Usability, Acceptance, and Effectiveness of a Coach-Guided, App-Based Mental Health Intervention: The beWell Tirol Pilot Study

Usability, Acceptance, and Effectiveness of a Coach-Guided, App-Based Mental Health Intervention: The beWell Tirol Pilot 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 Research Article Usability, Acceptance, and Effectiveness of a Coach-Guided, App-Based Mental Health Intervention: The beWell Tirol Pilot Study Sebastian Granz, Vera Demuth, Alex Hofer, Elisabeth Weiss, Markus Canazei, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8152998/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Mental illness is highly prevalent and associated with significant public health costs. Despite this, less than half of those affected receive treatment, often due to barriers such as stigma and accessibility. Internet-based interventions (IBIs) have shown promise in improving well-being and reducing psychological distress, yet many lack personalization and sufficient guidance. Addressing these limitations, beWell Tirol integrates cognitive behavioral content, individualized task selection, and on-demand coaching within a self-determined, app-based format, allowing users to autonomously select and adapt exercises according to their individual needs and preferences. Objective This study evaluates the usability, acceptance, and effectiveness of a digital mental health intervention, "beWell Tirol," a 4-month app-based program accompanied by a coach, targeting individuals experiencing high psychological distress. Methods A total of 25 participants with above-average psychological distress completed the 4-month intervention. The program included a selection of cognitive behavioral therapy-based exercises, which were available via the app, as well as bi-weekly coaching sessions. Assessments were conducted at baseline, post-intervention, and three months post-intervention, measuring usability (System Usability Scale, SUS) and satisfaction (primary outcomes), as well as psychological distress, resilience, self-efficacy, stress coping strategies, emotion regulation, and quality of life (secondary outcomes). Statistical analyses included repeated measures ANOVA and t-tests. Results The intervention demonstrated excellent usability (SUS score: M = 91.8, SD = 7.5) and high satisfaction. Significant improvements were observed in resilience, self-efficacy, psychological distress (Perceived Stress Scale, Brief Symptom Inventory), emotion regulation, and multiple dimensions of quality of life (p < .05). These improvements were partially sustained at follow-up. Conclusion The program was well accepted, demonstrated high usability, and was effective in improving mental well-being. The results support the feasibility and effectiveness of a coach-guided, self-determined digital intervention for promoting mental health. Future research should explore larger samples, including control groups. Trial registration ClinicalTrials.gov, NCT07300410, 09/12/2025, Retrospectively registered coaching cognitive behavioral therapy digital mental health prevention self-determination theory usability Figures Figure 1 Figure 2 Figure 3 Background Mental illness has a very high prevalence in the population (with e.g. 30% in Germany) and is associated with high public costs [ 1 , 2 ]. This is also reflected in working life: in Austria, 11.4% of all sick days are due to mental illness, with an increasing trend since 2018 [ 3 ]. At the same time, less than half of those suffering from mental illness receive treatment [ 4 ]. The reasons cited in the literature include stigma, lack of prevention, insufficient low-threshold outpatient services, long waiting times, inequality between mental and physical health care, and insufficient financial support [ 2 , 5 ]. A longitudinal study highlighted the relationship between psychological distress and healthcare costs, emphasizing the importance of prevention to reduce costs and distress [ 6 ]. The COVID-19 pandemic and related measures have been associated with increased psychological distress worldwide [ 7 ], with symptoms like anxiety, depression, and sleep disturbances [ 8 , 9 ]. A study in Tyrol (Austria) and South Tyrol (Italy) found that 15% of the general population experienced psychological distress, particularly women, single individuals, low-income groups, and the unemployed [ 10 , 11 ]. Follow-up results suggest these burdens remained stable from November 2020 to February 2022 [ 12 ]. For instance, the incidence of depression can be reduced by 21% through the implementation of preventive measures, as opposed to the incidence in control groups [ 13 , 14 ]. In this context, one possibility for preventing and reducing resources and costs is internet-based interventions. Studies have demonstrated that they are generally well accepted across diverse populations, regardless of age, gender, education, or profession [ 15 , 16 ]. Ecological momentary interventions (EMI) potentially address typical barriers associated with face-to-face mental health treatment, like an associated stigma, long waiting times, and general accessibility [ 17 , 18 ]. The effectiveness of digital mental health interventions is commonly evaluated through various psychological outcomes. Meta analyses show that the most frequently studied outcomes include depressive and anxiety symptoms [ 19 ], overall psychological well-being [ 20 ], quality of life, and stress [ 21 , 19 ]. App-based mental health interventions have been shown to be effective in addressing a wide range of psychological symptoms, increasing well-being among users, and reducing the symptoms of mental disorders [ 22 ]. Regarding the type of intervention, cognitive behavioral therapy (CBT)-based interventions show significantly higher effectiveness compared to interventions focusing solely on providing educational information [ 23 ], especially when using techniques involving self-monitoring, reflection, cognitive restructuring, and relaxation [ 22 ]. The inclusion of interactive and skill-based exercises is significant for sustaining engagement and facilitating long-term behavior change [ 24 ]. To comprehensively assess the psychological impact of digital interventions, the present study focuses on resilience, self-efficacy, coping strategies, emotion regulation, psychological distress, and quality of life. These constructs represent empirically validated indicators of mental health and well-being. Resilience and self-efficacy are important personal resources that enhance adaptive coping and buffer against psychological distress [ 25 , 26 ]. Emotion regulation, particularly cognitive reappraisal, supports psychological flexibility and stress reduction [ 27 , 28 ]. Coping strategies capture behavioral and cognitive adaptations to everyday stressors [ 29 ], whereas reductions in psychological distress and improvements in quality of life reflect overall improvements in mental health [ 20 , 30 ]. Together, these outcomes allow for the assessment of both symptom reduction and positive psychological adaptation, aligning with contemporary approaches to digital mental health research. Web-based self-help programs are generally offered unaccompanied (without a coach) or accompanied (with a coach), which differ in the extent of professional involvement and are typically referred to as unguided, minimally guided, and guided interventions [ 31 ]. Coaches in guided interventions can be responsible for supporting participants by clarifying comprehension questions, providing individualized feedback, reinforcing engagement, and facilitating progress through the program [ 32 , 33 ]. Several previous studies have highlighted the importance of personalized guidance in enhancing outcomes [ 21 ]. Interventions with therapist or coach support consistently show greater efficacy than unguided self-help approaches, with guided interventions yielding larger effect sizes [ 34 , 35 ] and perceived effectiveness [ 36 ] as well as higher completion rates [ 37 ]. Despite the proliferation of digital mental health interventions, many validated programs in German-speaking countries still follow structured, session-based designs that limit user autonomy. HelloBetter (GET.ON) is a leading example in the German-speaking context and one of the most extensively researched and validated e-mental health platforms in Europe. Multiple randomized controlled trials have confirmed its effectiveness in reducing symptoms of depression, anxiety, and stress [ 38 , 24 ]. However, while its modules provide high treatment fidelity, they follow a predefined sequence with limited flexibility or user-controlled content selection, reflecting a more prescriptive therapeutic model. In contrast, the beWell Tirol program presented here adopts a self-determined design that lets participants freely choose and arrange exercises according to their needs and personal preferences. This design, which supports autonomy, draws on Self-Determination Theory [ 39 ] and targets the fulfillment of the psychological needs for autonomy, competence, and relatedness. These factors are known to enhance intrinsic motivation and engagement [ 40 ]. By combining evidence-based content with a flexible and adaptive structure, beWell extends beyond existing validated internet-based intervention programs, offering a novel approach to preventive mental health promotion. Aim of the study This study aimed to develop and investigate a 4-month, app-based program designed to increase well-being. The program is based on cognitive behavioral techniques and is accompanied by coaching. The program was tested on a target group consisting of people with high psychological distress who are especially likely to want to improve their well-being and who may be affected by barriers associated with traditional face-to-face mental health treatment. The primary outcomes investigated are the usability of the provided app-based intervention and the satisfaction of the program among the target group. Second, the effectiveness of the intervention was investigated by examining the change in psychological parameters, such as psychological distress, quality of life, resilience, self-efficacy expectation, coping, and emotion regulation. Materials and methods Participants A total of 1,649 participants from the general population from Tyrol (Austria) and South Tyrol (Italy) completed a series of questionnaires via the computer-based health evaluation system (CHES; [ 41 ]). For this study, only those who reported increased psychological distress on the 53-item Brief-Symptom-Checklist (BSCL) were included. The BSCL measures nine symptom patterns of mental health problems. Items are rated on a 0 (no at all) to 4 (extremely) Likert-scale; the total score ranges from 0 to 212 [ 42 ]. 129 participants from the general population showed above-average psychological distress, defined as a Global Severity Index (GSI) T-score > 60, based on age and gender, 5 of whom were non-German-speaking. Ultimately, 124 participants were invited via email to participate in the beWell Tirol program, including a flyer with all relevant information. 40 participants expressed interest in the intervention and were sent access data for the beWell app. In addition, an initial meeting with a coach was arranged. Two people initially confirmed their interest in the program but did not start it. Thirteen participants dropped out during the program, and 25 completed the intervention (see Fig. 1 ). Intervention This beWell Tirol program is based on the techniques and exercises created for the care4stress program by the University of Innsbruck in collaboration with Evaluation Software Development [ 43 ]. The intervention period was set at 4 months. During this period, each participant had bi-weekly video conferences with their coach. Functions of the app Participants downloaded the beWell Tirol app (see Fig. 2 ) and logged in with their credentials, which were provided by the coach. Through the app, they could access the questionnaires, an overview of all exercises and topics, and a chat and video conferencing function to communicate with their coach. The beWell Tirol app features a user interface in the German language. From left to right: home area, overview of planned exercises, problem-solving training, and video consultation overview. Exercises The beWell Tirol app included eight modules, each consisting of psychoeducational texts, videos, interactive tasks, and screening questionnaires. There was no set order for the exercises or topics. This intervention was unique in that participants could choose their own exercises for a time period of two weeks, based on discussions with their coach and their individual needs. After every two weeks, modules and exercises could be added, excluded, or continued. Encouraging participants to actively participate in the module selection process enabled them to collaborate with their coach from the outset. This enabled the coach to activate the app's content individually, preventing participants from being overwhelmed by a wealth of options. The 9 modules are shown in Table 1 . Each module consisted of an introductory section, followed by either a short screening questionnaire (e.g., on perceived stress in Module 3 – “Stress and Relaxation”) or a quiz (e.g., in Module 7 – “Positive Psychology”). This was followed by a short psychoeducational text. The exercises consisted of a series of established CBT techniques that were made accessible via video (e.g., progressive muscle relaxation), audio (e.g., breathing exercises, imagination exercises), diary function (e.g. gratitude diary, stress diary), and explanatory text modules (e.g., informal mindfulness exercise, joyful activities). Each exercise was assigned to a specific topic (e.g., journaling, mindfulness, relaxation) and the theoretical background of each topic was explained on a psychoeducational page. The exercises were explained in detail via audio files, texts, or short videos. Participants could plan their exercises using a digital calendar and set reminders for training sessions within the app. Table 1 beWell Excersises Topic Exercises Medium Relaxation Progressive muscle relaxation Video Breathing exercises Audio Imagination exercise Audio Mindfulness Informal mindfulness exercise Text 5-4-3-2-1 method Audio Body scan Audio meditation Audio Journaling Wellbeing journal Digital diary Gratitude diary Digital diary Stress diary Digital diary Positive Psychology Social relationships Pre-structured text entry Character strengths Questionnaire, text fields Joyful activities Pre-structured text entry Daily Structuring ABC Daily Planning Pre-structured text entry Physical Activity Physical Activity Diary with charts Pre-structured text entry, charts Problem Solving Problem Solving Pre-structured text entry Balanced Diet Educational text Sleep Educational text Role of the coach The coaches were psychologists who had received specific training in the use and delivery of the program’s exercises. An instruction manual was created for each exercise. Bi-weekly video conferences were held in the app and lasted a maximum of 30 minutes. These sessions aimed to identify appropriate, tailored exercises for each participant, to reflect on their suitability, discuss their integration into daily routines, and to motivate participants to engage in regular practice. Assessment Points The questionnaires were made available in the app at three time points: at the beginning of the intervention (prior to the first meeting with the coach), right after the intervention ended, and three months post-intervention. Participants were notified of new questionnaires via push notifications on the app. Questionnaires Usability The System Usability Scale (SUS; [ 44 ]) is a widely used, standardized questionnaire designed to evaluate the perceived usability of a system. Consisting of 10 items, it is rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The items alternate between positive and negative statements to reduce response bias. The total SUS score ranges from 0 to 100 with higher scores indicating better usability. A SUS score above 68 generally indicates above-average usability, while a score above 80.3 indicates excellent usability [ 45 ]. For comparison, widely used applications such as WhatsApp ( M = 87.32) and YouTube ( M = 84.02) demonstrate very high usability [ 46 ]. In contrast, the average SUS score for digital health applications (excluding physical activity apps) is 68.05 [ 47 ], indicating moderate usability. The SUS has been validated across multiple studies and remains a robust tool for assessing usability in various domains, including software, websites, and medical applications [ 45 , 47 ]. Participant satisfaction To further evaluate the satisfaction with the intervention, participants rated 10 additional statements [ 48 ]. These statements covered general satisfaction, the impact on well-being, satisfaction with the background information, satisfaction with the coach, and satisfaction with the coaching format. Participants could rate the statements with either “strongly disagree”, “disagree”, “agree”, or “strongly agree”. Resilience Resilience was measured using the Resilience Scale (RS-13; [ 49 ]), which consists of 13 items on a seven-point Likert scale (1 “strongly disagree” to 7 “strongly agree”). Scores range from 13 to 91 with higher scores indicating greater resilience. A score ≤ 66 reflects low resilience, scores between 67 and 72 indicate moderate resilience, and scores ≥ 73 indicate high resilience [ 50 , 51 ]. Self-efficacy expectation General self-efficacy expectation was measured using a one-dimensional self-efficacy expectation scale (SWE; [ 52 ]) rooted in social cognitive theory [ 53 ]. This scale emphasizes the role of self-perceived efficacy in addressing situational demands. It consists of ten items on a Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). The sum score ranges from 10 to 40 [ 52 ]. Higher scores indicate greater self-efficacy, a valuable resource for positive mental health [ 26 , 54 ]. Psychological Distress Psychological distress was measured using the 10-Item Perceived Stress Scale [ 55 ], which assesses whether individuals perceive situations in their lives as excessively uncontrollable and overwhelming. Scores ranging from 0 to 13 indicate low stress, scores ranging from 14 to 26 indicate moderate stress, and scores ranging from 27 to 40 indicate high perceived stress [ 56 ]. Additionally, somatization, depression, and anxiety syndromes were assessed using the short version of the Brief Symptom Inventory (BSI-18; [ 57 ]). The total score on the BSI-18 is referred to as the Global Severity Index (GSI) and reflects the general psychological distress. Respondents answer the 18 items on a five-point Likert scale from ‘not at all’ (= 0) to ‘very strongly’ (= 4). The GSI score ranges from 0 to 72 with higher scores indicating higher general psychological distress. Emotion Regulation Emotion regulation strategies were assessed using the 10-item Emotion Regulation Questionnaire (ERQ; [ 58 ]). The ERQ measures two strategies, cognitive reappraisal (6 items) and expressive suppression (4 items), each rated on a 7-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). Quality of Life Quality of life was evaluated using the WHOQOL-BREF. The WHOQOL Group developed this quality of life assessment with fifteen international field centers in an attempt to create a cross-culturally applicable assessment. The 26-item short version of the questionnaire assesses four dimensions: physical well-being, psychological well-being, social relationships, and environment [ 59 ]. Coping strategies The Stressverarbeitungsfragebogen SVF-ak 42 [ 60 ] is a standardized self-report questionnaire designed to assess individual stress coping strategies. Consisting of 42 items, it captures both positive and negative coping mechanisms. Participants rate the frequency of their stress responses on a 4-point Likert scale. For both positive and negative strategies, summarized scales with a range from 0 to 6 were formed. The SVF-ak 42 provides a structured and validated measure of stress processing, making it a suitable tool for evaluating psychological interventions and stress-related outcomes. Coach Evaluations In addition to the self-report measures completed by the participants, a structured coach evaluation was administered to capture external observations of the participants’ motivation, engagement, and behavioral changes during the beWell Tirol intervention. The Coach Questionnaire consisted of 28 items that were rated after program completion. The questionnaire included both Likert-scale and open-ended questions. The items assessed various aspects of the intervention process, such as participant motivation, adherence to exercises, self-reflection, perceived behavioral changes, and the integration of learned strategies into daily life. Several items of the coach questionnaire were based on the Stundenbogen für die Allgemeine und Differentielle Einzelpsychotherapie (STEP; [ 61 ]), a well-established instrument for assessing the quality of the therapeutic process and the outcomes of supervision. It covers the dimensions of motivational clarification, problem solving, and relationship quality. In addition, three goal attainment items (GAS; [ 62 ]) were included in the coach questionnaire to evaluate the extent to which participants achieved their individual behavioral and emotional goals. Higher scores on these scales reflect more positive evaluations of the intervention process and greater perceived goal attainment. Statistical Analysis Inferential statistical analyses were performed using the IBM SPSS 30.0 statistical program. A general α-error level of 5% was defined to reject the null hypotheses, which was corrected according to Bonferroni-Holm [ 63 ]. Statistical tests were two-tailed. Since no outliers were detected, repeated measures ANOVAS were conducted to test for differences between the time points regarding the outcome measures. Repeated measures t-tests were used for single comparisons between time points. Effect sizes were calculated according to Cohan [ 64 ]. Results Sociodemographic data of the study sample are presented in Table 2. Table 2 : Sociodemographic information. Usability The SUS score recorded at T2 showed a mean value of M (24) = 91.8 ( SD = 7.5), thereby indicating excellent usability. Participant satisfaction 100% of study participants agreed with the statement that they were satisfied with the app. Regarding the program’s impact on well-being, 95,8% agreed that the program had had a positive impact on their well-being and that they thought that it would be successful as a comprehensive prevention program in Tyrol. 91,2% agreed that they knew people who would benefit from the program. All respondents found the background information on the program modules helpful. Regarding satisfaction with the coach, 100% of study participants agreed that the support from a coach was useful, while only 4,2% stated that the program would have been similarly successful without the involvement of a coach. All participants agreed that they were satisfied with the coaching format of the one-on-one video coaching sessions, while only 19,6% would have liked personal (face-to-face) meetings, and 8,3% would have liked video sessions in a group setting with other participants. All agreement rates to the satisfaction are shown in Fig. 3 . Health-related parameters Repeated measures ANOVAs revealed statistically significant differences (p < .05) in the following parameters: resilience, psychological distress (PSS10), self-efficacy expectation, WHOQOL-BREF global, WHOQOL-BREF physical health, WHOQOL-BREF psychological health, WHOQOL-BREF environment, ERQ reappraisal, and ERQ suppression. The means and standard deviations for all the parameters investigated are presented in Table 3. Single comparisons showed a significant increase in resilience from T1 to T2 (T = 4.5, df = 22, p < .001, d = 1.9). Regarding psychological distress, significant decreases were found in the PSS10 score from T1 to T2 (T = 3.3, df = 22, p = .002, d = 1.4) and in the BSI global severity index from T1 to T3 (T = 3.0, df = 21, p = .007, d = 1.3). Self-efficacy expectations significantly increased from T1 to T2 (T = 4.8, df = 23, p < .001, d = 2) and from T1 to T3 (T = 2.9, df = 21, p = .008, d = 1.3). A significant increase in quality of life (WHOQOL-BREF global) was found from T1 to T2 (T = 4.4, df = 23, p < .001, d = 1.8) and from T1 to T3 (T = 2.8, df = 21, p = .011, d = 1.2). In terms of WHOQOL-BREF physical health, a significant increase was detected from T1 to T2 (T = 4.7, df = 23, p < .001, d = 2) and from T1 to T3 (T = 3.2, df = 21, p = .004, d = 1.4). There was also a significant increase in the WHOQOL-BREF psychological health score from T1 to T2 (T = 3.2, df = 23, p = .004, d = 1.3). Regarding WHOQOL-BREF social relationships, a significant increase was found from T1 to T2 (T = 2.8, df = 23, p = .011, d = 1.2). Lastly, WHOQOL-BREF environment significantly increased from T1 to T2 (T = 3.9, df = 23, p < .001, d = 1.6). In terms of emotion regulation, the results showed a significant increase from T1 to T2 (T = 3.3, df = 22, p = .003, d = 1.4) and from T1 to T3 (T = 3.0, df = 20, p = .007, d = 1.3) regarding ERQ reappraisal. For ERQ suppression, a significant decrease was found from T1 to T2 (T = 2.9, df = 23, p = .009, d = 1.2). Positive coping strategies (SVF-ak 42) increased significantly from T1 to T3 (T = 2.8, df = 21, p = .01, d = 1.2), while no significant changes were found in terms of negative coping strategies. Means and standard deviations of investigated parameters. * Indicates a significant difference from the timepoint T1. The n used for statistical tests is based on the smaller n in each time point comparison. Coach Evaluations Coach evaluations were collected using a 28-item coach questionnaire. Several of the items were based on the STEP-SV and GAS questionnaires. Process ratings based on the STEP-SV revealed positive evaluations across all three dimensions: therapeutic relationship (M(24) = 6.4, SD = 0.5), problem-solving support (M(24) = 6.2, SD = 0.7), and motivational clarification (M(24) = 4.8, SD = 0.4). Goal attainment ratings (GAS) showed that, on average, participants achieved 74.7% (SD = 12.2) of their individual goals. Program-specific items revealed similarly positive impressions: coaches rated participants’ overall motivation as high (M(24) = 82.9, SD = 10.2) and agreed that participants would continue practicing some of the learned exercises (M(24) = 3.4, SD = 0.7). Together, these findings reflect a favorable coach perspective on participant engagement, motivation, and goal achievement throughout the intervention, complementing the self-reported improvements in psychological outcomes. Discussion This study evaluated the usability and effectiveness of a coach-guided, app-based intervention designed to enhance well-being and reduce psychological distress. Overall, the intervention demonstrated high usability and participant satisfaction, accompanied by improvements in several psychological outcomes. BeWell Tirol adopts a self-determined approach that allows participants to freely select and arrange exercises based on their individual needs. According to Self-Determination Theory [ 39 ], such autonomy-supportive designs foster the fulfillment of the basic psychological needs for autonomy, competence, and relatedness, thereby enhancing intrinsic motivation and engagement [ 40 ]. This is consistent with prior evidence indicating that autonomy-supportive digital environments promote adherence and self-regulation [ 65 ]. This autonomy-supportive design distinguishes beWell Tirol from most existing app-based interventions, which rely on standardized, prefabricated exercises rather than individualized or self-directed content. Furthermore, while most evidence-based digital programs focus on reducing symptoms, beWell Tirol emphasizes building resilience and self-efficacy. These are outcomes associated with preventive, resource-oriented approaches to mental health [ 25 ]. In total, 25 participants with above-average psychological distress (BSCL Global Severity Index T > 60) completed the intervention. The findings demonstrate excellent usability, high participant satisfaction, and significant improvements across several psychological outcomes. Usability and Satisfaction The app’s usability, as measured by SUS, was rated exceptionally high (M = 91.77), surpassing the benchmark for excellent usability. This result suggests that the app was well designed, intuitive, and user friendly. Additionally, participant satisfaction was high, with all users reporting a positive experience with the app and its features. The coaching aspect was particularly valued, with nearly all participants stating that the presence of a coach was crucial to the program’s success. These findings align with previous studies highlighting the benefits of guided over unguided digital mental health interventions [ 34 , 35 ]. In addition, guided formats may be particularly suitable for identifying and addressing acute crises, providing timely support that unguided interventions cannot offer [ 66 ]. The exceptionally high usability is particularly noteworthy given its crucial role in determining the adoption and sustained use of digital health technologies. Poor usability can undermine user engagement and consequently, limit the potential health benefits of such interventions [ 67 ]. For example, [ 68 ] demonstrated that higher usability ratings of self-management health apps were associated with increased exercise engagement and improved quality of life among patients with breast cancer. Moreover, the effectiveness of health apps depends not only on usability but also on personalization, both of which critically shape the overall user experience and therapeutic impact [ 69 ]. For example, a study on the mental health of undergraduate students found that personalized and easily accessible mental health apps enhance resilience and reduce anxiety [ 70 ]. Furthermore, the long-term use and implementation of digital health interventions heavily rely on both usability and user satisfaction [ 71 ]. Effectiveness of the Intervention Significant improvements were observed in resilience, self-efficacy, quality of life (global, physical health, psychological health, and environment), perceived stress, emotion regulation, and positive coping strategies. These results align with previous studies suggesting that structured cognitive-behavioral interventions can effectively enhance mental well-being [ 13 , 22 ]. Notably, improvements in self-efficacy, global quality of life, and reappraisal were sustained at the follow-up assessment, suggesting that the intervention had lasting benefits. The reduction in psychological distress and perceived stress further supports the efficacy of the intervention. Although the decrease in distress was not significant immediately after the intervention, it became so at follow-up. This may reflect a delayed effect, as participants continued to implement learned strategies in their daily lives and still had access to the final modules of the intervention during the follow-up period. Previous studies have reported similar patterns, where sustained engagement with intervention techniques yields prolonged psychological benefits [ 23 ]. Role of Coaching The strong preference for coach support is consistent with existing evidence showing that guided interventions yield higher adherence and effectiveness than unguided approaches [ 37 ]. Across the 4-month intervention period, coaches spent approximately 3 hours per participant in total, as most feedback sessions lasted no longer than 20 minutes and were conducted every two weeks. Given that only 4.2% of participants believed the program would have been similarly effective without a coach, these results highlight not only the importance of personalized guidance in digital interventions but also the cost efficiency of this hybrid format. This efficiency of guided digital mental health interventions is further supported by findings from a study involving participants in a pediatric digital mental health intervention (DMHI). In that study, reliable improvements in anxiety and depressive symptoms were observed following 2 sessions of 30 minutes of web-based intervention, and after 6 sessions, these changes stabilized [ 66 ]. Another study on mental well-being and emotional intelligence found that participants spent an average of 45 minutes per week engaging with their coach and participating in various related activities [ 72 ]. As there is currently no conclusive evidence regarding the optimal level of guidance, offering guidance on demand appears to be the most effective approach [ 32 ]. BeWell Tirol has implemented this concept by providing personalized tasks and guided feedback sessions on demand, thereby creating a unique and cost-effective model. Coach Evaluations Coach evaluations complemented participant self-reports by providing an external perspective on intervention processes. Coaches consistently reported strong therapeutic relationships, adequate problem-solving support, and high participant motivation. The observed average goal attainment rate of 74.7% and the positive ratings of continued exercise usage suggest that participants actively applied the learned strategies beyond the intervention period, supporting the program’s long-term feasibility. Limitations and Future Directions Despite these promising findings, several limitations must be considered. First, the sample size of participants who completed the intervention was relatively small (N = 25), which limits the generalizability of the results. Future studies should aim to recruit a larger sample size to enhance statistical power. Second, the absence of a control group prevented definitive conclusions about the intervention's efficacy relative to other treatment approaches such as coaching/counselling or psychotherapy in presence. A randomized controlled trial (RCT) would provide more robust evidence of the program’s effectiveness. Additionally, although the app was highly rated in terms of its usability and coaching support, the engagement data (e.g., frequency of app use, duration of exercise completion) were not analyzed in detail. Future research should examine usage patterns to better understand how engagement influences outcomes. Lastly, although the follow-up assessments indicated sustained benefits, it would be valuable to conduct longer-term follow-ups beyond three months to assess the intervention's durability. From a public health perspective, the accessibility and affordability of guided digital interventions are key to addressing treatment gaps in mental health care. Given the increasing prevalence of psychological distress and the limited availability of therapeutic services, programs like beWell Tirol could serve as low-threshold, preventive tools that complement existing healthcare systems. Integrating such interventions into workplace health promotion or primary care settings could help reduce the societal and economic burden associated with mental illness [ 73 ]. Positioning such programs within a stepped-care model would further enhance their utility, as they can serve as an initial or adjunctive level of care prior to more intensive therapeutic interventions [ 74 ]. In addition, their transdiagnostic nature allows for broad application across symptom domains without the need for elaborate diagnostic procedures, thereby facilitating early and flexible access to psychological support [ 75 ]. Furthermore, the relatively low coaching time of approximately three hours per participant over 4 months demonstrates that such programs can be implemented efficiently and at scale, making them highly feasible for broader application in public health contexts. These findings highlight the potential of hybrid digital coaching models to address the treatment gap in mental health care and to be incorporated into preventive strategies. Conclusion This study provides evidence that a coach-guided, app-based intervention can significantly enhance well-being and reduce psychological distress among individuals experiencing elevated stress levels. The intervention’s high usability ratings and participant satisfaction further highlight the feasibility of such digital interventions. Future research should focus on expanding sample sizes, incorporating control groups, and analyzing engagement data to optimize the design of the intervention and maximize its effectiveness. Declarations Ethics approval and consent to participate All procedures contributing to this work complied with the standards and were approved by the Ethics Committee of the Medical University Innsbruck (reference number: 1147/2020) and were conducted according to GCP standards on human experimentation and the Helsinki Declaration of 1975, as revised in 2008. Written informed consent to participate was obtained from all participants prior to enrollment in the study. Consent for publication Not applicable Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to ethical and privacy restrictions but are available from the corresponding author on reasonable request. Competing interests Gerhard Rumpold and Bernhard Holzner hold IPRs on the software CHES used in the study. Funding This work was supported by the federal state of Tyrol (grant no. F.21427) awarded to Alex Hofer. The funder of this study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Authors' contributions BH, AH and BFA designed the study and were responsible for its conceptualization together with all authors. BH, SG and VD were responsible for data curation and the statistical analysis. SG and VD interpreted the data, managed the literature searches, and wrote the first draft of the manuscript with support from LW. All authors participated in the critical revision of the manuscript and approved the completed version. Acknowledgments The authors would like to thank all participants for their time and commitment to the beWell Tirol program, which was supported by the federal state of Tyrol. Generative AI tools (ChatGPT) and translation software (DeepL) were used to refine the language and translate the manuscript. The authors critically reviewed and approved all content. 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Supplementary Files ParticipantSatisfactionScalebeWell.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 08 Jan, 2026 Editor assigned by journal 30 Dec, 2025 Editor invited by journal 24 Dec, 2025 Submission checks completed at journal 23 Dec, 2025 First submitted to journal 23 Dec, 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. 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07:55:24","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":208974,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8152998/v1/56d407f342e416128f0743c7.html"},{"id":100366190,"identity":"32da4fd7-b538-4232-8906-d4bb614390b0","added_by":"auto","created_at":"2026-01-16 07:56:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21829,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFlow Diagram of study participants\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8152998/v1/7783109eb483d58eec2f5878.png"},{"id":100124657,"identity":"8cec9d87-df04-41c7-9668-e55294b10dd9","added_by":"auto","created_at":"2026-01-13 09:12:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":363841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe beWell Tirol app features a user interface in the German language. From left to right: home area, overview of planned exercises, problem-solving training, and video consultation overview.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8152998/v1/22a00154234852041c5d4dee.png"},{"id":100124659,"identity":"db736cb5-b276-408e-89e0-802ec38680dc","added_by":"auto","created_at":"2026-01-13 09:12:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65267,"visible":true,"origin":"","legend":"\u003cp\u003eAgreement rate to satisfaction statements (n=24\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8152998/v1/22a4a4a7639bfbd2defef016.png"},{"id":100730706,"identity":"3be2a7ad-ef9a-4276-bb1b-f6bdb66b93ad","added_by":"auto","created_at":"2026-01-20 21:24:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1418901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8152998/v1/f78a7175-b259-4af4-a5ef-2eae2ea91641.pdf"},{"id":100366483,"identity":"cf552205-d945-4175-afa4-2cc11c5636fe","added_by":"auto","created_at":"2026-01-16 07:56:19","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14437,"visible":true,"origin":"","legend":"","description":"","filename":"ParticipantSatisfactionScalebeWell.docx","url":"https://assets-eu.researchsquare.com/files/rs-8152998/v1/088228bdec2d7bc8321d82ef.docx"}],"financialInterests":"Competing interest reported. Gerhard Rumpold and Bernhard Holzner hold IPRs on the software CHES used in the study.","formattedTitle":"Usability, Acceptance, and Effectiveness of a Coach-Guided, App-Based Mental Health Intervention: The beWell Tirol Pilot Study","fulltext":[{"header":"Background","content":"\u003cp\u003eMental illness has a very high prevalence in the population (with e.g. 30% in Germany) and is associated with high public costs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This is also reflected in working life: in Austria, 11.4% of all sick days are due to mental illness, with an increasing trend since 2018 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. At the same time, less than half of those suffering from mental illness receive treatment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The reasons cited in the literature include stigma, lack of prevention, insufficient low-threshold outpatient services, long waiting times, inequality between mental and physical health care, and insufficient financial support [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A longitudinal study highlighted the relationship between psychological distress and healthcare costs, emphasizing the importance of prevention to reduce costs and distress [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic and related measures have been associated with increased psychological distress worldwide [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with symptoms like anxiety, depression, and sleep disturbances [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A study in Tyrol (Austria) and South Tyrol (Italy) found that 15% of the general population experienced psychological distress, particularly women, single individuals, low-income groups, and the unemployed [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Follow-up results suggest these burdens remained stable from November 2020 to February 2022 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor instance, the incidence of depression can be reduced by 21% through the implementation of preventive measures, as opposed to the incidence in control groups [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this context, one possibility for preventing and reducing resources and costs is internet-based interventions. Studies have demonstrated that they are generally well accepted across diverse populations, regardless of age, gender, education, or profession [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Ecological momentary interventions (EMI) potentially address typical barriers associated with face-to-face mental health treatment, like an associated stigma, long waiting times, and general accessibility [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe effectiveness of digital mental health interventions is commonly evaluated through various psychological outcomes. Meta analyses show that the most frequently studied outcomes include depressive and anxiety symptoms [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], overall psychological well-being [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], quality of life, and stress [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eApp-based mental health interventions have been shown to be effective in addressing a wide range of psychological symptoms, increasing well-being among users, and reducing the symptoms of mental disorders [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding the type of intervention, cognitive behavioral therapy (CBT)-based interventions show significantly higher effectiveness compared to interventions focusing solely on providing educational information [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], especially when using techniques involving self-monitoring, reflection, cognitive restructuring, and relaxation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The inclusion of interactive and skill-based exercises is significant for sustaining engagement and facilitating long-term behavior change [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo comprehensively assess the psychological impact of digital interventions, the present study focuses on resilience, self-efficacy, coping strategies, emotion regulation, psychological distress, and quality of life. These constructs represent empirically validated indicators of mental health and well-being. Resilience and self-efficacy are important personal resources that enhance adaptive coping and buffer against psychological distress [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Emotion regulation, particularly cognitive reappraisal, supports psychological flexibility and stress reduction [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Coping strategies capture behavioral and cognitive adaptations to everyday stressors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], whereas reductions in psychological distress and improvements in quality of life reflect overall improvements in mental health [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Together, these outcomes allow for the assessment of both symptom reduction and positive psychological adaptation, aligning with contemporary approaches to digital mental health research.\u003c/p\u003e \u003cp\u003eWeb-based self-help programs are generally offered unaccompanied (without a coach) or accompanied (with a coach), which differ in the extent of professional involvement and are typically referred to as unguided, minimally guided, and guided interventions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Coaches in guided interventions can be responsible for supporting participants by clarifying comprehension questions, providing individualized feedback, reinforcing engagement, and facilitating progress through the program [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Several previous studies have highlighted the importance of personalized guidance in enhancing outcomes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Interventions with therapist or coach support consistently show greater efficacy than unguided self-help approaches, with guided interventions yielding larger effect sizes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and perceived effectiveness [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] as well as higher completion rates [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the proliferation of digital mental health interventions, many validated programs in German-speaking countries still follow structured, session-based designs that limit user autonomy. HelloBetter (GET.ON) is a leading example in the German-speaking context and one of the most extensively researched and validated e-mental health platforms in Europe. Multiple randomized controlled trials have confirmed its effectiveness in reducing symptoms of depression, anxiety, and stress [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, while its modules provide high treatment fidelity, they follow a predefined sequence with limited flexibility or user-controlled content selection, reflecting a more prescriptive therapeutic model.\u003c/p\u003e \u003cp\u003eIn contrast, the beWell Tirol program presented here adopts a self-determined design that lets participants freely choose and arrange exercises according to their needs and personal preferences. This design, which supports autonomy, draws on Self-Determination Theory [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and targets the fulfillment of the psychological needs for autonomy, competence, and relatedness. These factors are known to enhance intrinsic motivation and engagement [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. By combining evidence-based content with a flexible and adaptive structure, beWell extends beyond existing validated internet-based intervention programs, offering a novel approach to preventive mental health promotion.\u003c/p\u003e \u003cp\u003eAim of the study\u003c/p\u003e \u003cp\u003eThis study aimed to develop and investigate a 4-month, app-based program designed to increase well-being. The program is based on cognitive behavioral techniques and is accompanied by coaching. The program was tested on a target group consisting of people with high psychological distress who are especially likely to want to improve their well-being and who may be affected by barriers associated with traditional face-to-face mental health treatment.\u003c/p\u003e \u003cp\u003eThe primary outcomes investigated are the usability of the provided app-based intervention and the satisfaction of the program among the target group.\u003c/p\u003e \u003cp\u003eSecond, the effectiveness of the intervention was investigated by examining the change in psychological parameters, such as psychological distress, quality of life, resilience, self-efficacy expectation, coping, and emotion regulation.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 1,649 participants from the general population from Tyrol (Austria) and South Tyrol (Italy) completed a series of questionnaires via the computer-based health evaluation system (CHES; [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]). For this study, only those who reported increased psychological distress on the 53-item Brief-Symptom-Checklist (BSCL) were included. The BSCL measures nine symptom patterns of mental health problems. Items are rated on a 0 (no at all) to 4 (extremely) Likert-scale; the total score ranges from 0 to 212 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. 129 participants from the general population showed above-average psychological distress, defined as a Global Severity Index (GSI) T-score\u0026thinsp;\u0026gt;\u0026thinsp;60, based on age and gender, 5 of whom were non-German-speaking.\u003c/p\u003e \u003cp\u003eUltimately, 124 participants were invited via email to participate in the beWell Tirol program, including a flyer with all relevant information. 40 participants expressed interest in the intervention and were sent access data for the beWell app. In addition, an initial meeting with a coach was arranged. Two people initially confirmed their interest in the program but did not start it. Thirteen participants dropped out during the program, and 25 completed the intervention (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIntervention\u003c/h3\u003e\n\u003cp\u003eThis beWell Tirol program is based on the techniques and exercises created for the care4stress program by the University of Innsbruck in collaboration with Evaluation Software Development [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The intervention period was set at 4 months. During this period, each participant had bi-weekly video conferences with their coach.\u003c/p\u003e\n\u003ch3\u003eFunctions of the app\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003eParticipants downloaded the beWell Tirol app (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and logged in with their credentials, which were provided by the coach. Through the app, they could access the questionnaires, an overview of all exercises and topics, and a chat and video conferencing function to communicate with their coach.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe beWell Tirol app features a user interface in the German language. From left to right: home area, overview of planned exercises, problem-solving training, and video consultation overview.\u003c/em\u003e \u003c/p\u003e\n\u003ch3\u003eExercises\u003c/h3\u003e\n\u003cp\u003eThe beWell Tirol app included eight modules, each consisting of psychoeducational texts, videos, interactive tasks, and screening questionnaires. There was no set order for the exercises or topics. This intervention was unique in that participants could choose their own exercises for a time period of two weeks, based on discussions with their coach and their individual needs. After every two weeks, modules and exercises could be added, excluded, or continued.\u003c/p\u003e \u003cp\u003eEncouraging participants to actively participate in the module selection process enabled them to collaborate with their coach from the outset. This enabled the coach to activate the app's content individually, preventing participants from being overwhelmed by a wealth of options.\u003c/p\u003e \u003cp\u003eThe 9 modules are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Each module consisted of an introductory section, followed by either a short screening questionnaire (e.g., on perceived stress in Module 3 \u0026ndash; \u0026ldquo;Stress and Relaxation\u0026rdquo;) or a quiz (e.g., in Module 7 \u0026ndash; \u0026ldquo;Positive Psychology\u0026rdquo;). This was followed by a short psychoeducational text.\u003c/p\u003e \u003cp\u003eThe exercises consisted of a series of established CBT techniques that were made accessible via video (e.g., progressive muscle relaxation), audio (e.g., breathing exercises, imagination exercises), diary function (e.g. gratitude diary, stress diary), and explanatory text modules (e.g., informal mindfulness exercise, joyful activities). Each exercise was assigned to a specific topic (e.g., journaling, mindfulness, relaxation) and the theoretical background of each topic was explained on a psychoeducational page. The exercises were explained in detail via audio files, texts, or short videos. Participants could plan their exercises using a digital calendar and set reminders for training sessions within the app.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ebeWell Excersises\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExercises\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelaxation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProgressive muscle relaxation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVideo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreathing exercises\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAudio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImagination exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAudio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMindfulness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInformal mindfulness exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eText\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-4-3-2-1 method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAudio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody scan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAudio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emeditation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAudio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJournaling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWellbeing journal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital diary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGratitude diary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital diary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress diary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital diary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive Psychology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial relationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-structured text entry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacter strengths\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuestionnaire, text fields\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJoyful activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-structured text entry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily Structuring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABC Daily Planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-structured text entry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical Activity Diary with charts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-structured text entry, charts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProblem Solving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProblem Solving\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-structured text entry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalanced Diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducational text\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducational text\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eRole of the coach\u003c/h3\u003e\n\u003cp\u003eThe coaches were psychologists who had received specific training in the use and delivery of the program\u0026rsquo;s exercises. An instruction manual was created for each exercise. Bi-weekly video conferences were held in the app and lasted a maximum of 30 minutes. These sessions aimed to identify appropriate, tailored exercises for each participant, to reflect on their suitability, discuss their integration into daily routines, and to motivate participants to engage in regular practice.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssessment Points\u003c/h2\u003e \u003cp\u003eThe questionnaires were made available in the app at three time points: at the beginning of the intervention (prior to the first meeting with the coach), right after the intervention ended, and three months post-intervention. Participants were notified of new questionnaires via push notifications on the app.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuestionnaires\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eUsability\u003c/h2\u003e \u003cp\u003eThe System Usability Scale (SUS; [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]) is a widely used, standardized questionnaire designed to evaluate the perceived usability of a system. Consisting of 10 items, it is rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The items alternate between positive and negative statements to reduce response bias. The total SUS score ranges from 0 to 100 with higher scores indicating better usability. A SUS score above 68 generally indicates above-average usability, while a score above 80.3 indicates excellent usability [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor comparison, widely used applications such as WhatsApp (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;87.32) and YouTube (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;84.02) demonstrate very high usability [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In contrast, the average SUS score for digital health applications (excluding physical activity apps) is 68.05 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], indicating moderate usability. The SUS has been validated across multiple studies and remains a robust tool for assessing usability in various domains, including software, websites, and medical applications [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eParticipant satisfaction\u003c/h2\u003e \u003cp\u003eTo further evaluate the satisfaction with the intervention, participants rated 10 additional statements [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These statements covered general satisfaction, the impact on well-being, satisfaction with the background information, satisfaction with the coach, and satisfaction with the coaching format. Participants could rate the statements with either \u0026ldquo;strongly disagree\u0026rdquo;, \u0026ldquo;disagree\u0026rdquo;, \u0026ldquo;agree\u0026rdquo;, or \u0026ldquo;strongly agree\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eResilience\u003c/h2\u003e \u003cp\u003eResilience was measured using the Resilience Scale (RS-13; [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]), which consists of 13 items on a seven-point Likert scale (1 \u0026ldquo;strongly disagree\u0026rdquo; to 7 \u0026ldquo;strongly agree\u0026rdquo;). Scores range from 13 to 91 with higher scores indicating greater resilience. A score\u0026thinsp;\u0026le;\u0026thinsp;66 reflects low resilience, scores between 67 and 72 indicate moderate resilience, and scores\u0026thinsp;\u0026ge;\u0026thinsp;73 indicate high resilience [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSelf-efficacy expectation\u003c/h2\u003e \u003cp\u003eGeneral self-efficacy expectation was measured using a one-dimensional self-efficacy expectation scale (SWE; [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]) rooted in social cognitive theory [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This scale emphasizes the role of self-perceived efficacy in addressing situational demands. It consists of ten items on a Likert scale ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 5 (\u0026ldquo;strongly agree\u0026rdquo;). The sum score ranges from 10 to 40 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Higher scores indicate greater self-efficacy, a valuable resource for positive mental health [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePsychological Distress\u003c/h2\u003e \u003cp\u003ePsychological distress was measured using the 10-Item Perceived Stress Scale [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], which assesses whether individuals perceive situations in their lives as excessively uncontrollable and overwhelming. Scores ranging from 0 to 13 indicate low stress, scores ranging from 14 to 26 indicate moderate stress, and scores ranging from 27 to 40 indicate high perceived stress [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, somatization, depression, and anxiety syndromes were assessed using the short version of the Brief Symptom Inventory (BSI-18; [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]). The total score on the BSI-18 is referred to as the Global Severity Index (GSI) and reflects the general psychological distress. Respondents answer the 18 items on a five-point Likert scale from \u0026lsquo;not at all\u0026rsquo; (=\u0026thinsp;0) to \u0026lsquo;very strongly\u0026rsquo; (=\u0026thinsp;4). The GSI score ranges from 0 to 72 with higher scores indicating higher general psychological distress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEmotion Regulation\u003c/h2\u003e \u003cp\u003eEmotion regulation strategies were assessed using the 10-item Emotion Regulation Questionnaire (ERQ; [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]). The ERQ measures two strategies, cognitive reappraisal (6 items) and expressive suppression (4 items), each rated on a 7-point Likert scale ranging from 1 (\u0026ldquo;strongly disagree\u0026rdquo;) to 7 (\u0026ldquo;strongly agree\u0026rdquo;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eQuality of Life\u003c/h2\u003e \u003cp\u003eQuality of life was evaluated using the WHOQOL-BREF. The WHOQOL Group developed this quality of life assessment with fifteen international field centers in an attempt to create a cross-culturally applicable assessment. The 26-item short version of the questionnaire assesses four dimensions: physical well-being, psychological well-being, social relationships, and environment [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCoping strategies\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eStressverarbeitungsfragebogen\u003c/em\u003e SVF-ak 42 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] is a standardized self-report questionnaire designed to assess individual stress coping strategies. Consisting of 42 items, it captures both positive and negative coping mechanisms. Participants rate the frequency of their stress responses on a 4-point Likert scale. For both positive and negative strategies, summarized scales with a range from 0 to 6 were formed. The SVF-ak 42 provides a structured and validated measure of stress processing, making it a suitable tool for evaluating psychological interventions and stress-related outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCoach Evaluations\u003c/h2\u003e \u003cp\u003e In addition to the self-report measures completed by the participants, a structured coach evaluation was administered to capture external observations of the participants\u0026rsquo; motivation, engagement, and behavioral changes during the beWell Tirol intervention.\u003c/p\u003e \u003cp\u003eThe Coach Questionnaire consisted of 28 items that were rated after program completion. The questionnaire included both Likert-scale and open-ended questions. The items assessed various aspects of the intervention process, such as participant motivation, adherence to exercises, self-reflection, perceived behavioral changes, and the integration of learned strategies into daily life.\u003c/p\u003e \u003cp\u003eSeveral items of the coach questionnaire were based on the Stundenbogen f\u0026uuml;r die Allgemeine und Differentielle Einzelpsychotherapie (STEP; [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]), a well-established instrument for assessing the quality of the therapeutic process and the outcomes of supervision. It covers the dimensions of motivational clarification, problem solving, and relationship quality. In addition, three goal attainment items (GAS; [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]) were included in the coach questionnaire to evaluate the extent to which participants achieved their individual behavioral and emotional goals.\u003c/p\u003e \u003cp\u003eHigher scores on these scales reflect more positive evaluations of the intervention process and greater perceived goal attainment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eInferential statistical analyses were performed using the IBM SPSS 30.0 statistical program. A general α-error level of 5% was defined to reject the null hypotheses, which was corrected according to Bonferroni-Holm [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Statistical tests were two-tailed. Since no outliers were detected, repeated measures ANOVAS were conducted to test for differences between the time points regarding the outcome measures. Repeated measures t-tests were used for single comparisons between time points. Effect sizes were calculated according to Cohan [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSociodemographic data of the study sample are presented in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003cem\u003eSociodemographic information.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cimg 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\" width=\"535\" height=\"593\"\u003e\u003c/em\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eUsability\u003c/h2\u003e\n \u003cp\u003eThe SUS score recorded at T2 showed a mean value of \u003cem\u003eM\u003c/em\u003e(24)\u0026thinsp;=\u0026thinsp;91.8 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.5), thereby indicating excellent usability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipant satisfaction\u003c/h2\u003e\n \u003cp\u003e100% of study participants agreed with the statement that they were satisfied with the app. Regarding the program\u0026rsquo;s impact on well-being, 95,8% agreed that the program had had a positive impact on their well-being and that they thought that it would be successful as a comprehensive prevention program in Tyrol. 91,2% agreed that they knew people who would benefit from the program. All respondents found the background information on the program modules helpful.\u003c/p\u003e\n \u003cp\u003eRegarding satisfaction with the coach, 100% of study participants agreed that the support from a coach was useful, while only 4,2% stated that the program would have been similarly successful without the involvement of a coach. All participants agreed that they were satisfied with the coaching format of the one-on-one video coaching sessions, while only 19,6% would have liked personal (face-to-face) meetings, and 8,3% would have liked video sessions in a group setting with other participants. All agreement rates to the satisfaction are shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eHealth-related parameters\u003c/h2\u003e\n \u003cp\u003eRepeated measures ANOVAs revealed statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) in the following parameters: resilience, psychological distress (PSS10), self-efficacy expectation, WHOQOL-BREF global, WHOQOL-BREF physical health, WHOQOL-BREF psychological health, WHOQOL-BREF environment, ERQ reappraisal, and ERQ suppression. The means and standard deviations for all the parameters investigated are presented in Table\u0026nbsp;3.\u003c/p\u003e\n \u003cp\u003eSingle comparisons showed a significant increase in resilience from T1 to T2 (T\u0026thinsp;=\u0026thinsp;4.5, df\u0026thinsp;=\u0026thinsp;22, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.9). Regarding psychological distress, significant decreases were found in the PSS10 score from T1 to T2 (T\u0026thinsp;=\u0026thinsp;3.3, df\u0026thinsp;=\u0026thinsp;22, p\u0026thinsp;=\u0026thinsp;.002, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.4) and in the BSI global severity index from T1 to T3 (T\u0026thinsp;=\u0026thinsp;3.0, df\u0026thinsp;=\u0026thinsp;21, p\u0026thinsp;=\u0026thinsp;.007, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.3).\u003c/p\u003e\n \u003cp\u003eSelf-efficacy expectations significantly increased from T1 to T2 (T\u0026thinsp;=\u0026thinsp;4.8, df\u0026thinsp;=\u0026thinsp;23, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) and from T1 to T3 (T\u0026thinsp;=\u0026thinsp;2.9, df\u0026thinsp;=\u0026thinsp;21, p\u0026thinsp;=\u0026thinsp;.008, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.3).\u003c/p\u003e\n \u003cp\u003eA significant increase in quality of life (WHOQOL-BREF global) was found from T1 to T2 (T\u0026thinsp;=\u0026thinsp;4.4, df\u0026thinsp;=\u0026thinsp;23, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.8) and from T1 to T3 (T\u0026thinsp;=\u0026thinsp;2.8, df\u0026thinsp;=\u0026thinsp;21, p\u0026thinsp;=\u0026thinsp;.011, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.2). In terms of WHOQOL-BREF physical health, a significant increase was detected from T1 to T2 (T\u0026thinsp;=\u0026thinsp;4.7, df\u0026thinsp;=\u0026thinsp;23, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) and from T1 to T3 (T\u0026thinsp;=\u0026thinsp;3.2, df\u0026thinsp;=\u0026thinsp;21, p\u0026thinsp;=\u0026thinsp;.004, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.4). There was also a significant increase in the WHOQOL-BREF psychological health score from T1 to T2 (T\u0026thinsp;=\u0026thinsp;3.2, df\u0026thinsp;=\u0026thinsp;23, p\u0026thinsp;=\u0026thinsp;.004, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.3). Regarding WHOQOL-BREF social relationships, a significant increase was found from T1 to T2 (T\u0026thinsp;=\u0026thinsp;2.8, df\u0026thinsp;=\u0026thinsp;23, p\u0026thinsp;=\u0026thinsp;.011, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.2). Lastly, WHOQOL-BREF environment significantly increased from T1 to T2 (T\u0026thinsp;=\u0026thinsp;3.9, df\u0026thinsp;=\u0026thinsp;23, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.6).\u003c/p\u003e\n \u003cp\u003eIn terms of emotion regulation, the results showed a significant increase from T1 to T2 (T\u0026thinsp;=\u0026thinsp;3.3, df\u0026thinsp;=\u0026thinsp;22, p\u0026thinsp;=\u0026thinsp;.003, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.4) and from T1 to T3 (T\u0026thinsp;=\u0026thinsp;3.0, df\u0026thinsp;=\u0026thinsp;20, p\u0026thinsp;=\u0026thinsp;.007, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.3) regarding ERQ reappraisal. For ERQ suppression, a significant decrease was found from T1 to T2 (T\u0026thinsp;=\u0026thinsp;2.9, df\u0026thinsp;=\u0026thinsp;23, p\u0026thinsp;=\u0026thinsp;.009, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.2).\u003c/p\u003e\n \u003cp\u003ePositive coping strategies (SVF-ak 42) increased significantly from T1 to T3 (T\u0026thinsp;=\u0026thinsp;2.8, df\u0026thinsp;=\u0026thinsp;21, p\u0026thinsp;=\u0026thinsp;.01, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.2), while no significant changes were found in terms of negative coping strategies.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cimg 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\" width=\"566\" height=\"706\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eMeans and standard deviations of investigated parameters. * Indicates a significant difference from the timepoint T1. The n used for statistical tests is based on the smaller n in each time point comparison.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eCoach Evaluations\u003c/h2\u003e\n \u003cp\u003eCoach evaluations were collected using a 28-item coach questionnaire. Several of the items were based on the STEP-SV and GAS questionnaires. Process ratings based on the STEP-SV revealed positive evaluations across all three dimensions: therapeutic relationship (M(24)\u0026thinsp;=\u0026thinsp;6.4, SD\u0026thinsp;=\u0026thinsp;0.5), problem-solving support (M(24)\u0026thinsp;=\u0026thinsp;6.2, SD\u0026thinsp;=\u0026thinsp;0.7), and motivational clarification (M(24)\u0026thinsp;=\u0026thinsp;4.8, SD\u0026thinsp;=\u0026thinsp;0.4).\u003c/p\u003e\n \u003cp\u003eGoal attainment ratings (GAS) showed that, on average, participants achieved 74.7% (SD\u0026thinsp;=\u0026thinsp;12.2) of their individual goals. Program-specific items revealed similarly positive impressions: coaches rated participants\u0026rsquo; overall motivation as high (M(24)\u0026thinsp;=\u0026thinsp;82.9, SD\u0026thinsp;=\u0026thinsp;10.2) and agreed that participants would continue practicing some of the learned exercises (M(24)\u0026thinsp;=\u0026thinsp;3.4, SD\u0026thinsp;=\u0026thinsp;0.7).\u003c/p\u003e\n \u003cp\u003eTogether, these findings reflect a favorable coach perspective on participant engagement, motivation, and goal achievement throughout the intervention, complementing the self-reported improvements in psychological outcomes.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the usability and effectiveness of a coach-guided, app-based intervention designed to enhance well-being and reduce psychological distress. Overall, the intervention demonstrated high usability and participant satisfaction, accompanied by improvements in several psychological outcomes. BeWell Tirol adopts a self-determined approach that allows participants to freely select and arrange exercises based on their individual needs. According to Self-Determination Theory [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], such autonomy-supportive designs foster the fulfillment of the basic psychological needs for autonomy, competence, and relatedness, thereby enhancing intrinsic motivation and engagement [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This is consistent with prior evidence indicating that autonomy-supportive digital environments promote adherence and self-regulation [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. This autonomy-supportive design distinguishes beWell Tirol from most existing app-based interventions, which rely on standardized, prefabricated exercises rather than individualized or self-directed content. Furthermore, while most evidence-based digital programs focus on reducing symptoms, beWell Tirol emphasizes building resilience and self-efficacy. These are outcomes associated with preventive, resource-oriented approaches to mental health [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn total, 25 participants with above-average psychological distress (BSCL Global Severity Index T\u0026thinsp;\u0026gt;\u0026thinsp;60) completed the intervention. The findings demonstrate excellent usability, high participant satisfaction, and significant improvements across several psychological outcomes.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eUsability and Satisfaction\u003c/h2\u003e \u003cp\u003eThe app\u0026rsquo;s usability, as measured by SUS, was rated exceptionally high (M\u0026thinsp;=\u0026thinsp;91.77), surpassing the benchmark for excellent usability. This result suggests that the app was well designed, intuitive, and user friendly. Additionally, participant satisfaction was high, with all users reporting a positive experience with the app and its features. The coaching aspect was particularly valued, with nearly all participants stating that the presence of a coach was crucial to the program\u0026rsquo;s success. These findings align with previous studies highlighting the benefits of guided over unguided digital mental health interventions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, guided formats may be particularly suitable for identifying and addressing acute crises, providing timely support that unguided interventions cannot offer [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The exceptionally high usability is particularly noteworthy given its crucial role in determining the adoption and sustained use of digital health technologies. Poor usability can undermine user engagement and consequently, limit the potential health benefits of such interventions [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. For example, [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] demonstrated that higher usability ratings of self-management health apps were associated with increased exercise engagement and improved quality of life among patients with breast cancer. Moreover, the effectiveness of health apps depends not only on usability but also on personalization, both of which critically shape the overall user experience and therapeutic impact [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. For example, a study on the mental health of undergraduate students found that personalized and easily accessible mental health apps enhance resilience and reduce anxiety [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Furthermore, the long-term use and implementation of digital health interventions heavily rely on both usability and user satisfaction [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eEffectiveness of the Intervention\u003c/h2\u003e \u003cp\u003eSignificant improvements were observed in resilience, self-efficacy, quality of life (global, physical health, psychological health, and environment), perceived stress, emotion regulation, and positive coping strategies. These results align with previous studies suggesting that structured cognitive-behavioral interventions can effectively enhance mental well-being [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Notably, improvements in self-efficacy, global quality of life, and reappraisal were sustained at the follow-up assessment, suggesting that the intervention had lasting benefits.\u003c/p\u003e \u003cp\u003eThe reduction in psychological distress and perceived stress further supports the efficacy of the intervention. Although the decrease in distress was not significant immediately after the intervention, it became so at follow-up. This may reflect a delayed effect, as participants continued to implement learned strategies in their daily lives and still had access to the final modules of the intervention during the follow-up period. Previous studies have reported similar patterns, where sustained engagement with intervention techniques yields prolonged psychological benefits [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eRole of Coaching\u003c/h2\u003e \u003cp\u003eThe strong preference for coach support is consistent with existing evidence showing that guided interventions yield higher adherence and effectiveness than unguided approaches [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Across the 4-month intervention period, coaches spent approximately 3 hours per participant in total, as most feedback sessions lasted no longer than 20 minutes and were conducted every two weeks. Given that only 4.2% of participants believed the program would have been similarly effective without a coach, these results highlight not only the importance of personalized guidance in digital interventions but also the cost efficiency of this hybrid format. This efficiency of guided digital mental health interventions is further supported by findings from a study involving participants in a pediatric digital mental health intervention (DMHI). In that study, reliable improvements in anxiety and depressive symptoms were observed following 2 sessions of 30 minutes of web-based intervention, and after 6 sessions, these changes stabilized [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Another study on mental well-being and emotional intelligence found that participants spent an average of 45 minutes per week engaging with their coach and participating in various related activities [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. As there is currently no conclusive evidence regarding the optimal level of guidance, offering guidance on demand appears to be the most effective approach [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. BeWell Tirol has implemented this concept by providing personalized tasks and guided feedback sessions on demand, thereby creating a unique and cost-effective model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eCoach Evaluations\u003c/h2\u003e \u003cp\u003e Coach evaluations complemented participant self-reports by providing an external perspective on intervention processes. Coaches consistently reported strong therapeutic relationships, adequate problem-solving support, and high participant motivation.\u003c/p\u003e \u003cp\u003eThe observed average goal attainment rate of 74.7% and the positive ratings of continued exercise usage suggest that participants actively applied the learned strategies beyond the intervention period, supporting the program\u0026rsquo;s long-term feasibility.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLimitations and Future Directions\u003c/h3\u003e\n\u003cp\u003eDespite these promising findings, several limitations must be considered. First, the sample size of participants who completed the intervention was relatively small (N\u0026thinsp;=\u0026thinsp;25), which limits the generalizability of the results. Future studies should aim to recruit a larger sample size to enhance statistical power. Second, the absence of a control group prevented definitive conclusions about the intervention's efficacy relative to other treatment approaches such as coaching/counselling or psychotherapy in presence. A randomized controlled trial (RCT) would provide more robust evidence of the program\u0026rsquo;s effectiveness.\u003c/p\u003e \u003cp\u003eAdditionally, although the app was highly rated in terms of its usability and coaching support, the engagement data (e.g., frequency of app use, duration of exercise completion) were not analyzed in detail. Future research should examine usage patterns to better understand how engagement influences outcomes. Lastly, although the follow-up assessments indicated sustained benefits, it would be valuable to conduct longer-term follow-ups beyond three months to assess the intervention's durability.\u003c/p\u003e \u003cp\u003eFrom a public health perspective, the accessibility and affordability of guided digital interventions are key to addressing treatment gaps in mental health care. Given the increasing prevalence of psychological distress and the limited availability of therapeutic services, programs like beWell Tirol could serve as low-threshold, preventive tools that complement existing healthcare systems. Integrating such interventions into workplace health promotion or primary care settings could help reduce the societal and economic burden associated with mental illness [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Positioning such programs within a stepped-care model would further enhance their utility, as they can serve as an initial or adjunctive level of care prior to more intensive therapeutic interventions [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. In addition, their transdiagnostic nature allows for broad application across symptom domains without the need for elaborate diagnostic procedures, thereby facilitating early and flexible access to psychological support [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the relatively low coaching time of approximately three hours per participant over 4 months demonstrates that such programs can be implemented efficiently and at scale, making them highly feasible for broader application in public health contexts. These findings highlight the potential of hybrid digital coaching models to address the treatment gap in mental health care and to be incorporated into preventive strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides evidence that a coach-guided, app-based intervention can significantly enhance well-being and reduce psychological distress among individuals experiencing elevated stress levels. The intervention\u0026rsquo;s high usability ratings and participant satisfaction further highlight the feasibility of such digital interventions. Future research should focus on expanding sample sizes, incorporating control groups, and analyzing engagement data to optimize the design of the intervention and maximize its effectiveness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures contributing to this work complied with the standards and were approved by the Ethics Committee of the Medical University Innsbruck (reference number: 1147/2020) and were conducted according to GCP standards on human experimentation and the Helsinki Declaration of 1975, as revised in 2008. Written informed consent to participate was obtained from all participants prior to enrollment in the study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to ethical and privacy restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGerhard Rumpold and Bernhard Holzner hold IPRs on the software CHES used in the study.\u003c/p\u003e\n\u003ch5\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/h5\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;the\u0026nbsp;federal state of Tyrol (grant no. F.21427) awarded to Alex Hofer. The funder of this study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.\u003c/p\u003e\n\u003ch5\u003e\u003cem\u003eAuthors' contributions\u0026nbsp;\u003c/em\u003e\u003c/h5\u003e\n\u003cp\u003eBH, AH and BFA designed the study and\u0026nbsp;were responsible for its conceptualization together with all authors. BH, SG and VD were responsible for data curation and the statistical analysis.\u0026nbsp;SG and VD interpreted the data, managed the literature searches, and wrote the first draft of the manuscript with support from LW. All authors participated in the critical revision of the manuscript and approved the completed version.\u003c/p\u003e\n\u003ch5\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/h5\u003e\n\u003cp\u003eThe authors would like to thank all participants for their time and commitment to the\u0026nbsp;\u003cem\u003ebeWell Tirol\u003c/em\u003e program, which was supported by the federal state of Tyrol.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Generative AI tools (ChatGPT) and translation software (DeepL) were used to refine the language and translate the manuscript. The authors critically reviewed and approved all content.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eN\u0026uuml;bling R, B\u0026auml;r T, Jeschke K, Ochs M, Sarubin N, Schmidt J. Versorgung psychisch kranker Erwachsener in Deutschland: Bedarf und Inanspruchnahme sowie Effektivit\u0026auml;t und Effizienz von Psychotherapie. 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[65] p. 6 p. 7 p. 8 p. 9 p. 70 Issue Table of Contents Scandinavian Journal of Statistics, Vol. 6, No. 2 (1979), pp. 49-96 Front Matter On the Efficiency of Estimates of a Spectral Density [pp. 49-56] Infinite Divisibility in Theory and Practice [with Discussion and Reply] [pp. 57-64] A Simple Sequentially Rejective Multiple Test Procedure [pp. 65-70] A Statistical Analysis of the Mating Behaviour of Euchaeta norvegica (Copepoda: Calanoida) [pp. 71-76] A Note on the Behaviour of Occurrence/Exposure Rates When the Survival Distribution Is Not Exponential [pp. 77-80] Small Sample Tests against an Ordered Set of Binomial Probabilities [pp. 81-85] A Note on the Applicability of Sequence Compound Decision Schemes [pp. 86-88] Extension of Some Results by Reiers\u0026oslash;l to Multivariate Models [pp. 89-91] Brief Information on Current Unpublished Research in Scandinavia [pp. 92-96] Back Matter.\u003c/li\u003e\n\u003cli\u003eCohen J. 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Feasibility of a Digital Coaching Program for Improving Mental Well-Being and Emotional Intelligence: Pragmatic Retrospective Cohort Study. JMIR Formative Research. 2025;9(1):e71828.\u003c/li\u003e\n\u003cli\u003eBuntrock C. Cost-effectiveness of digital interventions for mental health: current evidence, common misconceptions, and future directions. Frontiers in Digital Health. 2024;6:1486728.\u003c/li\u003e\n\u003cli\u003eJagayat JK, Kumar A, Shao Y, Pannu A, Patel C, Shirazi A, et al. Incorporating a stepped care approach into internet-based cognitive behavioral therapy for depression: randomized controlled trial. JMIR mental health. 2024;11(1):e51704.\u003c/li\u003e\n\u003cli\u003eKolaas K, Berman AH, Hedman-Lagerl\u0026ouml;f E, Linds\u0026auml;ter E, Hybelius J, Axelsson E. Internet-delivered transdiagnostic psychological treatments for individuals with depression, anxiety or both: a systematic review with meta-analysis of randomised controlled trials. BMJ open. 2024;14(4):e075796.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"coaching, cognitive behavioral therapy, digital mental health, prevention, self-determination theory, usability","lastPublishedDoi":"10.21203/rs.3.rs-8152998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8152998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMental illness is highly prevalent and associated with significant public health costs. Despite this, less than half of those affected receive treatment, often due to barriers such as stigma and accessibility. Internet-based interventions (IBIs) have shown promise in improving well-being and reducing psychological distress, yet many lack personalization and sufficient guidance. Addressing these limitations, beWell Tirol integrates cognitive behavioral content, individualized task selection, and on-demand coaching within a self-determined, app-based format, allowing users to autonomously select and adapt exercises according to their individual needs and preferences.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study evaluates the usability, acceptance, and effectiveness of a digital mental health intervention, \"beWell Tirol,\" a 4-month app-based program accompanied by a coach, targeting individuals experiencing high psychological distress.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 25 participants with above-average psychological distress completed the 4-month intervention. The program included a selection of cognitive behavioral therapy-based exercises, which were available via the app, as well as bi-weekly coaching sessions. Assessments were conducted at baseline, post-intervention, and three months post-intervention, measuring usability (System Usability Scale, SUS) and satisfaction (primary outcomes), as well as psychological distress, resilience, self-efficacy, stress coping strategies, emotion regulation, and quality of life (secondary outcomes). Statistical analyses included repeated measures ANOVA and t-tests.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe intervention demonstrated excellent usability (SUS score: M\u0026thinsp;=\u0026thinsp;91.8, SD\u0026thinsp;=\u0026thinsp;7.5) and high satisfaction. Significant improvements were observed in resilience, self-efficacy, psychological distress (Perceived Stress Scale, Brief Symptom Inventory), emotion regulation, and multiple dimensions of quality of life (p\u0026thinsp;\u0026lt;\u0026thinsp;.05). These improvements were partially sustained at follow-up.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe program was well accepted, demonstrated high usability, and was effective in improving mental well-being. The results support the feasibility and effectiveness of a coach-guided, self-determined digital intervention for promoting mental health. Future research should explore larger samples, including control groups.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eClinicalTrials.gov, NCT07300410, 09/12/2025, Retrospectively registered\u003c/p\u003e","manuscriptTitle":"Usability, Acceptance, and Effectiveness of a Coach-Guided, App-Based Mental Health Intervention: The beWell Tirol Pilot Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 09:12:39","doi":"10.21203/rs.3.rs-8152998/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-01-08T07:44:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-30T10:38:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-24T08:17:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-23T12:21:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-23T12:10:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3dbde55c-1470-4bd8-8f38-b0ee5934c695","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T09:12:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 09:12:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8152998","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8152998","identity":"rs-8152998","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}