Basics of qualitative research: Grounded theory procedures and techniques

A user-centered model for designing consumer mobile health (mHealth) applications (apps)

Background: College students are increasingly reporting common mental health problems, such as depression and anxiety, and they frequently encounter barriers to seeking traditional mental health treatments. Digital mental health interventions, such as those delivered via the Web and apps, offer the potential to improve access to mental health treatment. Objective: This study aimed to review the literature on digital mental health interventions focused on depression, anxiety, and enhancement of psychological well-being among samples of college students to identify the effectiveness, usability, acceptability, uptake, and adoption of such programs. Methods: We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (registration number CRD42018092800), and the search strategy was conducted by a medical research librarian in the following databases: MEDLINE (Ovid), EMBASE (Elsevier), PsycINFO (EbscoHost), the Cochrane Library (Wiley), and Web of Science (Thomson Reuters) from the date of inception to April 2019. Data were synthesized using a systematic narrative synthesis framework, and formal quality assessments were conducted to address the risk of bias. Results: A total of 89 studies met the inclusion criteria. The majority of interventions (71/89, 80%) were delivered via a website, and the most common intervention was internet-based cognitive behavioral therapy (28, 31%). Many programs (33, 37%) featured human support in the form of coaching. The majority of programs were either effective (42, 47%) or partially effective (30, 34%) in producing beneficial changes in the main psychological outcome variables. Approximately half of the studies (45, 51%) did not present any usability or acceptability outcomes, and few studies (4, 4%) examined a broad implementation of digital mental health interventions on college campuses. Quality assessments revealed a moderate-to-severe risk of bias in many of the studies. Conclusions: Results suggest that digital mental health interventions can be effective for improving depression, anxiety, and psychological well-being among college students, but more rigorous studies are needed to ascertain the effective elements of these interventions. Continued research on improving the user experience of, and thus user engagement with, these programs appears vital for the sustainable implementation of digital mental health interventions on college campuses.

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Digital Mental Health Interventions for Depression, Anxiety, and Enhancement of Psychological Well-Being Among College Students: Systematic Review

A field of personal analytics has emerged around self-monitoring practices, which includes the visualization and interpretation of the data produced. This paper explores personal analytics from the perspective of self-optimization, arguing that the ways in which people confront and engage with visualized personal data are as significant as the technology itself. The paper leans on the concept of the “data double”: the conversion of human bodies and minds into data flows that can be figuratively reassembled for the purposes of personal reflection and interaction. Based on an empirical study focusing on heart-rate variability measurement, the discussion underlines that a distanced theorizing of personal analytics is not sufficient if one wants to capture affective encounters between humans and their data doubles. Research outcomes suggest that these explanations can produce permanence and stability while also profoundly changing ways in which people reflect on themselves, on others and on their daily lives.

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Visualized and Interacted Life: Personal Analytics and Engagements with Data Doubles

Mental health problems are common and pose a tremendous societal burden in terms of cost, morbidity, quality of life, and mortality. The great majority of people experience barriers that prevent access to treatment, aggravated by a lack of mental health specialists. Digital mental health is potentially useful in meeting the treatment needs of large numbers of people. A growing number of efficacy trials have shown strong outcomes for digital mental health treatments. Yet despite their positive findings, there are very few examples of successful implementations and many failures. Although the research-to-practice gap is not unique to digital mental health, the inclusion of technology poses unique challenges. We outline some of the reasons for this gap and propose a collection of methods that can result in sustainable digital mental health interventions. These methods draw from human-computer interaction and implementation science and are integrated into an Accelerated Creation-to-Sustainment (ACTS) model. The ACTS model uses an iterative process that includes 2 basic functions (design and evaluate) across 3 general phases (Create, Trial, and Sustain). The ultimate goal in using the ACTS model is to produce a functioning technology-enabled service (TES) that is sustainable in a real-world treatment setting. We emphasize the importance of the service component because evidence from both research and practice has suggested that human touch is a critical ingredient in the most efficacious and used digital mental health treatments. The Create phase results in at least a minimally viable TES and an implementation blueprint. The Trial phase requires evaluation of both effectiveness and implementation while allowing optimization and continuous quality improvement of the TES and implementation plan. Finally, the Sustainment phase involves the withdrawal of research or donor support, while leaving a functioning, continuously improving TES in place. The ACTS model is a step toward bringing implementation and sustainment into the design and evaluation of TESs, public health into clinical research, research into clinics, and treatment into the lives of our patients.

Accelerating Digital Mental Health Research From Early Design and Creation to Successful Implementation and Sustainment

mHealth applications, or “apps” as they are more commonly known, offer the opportunity to improve healthcare delivery and clinical outcomes. The ability to monitor patients remotely can enable patient risk-factor management and improve treatment compliance, thus allowing early detection of medical complications and ultimately preventing unnecessary hospitalizations. In addition to enhancing the delivery of care, one of the most significant opportunities that mHealth offers is in the consumer health domain, allowing patients to actively engage in and self manage their condition. Mobile phones also allow for the design of timely interventions based on user behavior. mHealth-mediated behavioral interventions can produce cognitive, behavioral, emotional, and social health-oriented responses. As such, mHealth apps are particularly appropriate for problems where treatments depend on patient behavioral change, such as those related to smoking, obesity, diabetes, and other chronic conditions. Despite the many potential benefits of mHealth apps, pilot studies aimed at evaluating the effectiveness of mHealth interventions have yielded mixed results. Furthermore, a quarter of all app downloads are used only once. Consumers often do not return to applications that do not immediately engage them, therefore undermining the intervention’s potential effectiveness. Regrettably, there is no simple formula for designing engaging and effective mHealth apps. Currently, many electronic health (eHealth) and mHealth interventions are designed on the basis of existing healthcare system constructs and may not be as effective as those that involve end users in the design process. Moreover, designers often base their designs on assumptions that are not validated with primary user input. The resulting systems may lack key features, and subsequent evaluations of the effectiveness of the interventions may be compromised. For this reason, we employ the user-centered design (UCD) process—an evidence-based approach informed by the needs and understanding of a specific end-user group. In our experience, designing mHealth and other apps, UCD plays a key role in achieving user engagement, thus improving the likelihood of the intervention’s effectiveness. The World Health Organization agrees, and advises that user evaluation be incorporated into the mHealth project lifecycle to ensure effective outcomes. mHealth Consumer Apps The Case for User-Centered Design

Cassie McDaniel

Papers

mHealth Consumer Apps: The Case for User-Centered Design