Effectiveness of a mental health mobile application for the academic community: A Longitudinal Study

Effectiveness of a mental health mobile application for the academic community: A Longitudinal Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Effectiveness of a mental health mobile application for the academic community: A Longitudinal Study John Robert C. Rilveria, Lorelie C. Grepo, Paola Marie Q. Lim, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6321910/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Research on technology-based interventions for mental health continues to establish its significance within the landscape of mental health services. Evidence has shown positive modest effects of using mental health applications on various mental health outcomes. This study explores the effectiveness of the Social Activity Guardian and Intervention Project (SAGIP) mental health mobile application designed for the University of the Philippines academic community. Using an experimental repeated measures design, the effectiveness of using the SAGIP app was evaluated over a 6-month period. This research compared participants from the experimental group (users of the SAGIP app) and the control group (non-users of the SAGIP app) while analyzing the interplay among different target variables like app usage, psychological distress scores, and psychological well-being scores. The mixed analysis of variance showed a significant difference between the experimental group and the control group. Using the SAGIP mental health app is associated with a non-linear improvement in users’ psychological well-being and a reduction of their psychological distress over time. Furthermore, based on the latent growth curve modeling, when controlling for the effects of psychological distress over time, participants who use the SAGIP mental health app experience better improvements in their psychological well-being compared to those who did not use the app. Nonetheless, user engagement in the form of frequency and duration of use was not a significant factor in well-being outcomes for those who use the SAGIP mental health app. Health sciences/Health care/Quality of life Scientific community and society/Social sciences/Psychology/Human behaviour Scientific community and society/Social sciences/Education mental health well-being distress mobile application academic community Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Mental health concerns have been on the rise worldwide. This was exacerbated by the COVID-19 pandemic that started in 2019 and is currently contributing to the increased need for mental health interventions. A meta-analysis of mental health research revealed that there was a 4–19% increase in mental health problem prevalence for the general population during the pandemic (World Health Organization, 2022 ). Likewise, in the Philippines, an exponential rise in the cases of mental health concerns was reported. However, the national information on mental health services in the Philippines reveals significant gaps and inconsistencies in the delivery of mental healthcare, leading to challenges in the provision of accessible and affordable mental healthcare (Lally et al., 2019 ). According to the World Health Organization ( 2021 ), the Philippines has 1.68 mental health professionals for every 100,000 Filipinos, compared to a global median of 13 per 100,000. In addition, several cultural, social, geographic, technological, and economic factors contribute to the challenges and barriers in seeking and providing mental health services in the country. With the increasing rate of mental health issues, as well as the existing discrepancy between healthcare demand and the provision of services, there is a need to look into the potential of alternative and emerging mental health interventions (Srivastava et al., 2020 ). Over the last decade, technology-based mental health interventions have grown rapidly, making them more accessible and cost-effective in the delivery of evidence-based healthcare services (Torous & Huffman, 2022 ). With the increasing ownership of smartphones and accessibility over the internet, combined with the surging demand for mental health support and services, mobile mental health applications are drawing more interest from consumers and mental health providers (Clay, 2021 ; Li et al., 2020 ). Mental health applications can easily be searched, downloaded, and accessed, with more than 10,000 existing today (Torous et al., 2019). Mental health applications are gaining popularity due to their “self-help” functions and features that are accessible anytime and anywhere (Köhnen et al., 2021 ). Moreover, mental health applications protect the identities of users as they can be used anonymously and confidentially (Kauer et al., 2014 ), preventing an individual from being stigmatized for seeking help and support. In addition, mental health applications are gradually becoming promising early intervention and support tools that can help users at the time they need it, at their earliest convenience, until they are able to see a mental health professional (Oppenheim, 2019 ). These technologies can supplement the system of care provided by a professional through the reinforcement of strategies, skills training, and information tracking. There is an optimistic outlook regarding the potential benefits of using mental health applications (Eis et al., 2022; Fischer et al., 2020). When comparing the effectiveness of face-to-face therapy, smartphone mobile application intervention, and the combination of face-to-face therapy and smartphone mobile application intervention, there are promising results in the reduction of depression, anxiety, and stress for college students (Oliveira et al., 2021 ; Borjalilu et al., 2019 ). Post hoc tests showed that blended therapy had the greatest improvement in scores, suggesting the combination of face-to-face therapy and mobile platforms to support the mental health of people in their everyday lives. Likewise, the research of Marshall et al. ( 2021 ) revealed the effectiveness of mobile applications for managing anxiety and depression symptoms using a multiple baseline research design for people undertaking psychotherapy and/or psychotropic medications concurrently. Those with anxiety symptoms and mixed anxiety and depression features had better outcomes than those with depression symptoms alone. In addition, the intervention was seen as more beneficial for those who have a shorter history of mental illness. The said results were generally maintained at 6-month follow-up. Furthermore, a meta-analytic review of mobile mental health applications showed significant results in reductions in depression and stress scores (Khamedian et al., 2020) with small to medium effect sizes (Lecomte et al., 2020). Aside from symptom reduction for users with mental health concerns, other mental health apps target the general population for well-being or quality of life outcomes (Conley et al., 2022; Hwang et al., 2021 ; Linardon et al., 2019 ). Specific features of mental health applications like meditation exercises (Yang et al., 2018 ; Carissoli et al., 2015 ), breathing-retraining exercises (Pham et al., 2016 ), informational resource (Arean et al., 2016), problem-solving applications (Mohr et al., 2017), mood monitoring (Backer & Rickard, 2018), and mental health screening (Hwang et al., 2019) were found to be useful in enhancing well-being, reducing stress-related experiences, and supplementing psychological interventions in improving mental health functioning. The literature on digital and technological mental health demonstrates the capacity of mental health applications to offer a number of potential benefits to supplement psychiatric treatment (Firth et al., 2017 ) and provide people with self-management tools to support their mental health (Ben-Zeev, et al., 2021 ). Nonetheless, a systematic review of these technology-based mental health interventions among the youth found that these interventions may only be of clinical significance when use is highly supervised, when there is consistency in usage, and when there are relevant and interactive features other than just providing digital educational materials (Garrido, et al., 2019 ). In this regard, the effectiveness of mental health apps is still contested (Chandrashekar, 2018 ) due to usage variance (i.e., frequency of use, duration of use, consistency of use, type of content, etc.). There are still research gaps in terms of what makes a mental health app effective. Despite the prevalence of mental health applications, data shows that the usage behavior tells a different story. Most mobile health applications that are downloaded are only used once, or within two weeks, then dropped by the users (Koh et al., 2022 ). Factors that contribute to low acceptance and adoption of mobile applications include lack of trust, privacy and security concerns, mismatch between the user expectations and app design, and poor usability. In addition, the lack of regulation and the lack of empirical evidence showing effectiveness solely attributed to mental health app usage contribute to the reluctance to use mental health applications. According to Lecomte et al. (2020), only around 5% of the existing mental health apps have been evaluated strictly in terms of their inherent effectiveness, teasing out other confounding variables. Furthermore, research on the effectiveness of utilizing mental health applications remains to be mixed. While there are studies suggesting that mental health applications are effective, some say otherwise (Donker et al., 2013 ), with some studies having inconclusive results (Weisel et al., 2019 ). There are still potential barriers to patient app use as well as to larger-scale adoption which include concerns surrounding safety, credibility, unfamiliarity, usability, engagement, personalization, and information governance. Therefore, more rigorous research on the effectiveness of mental health interventions is recommended (Powell et al, 2014 ). The Asia-Pacific region is at the top when it comes to the heaviest mobile data traffic, particularly in Brazil, China, India, Indonesia, Philippines, Vietnam, and Japan. The widespread ownership of mobile devices and internet access in Asia presents a significant opportunity to enhance mental health care in the digital and technological space. Mobile mental health applications can serve as a layer of mental health support for users and as a platform to connect mental health professionals with users, helping to bridge the gap in access to services (Li et al., 2020 ). To further investigate the effectiveness of mental health apps, this study evaluated the SAGIP mental health app designed for the academic community, particularly at the University of the Philippines (UP). Representative users consisting of students, teaching and non-teaching personnel used the SAGIP app, and mental health outcomes (i.e., psychological well-being and psychological distress) were measured over time in a 6-month study. Research Objectives Research Objectives The current study aimed to test the basic effectiveness of the SAGIP mental health application. Particularly, the following hypotheses were tested: 1. There is a significant difference between participants who used the SAGIP mental health application and participants who did not use the app in terms of their psychological well-being. a. Participants who used the SAGIP mental health application will experience improvements in their psychological well-being over time. 2. There is a significant difference between participants who used the SAGIP mental health application and participants who did not use the app in terms of their psychological distress. b. Participants who used the SAGIP mental health application will experience reductions in their psychological distress over time. 3. Using the SAGIP mental health application can significantly improve psychological well-being while controlling for the effects of psychological distress levels. a. The frequency and duration of using the SAGIP mental health application can significantly influence improvement in psychological well-being. Methods This paper is part of a broader research project that primarily involved two phases: 1) the user-centered design and development of the Social Activity Guardian and Intervention Project (SAGIP) mental health mobile application and 2) the evaluation of the SAGIP app through a longitudinal and cross-sectional mixed methods approach. This paper focuses on the Phase 2 quantitative longitudinal investigation of the effectiveness of using the SAGIP app. The research employed an experimental repeated measures design to evaluate the effectiveness of using the SAGIP app over time. This research compared participants from the experimental group (users of the SAGIP app) and the control group (non-users of the SAGIP app) while analyzing the interplay among different target variables like app usage, psychological distress scores, and psychological well-being scores. Intervention: SAGIP App The SAGIP app was developed using a user-centered design framework that involved students, faculty, non-teaching staff, and mental health professionals in the University of the Philippines. User requirements were elicited through Focus Group Discussion sessions and in-depth interviews. Details of the app development are discussed in another paper. SAGIP is a self-guided app with 3 main features: Directory, Resources, and Toolkits. The Directory contains contact information for mental health support providers within and outside the University campuses. The Resources are psychoeducational materials in the form of articles, audio, and video resources curated by clinical psychologists based on the needs identified from the interviews and FGD sessions with students, faculty, and non-teaching staff. Finally, the Toolkits contain worksheets and guided exercises (i.e., problem-solving, meditation, cognitive restructuring, coping, acceptance, self-compassion, support-seeking, emotion regulation, etc.) that help enhance, manage, or tackle general mental health concerns. Other app features include Assessment of Well-being, personalization of contents and resources through the Recommended and Explore features, Daily Quotes, Notifications, Badges for completion of worksheets and exercises, and customization of the app’s settings. Brief descriptions of the SAGIP app features are shown in Fig. 1 . Screenshots of the SAGIP app pages are shown in the Appendix. Study Site and Participants The University of the Philippines (UP) was chosen as the research site because its population is a good representation of the academic community, with its students, faculty, and staff coming from different campuses all over the Philippines. Sample Size The study targeted two hundred (n = 200) participants (including students, faculty, and staff) from the UP community. This is based on an a priori power analysis for a repeated measures, within-between interaction (2 groups and 5 repeated measures, alpha level = 0.05, power = 0.80, effect size = 0.25) using G*Power. This sample size is also consistent with the recommendation for latent growth curve modeling for unbiased estimates (Shi et al., 2021 ). Participants were randomly assigned to two groups: experimental “app” group and waitlist control “no app” group. Each group should have at least 100 participants composed of 80 students, 10 faculty, and 10 non-teaching staff. This breakdown is based on the population of about 25,000 students and 5,000 faculty and staff for the entire UP system. The faculty and staff comprise about 17% of the population. This will be about 17, rounded up to 20 in every 100 participants. A total of 224 participants expressed interest to participate in the study. Sampling and Recruitment Participants were recruited online via convenience sampling. Emails were sent through the official mailing lists of the campuses, and online advertisements were posted on university-affiliated social media pages. Interested participants were asked to sign up online via Qualtrics. The sign-up form included a study description and a screening questionnaire. Those who signed up were screened for their eligibility for the study and contacted by the research team. Inclusion Criteria . Participants qualified for the study if they are: a) currently enrolled or working in the University (UP student or UP employee) throughout the duration of the study, b) owning at least one smartphone with access to a minimum of 5mbps internet connectivity, c) willing to commit to participating in the study by using the app at least once a week and answering the periodic assessment measures for 6 months, d) at least 18 years old. Exclusion Criteria. Prospective participants who would fall under any of the following exclusion criteria were ineligible to participate: a) not officially enrolled nor working in the University, b) currently diagnosed with a mental health condition, c) currently taking psychotropic medications, d) currently seeing a mental health professional, e) currently using other mental health mobile application, f) below 18 years old. Withdrawal criteria. Participants who expressed that they no longer want to continue with the study were excluded from the study. Participants who, upon periodic evaluation, had highly elevated psychological distress scores (severe to extremely severe) were also referred to a mental health professional and asked whether they still want to continue their participation. For this study, no participants were withdrawn for these aforementioned reasons. Nonetheless, data of participants were withdrawn (i.e., not included for data analysis) if there are more than 15% missing responses in each of the questionnaires they accomplished over the six-month period. From the 224 participants who were recruited and assigned to either the experimental group or control group, only the data from 194 participants were used for data analysis (with n = 93 for the experimental group and n = 101 for the control group) after data cleaning. Study Procedure Participants (n = 224) were randomly assigned to either the experimental “app” group or the control “no app” group. Both groups were invited to join an online orientation via Zoom webinar to learn about the nature of the study and their roles and expectations from them as participants. Participants were asked to replace their profile pictures and names to avoid identification. They were also asked to close their cameras for security purposes. Orientation of the experimental “app” group included the installation of the SAGIP mobile application with user guide. The orientation sessions were recorded and shared to those participants who were unable to attend. After the orientation sessions, participants were asked to complete the informed consent and agreement forms online via Qualtrics. The study was conducted remotely, and all interactions with the participants were virtual via email and Zoom meetings. Likewise, the assessment measures for the longitudinal study were administered online through Qualtrics. Participants in the experimental “app” group were asked to use the mental health application for 6 months (Months 1 to 6) while the participants in the waitlist control “no app” group were only introduced to the application and was informed that they are on the waitlist for the app for the first three months, and the research team will continue to monitor their well-being during this time. A baseline survey was administered at the beginning of the study and periodic online assessments were administered at the end of Months 1, 2, and 3. At the end of month 3, the waitlist control group were given instructions on how to access the app and were allowed to use the SAGIP app up to month 6. At the end of month 6, a follow-up online survey was conducted. Measures and Data Collection Quantitative data were collected at five time points (pre-study assessment, three monthly assessments, and post-study assessment). The following variables were measured to evaluate SAGIP app’s effectiveness: 1. Psychological Distress The Depression Anxiety Stress Scale-21 short form version was administered to measure general psychological distress. The three subscales of depression, anxiety, and stress have 7 items each that are rated from 0 to 3. The general distress score is calculated by getting the sum of the ratings for all items (Zanon et al., 2021; Lovibond, S.H. & Lovibond, P.F., 1995). 2. Psychological Well-being The participants’ psychological well-being was measured using the shortened 18-item Psychological Well-being Scale (Ryff, 1995) with 6 constructs: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance. 3. App usage The usage of the SAGIP app was collected automatically from the app. This includes the frequency and duration of use and the pages/features of the app visited. Prior to participation, those who signed up were administered a questionnaire for mental health (depression, anxiety, stress, and psychological well-being) as part of a screening and to establish baseline measures. After the random assignment to experimental and control groups and their respective orientation to the study, quantitative data collection followed at the end of each month for the first 3 months of the study. Links to the monthly surveys were sent to the participants through email. The final quantitative data collection was done at the end of month 6 where both groups have experienced using the application. Data Analysis Each of the measures was first analyzed and reported using descriptive statistics. This included the multiple time point measures of Psychological Well-being, Psychological Distress scores, and the mental health app usage. In testing the first two research hypotheses, comparing the well-being and distress scores of SAGIP app users and non-users over time, a mixed analysis of variance was conducted. In testing the last set of research hypotheses looking at the growth trajectory of the effectiveness of using the SAGIP mental health app while controlling for the effects of psychological distress and looking at whether app usage is related to changes in psychological well-being over time, Latent Growth Curve Modeling was used. The IBM Statistical Package for the Social Sciences (SPSS) version 30 with the Analysis of Moment Structures (AMOS) module was utilized as the statistical tool for data analysis. Data from participants were removed from inclusion in the analysis if they did not complete the pre-study assessment, all three monthly assessments, and the post-study assessment. Answering at least 85% of the items in each questionnaire was considered as completion of the said assessment. A 15% missing rate falls under the acceptable range of missing data in longitudinal studies (Roberts et al., 2017; Schlomer et al., 2010). The pattern of the missing responses was first analyzed using Little’s MCAR test (Li, 2013), and the results supported the assumption that the data in question were missing completely at random. This indicates that there was no specific pattern of missingness found in the dataset and that the missing data occur randomly and independently from the variables of interest in the study (Roberts et al., 2017). Multiple imputation method was used to treat the missing data. It involves the repeated generation of replacement values for missing data based on statistical characteristics of the existing data and a pooled analysis of the imputed data sets to arrive at overall estimates (Woods et al., 2024; Li et al., 2015). Ethical Approval This study was approved by the University of the Philippines Manila Research Ethics Board (UPMREB 2023-0053-01). Results Participant Demographics A summary of the participants’ demographic characteristics is provided in Table 1. Majority of the participants in the study were female, students, and aged 25 years old and below. Table 1 Demographic Characteristics of Participants Baseline characteristic Experimental Wait-list Control Full sample n % n % n % Gender Female 59 63.44 60 59.41 119 61.34 Male 25 26.88 31 30.69 56 28.86 Prefer not to say 2 2.15 1 0.99 3 1.55 Did not indicate 7 7.53 9 8.91 16 8.25 Total 93 100.00 101 100.00 194 100.00 Occupation Student 82 88.17 83 82.18 165 85.05 Faculty and Staff 11 11.83 18 17.82 29 14.95 Total 93 100.00 101 100.00 194 100.00 Age 18-25 54 58.06 51 50.50 105 54.12 26-35 25 26.88 28 27.72 53 27.32 36-45 6 6.45 6 5.94 12 6.19 46 and up 1 1.08 7 6.93 8 4.12 Did not indicate 7 7.53 9 8.91 16 8.25 Total 93 100.00 101 100.00 194 100.00 General comparison between the experimental group and control group over time The first and second research hypotheses tested the significant differences between participants from the experimental group and participants from the control group in terms of their psychological well-being and psychological distress scores, respectively. A mixed Analysis of Variance was conducted to test these hypotheses, considering the data from baseline assessment (Time 1) to the last assessment (Time 5). The results are shown in Table 2. Within-subjects effects indicate a significant non-linear change in psychological well-being scores, F (1,192) = 11.189, p < .001, η2p = .055 and psychological distress scores, F (1,192) = 57.561, p < .001, η2p = .231 over time. Moreover, between-subjects effects indicate a significant difference between participants in the experimental group and participants in the control group in terms of their psychological well-being, F (1,192) = 86.91, p < .001, η2p = .312 and psychological distress scores, F (1,192) = 14.433, p < .001, η2p = .070. A significant interaction effect was also found between time and group for both psychological well-being, F (1,192) = 4.659, p < .05, η2p = .024 and psychological distress outcomes, F (1,192) = 3.873, p < .05, η2p = .025. Table 2 Two-way Mixed ANOVA Statistics Dependent Variables ANOVA Effect F df η2p Psychological Well-being Group 86.91** 1,192 .312 Time Linear 21.018** 1,192 .099 Quadratic 4.644* 1,192 .024 Cubic 0.372 1,192 .002 Quartic 11.189** 1,192 .055 Time x Group Linear 152.503** 1,192 .443 Quadratic .501 1,192 .003 Cubic 15.876** 1,192 .076 Quartic 4.659* 1,192 .024 Psychological Distress Group 14.433** 1,192 .070 Time Linear 190.413** 1,192 .498 Quadratic 30.040** 1,192 .135 Cubic 46.60** 1,192 .195 Quartic 57.561** 1,192 .231 Time x Group Linear 2.357 1,192 .012 Quadratic 63.130** 1,192 .247 Cubic 3.873* 1,192 .020 Quartic 4.992* 1,192 .025 Note. N = 194 ** p < .001. * p <.05 Based on these results, the rate of change in psychological well-being scores and psychological distress scores most likely fit a quartic function for both groups. This suggests fluctuations in the scores over time (Figures 2 and 3). It can be observed that from baseline (Time 1) to the end of month 3 (Time 4), there is an observable increase in the psychological well-being scores of participants who use the SAGIP app. On the other hand, there is an observable decline in the psychological well-being scores of participants in the control group. In addition, there is an erratic rate of change in psychological distress scores over time for participants in the control group compared to a more consistent decrease in psychological distress scores over time for participants in the experimental group. It’s interesting to note that there is a sudden rise at the end of month 3 (Time 4) and an eventual drop of psychological distress scores at the end of month 6 (Time 5) that can be observed for both groups. After the end of month 3, participants in the control group were given the opportunity to use the SAGIP app, and participants in the experimental group continued using the app until the end of month 6. At these time points, a very slight increase in psychological well-being and slight decrease in psychological distress scores can be observed for participants in the experimental group. On the other hand, there is an observable improvement in psychological well-being and sharp reduction in psychological distress scores for participants in the control group who just started using the SAGIP app. Overall, the results indicate that using the SAGIP app for the first time facilitated an increase in psychological well-being (as reflected from the experience of the participants in the control group). Consistent use of the app for longer periods contributed to continuous gains in psychological well-being (as reflected from the experience of the participants in the experimental group who have been using the app continuously for six months). Nonetheless, the rate of improvement in psychological well-being begins to slow down toward the sixth month of using the app for the participants in the experimental group. Also, while there are statistically significant differences between the groups, the participants from both groups experienced the same pattern of change in their psychological distress scores over time . Effectiveness of the SAGIP mental health app usage The last research hypothesis tested the rate of improvement of psychological well-being as a function of SAGIP app usage and psychological distress scores over time. In doing so, a Latent Growth Curve modeling was conducted to compare the levels of psychological well-being between users of the SAGIP mental health application (as part of the experimental group) versus those who did not use the application (as part of the control group) in 3 months. Based on the previous result, participants from both groups are significantly different in terms of their levels of depression, anxiety, and stress over time (see Table 3). Therefore, psychological distress scores over time were controlled and treated as time-varying covariates. Table 3 Maximum likelihood parameter estimates based on the latent growth curve model (well-being of app users vs non-users with psychological distress as time-varying covariates) Paths Estimates Standard Error Time-invariant covariate Group —> Intercept .350 1.360 Group —> Slope -8.218 .750 Time-varying covariates T1_DASS —> T1_Well-being .326* .052 T2_DASS —> T2_Well-being .247* .040 T3_DASS —> T3_Well-being .224* .049 T4_DASS —> T4_Well-being .034 .055 Note. Fit indices: ((χ2 (18) = 24.533, NFI = 0.960, CFI = 0.989, IFI = 0.989, TLI = 0.978, RMSEA = 0.043 [0.000, 0.083]) * p < .001 The growth curve analysis revealed a satisfactory overall model fit (χ 2 (18) = 24.533, NFI = 0.960, CFI = 0.989, IFI = 0.989, TLI = 0.978, RMSEA = 0.043 [0.000, 0.083]). The parameter estimates show that at baseline, members of the control group did not differ with members of the experimental group in terms of their levels of psychological well-being (β o = .350, p > .05). This supports the experimental manipulation such that the potential difference between the groups can be attributed to the mental health application usage vs non-usage in the succeeding months. A significant negative slope was found, which indicates that the rate of change in the psychological well-being of users is significantly higher than the rate of change in the psychological well-being of non-users from baseline to the end of the third month of assessment (β = -8.218, p < .001). Furthermore, the level of psychological well-being of users of the SAGIP mental health application significantly increases, especially from the end of the first month to the end of third month compared to non-users (see Table 4). There are 48.4%, 30.7%, 55.7%, and 93.2% of variance in psychological well-being from baseline to the end of the third month, respectively, being explained by the SAGIP app usage after controlling for the effects of depression, anxiety, and stress symptoms. The results show a degree of effectiveness of using the SAGIP mental health application in improving levels of psychological well-being while controlling for levels of psychological distress. Table 4 Squared multiple correlations based on the latent growth curve model (well-being of app users vs non-users with psychological distress as time-varying covariates) Variables Estimates Intercept .001 Slope .489 T1_Well-being .484 T2_Well-being .307 T3_Well-being .557 T4_Well-being .932 Relationship between frequency and duration of SAGIP app usage and its effectiveness In analyzing the influence of frequency and duration of SAGIP app usage in relation to well-being, the results yielded insignificant results (see Table 5). The growth curve analysis revealed a poor model fit (χ2 (31) = 166.384, NFI = 0.750, CFI = 0.774, IFI = 0.787, TLI = 0.520, RMSEA = 0.218 [0.186, 0.251). These results indicate that the app usage in terms of frequency and duration does not explain changes in the psychological well-being of the app users. Regardless of how frequently and how long the users engage with the SAGIP app, their psychological well-being increases. Accessing specific contents of the app at the time they are needed is likely beneficial enough to facilitate increases in well-being no matter the frequency and duration of its use. Table 5 Maximum likelihood parameter estimates based on the latent growth curve model (well-being of app users with app frequency and duration as time-varying covariates) Paths Estimates Standard Error Time-varying covariates T1_Frequency —> T1_Well-being -.025 .037 T1_Duration —> T1_Well-being .001 .001 T2_Frequency —> T2_Well-being -.034 .036 T2_Duration —> T2_Well-being .002 .001 T3_Frequency —> T3_Well-being .038 .047 T3_Duration —> T3_Well-being .000 .001 T4_Frequency —> T4_Well-being .030 .093 T4_Duration —> T4_Well-being .002 .003 Discussion Several points of discussion were raised based on the results of the study. First, the overall findings show that using the SAGIP mental health application proved to be effective in improving psychological well-being and reducing psychological distress among those who used it compared to those who did not use the application. This is consistent with the significant results shown in the literature about the effectiveness of mental health applications targeting distress and well-being (Oliveira et al., 2021; Lecomte et al., 2020; Fischer et al., 2020; Baumel et al., 2019; Firth et al., 2017) with modest effect sizes (Wiesel et al., 2019). Using a mental health application like the SAGIP app can be an adjunctive tool for helping users support their mental health, particularly in terms of their sense of well-being and level of general distress. The SAGIP app contains three main features: mental health directories, psychoeducational resources, and mental health toolkits (worksheets and exercises). Arguably, the self-guided nature of the SAGIP app allowed for flexibility and ease of use, making it more likely for participants to use it at their own pace whenever they need or want to (Ahmed et al., 2021). There were also no restrictions imposed on participants regarding what contents or features of the app they should use or access. The freedom to use respects their autonomy as users, which can be related to the improved sense of well-being associated with its use (Müller et al., 2023). Furthermore, the SAGIP app contains a feature that provides opportunities for users to earn points that can be accumulated to earn “badges” by completing certain tasks, exercises, and activities in the app. The badges can then be used in nourishing and growing a virtual mental health plant, symbolizing the users’ continuous engagement and personal development in the app. In this way, there is a gamification and incentivization experience of using the app that further enhances the user experience. Research has shown that gamification integrates theories of self-determination and motivational psychology (Deci & Ryan, 2000). Gamified features embedded in mental health applications enhance emotional well-being and stress management by offering virtual rewards, opportunities to track progress, and meaningful tasks (Castellano-Tejedor & Cencerrado, 2024). On the other hand, amidst the general pattern of effectiveness of using the SAGIP app is a specific pattern of sudden increase in distress of participants from both groups from the end of the second month to the end of the third month. This may be attributed to the part of the semester where students, faculty, and staff in the University experience distress due to various academic demands (i.e., examinations, checking of papers, submission of requirements, etc.) towards the end of the semester. Despite this increase in distress, users of the SAGIP app still experienced a continuous improvement in well-being, in contrast to non-users who showed a steady decline in well-being. Looking closely at the movement of well-being and distress scores over time, a notable non-linear increase in well-being and a non-linear decrease in distress was observed for participants who used the SAGIP app, which indicates variability in the rate of increase in well-being or decrease in distress levels per month. While there is a general trend of increase in well-being and decrease in distress throughout the study for participants who used the SAGIP app, the fluctuations over time suggest other variables that may be at play that were not taken into account in the study. However, this reflects a more realistic view of progress in longitudinal intervention studies (Hayes et al., 2007). Research has shown that across various mental health interventions, discontinuous and non-linear types of changes are more likely to happen (Collins, 2006). This is partly explained by dynamical systems theory that predicts an increase in variability and destabilization of “old patterns” or habits triggered by the introduction and perpetuation of an intervention (Vallacher et al., 2002). With the introduction of the SAGIP app and its continued usage, users become subjected to a dynamical system where the current psychological system of the person is disturbed and reorganized because new habits and new learnings are being formed until homeostasis is achieved. The learning resources, worksheets, exercises, and activities provide opportunities for users to learn, unlearn, and re-learn mental health information. Over the six months of the study, there may be time points characterized by slow and steady movement of well-being and distress scores, while there are critical points associated with rapid and sharp changes (Schiepek et al., 2003). Consistent with the dynamic systems theory , the psychological system will eventually stabilize when the habits of using the app and applying mental health strategies become more integrated within the user. This may also explain why from the end of the 3rd month (Time 4) to the end of the sixth month (Time 5), associated well-being and distress scores begin to stabilize for users of the SAGIP mental health app. On the other hand, participants from the control group who only began to use the SAGIP app at the end of the 3rd month experienced more observable changes in their well-being and distress scores. From a more pragmatic standpoint, the rate of increase being higher for those who just started using the app (previously part of the control group) compared to those who have already been using the app for three months (part of the experimental group) may also be influenced by factors associated with app usage. It is also possible that participants from the experimental group may have already exhausted the available contents and features of the app by the end of the sixth month. Eventually, well-being gains may plateau because users have already fully utilized what the app can offer. This has potential implications for the longer-term sustainability of app usage due to content limitations. According to Alqahtani & Orji (2020), mobile mental health application users are more likely to be engaged and experience better outcomes when there are opportunities for continuous improvement of the app they are using (e.g., updating the app more frequently to comply with new phone features and other software requirements, offering a new variety of options, functionalities, and content). Furthermore, the current study focused on the effects of mental health app usage versus non-usage on well-being outcomes while controlling for distress levels. A significant difference between the experimental and control groups is still observed even after accounting for time-varying covariates. Participants who used the SAGIP app experienced better well-being gains compared to participants who did not use the SAGIP app regardless of co-existing levels of distress across the time points. This finding further reinforces the well-being enhancement effect of mental health applications independent of other inherent characteristics and variables found in the literature (Conley et al., 2022; Hwang et al., 2021; Linardon et al., 2019). In line with this, it is interesting to find that the users’ varying levels of distress have significant positive effects on their corresponding well-being levels from baseline to the end of the second month. This finding reflects the dual-continuum model of mental health, where psychological distress (i.e., depression, anxiety, and stress) and psychological well-being occur in a distinct but related continuum (Mason Stephens et al., 2023). Mental health and well-being do not just refer to the absence of distress or psychopathologies but emphasize individual and societal functioning (Westerhorf & Keys, 2010). It is possible for high levels of distress to occur simultaneously with high levels of well-being. Also, it is likely that an increase in levels of distress can be an impetus for the enhancement of well-being (Keys & Lopez, 2009) through the use of well-being services or tools like the SAGIP app. Interestingly, this simultaneous increase in distress and well-being is no longer observable from the end of the second month to the end of the third month. This may be explained by the fact that by the end of the second month, the distress levels have also been reduced while well-being levels continuously increase over time as a function of the SAGIP app use. This result is supported by the extant literature on the significant relationship between distress and well-being, which shows that while both constructs are functionally independent, a percentage of changes in distress can still explain a percentage of changes in well-being and vice versa (Mason Stephens et al., 2023). From a longitudinal standpoint, there is a point in one’s mental health state that distress and well-being are interrelated, but through interventions, the two eventually become more increasingly distinct. From the end of the third month onwards, the SAGIP mental health app eventually becomes the more relevant and stable predictor of well-being. Finally, the frequency and duration of app use were found to be not related to the increase in well-being scores, contrary to the findings in the literature regarding positive relationship between greater user engagement and better outcomes (Cloonan et al., 2023; Baumel et al., 2019; Weisel et al., 2019). The commonly established dose-response effect in longitudinal intervention studies (Chang et al., 2023; Connolly et al., 2021) and routinely delivered interventions (Robinson et al., 2020) where frequency and duration of app use explain percentage of changes in mental health outcomes was not found. The beneficial effects likely come from the content of the application more than the frequency and duration of usage. It is possible that users who only use the app for a few times can already find it beneficial if they are able to access certain features or content of the app useful for them at that time. Conversely, those who frequently use the app may not benefit as much if they do not access certain features beneficial for them or appropriate to their needs (Conley et al., 2022). Nevertheless, user engagement may still be a critical factor in influencing changes in outcomes but from a methodological standpoint, the use of simple engagement metrics like frequency and duration may not be sufficient to capture the impact of engagement on mental health outcomes (Chang et al., 2023). Conclusion Using the SAGIP mental health app was tested to provide opportunities to enhance users’ psychological well-being and reduce psychological distress. The effect is characterized as a non-linear increase in well-being and decrease in distress over time. Even when controlling for effects of psychological distress over time, results have shown that those who use the SAGIP mental health app experience better improvements in their psychological well-being compared to those who did not use the app. Furthermore, user engagement in the form of frequency and duration of use was not a significant factor in well-being outcomes for those who use the SAGIP mental health app. This study has several implications for practice and research on digital mental health and mobile interventions. First, features such as self-guided functions and resources including psychoeducational materials and toolkits, are crucial in illustrating the type and extent of outcomes that users may experience with the mental health app. Second, the study's longitudinal approach, featuring five time points and periodic assessments, effectively captured the non-linear fluctuations in well-being and distress outcomes over time. Third, the results of the study revealed potential areas for improvement and further refinement of the SAGIP mental health app, particularly accounting for continuous updating and expansion of its features, contents, and functions to promote better engagement. Fourth, the study was able to tap into the interrelatedness of two variables: psychological well-being and psychological distress. Lastly, the results offered insights into the relationship between user engagement and well-being outcomes that may be relevant in conducting future studies on mental health app effectiveness. Limitations It is important to note several limitations of this study. First, despite the significant results in most hypotheses testing, the sample size can be considered a limiting factor, especially when looking at the effect sizes found in the results. The final number of valid cases used in the data analysis fell below the calculated minimum sample size based on the power analysis. A number of participants had more than 15% missing data on each assessment measure, so their data were no longer included in the analysis. A larger sample size may be warranted to account for possible attrition and missing data and better capture the statistical power of the study to enhance its generalizability (Andrade, 2020). Second, the outcome measures focused on psychological well-being and psychological distress variables. Other mental health outcomes may be influenced by the use of technological and digital mental health tools, which could be interesting to explore, such as quality of life and specific symptom severity measures. Furthermore, only the total composite scores on the Psychological Well-being Scale and Depression, Anxiety, and Stress Scale were used. It would be interesting to investigate how the specific dimensions of well-being and distress relate to using the SAGIP mental health application at a multivariate level. Third, the six-month duration of the study can be considered a relatively short-term intervention period (Prakash et al., 2025). In line with this, while the study has a follow-up period (from the end of the fourth month to the end of the sixth month with participants from the experimental group continuing and control group commencing the use of SAGIP app), the assessment of outcomes only captured the aggregate measures in those last three months. It would be more interesting to obtain the outcomes monthly similar to what was done in the first three months of the study. Fourth, participants used the SAGIP app without further direction after the initial set-up and orientation. In this case, the use of the app was not standardized. This variability in the manner of using the app plays a crucial role in determining the true effectiveness of using the SAGIP app (Lai et al., 2024). Only the frequency and duration of app use were utilized as predictors of well-being over time. Other usage metrics, such as the kinds of content in the app being accessed and features of the app being used, may provide added information about its effectiveness. Lastly, there are technical limitations in the contents, adaptive functionalities, and the need for further app development, as reflected in the plateauing of well-being outcomes in the last month of assessment for participants in the experimental group. This suggests that users may eventually exhaust the contents and capabilities of what the SAGIP can offer which may no longer contribute to enhancement of well-being. Recommendations Recommendations for future research include further analysis of engagement and effectiveness, particularly utilizing other user engagement metrics: not just quantity but quality of interactions, daily activity metrics, session interval, session depth, conversion rate, completion rate, perceived usability, aesthetic appeal (Bitrián et al., 2021; Holdener et al., 2020). In relation to this, the user experience can also be captured with qualitative methods like interviews, focus group discussions, or open-ended survey questions to identify recommendations for further refinement of the SAGIP app. Future research on the effectiveness of the SAGIP mental health application should include participants with varying levels of symptom severity. It is important to investigate which specific symptoms and levels of severity allow for the use of the SAGIP mental health app to still produce clinically significant outcomes. This has implications for establishing its potential and limitations as an intervention tool. It is also interesting to compare the effectiveness of various strategies and exercises used in the SAGIP app, such as mood tracking, meditation and mindfulness, emotion regulation, problem-solving, interpersonal skills, self-compassion, etc. in promoting mental health. From a methodological perspective, an ecological momentary assessment may be warranted to improve data collection, assess the daily dynamics and experiences of users, and potentially enhance user engagement (Magallón-Neri et al., 2016). With the proliferation of multiple mental health mobile applications available in the Philippines, it would be best to have a comparative study on the degree of effectiveness of a select number of mental health applications. In relation to establishing incremental validity, there is a need to determine how much predictive effects can the SAGIP app still contribute over and beyond the effects of other existing mental health mobile applications on various mental health outcomes. Declarations Acknowledgments This work was funded by the University of the Philippines (UP) System Office of the Vice President for Academic Affairs (OVPAA) Emerging Interdisciplinary Research Program (OVPAA-EIDR-C09-07). Author Contributions JCR contributed to the conceptualization of the study, formulation of the methodology and identification of the measures to be used in the study, writing of the study protocol, data collection and investigation, data analysis and writing of this paper. Furthermore, JCR served as the primary mental health professional who monitored participants and provided support to those with elevated DASS scores who sought help. LCG was involved in the conceptualization of the study, writing the research protocol, participant recruitment, data collection, data analysis, writing of the manuscript, project administration, supervision and funding acquisition. JNM and BPC helped in the conceptualization and methodology of the study and writing of the study protocol. PQL was involved in the data collection, data cleaning and data analysis. BDT and SCDL assisted in the participant recruitment, data collection and coordination with the participants for their compensation. ICI reviewed and submitted the study protocol and handled administrative processes required by the Ethics Review Committee. All authors read and approved the final manuscript. Conflicts of Interest The authors declare no conflicts of interest related to this study. Data Availability Statement All data supporting the findings of the study are available from the corresponding authors upon reasonable request. Code Availability The codes used for data analysis are available upon request. References Ahmed, A., Ali, N., Giannicchi, A., Abd-alrazaq, A. A., Ahmed, M. A. S., Aziz, S., & Househ, M. (2021). Mobile applications for mental health self-care: A scoping review. Computer Methods and Programs in Biomedicine Update, 1 , 100041. https://doi.org/10.1016/j.cmpbup.2021.100041 Alqahtani, F., & Orji, R. (2020). Insights from user reviews to improve mental health apps. Health Informatics Journal, 26 (3), 2042–2066. https://doi.org/10.1177/1460458219896492 Andrade C. (2020). Sample Size and its Importance in Research. Indian Journal of Psychological Medicine, 42 (1), 102–103. https://doi.org/10.4103/IJPSYM.IJPSYM_504_19 Arean, P. A., Hallgren, K. A., Jordan, J. T., Gazzaley, A., Atkins, D. C., Heagerty, P. J., & Anguera, J. A. (2016). The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial. Journal of Medical Internet Research, 18 (12), e330. https://doi.org/10.2196/jmir.6482 Bakker, D., & Rickard, N. (2018). Engagement in mobile phone app for self-monitoring of emotional wellbeing predicts changes in mental health: MoodPrism. Journal of Affective Disorders, 227 , 432–442. https://doi.org/10.1016/j.jad.2017.11.016 Baumel, A., Muench, F., Edan, S., & Kane, J. M. (2019). Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage Analysis. Journal of Medical Internet Research, 21 (9), e14567. https://doi.org/10.2196/14567 Ben-Zeev, D., Razzano, L. A., Pashka, N. J., & Levin, C. E. (2021). Cost of mHealth Versus Clinic-Based Care for Serious Mental Illness: Same Effects, Half the Price Tag. Psychiatric Services , 72 (4), 448–451. https://doi.org/10.1176/appi.ps.202000349 Bitrián, P., Buil, I., & Catalán, S. (2021). Enhancing user engagement: The role of gamification in mobile apps. Journal of Business Research, 132 (1), 170–185. https://doi.org/10.1016/j.jbusres.2021.04.028 Borjalilu, S., Mazaheri, M. A., & Talebpour, A. (2019). Effectiveness of Mindfulness-Based Stress Management in The Mental Health of Iranian University Students: A Comparison of Blended Therapy, Face-to-Face Sessions, and mHealth App (Aramgar). Iranian Journal of Psychiatry and Behavioral Sciences, 13 (2). https://doi.org/10.5812/ijpbs.84726 Carissoli, C., Villani, D., & Riva, G. (2015). Does a meditation protocol supported by a mobile application help people reduce stress? Suggestions from a controlled pragmatic trial. Cyberpsychology, Behavior and Social Networking, 18 (1), 46–53. https://doi.org/10.1089/cyber.2014.0062 Castellano-Tejedor, C., & Cencerrado, A. (2024). Gamification for Mental Health and Health Psychology: Insights at the First Quarter Mark of the 21st Century. International Journal of Environmental Research and Public Health, 21 (8), 990. https://doi.org/10.3390/ijerph21080990 Chandrashekar P. (2018). Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental health mobile apps. mHealth, 4 , 6. https://doi.org/10.21037/mhealth.2018.03.02 Chang, S., Gray, L., Torous, J. (2023). Smartphone app engagement and clinical outcomes in a hybrid clinic. Psychiatry Research , 319 , 115015. https://doi.org/10.1016/j.psychres.2022.115015 Clay, R. (2021, January 1). Mental health apps are gaining traction. APA. https://www.apa.org/monitor/2021/01/trends-mental-health-apps Cloonan, S., Fowers, R., Huberty, J., & Stecher, C. (2023). Meditation app habits and mental health: A longitudinal study of meditation app users during the COVID-19 pandemic. Mindfulness, 14 (9), 2276–2286. https://doi.org/10.1007/s12671-023-02217-1 Conley, C. S., Raposa, E. B., Bartolotta, K., Broner, S. E., Hareli, M., Forbes, N., Christensen, K. M., & Assink, M. (2022). The Impact of Mobile Technology-Delivered Interventions on Youth Well-being: Systematic Review and 3-Level Meta-analysis. JMIR Mental Health, 9 (7), e34254. https://doi.org/10.2196/34254 Connolly, S. L., Hogan, T. P., Shimada, S. L., & Miller, C. J. (2021). Leveraging Implementation Science to Understand Factors Influencing Sustained Use of Mental Health Apps: a Narrative Review. Journal of Technology in Behavioral Science, 6 (2), 184–196. https://doi.org/10.1007/s41347-020-00165-4 Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11 (4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01 Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M. R., & Christensen, H. (2013). Smartphones for smarter delivery of mental health programs: a systematic review. Journal of Medical Internet Research, 15 (11), e247. https://doi.org/10.2196/jmir.2791 Eis, S., Solà-Morales, O., Duarte-Díaz, A., Vidal-Alaball, J., Perestelo-Pérez, L., Robles, N., & Carrion, C. (2022). Mobile Applications in Mood Disorders and Mental Health: Systematic Search in Apple App Store and Google Play Store and Review of the Literature. International Journal of Environmental Research and Public Health, 19 (4), 2186. https://doi.org/10.3390/ijerph19042186 Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., & Sarris, J. (2017). The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 16 (3), 287–298. https://doi.org/10.1002/wps.20472 Fischer, R., Bortolini, T., Karl, J. A., Zilberberg, M., Robinson, K., Rabelo, A., Gemal, L., Wegerhoff, D., Nguyễn, T. B. T., Irving, B., Chrystal, M., & Mattos, P. (2020). Rapid Review and Meta-Meta-Analysis of Self-Guided Interventions to Address Anxiety, Depression, and Stress During COVID-19 Social Distancing. Frontiers in Psychology, 11 , 563876. https://doi.org/10.3389/fpsyg.2020.563876 Garrido, S., Millington, C., Cheers, D., Boydell, K., Schubert, E., Meade, T., & Nguyen, Q. V. (2019). What Works and What Doesn't Work? A Systematic Review of Digital Mental Health Interventions for Depression and Anxiety in Young People. Frontiers in Psychiatry, 10 , 759. https://doi.org/10.3389/fpsyt.2019.00759 Hayes, A. M., Laurenceau, J. P., Feldman, G., Strauss, J. L., & Cardaciotto, L. (2007). Change is not always linear: the study of nonlinear and discontinuous patterns of change in psychotherapy. Clinical Psychology Review, 27 (6), 715–723. https://doi.org/10.1016/j.cpr.2007.01.008 Holdener, M., Gut, A., & Angerer, A. (2020). Applicability of the User Engagement Scale to Mobile Health: A Survey-Based Quantitative Study. JMIR mHealth and uHealth, 8 (1), e13244. https://doi.org/10.2196/13244 Hwang, W. J., Ha, J. S., & Kim, M. J. (2021). Research Trends on Mobile Mental Health Application for General Population: A Scoping Review. International Journal of Environmental Research and Public Health, 18 (5), 2459. https://doi.org/10.3390/ijerph18052459 Hwang, W. J., & Jo, H. H. (2019). Evaluation of the Effectiveness of Mobile App-Based Stress-Management Program: A Randomized Controlled Trial. International Journal of Environmental Research and Public Health, 16 (21), 4270. https://doi.org/10.3390/ijerph16214270 Kauer, S. D., Mangan, C., & Sanci, L. (2014). Do online mental health services improve help-seeking for young people? A systematic review. Journal of Medical Internet Research, 16 (3), e66. https://doi.org/10.2196/jmir.3103 Keyes, C. L. M. (2009). Toward a science of mental health. In S. J. Lopez & C. R. Snyder (Eds.), Oxford Handbook of Positive Psychology (2nd ed., pp. 89–95). Oxford University Press. Khademian, F., Aslani, A., & Bastani, P. (2020). The effects of mobile apps on stress, anxiety, and depression: overview of systematic reviews. International Journal of Technology Assessment in Health Care, 37 , e4. https://doi.org/10.1017/S0266462320002093 Koh, J., Tng, G. Y. Q., & Hartanto, A. (2022). Potential and Pitfalls of Mobile Mental Health Apps in Traditional Treatment: An Umbrella Review. Journal of Personalized Medicine, 12 (9), 1376. https://doi.org/10.3390/jpm12091376 Köhnen, M., Dreier, M., Seeralan, T., Kriston, L., Härter, M., Baumeister, H., & Liebherz, S. (2021). Evidence on Technology-Based Psychological Interventions in Diagnosed Depression: Systematic Review. JMIR Mental Health, 8 (2), e21700. https://doi.org/10.2196/21700 Lai, L., Sanatkar, S., Mackinnon, A., Deady, M., Petrie, K., Lipscomb, R., Counson, I., Francis-Taylor, R., Dean, K., & Harvey, S. (2024). Testing the Effectiveness of a Mobile Smartphone App Designed to Improve the Mental Health of Junior Physicians: Protocol for a Randomized Controlled Trial. JMIR Research Protocols, 13 , e58288. https://doi.org/10.2196/58288 Lally, J., Tully, J., & Samaniego, R. (2019). Mental health services in the Philippines. BJPsych International, 16 (3), 62–64. https://doi.org/10.1192/bji.2018.34 Lecomte, T., Potvin, S., Corbière, M., Guay, S., Samson, C., Cloutier, B., Francoeur, A., Pennou, A., & Khazaal, Y. (2020). Mobile Apps for Mental Health Issues: Meta-Review of Meta-Analyses. JMIR mHealth and uHealth, 8 (5), e17458. https://doi.org/10.2196/17458 Li, C. (2013). Little’s Test of Missing Completely at Random. The Stata Journal, 13 (4), 795-809. https://doi.org/10.1177/1536867X1301300407 Li, H., Lewis, C., Chi, H., Singleton, G., & Williams, N. (2020). Mobile health applications for mental illnesses: An Asian context. Asian Journal of Psychiatry, 54 , 102209. https://doi.org/10.1016/j.ajp.2020.102209 Li, P., Stuart, E. A., & Allison, D. B. (2015). Multiple Imputation: A Flexible Tool for Handling Missing Data. JAMA, 314 (18), 1966–1967. https://doi.org/10.1001/jama.2015.15281 Linardon, J., Cuijpers, P., Carlbring, P., Messer, M., & Fuller-Tyszkiewicz, M. (2019). The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 18 (3), 325–336. https://doi.org/10.1002/wps.20673 Magallón-Neri, E., Kirchner-Nebot, T., Forns-Santacana, M., Calderón, C., & Planellas, I. (2016). Ecological Momentary Assessment with smartphones for measuring mental health problems in adolescents. World Journal of Psychiatry, 6 (3), 303–310. https://doi.org/10.5498/wjp.v6.i3.303 Marshall, J. M., Dunstan, D. A., & Bartik, W. (2021). Smartphone Psychological Therapy During COVID-19: A Study on the Effectiveness of Five Popular Mental Health Apps for Anxiety and Depression. Frontiers in Psychology, 12 , 775775. https://doi.org/10.3389/fpsyg.2021.775775 Mason Stephens, J., Iasiello, M., Ali, K., van Agteren, J., & Fassnacht, D. B. (2023). The Importance of Measuring Mental Wellbeing in the Context of Psychological Distress: Using a Theoretical Framework to Test the Dual-Continua Model of Mental Health. Behavioral Sciences (Basel, Switzerland), 13 (5), 436. https://doi.org/10.3390/bs13050436 Mohr, D. C., Tomasino, K. N., Lattie, E. G., Palac, H. L., Kwasny, M. J., Weingardt, K., Karr, C. J., Kaiser, S. M., Rossom, R. C., Bardsley, L. R., Caccamo, L., Stiles-Shields, C., & Schueller, S. M. (2017). IntelliCare: An Eclectic, Skills-Based App Suite for the Treatment of Depression and Anxiety. Journal of Medical Internet Research, 19 (1), e10. https://doi.org/10.2196/jmir.6645 Müller, R., Primc, N., & Kuhn, E. (2023). 'You have to put a lot of trust in me': autonomy, trust, and trustworthiness in the context of mobile apps for mental health. Medicine, Health Care, and Philosophy, 26 (3), 313–324. https://doi.org/10.1007/s11019-023-10146-y Oliveira, C., Pereira, A., Vagos, P., Nóbrega, C., Gonçalves, J., & Afonso, B. (2021). Effectiveness of Mobile App-Based Psychological Interventions for College Students: A Systematic Review of the Literature. Frontiers in Psychology, 12 , 647606. https://doi.org/10.3389/fpsyg.2021.647606 Oppenheim, S. (2019, January 16 ). Should you trust an app with your mental health? Forbes Magazine. https://www.forbes.com/sites/serenaoppenheim/2019/01/16/should-you-trust-an-app-with-your-mental-health/?sh=380b2aeb24b8 Pham, Q., Khatib, Y., Stansfeld, S., Fox, S., & Green, T. (2016). Feasibility and Efficacy of an mHealth Game for Managing Anxiety: "Flowy" Randomized Controlled Pilot Trial and Design Evaluation. Games for Health Journal, 5 (1), 50–67. https://doi.org/10.1089/g4h.2015.0033 Powell, B. J., Proctor, E. K., & Glass, J. E. (2014). A Systematic Review of Strategies for Implementing Empirically Supported Mental Health Interventions. Research on Social Work Practice, 24 (2), 192–212. https://doi.org/10.1177/1049731513505778 Prakash, G., Sunil Kumar, D., Arun, V., Yadav, D., Gopi, A., & Garg, R. (2025). Development and validation of android mobile application in the management of mental health. Clinical Epidemiology and Global Health, 31 , 101894. https://doi.org/10.1016/j.cegh.2024.101894 Roberts, M. B., Sullivan, M. C., & Winchester, S. B. (2017). Examining solutions to missing data in longitudinal nursing research . Journal for Specialists in Pediatric Nursing: JSPN, 22 (2), 10.1111/jspn.12179. https://doi.org/10.1111/jspn.12179 Robinson, L., Delgadillo, J., & Kellett, S. (2020). The dose-response effect in routinely delivered psychological therapies: A systematic review. Psychotherapy Research: Journal of the Society for Psychotherapy Research, 30 (1), 79–96. https://doi.org/10.1080/10503307.2019.1566676 Ryff, C. D., & Keyes, C. L. M. (1995). The structure of psychological well-being revisited. Journal of Personality and Social Psychology, 69 (4), 719–727. Schiepek, G., Eckert, H., & Weihrauch, S. (2003). Critical Fluctuations and Clinical Change: Data-Based Assessment in Dynamic Systems. Constructivism in the Human Sciences, 8 (1), 57–84. Schlomer, G. L., Bauman, S., & Card, N. A. (2010). Best practices for missing data management in counseling psychology. Journal of Counseling Psychology, 57 (1), 1–10. https://doi.org/10.1037/a0018082 Shi, D., DiStefano, C., Zheng, X., Liu, R., & Jiang, Z. (2021). Fitting Latent Growth Models with Small Sample Sizes and Non-normal Missing Data. International Journal of Behavioral Development, 45 (2), 179–192. https://doi.org/10.1177/0165025420979365 Srivastava, K., Chaudhury, S., Dhamija, S., Prakash, J., & Chatterjee, K. (2020). Digital technological interventions in mental health care. Industrial Psychiatry Journal, 29 (2), 181–184. https://doi.org/10.4103/ipj.ipj_32_21 Torous, J., Andersson, G., Bertagnoli, A., Christensen, H., Cuijpers, P., Firth, J., Haim, A., Hsin, H., Hollis, C., Lewis, S., Mohr, D. C., Pratap, A., Roux, S., Sherrill, J., & Arean, P. A. (2019). Towards a consensus around standards for smartphone apps and digital mental health. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 18 (1), 97–98. https://doi.org/10.1002/wps.20592 Torous, J., & Huffman, J. (2022). Mobile mental health: Bridging psychiatry and neurology through engaging innovations. General Hospital Psychiatry, 75 , 90–91. https://doi.org/10.1016/j.genhosppsych.2021.05.008 Vallacher, R. R., Read, S. J., & Nowak, A. (2002). The Dynamical Perspective in Personality and Social Psychology. Personality and Social Psychology Review, 6 (4), 264–273. https://doi.org/10.1207/s15327957pspr0604_01 Weisel, K. K., Fuhrmann, L. M., Berking, M., Baumeister, H., Cuijpers, P., & Ebert, D. D. (2019). Standalone smartphone apps for mental health-a systematic review and meta-analysis. NPJ digital medicine, 2 , 118. https://doi.org/10.1038/s41746-019-0188-8 Westerhof, G. J., & Keyes, C. L. (2010). Mental Illness and Mental Health: The Two Continua Model Across the Lifespan. Journal of Adult Development, 17 (2), 110–119. https://doi.org/10.1007/s10804-009-9082-y Woods, A. D., Gerasimova, D., Van Dusen, B., Nissen, J., Bainter, S., Uzdavines, A., Davis‐Kean, P. E., Halvorson, M., King, K. M., Logan, J. A. R., Xu, M., Vasilev, M. R., Clay, J. M., Moreau, D., Joyal‐Desmarais, K., Cruz, R. A., Brown, D. M. Y., Schmidt, K., & Elsherif, M. M. (2024). Best practices for addressing missing data through multiple imputation. Infant and Child Development, 33 (1), Article e2407. https://doi.org/10.1002/icd.2407 World Health Organization. (2022, March 2). Mental Health and COVID-19: Early evidence of the pandemic’s impact . World Health Organization. http://www.jstor.org/stable/resrep44578 World Health Organization. (2021). Mental Health Atlas 2020 . WHO. https://apps.who.int/iris/handle/10665/345946 Yang, E., Schamber, E., Meyer, R. M. L., & Gold, J. I. (2018). Happier Healers: Randomized Controlled Trial of Mobile Mindfulness for Stress Management. Journal of Alternative and Complementary Medicine (New York, N.Y.), 24 (5), 505–513. https://doi.org/10.1089/acm.2015.0301 Zanon, C., Brenner, R. E., Baptista, M. N., Vogel, D. L., Rubin, M., Al-Darmaki, F. R., Gonçalves, M., Heath, P. J., Liao, H. Y., Mackenzie, C. S., Topkaya, N., Wade, N. G., & Zlati, A. (2021). Examining the Dimensionality, Reliability, and Invariance of the Depression, Anxiety, and Stress Scale-21 (DASS-21) Across Eight Countries. Assessment, 28 (6), 1531–1544. https://doi.org/10.1177/1073191119887449 Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Feb, 2026 Reviews received at journal 01 Feb, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviews received at journal 01 Aug, 2025 Reviewers agreed at journal 01 Aug, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers invited by journal 08 May, 2025 Editor assigned by journal 23 Apr, 2025 Submission checks completed at journal 28 Mar, 2025 First submitted to journal 27 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6321910","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":453968332,"identity":"20fea4f7-9985-4381-a3d6-a1b5a813016c","order_by":0,"name":"John Robert C. Rilveria","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACxgYGhgMPgAw2BuYDMAE2wloSwFrYEojTAgZgtQw8BsRpYW4/+/BAYptNPh/7mW/SBQw2shsO8B57gNdhPekGQC1plm08udukZzCkGW84wJdugFfLDDagX84cNmBjAGrhYTicuOEAj5kEEVr+G7Dxv3kG1PKfWC0VBwzYJHLYgFoOEKGlJw2kJRmo5Zmx9QyDZOOZh/nS8GoxbD/G/OGDgZ2BfH/yw9sFFXayfcd7j+HX0oDEYWYABRUzDz4NDAzyyBxmCEVAyygYBaNgFIw4AACMpUVOY9VkMgAAAABJRU5ErkJggg==","orcid":"","institution":"University of the Philippines Diliman","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"Robert C.","lastName":"Rilveria","suffix":""},{"id":453968333,"identity":"baee4d4b-cc17-40df-8cea-c8d686602ae6","order_by":1,"name":"Lorelie C. Grepo","email":"","orcid":"","institution":"University of the Philippines Diliman","correspondingAuthor":false,"prefix":"","firstName":"Lorelie","middleName":"C.","lastName":"Grepo","suffix":""},{"id":453968334,"identity":"722fe6ea-7c20-4bb7-90e4-4c158b4cdea8","order_by":2,"name":"Paola Marie Q. Lim","email":"","orcid":"","institution":"University of the Philippines Diliman","correspondingAuthor":false,"prefix":"","firstName":"Paola","middleName":"Marie Q.","lastName":"Lim","suffix":""},{"id":453968335,"identity":"ecb85e97-1e34-4e8f-8a3b-a83a0a946e5e","order_by":3,"name":"Joy Ann N. Malapit","email":"","orcid":"","institution":"University of the Philippines Diliman","correspondingAuthor":false,"prefix":"","firstName":"Joy","middleName":"Ann N.","lastName":"Malapit","suffix":""},{"id":453968336,"identity":"08518845-b070-4ce3-9f33-a94a517e4000","order_by":4,"name":"Belinda Esther D. Tan","email":"","orcid":"","institution":"University of the Philippines Diliman","correspondingAuthor":false,"prefix":"","firstName":"Belinda","middleName":"Esther D.","lastName":"Tan","suffix":""},{"id":453968337,"identity":"6c410465-af1a-40c1-8cf1-79c668a5238c","order_by":5,"name":"Benette P. Custodio","email":"","orcid":"","institution":"University of the Philippines Diliman","correspondingAuthor":false,"prefix":"","firstName":"Benette","middleName":"P.","lastName":"Custodio","suffix":""},{"id":453968338,"identity":"f60d82c4-0b24-44f6-ad84-c35b2bb5959e","order_by":6,"name":"Stephanie Caridad D. Landicho","email":"","orcid":"","institution":"University of the Philippines Los Baños","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"Caridad D.","lastName":"Landicho","suffix":""},{"id":453968339,"identity":"3bb64759-38ff-4e5a-821a-748b520facdc","order_by":7,"name":"Iris Thiele C. Isip-Tan","email":"","orcid":"","institution":"University of the Philippines Manila","correspondingAuthor":false,"prefix":"","firstName":"Iris","middleName":"Thiele C.","lastName":"Isip-Tan","suffix":""}],"badges":[],"createdAt":"2025-03-27 15:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6321910/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6321910/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82776274,"identity":"b98887b8-1cc8-45c4-89c4-17dc4c59776a","added_by":"auto","created_at":"2025-05-15 07:22:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":154864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAGIP app home page and features\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6321910/v1/cc167428d081312b6147fbf2.png"},{"id":82776273,"identity":"bce42d42-e973-4df0-afbe-927e3f391402","added_by":"auto","created_at":"2025-05-15 07:22:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67627,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage levels of psychological well-being between participants from the experimental group and participants from the control group from baseline to end of month 6\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6321910/v1/df057e31049aa9e2d73c2cc7.png"},{"id":82777214,"identity":"7d463d17-31ba-44f8-a44a-bc676d5e522c","added_by":"auto","created_at":"2025-05-15 07:30:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage levels of psychological distress between participants from the experimental group and participants from the control group from baseline (Time 1) to follow up at the end of month 6 (Time 5)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6321910/v1/d3f0f790a89765cbc3f2343d.png"},{"id":82776277,"identity":"eb309600-8737-4024-840c-7e623b3578c4","added_by":"auto","created_at":"2025-05-15 07:22:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":113977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStandardized path coefficients of latent growth curve model of psychological well-being over four time periods predicted by the use of the SAGIP app with \u003c/em\u003epsychological distress\u003cem\u003e as time-varying covariates\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6321910/v1/1c3e8cd5f784c9db38d552ee.png"},{"id":82776282,"identity":"0908014f-7e0f-4493-824e-f782f199520b","added_by":"auto","created_at":"2025-05-15 07:22:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":143702,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStandardized path coefficients of latent growth curve model of psychological well-being over four time periods predicted by SAGIP app frequency and duration of use\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6321910/v1/1a791154ff9f6ceaf89d87f6.png"},{"id":82778751,"identity":"18bc4e45-6894-47a5-bb5c-2ee47afe2646","added_by":"auto","created_at":"2025-05-15 07:46:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1905302,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6321910/v1/cba9b9e8-4b87-438e-931f-80a03efcf1d7.pdf"},{"id":82777216,"identity":"8cd268a5-534f-44e5-b9d1-57dcc391815f","added_by":"auto","created_at":"2025-05-15 07:30:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2971396,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6321910/v1/ffc4c2bdbdc80ec4c24462fd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effectiveness of a mental health mobile application for the academic community: A Longitudinal Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMental health concerns have been on the rise worldwide. This was exacerbated by the COVID-19 pandemic that started in 2019 and is currently contributing to the increased need for mental health interventions. A meta-analysis of mental health research revealed that there was a 4\u0026ndash;19% increase in mental health problem prevalence for the general population during the pandemic (World Health Organization, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Likewise, in the Philippines, an exponential rise in the cases of mental health concerns was reported. However, the national information on mental health services in the Philippines reveals significant gaps and inconsistencies in the delivery of mental healthcare, leading to challenges in the provision of accessible and affordable mental healthcare (Lally et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to the World Health Organization (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the Philippines has 1.68 mental health professionals for every 100,000 Filipinos, compared to a global median of 13 per 100,000. In addition, several cultural, social, geographic, technological, and economic factors contribute to the challenges and barriers in seeking and providing mental health services in the country. With the increasing rate of mental health issues, as well as the existing discrepancy between healthcare demand and the provision of services, there is a need to look into the potential of alternative and emerging mental health interventions (Srivastava et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the last decade, technology-based mental health interventions have grown rapidly, making them more accessible and cost-effective in the delivery of evidence-based healthcare services (Torous \u0026amp; Huffman, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). With the increasing ownership of smartphones and accessibility over the internet, combined with the surging demand for mental health support and services, mobile mental health applications are drawing more interest from consumers and mental health providers (Clay, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mental health applications can easily be searched, downloaded, and accessed, with more than 10,000 existing today (Torous et al., 2019). Mental health applications are gaining popularity due to their \u0026ldquo;self-help\u0026rdquo; functions and features that are accessible anytime and anywhere (K\u0026ouml;hnen et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, mental health applications protect the identities of users as they can be used anonymously and confidentially (Kauer et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), preventing an individual from being stigmatized for seeking help and support. In addition, mental health applications are gradually becoming promising early intervention and support tools that can help users at the time they need it, at their earliest convenience, until they are able to see a mental health professional (Oppenheim, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These technologies can supplement the system of care provided by a professional through the reinforcement of strategies, skills training, and information tracking.\u003c/p\u003e \u003cp\u003eThere is an optimistic outlook regarding the potential benefits of using mental health applications (Eis et al., 2022; Fischer et al., 2020). When comparing the effectiveness of face-to-face therapy, smartphone mobile application intervention, and the combination of face-to-face therapy and smartphone mobile application intervention, there are promising results in the reduction of depression, anxiety, and stress for college students (Oliveira et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Borjalilu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Post hoc tests showed that blended therapy had the greatest improvement in scores, suggesting the combination of face-to-face therapy and mobile platforms to support the mental health of people in their everyday lives. Likewise, the research of Marshall et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) revealed the effectiveness of mobile applications for managing anxiety and depression symptoms using a multiple baseline research design for people undertaking psychotherapy and/or psychotropic medications concurrently. Those with anxiety symptoms and mixed anxiety and depression features had better outcomes than those with depression symptoms alone. In addition, the intervention was seen as more beneficial for those who have a shorter history of mental illness. The said results were generally maintained at 6-month follow-up. Furthermore, a meta-analytic review of mobile mental health applications showed significant results in reductions in depression and stress scores (Khamedian et al., 2020) with small to medium effect sizes (Lecomte et al., 2020).\u003c/p\u003e \u003cp\u003eAside from symptom reduction for users with mental health concerns, other mental health apps target the general population for well-being or quality of life outcomes (Conley et al., 2022; Hwang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Linardon et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Specific features of mental health applications like meditation exercises (Yang et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Carissoli et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), breathing-retraining exercises (Pham et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), informational resource (Arean et al., 2016), problem-solving applications (Mohr et al., 2017), mood monitoring (Backer \u0026amp; Rickard, 2018), and mental health screening (Hwang et al., 2019) were found to be useful in enhancing well-being, reducing stress-related experiences, and supplementing psychological interventions in improving mental health functioning.\u003c/p\u003e \u003cp\u003eThe literature on digital and technological mental health demonstrates the capacity of mental health applications to offer a number of potential benefits to supplement psychiatric treatment (Firth et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and provide people with self-management tools to support their mental health (Ben-Zeev, et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nonetheless, a systematic review of these technology-based mental health interventions among the youth found that these interventions may only be of clinical significance when use is highly supervised, when there is consistency in usage, and when there are relevant and interactive features other than just providing digital educational materials (Garrido, et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this regard, the effectiveness of mental health apps is still contested (Chandrashekar, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) due to usage variance (i.e., frequency of use, duration of use, consistency of use, type of content, etc.). There are still research gaps in terms of what makes a mental health app effective. Despite the prevalence of mental health applications, data shows that the usage behavior tells a different story. Most mobile health applications that are downloaded are only used once, or within two weeks, then dropped by the users (Koh et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Factors that contribute to low acceptance and adoption of mobile applications include lack of trust, privacy and security concerns, mismatch between the user expectations and app design, and poor usability. In addition, the lack of regulation and the lack of empirical evidence showing effectiveness solely attributed to mental health app usage contribute to the reluctance to use mental health applications. According to Lecomte et al. (2020), only around 5% of the existing mental health apps have been evaluated strictly in terms of their inherent effectiveness, teasing out other confounding variables.\u003c/p\u003e \u003cp\u003eFurthermore, research on the effectiveness of utilizing mental health applications remains to be mixed. While there are studies suggesting that mental health applications are effective, some say otherwise (Donker et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), with some studies having inconclusive results (Weisel et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There are still potential barriers to patient app use as well as to larger-scale adoption which include concerns surrounding safety, credibility, unfamiliarity, usability, engagement, personalization, and information governance. Therefore, more rigorous research on the effectiveness of mental health interventions is recommended (Powell et al, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Asia-Pacific region is at the top when it comes to the heaviest mobile data traffic, particularly in Brazil, China, India, Indonesia, Philippines, Vietnam, and Japan. The widespread ownership of mobile devices and internet access in Asia presents a significant opportunity to enhance mental health care in the digital and technological space. Mobile mental health applications can serve as a layer of mental health support for users and as a platform to connect mental health professionals with users, helping to bridge the gap in access to services (Li et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo further investigate the effectiveness of mental health apps, this study evaluated the SAGIP mental health app designed for the academic community, particularly at the University of the Philippines (UP). Representative users consisting of students, teaching and non-teaching personnel used the SAGIP app, and mental health outcomes (i.e., psychological well-being and psychological distress) were measured over time in a 6-month study.\u003c/p\u003e\n\u003ch3\u003eResearch Objectives\u003c/h3\u003e\n\u003ch2\u003eResearch Objectives\u003c/h2\u003e\n\u003cp\u003eThe current study aimed to test the basic effectiveness of the SAGIP mental health application. Particularly, the following hypotheses were tested:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;There is a significant difference between participants who used the SAGIP mental health application and participants who did not use the app in terms of their psychological well-being.\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;Participants who used the SAGIP mental health application will experience improvements in their psychological well-being over time.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;There is a significant difference between participants who used the SAGIP mental health application and participants who did not use the app in terms of their psychological distress.\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;Participants who used the SAGIP mental health application will experience reductions in their psychological distress over time.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;Using the SAGIP mental health application can significantly improve psychological well-being while controlling for the effects of psychological distress levels.\u003c/p\u003e\n\u003cp\u003ea. \u0026nbsp; The frequency and duration of using the SAGIP mental health application can significantly influence improvement in psychological well-being.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cp\u003eThis paper is part of a broader research project that primarily involved two phases: 1) the user-centered design and development of the Social Activity Guardian and Intervention Project (SAGIP) mental health mobile application and 2) the evaluation of the SAGIP app through a longitudinal and cross-sectional mixed methods approach. This paper focuses on the Phase 2 quantitative longitudinal investigation of the effectiveness of using the SAGIP app.\u003c/p\u003e \u003cp\u003eThe research employed an experimental repeated measures design to evaluate the effectiveness of using the SAGIP app over time. This research compared participants from the experimental group (users of the SAGIP app) and the control group (non-users of the SAGIP app) while analyzing the interplay among different target variables like app usage, psychological distress scores, and psychological well-being scores.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIntervention: SAGIP App\u003c/h3\u003e\n\u003cp\u003eThe SAGIP app was developed using a user-centered design framework that involved students, faculty, non-teaching staff, and mental health professionals in the University of the Philippines. User requirements were elicited through Focus Group Discussion sessions and in-depth interviews. Details of the app development are discussed in another paper.\u003c/p\u003e \u003cp\u003eSAGIP is a self-guided app with 3 main features: Directory, Resources, and Toolkits. The \u003cem\u003eDirectory\u003c/em\u003e contains contact information for mental health support providers within and outside the University campuses. The \u003cem\u003eResources\u003c/em\u003e are psychoeducational materials in the form of articles, audio, and video resources curated by clinical psychologists based on the needs identified from the interviews and FGD sessions with students, faculty, and non-teaching staff. Finally, the \u003cem\u003eToolkits\u003c/em\u003e contain worksheets and guided exercises (i.e., problem-solving, meditation, cognitive restructuring, coping, acceptance, self-compassion, support-seeking, emotion regulation, etc.) that help enhance, manage, or tackle general mental health concerns. Other app features include Assessment of Well-being, personalization of contents and resources through the Recommended and Explore features, Daily Quotes, Notifications, Badges for completion of worksheets and exercises, and customization of the app\u0026rsquo;s settings. Brief descriptions of the SAGIP app features are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Screenshots of the SAGIP app pages are shown in the Appendix.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eStudy Site and Participants\u003c/h3\u003e\n\u003cp\u003eThe University of the Philippines (UP) was chosen as the research site because its population is a good representation of the academic community, with its students, faculty, and staff coming from different campuses all over the Philippines.\u003c/p\u003e\n\u003ch3\u003eSample Size\u003c/h3\u003e\n\u003cp\u003eThe study targeted two hundred (n\u0026thinsp;=\u0026thinsp;200) participants (including students, faculty, and staff) from the UP community. This is based on an a priori power analysis for a repeated measures, within-between interaction (2 groups and 5 repeated measures, alpha level\u0026thinsp;=\u0026thinsp;0.05, power\u0026thinsp;=\u0026thinsp;0.80, effect size\u0026thinsp;=\u0026thinsp;0.25) using G*Power. This sample size is also consistent with the recommendation for latent growth curve modeling for unbiased estimates (Shi et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticipants were randomly assigned to two groups: experimental \u0026ldquo;app\u0026rdquo; group and waitlist control \u0026ldquo;no app\u0026rdquo; group. Each group should have at least 100 participants composed of 80 students, 10 faculty, and 10 non-teaching staff. This breakdown is based on the population of about 25,000 students and 5,000 faculty and staff for the entire UP system. The faculty and staff comprise about 17% of the population. This will be about 17, rounded up to 20 in every 100 participants. A total of 224 participants expressed interest to participate in the study.\u003c/p\u003e\n\u003ch3\u003eSampling and Recruitment\u003c/h3\u003e\n\u003cp\u003eParticipants were recruited online via convenience sampling. Emails were sent through the official mailing lists of the campuses, and online advertisements were posted on university-affiliated social media pages. Interested participants were asked to sign up online via Qualtrics. The sign-up form included a study description and a screening questionnaire. Those who signed up were screened for their eligibility for the study and contacted by the research team.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInclusion Criteria\u003c/b\u003e. Participants qualified for the study if they are: a) currently enrolled or working in the University (UP student or UP employee) throughout the duration of the study, b) owning at least one smartphone with access to a minimum of 5mbps internet connectivity, c) willing to commit to participating in the study by using the app at least once a week and answering the periodic assessment measures for 6 months, d) at least 18 years old.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion Criteria.\u003c/b\u003e Prospective participants who would fall under any of the following exclusion criteria were ineligible to participate: a) not officially enrolled nor working in the University, b) currently diagnosed with a mental health condition, c) currently taking psychotropic medications, d) currently seeing a mental health professional, e) currently using other mental health mobile application, f) below 18 years old.\u003c/p\u003e \u003cp\u003e\u003cb\u003eWithdrawal criteria.\u003c/b\u003e Participants who expressed that they no longer want to continue with the study were excluded from the study. Participants who, upon periodic evaluation, had highly elevated psychological distress scores (severe to extremely severe) were also referred to a mental health professional and asked whether they still want to continue their participation. For this study, no participants were withdrawn for these aforementioned reasons.\u003c/p\u003e \u003cp\u003eNonetheless, data of participants were withdrawn (i.e., not included for data analysis) if there are more than 15% missing responses in each of the questionnaires they accomplished over the six-month period. From the 224 participants who were recruited and assigned to either the experimental group or control group, only the data from 194 participants were used for data analysis (with n\u0026thinsp;=\u0026thinsp;93 for the experimental group and n\u0026thinsp;=\u0026thinsp;101 for the control group) after data cleaning.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Procedure\u003c/h2\u003e \u003cp\u003eParticipants (n\u0026thinsp;=\u0026thinsp;224) were randomly assigned to either the experimental \u0026ldquo;app\u0026rdquo; group or the control \u0026ldquo;no app\u0026rdquo; group. Both groups were invited to join an online orientation via Zoom webinar to learn about the nature of the study and their roles and expectations from them as participants. Participants were asked to replace their profile pictures and names to avoid identification. They were also asked to close their cameras for security purposes. Orientation of the experimental \u0026ldquo;app\u0026rdquo; group included the installation of the SAGIP mobile application with user guide. The orientation sessions were recorded and shared to those participants who were unable to attend. After the orientation sessions, participants were asked to complete the informed consent and agreement forms online via Qualtrics.\u003c/p\u003e \u003cp\u003e The study was conducted remotely, and all interactions with the participants were virtual via email and Zoom meetings. Likewise, the assessment measures for the longitudinal study were administered online through Qualtrics.\u003c/p\u003e \u003cp\u003eParticipants in the experimental \u0026ldquo;app\u0026rdquo; group were asked to use the mental health application for 6 months (Months 1 to 6) while the participants in the waitlist control \u0026ldquo;no app\u0026rdquo; group were only introduced to the application and was informed that they are on the waitlist for the app for the first three months, and the research team will continue to monitor their well-being during this time. A baseline survey was administered at the beginning of the study and periodic online assessments were administered at the end of Months 1, 2, and 3. At the end of month 3, the waitlist control group were given instructions on how to access the app and were allowed to use the SAGIP app up to month 6. At the end of month 6, a follow-up online survey was conducted.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eMeasures and Data Collection\u003c/h2\u003e\n\u003cp\u003eQuantitative data were collected at five time points (pre-study assessment, three monthly assessments, and post-study assessment). The following variables were measured to evaluate SAGIP app\u0026rsquo;s effectiveness:\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; Psychological Distress\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Depression Anxiety Stress Scale-21 short form version was administered to measure general psychological distress. The three subscales of depression, anxiety, and stress have 7 items each that are rated from 0 to 3. The general distress score is calculated by getting the sum of the ratings for all items (Zanon et al., 2021; Lovibond, S.H. \u0026amp; Lovibond, P.F., 1995).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; Psychological Well-being\u003c/p\u003e\n\u003cp\u003eThe participants\u0026rsquo; psychological well-being was measured using the shortened 18-item Psychological Well-being Scale (Ryff, 1995) with 6 constructs: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; App usage\u003c/p\u003e\n\u003cp\u003eThe usage of the SAGIP app was collected automatically from the app. This includes the frequency and duration of use and the pages/features of the app visited.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior to participation, those who signed up were administered a questionnaire for mental health (depression, anxiety, stress, and psychological well-being) as part of a screening and to establish baseline measures. After the random assignment to experimental and control groups and their respective orientation to the study, quantitative data collection followed at the end of each month for the first 3 months of the study. Links to the monthly surveys were sent to the participants through email. The final quantitative data collection was done at the end of month 6 where both groups have experienced using the application.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eEach of the measures was first analyzed and reported using descriptive statistics. This included the multiple time point measures of Psychological Well-being, Psychological Distress scores, and the mental health app usage. In testing the first two research hypotheses, comparing the well-being and distress scores of SAGIP app users and non-users over time, a mixed analysis of variance was conducted. In testing the last set of research hypotheses looking at the growth trajectory of the effectiveness of using the SAGIP mental health app while controlling for the effects of psychological distress and looking at whether app usage is related to changes in psychological well-being over time, Latent Growth Curve Modeling was used. The IBM Statistical Package for the Social Sciences (SPSS) version 30 with the Analysis of Moment Structures (AMOS) module was utilized as the statistical tool for data analysis.\u003c/p\u003e\n\u003cp\u003eData from participants were removed from inclusion in the analysis if they did not complete the pre-study assessment, all three monthly assessments, and the post-study assessment. Answering at least 85% of the items in each questionnaire was considered as completion of the said assessment. A 15% missing rate falls under the acceptable range of missing data in longitudinal studies (Roberts et al., 2017; Schlomer et al., 2010). The pattern of the missing responses was first analyzed using Little\u0026rsquo;s MCAR test (Li, 2013), and the results supported the assumption that the data in question were missing completely at random. This indicates that there was no specific pattern of missingness found in the dataset and that the missing data occur randomly and independently from the variables of interest in the study (Roberts et al., 2017). Multiple imputation method was used to treat the missing data. It involves the repeated generation of replacement values for missing data based on statistical characteristics of the existing data and a pooled analysis of the imputed data sets to arrive at overall estimates (Woods et al., 2024; Li et al., 2015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the University of the Philippines Manila Research Ethics Board (UPMREB 2023-0053-01).\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eParticipant Demographics\u003c/h2\u003e\n\u003cp\u003eA summary of the participants\u0026rsquo; demographic characteristics is provided in Table 1. Majority of the participants in the study were female, students, and aged 25 years old and below.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDemographic Characteristics of Participants\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline characteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperimental\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWait-list Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e63.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e59.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e61.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e26.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e30.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e28.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Prefer not to say\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Did not indicate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e88.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e82.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e85.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFaculty and Staff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e17.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e14.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18-25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e58.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e50.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e54.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e26-35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e26.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e27.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e27.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36-45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e46 and up\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Did not indicate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cbr\u003e\u003c/h2\u003e\n\u003ch2\u003eGeneral comparison between the experimental group and control group over time\u003c/h2\u003e\n\u003cp\u003eThe first and second research hypotheses tested the significant differences between participants from the experimental group and participants from the control group in terms of their psychological well-being and psychological distress scores, respectively. A mixed Analysis of Variance was conducted to test these hypotheses, considering the data from baseline assessment (Time 1) to the last assessment (Time 5). The results are shown in Table 2. Within-subjects effects indicate a significant non-linear change in psychological well-being scores, \u003cem\u003eF\u003c/em\u003e (1,192) = 11.189, p \u0026lt; .001, \u0026eta;2p = .055 and psychological distress scores, \u003cem\u003eF\u003c/em\u003e (1,192) = 57.561, p \u0026lt; .001, \u0026eta;2p = .231 over time. Moreover, between-subjects effects indicate a significant difference between participants in the experimental group and participants in the control group in terms of their psychological well-being, \u003cem\u003eF\u003c/em\u003e (1,192) = 86.91, p \u0026lt; .001, \u0026eta;2p = .312 and psychological distress scores, \u003cem\u003eF\u003c/em\u003e (1,192) = 14.433, p \u0026lt; .001, \u0026eta;2p = .070. A significant interaction effect was also found between time and group for both psychological well-being, \u003cem\u003eF\u003c/em\u003e (1,192) = 4.659, p \u0026lt; .05, \u0026eta;2p = .024 and psychological distress outcomes, \u003cem\u003eF\u003c/em\u003e (1,192) = 3.873, p \u0026lt; .05, \u0026eta;2p = .025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eTwo-way Mixed ANOVA Statistics\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"534\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 284px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eANOVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026eta;2p\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePsychological Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e86.91**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e21.018**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e4.644*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCubic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQuartic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e11.189**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eTime x Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e152.503**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCubic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e15.876**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQuartic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e4.659*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePsychological Distress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e14.433**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e190.413**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e30.040**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCubic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e46.60**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQuartic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e57.561**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eTime x Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e2.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQuadratic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e63.130**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCubic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e3.873*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eQuartic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e4.992*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eN = 194\u003c/p\u003e\n\u003cp\u003e** \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. * \u003cem\u003ep\u003c/em\u003e \u0026lt;.05\u003c/p\u003e\n\u003cp\u003eBased on these results, the rate of change in psychological well-being scores and psychological distress scores most likely fit a quartic function for both groups. This suggests fluctuations in the scores over time (Figures 2 and 3). It can be observed that from baseline (Time 1) to the end of month 3 (Time 4), there is an observable increase in the psychological well-being scores of participants who use the SAGIP app. On the other hand, there is an observable decline in the psychological well-being scores of participants in the control group. In addition, there is an erratic rate of change in psychological distress scores over time for participants in the control group compared to a more consistent decrease in psychological distress scores over time for participants in the experimental group. It\u0026rsquo;s interesting to note that there is a sudden rise at the end of month 3 (Time 4) and an eventual drop of psychological distress scores at the end of month 6 (Time 5) that can be observed for both groups.\u003c/p\u003e\n\u003cp\u003eAfter the end of month 3, participants in the control group were given the opportunity to use the SAGIP app, and participants in the experimental group continued using the app until the end of month 6. At these time points, a very slight increase in psychological well-being and slight decrease in psychological distress scores can be observed for participants in the experimental group. On the other hand, there is an observable improvement in psychological well-being and sharp reduction in psychological distress scores for participants in the control group who just started using the SAGIP app.\u003c/p\u003e\n\u003cp\u003eOverall, the results indicate that using the SAGIP app for the first time facilitated an increase in psychological well-being (as reflected from the experience of the participants in the control group). Consistent use of the app for longer periods contributed to continuous gains in psychological well-being (as reflected from the experience of the participants in the experimental group who have been using the app continuously for six months). Nonetheless, the rate of improvement in psychological well-being begins to slow down toward the sixth month of using the app for the participants in the experimental group. Also, while there are statistically significant differences between the groups, the participants from both groups experienced the same pattern of change in their psychological distress scores over time .\u003c/p\u003e\n\u003ch2\u003eEffectiveness of the SAGIP mental health app usage\u003c/h2\u003e\n\u003cp\u003eThe last research hypothesis tested the rate of improvement of psychological well-being as a function of SAGIP app usage and psychological distress scores over time. In doing so, a Latent Growth Curve modeling was conducted to compare the levels of psychological well-being between users of the SAGIP mental health application (as part of the experimental group) versus those who did not use the application (as part of the control group) in 3 months. Based on the previous result, participants from both groups are significantly different in terms of their levels of depression, anxiety, and stress over time (see Table 3). Therefore, psychological distress scores over time were controlled and treated as time-varying covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMaximum likelihood parameter estimates based on the latent growth curve model (well-being of app users vs non-users with psychological distress as time-varying covariates)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"516\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaths\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime-invariant covariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup \u0026mdash;\u0026gt; Intercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup \u0026mdash;\u0026gt; Slope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e-8.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e.750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime-varying covariates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1_DASS \u0026mdash;\u0026gt; T1_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.326*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2_DASS \u0026mdash;\u0026gt; T2_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.247*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3_DASS \u0026mdash;\u0026gt; T3_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.224*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT4_DASS \u0026mdash;\u0026gt; T4_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eFit indices: ((\u0026chi;2 (18)\u0026thinsp;=\u0026thinsp;24.533, NFI\u0026thinsp;=\u0026thinsp;0.960, CFI\u0026thinsp;=\u0026thinsp;0.989, IFI\u0026thinsp;=\u0026thinsp;0.989, TLI\u0026thinsp;=\u0026thinsp;0.978, RMSEA\u0026thinsp;=\u0026thinsp;0.043 [0.000, 0.083])\u003c/p\u003e\n \u003cp\u003e*\u003cem\u003e\u0026nbsp;p \u0026lt; .001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe growth curve analysis revealed a satisfactory overall model fit (\u0026chi;\u003csup\u003e2\u003c/sup\u003e(18)\u0026thinsp;=\u0026thinsp;24.533, NFI\u0026thinsp;=\u0026thinsp;0.960, CFI\u0026thinsp;=\u0026thinsp;0.989, IFI\u0026thinsp;=\u0026thinsp;0.989, TLI\u0026thinsp;=\u0026thinsp;0.978, RMSEA\u0026thinsp;=\u0026thinsp;0.043 [0.000, 0.083]). The parameter estimates show that at baseline, members of the control group did not differ with members of the experimental group in terms of their levels of psychological well-being (\u0026beta;\u003csub\u003eo\u003c/sub\u003e = .350, p \u0026gt; .05). This supports the experimental manipulation such that the potential difference between the groups can be attributed to the mental health application usage vs non-usage in the succeeding months. A significant negative slope was found, which indicates that the rate of change in the psychological well-being of users is significantly higher than the rate of change in the psychological well-being of non-users from baseline to the end of the third month of assessment (\u0026beta; = -8.218, p \u0026lt; .001). Furthermore, the level of psychological well-being of users of the SAGIP mental health application significantly increases, especially from the end of the first month to the end of third month compared to non-users (see Table 4). There are 48.4%, 30.7%, 55.7%, and 93.2% of variance in psychological well-being from baseline to the end of the third month, respectively, being explained by the SAGIP app usage after controlling for the effects of depression, anxiety, and stress symptoms.\u003c/p\u003e\n\u003cp\u003eThe results show a degree of effectiveness of using the SAGIP mental health application in improving levels of psychological well-being while controlling for levels of psychological distress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cem\u003eSquared multiple correlations based on the latent growth curve model \u0026nbsp;(well-being of app users vs non-users with psychological distress as time-varying covariates)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"348\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT4_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003eRelationship between frequency and duration of SAGIP app usage and its effectiveness\u003c/h2\u003e\n\u003cp\u003eIn analyzing the influence of frequency and duration of SAGIP app usage in relation to well-being, the results yielded insignificant results (see Table 5). The growth curve analysis revealed a poor model fit (\u0026chi;2 (31)\u0026thinsp;=\u0026thinsp;166.384, NFI\u0026thinsp;=\u0026thinsp;0.750, CFI\u0026thinsp;=\u0026thinsp;0.774, IFI\u0026thinsp;=\u0026thinsp;0.787, TLI\u0026thinsp;=\u0026thinsp;0.520, RMSEA\u0026thinsp;=\u0026thinsp;0.218 [0.186, 0.251). These results indicate that the app usage in terms of frequency and duration does not explain changes in the psychological well-being of the app users. Regardless of how frequently and how long the users engage with the SAGIP app, their psychological well-being increases. Accessing specific contents of the app at the time they are needed is likely beneficial enough to facilitate increases in well-being no matter the frequency and duration of its use.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMaximum likelihood parameter estimates based on the latent growth curve model (well-being of app users with app frequency and duration as time-varying covariates)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaths\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime-varying covariates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1_Frequency \u0026mdash;\u0026gt; T1_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e-.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1_Duration \u0026mdash;\u0026gt; T1_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2_Frequency \u0026mdash;\u0026gt; T2_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e-.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2_Duration \u0026mdash;\u0026gt; T2_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3_Frequency \u0026mdash;\u0026gt; T3_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3_Duration \u0026mdash;\u0026gt; T3_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT4_Frequency \u0026mdash;\u0026gt; T4_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT4_Duration \u0026mdash;\u0026gt; T4_Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eSeveral points of discussion were raised based on the results of the study. First, the overall findings show that using the SAGIP mental health application proved to be effective in improving psychological well-being and reducing psychological distress among those who used it compared to those who did not use the application. This is consistent with the significant results shown in the literature about the effectiveness of mental health applications targeting distress and well-being (Oliveira et al., 2021; Lecomte et al., 2020; Fischer et al., 2020; Baumel et al., 2019; Firth et al., 2017) with modest effect sizes (Wiesel et al., 2019). Using a mental health application like the SAGIP app can be an adjunctive tool for helping users support their mental health, particularly in terms of their sense of well-being and level of general distress. The SAGIP app contains three main features: mental health directories, psychoeducational resources, and mental health toolkits (worksheets and exercises). Arguably, the self-guided nature of the SAGIP app allowed for flexibility and ease of use, making it more likely for participants to use it at their own pace whenever they need or want to (Ahmed et al., 2021). There were also no restrictions imposed on participants regarding what contents or features of the app they should use or access. The freedom to use respects their autonomy as users, which can be related to the improved sense of well-being associated with its use (M\u0026uuml;ller et al., 2023). Furthermore, the SAGIP app contains a feature that provides opportunities for users to earn points that can be accumulated to earn \u0026ldquo;badges\u0026rdquo; by completing certain tasks, exercises, and activities in the app. The badges can then be used in nourishing and growing a virtual mental health plant, symbolizing the users\u0026rsquo; continuous engagement and personal development in the app. In this way, there is a gamification and incentivization experience of using the app that further enhances the user experience. Research has shown that gamification integrates theories of self-determination and motivational psychology (Deci \u0026amp; Ryan, 2000). Gamified features embedded in mental health applications enhance emotional well-being and stress management by offering virtual rewards, opportunities to track progress, and meaningful tasks (Castellano-Tejedor \u0026amp; Cencerrado, 2024).\u003c/p\u003e\n\u003cp\u003eOn the other hand, amidst the general pattern of effectiveness of using the SAGIP app is a specific pattern of sudden increase in distress of participants from both groups from the end of the second month to the end of the third month. This may be attributed to the part of the semester where students, faculty, and staff in the University experience distress due to various academic demands (i.e., examinations, checking of papers, submission of requirements, etc.) towards the end of the semester. Despite this increase in distress, users of the SAGIP app still experienced a continuous improvement in well-being, in contrast to non-users who showed a steady decline in well-being.\u003c/p\u003e\n\u003cp\u003eLooking closely at the movement of well-being and distress scores over time, a notable non-linear increase in well-being and a non-linear decrease in distress was observed for participants who used the SAGIP app, which indicates variability in the rate of increase in well-being or decrease in distress levels per month. While there is a general trend of increase in well-being and decrease in distress throughout the study for participants who used the SAGIP app, the fluctuations over time suggest other variables that may be at play that were not taken into account in the study. However, this reflects a more realistic view of progress in longitudinal intervention studies (Hayes et al., 2007). Research has shown that across various mental health interventions, discontinuous and non-linear types of changes are more likely to happen (Collins, 2006). This is partly explained by \u003cem\u003edynamical systems theory\u0026nbsp;\u003c/em\u003ethat predicts an increase in variability and destabilization of \u0026ldquo;old patterns\u0026rdquo; or habits triggered by the introduction and perpetuation of an intervention (Vallacher et al., 2002). With the introduction of the SAGIP app and its continued usage, users become subjected to a dynamical system where the current psychological system of the person is disturbed and reorganized because new habits and new learnings are being formed until homeostasis is achieved. The learning resources, worksheets, exercises, and activities provide opportunities for users to learn, unlearn, and re-learn mental health information. Over the six months of the study, there may be time points characterized by slow and steady movement of well-being and distress scores, while there are critical points associated with rapid and sharp changes (Schiepek et al., 2003). Consistent with the \u003cem\u003edynamic systems theory\u003c/em\u003e, the psychological system will eventually stabilize when the habits of using the app and applying mental health strategies become more integrated within the user. This may also explain why from the end of the 3rd month (Time 4) to the end of the sixth month (Time 5), associated well-being and distress scores begin to stabilize for users of the SAGIP mental health app. On the other hand, participants from the control group who only began to use the SAGIP app at the end of the 3rd month experienced more observable changes in their well-being and distress scores.\u003c/p\u003e\n\u003cp\u003eFrom a more pragmatic standpoint, the rate of increase being higher for those who just started using the app (previously part of the control group) compared to those who have already been using the app for three months (part of the experimental group) may also be influenced by factors associated with app usage. It is also possible that participants from the experimental group may have already exhausted the available contents and features of the app by the end of the sixth month. Eventually, well-being gains may plateau because users have already fully utilized what the app can offer. This has potential implications for the longer-term sustainability of app usage due to content limitations. According to Alqahtani \u0026amp; Orji (2020), mobile mental health application users are more likely to be engaged and experience better outcomes when there are opportunities for continuous improvement of the app they are using (e.g., updating the app more frequently to comply with new phone features and other software requirements, offering a new variety of options, functionalities, and content).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the current study focused on the effects of mental health app usage versus non-usage on well-being outcomes while controlling for distress levels. A significant difference between the experimental and control groups is still observed even after accounting for time-varying covariates. Participants who used the SAGIP app experienced better well-being gains compared to participants who did not use the SAGIP app regardless of co-existing levels of distress across the time points. This finding further reinforces the well-being enhancement effect of mental health applications independent of other inherent characteristics and variables found in the literature (Conley et al., 2022; Hwang et al., 2021; Linardon et al., 2019).\u003c/p\u003e\n\u003cp\u003eIn line with this, it is interesting to find that the users\u0026rsquo; varying levels of distress have significant positive effects on their corresponding well-being levels from baseline to the end of the second month. This finding reflects the dual-continuum model of mental health, where psychological distress (i.e., depression, anxiety, and stress) and psychological well-being occur in a distinct but related continuum (Mason Stephens et al., 2023). Mental health and well-being do not just refer to the absence of distress or psychopathologies but emphasize individual and societal functioning (Westerhorf \u0026amp; Keys, 2010). It is possible for high levels of distress to occur simultaneously with high levels of well-being. Also, it is likely that an increase in levels of distress can be an impetus for the enhancement of well-being (Keys \u0026amp; Lopez, 2009) through the use of well-being services or tools like the SAGIP app.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, this simultaneous increase in distress and well-being is no longer observable from the end of the second month to the end of the third month. This may be explained by the fact that by the end of the second month, the distress levels have also been reduced while well-being levels continuously increase over time as a function of the SAGIP app use. This result is supported by the extant literature on the significant relationship between distress and well-being, which shows that while both constructs are functionally independent, a percentage of changes in distress can still explain a percentage of changes in well-being and vice versa (Mason Stephens et al., 2023). From a longitudinal standpoint, there is a point in one\u0026rsquo;s mental health state that distress and well-being are interrelated, but through interventions, the two eventually become more increasingly distinct. From the end of the third month onwards, the SAGIP mental health app eventually becomes the more relevant and stable predictor of well-being.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, the frequency and duration of app use were found to be not related to the increase in well-being scores, contrary to the findings in the literature regarding positive relationship between greater user engagement and better outcomes (Cloonan et al., 2023; Baumel et al., 2019; Weisel et al., 2019). The commonly established dose-response effect in longitudinal intervention studies (Chang et al., 2023; Connolly et al., 2021) and routinely delivered interventions (Robinson et al., 2020) where frequency and duration of app use explain percentage of changes in mental health outcomes was not found. The beneficial effects likely come from the content of the application more than the frequency and duration of usage. It is possible that users who only use the app for a few times can already find it beneficial if they are able to access certain features or content of the app useful for them at that time. Conversely, those who frequently use the app may not benefit as much if they do not access certain features beneficial for them or appropriate to their needs (Conley et al., 2022). Nevertheless, user engagement may still be a critical factor in influencing changes in outcomes but from a methodological standpoint, the use of simple engagement metrics like frequency and duration may not be sufficient to capture the impact of engagement on mental health outcomes (Chang et al., 2023).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUsing the SAGIP mental health app was tested to provide opportunities to enhance users\u0026rsquo; psychological well-being and reduce psychological distress. The effect is characterized as a non-linear increase in well-being and decrease in distress over time. Even when controlling for effects of psychological distress over time, results have shown that those who use the SAGIP mental health app experience better improvements in their psychological well-being compared to those who did not use the app. Furthermore, user engagement in the form of frequency and duration of use was not a significant factor in well-being outcomes for those who use the SAGIP mental health app.\u003c/p\u003e\n\u003cp\u003eThis study has several implications for practice and research on digital mental health and mobile interventions. First, features such as self-guided functions and resources including psychoeducational materials and toolkits, are crucial in illustrating the type and extent of outcomes that users may experience with the mental health app. Second, the study\u0026apos;s longitudinal approach, featuring five time points and periodic assessments, effectively captured the non-linear fluctuations in well-being and distress outcomes over time. Third, the results of the study revealed potential areas for improvement and further refinement of the SAGIP mental health app, particularly accounting for continuous updating and expansion of its features, contents, and functions to promote better engagement. Fourth, the study was able to tap into the interrelatedness of two variables: psychological well-being and psychological distress. Lastly, the results offered insights into the relationship between user engagement and well-being outcomes that may be relevant in conducting future studies on mental health app effectiveness.\u003c/p\u003e\n\u003ch2\u003eLimitations\u003c/h2\u003e\n\u003cp\u003eIt is important to note several limitations of this study. First, despite the significant results in most hypotheses testing, the sample size can be considered a limiting factor, especially when looking at the effect sizes found in the results. The final number of valid cases used in the data analysis fell below the calculated minimum sample size based on the power analysis. A number of participants had more than 15% missing data on each assessment measure, so their data were no longer included in the analysis. A larger sample size may be warranted to account for possible attrition and missing data and better capture the statistical power of the study to enhance its generalizability (Andrade, 2020). Second, the outcome measures focused on psychological well-being and psychological distress variables. Other mental health outcomes may be influenced by the use of technological and digital mental health tools, which could be interesting to explore, such as quality of life and specific symptom severity measures. Furthermore, only the total composite scores on the Psychological Well-being Scale and Depression, Anxiety, and Stress Scale were used. It would be interesting to investigate how the specific dimensions of well-being and distress relate to using the SAGIP mental health application at a multivariate level. Third, the six-month duration of the study can be considered a relatively short-term intervention period (Prakash et al., 2025). In line with this, while the study has a follow-up period (from the end of the fourth month to the end of the sixth month with participants from the experimental group continuing and control group commencing the use of SAGIP app), the assessment of outcomes only captured the aggregate measures in those last three months. It would be more interesting to obtain the outcomes monthly similar to what was done in the first three months of the study. Fourth, participants used the SAGIP app without further direction after the initial set-up and orientation. In this case, the use of the app was not standardized. This variability in the manner of using the app plays a crucial role in determining the true effectiveness of using the SAGIP app (Lai et al., 2024). Only the frequency and duration of app use were utilized as predictors of well-being over time. Other usage metrics, such as the kinds of content in the app being accessed and features of the app being used, may provide added information about its effectiveness. Lastly, there are technical limitations in the contents, adaptive functionalities, and the need for further app development, as reflected in the plateauing of well-being outcomes in the last month of assessment for participants in the experimental group. This suggests that users may eventually exhaust the contents and capabilities of what the SAGIP can offer which may no longer contribute to enhancement of well-being.\u003c/p\u003e\n\u003ch2\u003eRecommendations\u003c/h2\u003e\n\u003cp\u003eRecommendations for future research include further analysis of engagement and effectiveness, particularly utilizing other user engagement metrics: not just quantity but quality of interactions, daily activity metrics, session interval, session depth, conversion rate, completion rate, perceived usability, aesthetic appeal (Bitri\u0026aacute;n et al., 2021; Holdener et al., 2020). In relation to this, the user experience can also be captured with qualitative methods like interviews, focus group discussions, or open-ended survey questions to identify recommendations for further refinement of the SAGIP app.\u003c/p\u003e\n\u003cp\u003eFuture research on the effectiveness of the SAGIP mental health application should include participants with varying levels of symptom severity. It is important to investigate which specific symptoms and levels of severity allow for the use of the SAGIP mental health app to still produce clinically significant outcomes. This has implications for establishing its potential and limitations as an intervention tool.\u003c/p\u003e\n\u003cp\u003eIt is also interesting to compare the effectiveness of various strategies and exercises used in the SAGIP app, such as mood tracking, meditation and mindfulness, emotion regulation, problem-solving, interpersonal skills, self-compassion, etc. in promoting mental health.\u003c/p\u003e\n\u003cp\u003eFrom a methodological perspective, an ecological momentary assessment may be warranted to improve data collection, assess the daily dynamics and experiences of users, and potentially enhance user engagement (Magall\u0026oacute;n-Neri et al., 2016).\u003c/p\u003e\n\u003cp\u003eWith the proliferation of multiple mental health mobile applications available in the Philippines, it would be best to have a comparative study on the degree of effectiveness of a select number of mental health applications. In relation to establishing incremental validity, there is a need to determine how much predictive effects can the SAGIP app still contribute over and beyond the effects of other existing mental health mobile applications on various mental health outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the University of the Philippines (UP) System Office of the Vice President for Academic Affairs (OVPAA) Emerging Interdisciplinary Research Program (OVPAA-EIDR-C09-07).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJCR contributed to the conceptualization of the study, formulation of the methodology and identification of the measures to be used in the study, writing of the study protocol, data collection and investigation, data analysis and writing of this paper. Furthermore, JCR served as the primary mental health professional who monitored participants and provided support to those with elevated DASS scores who sought help. LCG was involved in the conceptualization of the study, writing the research protocol, participant recruitment, data collection, data analysis, writing of the manuscript, project administration, supervision and funding acquisition. JNM and BPC helped in the conceptualization and methodology of the study and writing of the study protocol. PQL was involved in the data collection, data cleaning and data analysis. BDT and SCDL assisted in the participant recruitment, data collection and coordination with the participants for their compensation. ICI reviewed and submitted the study protocol and handled administrative processes required by the Ethics Review Committee. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of the study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe codes used for data analysis are available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed, A., Ali, N., Giannicchi, A., Abd-alrazaq, A. A., Ahmed, M. A. S., Aziz, S., \u0026amp; Househ, M. (2021). Mobile applications for mental health self-care: A scoping review. \u003cem\u003eComputer Methods and Programs in Biomedicine Update, 1\u003c/em\u003e, 100041. https://doi.org/10.1016/j.cmpbup.2021.100041\u003c/li\u003e\n\u003cli\u003eAlqahtani, F., \u0026amp; Orji, R. (2020). Insights from user reviews to improve mental health apps. \u003cem\u003eHealth Informatics Journal, 26\u003c/em\u003e(3), 2042\u0026ndash;2066. https://doi.org/10.1177/1460458219896492\u003c/li\u003e\n\u003cli\u003eAndrade C. (2020). Sample Size and its Importance in Research. \u003cem\u003eIndian Journal of \u003c/em\u003e\u003cem\u003ePsychological Medicine, 42\u003c/em\u003e(1), 102\u0026ndash;103. https://doi.org/10.4103/IJPSYM.IJPSYM_504_19\u003c/li\u003e\n\u003cli\u003eArean, P. A., Hallgren, K. A., Jordan, J. T., Gazzaley, A., Atkins, D. C., Heagerty, P. J., \u0026amp; Anguera, J. A. (2016). The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial. \u003cem\u003eJournal of Medical Internet Research, 18\u003c/em\u003e(12), e330. https://doi.org/10.2196/jmir.6482\u003c/li\u003e\n\u003cli\u003eBakker, D., \u0026amp; Rickard, N. (2018). Engagement in mobile phone app for self-monitoring of emotional wellbeing predicts changes in mental health: MoodPrism. \u003cem\u003eJournal of Affective Disorders, 227\u003c/em\u003e, 432\u0026ndash;442. https://doi.org/10.1016/j.jad.2017.11.016\u003c/li\u003e\n\u003cli\u003eBaumel, A., Muench, F., Edan, S., \u0026amp; Kane, J. M. (2019). Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage Analysis. \u003cem\u003eJournal of Medical Internet Research, 21\u003c/em\u003e(9), e14567. https://doi.org/10.2196/14567\u003c/li\u003e\n\u003cli\u003eBen-Zeev, D., Razzano, L. A., Pashka, N. J., \u0026amp; Levin, C. E. (2021). Cost of mHealth Versus Clinic-Based Care for Serious Mental Illness: Same Effects, Half the Price Tag. \u003cem\u003ePsychiatric Services\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(4), 448\u0026ndash;451. https://doi.org/10.1176/appi.ps.202000349\u003c/li\u003e\n\u003cli\u003eBitri\u0026aacute;n, P., Buil, I., \u0026amp; Catal\u0026aacute;n, S. (2021). Enhancing user engagement: The role of gamification in mobile apps. \u003cem\u003eJournal of Business Research, 132\u003c/em\u003e(1), 170\u0026ndash;185. https://doi.org/10.1016/j.jbusres.2021.04.028\u003c/li\u003e\n\u003cli\u003eBorjalilu, S., Mazaheri, M. A., \u0026amp; Talebpour, A. (2019). Effectiveness of Mindfulness-Based Stress Management in The Mental Health of Iranian University Students: A Comparison of Blended Therapy, Face-to-Face Sessions, and mHealth App (Aramgar). \u003cem\u003eIranian Journal of Psychiatry and Behavioral Sciences, 13\u003c/em\u003e(2). https://doi.org/10.5812/ijpbs.84726\u003c/li\u003e\n\u003cli\u003eCarissoli, C., Villani, D., \u0026amp; Riva, G. (2015). Does a meditation protocol supported by a mobile application help people reduce stress? Suggestions from a controlled pragmatic trial. \u003cem\u003eCyberpsychology, Behavior and Social Networking, 18\u003c/em\u003e(1), 46\u0026ndash;53. https://doi.org/10.1089/cyber.2014.0062\u003c/li\u003e\n\u003cli\u003eCastellano-Tejedor, C., \u0026amp; Cencerrado, A. (2024). Gamification for Mental Health and Health Psychology: Insights at the First Quarter Mark of the 21st Century. \u003cem\u003eInternational Journal of Environmental Research and Public Health, 21\u003c/em\u003e(8), 990. https://doi.org/10.3390/ijerph21080990\u003c/li\u003e\n\u003cli\u003eChandrashekar P. (2018). Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental health mobile apps. \u003cem\u003emHealth, 4\u003c/em\u003e, 6. https://doi.org/10.21037/mhealth.2018.03.02\u003c/li\u003e\n\u003cli\u003eChang, S., Gray, L., Torous, J. (2023). Smartphone app engagement and clinical outcomes in a hybrid clinic. \u003cem\u003ePsychiatry Research\u003c/em\u003e, \u003cem\u003e319\u003c/em\u003e, 115015. https://doi.org/10.1016/j.psychres.2022.115015\u003c/li\u003e\n\u003cli\u003eClay, R. (2021, January 1). \u003cem\u003eMental health apps are gaining traction.\u003c/em\u003e APA. https://www.apa.org/monitor/2021/01/trends-mental-health-apps\u003c/li\u003e\n\u003cli\u003eCloonan, S., Fowers, R., Huberty, J., \u0026amp; Stecher, C. (2023). Meditation app habits and mental health: A longitudinal study of meditation app users during the COVID-19 pandemic. \u003cem\u003eMindfulness, 14\u003c/em\u003e(9), 2276\u0026ndash;2286. https://doi.org/10.1007/s12671-023-02217-1\u003c/li\u003e\n\u003cli\u003eConley, C. S., Raposa, E. B., Bartolotta, K., Broner, S. E., Hareli, M., Forbes, N., Christensen, K. M., \u0026amp; Assink, M. (2022). The Impact of Mobile Technology-Delivered Interventions on Youth Well-being: Systematic Review and 3-Level Meta-analysis. \u003cem\u003eJMIR Mental Health, 9\u003c/em\u003e(7), e34254. https://doi.org/10.2196/34254\u003c/li\u003e\n\u003cli\u003eConnolly, S. L., Hogan, T. P., Shimada, S. L., \u0026amp; Miller, C. J. (2021). Leveraging \u003c/li\u003e\n\u003cli\u003eImplementation Science to Understand Factors Influencing Sustained Use of Mental Health Apps: a Narrative Review. \u003cem\u003eJournal of Technology in Behavioral Science, 6\u003c/em\u003e(2), 184\u0026ndash;196. https://doi.org/10.1007/s41347-020-00165-4\u003c/li\u003e\n\u003cli\u003eDeci, E. L., \u0026amp; Ryan, R. M. (2000). The \u0026ldquo;What\u0026rdquo; and \u0026ldquo;Why\u0026rdquo; of Goal Pursuits: Human Needs and the Self-Determination of Behavior. \u003cem\u003ePsychological Inquiry, 11\u003c/em\u003e(4), 227\u0026ndash;268. https://doi.org/10.1207/S15327965PLI1104_01\u003c/li\u003e\n\u003cli\u003eDonker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M. R., \u0026amp; Christensen, H. (2013). Smartphones for smarter delivery of mental health programs: a systematic review. \u003cem\u003eJournal of Medical Internet Research, 15\u003c/em\u003e(11), e247. https://doi.org/10.2196/jmir.2791\u003c/li\u003e\n\u003cli\u003eEis, S., Sol\u0026agrave;-Morales, O., Duarte-D\u0026iacute;az, A., Vidal-Alaball, J., Perestelo-P\u0026eacute;rez, L., Robles, N., \u0026amp; Carrion, C. (2022). Mobile Applications in Mood Disorders and Mental Health: Systematic Search in Apple App Store and Google Play Store and Review of the Literature. \u003cem\u003eInternational Journal of Environmental Research and Public Health, 19\u003c/em\u003e(4), 2186. https://doi.org/10.3390/ijerph19042186\u003c/li\u003e\n\u003cli\u003eFirth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., \u0026amp; Sarris, J. (2017). The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. \u003cem\u003eWorld Psychiatry: Official Journal of the World Psychiatric Association (WPA), 16\u003c/em\u003e(3), 287\u0026ndash;298. https://doi.org/10.1002/wps.20472\u003c/li\u003e\n\u003cli\u003eFischer, R., Bortolini, T., Karl, J. A., Zilberberg, M., Robinson, K., Rabelo, A., Gemal, L., Wegerhoff, D., Nguyễn, T. B. T., Irving, B., Chrystal, M., \u0026amp; Mattos, P. (2020). Rapid Review and Meta-Meta-Analysis of Self-Guided Interventions to Address Anxiety, Depression, and Stress During COVID-19 Social Distancing. \u003cem\u003eFrontiers in Psychology, 11\u003c/em\u003e, 563876. https://doi.org/10.3389/fpsyg.2020.563876\u003c/li\u003e\n\u003cli\u003eGarrido, S., Millington, C., Cheers, D., Boydell, K., Schubert, E., Meade, T., \u0026amp; Nguyen, Q. V. (2019). What Works and What Doesn\u0026apos;t Work? A Systematic Review of Digital Mental Health Interventions for Depression and Anxiety in Young People. \u003cem\u003eFrontiers in Psychiatry, 10\u003c/em\u003e, 759. https://doi.org/10.3389/fpsyt.2019.00759\u003c/li\u003e\n\u003cli\u003eHayes, A. M., Laurenceau, J. P., Feldman, G., Strauss, J. L., \u0026amp; Cardaciotto, L. (2007). Change is not always linear: the study of nonlinear and discontinuous patterns of change in psychotherapy. \u003cem\u003eClinical Psychology Review, 27\u003c/em\u003e(6), 715\u0026ndash;723. https://doi.org/10.1016/j.cpr.2007.01.008\u003c/li\u003e\n\u003cli\u003eHoldener, M., Gut, A., \u0026amp; Angerer, A. (2020). Applicability of the User Engagement Scale to Mobile Health: A Survey-Based Quantitative Study. \u003cem\u003eJMIR mHealth and uHealth, 8\u003c/em\u003e(1), e13244. https://doi.org/10.2196/13244\u003c/li\u003e\n\u003cli\u003eHwang, W. J., Ha, J. S., \u0026amp; Kim, M. J. (2021). Research Trends on Mobile Mental Health Application for General Population: A Scoping Review. \u003cem\u003eInternational Journal of Environmental Research and Public Health, 18\u003c/em\u003e(5), 2459. https://doi.org/10.3390/ijerph18052459\u003c/li\u003e\n\u003cli\u003eHwang, W. J., \u0026amp; Jo, H. H. (2019). Evaluation of the Effectiveness of Mobile App-Based Stress-Management Program: A Randomized Controlled Trial. \u003cem\u003eInternational Journal of Environmental Research and Public Health, 16\u003c/em\u003e(21), 4270. https://doi.org/10.3390/ijerph16214270\u003c/li\u003e\n\u003cli\u003eKauer, S. D., Mangan, C., \u0026amp; Sanci, L. (2014). Do online mental health services improve help-seeking for young people? A systematic review. \u003cem\u003eJournal of Medical Internet Research, 16\u003c/em\u003e(3), e66. https://doi.org/10.2196/jmir.3103\u003c/li\u003e\n\u003cli\u003eKeyes, C. L. M. (2009). Toward a science of mental health. In S. J. Lopez \u0026amp; C. R. Snyder (Eds.), \u003cem\u003eOxford Handbook of Positive Psychology\u003c/em\u003e (2nd ed., pp. 89\u0026ndash;95). Oxford University Press.\u003c/li\u003e\n\u003cli\u003eKhademian, F., Aslani, A., \u0026amp; Bastani, P. (2020). The effects of mobile apps on stress, anxiety, and depression: overview of systematic reviews. \u003cem\u003eInternational Journal of Technology Assessment in Health Care, 37\u003c/em\u003e, e4. https://doi.org/10.1017/S0266462320002093\u003c/li\u003e\n\u003cli\u003eKoh, J., Tng, G. Y. Q., \u0026amp; Hartanto, A. (2022). Potential and Pitfalls of Mobile Mental Health Apps in Traditional Treatment: An Umbrella Review. \u003cem\u003eJournal of Personalized Medicine, 12\u003c/em\u003e(9), 1376. https://doi.org/10.3390/jpm12091376\u003c/li\u003e\n\u003cli\u003eK\u0026ouml;hnen, M., Dreier, M., Seeralan, T., Kriston, L., H\u0026auml;rter, M., Baumeister, H., \u0026amp; Liebherz, S. (2021). Evidence on Technology-Based Psychological Interventions in Diagnosed Depression: Systematic Review. \u003cem\u003eJMIR Mental Health, 8\u003c/em\u003e(2), e21700. https://doi.org/10.2196/21700\u003c/li\u003e\n\u003cli\u003eLai, L., Sanatkar, S., Mackinnon, A., Deady, M., Petrie, K., Lipscomb, R., Counson, I., Francis-Taylor, R., Dean, K., \u0026amp; Harvey, S. (2024). Testing the Effectiveness of a Mobile Smartphone App Designed to Improve the Mental Health of Junior Physicians: Protocol for a Randomized Controlled Trial. \u003cem\u003eJMIR Research Protocols, 13\u003c/em\u003e, e58288. https://doi.org/10.2196/58288\u003c/li\u003e\n\u003cli\u003eLally, J., Tully, J., \u0026amp; Samaniego, R. (2019). Mental health services in the Philippines. \u003cem\u003eBJPsych \u003c/em\u003e\u003cem\u003eInternational, 16\u003c/em\u003e(3), 62\u0026ndash;64. https://doi.org/10.1192/bji.2018.34\u003c/li\u003e\n\u003cli\u003eLecomte, T., Potvin, S., Corbi\u0026egrave;re, M., Guay, S., Samson, C., Cloutier, B., Francoeur, A., Pennou, A., \u0026amp; Khazaal, Y. (2020). Mobile Apps for Mental Health Issues: Meta-Review of Meta-Analyses. \u003cem\u003eJMIR mHealth and uHealth, 8\u003c/em\u003e(5), e17458. https://doi.org/10.2196/17458\u003c/li\u003e\n\u003cli\u003eLi, C. (2013). Little\u0026rsquo;s Test of Missing Completely at Random. \u003cem\u003eThe Stata Journal, 13\u003c/em\u003e(4), 795-809. https://doi.org/10.1177/1536867X1301300407\u003c/li\u003e\n\u003cli\u003eLi, H., Lewis, C., Chi, H., Singleton, G., \u0026amp; Williams, N. (2020). Mobile health applications for mental illnesses: An Asian context. \u003cem\u003eAsian Journal of Psychiatry, 54\u003c/em\u003e, 102209. https://doi.org/10.1016/j.ajp.2020.102209\u003c/li\u003e\n\u003cli\u003eLi, P., Stuart, E. A., \u0026amp; Allison, D. B. (2015). Multiple Imputation: A Flexible Tool for Handling Missing Data. \u003cem\u003eJAMA, 314\u003c/em\u003e(18), 1966\u0026ndash;1967. https://doi.org/10.1001/jama.2015.15281\u003c/li\u003e\n\u003cli\u003eLinardon, J., Cuijpers, P., Carlbring, P., Messer, M., \u0026amp; Fuller-Tyszkiewicz, M. (2019). The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. \u003cem\u003eWorld Psychiatry: Official Journal of the World Psychiatric Association (WPA), 18\u003c/em\u003e(3), 325\u0026ndash;336. https://doi.org/10.1002/wps.20673\u003c/li\u003e\n\u003cli\u003eMagall\u0026oacute;n-Neri, E., Kirchner-Nebot, T., Forns-Santacana, M., Calder\u0026oacute;n, C., \u0026amp; Planellas, I. (2016). Ecological Momentary Assessment with smartphones for measuring mental health problems in adolescents. \u003cem\u003eWorld Journal of Psychiatry, 6\u003c/em\u003e(3), 303\u0026ndash;310. https://doi.org/10.5498/wjp.v6.i3.303\u003c/li\u003e\n\u003cli\u003eMarshall, J. M., Dunstan, D. A., \u0026amp; Bartik, W. (2021). Smartphone Psychological Therapy During COVID-19: A Study on the Effectiveness of Five Popular Mental Health Apps for Anxiety and Depression. \u003cem\u003eFrontiers in Psychology, 12\u003c/em\u003e, 775775. https://doi.org/10.3389/fpsyg.2021.775775\u003c/li\u003e\n\u003cli\u003eMason Stephens, J., Iasiello, M., Ali, K., van Agteren, J., \u0026amp; Fassnacht, D. B. (2023). The Importance of Measuring Mental Wellbeing in the Context of Psychological Distress: Using a Theoretical Framework to Test the Dual-Continua Model of Mental Health. \u003cem\u003eBehavioral Sciences (Basel, Switzerland), 13\u003c/em\u003e(5), 436. https://doi.org/10.3390/bs13050436\u003c/li\u003e\n\u003cli\u003eMohr, D. C., Tomasino, K. N., Lattie, E. G., Palac, H. L., Kwasny, M. J., Weingardt, K., Karr, C. J., Kaiser, S. M., Rossom, R. C., Bardsley, L. R., Caccamo, L., Stiles-Shields, C., \u0026amp; Schueller, S. M. (2017). IntelliCare: An Eclectic, Skills-Based App Suite for the Treatment of Depression and Anxiety. \u003cem\u003eJournal of Medical Internet Research, 19\u003c/em\u003e(1), e10. https://doi.org/10.2196/jmir.6645\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller, R., Primc, N., \u0026amp; Kuhn, E. (2023). \u0026apos;You have to put a lot of trust in me\u0026apos;: autonomy, trust, and trustworthiness in the context of mobile apps for mental health. \u003cem\u003eMedicine, Health Care, and Philosophy, 26\u003c/em\u003e(3), 313\u0026ndash;324. https://doi.org/10.1007/s11019-023-10146-y\u003c/li\u003e\n\u003cli\u003eOliveira, C., Pereira, A., Vagos, P., N\u0026oacute;brega, C., Gon\u0026ccedil;alves, J., \u0026amp; Afonso, B. (2021). Effectiveness of Mobile App-Based Psychological Interventions for College Students: A Systematic Review of the Literature. \u003cem\u003eFrontiers in Psychology, 12\u003c/em\u003e, 647606. https://doi.org/10.3389/fpsyg.2021.647606\u003c/li\u003e\n\u003cli\u003eOppenheim, S. (2019, January 16\u003cem\u003e). Should you trust an app with your mental health?\u003c/em\u003e Forbes Magazine. https://www.forbes.com/sites/serenaoppenheim/2019/01/16/should-you-trust-an-app-with-your-mental-health/?sh=380b2aeb24b8\u003c/li\u003e\n\u003cli\u003ePham, Q., Khatib, Y., Stansfeld, S., Fox, S., \u0026amp; Green, T. (2016). Feasibility and Efficacy of an mHealth Game for Managing Anxiety: \u0026quot;Flowy\u0026quot; Randomized Controlled Pilot Trial and Design Evaluation. \u003cem\u003eGames for Health Journal, 5\u003c/em\u003e(1), 50\u0026ndash;67. https://doi.org/10.1089/g4h.2015.0033\u003c/li\u003e\n\u003cli\u003ePowell, B. J., Proctor, E. K., \u0026amp; Glass, J. E. (2014). A Systematic Review of Strategies for Implementing Empirically Supported Mental Health Interventions. \u003cem\u003eResearch on Social Work Practice, 24\u003c/em\u003e(2), 192\u0026ndash;212. https://doi.org/10.1177/1049731513505778\u003c/li\u003e\n\u003cli\u003ePrakash, G., Sunil Kumar, D., Arun, V., Yadav, D., Gopi, A., \u0026amp; Garg, R. (2025). Development and validation of android mobile application in the management of mental health. \u003cem\u003eClinical Epidemiology and Global Health, 31\u003c/em\u003e, 101894. https://doi.org/10.1016/j.cegh.2024.101894\u003c/li\u003e\n\u003cli\u003eRoberts, M. B., Sullivan, M. C., \u0026amp; Winchester, S. B. (2017). Examining solutions to missing data in longitudinal nursing research\u003cem\u003e. Journal for Specialists in Pediatric Nursing: JSPN, 22\u003c/em\u003e(2), 10.1111/jspn.12179. https://doi.org/10.1111/jspn.12179\u003c/li\u003e\n\u003cli\u003eRobinson, L., Delgadillo, J., \u0026amp; Kellett, S. (2020). The dose-response effect in routinely delivered psychological therapies: A systematic review. \u003cem\u003ePsychotherapy Research: Journal of the Society for Psychotherapy Research, 30\u003c/em\u003e(1), 79\u0026ndash;96. https://doi.org/10.1080/10503307.2019.1566676\u003c/li\u003e\n\u003cli\u003eRyff, C. D., \u0026amp; Keyes, C. L. M. (1995). The structure of psychological well-being revisited. \u003cem\u003eJournal of Personality and Social Psychology, 69\u003c/em\u003e(4), 719\u0026ndash;727. \u003c/li\u003e\n\u003cli\u003eSchiepek, G., Eckert, H., \u0026amp; Weihrauch, S. (2003). Critical Fluctuations and Clinical Change: Data-Based Assessment in Dynamic Systems. \u003cem\u003eConstructivism in the Human Sciences, 8\u003c/em\u003e(1), 57\u0026ndash;84.\u003c/li\u003e\n\u003cli\u003eSchlomer, G. L., Bauman, S., \u0026amp; Card, N. A. (2010). Best practices for missing data management in counseling psychology. \u003cem\u003eJournal of Counseling Psychology, 57\u003c/em\u003e(1), 1\u0026ndash;10. https://doi.org/10.1037/a0018082\u003c/li\u003e\n\u003cli\u003eShi, D., DiStefano, C., Zheng, X., Liu, R., \u0026amp; Jiang, Z. (2021). Fitting Latent Growth Models with Small Sample Sizes and Non-normal Missing Data. \u003cem\u003eInternational Journal of Behavioral Development, 45\u003c/em\u003e(2), 179\u0026ndash;192. https://doi.org/10.1177/0165025420979365\u003c/li\u003e\n\u003cli\u003eSrivastava, K., Chaudhury, S., Dhamija, S., Prakash, J., \u0026amp; Chatterjee, K. (2020). Digital technological interventions in mental health care. \u003cem\u003eIndustrial Psychiatry Journal, 29\u003c/em\u003e(2), 181\u0026ndash;184. https://doi.org/10.4103/ipj.ipj_32_21\u003c/li\u003e\n\u003cli\u003eTorous, J., Andersson, G., Bertagnoli, A., Christensen, H., Cuijpers, P., Firth, J., Haim, A., Hsin, H., Hollis, C., Lewis, S., Mohr, D. C., Pratap, A., Roux, S., Sherrill, J., \u0026amp; Arean, P. A. (2019). Towards a consensus around standards for smartphone apps and digital mental health. \u003cem\u003eWorld Psychiatry: Official Journal of the World Psychiatric Association (WPA), 18\u003c/em\u003e(1), 97\u0026ndash;98. https://doi.org/10.1002/wps.20592\u003c/li\u003e\n\u003cli\u003eTorous, J., \u0026amp; Huffman, J. (2022). Mobile mental health: Bridging psychiatry and neurology through engaging innovations. \u003cem\u003eGeneral Hospital Psychiatry, 75\u003c/em\u003e, 90\u0026ndash;91. https://doi.org/10.1016/j.genhosppsych.2021.05.008\u003c/li\u003e\n\u003cli\u003eVallacher, R. R., Read, S. J., \u0026amp; Nowak, A. (2002). The Dynamical Perspective in Personality and Social Psychology. \u003cem\u003ePersonality and Social Psychology Review, 6\u003c/em\u003e(4), 264\u0026ndash;273. https://doi.org/10.1207/s15327957pspr0604_01\u003c/li\u003e\n\u003cli\u003eWeisel, K. K., Fuhrmann, L. M., Berking, M., Baumeister, H., Cuijpers, P., \u0026amp; Ebert, D. D. (2019). Standalone smartphone apps for mental health-a systematic review and meta-analysis. \u003cem\u003eNPJ digital medicine, 2\u003c/em\u003e, 118. https://doi.org/10.1038/s41746-019-0188-8\u003c/li\u003e\n\u003cli\u003eWesterhof, G. J., \u0026amp; Keyes, C. L. (2010). Mental Illness and Mental Health: The Two Continua Model Across the Lifespan. \u003cem\u003eJournal of Adult Development, 17\u003c/em\u003e(2), 110\u0026ndash;119. https://doi.org/10.1007/s10804-009-9082-y\u003c/li\u003e\n\u003cli\u003eWoods, A. D., Gerasimova, D., Van Dusen, B., Nissen, J., Bainter, S., Uzdavines, A., Davis‐Kean, P. E., Halvorson, M., King, K. M., Logan, J. A. R., Xu, M., Vasilev, M. R., Clay, J. M., Moreau, D., Joyal‐Desmarais, K., Cruz, R. A., Brown, D. M. Y., Schmidt, K., \u0026amp; Elsherif, M. M. (2024). Best practices for addressing missing data through multiple imputation. \u003cem\u003eInfant and Child Development, 33\u003c/em\u003e(1), Article e2407. https://doi.org/10.1002/icd.2407\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2022, March 2). \u003cem\u003eMental Health and COVID-19: Early evidence of \u003c/em\u003e\u003cem\u003ethe pandemic\u0026rsquo;s impact\u003c/em\u003e. World Health Organization. http://www.jstor.org/stable/resrep44578\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2021). \u003cem\u003eMental Health Atlas 2020\u003c/em\u003e. WHO. https://apps.who.int/iris/handle/10665/345946\u003c/li\u003e\n\u003cli\u003eYang, E., Schamber, E., Meyer, R. M. L., \u0026amp; Gold, J. I. (2018). Happier Healers: Randomized Controlled Trial of Mobile Mindfulness for Stress Management. \u003cem\u003eJournal of Alternative and Complementary Medicine (New York, N.Y.), 24\u003c/em\u003e(5), 505\u0026ndash;513. https://doi.org/10.1089/acm.2015.0301\u003c/li\u003e\n\u003cli\u003eZanon, C., Brenner, R. E., Baptista, M. N., Vogel, D. L., Rubin, M., Al-Darmaki, F. R., Gon\u0026ccedil;alves, M., Heath, P. J., Liao, H. Y., Mackenzie, C. S., Topkaya, N., Wade, N. G., \u0026amp; Zlati, A. (2021). Examining the Dimensionality, Reliability, and Invariance of the Depression, Anxiety, and Stress Scale-21 (DASS-21) Across Eight Countries. \u003cem\u003eAssessment, 28\u003c/em\u003e(6), 1531\u0026ndash;1544. https://doi.org/10.1177/1073191119887449\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-mental-health-research","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjmentalhealth","sideBox":"Learn more about [npj Mental Health Research](https://www.nature.com/npjmentalhealth/)","snPcode":"44184","submissionUrl":"https://mts-npjmentalhealth.nature.com/cgi-bin/main.p...","title":"npj Mental Health Research","twitterHandle":"@npjmentalhealth\n","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"npj","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mental health, well-being, distress, mobile application, academic community","lastPublishedDoi":"10.21203/rs.3.rs-6321910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6321910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eResearch on technology-based interventions for mental health continues to establish its significance within the landscape of mental health services. Evidence has shown positive modest effects of using mental health applications on various mental health outcomes. This study explores the effectiveness of the Social Activity Guardian and Intervention Project (SAGIP) mental health mobile application designed for the University of the Philippines academic community. Using an experimental repeated measures design, the effectiveness of using the SAGIP app was evaluated over a 6-month period. This research compared participants from the experimental group (users of the SAGIP app) and the control group (non-users of the SAGIP app) while analyzing the interplay among different target variables like app usage, psychological distress scores, and psychological well-being scores. The mixed analysis of variance showed a significant difference between the experimental group and the control group. Using the SAGIP mental health app is associated with a non-linear improvement in users\u0026rsquo; psychological well-being and a reduction of their psychological distress over time. Furthermore, based on the latent growth curve modeling, when controlling for the effects of psychological distress over time, participants who use the SAGIP mental health app experience better improvements in their psychological well-being compared to those who did not use the app. Nonetheless, user engagement in the form of frequency and duration of use was not a significant factor in well-being outcomes for those who use the SAGIP mental health app.\u003c/p\u003e","manuscriptTitle":"Effectiveness of a mental health mobile application for the academic community: A Longitudinal Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 07:22:49","doi":"10.21203/rs.3.rs-6321910/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-04T16:17:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-01T14:37:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236317690128011854142326622327782884140","date":"2026-01-26T15:48:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264422132454294413958437227384844544427","date":"2026-01-19T03:44:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8043257489327761382814903244228802639","date":"2025-11-26T04:19:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219996487158418750031696579392705514148","date":"2025-11-25T12:14:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79466627528997299419181099442907870839","date":"2025-08-04T04:13:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T23:06:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61305182294412907085651481180513217072","date":"2025-08-01T17:26:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201605579614124042104775520012989022799","date":"2025-07-29T21:07:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-08T16:01:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-23T18:20:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-28T15:05:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Mental Health Research","date":"2025-03-27T15:25:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-mental-health-research","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjmentalhealth","sideBox":"Learn more about [npj Mental Health Research](https://www.nature.com/npjmentalhealth/)","snPcode":"44184","submissionUrl":"https://mts-npjmentalhealth.nature.com/cgi-bin/main.p...","title":"npj Mental Health Research","twitterHandle":"@npjmentalhealth\n","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"npj","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9bc78f9-aea1-43b6-9b9e-2441d7785a11","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":48276999,"name":"Health sciences/Health care/Quality of life"},{"id":48277000,"name":"Scientific community and society/Social sciences/Psychology/Human behaviour"},{"id":48277001,"name":"Scientific community and society/Social sciences/Education"}],"tags":[],"updatedAt":"2026-05-13T05:40:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-15 07:22:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6321910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6321910","identity":"rs-6321910","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}