Young adults' adoption and use of mental health apps: efficient, effective, but no replacement for in-person care.

Objective: Millions of people use mobile phone or computer-based applications-apps-to maintain their mental health and connect with treatment providers through text- and video-based chat functions. The present study sought to understand young adults' motivations for adopting this technology using the unified theory of acceptance and use of technology (UTAUT), how they use mental health apps, and the gratifications they receive from using mental health apps. Participants: One hundred-eighteen mental health app users responded to an online survey. Methods: A survey was conducted with students at a Midwestern university. The survey included questions regarding current mental health services, mental health apps used, UTAUT and gratifications survey items. Results: A regression analysis suggested users' performance expectancies, effort expectancies, and facilitating conditions predicted the adoption of mental health apps. Young adults most often use mental health apps for stress relief. While they preferred in-person treatment, users described mental health apps as efficient and helpful. Conclusion: Overall, the results reinforce the sense of optimism for the future of mental health apps, suggesting the potential for apps to supplement-but not necessarily replace-in-person care.

Urban/Rural Digital Divide Exists in Older Adults: Does It Vary by Racial/Ethnic Groups?

Despite the overall increase in older adults' internet use, the digital divide within older Americans remains substantial, particularly for those in rural areas and with racial/ethnic minority backgrounds. The current study examines how one's residential area and racial/ethnicity relate to internet access, both singularly and in concert. Data were from the 2016 Health and Retirement Study. The sample consisted of 17,372 Americans aged 50 and above. Logistic regression analyses were performed to test the direct effects of residence and race/ethnicity and their interaction effects on internet use. The odds of internet use were significantly lower for older adults living in suburban and rural residences as well as for Black and Hispanic individuals. Furthermore, rural living reduced the probability of using the internet more for Blacks than Whites. These findings underscore the need for targeted interventions to narrow the digital divide, with particular attention required for older Blacks in rural communities.

Smartphone Users' Persuasion Knowledge in the Context of Consumer mHealth Apps: Qualitative Study.

BACKGROUND: Persuasion knowledge, commonly referred to as advertising literacy, is a cognitive dimension that embraces recognition of advertising, its source and audience, and understanding of advertisers' persuasive and selling intents as well as tactics. There is little understanding of users' awareness of organizations that develop or sponsor mobile health (mHealth) apps, especially in light of personal data privacy. Persuasion knowledge or recognition of a supporting organization's presence, characteristics, competencies, intents, and persuasion tactics are crucial to investigate because app users have the right to know about entities that support apps and make informed decisions about app usage. The abundance of free consumer mHealth apps, especially those in the area of fitness, often makes it difficult for users to identify apps' dual purposes, which may be related to not only helping the public manage health but also promoting the supporting organization itself and collecting users' information for further consumer targeting by third parties. OBJECTIVE: This study aims to investigate smartphone users' awareness of mHealth apps' affiliations with 3 different types of supporting organizations (commercial, government, and nonprofit); differences in users' persuasion knowledge and mHealth app quality and credibility evaluations related to each of the 3 organization types; and users' coping mechanisms for dealing with personal information management within consumer mHealth apps. METHODS: In-depth semistructured interviews were conducted with 25 smartphone users from a local community in midwestern United States. Interviews were thematically analyzed using inductive and deductive approaches. RESULTS: Participants indicated that their awareness of and interest in mHealth app-supporting organizations were secondary to the app's health management functions. After being probed, participants showed a high level of persuasion knowledge regarding the types of app-supporting organizations and their promotional intents. They thought that commercial companies sponsored mHealth apps mostly as entertainment tools, whereas noncommercial entities sponsored mHealth apps for users' education. They assigned self-promotional motives to commercial organizations; however, they associated commercial mHealth apps with good quality and functioning. Noncommercial entities were perceived as more credible. Participants were concerned about losing control over personal information within mHealth apps supported by different organizations. They used alternative digital identities to protect themselves from privacy invasion and advertising spam. They were willing to trade some personal information for high-quality commercial mHealth apps. There was a sense of fatalism in discussing privacy risks linked to mHealth app usage, and some participants did not perceive the risks to be serious. CONCLUSIONS: The discussion of and recommendations for the safe and ethical use of mHealth apps associated with organizations' promotional strategies and personal data protection are provided to ensure users' awareness of and enhanced control over digitalized personal information flows. The theoretical implications are discussed in the context of the Persuasion Knowledge Model and dual-processing theories.

Detecting clinically related content in online patient posts.

Patients with chronic health conditions use online health communities to seek support and information to help manage their condition. For clinically related topics, patients can benefit from getting opinions from clinical experts, and many are concerned about misinformation and biased information being spread online. However, a large volume of community posts makes it challenging for moderators and clinical experts, if there are any, to provide necessary information. Automatically identifying forum posts that need validated clinical resources can help online health communities efficiently manage content exchange. This automation can also assist patients in need of clinical expertise by getting proper help. We present our results on testing text classification models that efficiently and accurately identify community posts containing clinical topics. We annotated 1817 posts comprised of 4966 sentences of an existing online diabetes community. We found that our classifier performed the best (F-measure: 0.83, Precision: 0.79, Recall:0.86) when using Naïve Bayes algorithm, unigrams, bigrams, trigrams, and MetaMap Symantic Types. Training took 5 s. The classification process took a fraction of 1 s. We applied our classifier to another online diabetes community, and the results were: F-measure: 0.63, Precision: 0.57, Recall: 0.71. Our results show our model is feasible to scale to other forums on identifying posts containing clinical topic with common errors properly addressed.

A qualitative study of user perceptions of mobile health apps.

BACKGROUND: Mobile apps for health exist in large numbers today, but oftentimes, consumers do not continue to use them after a brief period of initial usage, are averse toward using them at all, or are unaware that such apps even exist. The purpose of our study was to examine and qualitatively determine the design and content elements of health apps that facilitate or impede usage from the users' perceptive. METHODS: In 2014, six focus groups and five individual interviews were conducted in the Midwest region of the U.S. with a mixture of 44 smartphone owners of various social economic status. The participants were asked about their general and health specific mobile app usage. They were then shown specific features of exemplar health apps and prompted to discuss their perceptions. The focus groups and interviews were audio recorded, transcribed verbatim, and coded using the software NVivo. RESULTS: Inductive thematic analysis was adopted to analyze the data and nine themes were identified: 1) barriers to adoption of health apps, 2) barriers to continued use of health apps, 3) motivators, 4) information and personalized guidance, 5) tracking for awareness and progress, 6) credibility, 7) goal setting, 8) reminders, and 9) sharing personal information. The themes were mapped to theories for interpretation of the results. CONCLUSIONS: This qualitative research with a diverse pool of participants extended previous research on challenges and opportunities of health apps. The findings provide researchers, app designers, and health care providers insights on how to develop and evaluate health apps from the users' perspective.

Answers to Health Questions: Internet Search Results Versus Online Health Community Responses.

BACKGROUND: About 6 million people search for health information on the Internet each day in the United States. Both patients and caregivers search for information about prescribed courses of treatments, unanswered questions after a visit to their providers, or diet and exercise regimens. Past literature has indicated potential challenges around quality in health information available on the Internet. However, diverse information exists on the Internet-ranging from government-initiated webpages to personal blog pages. Yet we do not fully understand the strengths and weaknesses of different types of information available on the Internet. OBJECTIVE: The objective of this research was to investigate the strengths and challenges of various types of health information available online and to suggest what information sources best fit various question types. METHODS: We collected questions posted to and the responses they received from an online diabetes community and classified them according to Rothwell's classification of question types (fact, policy, or value questions). We selected 60 questions (20 each of fact, policy, and value) and the replies the questions received from the community. We then searched for responses to the same questions using a search engine and recorded the RESULTS: Community responses answered more questions than did search results overall. Search results were most effective in answering value questions and least effective in answering policy questions. Community responses answered questions across question types at an equivalent rate, but most answered policy questions and the least answered fact questions. Value questions were most answered by community responses, but some of these answers provided by the community were incorrect. Fact question search results were the most clinically valid. CONCLUSIONS: The Internet is a prevalent source of health information for people. The information quality people encounter online can have a large impact on them. We present what kinds of questions people ask online and the advantages and disadvantages of various information sources in getting answers to those questions. This study contributes to addressing people's online health information needs.

Keep Using My Health Apps: Discover Users' Perception of Health and Fitness Apps with the UTAUT2 Model.

BACKGROUND: Health and fitness applications (apps) are one of the major app categories in the current mobile app market. Few studies have examined this area from the users' perspective. This study adopted the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) Model to examine the predictors of the users' intention to adopt health and fitness apps. MATERIALS AND METHODS: A survey (n=317) was conducted with college-aged smartphone users at a Midwestern university in the United States. RESULTS: Performance expectancy, hedonic motivations, price value, and habit were significant predictors of users' intention of continued usage of health and fitness apps. However, effort expectancy, social influence, and facilitating conditions were not found to predict users' intention of continued usage of health and fitness apps. CONCLUSIONS: This study extends the UTATU2 Model to the mobile apps domain and provides health professions, app designers, and marketers with the insights of user experience in terms of continuously using health and fitness apps.