A growing market for mental health applications and increasing evidence for the efficacy of these applications have made apps a popular mode of mental healthcare delivery. However, given the gravity of mental illnesses, the potential harms of using these applications must be continually investigated. In this study, we conducted a thematic analysis using user-comments left on depression self-management applications. We analyzed 6,253 reviews from thirty-six, systematically selected apps from the Google Play and Apple App stores. We identified four themes regarding the potential, unintentional harms caused by these applications. This study uniquely contributes to the literature by examining the reported harms to users caused by depression self-management apps and contextualizing them in an ethical framework. We provide recommendations to developers for creating ethical depression self-management apps and resources for practitioners and consumers to aid in screening apps.

With the increasing usage of mental health applications (MHAs), there is growing concern regarding their data privacy practices. Analyzing 437 user reviews from 83 apps, we outline users’ predominant privacy and security concerns with currently available apps. We then compare those concerns to criteria from two prominent app evaluation websites – Privacy Not Included and One Mind PsyberGuide. Our findings show that MHA users have myriad data privacy and security concerns including a user’s control over their own data, but these concerns do not often overlap with those of experts from evaluation websites who focus more on issues such as required password strength. We highlight this disconnect and propose solutions in how the mental health care ecosystem can provide better guidance to MHA users and experts from the fields of privacy and security and mental health technology in choosing and evaluating, respectively, potentially useful mental health apps.

OBJECTIVES: Ambient clinical documentation technology uses automatic speech recognition (ASR) and natural language processing (NLP) to turn patient-clinician conversations into clinical documentation. It is a promising approach to reducing clinician burden and improving documentation quality. However, the performance of current-generation ASR remains inadequately validated. In this study, we investigated the impact of non-lexical conversational sounds (NLCS) on ASR performance. NLCS, such as Mm-hm and Uh-uh, are commonly used to convey important information in clinical conversations, for example, Mm-hm as a "yes" response from the patient to the clinician question "are you allergic to antibiotics?" MATERIALS AND METHODS: In this study, we evaluated 2 contemporary ASR engines, Google Speech-to-Text Clinical Conversation ("Google ASR"), and Amazon Transcribe Medical ("Amazon ASR"), both of which have their language models specifically tailored to clinical conversations. The empirical data used were from 36 primary care encounters. We conducted a series of quantitative and qualitative analyses to examine the word error rate (WER) and the potential impact of misrecognized NLCS on the quality of clinical documentation. RESULTS: Out of a total of 135 647 spoken words contained in the evaluation data, 3284 (2.4%) were NLCS. Among these NLCS, 76 (0.06% of total words, 2.3% of all NLCS) were used to convey clinically relevant information. The overall WER, of all spoken words, was 11.8% for Google ASR and 12.8% for Amazon ASR. However, both ASR engines demonstrated poor performance in recognizing NLCS: the WERs across frequently used NLCS were 40.8% (Google) and 57.2% (Amazon), respectively; and among the NLCS that conveyed clinically relevant information, 94.7% and 98.7%, respectively. DISCUSSION AND CONCLUSION: Current ASR solutions are not capable of properly recognizing NLCS, particularly those that convey clinically relevant information. Although the volume of NLCS in our evaluation data was very small (2.4% of the total corpus; and for NLCS that conveyed clinically relevant information: 0.06%), incorrect recognition of them could result in inaccuracies in clinical documentation and introduce new patient safety risks.

While easy patient access to laboratory test results is necessary for patient engagement in their healthcare, it is not sufficient to enable patients to thrive in this role ? they must be able to understand and act upon these data. In this study, we analyzed posts to an online health community (OHC) that contained a patient's laboratory test results. The objective was to understand the nature of patients' questions related to these data to gain insights into how to better support patients as they individually and collaboratively make sense of their data. We found that patients seek help on the OHC to understand and use their laboratory test results at multiple points in the medical care trajectory. Specifically, in the diagnosis phase, patients tend to be focused on comprehending their data, to be receiving emotionally charged results and, of course, to be engaging the OHC in naming their medical issues. In the treatment phase, patients are often using their laboratory test results to ask more focused questions to identify treatment options, to seek treatment guidance from peers, and to predict the likely course of their disease. Throughout both phases, individuals are highly engaged in the medical process and put in substantial effort to proactively prepare for their care and interactions with doctors. They enlist the OHC in these efforts for many reasons such as a lack of confidence in their doctor. We discuss how gaps in the provision of healthcare services lead to the significant work involved in managing the complex and dynamic interplay between OHCs and the healthcare system. We offer design recommendations both for technologies that provide patients with access to their medical records and for OHCs that will likely continue to play an important role in filling gaps in healthcare services.

Various contact tracing approaches have been applied to help contain the spread of COVID-19, with technology-based tracing and human tracing among the most widely adopted. However, governments and communities worldwide vary in their adoption of digital contact tracing, with many instead choosing the human approach. We investigate how people perceive the respective benefits and risks of human and digital contact tracing through a mixed-methods survey with 291 respondents from the United States. Participants perceived digital contact tracing as more beneficial for protecting privacy, providing convenience, and ensuring data accuracy, and felt that human contact tracing could help provide security, emotional reassurance, advice, and accessibility. We explore the role of self-tracking technologies in public health crisis situations, highlighting how designs must adapt to promote societal benefit rather than just self-understanding. We discuss how future digital contact tracing can better balance the benefits of human tracers and technology amidst the complex contact tracing process and context.

People with multiple chronic conditions (MCC) often face complex and overwhelming conflicts in their personal health management. However, little is known about how technology can help users to address these challenges. Better understanding the practices involved in conflict resolution is necessary to guide the design of technology aiming to support this population. This interview study investigated the strategies seniors use to overcome conflicts involving different health issues, their self-care tasks, risks of another illness or complication, and their personal values. We report on the different strategies used to address these conflicts, such as seeking advice and information from different sources to prioritize and compromise. Compromising often involves purposefully deciding against conventional treatments or self-care activities. We argue that rich information and flexibility are required to support decision making in MCC self-care, and advocate for a holistic perspective in technology design for health management. These implications also apply to systems focused on a single illness, as many of their users might live with other conditions.

OBJECTIVE: Facial masks are an essential personal protective measure to fight the COVID-19 (coronavirus disease) pandemic. However, the mask adoption rate in the United States is still less than optimal. This study aims to understand the beliefs held by individuals who oppose the use of facial masks, and the evidence that they use to support these beliefs, to inform the development of targeted public health communication strategies. MATERIALS AND METHODS: We analyzed a total of 771 268 U.S.-based tweets between January to October 2020. We developed machine learning classifiers to identify and categorize relevant tweets, followed by a qualitative content analysis of a subset of the tweets to understand the rationale of those opposed mask wearing. RESULTS: We identified 267 152 tweets that contained personal opinions about wearing facial masks to prevent the spread of COVID-19. While the majority of the tweets supported mask wearing, the proportion of anti-mask tweets stayed constant at about a 10% level throughout the study period. Common reasons for opposition included physical discomfort and negative effects, lack of effectiveness, and being unnecessary or inappropriate for certain people or under certain circumstances. The opposing tweets were significantly less likely to cite external sources of information such as public health agencies' websites to support the arguments. CONCLUSIONS: Combining machine learning and qualitative content analysis is an effective strategy for identifying public attitudes toward mask wearing and the reasons for opposition. The results may inform better communication strategies to improve the public perception of wearing masks and, in particular, to specifically address common anti-mask beliefs.

OBJECTIVE: Alert fatigue limits the effectiveness of medication safety alerts, a type of computerized clinical decision support (CDS). Researchers have suggested alternative interactive designs, as well as tailoring alerts to clinical roles. As examples, alerts may be tiered to convey risk, and certain alerts may be sent to pharmacists. We aimed to evaluate which variants elicit less alert fatigue. MATERIALS AND METHODS: We searched for articles published between 2007 and 2017 using the PubMed, Embase, CINAHL, and Cochrane databases. We included articles documenting peer-reviewed empirical research that described the interactive design of a CDS system, to which clinical role it was presented, and how often prescribers accepted the resultant advice. Next, we compared the acceptance rates of conventional CDS-presenting prescribers with interruptive modal dialogs (ie, "pop-ups")-with alternative designs, such as role-tailored alerts. RESULTS: Of 1011 articles returned by the search, we included 39. We found different methods for measuring acceptance rates; these produced incomparable results. The most common type of CDS-in which modals interrupted prescribers-was accepted the least often. Tiering by risk, providing shortcuts for common corrections, requiring a reason to override, and tailoring CDS to match the roles of pharmacists and prescribers were the most common alternatives. Only 1 alternative appeared to increase prescriber acceptance: role tailoring. Possible reasons include the importance of etiquette in delivering advice, the cognitive benefits of delegation, and the difficulties of computing "relevance." CONCLUSIONS: Alert fatigue may be mitigated by redesigning the interactive behavior of CDS and tailoring CDS to clinical roles. Further research is needed to develop alternative designs, and to standardize measurement methods to enable meta-analyses.

Infertility is a global health concern that affects countless couples trying to conceive a child. Effective fertility treatment requires continuous monitoring of a wide range of health indicators through self-tracking. The process of collecting and interpreting data and information about fertility is complex, and much of the burden falls on women. In this study, we analyzed patient-generated content in a popular online health community dedicated to fertility issues. The objective was to understand the process in which women engage in tracking relevant information, and the challenges they face. Leveraging the Personal Informatics Model, we describe women's self-tracking experiences during their fertility cycles. We discuss how a complex and highly personalized context leads to responsibility, pressure, and emotional burden on women performing self-tracking activities, as well as the role of collaboration in creating individualized solutions. Finally, we provide implications for technologies aiming to support women with fertility care needs.