With the 2 new references Jiang, J., Zhu, Q., Zheng, Y., Zhu, Y., Li, Y., & Huo, Y. (2019). Perceptions and acceptance of mHealth in patients with cardiovascular diseases: a cross-sectional study. JMIR mHealth and uHealth, 7(2), e10117.
Klimis, H., Thakkar, J., & Chow, C. K. (2018). Breaking barriers: mobile health interventions for cardiovascular disease. Canadian Journal of Cardiology, 34(7), 905-913.
• Compared and contrasted with 2 studies (designs, sample, sample size, measures, p values, statistical significance of findings, decision criteria, strengths, weaknesses, etc.) lient:
Sample: The first study, published in 2016 by Mitsuru et al., is a RCT evaluating the effectiveness of a text message-based surveillance system in reporting disease outbreaks in Kenya from November 2013 to April 2014. Sixty-six healthcare facilities were randomly assigned to the intervention group (IG) and sixty-five were randomly assigned to the control group (CG). Both groups were asked to report cases of EID using the standard, paper-based reporting system. The IG received additional training on how to use a mobile outbreak alert system (mSOS) and was required to submit alerts using both systems. At the conclusion of the six-month study, the IG had reported 130 cases and the CG had reported 39 cases. 19.2% of IG-reported cases led to response actions being taken as compared to 2.6% in the CG. Use of mSOS did increase the number of reported cases but resulting action steps were still minimal. This study demonstrates the usability of a mobile phone-based system, but the solution fails to impact the response taken to control disease. Study limitations include a large drop-out rate (47.4%) for participating facilities and confounding variables such as the ineffectiveness of paper-based reporting.
Similarly, Ratnayake et al. (2016) published a prospective cohort study testing the sensitivity and timeliness of a mobile phone, community event-based surveillance (CEBS) system reporting Ebola virus cases during the 2014/2016 epidemic in Sierra Leone. The study participants, including 7,416 “community health monitors” and 137 “surveillance supervisors”, reported Ebola virus “trigger events” via mobile phones. This method was compared to the national surveillance system, which consisted of “contact tracing, healthcare facility surveillance, and a telephone hotline for reporting events” (Ratnayake et al., 2016, p. 1). The study found that the intervention was responsible for 30% of reported cases during the study timeframe and cases were generally reported more rapidly through CEBS than through the existing surveillance routes. The study lacks treatment fidelity as no discussion was provided on how the sample size changed or whether participants received training.
Among patient with established cardiovascular disease, does the interventions of mobile health compare to in-personal visit effect adherence to pharmacologic and nonpharmacologic therapy and secondary prevention of cardiovascular disease.
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