Technology ecosystems

Burgess, Eleanor R.; Zhang, Renwen; Ernala, Sindhu Kiranmai; Feuston, Jessica L.; Choudhury, Munmun De; Czerwinski, Mary; Aguilera, Adrián; Schueller, Stephen M.; Reddy, Madhu

doi:10.1145/3434564

Cited by 18 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our findings that people are increasingly using various technologies-both those specifically designed for mental health support as well as those that are not-are consistent with studies that explore the various ways people use technology to self-manage their mental health [32][33][34]. These findings also support recent calls for understanding how the technology ecosystem might impact mental health and lead to opportunities for prevention and intervention tools [35]. Some efforts, such as Google's integration of mental health screening into their search engine, have been launched [36].…”

Section: Discussionsupporting

confidence: 87%

Rise in Use of Digital Mental Health Tools and Technologies in the United States During the COVID-19 Pandemic: Survey Study

Sorkin¹,

Janio²,

Eikey³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

Background Accompanying the rising rates of reported mental distress during the COVID-19 pandemic has been a reported increase in the use of digital technologies to manage health generally, and mental health more specifically. Objective The objective of this study was to systematically examine whether there was a COVID-19 pandemic–related increase in the self-reported use of digital mental health tools and other technologies to manage mental health. Methods We analyzed results from a survey of 5907 individuals in the United States using Amazon Mechanical Turk (MTurk); the survey was administered during 4 week-long periods in 2020 and survey respondents were from all 50 states and Washington DC. The first set of analyses employed two different logistic regression models to estimate the likelihood of having symptoms indicative of clinical depression and anxiety, respectively, as a function of the rate of COVID-19 cases per 10 people and survey time point. The second set employed seven different logistic regression models to estimate the likelihood of using seven different types of digital mental health tools and other technologies to manage one’s mental health, as a function of symptoms indicative of clinical depression and anxiety, rate of COVID-19 cases per 10 people, and survey time point. These models also examined potential interactions between symptoms of clinical depression and anxiety, respectively, and rate of COVID-19 cases. All models controlled for respondent sociodemographic characteristics and state fixed effects. Results Higher COVID-19 case rates were associated with a significantly greater likelihood of reporting symptoms of depression (odds ratio [OR] 2.06, 95% CI 1.27-3.35), but not anxiety (OR 1.21, 95% CI 0.77-1.88). Survey time point, a proxy for time, was associated with a greater likelihood of reporting clinically meaningful symptoms of depression and anxiety (OR 1.19, 95% CI 1.12-1.27 and OR 1.12, 95% CI 1.05-1.19, respectively). Reported symptoms of depression and anxiety were associated with a greater likelihood of using each type of technology. Higher COVID-19 case rates were associated with a significantly greater likelihood of using mental health forums, websites, or apps (OR 2.70, 95% CI 1.49-4.88), and other health forums, websites, or apps (OR 2.60, 95% CI 1.55-4.34). Time was associated with increased odds of reported use of mental health forums, websites, or apps (OR 1.20, 95% CI 1.11-1.30), phone-based or text-based crisis lines (OR 1.20, 95% CI 1.10-1.31), and online, computer, or console gaming/video gaming (OR 1.12, 95% CI 1.05-1.19). Interactions between COVID-19 case rate and mental health symptoms were not significantly associated with any of the technology types. Conclusions Findings suggested increased use of digital mental health tools and other technologies over time during the early stages of the COVID-19 pandemic. As such, additional effort is urgently needed to consider the quality of these products, either by ensuring users have access to evidence-based and evidence-informed technologies and/or by providing them with the skills to make informed decisions around their potential efficacy.

show abstract

Section: Discussionsupporting

confidence: 87%

Rise in Use of Digital Mental Health Tools and Technologies in the United States During the COVID-19 Pandemic: Survey Study

Sorkin¹,

Janio²,

Eikey³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Prior studies have found that individuals infrastructure different resources to achieve goals of self-care in various contexts, including gender transition [ 71 ], telehealth [ 7 ], mental illnesses [ 13 , 72 ], and navigation of the healthcare system, more generally [ 35 ]. Our study extends this understanding to T1D, a chronic illness requiring everyday management routines that encompass both medication adherence and careful control over various lifestyle factors.…”

Section: Discussionmentioning

confidence: 99%

“…In other chronic conditions like mental illnesses, existing scholarship in HCI and CSCW suggests that people use technology ecosystems for self-management [ 13 , 72 ] for purposes like seeking help and balancing life and episodes [ 72 ]. These findings prompt further exploration of managing comorbidity of T1D, such as mental health challenges of AYAs with T1D.…”

Section: Discussionmentioning

confidence: 99%

“Obviously, Nothing's Gonna Happen in Five Minutes”: How Adolescents and Young Adults Infrastructure Resources to Learn Type 1 Diabetes Management

Xu,

Jost,

Messer

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Learning personalized self-management routines is pivotal for people with type 1 diabetes (T1D), particularly early in diagnosis. Context-aware technologies, such as hybrid closed-loop (HCL) insulin pumps, are important tools for diabetes self-management. However, clinicians have observed that practices using these technologies involve significant individual differences. We conducted interviews with 20 adolescents and young adults who use HCL insulin pump systems for managing T1D, and we found that these individuals leverage both technological and non-technological means to maintain situational awareness about their condition. We discuss how these practices serve to infrastructure their self-management routines, including medical treatment, diet, and glucose measurement-monitoring routines. Our study provides insights into adolescents’ and young adults’ lived experiences of using HCL systems and related technology to manage diabetes, and contributes to a more nuanced understanding of how the HCI community can support the contextualized management of diabetes through technology design.

show abstract

“…The current ecosystem of technologies provides limited coordination between people’s data, despite a push to create platforms that aggregate health data [ 28 ]. A lack of data aggregation can make it difficult to support coordination across people’s goals, because people use multiple tools to manage their mental health [ 15 ]. For example, a person might use an exercise app to manage physical activity, and a sleep app to improve sleep patterns.…”

Section: Discussionmentioning

confidence: 99%

A Longitudinal Goal Setting Model for Addressing Complex Personal Problems in Mental Health

Agapie

Areán

Hsieh

et al. 2022

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

Goal setting is critical to achieving desired changes in life. Many technologies support defining and tracking progress toward goals, but these are just some parts of the process of setting and achieving goals. People want to set goals that are more complex than the ones supported through technology. Additionally, people use goal-setting technologies longitudinally, yet the understanding of how people's goals evolve is still limited. We study the collaborative practices of mental health therapists and clients for longitudinally setting and working toward goals through semi-structured interviews with 11 clients and 7 therapists who practiced goal setting in their therapy sessions. Based on the results, we create the Longitudinal Goal Setting Model in mental health, a three-stage model. The model describes how clients and therapists select among multiple complex problems, simplify complex problems to specific goals, and adjust goals to help people address complex issues. Our findings show collaboration between clients and therapists can support transformative reflection practices that are difficult to achieve without the therapist, such as seeing problems through new perspectives, questioning and changing practices, or addressing avoided issues.

show abstract

Technology ecosystems

Cited by 18 publications

References 8 publications

Rise in Use of Digital Mental Health Tools and Technologies in the United States During the COVID-19 Pandemic: Survey Study

Rise in Use of Digital Mental Health Tools and Technologies in the United States During the COVID-19 Pandemic: Survey Study

“Obviously, Nothing's Gonna Happen in Five Minutes”: How Adolescents and Young Adults Infrastructure Resources to Learn Type 1 Diabetes Management

A Longitudinal Goal Setting Model for Addressing Complex Personal Problems in Mental Health

Contact Info

Product

Resources

About