Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?

Abstract

The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed noninfected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data

“…If specificity versus flu and other respiratory viruses cannot be demonstrated, early-warning systems triggered on wearable data should be considered as more non-specific “infection screening,” and therefore be coupled with appropriate confirmatory testing mechanisms that can help to quickly relieve self-imposed quarantine of non-COVID-19 infections. 17 , 18 …”

“… 14 , 16 Therefore, wearables could potentially support use cases, such as return to work and college reopening, 18 where most of the cohort can be asked to wear the sensors frequently. 17 To better understand feasibility, however, further research is needed. First, it is important to accurately quantify the sensitivity of wearable-based alert systems in prospective validation, and especially for asymptomatic/pre-symptomatic patients (who collectively seem to be responsible for more than 40% of the total infections).…”

“…In addition to being used in aggregate form for population-level hotspot detection, PGHD is also being proposed as a candidate for individual-level applications, such as to support detection and monitoring of COVID-19 and other respiratory viruses. 14 , 15 , 16 , 17 , 18 …”

Emerging Telemedicine Tools for Remote COVID-19 Diagnosis, Monitoring, and Management

“…59 Additionally, monitoring physiological signals of patients continuously may offer a deeper understanding of the development of COVID-19 infections as well as the process of recovery or potential long-term sequelae. 60 , 61 Population level mHealth monitoring will unveil the true incidence among communities, guide local reopening policies, and provide an early warning system to help reduce viral transmission and mortality rate. In this section, we summarize the clinical translation of physiological biomarkers that are strongly related with COVID-19 symptoms, introduce the wearable sensor platforms developed to track them, and then discuss the current progress of using these wearable vital sign monitors and the collected relevant data for COVID-19 monitoring and management.…”

Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification

“…Nowadays, mobile devices in all everyday tasks are increasing, and their usage allows users to stay connected and communicate with ease [1,2]. The current pandemic situation discourages social interaction and personal contacts, enhancing the role of technology in promoting social distancing while being connected [3,4] and active, avoiding sedentary positions [5,6]. Several studies use mobile devices to identify human activities and create a personal agenda to track people [7][8][9][10].…”