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Development and feasibility of stratified primary care physiotherapy integrated with eHealth in patients with neck and/or shoulder complaints: results of a mixed methods study

Background Providing individualized care based on the context and preferences of the patient is important. Knowledge on both prognostic risk stratification and blended eHealth care in musculoskeletal conditions is increasing and seems promising. Stratification can be used to match patients to the most optimal content and intensity of treatment as well as mode of treatment delivery (i.e. face-to-face or blended with eHealth). However, research on the integration of stratified and blended eHealth care with corresponding matched treatment options for patients with neck and/or shoulder complaints is lacking. Methods This study was a mixed methods study comprising the development of matched treatment options, followed by an evaluation of the feasibility of the developed Stratified Blended Physiotherapy approach. In the first phase, three focus groups with physiotherapists and physiotherapy experts were conducted. The second phase investigated the feasibility (i.e. satisfaction, usability and experiences) of the Stratified Blended Physiotherapy approach for both physiotherapists and patients in a multicenter single-arm convergent parallel mixed methods feasibility study. Results In the first phase, matched treatment options were developed for six patient subgroups. Recommendations for content and intensity of physiotherapy were matched to the patient’s risk of persistent disabling pain (using the Keele STarT MSK Tool: low/medium/high risk). In addition, selection of mode of treatment delivery was matched to the patient’s suitability for blended care (using the Dutch Blended Physiotherapy Checklist: yes/no). A paperbased workbook and e-Exercise app modules were developed as two different mode of treatment delivery options, to support physiotherapists. Feasibility was evaluated in the second phase. Physiotherapists and patients were mildly satisfied with the new approach. Usability of the physiotherapist dashboard to set up the e-Exercise app was considered ‘OK’ by physiotherapists. Patients considered the e-Exercise app to be of ‘best imaginable’ usability. The paper-based workbook was not used. Conclusion Results of the focus groups led to the development of matched treatment options. Results of the feasibility study showed experiences with integrating stratified and blended eHealth care and have informed amendments to the Stratified Blended Physiotherapy approach for patients with neck and/or shoulder complaints ready to use within a future cluster randomized trial.

EACPR/AHA scientific statement. Clinical recommendations for cardiopulmonary exercise testing data assessment in specific patient populations

From an evidence-based perspective, cardiopulmonary exercise testing (CPX) is a well-supported assessment technique in both the United States (US) and Europe. The combination of standard exercise testing (ET) (ie, progressive exercise provocation in association with serial electrocardiograms [ECG], hemodynamics, oxygen saturation, and subjective symptoms) and measurement of ventilatory gas exchange amounts to a superior method to: 1) accurately quantify cardiorespiratory fitness (CRF), 2) delineate the physiologic system(s) underlying exercise responses, which can be applied as a means to identify the exercise-limiting pathophysiologic mechanism(s) and/or performance differences, and 3) formulate function-based prognostic stratification. Cardiopulmonary ET certainly carries an additional cost as well as competency requirements and is not an essential component of evaluation in all patient populations. However, there are several conditions of confirmed, suspected, or unknown etiology where the data gained from this form of ET is highly valuable in terms of clinical decision making

Clinical recommendations for cardiopulmonary exercise testing data assessment in specific patient populations. EACPR/AHA Joint Scientific Statement

From an evidence-based perspective, cardiopulmonary exercise testing (CPX) is a well-supported assessment technique in both the United States (US) and Europe. The combination of standard exercise testing (ET) [i.e. progressive exercise provocation in association with serial electrocardiograms (ECGs), haemodynamics, oxygen saturation, and subjective symptoms] and measurement of ventilatory gas exchange amounts to a superior method to: (i) accurately quantify cardiorespiratory fitness (CRF), (ii) delineate the physiologic system(s) underlying exercise responses, which can be applied as a means to identify the exercise-limiting pathophysiological mechanism(s) and/or performance differences, and (iii) formulate function-based prognostic stratification. Cardiopulmonary ET certainly carries an additional cost as well as competency requirements and is not an essential component of evaluation in all patient populations. However, there are several conditions of confirmed, suspected, or unknown aetiology where the data gained from this form of ET is highly valuable in terms of clinical decision making.1

The NIH minimal dataset for chronic low back pain: responsiveness and minimal

CC-BY-NC-NDSTUDY DESIGN:prospective cohort study.OBJECTIVE:To analyze responsiveness and minimal clinically important change (MCIC) of the US National Institutes of Health (NIH) minimal dataset for chronic low back pain (CLBP).SUMMARY OF BACKGROUND DATA:The NIH minimal dataset is a 40-item questionnaire developed to increase use of standardized definitions and measures for CLBP. Longitudinal validity of the total minimal dataset and the subscale Impact Stratification are unknown.METHODS:Total outcome scores on the NIH minimal dataset, Dutch Language Version, were calculated ranging from 0-100 points with higher scores representing worse functioning. Responsiveness and MCIC were determined with an anchor based method, calculating the area under the receiver operating characteristics (ROC) curve (AUC) and by determining the optimal cut-off point. Smallest detectable change (SDC) was calculated as a parameter of measurement error.RESULTS:In total 223 patients with CLBP were included. Mean total score on the NIH minimal dataset was 44 ± 14 points at baseline. The total outcome score was responsive to change with an AUC of 0.84. MCIC was 14 points with a sensitivity of 72% and specificity 82%, and SDC was 23 points. Mean total score on Impact Stratification (scale 8-50) was 34.4 ± 7.4 points at baseline, with an AUC of 0.91, an MCIC of 7.5 with a sensitivity 96% of and specificity of 78%, and an SDC of 14 points.CONCLUSION:The longitudinal validity of the NIH minimal dataset is adequate. An improvement of 14 points in total outcome score and 7.5 points in Impact Stratification can be interpreted as clinically important in individual patients. However, MCIC depends on baseline values and the method that is chosen to determine the optimal cut-off point. Furthermore, measurement error is larger than the MCIC. This means that individual change scores should be interpreted with caution.LEVEL OF EVIDENCE:4This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal

Construct validity of the octopus stratification algorithm for allocating patients with knee osteoarthritis into clinically relevant subgroups

Construct validity of the OCTOPuS stratification algorithm for allocating patients with knee osteoarthritis into subgroups

BACKGROUND: We recently developed a model of stratified exercise therapy, consisting of (i) a stratification algorithm allocating patients with knee osteoarthritis (OA) into one of the three subgroups ('high muscle strength subgroup' representing a post-traumatic phenotype, 'low muscle strength subgroup' representing an age-induced phenotype, and 'obesity subgroup' representing a metabolic phenotype) and (ii) subgroup-specific exercise therapy. In the present study, we aimed to test the construct validity of this algorithm.METHODS: Data from five studies (four exercise therapy trial cohorts and one cross-sectional cohort) were used to test the construct validity of our algorithm by 63 a priori formulated hypotheses regarding three research questions: (i) are the proportions of patients in each subgroup similar across cohorts? (15 hypotheses); (ii) are the characteristics of each of the subgroups in line with their proposed underlying phenotypes? (30 hypotheses); (iii) are the effects of usual exercise therapy in the 3 subgroups in line with the proposed effect sizes? (18 hypotheses).RESULTS: Baseline data from a total of 1211 patients with knee OA were analyzed for the first and second research question, and follow-up data from 584 patients who were part of an exercise therapy arm within a trial for the third research question. In total, the vast majority (73%) of the hypotheses were confirmed. Regarding our first research question, we found similar proportions in each of the three subgroups across cohorts, especially for three cohorts. Regarding our second research question, subgroup characteristics were almost completely in line with the proposed underlying phenotypes. Regarding our third research question, usual exercise therapy resulted in similar, medium to large effect sizes for knee pain and physical function for all three subgroups.CONCLUSION: We found mixed results regarding the construct validity of our stratification algorithm. On the one hand, it is a valid instrument to consistently allocate patients into subgroups that aligned our hypotheses. On the other hand, in contrast to our hypotheses, subgroups did not differ substantially in effects of usual exercise therapy. An ongoing trial will assess whether this algorithm accompanied by subgroup-specific exercise therapy improves clinical and economic outcomes.