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.

Conversational agents have gained their ground in our daily life and various domains including healthcare. Chronic condition self-management is one of the promising healthcare areas in which conversational agents demonstrate significant potential to contribute to alleviating healthcare burdens from chronic conditions. This survey paper introduces and outlines types of conversational agents, their generic architecture and workflow, the implemented technologies, and their application to chronic condition self-management.

Abstract The COVID‐19 crisis provided an opportunity for information professionals to rethink the role of information in individuals' decision making such as vaccine uptake. Unlike previous studies, which often considered information as a single factor among others, this study examined the impact of the quantity and trustworthiness of information on people's adoption of information for vaccination decisions based on the information adoption model. We analyzed COVID‐19 Preventive Behavior Survey data collected by the Massachusetts Institute of Technology from Facebook users (N = 82,213) in 15 countries between October 2020 and March 2021. The results of logistic regression analyses indicate that reasonable quantity and trustworthiness of information were positively related to COVID‐19 vaccination intent. But excessive and less than the desired amount of information was more likely to have negative impacts on vaccination intent. The degrees of trust in the mediums and in the sources were associated with the level of vaccine acceptance. But the effects of trustworthiness accorded to information sources showed variations across sources and mediums. Implications for information professionals and suggestions for policies are discussed.

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.

Abstract Given the aging world, it is important to understand older adults' everyday life information seeking (ELIS), which plays a vital role in healthy aging in later life. The present study analyzed national survey data collected by the Pew Research Center in 2016 based on Savolainen's ELIS model. The model's core concept, mastery of life, was measured in terms of general coping style to find information (cognitive vs. affective) and expectations about the findability of necessary information (optimistic vs. pessimistic). Weighted multiregression analyses were conducted to examine the associations between mastery of life and ELIS behavior of older Americans (65 years or older) in terms of breath and depth of interest in everyday topics, holding other sociocultural and demographic factors constant. The results showed a cognitive, as opposed to affective, coping style was associated with a wider range of ELIS topics and a deeper level of interest in political and cultural topics. However, whether an individual had an optimistic or pessimistic perspective toward information seeking was not associated with the variety or depth of interest in everyday topics. Neither dimension was related to the depth of interest in sports topics. Practical, theoretical, and methodological implications of the findings are provided.

This study explores knowledge organization behavior on the Web with respect to identifying the semantic relationships of health‐related concepts. In particular, this study aims to investigate the potentials of imparting richer collective intelligence to existing knowledge representation systems in health. The study focuses on detecting semantic relationships between semantic groups of major concepts mined from health consumers' descriptions of health issues and associated user‐generated metadata (i.e., tags). A total of 50,263 blogs and associated 341,720 tags were collected from Tumblr, a blogging social networking site. Text mining and semantic network analysis methods were used to explore the usage patterns at semantic type levels of the identified medical concepts in tags, in blogs, and between tags and blogs. More various associations among semantic types were identified both in tags and in blogs. These associations were more diverse and complicated than the relationships in the Unified Medical Language System Semantic Network. Among the groups of concepts in tags and blogs, groups showed relatively stronger and more diverse relationships with other groups of concepts. In addition, many direct and close relations were found between tags and blogs.