Keywords
digital mental health, mobile apps, barriers, medical students, Saudi Arabia
This study investigates the psychological and technological factors influencing the utilization of mental health applications among medical students at Umm Al-Qura University in the holy city of Makkah, Saudi Arabia, The study aims to evaluate the prevalence, perceptions, and barriers related to mental health applications usage, identify demographic and psychological factors influencing their adoption, and estimate the prevalence of mental health conditions among undergraduate medical students at Umm Al-Qura University in 2024.
A cross-sectional survey-based study was conducted at Umm Al-Qura University among 255 undergraduate medical students using a simple random sampling technique. Data were collected through a validated, self-administered (English) online questionnaire adapted from a previously published study. The questionnaire assessed sociodemographic characteristics, smartphone usage, mental health app use, and perceived barriers. The Statistical analysis included Structural Equation Modeling (SEM) and logistic regression, performed using R software version 4.2.2. And confidentiality was strictly maintained.
The SEM model exhibited an exemplary fit (CFI = 1.000, TLI = 1.029, RMSEA < 0.001, SRMR = 0.027). Cognitive Influence was significantly represented by variables such as the perceived importance of mental health and awareness of available resources. In contrast, neither Social Influence nor Technology Compatibility exhibited significant predictive pathways to Cognitive Influence. The covariance between Social Influence and Technology Compatibility was significant (p = 0.011), suggesting a potential indirect relationship. Observed variables, including “Technology helps positively” and “Peer influence item,” demonstrated strong factor loadings (p < 0.001).
This research underscores the intricate interplay between psychological, technological, and demographic variables in the adoption of mental health applications. Interventions designed to enhance cognitive awareness and promote tailored application development may significantly improve uptake among medical students.
digital mental health, mobile apps, barriers, medical students, Saudi Arabia
Mental health problems are higher among university students than among their peers. Medical students acquire more professional knowledge and skills than students in other majors. As a result, emotional disturbances and stress levels are very high amongst medical students.1 Medical students between the ages of 18 and 24 are more susceptible to developing mental diseases, such as anxiety disorders, psychiatric comorbidity, and major depressive disorder.2 Mental illnesses result in negative personal and professional consequences, such as physician impairment, social isolation, and burnout.1,2 Additionally, if these mental illnesses were left untreated, they may result in negative coping mechanisms, such as smoking, self-harm, substance abuse, and alcohol consumption.1
Medical students encounter numerous challenges that contribute to immense psychological and academic pressure. These pressures stem from various factors, including the intricate nature of the medical curriculum, demanding duty hours, the vast and complex body of medical knowledge, significant financial burdens associated with educational expenses, and notably reduced leisure time when compared to their counterparts in other professional fields.2
Mental health issues are highly prevalent among medical students. A systematic review and meta-analysis by Sarokhani et al. (2013) found that 33% (95% CI: 32-34%) of university students experienced depression.3 Similarly, Quek et al. (2019) reported a global prevalence of anxiety among medical students of 33.8% (95% CI: 29.2-38.7%).2 These findings underscore the urgent need for mental health support and targeted interventions in this population.
The prevalence of mental illness among medical students can be attributed to cultural or social reasons.4 While mental health disorders have been documented for many years, the rising prevalence of conditions such as anxiety and depression among medical students in developing countries warrants greater attention.5,6 Depression among medical students poses significant risks, including detrimental impacts on academic performance, increased suicidal ideation, and potential substance use. Several underlying factors contribute to this mental health challenge, notably the immense academic pressure these students face and the critical nature of their future responsibilities regarding patient safety. Additionally, the medical curriculum often presents a stark contrast to prior educational experiences. Additionally, the prevalence of depression does not differ significantly between male and female medical students.7
Over the past decade, technology has significantly impacted mental health care by facilitating access to mental health care and reducing costs through mobile apps.8 While extensive research has been conducted on this topic in Western countries, a notable scarcity of studies in other cultural contexts remains. For example, in Saudi Arabia, the prevalence of smartphone and internet usage is significantly high, supported by governmental initiatives such as the SEHA Virtual Hospital and various mobile health (mHealth) applications. Despite this conducive environment, there is a limited body of research focused specifically on the effectiveness and usage of digital mental health applications within this region.8
Undergraduate medical students are increasingly confronted with a range of mental health challenges, including elevated levels of depression, anxiety, and stress.2 Largely attributable to the rigorous academic environment, societal stigma, and inadequate access to traditional mental health services.2,9 In this context, digital mental health applications present a promising alternative; however, their acceptance, efficacy, and overall impact among medical students in Saudi Arabia remain underexplored.
Severe mental illnesses can pose a challenge to the successful use of digital mental health applications.9 Another factor that depends on the user is the user’s experience in using such applications.10 Additionally, the technology implemented in the application and the technical errors can be a barrier for the users.9,10 Considerations, such as technical assistance, program training, and customizing the application to be suitable for each user, are required.11
There exists a notable deficiency in the existing literature concerning medical students’ perceptions of digital mental health applications, as well as the barriers they encounter while utilizing these resources. This gap highlights the need for further investigation into the attitudes and experiences of medical students in relation to the accessibility and efficacy of such applications in supporting mental health. This study aims to investigate the utilization, perceptions, and barriers associated with digital mental health apps among medical students at Umm Al-Qura University. Additionally, it seeks to assess the demographic and psychological predictors of mental health applications engagement with this cohort.
This study employed a cross-sectional, survey-based design conducted over eight months, from November 2024 to June 2025, at Umm Al-Qura University — a leading public institution in Saudi Arabia comprising 35 colleges and institutes, 119 departments, and 215 distinct specializations. The study was carried out within the Faculty of Medicine, located in the Al-Abidia district on the southern periphery of the holy city of Makkah.
Participants included medical students from the first to the sixth academic year at Umm Al-Qura University who agreed to participate in the study. Recruitment was conducted through group leaders, and potential contamination was minimized by conducting interviews and distributing the questionnaire on campus.
The sample size was calculated as n = 255 after obtaining the total number of students from the academic office of the college. Calculations were performed using the Raosoft sample size calculator, with a 5% margin of error and a 90% confidence level.
A simple random sampling technique was applied to select students from each of the six academic years. Group leaders announced that a random subset of students would be invited to participate. Student selection was performed using a randomly generated list from the random.org website. Selected students received the survey link via email or WhatsApp, directing them to the Monkey Survey platform.
The questionnaire was adapted from a cross-sectional study conducted at the University of California10 and was administered in its original language (English), as all participants were proficient in English due to the nature of their medical curriculum. Written permission to use and adapt the questionnaire was obtained from the corresponding author. The adapted version was reviewed by preventive medicine consultants to ensure relevance, comprehensive coverage of key dimensions related to mental health applications, and inclusion of items addressing perceived barriers and student perceptions. A pilot test was conducted on a subsample of 10% of the study population (n = 25) to assess clarity, ease of completion, and feasibility.
The final survey consisted of six sections:
1. Sociodemographic data: Age, gender, academic year, income level, marital status, and caregiving responsibilities.
2. Technology and smartphone use: Access to devices, daily screen time, internet availability, and use of smartphones for mental health purposes.
3. Mental health app usage: Prior experience, perceived usefulness, cultural relevance, ease of use, and barriers (e.g., privacy concerns, technical limitations).
4. Healthcare utilization and resources: Professional help-seeking behaviors, previous mental health diagnoses, and informal coping strategies.
5. Stress and emotional well-being: Adapted items from validated screening tools assessing anxiety, depression, and emotional strain in the past year.
6. Perceptions and stigma: Attitudes toward mental illness, willingness to seek help, and openness to discussing psychological concerns.
The self-administered questionnaire required approximately 15 minutes to complete and was delivered online with an attached consent form. Ten trained data collectors received seven days of training in questionnaire administration and participant support. Data confidentiality was maintained throughout collection, cleaning, coding, and analysis, with access restricted to the study authors.
Due to copyright restrictions, the full questionnaire items cannot be reproduced in the manuscript or supplementary files. A complete citation and direct link to the original instrument are provided: Borghouts J, Eikey EV, Mark G, De Leon C, Schueller SM, Schneider M, et al. Understanding Mental Health App Use Among Community College Students: Web-Based Survey Study. Journal of Medical Internet Research. 2021;23(9):e27745. Available from: https://www.jmir.org/2021/9/e27745. The adapted version is available from the Corresponding Author upon reasonable request.
Descriptive statistics (frequencies and percentages) were used to summarize demographic characteristics. Structural Equation Modeling (SEM) was applied to examine the relationships between latent and observed variables, and to explore the influence of latent constructs on observable indicators. Logistic regression analysis identified demographic predictors of mental health app use. Analyses were conducted using R version 4.2.2, with statistical significance set at p < 0.05 and 95% confidence intervals reported.
The SEM framework included both a measurement model (linking latent variables to their observed indicators) and a structural model (specifying relationships among latent variables).
Cognitive Influence was measured by items such as:
- “Using mental health apps increases my chances of achieving things that are important to me.”
- “Using mental health apps helps me accomplish things more quickly.”
- “Using mental health apps increases my productivity.”
Social Influence was measured by items such as:
- “People who are important to me think that I should use mental health apps.”
- “I know it is necessary to use mental health apps.”
Technology Compatibility was measured by items such as:
- “Mental health apps are compatible with other technologies I use.”
- “I can get help from others when I have difficulties using mental health apps.”
In the structural model, Cognitive Influence was hypothesized to be influenced by both Social Influence and Technology Compatibility. Model fit indices and standardized estimates were reported.
This study was approved by the Institutional Review Board (IRB) of Umm Al-Qura University (Approval No.: HAPO-02-K-012-2024-10-2234). The study purpose was explained to participants, and an electronic consent form was provided at the start of the questionnaire thus Informed consent was obtained from all subject involved in the study via an item at the beginning of the survey.
Participation was voluntary and anonymous, and respondents could withdraw at any time. Only the primary author and co-authors had access to the data.
Out of the 255 participants, the majority were aged between 18 and 24 years (n = 230, 90.2%), and 51.8% were female (n = 132). Most respondents were single (n = 238, 93.3%) and affiliated with the College of Medicine at Umm Al-Qura University in Makkah (n = 235, 92.1%), reflecting a predominantly medical student population. Representation spanned all six academic years, with the highest proportions observed in the third year (n = 60, 23.5%) and fourth year (n = 61, 23.9%). More than half reported a monthly household income below 5,000 SAR (n = 157, 61.6%), and 17.6% (n = 45) indicated having children or dependents.
Technology access through smartphones (n = 201, 78.8%) and tablets (n = 190, 74.5%) were more commonly used than desktop or laptop computers (n = 116, 45.5%). A smaller proportion (n = 41, 16.1%) reported using non-smart mobile phones, while only five participants (2.0%) indicated they did not use any digital devices. Detailed demographic characteristics are presented in ( Table 1).
Among the 255 participants, only 21.6% (n = 55) reported having used mental health applications. Of those who used such applications, the majority 76.4% (n = 42) found them helpful. The most commonly cited reasons for non-usage included a lack of time 36.1% (n = 92) and a perception of not needing psychological support 33.1% (n = 84). Other reasons included not knowing about available applications 12.6% (n = 32), concerns about privacy 8.3% (n = 21), and a preference for in-person therapy 6.3% (n = 16). In terms of self-perception, 67.5% (n = 172) of participants reported a moderate mental health status, while 22.0% (n = 56) experienced symptoms of depression, and 34.5% (n = 88) reported symptoms of anxiety during the same period. The majority of students 62.4% (n = 159) rated mental health as “very important,” and 71.8% (n = 183) stated they were aware of resources that support mental well-being.
Technology access was high among the participants, with 78.8% (n = 201) using smartphones and 74.5% (n = 190) using tablets. However, only 45.5% (n = 116) reported regular use of desktops or laptops, and 2.0% (n = 5) of students indicated no access to any digital devices. These usage patterns suggest a digital readiness that could support mental health interventions via mobile applications, provided that issues related to awareness, time, and perceived need are addressed ( Table 2).
The fit statistics for the user model indicated a good overall model fit. The Chi-square test statistic was 6.367, with a p-value of 0.848, suggesting that the model fits well without significant deviation. Additional fit indices further supported the adequacy of the model. The Comparative Fit Index (CFI) was 1.000, exceeding the recommended threshold of 0.95 and indicating excellent fit. Similarly, the Tucker–Lewis Index (TLI) was 1.029, above the threshold of 0.90, confirming a strong model fit.
The Root Mean Square Error of Approximation (RMSEA) was 0.000, reflecting an ideal fit, with a 90% confidence interval ranging from 0.000 to 0.037, well within the acceptable range. The p-value for RMSEA at 0.05 was 0.980, providing strong evidence of close fit, while the p-value for RMSEA at 0.08 was 0.001, rejecting the hypothesis of poor fit. The Standardized Root Mean Square Residual (SRMR) value was 0.027, which is considered excellent since values below 0.08 indicate a good fit ( Table 3).
The latent variable estimates provided insight into the relationships between observed variables and their corresponding latent constructs. For the latent construct Cognitive Influence, the observed variables demonstrated strong factor loadings. Self-assessed mental health showed a factor loading of 0.587, with a significant z-value of 7.476 and a p-value < 0.001. Mental health importance had a loading of 0.385, also significant (z = 7.649, p < 0.001). Resource awareness exhibited a substantial loading of 0.500, with the same z-value (7.649) and p-value < 0.001. In contrast, the latent construct Social Influence included observed variables such as Others have knowledge, with a loading of 0.858 (z = 2.653, p = 0.008), and Peer influence item, which had a loading of 0.278 and a non-significant z-value. The latent construct Technology Compatibility was represented by two items: Tech fits mental goals (loading = 0.642, p < 0.001) and Tech helps positively (loading = 0.556, p < 0.001), both showing statistically significant associations ( Table 4).
Latent variable | Indicator | Estimate | Std. Err | z-value | P-value | Std.lv | Std.all |
---|---|---|---|---|---|---|---|
Cognitive Influence | Self-assessed mental health | 1.000 | - | - | - | 0.587 | 0.818 |
Mental health importance | 0.656 | 0.088 | 7.476 | 0.000** | 0.385 | 0.606 | |
Resource awareness | 0.851 | 0.111 | 7.649 | 0.000** | 0.500 | 0.635 | |
Social Influence | Peer influence item | 1.000 | - | - | - | 0.278 | 0.239 |
Others have knowledge | 3.089 | 1.164 | 2.653 | 0.008* | 0.858 | 0.791 | |
Technology Compatibility | Tech fits mental goals | 1.000 | - | - | - | 0.642 | 0.634 |
Tech helps positively | 0.865 | 0.144 | 6.022 | 0.000** | 0.556 | 0.519 |
The regression estimates assessed the relationships between latent constructs. The path from Social Influence to Cognitive Influence showed a negative estimate of -0.470, with a non-significant p-value of 0.821, suggesting no statistically significant association between these constructs. Conversely, the path from Technology Compatibility to Cognitive Influence demonstrated a positive estimate of 0.657, but this relationship was also non-significant (p = 0.482), indicating no strong association between these variables ( Table 5).
The covariance between Social Influence and Technology Compatibility was estimated at 0.165, with a significant z-value of 2.549 and a p-value of 0.011, indicating a positive relationship between these two latent variables ( Table 6).
Covariance | Estimate | Std. Err | z-value | P-value | Std.lv | Std. all |
---|---|---|---|---|---|---|
Social Influence ~~ Technology Compatibility | 0.165 | 0.065 | 2.549 | 0.011* | 0.925 | 0.925 |
The observed variables demonstrated strong factor loadings across various constructs. Self-assessed mental health had an estimate of 0.171, a z-value of 4.285, and a p-value < 0.001, indicating strong statistical significance. Mental health importance showed an estimate of 0.255, with a z-value of 9.028 and a p-value < 0.001, further supporting its significant contribution. Resource awareness had an estimate of 0.369, a z-value of 8.565, and a p-value < 0.001, confirming its significance. The Peer influence item recorded the highest estimate at 1.271, with a z-value of 11.001 and a p-value < 0.001, highlighting its substantial impact. Tech helps positively had an estimate of 0.835, a z-value of 9.316, and a p-value < 0.001, reflecting strong significance in the model. Tech fits mental goals showed an estimate of 0.615, a z-value of 7.078, and a p-value < 0.001, further affirming its contribution. All latent variables (Cognitive Influence, Social Influence, and Technology Compatibility) exhibited strong and statistically significant factor loadings ( Table 7).
Observed variable | Estimate | Std. Err | z-value | P-value | Std.lv | Std. all |
---|---|---|---|---|---|---|
Self-assessed mental health | 0.171 | 0.040 | 4.285 | 0.000** | 0.171 | 0.332 |
Mental health importance | 0.255 | 0.028 | 9.028 | 0.000** | 0.255 | 0.633 |
Resource awareness | 0.369 | 0.043 | 8.565 | 0.000** | 0.369 | 0.596 |
Peer influence item | 1.271 | 0.116 | 11.001 | 0.000** | 1.271 | 0.943 |
Others have knowledge | 0.441 | 0.252 | 1.754 | 0.079 | 0.441 | 0.375 |
Tech fits mental goals | 0.615 | 0.087 | 7.078 | 0.000** | 0.615 | 0.599 |
Tech helps positively | 0.835 | 0.090 | 9.316 | 0.000** | 0.835 | 0.730 |
Cognitive Influence (latent) | 0.251 | 0.061 | 4.152 | 0.000** | 0.729 | 0.729 |
Social Influence (latent) | 0.077 | 0.048 | 1.622 | 0.105 | 1.000 | 1.000 |
Tech Compatibility (latent) | 0.413 | 0.099 | 4.147 | 0.000** | 1.000 | 1.000 |
The logistic regression analysis examined the relationship between various demographic characteristics and the likelihood of using a mental health application. Among all predictors included in the model, two factors demonstrated statistically significant associations with app usage. First, being a third-year student was significantly associated with a lower likelihood of using the app (Estimate = -1.07, p = 0.039). Second, individuals without children or dependents were also significantly less likely to use the app (Estimate = -0.85, p = 0.039).
All other demographic variables — including age groups, gender, marital status, income level, and college affiliation — did not show statistically significant relationships with app usage, as their p-values exceeded the 0.05 threshold. Notably, some predictors, such as specific college affiliations, displayed extremely large coefficients and standard errors, suggesting potential issues with data sparsity or model instability for those categories.
These findings suggest that while most demographic characteristics did not significantly influence mental health app usage, certain life-stage factors — such as academic year and caregiving responsibilities — may play a more prominent role ( Table 8).
Predictor | Estimate | Std. Error | z value | P value |
---|---|---|---|---|
(Intercept) | 1.06 | 0.78 | 1.36 | 0.18 |
18-28 y | -0.74 | 0.80 | -0.91 | 0.36 |
More than 24 y | 0.06 | 1.18 | 0.05 | 0.96 |
Women | -0.32 | 0.32 | -1.00 | 0.32 |
Married | 0.86 | 0.89 | 0.96 | 0.34 |
Widowed | 17.19 | 2399.54 | 0.01 | 0.99 |
Divorced | -0.50 | 1.47 | -0.34 | 0.73 |
College of Medicine, Al-Qunfudhah | 0.20 | 0.74 | 0.27 | 0.79 |
College of Health Sciences, Al-Lith | 15.78 | 2399.55 | 0.01 | 0.99 |
College of Pharmacy | -15.76 | 2399.54 | -0.01 | 0.99 |
College of Applied Medical Sciences | -1.27 | 1.58 | -0.80 | 0.42 |
College of Nursing | 15.34 | 2399.54 | 0.01 | 0.99 |
College of Public Health and Health Informatics | 16.61 | 2399.54 | 0.01 | 0.99 |
Second year | -0.06 | 0.49 | -0.13 | 0.90 |
Third year | -1.07 | 0.52 | -2.06 | 0.04* |
Fourth year | 0.20 | 0.48 | 0.42 | 0.68 |
Fifth year | -1.17 | 0.69 | -1.71 | 0.09 |
Sixth year | -1.17 | 0.74 | -1.58 | 0.11 |
5000 to 15000 SR | 0.32 | 0.35 | 0.91 | 0.37 |
More than 15000 SR | 0.02 | 0.45 | 0.05 | 0.96 |
No children or dependents | -0.85 | 0.42 | -2.06 | 0.04* |
This study investigated the factors affecting the use of mental health applications among medical students at Umm Al-Qura University using SEM and logistic regression. Despite high smartphone ownership 201 (78.8%) and digital literacy, only 64 (25.1%) reported mental health app usage. These results reflect a broader underutilization of non-digital mental health tools, consistent with findings from a study conducted by Aldaweesh et al. (2024), which reported that most clinics use paper for assessment and the CBT tools, despite the wide use of smartphones.8 Despite their different population, these findings highlight a larger issue relevant to our study, the underutilization of digital mental health aids in technologically advanced workplaces. Our clinical findings show that students with many smartphones did not use mental health apps. This suggests that psychological, structural, or cultural barriers may prevent people from participating in different situations.12 In Saudi Arabia, privacy and stigma regarding mental health are major concerns.
A study conducted by Al Dweik et al. in the UAE found that the use of mental health applications post-COVID-19 has significantly accelerated. About 228 (95.0%) of the participants stated the importance of mental health services, and about 173 (72.0%) found virtual consultations as effective as in-person therapy. Moreover, 144 (60.0%) of mental visits were teleassessments.13 The data confirm our finding that life-stage characteristics and cognitive perceptions-particularly stress and mental health awareness-drive app usage among medical students.
SEM analysis showed excellent model fit (CFI = 1.000, TLI = 1.029, RMSEA <0.001, SRMR = 0.027). Key findings included the latent construct of Cognitive Influence being significantly linked to perceived mental health importance and resource awareness, which was a significant predictor of app use. This aligns with a web-based survey administered to a randomly selected sample of 500 community college students from April 16 to June 30, 2020. Borghouts et al. (2021) reported in their study that participants who used mental health applications had one or more of these factors: perceived stress, need to seek help for mental health concerns, social influence of other people, privacy concerns, and past use of mental health services. Additionally, stress leads to seeking help. Stress is one of the most common mental health issues. Approximately 220 (44.0%) of participants in their study suffered from stress. Borghouts et al. reported that there is a direct relationship between the stress that students experience and the use of mental health applications.10 Conversely, social influence and technology compatibility showed no significant direct pathways to cognitive influence, indicating possible mediation by unmeasured variables. Covariance analysis revealed a positive relationship between Social Influence and Technology Compatibility (p = 0.011), suggesting potential indirect effects on user behavior. Additionally, Ervasti et al. (2019) suggested that self-reported stress strongly correlated with stress management applications use in a survey study conducted at the University of Helsinki’s mailing lists.11 Our results indicate that in the Saudi student population, psychological relevance and internal motivation drive digital mental health engagement more than external social or technological factors, an important consideration for culturally sensitive intervention design.
Logistic regression identified two key demographic predictors for mental health app usage: third-year students and those without caregiving responsibilities had a decreased likelihood of usage (p = 0.039), possibly due to academic pressures. Students in this academic year may be less inclined or less likely to engage with mental health applications compared to other year groups. Those without dependents showed lower utilization, indicating caregiving responsibilities may heighten the need for such resources and might be associated with higher engagement in mental health support tools. This shows that academic-stage difficulties and obligations may increase support tool use. These findings align with Borghouts et al. (2021), who emphasized personal obligations and mental health requirements determine app usage,10 and with the findings from the national Healthy Minds Study, 2013-2021 by Lipson et al. (2022), who showed that students with more life demands are more likely to seek mental health support.14 Other factors such as age, gender, income, and college affiliation did not show significant associations, although some coefficient instability was noted in certain subgroups, necessitating further research. This is consistent with the survey study conducted by Apolinário-Hagen et al. (2019), which indicated that psychological moods impacted app engagement more than static demographics.15 Although there is extensive access to the internet in Saudi Arabia, the stigma associated with mental health is still severe. As a result, internal cognitive and emotional factors may have a greater impact on adoption than socioeconomic position.
Topics such as stigma, a lack of Arabic content, and worries about privacy are brought to light. These are issues that are particularly pertinent in the context of Saudi culture. Aldaweesh et al. stated that most clinicians were aware of consultation applications, such as Labayh, Famcare, and Estenarh. However, fewer clinicians were aware of applications that were used to provide mental health services. English-speaking patients were referred to use applications such as Headspace. The Arabic content in mental health applications is lacking. Only 1 (2.0%) clinician used the CBT-based Arabic applications.8 Our study found that students used mental health applications rarely, highlighting the need for culturally sensitive and distinct mental health solutions. Anonymous, Arabic-language, and religiously and socially compliant apps may be more accepted. Lipson et al. (2021) found that 30% of university students sought therapy, and only 5.4% of them sought therapy on campus in an annual cross-sectional survey.14 These findings collectively confirm our results and suggest that the utilization of mental health applications among medical students is more significantly influenced by cognitive perspectives and academic stage characteristics than by technological or demographic variables. This is the case when all factors are considered. The dissemination of additional information, the reduction of stigma, and the creation of applications that are compatible with cultural and linguistic requirements could all contribute to the increase in the use of mobile applications among Saudi medical students. Consequently, this underscores a significant possibility for intervention. Our results reinforce the view that life context, rather than static factors like gender or income, is a more reliable predictor of digital mental health tool adoption in student populations.
This study employed an analytical framework by integrating SEM with logistic regression to investigate both latent constructs and demographic predictors. The model fit indices indicate a high level of model adequacy, with CFI at 1.000, TLI at 1.029, RMSEA <0.001, and SRMR at 0.027, thereby signifying excellent internal consistency of the constructs examined. The analysis explored various validated constructs, including Technology Compatibility, Cognitive Influence, and Social Influence, utilizing observed variables that exhibited strong factor loadings. The selection of a diverse sample of medical students enhances the relevance of the findings, particularly concerning the demographic that is most susceptible to academic stress. Furthermore, comparisons with existing literature substantiate the findings and situate the research within broader global and regional contexts.
Certain pathways in the SEM model were not statistically significant, which constrains the ability to draw definitive conclusions regarding direct relationships among the constructs. The logistic regression analysis revealed substantial coefficients and standard errors within specific demographic subgroups, indicating potential issues of data sparsity or instability. The cross-sectional nature of the study precludes causal inferences, and reliance on self-reported data may introduce response bias, potentially compromising the reliability of the reported usage of mental health applications. Additionally, the limited generalizability of the findings is a consequence of the study being conducted within a single institution and the predominance of female participants. Lastly, the research did not account for unmeasured mediators or moderators that may exert influence over app usage behavior, suggesting areas for future inquiry.
This study explores the psychological and demographic factors affecting the usage of mental health applications among medical students. Although the structural model showed strong fit, key constructs like Social Influence and Technology Compatibility did not directly affect Cognitive Influence, indicating potential mediation by other variables. The findings indicated that third-year students and individuals without dependents were less likely to utilize mental health applications, highlighting the influence of life-stage factors on help-seeking behaviors and digital mental health engagement. The cultural emphasis on privacy, particularly among female users, underscores the need for features prioritizing confidentiality. The findings offer valuable implications for future research and mental health technology development. Addressing cognitive barriers and enhancing cultural relevance are crucial for increasing adoption in this demographic.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Umm Al-Qura University. Protocol code HAPO-02-K-012-2024-10-2234.
Due to ethical restrictions imposed by the Institutional Review Board (IRB) at Umm Al-Qura University, the dataset supporting the conclusions of this article is not publicly available, as it contains potentially identifiable and sensitive information. However, the data is available upon reasonable request to qualified researchers affiliated with recognized academic or research institutions. Requests can be directed to the corresponding author at (WTakrooni@moh.gov.sa).
We gratefully acknowledge the support of Taif Almehmadi, Farah Alhuthali, Manar Mukhaymir, Dana Althagafi, Ritaj Alharthi, Maiar Fatani, Abdulmajeed Halawani, and Lujain Bin Salman for their dedicated efforts in data collection.
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