Telerehabilitation for hip or knee osteoarthritis

Objectives

This is a protocol for a Cochrane Review (intervention). The objectives are as follows:

To assess the benefits and harms of telerehabilitation for patients with OA of the knee or hip.

Background

Osteoarthritis (OA) is a leading cause of mobility disability and impaired quality of life among older adults, with an estimated prevalence of around 250 million people affected worldwide (Cross 2014). Current estimates indicate that 14% to 44% of the general population will suffer from knee OA in their lifetime (Losina 2013; Murphy 2008), while one in four adults living until the age of 85 years will develop symptoms of hip OA (Murphy 2010). The prevalence of OA has been projected to double over the next two decades largely related to the overall ageing of the population and increasing rates of obesity and sedentary lifestyle (Hunter 2014).

The clinical hallmark of OA is joint pain which is initially movement‐related and intermittent but, as the disease progresses, a more constant and severe pattern may occur with progressive limitations in joint function. Current OA guidelines recommend a sequence of physical therapy modalities and weight loss, physical activity, and medications for pain relief, reserving surgical joint replacement as the last resort for the management of OA symptoms (Hochberg 2012; McAlindon 2014). Among those treatments, exercise has been shown to have small to moderate effects in improving pain and function in persons with knee and hip OA (Fransen 2014; Fransen 2015; Hurley 2018), which is similar to that provided by most oral non‐steroidal anti‐inflammatory drugs (NSAIDs) but without their associated gastrointestinal and cardiovascular risks (Costa 2017). Exercise therapy has been proven to be generally safe for OA patients across all disease stages and additionally to improve mood, balance and general well‐being (Bennell 2011). Uptake of the evidence‐based recommendations for exercise in clinical practice has, however, been suboptimal (Egerton 2018).

Several factors may impair the translation of evidence into practice including time constraints (both for health providers to instruct patients in exercising and for patients to attend multiple exercise sessions), poor patient compliance due to lack of motivation for lifestyle changes, and other health system and societal factors such as residence in underprivileged areas with poor health care, low socioeconomic status and lack of access to physical therapy and community‐level programmes (Allen 2018a; Hunter 2010). Interventions such as telerehabilitation, that are potentially low cost, widely accessible and time efficient, would enable greater access to healthcare support (Slater 2016). Consumers and stakeholders already support such initiatives, showing mostly positive perceptions when receiving care at distance and not viewing such interventions as a substitute for traditional care but as an additional option to increase accessibility (Cronström 2019; Hinman 2016; Hinman 2017; Lawford 2018).

Description of the condition

Osteoarthritis is the most prevalent type of chronic joint disease and involves changes in the whole joint including the subchondral bone, hyaline cartilage, synovium, menisci, peri‐articular muscles and other soft‐tissue structures including ligaments and tendons (Loeser 2012). The pathogenesis of OA is complex, involving mechanical, metabolic and inflammatory mechanisms (Liu‐Bryan 2015). Additional factors including psychosocial issues and neurological pain processing may also play a role and influence the pain experience. Osteoarthritis can affect any synovial joint but most commonly affects the knees, hips, hands and spine. Knee and hip OA contribute significantly to the overall burden of OA to individual patients and healthcare systems (Cross 2014).

A range of risk factors for the development and progression of knee and hip OA has been identified and can be classified into systemic risk factors, including older age, female gender and genetics; and local risk factors, including obesity, joint injury, joint shape abnormalities and peri‐articular muscle weakness (Zhang 2010). Targeting modifiable local risk factors is of utmost importance for the management of this condition in order to improve symptoms and quality of life as well as prevent functional decline and progression to joint replacement (Bennell 2012).

Description of the intervention

Telerehabilitation can be broadly defined as the delivery of rehabilitation services over telecommunication technologies such as websites, smartphone apps, videoconferencing systems and telephone, and can be considered as a subfield of telehealth (Russell 2007). It could provide a platform to deliver services offered by a number of health disciplines including physiotherapy, occupational therapy, dietetics, psychology and others. It may involve the full spectrum of client care including the client interview, physical assessment and diagnosis, treatment, maintenance activities, consultation, education and training. Telerehabilitation services have been developed as a way of increasing accessibility to healthcare, especially for rural populations, those with disability, or people living in highly‐populated cities where healthcare systems can be overcrowded. It overcomes some of the potential barriers to healthcare access such as travel (distance, traffic, transport), time consumed, high demand for the public health system (long waiting lists), lack of insurance cover for private care, and high costs for long‐term treatment (Kairy 2009; Lee 2018). Well‐designed telerehabilitation programmes have the potential to improve healthcare access and outcomes, particularly for chronic diseases, such as knee and hip OA.

How the intervention might work

The treatment of hip and knee osteoarthritis is based on a multidisciplinary and multifactorial approach with emphasis on non‐pharmacological treatments and active strategies (NICE 2014; RACGP 2018; Teo 2019). Clinical practice guidelines recommend exercise therapy, pain education, behavioural therapy, physical activity and healthy lifestyle, and encouragement of self‐management of the condition (AAOS 2013; AAOS 2017; NICE 2014; RACGP 2018). Exercise therapy aims to improve joint mobility, increase muscle strength and improve functionality for patients with osteoarthritis (AAOS 2013; AAOS 2017). Strategies of exercise and physical activity help to reduce joint stiffness due to the process of wear and tear that occurs in osteoarthritis. In addition, physical exercise may promote important hormonal benefits in regulating the mechanisms responsible for pain sensation (Gomolka 2019; Stagg 2011). Pain education and behaviour therapy helps to improve specific aspects related to the somatosensory system and assists in modifying behaviours that may amplify the pain sensation of individuals, besides teaching how to live and accept the pain condition without suffering from functional disability (British Pain Society 2013; Courtney 2017; Wijma 2016).

Previous randomised controlled trials in knee OA found that telerehabilitation strategies based on exercise and education were as effective as face‐to‐face physical therapy for improving pain, function and quality of life (Allen 2018b; Azma 2018). Two recent randomised controlled trials testing Internet‐based pain‐coping skills training and home exercise compared to education for people with knee and hip OA showed clinically meaningful improvements for pain and function for people with knee pain, but showed no significant differences for people with hip OA (Bennell 2017; Bennell 2018). Telerehabilitation services for knee and hip OA have also been reported as a feasible and acceptable mode of treatment by patients. Patients are generally satisfied with the services and usually understand telerehabilitation as a new option to increase accessibility and complement traditional rehabilitation services (Hinman 2017; Lawford 2018; Tousignant 2011).

Why it is important to do this review

There has been an increase in the number of trials investigating the effectiveness of telerehabilitation programmes (including virtual rehabilitation and Internet‐based interventions) for patients with chronic pain, especially knee and hip OA; however, trials present mixed results and clinical practice guidelines have not adopted a position on this type of intervention yet. There have not been any reviews published so far in this topic. Thus a well‐conducted Cochrane Review with meta‐analysis would be important to better inform clinicians, patients and policy makers about the effectiveness of these programmes for hip or knee OA. We will conduct this review according to the guidelines recommended by the Cochrane Musculoskeletal Group Editorial Board (Ghogomu 2014).

Objectives

To assess the benefits and harms of telerehabilitation for patients with OA of the knee or hip.

Methods

Criteria for considering studies for this review

Types of studies

We will include randomised controlled trials (RCTs), cross‐over RCTs and cluster RCTs. We will include studies reported as full text, those published as abstract only, and unpublished data. There will be no date or language restriction.

Types of participants

We will include adults (≥ 18 years) with a diagnosis of knee or hip OA according to the American College of Rheumatology (Altman 1986); European League Against Rheumatism; NICE recommendations for diagnosis of osteoarthritis or other diagnostic criteria (NICE 2014); or clinically or radiologically confirmed. We will include people of any level of health care (primary, secondary, or tertiary). We will exclude trials evaluating patients in the postoperative period and mixed populations of participants with rheumatoid arthritis and osteoarthritis, unless separate data were provided for osteoarthritis.

Types of interventions

We will consider telerehabilitation as the delivery of rehabilitation services over telecommunication networks or Internet, including website, smartphone apps, videoconferencing systems, computer software, phone calls and health coaching. We will consider all types of interventions delivered through telerehabilitation (e.g. education, behaviour therapy and exercise). We will categorise telerehabilitation according to the most predominant component of the intervention into three categories: (i) psychological interventions (behavioural and psychotherapeutic treatments designed to reduce psychological distress and maladaptive behaviour, e.g. CBT, counselling), or education; (ii) exercise and physical activity (this includes general or specific exercises and strategies to increase physical activity levels); (iii) others, including multicomponent interventions (e.g. psychological interventions and exercise as both key interventions).

We will include trials comparing telerehabilitation with no treatment, waiting list, usual care, advice, or a similar face‐to‐face intervention. We will only consider mixed interventions (combination of face‐to‐face and telerehabilitation) if the remote component was at least 75% of the total intervention. The main comparison that we report in the 'Summary of findings' table(s) will be telerehabilitation (remotely delivered) versus usual care.

Types of outcome measures

Major outcomes

The major outcomes will be pain intensity, physical function, radiographic joint structure changes, quality of life, serious adverse events, withdrawals due to adverse events and patient‐reported global perceived effect. We will follow the hierarchy of pain and physical function outcomes of the Cochrane Musculoskeletal Group, as follows.

• Pain intensity (when more than one is reported, we will give preference to the highest on the list)

  • Pain overall

  • Pain on walking

  • Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale

  • Pain on activities other than walking

  • WOMAC global scale

  • Lequesne osteoarthritis index

  • Global score

  • Other algofunctional scale

  • Patient’s global assessment

  • Physician’s global assessment

  • Other outcome

  • No continuous outcome reported

• Physical function (when more than one is reported, we will give preference to the highest on the list)

  • Global disability score

  • Walking disability

  • WOMAC disability subscore

  • Composite disability scores other than WOMAC

  • Disability other than walking

  • WOMAC global scale

  • Lequesne osteoarthritis index global score

  • Other algofunctional scale

• Radiographic joint structure changes according to the given hierarchy

  • Minimum joint‐space width

  • Median joint‐space width

  • Semi‐quantitative measurement

• Health‐related quality of life (in the following order of preference: SF‐12, EQ‐5D, physical and mental health domains of SF‐36, other algofunctional scale)

• Short‐term serious adverse events from trials (e.g., cardiovascular events, )

• Withdrawals due to adverse events (any adverse events reported, such as fatigue and worsening of pain)

• Patient‐reported global perceived effect (in the following order of preference: global perceived effect scales, patient global impression of change, other algofunctional scale)

Minor outcomes

• Anxiety (in the following order of preference: Hospital Anxiety and Depression Scale; the Spielberger State‐Trait Anxiety Inventory; other algofunctional scale)

• Depression (in the following order of preference: Hospital Anxiety and Depression Scale; Centre for Epidemiological Studies Depression Scale; Beck Depression Inventory; other algofunctional scale)

• Self‐efficacy (in the following order of preference: Arthritis Self‐Efficacy Scale; Pain Self‐Efficacy Questionnaire; Chronic Pain Self‐Efficacy Scale; other algofunctional scale)

• Fear avoidance (in the following order of preference: Knee Osteoarthritis Fears and Beliefs Questionnaire; Fear‐Avoidance Belief Questionnaire; other algofunctional scale)

• Pain catastrophisation (in the following order of preference: Pain Catastrophising Scale; other algofunctional scale)

• Intervention adherence (expressed in percentages or number of participants)

We will consider three time points: short term (≤ 3 months after randomisation), intermediate (> 3 months to < 12 months after randomisation), and long term (≥ 12 months after randomisation). If there are multiple time points classified within the same category, we will use the one that will be closest to the end of the treatment for short term, six months for intermediate term, and collect all available long‐term follow‐ups.

Search methods for identification of studies

Electronic searches

We will search the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (via OvidSP), Embase (via OvidSP), CINAHL (via EBSCO) and PsycINFO (via OvidSP). We will also conduct a search for ongoing trials on ClinicalTrials.gov and the World Health Organization (WHO) trials portal (www.who.int/ictrp/en). We will search all databases from their inception to the present, and we will impose no restriction on language of publication.

See Appendix 1 for the MEDLINE search strategy.

Searching other resources

We will check reference lists of all primary studies and review articles for additional references. We will search for errata or retractions from included studies published in full text on PubMed and report the date this was done.

Data collection and analysis

Selection of studies

Two review authors (BTS and IF) will independently screen titles and abstracts for inclusion of all records identified as a result of the search. We will retrieve the full text of potentially‐relevant reports and two review authors (BTS and IF) will independently screen the full text and identify studies for inclusion, and identify and record reasons for exclusion of the ineligible studies. We will resolve any disagreement through discussion or, if required, we will consult a third person (SK or LOPC). We will identify and exclude duplicates and collate multiple reports of the same study so that each study, rather than each report, is the unit of interest in the review. We will record the selection process in sufficient detail to complete a PRISMA flow diagram and 'Characteristics of excluded studies' table (PRISMA Group 2009).

Data extraction and management

We will use a data collection form for study characteristics and outcome data, which we will pilot on at least one study in the review. One review author (IF) will extract study characteristics from included studies. A second review author (BTS) will spot‐check study characteristics for accuracy against the trial report. We will extract the following study characteristics.

• Bibliometric data: authors, year of publication, study language

• Methods: study design, duration of study, settings, withdrawals, and date of study

• Participants: N, mean age, age range, sex, socioeconomic status, disease duration, severity of condition, diagnostic criteria, inclusion and exclusion criteria

• Interventions: intervention (including the type of technology used e.g., telephone, app, website), comparison, concomitant medications, and co‐interventions. We will collect the reporting of interventions according to the Template for Intervention Description and Replication (TIDieR) checklist (Hoffmann 2014; Yamato 2016) (Appendix 2)

• Outcomes: major and minor outcomes specified and collected, and time points reported

• Characteristics of the design of the trial as outlined below in the 'Assessment of risk of bias in included studies' section

• Notes: funding for trial, and notable declarations of interest of trial authors

• If the trial was prospectively registered or not

Two review authors (BTS and IF) will independently extract outcome data from included studies. 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 use the PlotDigitizer software (PlotDigitizer [Computer program]) to extract data from graphs or figures (we will extract these data in duplicate). We will resolve disagreements by consensus or by involving a third person (SK or LOPC). One review author (IF) will transfer data into the Review Manager 5 file (Review Manager 2014). We will double‐check that data are entered correctly by comparing the data presented in the systematic review with the study reports.

We will select data to extract based on the following decision rules.

• We will give preference to change scores if both change and endpoint values are available

• We will give preference to intention‐to‐treat (ITT) analysis data rather than 'per protocol' or 'as treated', if available

• If multiple time points are reported, we will use the one closest to three months for short term, six months for intermediate term, and collect all available long‐term follow‐ups

Main planned comparisons

Our primary comparison will be telerehabilitation versus usual care.

Our other main comparisons are grouped as follows.

• Telerehabilitation versus minimal interventions (i.e. waiting list, no treatment, advice)

• Telerehabilitation versus similar face‐to‐face treatments

Assessment of risk of bias in included studies

Two review authors (BTS and IF) will independently assess risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2019a). We will resolve any disagreements by discussion or by involving another author (SK or LOPC). We will assess the risk of bias according to the following domains.

• Random sequence generation (selection bias)

• Allocation concealment (selection bias)

• Blinding of participants and personnel (performance bias)

• Blinding of outcome assessment (detection bias)

• Incomplete outcome data (attrition bias)

• Selective outcome reporting (reporting bias)

• Other bias: we will assess other risk of bias such as whether trials were stopped early, differences between groups at baseline, differences between groups in timing of outcome assessment, and co‐intervention differences across groups

We will grade each potential source of bias as high, low or unclear risk, and provide a quote from the study report together with a justification for our judgment in the 'Risk of bias' table. We will summarise the 'Risk of bias' judgements across different studies for each of the domains listed. Where information on risk of bias relates to unpublished data or correspondence with a trialist, we will note this in the 'Risk of bias' table. When considering treatment effects, we will take into account the risk of bias for the studies that contribute to that outcome. We will present the figures generated by the 'Risk of bias' tool to provide summary assessments of the 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 systematic review.

Measures of treatment effect

We will analyse dichotomous data as risk ratios or Peto odds ratios when the outcome is a rare event (approximately less than 10%), and use 95% confidence intervals (CIs). Continuous data will be analysed as mean difference (MD) or standardised mean difference (SMD), depending on whether the same scale is used to measure an outcome, and 95% CIs. We will enter data presented as a scale with a consistent direction of effect across studies.
When different scales are used to measure the same conceptual outcome (e.g. disability), SMDs will be calculated instead, with corresponding 95% CIs. SMDs will be back‐translated to a typical scale (e.g. 0 to 10 for pain) by multiplying the SMD by a typical among‐person standard deviation (e.g. the standard deviation of the control group at baseline from the most representative trial) (Schünemann 2019b).

In the 'Effects of interventions' results section and the Comments column of the 'Summary of findings' table, we will report the absolute percentage difference, the relative percentage change from baseline, and for outcomes that show a clinically important difference between treatment groups, we will report the number needed to treat for an additional beneficial outcome (NNTB), or number needed to treat for an additional harmful outcome (NNTH).

For dichotomous outcomes we will calculate the NNTB or NNTH from the control group event rate and the relative risk using the Visual Rx NNT calculator (Cates 2008). We will calculate the NNTB for continuous measures using the Wells calculator (available at the CMSG Editorial office, musculoskeletal.cochrane.org). We will calculate NNTB or NNTH for outcomes showing a clinically significant benefit/harm. For input into the calculator we will assume a minimal clinically important difference (MCID) of 1.5 points on a 10‐point scale for pain, and 10 points on a 100‐point scale for function or disability.

For dichotomous outcomes, we will calculate the absolute change from the difference in the risks between the intervention and control group using GRADEpro and expressed as a percentage (GRADEpro GDT 2015). We will calculate the relative change as the risk ratio minus 1 and expressed as a percentage.

For continuous outcomes, we will calculate the absolute change by dividing the mean difference by the scale of the measure and expressing it as a percentage. We will calculate the relative difference as the absolute benefit (mean difference) divided by the baseline mean of the control group, and expressed as a percentage.

Unit of analysis issues

The unit of analysis will be the participant for all trials. Where multiple trial arms are reported in a single trial, we will include only the relevant arms. If two comparisons are combined in the same meta‐analysis, we will halve the control group to avoid double‐counting (e.g. two groups of different telerehabilitation interventions and one waiting‐list control).

If we identify cross‐over trials, we will extract data from the first phase of the trial to avoid potential carry‐over effects. If we identify studies that included more than one joint in the analysis, or cluster‐randomised trials, we will multiply the standard error of the effect estimate (from an analysis ignoring clustering) by the square root of the design effect (inflated variances), according to the method described in Chapter 23 of the Cochrane Handbook (Higgins 2019b). The meta‐analysis using the inflated variances will be performed using the generic inverse‐variance method.

Dealing with missing data

We will contact investigators or study sponsors in order to verify key study characteristics and obtain missing numerical outcome data where possible (e.g. when a study is identified as abstract only or when data are not available for all participants). When this is not possible, and we think the missing data introduces serious bias, we will explore the impact of including such studies in the overall assessment of results by a sensitivity analysis. We will clearly describe any assumptions and imputations to handle missing data and explore the effect of imputation by sensitivity analyses.

For dichotomous outcomes (e.g. number of withdrawals due to adverse events), we will calculate the withdrawal rate using the number of patients randomised in the group as the denominator. For continuous outcomes (e.g. mean change in pain score), we will calculate the MD or SMD based on the number of patients analysed at that time point. If the number of patients analysed is not presented for each time point, we will use the number of randomised patients in each group at baseline.

Where possible, we will compute missing standard deviations from other statistics such as standard errors, confidence intervals (CIs) or P values, according to the methods recommended in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2019a). If standard deviations cannot be calculated, we will impute them (e.g. from other studies in the meta‐analysis).

Assessment of heterogeneity

We will assess clinical and methodological diversity in terms of participants, interventions, outcomes and study characteristics for the included studies to determine whether a meta‐analysis is appropriate. We will do this by examining these data in the data extraction tables. We will assess statistical heterogeneity by visual inspection of the forest plot to assess for obvious differences in results between the studies, and using the I² and Chi² statistical tests.

As recommended in the Cochrane Handbookfor Systematic Reviews of Interventions (Deeks 2019), the interpretation of an I² value of 0% to 40% "might not be important"; 30% to 60% may represent "moderate heterogeneity"; 50% to 90% may represent "substantial heterogeneity"; and 75% to 100% represents "considerable heterogeneity". As noted in the Cochrane Handbookfor Systematic Reviews of Interventions, we will keep in mind that the importance of I² depends on: (i) magnitude and direction of effects and (ii) strength of evidence for heterogeneity. We will interpret the Chi² test so that a P value of less than or equal to 0.10 indicates evidence of statistical heterogeneity.

Assessment of reporting biases

We will create and examine a funnel plot to explore possible small‐study biases. In interpreting funnel plots, we will examine the different possible reasons for funnel plot asymmetry as outlined in section 10.4 of the Cochrane Handbookfor Systematic Reviews of Interventions and relate this to the results of the review. If we are able to pool more than 10 trials, we will undertake formal statistical tests to investigate funnel plot asymmetry, and will follow the recommendations in section 10.4 of the Cochrane Handbookfor Systematic Reviews of Interventions (Sterne 2017).

To assess outcome reporting bias, we will check trial protocols against published reports. For studies published after 1 July 2005, we will screen the Clinical Trial Register at the International Clinical Trials Registry Platform of the World Health Organisation (www.who.int/ictrp/en) for the a priori trial protocol. We will evaluate whether selective reporting of outcomes is present.

Data synthesis

We will use a random‐effects model for all meta‐analyses only where meta‐analysis is meaningful—that is if the treatments, participants and the underlying clinical question are similar enough for pooling to make sense. We will use alternative synthesis methods, such as summary of effect estimates (e.g., median, interquartile range with box‐and‐whisker plots) or the combination of P values in the circumstance where there is no, or minimal, information reported on the direction of effect (McKenzie 2019).

GRADE and 'Summary of findings' tables

We will create a 'Summary of findings' (SoF) table using the following outcomes: pain intensity, physical function, radiographic joint structure changes, quality of life, serious adverse events, withdrawals due to adverse events and patient‐reported global perceived effect. The main comparison in the SoF table will be telerehabilitation versus usual care at post‐treatment follow‐up. We will also create SoF tables for the other comparisons at post‐treatment follow‐up.

Two reviewers (BTS and IF) will independently assess the certainty of the evidence. We will use the five GRADE considerations (study limitations; inconsistency; imprecision; indirectness; and publication bias) to assess the certainty of a body of evidence as it relates to the studies which contribute 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 software to prepare the SoF tables (GRADEpro GDT 2015). We will justify all decisions to downgrade RCTs and the quality of studies using footnotes and we will make comments to aid the reader's understanding of the review where necessary.

We will use the following criteria to downgrade the certainty of evidence based on the five GRADE considerations.

1. Study design and risk of bias

We will consider comparisons at high risk of bias when more than 25% of participants in the comparison are from studies at high overall risk of bias (i.e. one or more bias domains judged as high risk) and we will downgrade one level for the quality of evidence. We will downgrade by two levels if 50% of participants in the comparison are from studies at high overall risk of bias.

2. Inconsistency

We will evaluate each direct comparison for consistency in the direction and magnitude of the effect sizes from individual trials, considering the width of the prediction interval and magnitude of the heterogeneity parameter. We will downgrade comparisons one level if we identify important and non‐explained heterogeneity through visual inspection or considerable heterogeneity in the I² test (> 50%). Evidence of serious inconsistency (heterogeneity in the I² test > 75%) will be downgraded by two levels.

3. Indirectness

We will downgrade by one level if more than 50% of the participants are assessed as being outside the target group. We will not downgrade this domain by two levels.

4. Imprecision

In cases where studies include relatively few patients and few events, and thus have wide confidence intervals around the estimate of the effect, the results are imprecise.

1. Dichotomous outcomes: A) When there is only one study or when there is more than one study, but the total number of events is < 300, we will downgrade the evidence by one level on this criterion. B) When the 95% confidence interval around the pooled or best estimate of effect includes both (i) no effect and (ii) appreciable benefit or appreciable harm, we will downgrade the evidence by one level. We will downgrade the evidence by two levels when there is imprecision due to both A) and B).

2. Continuous outcomes: A) When there is only one study or when there is more than one study but the total sample size is < 400, we will downgrade the evidence by one level. B) When the 95% confidence interval around the pooled or best estimate of effect includes no effect and the confidence interval crosses an effect size of SMD = 0.5 or MD > 10% of the scale in either direction, we will downgrade the evidence by one level. We will downgrade the evidence by two levels when there is imprecision due to both A) and B).

5. Publication bias

To assess publication bias, we plan to generate funnel plots if at least 10 trials examining the same intervention comparison are included in the review. If funnel plots shows asymmetry, we will downgrade the level of evidence by one level. We will not downgrade this domain by two levels.

We will use methods and recommendations described in section 8.5 and 8.7, and chapters 11 and 12, of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2019c; Schünemann 2019a; Schünemann 2019b). When assessing the overall quality, we may downgrade the evidence by one level for each factor, up to a maximum of three levels for all factors. If there are very severe problems for any one factor, we may downgrade the evidence by two levels due to that factor alone (Higgins 2019c).

Subgroup analysis and investigation of heterogeneity

We will conduct subgroup analyses to assess if pain and physical function differ between people with hip or knee osteoarthritis. We will also perform a subgroup on telerehabilitation modality (phone, website, app, video conference). We will use the formal test for subgroup interactions in Review Manager (Review Manager 2014).

Sensitivity analysis

We plan to carry out the following sensitivity analyses for the main comparison to investigate the robustness of the treatment effect of pain intensity and physical function for all time points.

1. Studies we judge as low risk of bias for selection bias

2. Studies we judge as low risk of bias for detection bias

We will follow the same decision of data synthesis and unit of analysis issues as the main analyses.

Interpreting results and reaching conclusions

We will follow the guidelines in Chapter 12 of the Cochrane Handbook for Systematic Reviews of Interventions for interpreting results (Schünemann 2019b), and will be aware of distinguishing a lack of evidence of effect from a lack of effect. We will base our conclusions only on findings from the quantitative or narrative synthesis of included studies for this review. We will avoid making recommendations for practice, and our implications for research will suggest priorities for future research and outline what the remaining uncertainties are in the area.

History

Protocol first published: Issue 6, 2020

Notes

This protocol is based on a template developed by the Cochrane Musculoskeletal Group editorial base.

Appendices

Appendix 1. MEDLINE Search strategies

1. randomized controlled trial.pt. 2. controlled clinical trial.pt. 3. randomized.ab. 4. placebo.ab. 5. clinical trials as topic.sh. 6. randomly.ab. 7. trial.ti. 8. 1 or 2 or 3 or 4 or 5 or 6 or 7 9. exp animals/ not humans.sh. 10. 8 not 9 11. exp Osteoarthritis/ 12. Osteoarthr$.tw. 13. (degenerative adj2 arthritis).tw. 14. (degeneration adj3 (hip$ or knee$ or menisc$)).mp 15. Arthrosis.tw. 16. 11 or 12 or 13 or 14 or 15 17. telemedicine.mp. or exp Telemedicine/ 18. telehealth.mp. 19. telerehabilitation.mp. 20. telecare.mp. 21. distance rehabilitation.mp. 22. electronic health.mp. 23. ehealth.mp. 24. home‐based.mp. 25. self‐management.mp 26. mobile health.mp. 27. mhealth.mp. 28. mobile medicine.mp. 29. phone.mp. 30. telephone.mp. or exp Telephone/ 31. smartphone.mp. 32. remotely delivered.mp. 33. mobile.mp.) 34. apps.mp. 35. application program*.mp. 36. application software*.mp. 37. mobile application*.mp. 38. SMS.mp. 39. text messaging.mp. or exp Text Messaging/ 40. texting.mp. 41. exp Internet/ or internet.mp. 42. online.mp. 43. internet‐based.mp. 44. web‐based.mp. 45. video conferenc*.mp. 46. tablet device.mp. 47. iPad.mp. 48. iPhone.mp. 49. distance.mp. 50. Wechat.mp 51. 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 or 27 or 28 or 29 or 30 or 31 or 32 or 33 or 34 or 35 or 36 or 37 or 38 or 39 or 40 or 41 or 42 or 43 or 44 or 45 or 46 or 47 or 48 or 49 or 50 52. 10 and 16 and 51

Appendix 2. Appendix 2

Item number	Item	Where located
		Primary paper (page or appendix number)	Other (details)
	BRIEF NAME
1.	Provide the name or a phrase that describes the intervention.
	WHY
2.	Describe any rationale, theory, or goal of the elements essential to the intervention.
	WHAT
3.	Materials: Describe any physical or informational materials used in the intervention, including those provided to participants or used in intervention delivery or in training of intervention providers. Provide information on where the materials can be accessed (e.g. online appendix, URL).
4.	Procedures: Describe each of the procedures, activities, and/or processes used in the intervention, including any enabling or support activities.
	WHO PROVIDED
5.	For each category of intervention provider (e.g. psychologist, nursing assistant), describe their expertise, background and any specific training given.
	HOW
6.	Describe the modes of delivery (e.g. face‐to‐face or by some other mechanism, such as internet or telephone) of the intervention and whether it was provided individually or in a group.
	WHERE
7.	Describe the type(s) of location(s) where the intervention occurred, including any necessary infrastructure or relevant features.
	WHEN and HOW MUCH
8.	Describe the number of times the intervention was delivered and over what period of time including the number of sessions, their schedule, and their duration, intensity or dose.
	TAILORING
9.	If the intervention was planned to be personalised, titrated or adapted, then describe what, why, when, and how.
	MODIFICATIONS
10.	If the intervention was modified during the course of the study, describe the changes (what, why, when, and how).
	HOW WELL
11.	Planned: If intervention adherence or fidelity was assessed, describe how and by whom, and if any strategies were used to maintain or improve fidelity, describe them.
12.	Actual: If intervention adherence or fidelity was assessed, describe the extent to which the intervention was delivered as planned.

Contributions of authors

Conception and design: Bruno Saragiotto, Tie Yamato, Chris Maher, Leonardo Costa, Christopher Williams, Steven Kamper, Bethan Richards and Blake Dear.

Drafting of the protocol: Bruno Saragiotto, Leticia Deveza, Iuri Fioratti and Leonardo Costa.

Critical revision of the protocol for important intellectual content: all authors.

Final approval of the protocol: all authors.

Sources of support

Internal sources

• none, Other

External sources

• Sao Paulo Research Foundation (FAPESP), Brazil

  BTS has received a research grant from the Sao Paulo Research Foundation (FAPESP, grant number 2016/24217‐7) to conduct a randomised controlled trial on telerehabilitation for chronic pain.

• National Health and Medical Research Council (NHMRC), Australia

  CGM holds a research fellowship from the National Health and Medical Research Council (NHMRC), Australia

• Sao Paulo Research Foundation (FAPESP), Brazil

  IF holds a PhD scholarship from the Sao Paulo Research Foundation (FAPESP), Brazil (process number 2018/15889‐7).

Declarations of interest

Bruno Saragiotto has received a research grant from the Sao Paulo Research Foundation (FAPESP‐Brazil, grant number 2016/24217‐7) to conduct a randomised controlled trial on telerehabilitation for chronic pain.

Iuri Fioratti: none known.

Leticia Deveza declares partial reimbursement of conference registration cost by Pfizer and receiving royalties from UptoDate, outside the submitted work.

Tie Yamato holds a research fellowship from the Sao Paulo Research Foundation (FAPESP‐Brazil, process number 2017/17484‐1).

Bethan Richards: none known.

Chris Maher holds a research fellowship from the National Health and Medical Research Council (NHMRC), Australia. GlaxoSmithKline, Pfizer, Australian Research Council, National Health and Medical Research Council, NSW Motor Accidents Authority, QLD Motor Accidents Insurance Commission, NSW WorkCover, SA WorkCover, Transport Accident Commission, Arthritis Australia, Physiotherapy Physio‐ therapy Research Foundation and a range of national physiotherapy associations ‐ GSK have provided supplementary funding for an investigator initiated NHMRC‐funded trial evaluating paracetamol for acute back pain. Pfizer provided study medicines for the PRE‐ CISE trial. The Australian Research Council, National Health and Medical Research Council, NSW Motor Accidents Authority, QLD Motor Accidents Insurance Commission, NSW WorkCover, SA WorkCover, Transport Accident Commission, Arthritis Australia, Physiotherapy Research Foundation and a range of national physiotherapy associations have provided research grants and fellowships to support his research. As an invited speaker at conferences he has had his expenses covered and also received small gifts such as a box of chocolates or a bottle of wine. He has received honoraria for marking theses, reviewing grants and preparing talks.

Blake Dear has no conflicts for the prepared work. His institution has received numerous competitive grants for his work; no grants were received for this work. BFD is part of a group MindSpot Clinic funded by the Australian Federal government to develop and provide an online mental health service, which is free to all Australians; BFD receives no royalties for this or any of his other work.

Christopher Williams: none known.

Leonardo Costa has received board membership and competitive grants from his work from FAPESP‐Brazil and CNPq‐Brazil over the last five years, none related to the topic of this review. He is fully employed as an associate professor at Universidade Cidade de Sao Paulo since 2010.

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