Myths and lessons of liberal intervention: The British campaign for the abolition of the Atlantic slave trade to Brazil

This is the Pre-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 Martinus NijhoffThis article takes issue with recent references to the British nineteenth century campaign for the abolition of the trans-Atlantic slave trade to Brazil that serve to bolster interventionist or imperialist agendas. In particular, such accounts reproduce two and a half myths about the campaign: that it can serve as a model for the present age; that the success of the campaign can be explained through the actions of the intervening party alone (with a corresponding neglect of those of the ‘target’ state); and the half-myth that the campaign’s success was due to military action (at the expense of institutional (legal) and normative factors and the capacity of the target state). I argue instead that this case – and interventions more generally – would benefit from an analysis that considers the role of force in relation to a series of residual institutional and cultural constraints within the liberal state and to political conditions in the target state. In light of the complexities and contingencies that these factors present the underlying lesson is that military force should be used sparingly, if at all

Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?

In healthcare, physical activity can be monitored in two ways: self-monitoring by the patient himself or external monitoring by health professionals. Regarding self-monitoring, wearable activity trackers allow automated passive data collection that educate and motivate patients. Wearing an activity tracker can improve walking time by around 1500 steps per day. However, there are concerns about measurement accuracy (e.g., lack of a common validation protocol or measurement discrepancies between different devices). For external monitoring, many innovative electronic tools are currently used in rheumatology to help support physician time management, to reduce the burden on clinic time, and to prioritize patients who may need further attention. In inflammatory arthritis, such as rheumatoid arthritis, regular monitoring of patients to detect disease flares improves outcomes. In a pilot study applying machine learning to activity tracker steps, we showed that physical activity was strongly linked to disease flares and that patterns of physical activity could be used to predict flares with great accuracy, with a sensitivity and specificity above 95%. Thus, automatic monitoring of steps may lead to improved disease control through potential early identification of disease flares. However, activity trackers have some limitations when applied to rheumatic patients, such as tracker adherence, lack of clarity on long-term effectiveness, or the potential multiplicity of trackers

Patient e-health platform for Rheumatoid Arthritis: accuracy and adherence factors

Background: Personal health records (PHRs) are patient-controlled repositories, capturing health data entered by individuals and providing information related their care. These tools improve treatment adherence but data are scarce concerning tool adherence. The accuracy of the self-recorded data remains controversial. We assessed how support measures improve PHR adoption determined the factors that influence the accuracy of self-recorded data and tool adherence of RA patients.Methods: A controlled randomized study with a PHR tool with integrated electronic health records developed by SANOIA. RA patients with ACR/EULAR 2010 criteria with web access randomized into 3 groups: Group 1 patients were given written information to create and manage a PHR; Group 2 patients received written information and a web technician hotline 48 hours after inclusion; Group 3 patients began their PHR with their rheumatologist during the consultation.Results: 56 RA patients were included (female: 73%, mean age: 57.1, mean DAS28: 3.04, mean RAPID-3: 2.93). Self-reported data accuracy was significantly higher in Groups 2 (73.7%) and 3 (82.4%) than in Group 1 (45.0%), (P = 0.04). Patient adherence was higher in Group 2 (78.9%) compared with Groups 1 (55.0%) and 3 (58.8%) (P = 0.45). Accuracy was correlated to adhesion (P <0.0001). Gender, age, disease durationand activity, treatments, and patient level of interest were not correlated to data accuracy or patient adherence.Conclusion: Information accuracy collected with PHR was relevant and better when patients were initially assisted either by their physician or by non-medical phone support. We also observed better adherence when patients were initially assisted

Patient e-health platform for Rheumatoid Arthritis: accuracy and adherence factors

Background: Personal health records (PHRs) are patient-controlled repositories, capturing health data entered by individuals and providing information related their care. These tools improve treatment adherence but data are scarce concerning tool adherence. The accuracy of the self-recorded data remains controversial. We assessed how support measures improve PHR adoption determined the factors that influence the accuracy of self-recorded data and tool adherence of RA patients.Methods: A controlled randomized study with a PHR tool with integrated electronic health records developed by SANOIA. RA patients with ACR/EULAR 2010 criteria with web access randomized into 3 groups: Group 1 patients were given written information to create and manage a PHR; Group 2 patients received written information and a web technician hotline 48 hours after inclusion; Group 3 patients began their PHR with their rheumatologist during the consultation.Results: 56 RA patients were included (female: 73%, mean age: 57.1, mean DAS28: 3.04, mean RAPID-3: 2.93). Self-reported data accuracy was significantly higher in Groups 2 (73.7%) and 3 (82.4%) than in Group 1 (45.0%), (P = 0.04). Patient adherence was higher in Group 2 (78.9%) compared with Groups 1 (55.0%) and 3 (58.8%) (P = 0.45). Accuracy was correlated to adhesion (P <0.0001). Gender, age, disease durationand activity, treatments, and patient level of interest were not correlated to data accuracy or patient adherence.Conclusion: Information accuracy collected with PHR was relevant and better when patients were initially assisted either by their physician or by non-medical phone support. We also observed better adherence when patients were initially assisted

Patient e-health platform for Rheumatoid Arthritis: accuracy and adherence factors

Background: Personal health records (PHRs) are patient-controlled repositories, capturing health data entered by individuals and providing information related their care. These tools improve treatment adherence but data are scarce concerning tool adherence. The accuracy of the self-recorded data remains controversial. We assessed how support measures improve PHR adoption determined the factors that influence the accuracy of self-recorded data and tool adherence of RA patients.Methods: A controlled randomized study with a PHR tool with integrated electronic health records developed by SANOIA. RA patients with ACR/EULAR 2010 criteria with web access randomized into 3 groups: Group 1 patients were given written information to create and manage a PHR; Group 2 patients received written information and a web technician hotline 48 hours after inclusion; Group 3 patients began their PHR with their rheumatologist during the consultation.Results: 56 RA patients were included (female: 73%, mean age: 57.1, mean DAS28: 3.04, mean RAPID-3: 2.93). Self-reported data accuracy was significantly higher in Groups 2 (73.7%) and 3 (82.4%) than in Group 1 (45.0%), (P = 0.04). Patient adherence was higher in Group 2 (78.9%) compared with Groups 1 (55.0%) and 3 (58.8%) (P = 0.45). Accuracy was correlated to adhesion (P <0.0001). Gender, age, disease durationand activity, treatments, and patient level of interest were not correlated to data accuracy or patient adherence.Conclusion: Information accuracy collected with PHR was relevant and better when patients were initially assisted either by their physician or by non-medical phone support. We also observed better adherence when patients were initially assisted

Physical Activity Assessment Using an Activity Tracker in Patients with Rheumatoid Arthritis and Axial Spondyloarthritis: Prospective Observational Study

International audienceBackground: Physical activity can be tracked using mobile devices and is recommended in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) management. The World Health Organization (WHO) recommends at least 150 min per week of moderate to vigorous physical activity (MVPA).Objective: The objectives of this study were to assess and compare physical activity and its patterns in patients with RA and axSpA using an activity tracker and to assess the feasibility of mobile devices in this population.Methods: This multicentric prospective observational study (ActConnect) included patients who had definite RA or axSpA, and a smartphone. Physical activity was assessed over 3 months using a mobile activity tracker, recording the number of steps per minute. The number of patients reaching the WHO recommendations was calculated. RA and axSpA were compared, using linear mixed models, for number of steps, proportion of morning steps, duration of total activity, and MVPA. Physical activity trajectories were identified using the K-means method, and factors related to the low activity trajectory were explored by logistic regression. Acceptability was assessed by the mean number of days the tracker was worn over the 3 months (ie, adherence), the percentage of wearing time, and by an acceptability questionnaire.Results: A total of 157 patients (83 RA and 74 axSpA) were analyzed; 36.3% (57/157) patients were males, and their mean age was 46 (standard deviation [SD] 12) years and mean disease duration was 11 (SD 9) years. RA and axSpA patients had similar physical activity levels of 16 (SD 11) and 15 (SD 12) min per day of MVPA (P=.80), respectively. Only 27.4% (43/157) patients reached the recommendations with a mean MVPA of 106 (SD 77) min per week. The following three trajectories were identified with constant activity: low (54.1% [85/157] of patients), moderate (42.7% [67/157] of patients), and high (3.2% [5/157] of patients) levels of MVPA. A higher body mass index was significantly related to less physical activity (odds ratio 1.12, 95% CI 1.11-1.14). The activity trackers were worn during a mean of 79 (SD 17) days over the 90 days follow-up. Overall, patients considered the use of the tracker very acceptable, with a mean score of 8 out 10.Conclusions: Patients with RA and axSpA performed insufficient physical activity with similar levels in both groups, despite the differences between the 2 diseases. Activity trackers allow longitudinal assessment of physical activity in these patients. The good adherence to this study and the good acceptability of wearing activity trackers confirmed the feasibility of the use of a mobile activity tracker in patients with rheumatic diseases

Fluctuation of pain is frequent in rheumatoid arthritis and axial spondyloarthritis: a 12 weeks prospective study of 165 patients

International audienc

Development and validation of a Type 1 and Type 2 diabetes-specific patient-reported experience measure e-questionnaire: Diabetes reported experience measures (DREMS)

Introduction: Successful diabetes management is associated with an effective partnership between People with Diabetes (PwD) and healthcare professionals. Though possible to measure using Patient-Reported Experience Measures (PREMs), none are specific to Type 1 or Type 2 Diabetes (T1D/T2D) and validated in French. Thus, we developed and validated the DREMS (Diabetes Reported Experience MeasureS) e-questionnaire. Methodology: DREMS is comprised of 18 items evaluating 5 different factors. Validation for use by PwT1D and PwT2D (recruited online) was performed using: Exploratory Factor Analysis (EFA); Confirmatory Factor Analysis (CFA) and Cronbach's Alpha. Test-retest reliability was evaluated through Intraclass Correlation Coefficients (ICC) in a subsample. Results: DREMS was tested by 2,513 respondents, including 942 PwT1D and 1,571 PwT2D. For both groups, EFA results indicated 18 items loaded substantially onto 5 clear factors. CFA showed all coefficients were significant in their respective factors. Goodness-of-fit, assessed using the Comparative Fit Index was >0.90 and by the RMSEA was <0.080. Cronbach's α for the entire DREMS e-questionnaire was ≥0.90. ICC was 0.87 for PwT1D (n = 136) and 0.74 for PwT2D (n = 169). Innovation: DREMS is the first validated French-language diabetes-specific PREM for both PwT1D and PwT2D and can be useful to evaluate and improve health care management and patient health