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BackgroundChronic diseases are generating a major health and societal burden worldwide. Healthy lifestyles, including physical activity (PA), have proven efficacy in the prevention and treatment of many chronic conditions. But, so far, national PA surveillance systems, as well as strategies for promotion of PA, have shown low impact. We hypothesize that personalized modular PA services, aligned with healthcare, addressing the needs of a broad spectrum of individual profiles may show cost-effectiveness and sustainability.MethodsThe current manuscript describes the protocol for regional implementation of collaborative self-management services to promote PA in Catalonia (7.5 M habitants) during the period 2017–2019. The protocols of three implementation studies encompassing a broad spectrum of individual needs are reported. They have a quasi-experimental design. That is, a non-randomized intervention group is compared to a control group (usual care) using propensity score methods wherein age, gender and population-based health risk assessment are main matching variables. The principal innovations of the PA program are: i) Implementation of well-structured modular interventions promoting PA; ii) Information and communication technologies (ICT) to facilitate patient accessibility, support collaborative management of individual care plans and reduce costs; and iii) Assessment strategies based on the Triple Aim approach during and beyond the program deployment.DiscussionThe manuscript reports a precise roadmap for large scale deployment of community-based ICT-supported integrated care services to promote healthy lifestyles with high potential for comparability and transferability to other sites.Trial registrationThis study protocol has been registered at ClinicalTrials.org (NCT02976064). Registered November 24th, 2016.Electronic supplementary materialThe online version of this article (10.1186/s12913-018-3363-8) contains supplementary material, which is available to authorized users.

BackgroundPatients suffering obstructive sleep apnea are mainly treated with continuous positive airway pressure (CPAP). Although it is a highly effective treatment, compliance with this therapy is problematic to achieve with serious consequences for the patients’ health. Unfortunately, there is a clear lack of clinical analytical tools to support the early prediction of compliant patients.MethodsThis work intends to take a further step in this direction by building compliance classifiers with CPAP therapy at three different moments of the patient follow-up, before the therapy starts (baseline) and at months 1 and 3 after the baseline.ResultsResults of the clinical trial shows that month 3 was the time-point with the most accurate classifier reaching an f1-score of 87% and 84% in cross-validation and test. At month 1, performances were almost as high as in month 3 with 82% and 84% of f1-score. At baseline, where no information of patients’ CPAP use was given yet, the best classifier achieved 73% and 76% of f1-score in cross-validation and test set respectively. Subsequent analyzes carried out with the best classifiers of each time point revealed baseline factors (i.e. headaches, psychological symptoms, arterial hypertension and EuroQol visual analog scale) closely related to the prediction of compliance independently of the time-point. In addition, among the variables taken only during the follow-up of the patients, Epworth and the average nighttime hours were the most important to predict compliance with CPAP.ConclusionsBest classifiers reported high performances after one month of treatment, being the third month when significant differences were achieved with respect to the baseline. Four baseline variables were reported relevant for the prediction of compliance with CPAP at each time-point. Two characteristics more were also highlighted for the prediction of compliance at months 1 and 3.Trial registrationClinicalTrials.gov Identifier, NCT03116958. Retrospectively registered on 17 April 2017.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0657-z) contains supplementary material, which is available to authorized users.

Due to the growing incidence of chronic diseases and aging populations, the pressure to control costs and the expectations of continuous improvements in the quality of service have increased the need to understand how healthcare is provided and to determine whether cost-effective improvements to care practices can be made. In the case of people suffering Obstructive Sleep Apnea, patients using self-administer nasal Continuous Positive Airway Pressure (CPAP) may receive information on the treatment only once they go to a visit with the lung specialist. In this paper, we propose an IoT-based Intelligent Monitoring System that relies on machine learning to achieve a threefold goal: (1) it is aimed at early detecting compliance in order to predict CPAP usage; (2) it monitors the actual adherence degree to the treatment to keep informed both the patient and the lung specialists; and (3) it sends recommendations to the patient to empower her/him and to better follow up.