This paper illustrates the processes and results of user acceptance and satisfaction tests for the voice-activated self-monitoring (VoiS) application conducted in the field. VoiS was designed and developed for individuals with diabetes (DM) and hypertension (HTN) to support their routine and convenient self-management using a smart speaker platform. VoiS is also accessible on users' mobile devices to visualize user-generated data. A total of nine adults with DM and HTN participated and were asked to use the VoiS system at home for a week to identify its acceptability in real-world conditions. Participants completed phone interviews to report operational errors and a structured survey to assess the acceptability of VoiS. The results showed that participants agreed or strongly agreed that VoiS was easy to use and useful, and they intended to continue using it. They were highly satisfied with VoiS. They also reported operational errors and challenges including device-related technical issues and the smart reminder feature of VoiS. Overall, participants perceived VoiS as an easy and useful tool for managing their conditions and felt motivated to monitor their biomarkers routinely.

This study suggests a way to utilize the existing medical ontology and natural language processing techniques to extract major medical concepts from lay vocabularies of health consumers on social media and group them based on the defined semantic types in the ontology. Diabetes-related discussions on Tumblr was used to test the efficiency of SpaCy and the Markov-Viterbi algorithm to map lay medical terms to the defined medical concepts in the UMLS. The system discussed in this paper can better analyze free texts, take care of word ambiguity and extract the lifestyle indicators from the daily life discussions of diabetic people on Tumblr. The findings of this study can contribute to developing health applications that track the health behavior of those living with chronic conditions such as diabetes. This approach can also assist researchers who are interested in processing lay languages used by health consumers to foster an understanding of their health behavior.

The integration of motivational strategies and self-management theory with mHealth tools is a promising approach to changing the behavior of patients with chronic disease. In this manuscript, we describe the development and current architecture of a prototype voice-activated self-monitoring application (VoiS) which is based on these theories. Unlike prior mHealth applications which require textual input, VoiS app relies on the more convenient and adaptable approach of asking users to verbally input markers of diabetes and hypertension control through a smart speaker. The VoiS app can provide real-time feedback based on these markers; thus, it has the potential to serve as a remote, regular, source of feedback to support behavior change. To enhance the usability and acceptability of the VoiS application, we will ask a diverse group of patients to use it in real-world settings and provide feedback on their experience. We will use this feedback to optimize tool performance, so that it can provide patients with an improved understanding of their chronic conditions. The VoiS app can also facilitate remote sharing of chronic disease control with healthcare providers, which can improve clinical efficacy and reduce the urgency and frequency of clinical care encounters. Because the VoiS app will be configured for use with multiple platforms, it will be more robust than existing systems with respect to user accessibility and acceptability.

OBJECTIVE: This article reports results from a systematic literature review of the current state of mobile health (mHealth) technologies that have the potential to support self-management for people with diabetes and hypertension. The review aims to (a) characterize mHealth technologies used or described in the mHealth literature and (b) summarize their effects on self-management for people with diabetes and hypertension from the clinical and technical standpoints. MATERIALS AND METHODS: A systematic literature review was conducted following PRISMA guidelines. Online databases were searched in September 2018 to identify eligible studies for review that had been published since 2007, the start of the smartphone era. Data were extracted from included studies based on the PICOS framework. RESULTS: Of the 11 studies included for in-depth review, 5 were clinical research examining patient health outcomes and 6 were technology-focused studies examining users' experiences with mHealth technologies under development. The most frequently used mHealth technology features involved self-management support (n = 11) followed by decision support (n = 6) and shared decision-making (n = 6). Most clinical studies reported benefits associated with mHealth interventions. These included reported improvements in objectively measured patient health outcomes (n = 3) and perceptual or behavioral outcomes (n = 4). DISCUSSION: Although most studies reported promising results in terms of the effects of mHealth interventions on patient health outcomes and experience, the strength of evidence was limited by the study designs. CONCLUSION: More randomized clinical trials are needed to examine the promise and limitations of mHealth technologies as assistive tools to facilitate the self-management of highly prevalent comorbidity of chronic conditions, such as diabetes and hypertension.