Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study.

Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this patient population. The aim of this study was to validate step counting with an activity-tracking application running the inbuilt Android smartphone pedometer virtual sensor in patients with RA. Two Android-based smartphones were tested in a treadmill test-bed setup at six walking speeds and compared to manual step counting as the gold standard. Guided by a facilitator, the participants walked 100 steps at each test speed, from 2.5 km/h to 5 km/h, wearing both devices simultaneously in a stomach pouch. A computer automatically recorded both the manually observed and the sensor step count. The overall difference in device step counts versus the observed was 5.9% mean absolute percentage error. Highest mean error was at the 2.5 km/h speed tests, where the mean error of the two devices was 18.5%. Both speed and cadence were negatively correlated to the absolute percentage error, which indicates that the greater the speed and cadence, the lower the resulting step counting error rate. There was no correlation between clinical parameters and absolute percentage error. In conclusion, the activity-tracking application using the inbuilt Android smartphone pedometer virtual sensor is valid for step counting in patients with RA. However, walking at very low speed and cadence may represent a challenge.

Interventions promoting recovery from depression for patients transitioning from outpatient mental health services to primary care: A scoping review.

INTRODUCTION: Major Depressive Disorder (MDD) is one of the most prevalent mental disorders worldwide with significant personal and public health consequences. After an episode of MDD, the likelihood of relapse is high. Therefore, there is a need for interventions that prevent relapse of depression when outpatient mental health care treatment has ended. This scoping review aimed to systematically map the evidence and identify knowledge gaps in interventions that aimed to promote recovery from MDD for patients transitioning from outpatient mental health services to primary care. MATERIALS AND METHODS: We followed the guidance by Joanna Briggs Institute in tandem with the PRISMA extension for Scoping Reviews checklist. Four electronic databases were systematically searched using controlled index-or thesaurus terms and free text terms, as well as backward and forward citation tracking of included studies. The search strategy was based on the identification of any type of intervention, whether simple, multicomponent, or complex. Three authors independently screened for eligibility and extracted data. RESULTS: 18 studies were included for review. The studies had high heterogeneity in design, methods, sample size, recovery rating scales, and type of interventions. All studies used several elements in their interventions; however, the majority used cognitive behavioural therapy conducted in outpatient mental health services. No studies addressed the transitioning phase from outpatient mental health services to primary care. Most studies included patients during their outpatient mental health care treatment of MDD. CONCLUSIONS: We identified several knowledge gaps. Recovery interventions for patients with MDD transitioning from outpatient mental health services to primary care are understudied. No studies addressed interventions in this transitioning phase or the patient's experience of the transitioning process. Research is needed to bridge this gap, both regarding interventions for patients transitioning from secondary to primary care, and patients' and health care professionals' experiences of the interventions and of what promotes recovery. REGISTRATION: A protocol was prepared in advance and registered in Open Science Framework (https://osf.io/ah3sv), published in the medRxiv server (https://doi.org/10.1101/2022.10.06.22280499) and in PLOS ONE (https://doi.org/10.1371/journal.pone.0291559).