirm different logical conditions between values of different variables in the data, and hence, to flag inconsistent variables and values;
Report the overall missing data points or in certain variables.
Data visualisation
Using different plots for data visualisation will be effective to recognise unique values, distributions, patterns, and irregular data points in variables. Visualising the data using bar and box plots and distribution plots will help to identify inconsistent data points. For example, data visualisation will be worthwhile to:
Identify the form of the distribution for different variables;
Discover the proportion of unique values in different variables;
Confirm the certain pattern in different variables;
Detect outlier data points;
Distinguish influential data points;
Discern the distribution of missing data points in different variables to create flag variables for potential description.
2. Data verification
The study data points flagged in the above step will be double‐checked against the publication report for errors in data extraction, and misunderstanding by reviewers in the data verification phase.
Influential data points confirmed to agree with the study report, but believed to be erroneous, will be checked with the trial's corresponding author to verify the study data. After data verification, we will classify suspected data points as:
Erroneous;
True extreme;
Idiopathic.
3. Data correction
Data correction will involve different techniques and methods based on the identification and irregularities of the data points. In data correction, we will rectify data points if they are erroneous, and consider imputation (with sensitivity analysis) if idiopathic. We will make the decision about outlier/true extremes after investigating their effect on results (sensitivity analysis) and consensus.
3a. Methods for correcting erroneous data points
The erroneous data points will be fixed after identification of their types.
Irrelevant data
Irrelevant variables and data points will be those that will not actually be needed, and created during the data recording and extraction process. We will check data by column for irrelevant variables, and by row for irrelevant observations/levels, and will drop an unimportant piece of information after we are sure.
Duplicate elimination
As data will be combined from different sources, there will be odds of having duplicate data points. A common indicator will be when two studies have the same reference ID or study ID. And therefore, we will simply remove them after confirmation.
Type conversion and creation of new variables
The variables should be in the specified types and formats. This will be checked quickly by looking over the variable types in each column summary, and changing them, as required.
Similarly, new labelled variables as categorical variables will be generated from numerical variables, and new numerical variables will be created from character variables, if needed.
The variables or values that can’t be transformed to a specified type will be converted to NA value (or any specified format), with a warning inserted to indicate which variable/value is incorrect and must be fixed with consultation.
Syntax errors
The syntax errors will be fixed.
The additional white spaces at the beginning, end, or in the middle of character/string variables will be deleted.
The character/string variables will be padded with spaces or other characters to a certain width.
The typos in the character/strings will be fixed, as these variables will be entered in many different ways and can have mistakes. We will replace all values in such variables with one unique value.
Special considerations will be given to values, such as 0, Not applicable, NA, None, N/A, Null, 9999, or 7777, as they will represent missing values. These values will be changed to one unique value for consistency.
Consistency errors
The consistency errors between two variables identified in data inspection will again be confirmed, and addressed accordingly.
Variable standardisation
All numerical and character variables will be changed to a standard format for data uniformity after all possible syntax errors are corrected.
For character/string variables, all values should be in lower or upper case.
For numerical variables, all values should be in a specified measurement unit and decimal points, as required.
Missing values
The identification of missing values as truly missing from studies, or missing in data extraction, will be important, and flagging will be used to distinct both. Also, to differentiate missing values from 'default' and 'unknown' values will be important, and flag variables will be used. The variable observations with missing values in the systematic review data set will not be dropped. The variables with missing values may be missing, and will be reported as missing or imputed.
In a systematic review data set, imputation will be performed only for specific variables, and will be applied only for outcome measures of variance (SD). If mean outcomes are available, but variance measures are missing or implausible, we will impute this SD. The previous method of imputation (which seems reasonable again) was to apply a simple imputation using the average SD from similar trials as an estimate of the missing value, and use it for imputation. 'Similar trials' will be based on two to three study characteristics (e.g. population source, outcome measure, treatment group) in the studies.
The flagged variables will be used to keep the missing data points information in the data set.
Scaling/normalisation
We will scale/normalise our outcome measures data based on calculated multiplicative factors for different outcomes, to minimise skewness for having different measurement units.
3b. Methods for correcting true extreme/outlier data points
The values of the variables that significantly differ from all other values will be known as outliers. The outliers might be influential data points if they have an impact on model coefficients, and will deviate the model from where most of the data points lie. We will keep these values, unless there is a good reason to remove these values after investigation.
3c. Methods for correcting idiopathic data points
Idiopathic data points will be those data points that are still suspect, without any clarification found. We will impute such data points with sensitivity analysis, to determine whether to use the original data points or imputed values for further analysis. The similar, stated imputation method will be used for such data points.
4. Data re‐verification
After data cleaning and imputation, we will re‐inspect the data to validate accuracy, and for the set rules and constraints. Some manual corrections and adjustments will be made, if otherwise not possible.
5. Data reporting
We will prepare a report about the changes made to clean the data, as data reporting will be as considerable as data cleaning. Each corrected data file will be saved with a suffix with the date last modified, in the following format, to track the changes over time (i.e. Month. Date. Year. Version; for example, data.Apr.12.2022.v1.csv). Similarly, each code file will be saved with the same format and name to match the changes made in the data, and for reproduction. We will also construct a logbook with the same name and format to track the changes made in the data for each variable or value, with the causes of errors/irregularities that should be prevented in future projects and data extraction.
The intermediate files created during data cleaning will be saved separately, and named in a specified format; their links will be stored in the logbook for tracking and reproduction.
Database created versions
The procedures for data cleaning will include saving the study database at three stages:
Original data collected by reviewers;
Corrected (errors) after data cleaning;
Corrected and imputed, as appropriate.
These dataset versions will allow assessment of the impact of data cleaning on the overall results in systematic reviews.
Appendix 4. The GRADE approach to evidence synthesis
We will use the following five GRADE considerations to assess the certainty of the evidence.
1. Study design and risk of bias
We will use the overall risk of bias judgment across studies providing data to assess study limitations. Low risk of bias will indicate no limitations; unclear risk of bias will indicate either no limitations or serious limitations; and high risk of bias will indicate either serious limitations or very serious limitations. If we judged the included studies at a low or unclear risk of bias, and they have no serious limitations, we will not downgrade the certainty of evidence. If the studies have an unclear or high risk of bias and serious limitations, we will downgrade by one level. If studies have a high risk of bias and very serious limitations, we will downgrade by two levels.
2. Inconsistency
Inconsistency refers to an unexplained heterogeneity of results. We will explore explanations for heterogeneity and then downgrade the certainty of evidence when a plausible explanation cannot be identified. We will take into consideration the following criteria:
Wide variance of point estimates across studies (note: direction of effect is not a criterion for inconsistency);
Minimal or no overlap of confidence intervals (CI), which suggests variation is more than what one would expect by chance alone;
Statistical criteria, including tests of heterogeneity, which test the null hypothesis that all studies have the same underlying magnitude of effect, and have a low P value (P < 0.05), indicating the results reject the null hypothesis.
If inconsistency can be explained by differences in populations, interventions, or outcomes, we will not downgrade the certainty of evidence, but rather offer different estimates across population groups, interventions, or outcomes. We will downgrade by one or two levels depending on the extent to which the magnitude of the effect remains unexplained, causing the uncertainty.
3. Indirectness
We are more confident in the results when we have direct evidence. Direct evidence consists of research that directly compares our interventions of interest, delivered to the populations of interest, and measures the outcomes that are important to people. Depending on how different the study populations, interventions, and outcome measures are, we will downgrade the certainty of evidence by one or two levels.
4. Imprecision
The precision of study results may be compromised when the sample size is limited, resulting in a wide confidence interval (CI) around the estimated effect. We will downgrade the quality of the evidence by two levels if the CI crosses the MID threshold (defined earlier in the methods).
For continuous outcomes, precision is compromised when the confidence interval is sufficiently wide that the estimate is consistent with conflicting recommendations (i.e. the 95% CI overlaps with both an effect and no effect, or fails to exclude important benefits or important harms). We will downgrade by one or two levels, as applicable. If we have not already downgraded based on CI width, we will consider downgrading if the sample size does not meet the optimal information size.
For dichotomous outcomes, imprecision is identified when there are very few events, and CIs around both relative and absolute estimates of effect include both benefit and harm.
5. Publication bias
Publication bias is a systematic under‐ or overestimation of the underlying beneficial or harmful effect due to the selective publication of studies. We plan to generate funnel plots to assess publication bias if at least ten trials examining the same intervention comparison are included in the review. If funnel plots show asymmetry, we will downgrade the quality of the evidence by one level. If there are several completed yet unpublished trials (e.g. listed in a trial registry), we will also suspect publication bias and downgrade by one level.
We will interpret the results of the GRADE assessment as follows.
High certainty: we are very confident that the true effect lies close to that of the estimate of the effect.
Moderate certainty: we are moderately confident in the effect estimate; the true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.
Low certainty: our confidence in the effect estimate is limited; the true effect may be substantially different from the estimate of the effect.
Very low certainty: we have very little confidence in the effect estimate; the true effect is likely to be substantially different from the estimate of effect.
We will adhere to the guidelines outlined in Chapter 12 of the Cochrane Handbook when interpreting results (McKenzie 2024). It is crucial to distinguish lack of evidence of an effect from a lack of effect. Therefore, our conclusions will exclusively rely on the outcomes derived from quantitative or narrative analyses of the studies we incorporate. We shall refrain from offering any practice‐based recommendations, and suggest research priorities while pinpointing any lingering uncertainties within the domain.
Appendix 5. Guidance documentation for all study characteristics extracted
|
Developed by the BACK Program team, Dalhousie University. Halifax, NS, Canada. Please cite as: Hayden JA, Ogilvie R, Singh S, Ellis J. Data extraction guidelines (Collaborative Review): Exercise treatment for chronic low back pain. 2022. Published in: Hayden JA, Ogilvie R, Kashif S, Singh S, Boulos L, Stewart SA, Wieland LS, Jesus‐Moraleida FR, Saragiotto BT, Yamato TP, de Zoete A, Bülow K, Almeida de Oliveira L, Bejarano G, Cancelliere C. Exercise treatments for chronic low back pain: a network meta‐analysis. Cochrane Database of Systematic Reviews 2023, Issue 6. Art. No.: CD015608. DOI: 10.1002/14651858.CD015608. |
|||
| Please note that this is intended to be a living document and is updated regularly to provide further clarification for data extraction items. The most recent version of this document is to be linked at the top of the data extraction form within DistillerSR for ease of reference for all team members. | |||
| This colour shading indicates branching fields (i.e. these fields will only be visible if the appropriate response to a previous question was selected). | |||
Please be consistent with data entry convention for free text boxes:
| |||
| Study Information | |||
| Study ID (First Author Last Name Year of Publication ‐ e.g., Smith 2007) | Format: ‘Last Name YYYY’ | ||
| Corresponding author last name | Record the corresponding author’s last name (even if they are the first author). | ||
| Corresponding author email address | Record the corresponding author’s email address. | ||
| Trial registration number reported (if yes, record) | Record the trial registration number, if yes. Format: ‘NCT01343927’ | ||
| Published protocol reported (if yes, record citation) | Record the protocol citation, if yes. Note: If you discover that a study has supplements or other materials necessary for full extraction, please email research co‐ordinator. | ||
| Research ethics board review reported (if yes, record name of the Review Board and REB#, if available) | Record if the trial reported receiving ethical approval. Format: ‘Boston University Institutional Review Board’ This study had approval from the SPECIFIC Research Ethics Board (e.g. Dalhousie University Research Ethics Board): Yes This study had institutional approval from SPECIFIC university (e.g. approved by Dalhousie University) and followed Helsinki Declaration OR followed by 'informed consent' (word ethics is not stated): Yes This study had ethical approval from our local institution (specific institution not provided): Yes |
||
| Conflict of interest statement reported (if yes, record conflict statement, or if study reports that there were no conflicts of interest, record as ‘None to declare’) | Copy and paste the conflict statement in the manuscript. If the authors declare no conflicts ‐ use this language in the Distiller text box: None to declare Format: ‘Dr. Saper reports grants from the National Center for Complementary and Integrative Health of the National Institutes of Health during the conduct of the study.’ Format: ‘None to declare’ |
||
| Funding source for trial reported (if yes, record the funding source, or if study was not funded, record as 'No funding received') | Record the name of the funding source(s), separating funding sources by a semicolon. If the trial reports that they did not receive funding, use statement: No funding received Format: ‘National Center for Complementary and Integrative Health of the National Institutes of Health; Centre for Yoga Therapy’ Format: ‘No funding received’ |
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| Study participant recruitment dates reported (format: December 2015 to December 2017) | Record all dates provided in the publication.
Format: ‘December 2009 to March 2010’ OR ‘2009 to 2010’ ‐ depending on detail given in publication |
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| Number of participants randomised (all groups combined) | Record number of participants randomised into the trial. | ||
| Are the majority of study participants older adults (age ≥ 60 years) OR is there a subgroup of participants of older adults aged ≥ 60 years presented? | Must be an older adult population (majority of participants is ≥ 60 years). Trial can indicate inclusion criteria greater than 60 years or report a subgroup of participants 60 years and older. If the study population is mixed age, then consider the mean and standard deviation to decide if > 75% is older than 60 years [e.g. mean age – (0.675 x SD) > 60 yr, assuming a normal distribution). Indicate ‘unclear’ if not clear from the full‐text publication. | ||
| Country/countries of study conduct | Record the full name of the country or countries of conduct, separating countries by a semicolon. If the country of conduct is not reported, record the country of the first author’s institution. Format: ‘United States; Canada; Denmark’ (full name of country) |
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| Population LBP duration | Record the LBP duration in the study population.
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| Inclusion criteria for pain duration (include units ‐ days/weeks/months) | Record the specific inclusion criteria that the study used with reference to symptom duration of LBP (e.g. ≥ 6 weeks, ≥ 2 months, etc.). Format: ‘3 months’, ‘≥ 12 weeks’ |
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| Pain duration (include units ‐ days/weeks/months) | Record the duration of LBP episode among all the participants randomised in the study. Prioritise extracting mean duration of population if it is provided. If not, extract % when possible. Extract by study group if study population duration is not available (example, Ex1 – 12 weeks, Ex2 – 13 weeks, Comp1 – 12 weeks) | ||
| Presence of leg pain and/or lower limb neurological symptoms (e.g. weakness, sensory deficits) | Record the proportion of the study population that had leg pain and/or lower limb neurologic symptoms.
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| Inclusion of any participants with specific‐cause LBP (if yes, what %) | Record the % of participants included in the trial with specific‐cause low back pain (must be less than 25% of participants to be eligible for inclusion, unless subgroups are presented) Format: 12% |
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| Population setting or source | Record the recruitment location for the study population.
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| Include or exclude study | Record if the study should be included or excluded. Stop extracting if you choose to exclude. | ||
| Reason for exclusion | Record the first reason for exclusion for this study (following the order presented).
Complete any mandatory questions with Not Reported or 9999 and click submit. |
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| Study information notes | Record any extra or unique information about the study (e.g. special study selection criteria, such as 'study was limited to military personnel', 'study was limited to people with average pain intensity in the previous week of 4 or greater on an 11‐point (0 to 10) numerical rating scale). | ||
| Network review primary outcomes reported | Record all the network review primary outcomes that the study measured and reported. (NOTES: Primary outcome refers to our review’s primary outcomes, NOT the trial’s primary outcomes. If a study appears to report other outcomes in a separate publication, not yet in hand, record the citation (if available) and outcomes in the very last text box in this form). Note ALL scales reported for each outcome below. For example, if both the RMDQ and ODI are reported for the functional limitations outcome, record both scales in the text box provided (but extract data only for RMDQ). Check the box for Adverse events if adverse events are mentioned in the publication (please CNTL F “adverse”).
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| Describe how adverse events are reported | Record information about how the trial collected information related to adverse events. If adverse events outcome data are not presented by study group, please include results here. If available, describe whether the adverse events were considered to be related to the intervention and how this was determined within each trial (e.g. by trialists or by an independent monitoring board) | ||
| Were adverse events measured systematically for all study participants, or only recorded ‘as reported’ by participants? | Record if adverse events were:
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| Network review secondary outcomes reported (indicate specific measure/scale) | Record all the network review secondary outcomes that were measured and reported in the study by checking all that apply, then listing all other available outcomes in the “other” checkbox. For the listed outcomes, indicate the scale/measure used to assess each secondary outcome in the available text boxes. (NOTES: Secondary outcome refers to our review’s secondary outcomes, NOT the trial’s secondary outcomes. If a study appears to report other outcomes in a separate publication, not yet in hand, record the citation (if available) and outcomes in the very last text box in this form). | ||
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| Pain/Function/Health‐related QoL/Depression measures/scales | Record the measure/scale used to assess the outcome of interest. If there are multiple measures/scales for an outcome, prioritise the following measures in the following order, when available:
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| Describe Pain/Functional limitations/Health‐related QoL/Depression measure/scale | Copy and paste any additional information about the measure/scale provided in the publication. | ||
| Considering Pain/Functional limitations/Health‐related QoL/Depression measure/scale direction, which is true? | Record which direction the measure/scale should be interpreted.
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| Minimum value of Pain/Functional limitations/Health‐related QoL/Depression measure/scale | Record the minimum value of the measure/scale used to assess the outcome of interest. Format: ‘0’ |
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| Maximum value of Pain/Functional limitations/Health‐related QoL/Depression measure/scale | Record the maximum value of the measure/scale used to assess the outcome of interest. Format: ‘100’ |
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| All available follow‐ups | Record all available follow‐up periods. Note: When extracting outcome data later on in the form, only extract immediate and very long‐term follow‐up data if no data are available for short‐term or long‐term follow‐up. To categorise a follow‐up period, follow the two steps below: 1. Put it in the category within its time frame: 6‐12, 13‐47, 48+ wks. 2. If a single study has two measurement periods falling within one follow‐up period, the one that gets selected is the one closest to 3, 6, 12mo. If there is more than 2 weeks between randomisation and the start of treatment, please note this in the notes section.
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| Immediate‐term follow‐up (< 6 weeks): Indicate unit of measure reported and exact # of weeks/months/other | Record the exact length of the immediate‐term follow‐up period. First select the unit of time used to measure, then state the exact length of time. | ||
| Short‐term follow‐up (closest to 3 months): Indicate unit of measure reported and exact # of weeks/months/other | Record the exact length of the short‐term follow‐up period. First select the unit of time used to measure, then state the exact length of time. | ||
| Moderate‐term follow‐up (closest to 6 months): Indicate unit of measure reported and exact # of weeks/months/other | Record the exact length of the moderate‐term follow‐up period. First select the unit of time used to measure, then state the exact length of time. | ||
| Long‐term follow‐up (closest to 12 months): Indicate unit of measure reported and exact # of weeks/months/other | Record the exact length of the long‐term follow‐up period. First select the unit of time used to measure, then state the exact length of time. | ||
| Outcomes and follow‐up notes | Record any unusual or unique details about the outcomes and their measurements, as well as the follow‐up time periods. | ||
| Study Design | |||
| Number of exercise groups | Record the number of exercise groups described in the publication. This will generate the appropriate data extraction fields below. | ||
| Number of non‐exercise comparison groups | Record the number of non‐exercise comparison groups described in the publication. This will generate the appropriate data extraction fields below. | ||
| IMPORTANT: Please remember to extract the exercise groups in the order that they appear in the publication. Exercise Group 1 should be the first exercise group, Exercise Group 2 should be the second exercise group described, etc. Comparison Group 1 should be the first non‐exercise comparison group described. | |||
| Exercise Groups | |||
| Treatment description | Record a thorough description of the treatment delivered to this treatment group. | ||
| All exercise types delivered to this group (check all that apply) | Record all exercise types delivered to the treatment group, regardless of the proportion of the treatment the exercise represented. | ||
| Dominant exercise type(s) delivered to this group (select up to two types, or mixed exercise if 3 or more types are equally dominant) | Record the dominant exercise type(s) delivered to this group (select up to two types, or mixed exercise if 3 or more types are equally dominant).
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| Was this exercise treatment sufficiently robust that it would be expected to be effective by both the research team, provider and participants? | Record if the treatment delivered in this group was a robust and plausible intervention designed for maximum outcome improvement, such that the research team, provider and participants would expect it to be effective. This is the data extractor’s judgement. | ||
| Does this group include a graded‐activity approach to the treatment delivery? | Graded activity = Graded activity includes baseline assessment of identified problematic activities and exercise progressed using a time‐contingent manner, regardless of pain, with use of quotas and pacing. Graded activity includes cognitive and/or behavioural principles, such as operant conditioning, with the use of strategies of positive reinforcement, and reassurance. The reporting of a gradual increase of exercise dosage doesn’t by itself characterise GA. | ||
| Is the treatment delivered to this group community‐based exercise? | Community‐based exercise = Community‐based exercises are exercise treatments delivered by a non‐healthcare professional. A non‐healthcare professional does not have advanced healthcare training and education and is not a member of a licensed, regulated health profession (e.g. community‐health workers or personal trainers are NOT healthcare professionals). Exercise treatments intended to be done alone (e.g. home exercise program) that are delivered by a healthcare professional, in‐person or through telehealth are not community‐based. | ||
| Exercise specificity | Record the correct exercise specificity.
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| Program design individualisation | Record the level of individualisation of the treatment.
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| Program delivery site | Record the delivery site of the treatment.
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| Program delivery mode | Record the delivery mode of the treatment.
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| Home exercise component | Record if the treatment included a home exercise component. | ||
| Indicate all co‐interventions provided to participants in this exercise group | Record all co‐interventions delivered to this group or indicate if no co‐interventions were delivered to the group. | ||
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| All participants in group received identical co‐intervention(s) | Record if all participants in the group received identical co‐intervention. | ||
| YOGA QUESTION SET | See end of document for further information about yoga‐specific questions | ||
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Dose and Duration Table For example: An exercise treatment was delivered in 30‐minute sessions (i.e. dose (hours per session)) with 6 sessions delivered over the course of 2 weeks (i.e. program duration (weeks)). If the exercise treatment included a clinic or community component and recommended home exercises, record information about the main exercise sessions below; record information about any home exercises in the dosage/adherence comment box below. | |||
| Dosage (minutes per session) | Record how long each session of exercise treatment delivery lasted, in minutes. Format: ‘60’ |
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| Number of sessions | Record the total number of sessions that each study participant was asked to attend in the treatment program. Format: ‘12’ |
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| Program duration (weeks) | Record the total length of time over which the treatment was delivered. Format: ‘12’ |
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| Therapist delivering exercise treatment | Record the professional designation of the therapist that delivered the treatment to this group.
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| Materials/equipment required for exercise program (if yes, what?) | Record if special materials or equipment were required for the treatment delivered to this group. It is not necessary that all participants used all required equipment. If yes, indicate what materials were required (e.g. yoga block, Pilates reformer, stability ball, hand weights) | ||
| Cost to participants (if yes, how much?) | Record if there was a cost to participants for the treatment delivered to this group. If yes, indicate the total cost of all sessions. Format: ‘$250’ |
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| % adherence/completion | Record the extent to which participants followed the study requirements. If adequate adherence is reported in the study, report this value. E.g. the mean number of main sessions attended was 8 out of 10 = 80% If adherence is not specifically reported, then this should be estimated from information about participation, based on the dominant site/approach of delivery, following best estimates:
For example, if a program includes a predominantly clinic‐based supervised program with a home component, estimate is based on “directly supervised by provider”. Format: ‘100%’ |
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| Dosage and adherence notes: | If necessary for clarification, describe the exercise treatment component(s) that the dose and duration above refer to; if there are additional components (e.g. home exercises not captured in a study‐defined adherence measure) describe this here and note any available adherence features of the group. | ||
| Participant description table | |||
| Number of participants randomised to this group | Record the number of participants originally randomised into this group. Format: ‘100’ |
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| Sex (% male) | Record the percentage of the population that is male. Format: ‘54%’ |
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| Age (mean or median) | Record the mean or median age of group participants. If age is only described for the whole study population and not by study group, then record it in Exercise Group 1 and make a note in the notes section of this table of that fact. | ||
| Group description notes | Record any unusual or unique details about the basic characteristics of this treatment group population. | ||
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Outcome Measurement Tables All follow‐up time points will be displayed in the outcome tables, regardless of what follow‐up time points are presented in the trial. Please leave cells empty (blank) in the column if the follow‐up time point is not presented. Prioritise the extraction of data in order above, as available: group follow‐up data based on intention‐to‐treat analysis (if unadjusted and adjusted are available, extract the primary reported), within group change scores, between group change scores, effect size. | |||
| Please indicate the measure of central tendency that is reported for the exercise group(s) in this study (choose mean, if available): | Record what type of statistic was used to measure the central tendency of pain outcome. Report mean, if available. | ||
| Please indicate the measure of spread that is reported for the exercise group(s) in this study (choose SD, 95% CI if available): | Record what type of statistic was used to measure the spread of pain outcome. Report standard deviation if available, or 95% confidence interval, if standard deviation is not reported. Note: Interquartile range is the same as (Q1, Q3). | ||
| What type of data are you extracting for this outcome? | Record the type of data you are extracting for the outcome. Prioritise the extraction of data in order below, as available: group follow‐up data based on intention‐to‐treat analysis (if unadjusted and adjusted are available, extract the primary reported), within group change scores, between group change scores, effect size.
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| Outcome notes | Please use this notes box, provided after each outcome table under exercise group 1, to note any important information about the outcome data reported or extracted. | ||
| All outcome tables | Extract data as reported in the publication, keeping the same number of decimal places. Do not round. Please leave cells empty (blank) in the column if the FU time point is not assessed in the trial. Only use 9999 if the follow‐up time point is assessed in the trial, but data are not presented for the outcome. | ||
| Describe minor adverse events for this group, including n (%), when available | Record the n and/or (%) of group that reported minor adverse events. If adverse events outcome data are not presented by study group, please record all results in exercise group 1. Format if both n and % reported: ’14 (80%) experienced headache, minor aches’ |
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| Describe major adverse events for this group, including n (%), when available | Record the n and/or (%) of group that reported major adverse events. If adverse events outcome data are not presented by study group, please record all results in exercise group 1. Format if both n and % reported: ’1 (3%) experienced cardiac arrest’ |
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| Comparison groups | |||
| Comparison group description | Record the treatment this group received (if any), including intensity/time/number of sessions. | ||
| Comparison type | Record the comparison type of this group.
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| Other conservative treatment type (check all that apply) | Record the ‘other conservative treatment’ type delivered to this group. See definitions above for co‐interventions. | ||
| This treatment group was a main treatment of interest of the trial | Record if this treatment was a main treatment of interest of the trial. | ||
| Number of participants randomised to this group | Record the number of participants originally randomised into this group. Format: ‘100’ |
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| Sex (% male) | Record the percentage of the population that is male. Format: ‘52%’ |
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| Age (mean or median) | Record the mean or median age of group participants. If age is only described for the whole study population and not by study group, then record it in Exercise Group 1 and make a note in the notes section of this table of that fact. | ||
| Adverse events reported for group (if minor or major, describe)? | Record % of group that reported minor or major adverse events. If adverse events outcome data are not presented by study group, please record all results in exercise group 1. Format: ‘60%’ |
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| Group description notes | Record any unusual or unique details about the basic characteristics of the comparison group population. This notes box appears only for Comparison Group 1; if more than 2 comparison groups are included in the study, please make notes about all comparison groups here. | ||
| Additional Study Notes | Record the citations for any linked publications referenced in this trial publication, additional supplements, etc. | ||
| YOGA QUESTION SET | This set of questions will appear if ‘yoga’ is selected as a dominant exercise type in any exercise group. | ||
| Characterisation of the type or style of yoga (check all that apply) | Record the named type of yoga delivered to participants. Check all that apply.
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| Components of the yoga treatment (check all that apply) | Record all components of the yoga treatment delivered to participants. Check all that apply.
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| Are specific poses listed or pictured? (check all that apply) | Record the information provided about specific poses delivered to participants. Check all that apply.
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| How was the yoga treatment designed? | Copy and paste the quote from the report regarding how the treatment was designed. | ||
| Does the report describe monitoring for treatment fidelity? | Record if the report described monitoring for treatment fidelity.
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| What does the report state about flexibility of the treatment delivery? | Record the level of flexibility of delivery of the yoga treatment delivered to participants.
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| What does the report state about the background of the teachers? | Record the level of training of the yoga teachers delivering the yoga treatment.
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Developed by the BACK Program team, Dalhousie University. Halifax, NS, Canada. Please cite as: Hayden JA, Ogilvie R, Singh S, Ellis J. Data extraction guidelines (Collaborative Review): Exercise therapy for chronic low back pain. 2022.
Contributions of authors
Lisandra Almeida was responsible for writing this protocol. Dr. Luciana Macedo, Dr. Lisa Carlesso, Dr. Anita Gross, Dr. Diego Silva, and Nora Bakaa reviewed the protocol. Dr. Jill Hayden, as the leader of the Collaborative Review team, helped with the decision‐making.
Sources of support
Internal sources
-
No sources of support provided, Other
None
External sources
-
Canadian Institutes of Health Research (CIHR), Canada
Canada CIHR provided funding for the Collaborative Review on exercise for low back project (Project Grant Competition, PJT‐173478, NPI Hayden)
Declarations of interest
LA: none LM: none AG: none LC: none SH: none NB: none DS: none JH: Canadian Institutes of Health Research (Grant / Contract)
New
References
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