Role of technology-based innovation in chronic disease management in rheumatology

Background Mobile health (mHealth) apps represent a promising approach for improving health outcomes in patients with chronic illness, but surprisingly few mHealth interventions have investigated the association between user engagement and health outcomes. We aimed to examine the efficacy of a recommended, commercially available gout self-management app for improving self-care behaviors and to assess self-reported user engagement of the app in a sample of adults with gout. Objective Our objective was to examine differences in self-reported user engagement between a recommended gout app (treatment group) and a dietary app (active control group) over 2 weeks as well as to examine any differences in self-care behaviors and illness perceptions. Methods Seventy-two adults with gout were recruited from the community and three primary and secondary clinics. Participants were randomized to use either Gout Central (n=36), a self-management app, or the Dietary Approaches to Stop Hypertension Diet Plan (n=36), an app based on a diet developed for hypertension, for 2 weeks. The user version of the Mobile Application Rating Scale (uMARS, scale: 1 to 5) was used after the 2 weeks to assess self-reported user engagement, which included an open-ended question. Participants also completed a self-report questionnaire on self-care behaviors (scale: 1-5 for medication adherence and diet and 0-7 for exercise) and illness perceptions (scale: 0-10) at baseline and after the 2-week trial. Independent samples t tests and analysis of covariance were used to examine differences between groups at baseline and postintervention. Results Participants rated the gout app as more engaging (mean difference –0.58, 95% CI –0.96 to –0.21) and more informative (mean difference –0.34, 95% CI –0.67 to –0.01) than the dietary app at the 2-week follow-up. The gout app group also reported a higher awareness of the importance of gout (mean difference –0.64, 95% CI –1.27 to –0.003) and higher knowledge/understanding of gout (mean difference –0.70, 95% CI –1.30 to –0.09) than the diet app group at follow-up. There were no significant differences in self-care behaviors between the two groups postintervention. The gout app group also demonstrated stronger negative beliefs regarding the impact of gout (mean difference –2.43, 95% CI –3.68 to –1.18), stronger beliefs regarding the severity of symptoms (mean difference –1.97, 95% CI –3.12 to –0.82), and a stronger emotional response to gout (mean difference –2.38, 95% CI –3.85 to –0.90) at follow-up. Participant feedback highlighted the importance of tracking health-related information, customizing to the target group/individual, providing more interactive features, and simplifying information. Conclusions Participants found the commercially available gout app more engaging. However, these findings did not translate into differences in self-care behaviors. The gout app group also demonstrated stronger negative illness perceptions at the follow-up. Overall, these findings suggest that the development of gout apps would benefit from a user-centered approach with a focus on daily, long-term self-care behaviors as well as modifying illness beliefs. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12617001052325; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373217.

Study Objectives: The objective of this study was to evaluate the performance of a miniaturized home sleep apnea test, called NightOwl. The system consists of a sensor placed on the fingertip and a cloud-based analytics software. The sensor acquires accelerometer and photoplethysmographic data. The software derives actigraphy from the former, and blood oxygen saturation and peripheral arterial tone, among other features, from the latter. Methods: Data of 101 participants who underwent an in-laboratory polysomnography (PSG), while wearing the NightOwl sensor, were collected. In order to establish an external benchmark, all PSG tests were edited by a somnologist of Younes Medical Technologies Ltd. (YMT) after analysis by the Michele Sleep Scoring System (MSSS). The respiratory event index (REI) derived by NightOwl (NightOwl-REI), the apnea-hypopnea index (AHI) derived by Ziekenhuis Oost-Limburg (ZOL-AHI), and the AHI derived by YMT (MSSS-AHI) were compared. Results: The NightOwl-REI had a high correlation with the MSSS-AHI (ρ = .87, P < .001), which was close to the correlation between the ZOL-AHI and MSSS-AHI (ρ = .84, P < .001). The NightOwl-REI and ZOL-AHI had a correlation of .77 ( P < .001). After categorization of the AHI, the agreement between the NightOwl-REI and the MSSS-AHI was .812 and the agreement between the ZOL-AHI and MSSS-AHI was .743, after double-labeling near-boundary participants. Conclusions: The NightOwl-REI achieved a close correlation and REI-categorization with the MSSS-AHI, especially in light of the significant inter-scorer variability of the analysis of the PSG. Citation: Massie F, Mendes de Almeida D, Dreesen P, Thijs I, Vranken J, Klerkx S. An evaluation of the NightOwl home sleep apnea testing system. J Clin Sleep Med. 2018;14(10):1791–1796.

Objective To assess the feasibility of Fractional exhaled Nitric Oxide (FeNO) as a simple, non-invasive, cost-effective and portable biomarker and decision support tool for risk stratification of COVID-19 patients. Methods We conducted a single-center prospective cohort study of COVID-19 patients whose FeNO levels were measured upon ward admission by the Vivatmo-me handheld device. Demographics, COVID-19 symptoms, and relevant hospitalization details were retrieved from the hospital databases. The patients were divided into those discharged to recover at home and those who died during hospitalization or required admission to an intensive care unit, internal medicine ward, or dedicated facility (severe outcomes group). Results Fifty-six patients were enrolled. The only significant demographic difference between the severe outcomes patients (n = 14) and the home discharge patients (n = 42) was age (64.21 ± 13.97 vs. 53.98 ± 15.57 years, respectively, P = .04). The admission FeNO measurement was significantly lower in the former group compared with the latter group (15.86 ± 14.74 vs. 25.77 ± 13.79, parts per billion [PPB], respectively, P = .008). Time to severe outcome among patients with FeNO measurements ≤11.8 PPB was significantly shorter compared with patients whose FeNO measured >11.8 PPB (19.25 ± 2.96 vs. 24.41 ± 1.09 days, respectively, 95% confidence interval [CI] 1.06 to 4.25). An admission FeNO ≤11.8 PPB was a significant risk factor for severe outcomes (odds ratio = 12.8, 95% CI: 2.78 to 58.88, P = .001), with a receiver operating characteristics curve of 0.752. Conclusions FeNO measurements by the Vivatmo-me handheld device can serve as a biomarker and COVID-19 support tool for medical teams. These easy-to-use, portable, and noninvasive devices may serve as valuable ED bedside tools during a pandemic.