Assessing College Students' Perceptions of and Intentions to Use a Mobile App for Mental Health

Mitchell, Katharine M; Holtz, Bree; McCarroll, Alexis M

doi:10.1089/tmj.2021.0106

Cited by 16 publications

(13 citation statements)

References 42 publications

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“…As expected, performance expectancy was a significant predictor of the intention to use the personalized health care service app. These results were in line with many previous studies on the intention to use health care-related services [22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Performance expectancy can be increased if it is possible to integrate mHealth services, with an existing health tracking app or a health information app that includes medication, treatment, and health checkup histories or hospital information such as the nearest hospital or reservation service.…”

Section: Comparison With Prior Worksupporting

confidence: 90%

“…Performance expectancy is defined as "the degree to which individuals believe that using the personalized health management service app will help improve their health" [20]; this construct is similar to PU in the TAM [19]. Many previous studies on accepting health care services have demonstrated that performance expectancy is a good predictor of behavioral intention [22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Thus, we proposed the following hypothesis.…”

Section: Research Model and Research Hypothesesmentioning

confidence: 99%

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Factors Predicting Older People’s Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study (Preprint)

Koo¹,

Park²,

Kang³

2022

Preprint

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BACKGROUND mHealth services enable real-time measurement of information on individuals' biosignals and environmental risk factors; accordingly, research on health management using mHealth is being actively conducted. This study investigates attitudes toward an mHealth personalized healthcare service app (currently in development, featuring wearable devices) among the elderly in South Korea. OBJECTIVE The study aims to identify factors that influence older people's intention to use mHealth and to verify whether the presence of chronic disease moderates the effect of the identified factors on their behavioral intentions. METHODS A cross-sectional study was conducted among 500 participants aged 60 to 75. The research hypotheses were tested using structural equation modeling, and indirect effects were verified through bootstrapping. Bootstrapping was performed 10,000 times, and the significance of the indirect effects was confirmed through the bias-corrected percentile method. RESULTS Of 477 participants, 278 (58.3%) had at least one chronic disease. Performance expectancy (beta=0.453, P=.003) and social influence (beta=0.693, P=<.001) had direct effects on behavioral intention. As a result of bootstrapping, facilitating conditions (beta=0.325, P=.006, 95% CI 0.115-0.759) were found to have a significant indirect effect on behavioral intention. Multi-group structural equation modeling testing the presence or absence of chronic disease revealed a significant difference in the path of device trust on performance expectancy (critical ratio=-2.165). Bootstrapping also confirmed that device trust (beta=0.122, P=.039, 95% CI 0.007-0.346) had a significant indirect effect on behavioral intention in people with chronic disease. CONCLUSIONS To encourage older people to use personalized healthcare services, it is important to clearly demonstrate and emphasize their benefits in terms of healthcare management. The awareness of people around them also plays an important role. In particular, it is necessary to promote such services at the family and community levels; this aspect is particularly important in the Confucian culture. In addition, support from service providers should be strengthened, so that older people trust that they have consistent access to technical support. Overall, if people with chronic diseases have confidence in a wearable device for measuring biosignals, a higher utilization rate could be achieved.

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Section: Comparison With Prior Worksupporting

confidence: 90%

Section: Research Model and Research Hypothesesmentioning

confidence: 99%

Factors Predicting Older People’s Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study (Preprint)

Koo¹,

Park²,

Kang³

2022

Preprint

View full text Add to dashboard Cite

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“…Psychological factors included intentions, attitudes, and beliefs. Intentions were assessed in 5 studies [67][68][69][70][71]117], stating that up to half of the respondents were generally willing to join online support groups [69,71,68] and that participants with higher depression scores were substantially more likely to express willingness to use web-based chat sites and self-help resources [64,67]. Concerning attitudes and beliefs toward web-based help seeking, 8 studies specifically addressed preferences in terms of comparison with offline help seeking-formal or informal-and showed that participants preferred face-to-face interventions over digital ones [69,72,73], although 1 study [73] found that internet-based information resources were the preferred support of Australian fathers.…”

Section: Overview Of Web-based Help Seeking and Depressive Symptomsmentioning

confidence: 99%

The Empowering Role of Web-Based Help Seeking on Depressive Symptoms: Systematic Review and Meta-analysis

Bizzotto¹,

Marciano²,

Bruijn³

et al. 2023

J Med Internet Res

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Background Most research on web-based help seeking for mental health problems has focused on the antecedents of this behavior. Therefore, little is known about the outcomes of web-based help seeking in general or in specific mental health issues. Objective This study was a systematic review and meta-analysis of the literature on the antecedents and consequences of web-based help-seeking behaviors for depressive symptoms. Methods A systematic literature search was carried out in 6 scientific databases, leading to 48 studies (for a total of 314,921 participants) included in the qualitative synthesis and 19 included in the meta-analysis. Results The results indicated a positive relationship between depressive symptoms and web-based help-seeking behaviors through online support groups (r=0.089; P=.009), and Generation Z (r=0.102; P=.008) tended to participate in support groups more than previous generations. In addition, web-based help seeking was positively related to empowerment (r=0.245; P=.004). Other forms of support reported included the internet and specific self-help tools, but no significant relationships were found with depressive symptoms. Conclusions More studies examining the outcomes are needed, together with a more rigorous assessment of web-based help-seeking behaviors. Ultimately, we propose a summary framework for the literature on this topic, including the antecedents, patterns of use, and outcomes of web-based help seeking in the context of depressive symptoms.

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“…Studies have shown that some college students are comfortable with mental health screening through mHealth modalities in the areas of performance expectancy and social influence [ 13 ]. Social influence and the stigma associated with mental health care greatly influence this age bracket (18-24 years) and their willingness to answer questions over their mobile devices.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Mobile Health to Manage Mental Health/Behavioral Health Disorders: Systematic Literature Review

Kruse¹,

Betancourt²,

Gonzales³

et al. 2022

JMIR Ment Health

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Background Mental health is a complex condition, highly related to emotion. The COVID-19 pandemic caused a significant spike in depression (from isolation) and anxiety (event related). Mobile Health (mHealth) and telemedicine offer solutions to augment patient care, provide education, improve symptoms of depression, and assuage fears and anxiety. Objective This review aims to assess the effectiveness of mHealth to provide mental health care by analyzing articles published in the last year in peer-reviewed, academic journals using strong methodology (randomized controlled trial). Methods We queried 4 databases (PubMed, CINAHL [Cumulative Index to Nursing and Allied Health Literature], Web of Science, and ScienceDirect) using a standard Boolean search string. We conducted this systematic literature review in accordance with the Kruse protocol and reported it in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 checklist (n=33). Results A total of 4 interventions (mostly mHealth) from 14 countries identified improvements in primary outcomes of depression and anxiety as well as in several secondary outcomes, namely, quality of life, mental well-being, cognitive flexibility, distress, sleep, self-efficacy, anger, decision conflict, decision regret, digestive disturbance, pain, and medication adherence. Conclusions mHealth interventions can provide education, treatment augmentation, and serve as the primary modality in mental health care. The mHealth modality should be carefully considered when evaluating modes of care. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42022343489; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=343489

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Assessing College Students' Perceptions of and Intentions to Use a Mobile App for Mental Health

Cited by 16 publications

References 42 publications

Factors Predicting Older People’s Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study (Preprint)

Factors Predicting Older People’s Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study (Preprint)

The Empowering Role of Web-Based Help Seeking on Depressive Symptoms: Systematic Review and Meta-analysis

Leveraging Mobile Health to Manage Mental Health/Behavioral Health Disorders: Systematic Literature Review

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