Effectiveness of the eVISualisation of physical activity and pain intervention (eVIS) in Swedish Interdisciplinary Pain Rehabilitation Programmes: study protocol for a registry-based randomised controlled clinical trial.

INTRODUCTION: Living with chronic pain often involves negative consequences. Interdisciplinary Pain Rehabilitation Programmes (IPRP) is considered superior to single-treatment measures in patients with chronic pain. Despite this, effects emerge suboptimal and more than 20% of patients deteriorate in patient-reported physical health outcomes after IPRP. A novel e-Health intervention, eVISualisation (eVIS) of physical activity and pain, was systematically developed to facilitate individualisation of physical activity levels. By adding elements of data collection, visualisation and communication of objectively measured physical activity and patient-reported outcomes (pain intensity, interference of pain, pharmaceutical consumption) to existing treatment modalities in IPRP, the IPRP team acquires prerequisites to adapt advice and physical activity prescriptions and to evaluate set activity goals. The overall aim is twofold. First, the aim is to evaluate the feasibility of the subsequent registry-based randomised controlled clinical trial (R-RCT). Second, the aim is to prospectively evaluate the effectiveness of the eVIS-intervention as a supplement to IPRP on our defined primary (physical health) and secondary outcomes. METHODS AND ANALYSIS: In the R-RCT, recruitment of 400 patients with chronic pain will be performed at 15 IPRP units. A random allocation to either IPRP + eVIS or to control group that will receive IPRP only will be performed. Data from the initial 30 participants completing the study period (6 months) will be included in a pilot study, where key feasibility outcomes (recruitment, randomisation, implementation, treatment integrity, data collection procedure, preliminary outcome measures) will be evaluated. Outcome variables will be extracted from the web application Pain And TRaining ON-line (PATRON) and from six national registries. Multivariate statistics and repeated measure analyses will be performed. Quality-adjusted life years and incremental cost-effectiveness ratio will be calculated for cost-effectiveness evaluation. ETHICS/DISSEMINATION: The Swedish Ethics Review Board granted approval (Dnr 2021/02109). Results will be disseminated through peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT05009459. Protocol V.1.

Predictors of Sickness Absence in a Clinical Population With Chronic Pain.

Chronic pain-related sickness absence is an enormous socioeconomic burden globally. Optimized interventions are reliant on a lucid understanding of the distribution of social insurance benefits and their predictors. This register-based observational study analyzed data for a 7-year period from a population-representative sample of 44,241 chronic pain patients eligible for interdisciplinary treatment (IDT) at specialist clinics. Sequence analysis was used to describe the sickness absence over the complete period and to separate the patients into subgroups based on their social insurance benefits over the final 2 years. The predictive performance of features from various domains was then explored with machine learning-based modeling in a nested cross-validation procedure. Our results showed that patients on sickness absence increased from 17% 5 years before to 48% at the time of the IDT assessment, and then decreased to 38% at the end of follow-up. Patients were divided into 3 classes characterized by low sickness absence, sick leave, and disability pension, with eight predictors of class membership being identified. Sickness absence history was the strongest predictor of future sickness absence, while other predictors included a 2008 policy, age, confidence in recovery, and geographical location. Information on these features could guide personalized intervention in the specialized healthcare. PERSPECTIVE: This study describes sickness absence in patients who visited a Swedish pain specialist interdisciplinary treatment clinic during the period 2005 to 2016. Predictors of future sickness absence are also identified that should be considered when adapting IDT programs to the patient's needs.