If you don't remember your password, you can reset it by entering your email address and clicking the Reset Password button. You will then receive an email that contains a secure link for resetting your password
If the address matches a valid account an email will be sent to __email__ with instructions for resetting your password
Corresponding author. Botnar Research Centre. Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Windmill Road, Headington, Oxford, OX3 7LD, UK. Tel.: +44-01865-227-374.
Centre for Rehabilitation Research, Nuffield Department of Rheumatology, Orthopaedics and Musculoskeletal Sciences, University of Oxford, Oxford, UKCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
Centre for Rehabilitation Research, Nuffield Department of Rheumatology, Orthopaedics and Musculoskeletal Sciences, University of Oxford, Oxford, UKCollege of Medicine and Health, University of Exeter, UK
First, mobility decline prediction model in older men and women living in the community.
•
Model performed well in terms of discrimination and showed greater net benefit compared than not using a model.
•
The simple scoring systems developed are easy-to-use and could be used during a routine clinical consultation.
•
Predictors with the strongest prognostic power are modifiable (eg, walking ability and balance).
Abstract
Objectives
The aim of this study is to develop and validate two models to predict 2-year risk of self-reported mobility decline among community-dwelling older adults.
Study Design and Setting
We used data from a prospective cohort study of people aged 65 years and over in England. Mobility status was assessed using the EQ-5D-5L mobility question. The models were based on the outcome: Model 1, any mobility decline at 2 years; Model 2, new onset of persistent mobility problems over 2 years. Least absolute shrinkage and selection operator logistic regression was used to select predictors. Model performance was assessed using C-statistics, calibration plot, Brier scores, and decision curve analyses. Models were internally validated using bootstrapping.
Results
Over 18% of participants who could walk reported mobility decline at year 2 (Model 1), and 7.1% with no mobility problems at baseline, reported new onset of mobility problems after 2 years (Model 2). Thirteen and 6 out of 31 variables were selected as predictors in Models 1 and 2, respectively. Models 1 and 2 had a C-statistic of 0.740 and 0.765 (optimism < 0.013), and Brier score = 0.136 and 0.069, respectively.
Conclusion
Two prediction models for mobility decline were developed and internally validated. They are based on self-reported variables and could serve as simple assessments in primary care after external validation.
We have derived and internally validated two easy-to-use prediction models that quantify absolute risk of mobility decline in community-dwelling older people aged 65 years and over.
•
Both models demonstrated moderate to good prediction to identify individuals at risk of mobility decline over a 2-year period.
What does this add to what is already known?
•
These models are the first prediction models for self-reported mobility decline in a community-dwelling older adults in England.
What is the implication, what should change now?
•
Our prediction models provide health care professionals with a small set of easy to collect variables that appear to discriminate well in the prediction of mobility decline in older adults living in the community.
•
After appropriate external validation, these models may be useful in informing clinical decision-making about the need for rehabilitation to prevent mobility decline and maintain independence in older age.
1. Introduction
In later life, declining mobility is an early predictor of dependence [
]. Generally, mobility limitations increase with age and affect more women than men. Prospective studies identify socioeconomic status, lifestyle habits, physiological and psychological factors, health conditions, social networks and participation, environmental, and organizational/policy factors [
These factors may be measured using self-reported or objective measures. Objective measures are recommended in the evaluation of an individual’s functional status, but are time-consuming and, therefore, difficult to implement in routine primary care. Self-reported measures have low response burden, can capture both current and historical information, and can assess psychological and social factors.
Prediction models estimate absolute risk of a particular outcome for individual people [
Ability of self-reported frailty components to predict incident disability, falls, and all-cause mortality: results from a population-based study of older British men.
]. Most studies used small sample sizes, were developed using data collected many decades ago, and have methodological limitations, such as the absence of key model performance measures, selection of predictor variables based on univariable analyses, and absence of multivariable analyses [
An accurate and user-friendly model to predict mobility decline in older adults could identify higher risk people in the community setting, enabling better targeting of rehabilitation and other interventions in clinical practice. We wanted to examine whether easy to collect self-reported measures of health and mobility could be used for this purpose.
The aim of this study is to develop and internally validate two risk prediction models for predicting the 2-year risk of mobility decline, using a cohort of older adults aged 65–100 years recruited in a community-based study in England.
2. Materials and methods
We followed the TRIPOD (Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) guidelines to report the development and validation of the prediction models [
We used data from the Oxford Pain, Activity and Lifestyle (OPAL) study which is a prospective cohort study of older people living in England. Full details of the cohort protocol and baseline characteristics are provided elsewhere [
]. Briefly, eligible individuals were identified from electronic record searches of 35 general practice lists across England and randomly selected from two age bands (65–74 and 75 years and over). Exclusion criteria were living in residential care or nursing home, terminal illness with life expectancy <6 months, severe health/social concerns sufficient to preclude approach, or unable to provide informed consent.
2.2 Cohort study population
Between October 2016 and March 2018, a sample of up to 400 patients from each participating practice were invited to take part in the cohort study (a total of 12,839 individuals) and 5,409 (42.1%) agreed to participate. After completion of the baseline questionnaire, follow-up data were collected by post annually for 2 years.
At 2-year follow up, 23.2% (n = 1,256/5,409) of the original cohort were lost to follow-up. Of these, 3.4% (n = 181/5,409) had died, 13.9% (n = 753/5,409) were due to withdrawal from the study by the participant (12.4%; n = 669/5,409) or general practitioner (1.6%; n = 84/5,409), and 6.0% (n = 322/5,409) were uncontactable (Appendix 1).
2.3 Baseline candidate predictors
The list of candidate predictors and how they were defined are described in Table 1 and Appendix 2. We identified 31 candidate predictors based on a recent systematic literature [
] to identify participants with mobility decline. Participants rated their mobility status as no problems, slight, moderate, severe problems, and unable to walk.
2.4.1 Model 1: 2-year risk of mobility decline.
All participants who were able to walk at baseline, regardless of their ability, were included (n = 5,358/5,409; 99%). Mobility decline was calculated by subtracting mobility score at 2 years from baseline scores and classifying participants using a binary outcome [no mobility decline (0) vs. mobility decline (1)].
2.4.2 Model 2: 2-year risk of new-onset persistent mobility problems.
People with no mobility problems at baseline were eligible (n = 3,193/5,409; 59%). New onset of persistent mobility problems over 2 years was defined as having any problems in mobility at both 1 and 2 years from baseline.
2.5 Sample size
The minimum sample size was based on Riley et al. [
] and a prespecified maximum number of predictor parameters of 50, the minimum sample size required to develop the model to minimize overfitting was estimated to be 4,585, with 853 events.
2.6 Statistical analysis
Nonlinearity between continuous predictors and the outcome was examined using fractional polynomials before multivariable modeling.
Missing data were imputed using multiple imputation by chained equation technique [
] (Appendix 3). Fifty imputed datasets were created. We included all candidate predictors and the outcome variable in the imputation model. To impute for sample attrition (Table 1b/Appendix 1), we included ethnicity, smoking status, place of residence, and cognitive function.
To minimize the risk of overfitting we applied a penalized approach, the least absolute shrinkage and selection operator (LASSO) [
], and forced the inclusion of mobility status at baseline in Model 1. Logistic regression using the LASSO was applied on each imputed dataset separately. The tuning parameter λ value for the prediction model was chosen from a grid of 100 λ values through 10-fold repeated cross-validation [
]. The λ that had the average mean-squared error within one standard error from the minimum error was chosen. Predictors selected in 80% of the imputed datasets were retained. Average regression coefficient estimates over all imputed datasets of the variables retained were calculated to obtain the final model.
Model performance was assessed by calculating discrimination, calibration, decision curve analysis (DCA), and overall performance [
]. Discrimination, assessed by the C-statistic, is the ability of the prediction model to differentiate between those who report mobility decline and do not report decline. Calibration—how closely predicted risk corresponds with observed risk—was assessed visually using calibration plots. DCA determines the clinical validity of the prediction model by measuring the net benefit over a range of different threshold probabilities. As the risk threshold probability increases, the cost–benefit ratio increases accordingly [
]. Brier score quantifies how close predictions are to the observed data. Scores range from 0 to 1, with 0 representing perfect concordance between predicted and observed values. The model performance (C-statistic and Brier score) was evaluated on each multiple imputation (MI) dataset and median and range was reported [
]. In each bootstrap sample, we repeated the entire modeling process, estimating the C-statistic in each bootstrap sample and then assessed the performance of each model in the first MI dataset. Optimism was estimated as the mean of the difference between the C-statistic of the bootstrap sample and that of the first MI sample. Optimism-corrected C-statistic was calculated as follows: C-statistic of the model developed in the first MI dataset − optimism.
To allow translation to clinical settings, we designed a simple scoring system based on procedures described by Sullivan et al. [
]. Penalized regression coefficients of the model were used to assign integer points to each level of each predictor. These score points were summed to derive a total risk score for each participant.
All analyses were carried out using STATA 16.1 and R 4.1.2. We used STATA for MI and R for model development, model performance, and internal validation [
Of the 5,358 eligible individuals who could walk at baseline (Model 1), 18.7% (n = 1,001) had one or more missing values for the candidate predictors. For most candidate predictors, the proportion of missing data was <5% (Table 2/Appendix 3).
An additional 916 (21.0% of 4,357) participants did not return questionnaires or did not complete the mobility question at year 2 (Fig. 1/Appendix 3).
3.2 Model 1: 2-year risk of mobility decline
3.2.1 Participants.
Of 5,358 participants who could walk at baseline, 174 had died by 2 years of follow-up. Thus, 5,184 participants were included (Fig. 1). The mean age of participants [standard deviation (SD)] was 74.7 years (6.6) and 51.8% (n = 2,683/5,184) were women. At baseline, most participants reported no mobility problems (60.7%; 3,146/5,184) and 5.4% (280/5,184) reported severe mobility problems.
Fig. 1Flowchart of the OPAL cohort study. OPAL, Oxford Pain, Activity and Lifestyle.
Fig. 2 shows the change in mobility status at year 2 (overall and stratified by mobility status). Among those with follow-up data at 2 years (79.4%; n = 4,115/5,184), 18.6% (n = 765/4,115) reported mobility decline (Fig. 2). Participants who reported slight mobility problems (lower panel, plot A) at baseline had the biggest decline over the 2 years of follow-up (35.6%). There were 427 (10.3%) individuals who improved their mobility at 2 years.
Fig. 2Absolute change in mobility status between baseline and year 2 for all participants and stratified by mobility status at baseline. (A) no mobility problems; (B) slight mobility problems; and (C) moderate or severe mobility problems).
The absolute change was calculated as the mobility score at year 2 minus the score at baseline (negative values indicate worse change in mobility status and positive values indicate improvement in mobility status). The upper panel represents participants who could walk at baseline and the lower panel represents participants who stratify by mobility status at baseline.
3.2.2 Selection of predictor variables and model fitting.
In addition to mobility status at baseline, 13 variables were identified as predictors of mobility decline at 2 years in ≥80% of the MI datasets (Table 2, Model 1).
Table 2Predictors of mobility decline selected in ≥80% of the imputed datasets
Model 1
Model 2
Predictor variables
LASSO regression (% times selected)
Average penalized coefficient
LASSO regression (% times selected)
Average penalized coefficient
Intercept
−6.757
−1.844
Age (65–100 yr)
100%
0.035
-
-
Adequacy of income
Have to be careful with money
100%
0.239
-
-
BMI (15–70 kg/m2)
100%
0.025
-
-
Usual walking pace
Stroll at any easy pace
100%
0.494
-
-
Very slow/slow
100%
0.573
100%
0.611
Difficulties maintaining balance
96%
0.166
90%
0.329
Confidence to walk
100%
0.056
100%
0.106
Use of walking aid outside
Sometimes
96%
0.158
-
-
Change in walking ability compared to last year (Better)
About the same
80%
−0.087
-
-
Worse
96%
0.141
100%
0.465
Lower limb pain in the last 6 wk
100%
0.226
-
-
Current pain/discomfort severity
98%
0.070
-
-
Number of health conditions
100%
0.117
88%
0.046
Physical tiredness
98%
0.092
-
-
Self-reported general health
100%
−0.011
100%
−0.015
Predictors forced to be included in the final model
Mobility problems at baseline (moderate/severe problems)
I have slight problems
100%
1.963
-
-
I have no problems
100%
2.387
-
-
Abbreviations: BMI, body mass index; LASSO, least absolute shrinkage and selection operator.
Model 1: 2-year risk of mobility decline; Model 2: 2-year risk of new-onset persistent mobility problems.
Older participants and those who perceived their income to be inadequate were more likely to report mobility decline at 2 years (Table 2, Model 1). Participants who reported a slow usual walking pace had difficulty maintaining their balance, had low confidence to walk long distances, rated their walking as worse compared to last year and reported sometimes using a walking aid outside, and were more likely to report a decline in mobility after 2 years. Lower limb pain and the presence of severe general pain or discomfort were also predictors of mobility decline. Other health-related factors associated with decline in mobility at 2 years were having a higher body mass index, greater number of health conditions, problems in daily life due to physical tiredness, and poor general health (Table 2).
3.2.3 Model performance.
Model 1 revealed a median C-statistic of 0.740 (range: 0.737–0.743) and the median Brier score was 0.136 (range: 0.135–0.137). The model showed good calibration: 95% confidence interval bars and some of the mean values (data points) intersect with the 45° line that indicates perfect agreement between the predicted and observed values of mobility decline at 2 years (Fig. 1/Appendix 4).
The DCA plot showed (Fig. 3A , Model 1) a clear net benefit gain of the prediction model over the entire range of selected thresholds (0–50%).
Fig. 3Decision curve analysis for participants in the OPAL cohort study. Model 1 (A) and Model 2 (B). OPAL, Oxford Pain, Activity and Lifestyle.
The optimism-corrected C-statistic estimate was 0.734 (optimism = 0.010). The model’s performance on internal validation showed only a slight reduction in the C-statistic, indicating low overfitting.
The y-axis shows the net benefit of Model 1 (A) and Model 2 (B). The x-axis of DCA is the threshold of the predicted probability using Model 1 (A) and Model 2 (B) to classify individuals with and without mobility decline. The DCA compares the net benefits of an intervention in three scenarios: intervention for all individuals regardless of prediction (black solid line), intervention for no individuals (black dotted line, horizontal line), and intervention for individuals based on the prediction models (black dashed line); Model 1: 2-year risk of mobility decline; Model 2: 2-year risk of new-onset persistent mobility problems. Models included predictors that selected at least 80% of the imputed datasets.
3.2.4 Point scoring system.
The point scoring system for Model 1 had a score range from 0 to 39 with predicted risks corresponding to each point score ranging from 0.9% (0 points) to 90.1% (39 points) (Tables 1 and 2/Appendix 5). The median C-statistic of this scoring system was 0.734 (range: 0.730–0.735), similar to the regression model indicating no noticeable loss in performance.
3.3 Model 2: 2-year risk of new-onset persistent mobility problems
3.3.1 Participants.
Participants with no mobility problems at baseline who were alive at 2 years of follow-up were included (n = 3,146) (Fig. 1). The mean age (standard deviation) was 73.3 (5.8) years, 51.0% (1,604/3,146) were women, and 7.1% (n = 201/2,832) had new onset of persistent mobility problems.
3.3.2 Selection of predictor variables and model fitting.
In total, 28 variables (42 parameters) were included in this analysis. Six variables were selected as predictors of new-onset persistent mobility decline (Table 2, Model 2). These were slow usual walking pace, difficulty maintaining balance, low confidence to walk long distances and worse walking ability compared to last year, and two health-related factors (greater number of health conditions and poor general health assessed by EuroQol Visual Analog Scale (EQ-VAS)).
3.3.3 Model performance.
The median C-statistic was 0.765 (range: 0.762–0.768). The median Brier score was 0.069 (range: 0.068–0.070). Model calibration was good, with close agreement between predicted and observed values of new onset of persistent mobility problems (Fig. 2/Appendix 4).
Fig. 3B (Model 2) displays the net benefit curves equation. The net benefit clinical decision-making is superior across the range of risk thresholds of roughly 8–40%.
The average optimism of the C-statistic estimate was 0.013.
3.3.4 Point scoring system.
The point scoring systems is given in Tables 3 and 4/Appendix 5. The final score ranged from 0 to 22 with predicted risks corresponding to each point score ranging from 4.5% (0 points) to 53.8% (22 points). The model showed good predictive validity, with median C-statistic of 0.754 (range: 0.750–0.757).
4. Discussion
We developed and internally validated two prediction models for self-reported mobility decline in community-dwelling older adults aged 65 years and over, using self-reported measures of health and mobility measures that are easy to collect and could be used in clinical practice. Both models demonstrated moderate to good prediction capacity to identify individuals at 2-year risk of mobility decline. Model calibration showed good agreement between the observed and predicted mobility change. Both models also showed greater net benefit across a range of thresholds compared with not using any model.
Many of the variables identified as important predictors in both our models were associated with mobility decline in a recent systematic literature we conducted to inform this work [
We found evidence that older age, perceived inadequacy of income, preclinical mobility modifications (ie, use of walking aid, change in walking ability), lower limb pain, comorbidities [with diabetes and arthritis the most important individual conditions in our study (data not shown)], physical tiredness, and self-reported general health are predictors of mobility decline. The evidence for balance difficulties, general pain/discomfort severity, and body mass index was previously limited or unclear, but our findings confirm these as predictors of mobility decline [
Self-reported predictors newly identified by this study include confidence to walk half a mile and self-reported speed of usual walking pace. Self-reported slow, or very slow, usual walking pace at baseline was one of the most important predictors of mobility decline in this study, and has previously been validated as a marker of measured walking speed [
Pain characteristics associated with the onset of disability in older adults: the maintenance of balance, independent living, intellect, and zest in the Elderly Boston Study.
]. Discrepancies may be due to the following reasons: differences in the way predictors and outcome were measured; the different sets of predictors/confounders included in the model; and/or those studies that did not consider severity of pain as an independent variable in their models.
Studies developing prediction models for the onset of mobility problems over a short period of time (less than 5 years of follow-up) are scarce [
Ability of self-reported frailty components to predict incident disability, falls, and all-cause mortality: results from a population-based study of older British men.
]. This study was based on high physically and cognitively functioning women, aged 70–80 years, in the United States. Both models included self-reported preclinical mobility limitations; however, our model (Model 2) had better performance and is simpler to implement because it is based on self-reported variables only. In a different model, Papachristou et al. included age, slow walking speed, physically inactivity, and exhaustion as predictors. The C-statistic of their model was smaller (C-statistic = 0.68), and measures of calibration were not assessed. Reynolds and Silverstein constructed a prediction model for onset of walking among community-dwelling adults aged 70 years and older. They reported a C-statistic = 0.82, for their full model including 42 baseline and time-dependent predictors but did not report the statistic for their abbreviated clinical model. None of the previous studies have used rigorous internal validation.
Our prediction models provide health care professionals with a small set of easy to collect variables that discriminate well in the prediction of mobility decline in older men and women living in the community. The number and format of variables in the risk prediction model, mean that it could be used during a routine clinical consultation. Some of these variables are also modifiable and can effectively be targeted through rehabilitation. First, there are several variables that are markers of reduced walking ability and balance. These include slow walking pace, reduced confidence to walk longer distance, and difficulty maintaining balance. We, and others, have demonstrated that walking ability can be improved through a progressive tailored rehabilitation program that targets walking confidence, walking ability (distance and speed), muscle strength, and balance [
The clinical effectiveness of a physiotherapy delivered physical and psychological group intervention for older adults with neurogenic claudication: the BOOST randomised controlled trial.
]. Second, lower limb pain, such as that arising from hip and knee osteoarthritis, is common in older adults and can be effectively managed through rehabilitation incorporating pain self-management, coping strategies, and exercise, as well as surgery [
Clinical effectiveness of a rehabilitation program integrating exercise, self-management, and active coping strategies for chronic knee pain: a cluster randomized trial.
]. General bodily pain severity was also a predictor suggesting that pain management should be considered more broadly (not just for lower limb pain) as it is often not prioritized against a backdrop of other health conditions by older people themselves [
The OPAL cohort study represents a large, representative prospective cohort study with baseline characteristics similar to those in general English population of the same age [
]. This study reports the first prediction model for self-reported mobility decline in a community-dwelling older adults in England. A wide range of predictors were used. The reproducibility of the model was ensured by using MI and penalized regression methods, so that only significant predictors were selected, and anomalous predictors were rejected, reducing the risk of overfitting.
There are several potential limitations to this study. Self-reported mobility variables can be criticized because of subjectivity, but they provide important information about the difficulties people perceive in their everyday environment, can incorporate time-varying estimations such as frequency of balance problems, and are thus clinically highly relevant. Self-reported predictors may be particularly useful in clinical settings face to face or by telephone, or postal follow-up of cohort study participants when more-intense testing is impractical because of time, cost, space, or venue-related concerns. Although the main study mobility outcome was based on EQ-5D-self-report, this is known to have strong validity for measuring mobility performance in older adults [
We excluded 51 participants who could not walk at baseline, and it is theoretically possible that they could have recovered during follow up. Third, we did not externally validate the models although bootstrap validation takes model over-optimism into account and is considered better than data splitting [
]. Fourth, there were differences between characteristics of participants who remained in the study and those who withdrew, and this may have introduced attrition bias. Although MI was used to reduce the potential biases, this may not overcome all the bias associated with withdrawals. Finally, the small number of participants who died during the first 2 years were excluded from the main analyses.
5. Conclusion
We have derived and internally validated two easy-to-use prediction models that quantify absolute risk of mobility decline in community-dwelling older people. Our models are based on self-reported health and mobility questions that are easy to collect and can inform clinical decision-making regarding the need for rehabilitation aimed at preventing mobility decline and maintaining independence in older age. The models displayed good discrimination.
Acknowledgments
The authors would like to thank the individuals who participated in The Oxford Pain, Activity and Lifestyle (OPAL) cohort study, the general practitioners and their staff for assisting with the identification and invitation of eligible individuals, and the NIHR Biomedical Research Center, Oxford for funding this study (grant reference PTC-RP-PG-0213-20002).
OPAL study team: Conway O, Darton F, Dutton S, Garrett A, Hagan D, Haywood D, Hewitt A, Lamb S, Marian I, Morris A, Nevay L, Nicolson P, Sanchez-Santos MT, Bruce J, Slark M, Vadher K, Ward L, Watson M, Williamson E, Arden N, Barker K, Collins G, Fairbank J, Fitch J, French D, Griffiths F, Hanson Z, Hutchinson C, Mallen C, and Petrou S.
OPAL general practice team: Grange Hill Surgery, Birmingham; Gosford Hill Medical Center, Oxford; River Brook Medical Center, Birmingham; The Key Medical Practice, Oxford; Summertown Health Center, Oxford; Alconbury and Brampton Surgeries, Cambridgeshire; Old Exchange Surgery, Cambridgeshire; The Wand Medical Center, Birmingham; Kingsfield Medical Center, Birmingham; Buckden and Little Paxton Surgery, Cambridgeshire; Temple Cowley Medical Group, Oxford; Keynell Covert Surgery, Birmingham; Burbury Medical Center, Birmingham; Hollow Way Medical Center, Oxford; Priory View Medical Center, Leeds; Newton Surgery, Leeds; Priory Fields Surgery, Cambridgeshire, Cromwell Place Surgery, Cambridgeshire; Craven Road Medical Center, Leeds; Queslett Medical Practice, Birmingham; Hall Street Medical Center, Saint Helens; Vauxhall Health Center, Liverpool; Ireland Wood Surgery, Leeds; Civic Medical Center, Wirral; Brownlow Group Practice, Liverpool; Wareham Surgery, Dorset; The Adam Practice, Poole; The Harvey Practice, Dorset; Three Chequers Medical Practice, Salisbury; Gate Medical Center, Birmingham; Rendcomb Surgery, Cirencester; Cotswold Medical Practice, Cheltenham; Brigstock and South Norwood Partnership, Croydon; Portland Practice, Gloucestershire; Eversley Medical Center, Croydon.
Supporting NIHR clinical research networks (CRN): Thames Valley and South Midlands, Eastern; Yorkshire and the Humber, North West Coast; Wessex, West of England; West Midlands, South London.
Ability of self-reported frailty components to predict incident disability, falls, and all-cause mortality: results from a population-based study of older British men.
Pain characteristics associated with the onset of disability in older adults: the maintenance of balance, independent living, intellect, and zest in the Elderly Boston Study.
The clinical effectiveness of a physiotherapy delivered physical and psychological group intervention for older adults with neurogenic claudication: the BOOST randomised controlled trial.
Clinical effectiveness of a rehabilitation program integrating exercise, self-management, and active coping strategies for chronic knee pain: a cluster randomized trial.
Funding: This research is funded by the NIHR Programme Grants for Applied Research (reference: PTC-RP-PG-0213-20002). Preparatory work for the program of research was supported by the NIHR Collaborations for Leadership in Applied Health Research and Care (CLAHRC). Christian D. Mallen is funded by the National Institute for Health Research (NIHR) Applied Research Collaboration (West Midlands), the NIHR School for Primary Care Research, and an NIHR Research Professorship in General Practice (NIHR-RP-2014-04-026). Julie Bruce is supported by NIHR Research Capability Funding via University Hospitals Coventry and Warwickshire.
Ethical considerations: Written informed consent was provided by all participants, and approval for the study was given by the London–Brent Research Ethics Committee (16/LO/0348) on March 10, 2016.
Disclosure: The views expressed in this article are those of the authors and not necessarily those of the NHS, the NIHR, our funding bodies, or the Department of Health and Social Care.
Conflict of interest statement: Sarah E. Lamb reports and declared competing interests of authors: Sarah E. Lamb was on the Health Technology Assessment (HTA) Additional Capacity Funding Board, HTA End of Life Care and Add-on Studies Board, HTA Prioritisation Group Board, and the HTA Trauma Board. All other authors declare no conflicts of interest.
Author contributions: M.T.S.S. participated in the data preparation, analysis, and interpretation; and the development and writing of the paper. E.W. participated in the OPAL study design, data collection and interpretation of the results of the paper. P.J.A.N., J.B., C.D.M., and F.G. participated in the OPAL study design and interpretation of findings. G.S.C. participated in the analysis and interpretation of findings. A.G., A.M., and M.S. participated in the design of the OPAL study, data collection, data preparation and interpretation of findings. S.E.L. conceived the study, secured funding, and oversaw all aspects as principal investigator. S.E.L. participated in the design and execution of the OPAL study, and the development and writing of the paper. All authors contributed and approved the final manuscript.
00223-2&id=gr1.jpg){kind=link}
00223-2&id=gr2.jpg){kind=link}
00223-2&id=gr3.jpg){kind=link}