User experience design to enhance the effectiveness of mobile technologies for the treatment of mental health

Stevens, Ryley S.; Polk, Kristin; Merrill, Colette; Feng, Fan; Weiss, Maxwell; Brosnan, Elise; Gerber, Matthew S.; Barnes, Laura E.

doi:10.1109/sieds.2018.8374724

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…By contrast, other studies argue that older participants who have barriers to using technology were more likely to drop out of the study [ 23 ]. Participants’ education level has shown relatively consistent results in previous studies: participants with a higher education level were more likely to remain in the study than those who were less educated [ 24 , 25 ]. Studies also found that participants who were unemployed, those with a low income, and those who were single were more likely to drop out [ 7 , 26 , 27 ].…”

Section: Introductionsupporting

confidence: 69%

Predictors of Dropout Among Psychosomatic Rehabilitation Patients During the COVID-19 Pandemic: Secondary Analysis of a Longitudinal Study of Digital Training

Gao,

Keller,

Becker

et al. 2023

J Med Internet Res

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Background High dropout rates are a common problem reported in web-based studies. Understanding which risk factors interrelate with dropping out from the studies provides the option to prevent dropout by tailoring effective strategies. Objective This study aims to contribute an understanding of the predictors of web-based study dropout among psychosomatic rehabilitation patients. We investigated whether sociodemographics, voluntary interventions, physical and mental health, digital use for health and rehabilitation, and COVID-19 pandemic–related variables determine study dropout. Methods Patients (N=2155) recruited from 4 psychosomatic rehabilitation clinics in Germany filled in a web-based questionnaire at T1, which was before their rehabilitation stay. Approximately half of the patients (1082/2155, 50.21%) dropped out at T2, which was after the rehabilitation stay, before and during which 3 voluntary digital trainings were provided to them. According to the number of trainings that the patients participated in, they were categorized into a comparison group or 1 of 3 intervention groups. Chi-square tests were performed to examine the differences between dropout patients and retained patients in terms of sociodemographic variables and to compare the dropout rate differences between the comparison and intervention groups. Logistic regression analyses were used to assess what factors were related to study dropout. Results The comparison group had the highest dropout rate of 68.4% (173/253) compared with the intervention groups’ dropout rates of 47.98% (749/1561), 50% (96/192), and 42.9% (64/149). Patients with a diagnosis of combined anxiety and depressive disorder had the highest dropout rate of 64% (47/74). Younger patients (those aged <50 y) and patients who were less educated were more likely to drop out of the study. Patients who used health-related apps and the internet less were more likely to drop out of the study. Patients who remained in their jobs and patients who were infected by COVID-19 were more likely to drop out of the study. Conclusions This study investigated the predictors of dropout in web-based studies. Different factors such as patient sociodemographics, physical and mental health, digital use, COVID-19 pandemic correlates, and study design can correlate with the dropout rate. For web-based studies with a focus on mental health, it is suggested to consider these possible dropout predictors and take appropriate steps to help patients with a high risk of dropping out overcome difficulties in completing the study.

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Section: Introductionsupporting

confidence: 69%

Predictors of Dropout Among Psychosomatic Rehabilitation Patients During the COVID-19 Pandemic: Secondary Analysis of a Longitudinal Study of Digital Training

Gao,

Keller,

Becker

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

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“…The design of a good user interface is mainly related to their graphic appearance. The user experience goes beyond; the user interface is only a component [18].…”

Section: Ux and Usabilitymentioning

confidence: 99%

“…Currently, it is difficult to agree on criteria and methods for analyzing the user experience [17,18]. However, UX considers the following components of the different business lines:…”

Section: Ux and Usabilitymentioning

confidence: 99%

Mobile Serious Game Design using User Experience: Modeling of Software Product Line Variability

Meftah

Retbi

Bennani

et al. 2019

Int. J. Emerg. Technol. Learn.

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In recent years, gamification has emerged as a new approach to increasing learner engagement. It covers a very wide range of games with very different purposes and with many fields of application. However, most of the gamification solutions proposed do not adopt the same modeling approach and little attention has been paid to mobile serious games (MSG) belonging to different pedagogical contexts. In order to overcome these difficulties, we have developed in this paper a generic model based on the Software Product Line (SPL) approach to manage the common and variable points of the MSG product set. We have also focused on the User eXperience (UX) concept to study the aspects that most affect the player’s experience in the context of MSGs. These aspects have been modeled in the form of features in the SPL Feature Model. MSG designers can use the model proposed during the development process, both to manage variability and to create an effective and fun learning environment.

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“…In various countries, one of them is the United States, based on the National Institute of Mental Health estimates that 31.1% of adults in the United States will experience mental disorders in some aspects of their lives. Face-to-face sessions with psychologists are the most standard approach to treatment, but it is not consistently feasible for participants to receive such treatment [2].Therefore, the current number of cases remains very worrying and should not be underestimated for mental illness. Around 450 million people worldwide suffer from mental and behavioral disorders [3].…”

Section: Introductionmentioning

confidence: 99%

Implementation of User Centered Design on Interface Design for Mental Health Selfcare Services

Pranata

Herdiani²,

Hadikusuma³

2022

mib

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Mental health is essential in performing maximum activities. During the COVID-19 pandemic that is spreading all over the world. Some activities are transformed into a new activity called "New Normal". With these new activities, the level of mental health in Indonesia has deteriorated. WHO as a world health organization made a mental health pyramid that handling of every level of mental health. There are 4 levels, which are promotive, preventive, monitoring and identifying psychosocial services, and specialist services. In the results of observations and analysis, digital-based services in Indonesia prioritize levels 3 and 4, while levels 1 and 2 are still infrequent, and the application is still not perfectly weighted. This study will design a mental health application by emphasizing level 1 and 2 treatment, namely self-care and storytelling sessions using the User Centered Design method, and the results of the evaluation used the usability testing method and the usability scale system. With this research, potential users obtain an interface resolution with a mood tracker, self-care, and storytelling features that users desire. By helping the user to perceive the history of the condition, getting self-care and storytelling rooms according to the user's condition, and helping the user's condition to bolster up and not add to the burden of the user's condition. The outcome of the evaluation that has been done, results that the average user completed each task for 16.59 seconds, the average result is 0 miss click and 0 miss click page, and the results from the System Usability Scale are 81.51 with the "Excellent" category, with the composition of solutions provided to answer user problems.

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User experience design to enhance the effectiveness of mobile technologies for the treatment of mental health

Cited by 7 publications

References 7 publications

Predictors of Dropout Among Psychosomatic Rehabilitation Patients During the COVID-19 Pandemic: Secondary Analysis of a Longitudinal Study of Digital Training

Predictors of Dropout Among Psychosomatic Rehabilitation Patients During the COVID-19 Pandemic: Secondary Analysis of a Longitudinal Study of Digital Training

Mobile Serious Game Design using User Experience: Modeling of Software Product Line Variability

Implementation of User Centered Design on Interface Design for Mental Health Selfcare Services

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