Abstract
Objectives
The Scleroderma Patient-centered Intervention Network (SPIN) Cohort uses the cohort multiple randomised controlled trial design to embed trials of online self-care interventions for people living with systemic sclerosis (SSc; scleroderma). To offer interventions to patients interested in using them, participants complete signalling items that query about the likelihood that patients would agree to participate in nine different hypothetical online programmes addressing common SSc-related problems. It is not known what factors influence patient-reported interest in participating in a particular online intervention and if intervention-specific signalling questions provide unique information or replicate broader characteristics, such as overall willingness to participate or self-efficacy. This study assessed factors that explain responses to intervention-specific signalling items.
Design
Cross-sectional survey.
Setting
SPIN Cohort participants enrolled at 42 centres from Canada, the USA, the UK, France, Spain and Mexico who completed study questionnaires from March 2014 to January 2018 were included.
Measures
Demographic and disease characteristics, self-efficacy and symptoms related to each specific intervention were completed in addition to signalling items. General likelihood of using interventions was calculating by taking the mean score of the remaining signalling questions.
Participants
1060 participants with complete baseline data were included in the analyses.
Results
For all individual signalling questions, controlling for other variables, the mean of the remaining signalling questions was the strongest predictor (standardised regression coefficient β from 0.61 (sleep) to 0.80 (self-management)). Smaller, but statistically significant, associations were found with the symptom associated with the respective signalling question and with general self-efficacy for 7 of 9 signalling questions.
Conclusions
The main factor associated with patients’ interest in participating in a disease-specific online self-care intervention is their general interest in participating in online interventions. Factors that may influence this general interest should be explored and taken into consideration when inviting patients to try online interventions.
Keywords: clinical trials, cmRCT, cohort multiple RCT, systemic sclerosis, scleroderma
Strengths and limitations of this study.
This is the first study to evaluate factors associated with patients indicating likelihood of using specific online interventions as part of signalling questions sometimes used in the cohort multiple randomised controlled trial design.
A large, international sample of patients with systemic sclerosis was analysed.
Factors examined included sociodemographic variables, general likelihood of using online interventions and symptoms or problems that would be addressed by the specific intervention.
The Scleroderma Patient-centered Intervention Network (SPIN) Cohort constitutes a convenience sample of patients with SSc receiving treatment at a SPIN recruiting centre, and patients at these centres may differ from those in other settings.
Patients with SSc in the SPIN Cohort complete questionnaires online, which may limit the generalisability of findings.
Introduction
Well-designed and conducted randomised controlled trials (RCTs) provide the best mechanism for evaluating the benefits and harms of healthcare interventions.1 2 Large-scale RCTs, however, are complex and expensive to conduct. Concerns have been raised that many RCTs have difficulty recruiting and enrolling patients, consent procedures do not reflect how patients make decisions in real clinical practice, long-term outcomes are often not available, many trials have limited real-world generalisability and the infrastructure needed for individual trials is prohibitively expensive.3–11
In response to these concerns, new approaches to RCTs have been proposed, including trial designs that use routinely collected health data or create data sources to facilitate patient recruitment and outcome assessment.4 12–16 One example is the cohort multiple RCT (cmRCT) design.4 In the cmRCT design, researchers set up an ongoing observational cohort that is designed from inception to serve as a framework for conducting trials. Participants who enrol in the cohort complete outcome measurements at regular intervals. When a trial is conducted using the cohort, a random selection of patients eligible for the trial is contacted and offered access to the intervention being tested. Patients who are eligible but not selected are not notified that the trial is occurring and therefore receive usual care. Outcomes for the two groups are compared post-trial using the cohort’s routine data collection procedures. In most examples of cmRCTs, prior to enrolment in the cohort, patients are informed and consent to the possibility that they may be participants in trials but would not be notified about the trial if they are assigned to usual care.17–22
Participants sometimes enrol in trials in order to receive a new intervention that would not be available to them as part of their usual care. In conventional trial designs in which participants consent to randomisation to a specific intervention or usual care, this may lead to withdrawal from the trial or disappointment bias reflected in patient-reported outcomes.1 4 In order to reduce this possibility, in the cmRCT design, cohort participants are not notified about specific trials being planned or conducted, except when they are offered access to an intervention as part of a trial. A potential problem with this approach is that a substantial number of patients offered an intervention that is undergoing testing may not accept it, since they did not enrol in the cohort with any expectation that it would be offered to them. This would dilute intervention effects estimated on an intention-to-treat basis, potentially substantially if the rate of accepted offers is low, as the intervention arm then includes a large proportion of patients receiving care as usual.23 A possible solution that has been suggested to reduce non-acceptance of intervention offers is to present cohort patients with a list of possible interventions as part of regular cohort data collection and ask if they would agree to use them if offered.4 Using this as a criteria for eligibility to participate in the trial is thought to increase the likelihood of accepting an intervention offer without disclosing the actual intervention that will be offered.
Systemic sclerosis (SSc), also known as scleroderma, is a rare autoimmune connective tissue disease characterised by vascular injury, immune dysfunction and an abnormal fibrotic process that can affect multiple organ systems including the skin, lungs, gastrointestinal tract and cardiovascular system.24 25 SSc is notable for the range of problems faced by people living with the disease, including limitations in physical mobility and hand function, pain, fatigue, sleep disturbance, depression, sexual dysfunction and body image distress from disfiguring changes in appearance.17 26–28 The Scleroderma Patient-centered Intervention Network (SPIN) was formed to develop, test and disseminate interventions to improve the health and quality of life of patients with SSc and to serve as a model for doing this in other rare diseases. To do this, SPIN uses the cmRCT design and maintains a large international cohort used to collect information about problems important to patients and as a framework for RCTs of internet-based rehabilitation, education, self-management and psychological interventions.17
As part of routine data collection via the SPIN Cohort, SPIN administers a series of signalling items that query about patients’ self-reported likelihood of using nine different online programmes that would address problems common in SSc, including fatigue, hand function and mobility, sleep difficulty, emotions and stress, concerns about body image and appearance, pain, low self-efficacy for managing different problems common in scleroderma, nutrition and diet, and difficulty exercising. It is not clear, however, what factors are associated with patient-reported likelihood of using interventions and whether responses reflect a general willingness to use online interventions versus the desire to address specific problems or symptoms. The objective of this study was to identify characteristics of SPIN Cohort participants associated with a greater reported likelihood that they would agree to use an online intervention if it were offered through SPIN, including sociodemographic characteristics, disease characteristics, a general willingness to use online interventions and symptoms or problems that would be presumed to be addressed by each specific intervention.
Patients and methods
Patients and procedure
The study sample consisted of participants enrolled in the SPIN Cohort17 who completed study questionnaires from March 2014 to January 2018. Patients were enrolled at 42 centres from Canada, the USA, the UK, France, Spain and Mexico. To be eligible for the SPIN Cohort, participants must be classified as having SSc according to 2013 American College of Rheumatology/European League Against Rheumatism criteria,29 be ≥18 years of age, be fluent in English, French or Spanish and be able to respond to questionnaires via the internet. The SPIN sample is a convenience sample. Eligible participants are invited by attending physicians or supervised nurse coordinators from SPIN centres to participate, and written informed consent is obtained. The local SPIN investigator provides medical data, which triggers an email invitation to participants with instructions for activating their SPIN account and completing SPIN Cohort measures online. Participants complete outcome measures on enrolment and subsequently every 3 months. Participants with limited or diffuse SSc who completed all study variables at baseline were included in the present study.
Measures
Sociodemographic and medical data
Patients provided demographic data, including age, sex and years of education. SPIN recruiting physicians provided medical data, including time since first non-Raynaud’s phenomenon symptoms, onset of Raynaud’s phenomenon and SSc diagnosis; SSc subtype (limited or diffuse cutaneous SSc)30; and modified Rodnan Skin Score.31
Signalling items
Nine signalling items were developed specifically for use in the SPIN Cohort to assess the self-reported likelihood that Cohort participants would agree to use online programmes designed to address one of nine problems related to living with scleroderma, including fatigue, hand function and mobility, sleep problems, emotions and stress, concerns about body image and appearance, pain, low self-efficacy for disease management, nutrition/diet and exercise. Each item (‘Please indicate how likely you would be to participate in an online program that addresses […]’) is rated on a numerical scale ranging from 0 (not likely at all) to 10 (very likely).
Self-Efficacy to Manage Chronic Disease Scale (SEMCD)
The six-item SEMCD Scale measures confidence in one’s ability to manage fatigue, pain, emotional distress and other symptoms as well as to reduce the need for medical care and reliance on medications.32 Respondents are asked to rate their current confidence in their ability to perform certain tasks regularly. Each item is rated on a 10-point rating scale ranging from 1 (not confident at all) to 10 (totally confident). The score for the scale is the mean of all items, with higher scores reflecting greater self-efficacy. The SEMCD scale has been validated in patients with SSc.33
Patient-Reported Outcomes Measurement Information System-29 (PROMIS-29v2)
The PROMIS-29 profile version 2.0 (PROMIS-29v2)34 measures patient-reported health status over the past 7 days, with four items for each of seven domains (physical function, anxiety, depression, fatigue, sleep disturbance, ability to participate in social roles and activities and pain interference) plus a single pain intensity item. Items are scored on a five-point scale (range 1–5), with different response options for different domains. The single pain intensity item is measured on an 11-point rating scale (0=no pain, 11=worst imaginable pain). Higher scores represent more of the domain being measured, that is, better physical function and ability to participate in social roles and activities but higher levels of anxiety, depression, fatigue, sleep disturbance, pain interference and pain intensity. Raw domain scores are obtained by summing item scores for each domain, which are converted into T-scores standardised for the general US population (mean=50, SD=10). The PROMIS-29v2 has been validated in patients with SSc.35
Cochin Hand Function Scale (CHFS)
The 18-item CHFS36 37 measures the ability to perform daily hand-related activities. Items are scored on a scale from 0 (yes, without difficulty) to 5 (impossible) and are grouped into five content categories: kitchen, dressing oneself, hygiene, the office and other. Total scores range from 0 to 90, and higher scores indicate more hand disability. The CHFS has been validated in SSc.37
Social Appearance Anxiety Scale (SAAS)
The SAAS is a 16-item measure examining fear of situations in which one’s appearance will be evaluated.38 Response options range from 1 (not at all) to 5 (extremely). To calculate a total score, the first item is reverse coded, and then all items are summed. Total scores range from 16 to 80, with higher scores indicating greater fear. The SAAS has been validated in SSc.39
Interference from gastrointestinal problems
Interference with daily activities from gastrointestinal problems was assessed using an 11-point numerical rating scale (range 0–10), with higher scores indicating more limitations.
Physical activity
Physical activity was assessed using a single item ‘Compared with other people your age, how would you rate your physical activity during the past year?’. Response options ranged from 1 (physically inactive) to 5 (very active).
Statistical analyses
Descriptive statistics were used to calculate the mean and SD for each signalling item. Pearson correlations between signalling question scores were calculated. To assess factors associated with self-reported likelihood of participating in an online programme, we conducted multiple linear regression analysis for each signalling question and entered sets of variables hierarchically. Independent variables included in the regression models were determined a priori and included: (A) demographic and disease characteristics including age, sex, disease duration (time since onset of first non-Raynaud symptom), modified Rodnan Skin Score and years of education; (B) general likelihood of using online interventions, calculating by taking the mean score of the remaining signalling questions; (C) self-efficacy to manage chronic disease; and (D) the symptom or problem corresponding with the intervention in each signalling item. The intervention-specific symptoms or problems were measured with the relevant PROMIS-29 domains for fatigue, sleep, depression and pain signalling items: physical activity for the exercise signalling item on exercise, CHFS for the hand function signalling item, the SAAS for the body image signalling item and a single-item numerical rating scale item on intestinal problems for the nutrition and diet signalling item. Standardised regression coefficients beta (β) are reported, as well as the total explained variance for each model (R2).
In addition to the main regression model, based on our findings, we conducted hierarchical regression models to quantify the amount of additional variance explained by the mean score of the remaining signalling questions and the intervention-specific symptom or problem variable. In these models, in step 1, the demographic and disease characteristics, and self-efficacy to manage chronic disease were included as independent variables. In step 2, the mean score of the remaining signalling questions was added, and the magnitude of the change in R2 was examined. In step 3, the symptom or problem corresponding with the intervention in each signalling item was added.
The assumption of normal distribution of residuals in the regression model was tested using a normal probability plot. Additionally, correlations between independent variables and tolerances were calculated to check for multicollinearity. Linearity of the model was assessed using partial residual plot. All analyses were conducted using Stata V.14.2.
Patient and public involvement
Since SPIN was conceived, SPIN Patient Advisory Board members have been involved in all stages of SPIN’s research (https://www.spinsclero.com/en/Team?teamID=f120d6a6-8bee-62ed-b515-ff0000ce1efe). They have engaged in projects that have helped to better understand important problems faced by people with SSc,17 27 28 to prioritise educational, psychosocial and rehabilitation tools to address these problems and to evaluate how best to develop, test and deliver interventions in a rare disease context. 17 40 Members of the SPIN Patient Advisory Board initially participated in the selection of topics to include in the SPIN Cohort assessments including the development of signalling items to include. Team members provided input on the use of the cmRCT design and were involved in decisions related to which international scleroderma treatment centres to approach for enrolment of patients.
Results
Sample characteristics
Of 1704 participants with submitted baseline self-report data, n=228 had no data for the SAAS, as SPIN stopped collecting data for this measure in English-speaking cohort participants after 7 November 2016. Of the 1476 eligible participants, there were 416 (28.2%) missing one or more variables. A commonly missing value was the time since the onset of the first non-Raynaud’s symptom (n=103). The remaining patients (n=313) were missing one or more demographic or patient-reported outcome measures (i.e., signalling or symptom measures).
In total, 1060 participants had complete data for all variables and were included in regression analyses, including 128 men (12%) and 932 women (88%; table 1). Most patients (71%) were married or living as married. Mean time since Raynaud’s onset was 14.6 (SD=11.6) years; mean time since first non-Raynaud’s symptoms was 11.3 (SD=8.5) years; mean time since diagnosis was 9.4 (SD=7.8) years. The mean signalling question scores ranged from 5.1 (body image) to 7.0 (exercise). Response frequencies for signalling items are shown in online supplementary appendix table A. Responses for each signalling question were skewed towards willingness to participate, with score of 10 (very likely to participate) being most frequently given for all nine items (range 22%–36%). As shown in table 2, correlations between signalling question scores ranged from 0.43 (sleep problems with exercise) to 0.71 (body image with emotions and stress).
Table 1.
Demographic characteristics (n=1060)
| Variable | Value |
| Demographic | |
| Age in years, mean (SD) | 54.6 (12.2) |
| Female sex, n (%) | 932 (88) |
| Education in years, mean (SD) | 15.0 (3.6) |
| Married or living as married, n (%) | 751 (71) |
| Country, n (%) | |
| Canada | 273 (26) |
| USA | 416 (39) |
| UK | 117 (11) |
| France | 218 (21) |
| Spain | 32 (3) |
| Mexico | 4 (0) |
| Disease characteristics | |
| Time since onset first non-Raynaud’s symptom or sign in years, mean (SD) | 11.3 (8.5) |
| Time since onset Raynaud’s in years, mean (SD)* | 14.6 (11.6) |
| Time since diagnosis in years, mean (SD)† | 9.4 (7.8) |
| Diffuse disease subtype, n (%) | 439 (41.4) |
| Modified Rodnan Skin Score, mean (SD)‡ | 8.1 (8.6) |
| Signalling question scores | |
| Fatigue, mean (SD) | 6.8 (3.2) |
| Hand function and mobility, mean (SD) | 6.8 (3.4) |
| Sleep problems, mean (SD) | 6.0 (3.7) |
| Emotions and stress, mean (SD) | 5.8 (3.6) |
| Body image and appearance, mean (SD) | 5.1 (3.7) |
| Pain, mean (SD) | 6.3 (3.4) |
| Self-management/coping strategies, mean (SD) | 6.6 (3.3) |
| Nutrition/diet, mean (SD) | 6.9 (3.2) |
| Exercise, mean (SD) | 7.0 (2.9) |
| Patient-reported outcome measures | |
| Self-efficacy to Manage Chronic Disease Scale, mean (SD) | 6.3 (2.2) |
| PROMIS-29 fatigue, mean (SD) | 55.9 (10.7) |
| PROMIS-29 sleep, mean (SD) | 52.8 (8.6) |
| PROMIS-29 depression, mean (SD) | 51.7 (9.3) |
| PROMIS-29 pain, mean (SD) | 56.4 (9.3) |
| Cochin Hand Function Scale, mean (SD) | 14.7 (16.4) |
| Social Appearance Anxiety Scale, mean (SD) | 29.6 (13.7) |
| Interference from gastrointestinal problems, mean (SD) | 2.7 (3.0) |
| Physical activity, mean (SD) | 1.7 (1.1) |
*Due to missing data: N=986.
†Due to missing data: N=1053.
‡Due to missing data: N=879.
PROMIS-29, Patient-Reported Outcomes Measurement Information System-29.
Table 2.
Correlations between signalling items (n=1060)
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
| (1) Fatigue | 1.00 | ||||||||
| (2) Hand function and mobility | 0.55 | 1.00 | |||||||
| (3) Sleep problems | 0.63 | 0.46 | 1.00 | ||||||
| (4) Emotions and stress | 0.60 | 0.47 | 0.61 | 1.00 | |||||
| (5) Concerns about body image | 0.49 | 0.46 | 0.52 | 0.71 | 1.00 | ||||
| (6) Pain | 0.62 | 0.59 | 0.58 | 0.61 | 0.53 | 1.00 | |||
| (7) Self-management | 0.60 | 0.63 | 0.53 | 0.65 | 0.60 | 0.69 | 1.00 | ||
| (8) Nutrition and diet | 0.53 | 0.49 | 0.48 | 0.57 | 0.52 | 0.52 | 0.65 | 1.00 | |
| (9) Exercise | 0.47 | 0.52 | 0.43 | 0.50 | 0.48 | 0.46 | 0.60 | 0.70 | 1.00 |
All correlations are significant with p<0.001.
bmjopen-2019-029542supp001.pdf (102.6KB, pdf)
Correlates of signalling items
Results from the multiple linear regression analyses are shown in table 3. R2 for the models ranged from 0.46 (exercise) to 0.64 (self-management). In all models, controlling for other variables, the mean of the remaining signalling questions was most strongly associated with a greater likelihood to participate in an intervention, with standardised regression coefficients ranging from β=0.61 (sleep) to β=0.80 (self-management). The symptom or problem corresponding with the respective signalling question was significantly associated with higher scores on 7 of the 9 of the signalling questions: fatigue (β=0.30, p<0.001), hand (β=0.21, p<0.001), sleep (β=0.43, p<0.001), emotions and stress (β=0.18, p<0.001), body image (β=0.28, p<0.001), pain (β=0.32, p<0.001) and nutrition/diet (β=0.07, p=0.004). For the remaining two signalling questions, self-efficacy was not statistically associated with reported likelihood of participating in a self-management programme (β=−0.03, p=0.124), and physical activity level was not associated with the exercise intervention signalling question (β=−0.04, p=0.130). Higher self-efficacy was significantly associated with higher scores on the signalling questions for seven items, including fatigue (β=0.10, p<0.001), hand (β=0.11, p<0.001), sleep (β=0.13, p<0.001), body image (β=0.09, p<0.001), pain (β=0.04, p=0.047), nutrition/diet (β=0.09, p<0.001) and exercise (β=0.16, p<0.001), but not for emotions and stress (β=0.03, p=0.131) or self-management (β=−0.03, p=0.124). Finally, there were six sociodemographic and disease variables included in each regression; between 0 and 2 were significantly associated with signalling question scores but β≤0.08 in all cases. Unstandardised regression coefficients (B) and their 95% CIs from the multivariate linear regression analyses are shown in online supplementary appendix tables B1-B9.
Table 3.
Multiple linear regression analyses of the relationship between sociodemographic and disease variables with the signalling questions (n=1060)
| Fatigue* | Hand function and mobility† | Sleep problems‡ | Emotions and stress§ | Body image¶ | Pain** | Self-management | Nutrition and diet†† | Exercise‡‡ | |
| β (p value) | β (p value) | β (p value) | β (p value) | β (p value) | β (p value) | β (p value) | β (p value) | β (p value) | |
| Age in years | 0.05 (0.03) | 0.02 (0.41) | 0.08 (<0.01) | −0.08 (<0.01) | −0.05 (0.02) | 0.01 (0.70) | 0.03 (0.18) | <−0.01 (0.95) | 0.01 (0.73) |
| Male sex | 0.06 (<0.01) | 0.02 (0.43) | 0.01 (0.60) | −0.02 (0.26) | −0.03 (0.11) | 0.04 (0.03) | <0.01 (0.84) | −0.05 (0.01) | −0.02 (0.31) |
| Disease duration | −0.02 (0.44) | −0.01 (0.58) | <−0.01 (0.94) | −0.03 (0.10) | −0.02 (0.35) | 0.01 (0.47) | −0.01 (0.78) | −0.02 (0.36) | <−0.01 (0.98) |
| Diffuse disease | −0.05 (0.02) | 0.02 (0.31) | −0.04 (0.08) | −0.02 (0.42) | 0.01 (0.63) | −0.03 (0.19) | 0.02 (0.34) | −0.01 (0.62) | 0.03 (0.27) |
| Education in years | <0.01 (0.87) | <−0.01 (0.99) | 0.03 (0.10) | 0.01 (0.60) | −0.06 (0.01) | −0.05 (<0.01) | −0.01 (0.47) | 0.06 (0.01) | 0.07 (<0.01) |
| Married or living as married | 0.03 (0.21) | 0.03 (0.19) | <−0.01 (0.95) | −0.04 (0.03) | <0.01 (0.88) | 0.02 (0.43) | 0.01 (0.45) | −0.01 (0.61) | −0.01 (0.69) |
| Self-efficacy | 0.10 (<0.01) | 0.11 (<0.01) | 0.13 (<0.01) | 0.03 (0.13) | 0.09 (<0.01) | 0.04 (0.05) | −0.03 (0.12) | 0.09 (<0.01) | 0.16 (<0.01) |
| Symptom measure | 0.30 (<0.01) | 0.21 (<0.01) | 0.43 (<0.01) | 0.18 (<0.01) | 0.28 (<0.01) | 0.32 (<0.01) | – | 0.07 (<0.01) | −0.04 (0.13) |
| Mean of remaining signalling items | 0.65 (<0.01) | 0.63 (<0.01) | 0.61 (<0.01) | 0.72 (<0.01) | 0.64 (<0.01) | 0.67 (<0.01) | 0.80 (<0.01) | 0.71 (<0.01) | 0.70 (<0.01) |
| R2 | 0.58 | 0.47 | 0.61 | 0.62 | 0.55 | 0.64 | 0.64 | 0.51 | 0.46 |
β: standardised regression coefficient.
*Symptom measures for the models: PROMIS-29 fatigue.
†Symptom measures for the models: Cochin Hand Function.
‡Symptom measures for the models; PROMIS-29 sleep.
§Symptom measures for the models: PROMIS-29 depression.
¶Symptom measures for the models: SAAS score.
**Symptom measures for the models: PROMIS-29 pain.
††Symptom measures for the models: interference of gastro-intestinal symptoms.
‡‡Symptom measures for the models: activity level.
PROMIS-29, Patient-Reported Outcomes Measurement Information System-29.
In the hierarchical analyses, R2 change was assessed for all nine models separately (online supplementary appendix tables B1-B9). The amount of additional variance explained by adding the mean of the other signalling items to the model ranged from 0.41 (hand function problems) to 0.60 (self-management). The amount of additional variance explained by adding the symptom or problem corresponding with the signalling item ranged from <0.01 (exercise) to 0.14 (sleep).
Regression diagnostics found no evidence for deviation from the assumption of normal distribution of residuals for any of the regression models based on a normal probability plot. All tolerance values were between 0.56 and 0.97, indicating that multicollinearity was not an issue for any of the regression models. Partial residual plots did not show any violation of the linearity assumption for any of the regression models.
Discussion
The main finding of this study was that the most important factor influencing patient-reported interest in using disease-specific online self-care interventions is general interest in using online interventions, which explained a substantial amount of additional variance for each model, ranging from 43% to 60%. The symptom or problem corresponding with the respective signalling question and higher self-efficacy was significantly associated with higher scores on 7 of the 9 of the signalling questions but added between <1% and 14% of additional explained variance.
Results from our study suggest that there is a generic factor determining interest in participation in online self-care interventions. Across settings, it has been shown that the intention to use technology and the uptake and implementation of technological innovations in practice are mainly predicted by general factors, including the perceived usefulness, the perceived ease of use, experience and greater technology confidence.41–43 Identifying if these underlying factors are indeed driving the general interest in our sample of patients with SSc could be useful, as these factors could then be taken into consideration in future trials when patients are invited to try novel online interventions in SPIN’s research context or in other research programmes.
To reduce non-acceptance of intervention offers in the cmRCT design, it has been suggested that cohort participants can be presented with a list of possible interventions as part of regular cohort data collection and asked if they would agree to use them if offered.4 It has been hypothesised that this process would identify the potential accepters in advance and consequently reduce dilution of the intervention effects. The results of our study suggest that such a signalling question may not need to be intervention specific, as a higher general interest in interventions was the main factor associated with higher scores on all signalling items.
Identifying factors associated with responses, however, cannot predict actual use of interventions. Recently, the suggested process of including patients with a high indicated interest on the cohort’s signalling item was applied in the SPIN-HAND feasibility trial, which was conducted via the SPIN Cohort. SPIN-HAND is an online hand exercise programme to improve hand function for patients with SSc. SPIN Cohort participants with at least mild hand function limitations (CHFS ≥3) and an indicated interest in using an online hand exercise intervention (hand signalling question ≥7) were randomised to be offered to use the SPIN-HAND programme or usual care for 3 months. Of the 40 SPIN Cohort participants that were included in the SPIN-HAND feasibility trial, 24 were allocated to the intervention arm and 16 to the control group. Patients in the intervention arm were offered to try the SPIN-HAND programme and, afterwards, to participate in an interview collecting their feedback. In total, 15 of 24 (62.5%) patients consented to use the SPIN-HAND intervention.43 Thus, uptake of the offer to try the intervention was low despite selecting patients based on their indicated interest. This result raises important questions about using signalling items as an eligibility criterion for participation in RCTs conducted using the cmRCT design, and it needs to be carefully evaluated how effective these items are at identifying potential accepters of interventions in advance. Since the SPIN-HAND feasibility trial with its small sample size provides only preliminary evidence, additional RCTs using the cmRCT design with larger samples are necessary to confirm this finding.
The present study has limitations that should be considered in interpreting its results. First, the SPIN Cohort constitutes a convenience sample of patients with SSc receiving treatment at a SPIN recruiting centre, and patients at these centres may differ from those in other settings. Additionally, patients with SSc in the SPIN Cohort complete questionnaires online, which may further limit the generalisability of findings, as all participants already have internet access and are comfortable using it in a research setting. Third, 28% of the enrolled patients were excluded from the analyses due to missing data. Fourth, the SPIN interventions under development to be tested through the cohort are all online self-care programmes, and this is reflected in the signalling questions that query about these online interventions. Based on our data, however, it is not possible to distinguish whether patients respond to the signalling items based on their interest in the content of the proposed programme (eg, their interest in self-management or non-pharmacological treatments) or whether the online nature of the programme drive their responses. Finally, this study explored an indicated interest (intention) in potentially trying an online intervention but not the patients’ actual participation in an intervention when it was offered to them. It remains to be elucidated to what degree these signalling questions may reflect actual acceptance of the offer when participants are invited to participate in an intervention. Recent experiences with the SPIN-HAND feasibility trial indicate that the predictive value of these questions may be lower than anticipated.
In sum, findings of the present study suggest that the main factor influencing patients’ interest in participating in a disease-specific online self-care intervention is their general interest in participating in these types of interventions. It should be further explored what factors may drive this general interest, as these factors may be taken into consideration when inviting patients to try novel (online) interventions in a research context.
Supplementary Material
Footnotes
Correction notice: This article has been corrected since it was published. The article title and collaborators are updated.
Collaborators: SPIN Investigators: Murray Baron, McGill University, Montreal, Quebec, Canada; Daniel E. Furst, Division of Rheumatology, Geffen School of Medicine, University of California, Los Angeles, California, USA; Karen Gottesman, Scleroderma Foundation, Los Angeles, California, USA; Maureen D. Mayes, University of Texas McGovern School of Medicine, Houston, Texas, USA; Robert Riggs, Scleroderma Foundation, Danvers, Massachusetts, USA; Maureen Sauve, Scleroderma Society of Ontario, Hamilton, Ontario; Fredrick Wigley, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Shervin Assassi, University of Texas McGovern School of Medicine, Houston, Texas, USA; Isabelle Boutron, Université Paris Descartes, and Assistance Publique-Hôpitaux de Paris, Paris, France; Angela Costa Maia, University of Minho, Braga, Portugal; Lindsay Cronin, Scleroderma Foundation, Western Pennsylvania Chapter, Pittsburgh, Pennsylvania, USA; Ghassan El-Baalbaki, Université du Québec à Montréal, Montreal, Quebec, Canada; Carolyn Ells, McGill University, Montreal, Quebec, Canada; Stephen Elrod, Scleroderma Foundation, Southern California Chapter, Los Angeles, California, USA; Cornelia van den Ende, Sint Maartenskliniek, Nijmegen, The Netherlands; Kim Fligelstone, Scleroderma & Raynaud’s UK, London, UK; Catherine Fortune, Scleroderma Society of Ontario, Hamilton, Ontario, Canada; Tracy Frech, University of Utah, Salt Lake City, Utah, USA; Amy Gietzen, Scleroderma Foundation, Tri-State Chapter, Binghamton, New York, USA; Dominique Godard, Association des Sclérodermiques de France, Sorel-Moussel, France; Geneviève Guillot, Sclérodermie Québec, Montreal, Quebec, Canada; Daphna Harel, New York University, New York, New York, USA; Shirley Haslam, Scleroderma Society of Ontario, Hamilton, Ontario, Canada; Monique Hinchcliff, Yale School of Medicine, New Haven, Connecticut, USA; Marie Hudson, McGill University, Montreal, Quebec, Canada; Ann Impens, Midwestern University, Downers Grove, Illinois, USA; Yeona Jang, McGill University, Montreal, Quebec, Canada; Sindhu R. Johnson, Toronto Scleroderma Program, Mount Sinai Hospital, Toronto Western Hospital, and University of Toronto, Toronto, Ontario, Canada; Ann Tyrell Kennedy, Federation of European Scleroderma Associations, Dublin, Ireland; Annett Körner, McGill University, Montreal, Quebec, Canada; Maggie Larche, McMaster University, Hamilton, Ontario, Canada; Catarina Leite, University of Minho, Braga, Portugal; Carlo Marra, Memorial University, St. John’s, Newfoundland, Canada; Christelle Nguyen, Université Paris Descartes, and Assistance Publique - Hôpitaux de Paris, Paris, France; Karen Nielsen, Scleroderma Society of Ontario, Hamilton, Ontario, Canada; Janet Pope, University of Western Ontario, London, Ontario, Canada; Alexandra Portales, Asociación Española de Esclerodermia, Madrid, Spain; Michelle Richard, Scleroderma Society of Nova Scotia, Halifax, Nova Scotia, Canada; Tatiana Sofia Rodriguez Reyna, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; Ken Rozee, Scleroderma Society of Nova Scotia, Halifax, Nova Scotia, Canada; Anne A. Schouffoer, Leiden University Medical Center, Leiden, The Netherlands; Russell J. Steele, Jewish General Hospital and McGill University, Montreal, Quebec, Canada; Nancy Stephens, Scleroderma Foundation, Michigan Chapter, Southflied, Michigan, USA; Maria E. Suarez-Almazor, University of Texas MD Anderson Cancer Center, Houston, Texas, USA; Durhane Wong-Rieger, Canadian Organization for Rare Disorders, Toronto, Ontario, Canada; Christian Agard, Centre Hospitalier Universitaire - Hôtel-Dieu de Nantes, Nantes, France; Alexandra Albert, Université Laval, Quebec, Quebec, Canada; Marc André, Centre Hospitalier Universitaire Gabriel-Montpied, Clermont-Ferrand, France; Guylaine Arsenault, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Ilham Benzidia, Assistance Publique - Hôpitaux de Paris, Hôpital St-Louis, Paris, France; Sabine Berthier, Centre Hospitalier Universitaire Dijon Bourgogne, Dijon, France; Lyne Bissonnette, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Gilles Boire, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Alessandra Bruns, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Patricia Carreira, Servicio de Reumatologia del Hospital 12 de Octubre, Madrid, Spain; Marion Casadevall, Assistance Publique - Hôpitaux de Paris, Hôpital Cochin, Paris, France; Benjamin Chaigne, Assistance Publique - Hôpitaux de Paris, Hôpital Cochin, Paris, France; Lorinda Chung, Stanford University, Stanford, California, USA; Pascal Cohen, Assistance Publique - Hôpitaux de Paris, Hôpital Cochin, Paris, France; Chase Correia, Northwestern University, Chicago, Illinois, USA; Pierre Dagenais, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Christopher Denton, Royal Free London Hospital, London, UK; Robyn Domsic, University of Pittsburgh, Pittsburgh, Pennsylvania, USA; Sandrine Dubois, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; James V. Dunne, St. Paul's Hospital and University of British Columbia, Vancouver, British Columbia, Canada; Bertrand Dunogue, Assistance Publique - Hôpitaux de Paris, Hôpital Cochin, Paris, France; Alexia Esquinca, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; Regina Fare, Servicio de Reumatologia del Hospital 12 de Octubre, Madrid, Spain; Dominique Farge-Bancel, Assistance Publique - Hôpitaux de Paris, Hôpital St-Louis, Paris, France; Paul R. Fortin, CHU de Québec - Université Laval, Quebec, Quebec, Canada; Anna Gill, Royal Free London Hospital, London, UK; Jessica Gordon, Hospital for Special Surgery, New York City, New York, USA; Brigitte Granel-Rey, Aix Marseille Université, and Assistance Publique - Hôpitaux de Marseille, Hôpital Nord, Marseille, France; Claire Grange, Centre Hospitalier Lyon Sud, Lyon, France; Genevieve Gyger, Jewish General Hospital and McGill University, Montreal, Quebec, Canada; Eric Hachulla, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; Pierre-Yves Hatron, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; Ariane L Herrick, University of Manchester, Salford Royal NHS Foundation Trust, Manchester, UK; Adrian Hij, Assistance Publique - Hôpitaux de Paris, Hôpital St-Louis, Paris, France; Monique Hinchcliff, Northwestern University, Chicago, Illinois, USA; Alena Ikic, Université Laval, Quebec, Quebec, Canada; Niall Jones, University of Alberta, Edmonton, Alberta, Canada; Artur Jose de B. Fernandes, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Suzanne Kafaja, University of California, Los Angeles, California, USA; Nader Khalidi, McMaster University, Hamilton, Ontario, Canada; Marc Lambert, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; David Launay, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; Patrick Liang, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Hélène Maillard, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; Nancy Maltez, University of Ottawa, Ottawa, Ontario, Canada; Joanne Manning, Salford Royal NHS Foundation Trust, Salford, UK; Isabelle Marie, CHU Rouen, Hôpital de Bois-Guillaume, Rouen, France; Maria Martin, Servicio de Reumatologia del Hospital 12 de Octubre, Madrid, Spain; Thierry Martin, Les Hôpitaux Universitaires de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France; Ariel Masetto, Université de Sherbrooke, Sherbrooke, Quebec, Canada; François Maurier, Hôpitaux Privés de Metz, Hôpital Belle-Isle, Metz, France; Arsene Mekinian, Assistance Publique - Hôpitaux de Paris, Hôpital St-Antoine, Paris, France; Sheila Melchor, Servicio de Reumatologia del Hospital 12 de Octubre, Madrid, Spain; Mandana Nikpour, St Vincent’s Hospital and University of Melbourne, Melbourne, Victoria, Australia; Vincent Poindron, Les Hôpitaux Universitaires de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France; Susanna Proudman, Royal Adelaide Hospital and University of Adelaide, Adelaide, South Australia, Australia; Alexis Régent, Assistance Publique - Hôpitaux de Paris, Hôpital Cochin, Paris, France; Sébastien Rivière, Assistance Publique - Hôpitaux de Paris, Hôpital St-Antoine, Paris, France; David Robinson, University of Manitoba, Winnipeg, Manitoba, Canada; Esther Rodriguez, Servicio de Reumatologia del Hospital 12 de Octubre, Madrid, Spain; Sophie Roux, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Perrine Smets, Centre Hospitalier Universitaire Gabriel-Montpied, Clermont-Ferrand, France; Doug Smith, University of Ottawa, Ottawa, Ontario, Canada; Vincent Sobanski, Centre Hospitalier Régional Universitaire de Lille, Hôpital Claude Huriez, Lille, France; Robert Spiera, Hospital for Special Surgery, New York City, New York, USA; Virginia Steen, Georgetown University, Washington, DC, USA; Wendy Stevens, St Vincent’s Hospital and University of Melbourne, Melbourne, Victoria, Australia; Evelyn Sutton, Dalhousie University, Halifax, Nova Scotia, Canada; Benjamin Terrier, Assistance Publique -Hôpitaux de Paris, Hôpital Cochin, Paris, France; Carter Thorne, Southlake Regional Health Centre, Newmarket, Ontario, Canada; John Varga, Northwestern University, Chicago, Illinois, USA; Pearce Wilcox, St. Paul's Hospital and University of British Columbia, Vancouver, British Columbia, Canada; Michelle Wilson, St Vincent’s Hospital and University of Melbourne, Melbourne, Victoria, Australia; Kylene Anne Aguila, Jewish General Hospital, Montreal, Quebec, Canada; Mara Cañedo Ayala, Jewish General Hospital, Montreal, Quebec, Canada; Andrea Carboni-Jiménez, Jewish General Hospital, Montreal, Quebec, Canada; Claire Fedoruk, Jewish General Hospital, Montreal, Quebec, Canada; Shadi Gholizadeh, McGill University, Montreal, Quebec, Canada, and San Diego State University and University of California, San Diego, California, USA; Sami Harb, Jewish General Hospital, Montreal, Quebec, Canada; Lydia Tao, Jewish General Hospital, Montreal, Quebec, Canada; Kimberly Turner, Jewish General Hospital, Montreal, Quebec, Canada.
Contributors: LK and BDT were responsible for the study conception. LM and the SPIN Investigators contributed to data collection. All authors contributed to data analysis and interpretation. LK, JC and BDT contributed to drafting the manuscript. All authors provided a critical revision of the manuscript and approved the final version of the manuscript. BDT is the guarantor.
Funding: SPIN has been funded by grants from the Canadian Institutes of Health Research (TR3-119192, PJT-148504, PJT-149073), the Canadian Initiative for Outcomes in Rheumatology Care and the Arthritis Society. In addition, SPIN has received institutional contributions from the Lady Davis Institute for Medical Research of the Jewish General Hospital, Montreal, Canada, and from McGill University, Montreal, Canada. SPIN has also received support from Scleroderma Canada, the Scleroderma Society of Ontario, Sclérodermie Québec, the Scleroderma Society of Nova Scotia, the Scleroderma Association of British Columbia, Scleroderma Manitoba, and the Scleroderma Society of Saskatchewan. BDT was supported by a Fonds de Recherche Québec – Santé researcher awards outside of the present work.
Competing interests: None declared.
Patient consent for publication: Not required.
Ethics approval: The SPIN Cohort study was approved by the Research Ethics Committee of the Jewish General Hospital, Montréal, Canada and by the research ethics committees of each participating centre.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data availability statement: Data are available on reasonable request.
Contributor Information
the SPIN Investigators:
Murray Baron, Daniel E. Furst, Karen Gottesman, Maureen D. Mayes, Robert Riggs, Maureen Sauve, Fredrick Wigley, Shervin Assassi, Isabelle Boutron, Angela Costa Maia, Lindsay Cronin, Ghassan El-Baalbaki, Carolyn Ells, Stephen Elrod, Cornelia van den Ende, Kim Fligelstone, Catherine Fortune, Tracy Frech, Amy Gietzen, Dominique Godard, Daphna Harel Geneviève Guillot, Shirley Haslam, Monique Hinchcliff, Marie Hudson, Ann Impens, Yeona Jang, Sindhu R. Johnson, Ann Tyrell Kennedy, Annett Körner, Maggie Larche, Catarina Leite, Carlo Marra, Christelle Nguyen, Karen Nielsen, Janet Pope, Alexandra Portales, Michelle Richard, Tatiana Sofia Rodriguez Reyna, Ken Rozee, Anne A. Schouffoer, Russell J. Steele, Nancy Stephens, Maria E. Suarez-Almazor, Durhane Wong-Rieger, Christian Agard, Alexandra Albert, Marc André, Guylaine Arsenault, Ilham Benzidia, Sabine Berthier, Lyne Bissonnette, Gilles Boire, Alessandra Bruns, Patricia Carreira, Marion Casadevall, Benjamin Chaigne, Lorinda Chung, Pascal Cohen, Chase Correia, Pierre Dagenais, Christopher Denton, Robyn Domsic, Sandrine Dubois, James V. Dunne, Bertrand Dunogue, Alexia Esquinca, Regina Fare, Dominique Farge-Bancel, Paul R. Fortin, Anna Gill, Jessica Gordon, Brigitte Granel-Rey, Claire Grange, Genevieve Gyger, Eric Hachulla, Pierre-Yves Hatron, Ariane L Herrick, Adrian Hij, Monique Hinchcliff, Alena Ikic, Niall Jones, Artur Jose de B. Fernandes, Suzanne Kafaja, Nader Khalidi, Marc Lambert, David Launay, Patrick Liang, Hélène Maillard, Nancy Maltez, Joanne Manning, Isabelle Marie, Maria Martin, Thierry Martin, Ariel Masetto, François Maurier, Mekinian Arsene, Sheila Melchor, Nikpour Mandana, Vincent Poindron, Susanna Proudman, Alexis Régent, Sébastien Rivière, David Robinson, Esther Rodriguez, Sophie Roux, Perrine Smets, Doug Smith, Vincent Sobanski, Robert Spiera, Virginia Steen, Wendy Stevens, Evelyn Sutton, Benjamin Terrier, Carter Thorne, John Varga, Pearce Wilcox, Michelle Wilson, Kylene Anne Aguila, Mara Cañedo Ayala, Andrea Carboni-Jiménez, Claire Fedoruk, Gholizadeh Shadi, Sami Harb, Lydia Tao, and Kimberly Turner
References
- 1. Torgerson DJ, Torgerson CJ. Designing randomised trials in health, education and the social sciences: an introduction. Basingstoke: Palgrave Macmillan, 2008. [Google Scholar]
- 2. Evans I, Thornton H, Chalmers I, et al. Testing treatments: better research for better healthcare. London: Pinter and Martin Ltd, 2011. [PubMed] [Google Scholar]
- 3. McDonald AM, Knight RC, Campbell MK, et al. What influences recruitment to randomised controlled trials? A review of trials funded by two UK funding agencies. Trials 2006;7:9 10.1186/1745-6215-7-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Relton C, Torgerson D, O'Cathain A, et al. Rethinking pragmatic randomised controlled trials: introducing the "cohort multiple randomised controlled trial" design. BMJ 2010;340 10.1136/bmj.c1066 [DOI] [PubMed] [Google Scholar]
- 5. Treweek S, Mitchell E, Pitkethly M, et al. Strategies to improve recruitment to randomised controlled trials. Cochrane Database Syst Rev 2010;1 10.1002/14651858.MR000013.pub5 [DOI] [PubMed] [Google Scholar]
- 6. Watson JM, Torgerson DJ. Increasing recruitment to randomised trials: a review of randomised controlled trials. BMC Med Res Methodol 2006;6:34 10.1186/1471-2288-6-34 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Campbell M, Snowdon C, Francis D, et al. Recruitment to randomised trials: strategies for trial enrolment and participation study. The steps study. Health Technol Assess 2007;11 10.3310/hta11480 [DOI] [PubMed] [Google Scholar]
- 8. Treweek S, Lockhart P, Pitkethly M, et al. Methods to improve recruitment to randomised controlled trials: cochrane systematic review and meta-analysis. BMJ Open 2013;3:e002360 10.1136/bmjopen-2012-002360 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Sully BGO, Julious SA, Nicholl J. A reinvestigation of recruitment to randomised, controlled, multicenter trials: a review of trials funded by two UK funding agencies. Trials 2013;14:166 10.1186/1745-6215-14-166 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. McDonald AM, Treweek S, Shakur H, et al. Using a business model approach and marketing techniques for recruitment to clinical trials. Trials 2011;12:74 10.1186/1745-6215-12-74 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Donovan JL, Paramasivan S, de Salis I, et al. Clear obstacles and hidden challenges: understanding recruiter perspectives in six pragmatic randomised controlled trials. Trials 2014;15 10.1186/1745-6215-15-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Mc Cord KA, Al-Shahi Salman R, Treweek S, et al. Routinely collected data for randomized trials: promises, barriers, and implications. Trials 2018;19:29 10.1186/s13063-017-2394-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. James S, Fröbert O, Lagerqvist B. Cardiovascular registries: a novel platform for randomised clinical trials. Heart 2012;98:1329–31. 10.1136/heartjnl-2012-301727 [DOI] [PubMed] [Google Scholar]
- 14. James S, Rao SV, Granger CB. Registry-based randomized clinical trials--a new clinical trial paradigm. Nat Rev Cardiol 2015;12:312–6. 10.1038/nrcardio.2015.33 [DOI] [PubMed] [Google Scholar]
- 15. van Staa T-P, Dyson L, McCann G, et al. The opportunities and challenges of pragmatic point-of-care randomised trials using routinely collected electronic records: evaluations of two exemplar trials. Health Technol Assess 2014;18:1–146. 10.3310/hta18430 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Anderson GL, Burns CJ, Larsen J, et al. Use of administrative data to increase the practicality of clinical trials: insights from the women's health Initiative. Clin Trials 2016;13:519–26. 10.1177/1740774516656579 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Kwakkenbos L, Jewett LR, Baron M, et al. The scleroderma patient-centered intervention network (spin) cohort: protocol for a cohort multiple randomised controlled trial (cmRCT) design to support trials of psychosocial and rehabilitation interventions in a rare disease context. BMJ Open 2013;3:e003563 10.1136/bmjopen-2013-003563 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Young-Afat DA, van Gils CH, van den Bongard HJGD, et al. The Utrecht cohort for multiple breast cancer intervention studies and long-term evaluation (umbrella): objectives, design, and baseline results. Breast Cancer Res Treat 2017;164:445–50. 10.1007/s10549-017-4242-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Gal R, Monninkhof EM, Groenwold RHH, et al. The effects of exercise on the quality of life of patients with breast cancer (the umbrella fit study): study protocol for a randomized controlled trial. Trials 2017;18:504 10.1186/s13063-017-2252-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. van der Velden JM, Verkooijen HM, Seravalli E, et al. Comparing conVEntional radiotherapy with stereotactIC body radiotherapy in patients with spinAL metastases: study protocol for an randomized controlled trial following the cohort multiple randomized controlled trial design. BMC Cancer 2016;16:909 10.1186/s12885-016-2947-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Burbach JPM, Verkooijen HM, Intven M, et al. Randomized controlled trial for pre-operAtive dose-escaLation boost in locally advanced rectal cancer (rectal boost study): study protocol for a randomized controlled trial. Trials 2015;16:58 10.1186/s13063-015-0586-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Couwenberg AM, Burbach MJP, Smits AB, et al. The impact of retractor SPONGE-assisted laparoscopic surgery on duration of hospital stay and postoperative complications in patients with colorectal cancer (sponge trial): study protocol for a randomized controlled trial. Trials 2016;17:132 10.1186/s13063-016-1256-x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Candlish J, Pate A, Sperrin M, et al. Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study. BMC Med Res Methodol 2017;17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Seibold J, Scleroderma, In Harris ED, et al. Kelley's textbook of rheumatology. Philadelphia: Elsevier, 2005: 1279–308. [Google Scholar]
- 25. Wigley FM, Hummers LK. Clinical features of systemic sclerosis : Hochberg MC, Silman AJ, Smolen JS, Philadelphia, Mosby: Rheumatology, 2003: 1463–80. [Google Scholar]
- 26. Kwakkenbos L, Delisle VC, Fox RS, et al. Psychosocial aspects of scleroderma. Rheum Dis Clin North Am 2015;41:519–28. 10.1016/j.rdc.2015.04.010 [DOI] [PubMed] [Google Scholar]
- 27. Thombs BD, van Lankveld W, Bassel M, et al. Psychological health and well-being in systemic sclerosis: state of the science and consensus research agenda. Arthritis Care Res 2010;62:1181–9. 10.1002/acr.20187 [DOI] [PubMed] [Google Scholar]
- 28. Bassel M, Hudson M, Taillefer SS, et al. Frequency and impact of symptoms experienced by patients with systemic sclerosis: results from a Canadian national survey. Rheumatology 2011;50:762–7. 10.1093/rheumatology/keq310 [DOI] [PubMed] [Google Scholar]
- 29. van den Hoogen F, Khanna D, Fransen J, et al. 2013 classification criteria for systemic sclerosis: an American College of rheumatology/European League against rheumatism collaborative initiative. Ann Rheum Dis 2013;72:1747–55. 10.1136/annrheumdis-2013-204424 [DOI] [PubMed] [Google Scholar]
- 30. LeRoy EC, Black C, Fleischmajer R, et al. Scleroderma (systemic sclerosis): classification, subsets and pathogenesis. J Rheumatol 1988;15:202–5. [PubMed] [Google Scholar]
- 31. Clements P, Lachenbruch P, Siebold J, et al. Inter and intraobserver variability of total skin thickness score (modified Rodnan TSS) in systemic sclerosis. J Rheumatol 1995;22:1281–5. [PubMed] [Google Scholar]
- 32. Ritter PL, Lorig K. The English and Spanish self-efficacy to manage chronic disease scale measures were validated using multiple studies. J Clin Epidemiol 2014;67:1265–73. 10.1016/j.jclinepi.2014.06.009 [DOI] [PubMed] [Google Scholar]
- 33. Riehm KE, Kwakkenbos L, Carrier M-E, et al. Validation of the self-efficacy for managing chronic disease scale: a scleroderma patient-centered intervention network cohort study. Arthritis Care Res 2016;68:1195–200. 10.1002/acr.22807 [DOI] [PubMed] [Google Scholar]
- 34. The NIH Patient-Reported Outcomes Measurement Information System Available: http://www.healthmeasures.net/explore-measurement-systems/promis [Accessed 3 Oct 2018].
- 35. Kwakkenbos L, Thombs BD, Khanna D, et al. Performance of the patient-reported outcomes measurement information System-29 in scleroderma: a scleroderma patient-centered intervention network cohort study. Rheumatology 2017;56:1302–11. 10.1093/rheumatology/kex055 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Duruöz MT, Poiraudeau S, Fermanian J, et al. Development and validation of a rheumatoid hand functional disability scale that assesses functional handicap. J Rheumatol 1996;23:1167–72. [PubMed] [Google Scholar]
- 37. Rannou F, Poiraudeau S, Berezné A, et al. Assessing disability and quality of life in systemic sclerosis: construct validities of the Cochin hand function scale, health assessment questionnaire (HAQ), systemic sclerosis HAQ, and medical outcomes study 36-Item short form health survey. Arthritis Rheum 2007;57:94–102. 10.1002/art.22468 [DOI] [PubMed] [Google Scholar]
- 38. Hart TA, Flora DB, Palyo SA, et al. Development and examination of the social appearance anxiety scale. Assessment 2008;15:48–59. 10.1177/1073191107306673 [DOI] [PubMed] [Google Scholar]
- 39. Mills SD, Kwakkenbos L, Carrier M-E, et al. Validation of the social appearance anxiety scale in patients with systemic sclerosis: a scleroderma patient-centered intervention network cohort study. Arthritis Care Res 2018;70:1557–62. 10.1002/acr.23514 [DOI] [PubMed] [Google Scholar]
- 40. Kwakkenbos L, Carrier ME, Boutron I, et al. Randomized feasibility trial of the scleroderma patient-centered intervention network hand exercise program (SPIN-HAND): study protocol. J Scleroderma Relat Disord 2018;3:S199–209. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Hofstede J, de Bie J, van Wijngaarden B, et al. Knowledge, use and attitude toward eHealth among patients with chronic lung diseases. Int J Med Inform 2014;83:967–74. 10.1016/j.ijmedinf.2014.08.011 [DOI] [PubMed] [Google Scholar]
- 42. Edwards L, Thomas C, Gregory A, et al. Are people with chronic diseases interested in using telehealth? A cross-sectional postal survey. J Med Internet Res 2014;16:e123 10.2196/jmir.3257 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. King WR, He J, Jun HE. A meta-analysis of the technology acceptance model. Information & Management 2006;43:740–55. 10.1016/j.im.2006.05.003 [DOI] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
bmjopen-2019-029542supp001.pdf (102.6KB, pdf)