Toward robust and sustainable digital mental health: Real-world validation of an AI-enabled vibro-acoustic smartphone application for stress management

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While many smartphone applications for stress management exist, most rely solely on self-reported outcomes and lack rigorous validation. We conducted a three-phase real-world study of an AI-enabled vibro-acoustic smartphone application. Phase 1 assessed subjective outcomes (depression, anxiety, stress, insomnia, mood, fatigue) over one month; Phase 2 measured salivary cortisol as an objective biomarker; and Phase 3 gathered user feedback (n = 1,487) after one week. Participants in Phase 1 were randomized in a 2:1 ratio (application: control) to maximize power in the intervention group, and salivary cortisol was collected once upon awakening to minimize diurnal variation, though this approach has acknowledged limitations. Results indicated improvements in depression, anxiety, insomnia, and mood in the application group, particularly among participants with high fatigue, as well as a significant reduction in cortisol levels. User feedback further suggested positive effects on relaxation, sleep, and willingness to continue use. These findings suggest that vibro-acoustic smartphone applications may contribute to stress reduction and psychological well-being in real-world settings. However, conclusions should be drawn cautiously given the modest sample sizes, reliance on a single cortisol measurement, and short-term follow-up. Larger and longer-term studies with multi-day biomarker protocols are needed to establish robustness and generalizability, but the present results highlight the potential of rigorously evaluated digital tools as complementary approaches in mental health care. Health sciences/Biomarkers Health sciences/Health care Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology digital mental health smartphone application AI stress management psychiatric support cortisol Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Mental illness is a highly prevalent and complex public health issue. The total number of people with any mental health disorder reached 792 million in 2017 [ 1 ]. Moreover, according to the World Health Organization, the number of people with common mental disorders (CMD), such as mild to severe depression and anxiety, is globally increasing over time [ 1 ]. The burden of these conditions highlights the urgent need for scalable, accessible, and cost-effective mental health interventions. In this context, mobile health (mHealth) applications have rapidly emerged as potential tools for promoting mental well-being and reducing symptoms of depression and anxiety. However, evidence on their efficacy remains mixed. For example, while many digital interventions show promise, evidence on the efficacy of mHealth applications in reducing depressive symptoms in pregnant and postpartum women remains inconclusive [ 2 ]. This reflects a broader challenge in digital mental health research: although smartphone applications are widely available and heavily promoted, the evaluation of their clinical effectiveness and the establishment of robust validation frameworks are still at an early stage. Previous studies have explored various types of digital mental health apps, including the Foundations app for well-being [ 3 ], the Flourishing App targeting workplace women [ 4 ], app-based mindfulness programs [ 5 ], and commercial programs such as Meru Health [ 6 ]. More recently, a randomized controlled trial demonstrated that a digital, data-driven intervention significantly reduced depressive and generalized anxiety symptoms in adults [ 7 ]. Other approaches include mindfulness-based stress reduction (MBSR) [ 8 ] programs incorporating subjective and limited physiological indicators and data-driven cognitive behavioral therapy (CBT)-based digital therapeutics [ 9 ]. Although these interventions demonstrate some improvements in subjective outcomes—such as stress, anxiety, or sleep quality—most remain limited to self-reported measures, with little integration of objective biomarkers or large-scale real-world validation. The potential of mobile health technologies to support psychiatric practice has been widely recognized [ 10 ]. However, questions remain regarding which applications are clinically useful and how their efficacy should be evaluated [ 11 ]. While biofeedback-based smartphone applications have shown promise in reducing stress [ 12 ], few have integrated both subjective assessments and objective biomarkers in real-world conditions. The primary objective of this study was to evaluate the usefulness of an AI-enabled vibro-acoustic smartphone application originally developed to support daily well-being. Although the application was designed as a general wellness tool, its core functions—including the estimation of psychological symptoms such as depression, anxiety, and fatigue, and the provision of tailored mood-improvement programs based on these estimations—suggest potential applications in supporting psychiatric care. Specifically, we aimed to test whether the application could provide evidence-based benefits that extend beyond temporary relaxation, demonstrating its value as a self-monitoring and self-care tool for mental health. If proven effective, such an application could enable individuals to perform regular self-checks of their mental state, engage in personalized self-care interventions, and, when necessary, be guided more smoothly to professional psychiatric services. In this way, the application could function as both a preventive strategy for maintaining well-being and a supportive pathway to clinical care. The primary objective of this study was to evaluate the usefulness of an AI-enabled vibro-acoustic smartphone application originally developed to support daily well-being. Although the application was designed as a general wellness tool, its core functions—including the estimation of psychological symptoms such as depression, anxiety, and fatigue, and the provision of tailored mood-improvement programs based on these estimations—suggest potential applications in supporting psychiatric care. Specifically, we aimed to test whether the application could provide evidence-based benefits that extend beyond temporary relaxation, demonstrating its value as a self-monitoring and self-care tool for mental health. If proven effective, such an application could enable individuals to perform regular self-checks of their mental state, engage in personalized self-care interventions, and, when necessary, be guided more smoothly to professional psychiatric services. In this way, the application could function as both a preventive strategy for maintaining well-being and a supportive pathway to clinical care. Based on previous research demonstrating the potential of biofeedback and vibro-acoustic stimulation for stress reduction [ 12 , 13 ], we formulated the following hypotheses: 1. Subjective outcomes (Phase 1): After one month of application use, participants in the application group would show significant reductions in depression, anxiety, stress, and fatigue, as well as improvements in insomnia and mood scores, compared to the control group. 2. Objective outcomes (Phase 2): After one month of application use, participants in the application group would exhibit a significant reduction in salivary cortisol levels compared to baseline, whereas no such change would be observed in the control group. 3. User perception and retention (Phase 3): After one week of application use, a majority of participants would report perceived relaxation benefits, improvements in sleep, and a positive intention to continue using the application. Together, these hypotheses reflect the assumption that the application would demonstrate stability of outcomes over time, robustness across different measurement methods (subjective scales and biological markers), and sustainability through favorable user acceptance in real-world conditions. Figure 1 summarizes these previous studies and highlights how most digital mental health applications are limited to subjective assessments, underscoring the novelty of our study in combining subjective and objective measures. Methods Application Design The AI-enabled vibro-acoustic smartphone application used in this study has been described in detail previously [ 14 ]. Figure 2 illustrates the application design, which analyzes facial color changes in the cheek area from facial videos to estimate heart rate. This method employs photoplethysmography (PPG), capturing subtle skin color changes caused by blood flow, and has been validated in previous studies [ 15 , 16 ]. Based on this physiological data and brief self-perception questions about facial appearance, the system delivers individualized content comprising synchronized vibration and music, with tempo gradually decreasing from the user’s heart rate to 50 beats per minute. When indicated, voice guidance modules (e.g., mindfulness, self-compassion, or poetic voice reading) are integrated. Stress reduction intervention Based on the psychological stress level, a personalized stress reduction strategy is implemented, centered on tactile and auditory tempo modulation through two primary mechanisms: Tactile Tempo Change (Vibration) The vibration starts at the same tempo as the user's heart rate and gradually slows to 50 bpm, inducing relaxation through slow rhythmic modulation. Auditory Tempo Change (Music) When the vibration tempo drops to 60 bpm, the music begins and its tempo gradually slows to synchronize with the ongoing vibration. The type of music is adapted to the user's stress level:Levels 8–10 (low stress): Calm ambient music. Levels 4–7: Ambient music with subtle natural sounds (waves, wind, river, rain, birdsong). Levels 1–3 (high stress): A mindfulness voice guide is added to the music to actively promote relaxation. This intervention is supported by scientific findings demonstrating that gradually decreasing music tempo enhances parasympathetic nervous system activity, reduces cortisol levels, and lowers subjective stress ratings [ 17 ]. Additionally, music has been shown to facilitate stress recovery by promoting physiological relaxation and reducing stress-related biomarkers [ 18 ]. Furthermore, tactile vibration stimulation has been reported to regulate autonomic nervous system responses and contribute to stress reduction [ 19 ]. A preliminary study has confirmed the effectiveness of combining these techniques [ 20 ]. Supplementary Voice Guidance While vibration and music form the core intervention, voice guidance serves as a supplementary feature, particularly for users experiencing severe psychological distress (Levels 1–3). Depending on the user's condition, one of seven mindfulness guides is activated and seamlessly integrated with the music. This voice guidance, featuring relaxation exercises, breathing techniques, and present-moment awareness, provides additional support for emotional regulation and relaxation. While not a primary component of the intervention, it enhances user engagement by fostering connection and trust through human voice interaction. Psychological Assessments To evaluate subjective psychological outcomes, we used validated self-report questionnaires widely applied in Japanese populations: Depression Anxiety Stress Scales–21 (DASS-21): A brief measure assessing depression, anxiety, and stress, validated in both clinical and community samples [21]. Athens Insomnia Scale (AIS): An 8-item tool for quantifying insomnia symptoms. The Japanese version has shown good reliability and validity [22,23]. Positive and Negative Affect Schedule (PANAS): Measures mood in terms of positive and negative affect. The Japanese version demonstrated comparable factor structure and validity [24,25]. Chalder Fatigue Scale (CFS): Widely used for assessing fatigue. The Japanese version was validated among university students [26,27]. A cut-off score of ≥15 was used to identify participants with severe fatigue, based on population norms [28,29]. Salivary Cortisol Analysis In addition to psychological measures, salivary cortisol was collected as an objective biomarker of stress. Rationale: Cortisol is a well-established index of hypothalamic–pituitary–adrenal (HPA) axis activity and stress response [ 30 ]. Sampling procedure: Participants were instructed to refrain from eating or drinking (except water) after 9 PM and to collect saliva samples immediately after awakening to minimize diurnal variation. Storage and analysis: Samples were frozen at − 20°C and transported to the laboratory for analysis. Cortisol concentrations were determined using enzyme-linked immunosorbent assay (ELISA), following standardized protocols for salivary biomarker assessment [ 30 , 31 ]. Japanese context: Prior work has validated the use of salivary cortisol for stress research in Japanese populations [ 31 ]. Limitations: In line with expert consensus guidelines [ 30 ], the cortisol awakening response (CAR) measured across multiple consecutive days is recommended as the gold standard for evaluating HPA axis reactivity. However, due to practical and logistical constraints, this study assessed cortisol only once upon awakening. While this single-sample approach has been applied in prior stress research [ 31 ], it provides a limited picture of HPA axis dynamics. Future studies should therefore adopt multi-day CAR protocols to increase robustness and generalizability. Study Design This prospective study, conducted in Tokyo, Japan, from 2021 to 2023, enrolled Japanese men and women aged 20–60 years. Participants were recruited through a testing company’s database of registered members, enabling the efficient recruitment of individuals with diverse characteristics. Participants with serious cardiovascular, hepatic, renal, respiratory, endocrine, or metabolic disorders; those with a medical history of such conditions; and those taking medications that could affect the autonomic nervous system were excluded, as were patients deemed unsuitable by the investigator. The study was approved by the Committee for the Protection of Human Subjects (CPHS) of POLA Chemical Industries, Inc., Kanagawa, Japan (Approval # 2021F78, 2021F83, and 2022F7) and was conducted according to the Ethical Guidelines for Medical and Health Research Involving Human Subjects (Japan’s Ministry of Education, Culture, Sports, Science, and Technology and Ministry of Health, Labour and Welfare, 2014, revised in 2017) and the Declaration of Helsinki (revised in 2013). All participants received a detailed explanation of the study’s objectives and procedures and provided written informed consent. This study was divided into three phases, each focusing on a different aspect of the application’s effectiveness in stress management (Table 1 ). The study used a combination of subjective surveys and biological analyses to provide a holistic evaluation of the application’s impact on stress across multiple methods and timeframes [ 32 – 34 ]. Table 1 Study design and participant characteristics across the three phases. Phase Study type Outcomes / Scales Duration of application use Participants (n) Sex distribution Mean age ± SD (years) 1 Subjective survey DASS-21 (Depression, Anxiety, Stress); AIS; PANAS (Positive/Negative Affect); CFS 1 month 76 Female only 30.2 ± 3.5 2 Objective survey Salivary cortisol 1 month 93 Male + Female 44.5 ± 9.7 3 Questionnaire survey Relaxation, Sleep, Intention to continue use 1 week 1487 862 Male, 625 Female 39.5 ± 10.6 Phase 1 evaluated subjective psychological outcomes using validated self-report scales, including the Depression, Anxiety, and Stress Scales–21 (DASS-21), Athens Insomnia Scale (AIS), Positive and Negative Affect Schedule (PANAS), and Chalder Fatigue Scale (CFS). Phase 2 assessed objective physiological stress using salivary cortisol as a biomarker under standardized sampling conditions. Phase 3 investigated user acceptability and feasibility through a large-scale questionnaire survey. This multi-phase approach provides complementary subjective, physiological, and usability data, ensuring stability and robustness of the findings, and supporting the sustainability of real-world application. Values represent the number of participants, sex distribution, and mean age ± standard deviation (SD) for each phase. [Insert Table 1 here] Phase 1: Subjective Stress Improvement Phase 1 focused on measuring subjective stress and mood improvement using seven validated scales: Depression, Anxiety, Stress, Insomnia, Positive Mood, Negative Mood, and Fatigue. The research question aimed to determine whether the application would reduce subjective stress, improve mood, and alleviate insomnia within one month. It was hypothesized that the application group would show significant reductions in depression, anxiety, stress, and fatigue as well as improvements in mood and insomnia scores, compared to the control group. The independent variable was the use of the application (yes/no; application group vs. control group), while the dependent variables were scores on validated scales such as the DASS-21 for psychological stress [ 21 ], AIS for sleep quality [ 22 , 23 ], PANAS for mood [ 24 , 25 ], and CFS for energy levels [ 26 , 27 ]. Seventy-six female participants, aged 25–35 years, were randomized in a 2:1 ratio (application: control). This unequal allocation was adopted to maximize the statistical power for detecting changes in the application group while still maintaining a sufficient control group for comparison, given the practical constraints of recruitment and resources. Unequal randomization ratios have been frequently employed in early-stage intervention studies where the primary focus is on characterizing effects within the intervention group [ 7 ]. Participants completed surveys before and after the intervention, providing data on psychological stress, sleep quality, mood, and energy levels. A subgroup with high fatigue (initial CFS score ≥ 15) was identified and classified as the high-fatigue group. A previous study published in PLOS ONE [ 35 ] reported that the mean CFS score of doctors following an overnight on-call duty was 18.4 (SD = 6.6), indicating a significant increase in fatigue levels due to work-related stress. Based on this, we set the threshold at 15 in our study, considering the standard deviation and the range at which fatigue becomes more pronounced. Furthermore, Cella and Chalder [ 36 ] reported that the mean CFS score in the general population is 14.2 (SD = 4.6). Since a score of 15 or higher exceeds the normal fatigue range, this threshold provides a reasonable basis for identifying participants with substantial fatigue in our analysis. Phase 2: Objective Stress Improvement Phase 2 assessed objective biological markers of stress, particularly salivary cortisol levels. Cortisol was chosen for this study because it is a well-established and widely recognized biomarker for stress, providing a robust foundation for evaluating the effectiveness of the application. The research question sought to determine whether the application would reduce salivary cortisol levels after one month of use. It was hypothesized that the application group would show a significant reduction in salivary cortisol levels compared to baseline. The independent variable was the use of the application (yes/no; application group vs. control group), whereas the dependent variable was the change in salivary cortisol levels. Ninety-three male and female participants, aged 30–59 years, were assessed for changes in cortisol levels. Saliva samples were collected immediately after waking up to measure morning cortisol levels and to minimize diurnal variations. Participants were instructed to follow specific procedures, including avoiding excessive exercise, finishing eating by 9 PM, and refraining from food and drink (except water) until sample collection. Saliva samples were frozen and transported for analysis by Macromill Inc. Although significant improvements were observed in the application group, it should be noted that these findings are based on a single morning cortisol measurement. As highlighted by methodological guidelines [ 30 ], single-point assessments may not capture day-to-day variability in HPA axis reactivity. Accordingly, future research should implement multi-day sampling of the cortisol awakening response to provide more comprehensive biomarker validation. Phase 3: User Perception and Retention Phase 3 evaluated user perceptions of the application’s effectiveness and the likelihood of continued use after one week. The research question aimed to determine whether users perceived the application as effective, and whether they intended to continue using it. It was hypothesized that the majority of users would report feeling relaxed and experiencing improved sleep, and they would express a positive intention to continue using the application. The independent variable was the use of the application for one week, and the dependent variables were responses to a questionnaire assessing relaxation, sleep improvement, and intention to continue using the app. In total. 1,487 participants (862 males and 625 females, aged 20–59 years) completed the questionnaire after using the application for one week. The survey included questions about whether the participants felt relaxed in their minds and bodies, found it easier to fall asleep, and intended to continue using the application. The collected data provided insights into immediate user satisfaction and the potential for long-term integration of the application into daily life. Participant Selection and Study Size Justification Participants in Phases 1 and 2 were pre-screened using a 5-point questionnaire to exclude those with mild stress levels, with those scoring between 2 and 5 qualifying for inclusion. In Phase 3, no pre-screening was conducted, as it aimed to assess general user acceptance and intention to continue using the app, regardless of initial stress levels. In all phases, participants downloaded and used the application independently in their everyday environments for one month (Phases 1 and 2) or one week (Phase 3). The sample size for each phase was determined based on available resources, while ensuring statistical validity. Phases 1 and 2 were designed as smaller-scale experimental studies to assess the psychological and biological effects of the application in detail, whereas Phase 3 employed a large-scale survey to evaluate broader user experiences and acceptance. Statistical Analysis Data were summarized as mean and standard errors (SE). The pre- and post-application values of the stress parameters were compared using the Wilcoxon signed-rank sum tests. This non-parametric test was chosen because several outcome measures did not meet the assumptions of normality and because the sample sizes in Phases 1 and 2 were relatively small, making Wilcoxon more robust than parametric alternatives under these conditions [ 18 ]. The Bonferroni correction was applied for multiple testing corrections wherever applicable. Missing values were minimal (< 5%) and were handled using listwise deletion. Given the small proportion and the absence of systematic patterns of missingness, this approach was considered acceptable and unlikely to bias the results [ 18 ]. Statistical analysis was performed using JMP® 17 (SAS Institute Japan) with a significance level set at p < 0.05. Results Participants Table 1 presents the participants’ general characteristics. The average age of the participants was 30.2 ± 3.5 years in Phase 1, 44.5 ± 9.7 years in Phase 2, and 39.5 ± 10.6 years in Phase 3. Phase 1: Subjective Stress Improvement After one month, subjective stress values remained unchanged in the control group, whereas the application-using group exhibited significant improvements in depression, anxiety, stress, insomnia, and positive/negative mood scales. No change was observed in the Fatigue Scale in either group; however, analyses of the highly fatigued subgroup revealed a significant improvement only in the application-using group (Fig. 2 ). For detailed numerical values, see Supplementary Table 1 (subjective outcomes). Phase 2: Objective Stress Improvement After one month, the application group exhibited significant improvements in salivary cortisol levels, but the control group values remained unchanged (Fig. 3 ). For detailed numerical values, see Supplementary Table 2 (objective outcomes). Phase 3: User Perception and Retention After one week of application use, 67% of the participants reported that their therapy was effective in relaxing both the body and mind, while 46% felt that it had contributed to improved sleep. Additionally, 73% of the participants expressed a positive intention to continue with therapy, indicating a desire for either daily or occasional use (Fig. 4 ). Discussion In this study, we evaluated a vibro-acoustic smartphone application using a three-phase design. The application was associated with improvements in depressive, anxiety, stress, insomnia, and mood outcomes (Phase 1), as well as reductions in salivary cortisol levels (Phase 2), and favorable user perceptions of relaxation, sleep, and continued use (Phase 3). Together, these findings suggest that the application may contribute to psychological well-being and stress reduction in real-world settings. Digital mental health applications are increasingly common, but relatively few have been validated through rigorous clinical or biomarker-based studies. Prior evidence has been inconsistent, with many interventions lacking standardized assessments or objective outcomes. The present study adds to the literature by combining validated self-report scales, a biological stress marker, and feasibility evaluation in a unified framework. This multi-layered approach provides encouraging initial evidence to support the potential role of digital interventions in psychiatry. The results are broadly consistent with the themes of this collection. Indicators of stability were observed through convergent improvements across subjective and objective outcomes. Robustness was partially supported by the multi-phase design, although the modest sample sizes and limited biomarker assessment warrant caution. Sustainability was suggested by user engagement and willingness to continue, highlighting the potential for long-term integration of such tools into daily life. However, these outcomes should be interpreted as preliminary and require replication in larger and more diverse samples. Importantly, while this study provides initial evidence, several limitations restrict the strength of conclusions. First, the use of a single morning salivary cortisol measurement limits the ability to capture daily variability in hypothalamic–pituitary–adrenal (HPA) axis activity. Future studies should implement multi-day cortisol awakening response (CAR) protocols, as recommended by expert consensus guidelines [ref]. Second, the relatively small sample sizes in Phases 1 and 2 reduce statistical power and generalizability. Third, the short duration of follow-up prevents conclusions regarding long-term sustainability. Finally, the study population was limited to Japanese participants, and further research is needed to examine cross-cultural generalizability. From a psychiatric perspective, these findings have potential implications. Patients are often exposed to unregulated wellness apps that lack evidence and may delay appropriate care. By contrast, the present application was examined across both psychological and biological outcomes, providing a rare example of an evidence-based digital tool that could support both self-care and clinical practice. As mental health needs continue to expand globally, rigorously evaluated digital applications represent not only a promising innovation but also a potential component of comprehensive psychiatric care. Conclusion From a psychiatrist’s standpoint, this study provides encouraging initial evidence that objectively validated digital tools can support clinical practice. The vibro-acoustic smartphone application was associated with improvements in psychological symptoms, reductions in salivary cortisol, and favorable user acceptance. These findings suggest that digital interventions of this kind may offer a promising approach to stress management and self-care in real-world conditions. Nevertheless, the conclusions should be interpreted with caution given the methodological limitations, including modest sample sizes, a single morning cortisol assessment, and relatively short follow-up. Future research employing larger, more diverse cohorts, multi-day biomarker protocols, and extended observation periods will be essential to confirm the reliability and generalizability of these results. Taken together, the present study highlights the potential role of rigorously evaluated digital mental health applications as complementary tools for prevention, self-monitoring, and clinical support. Abbreviations standard errors (SE) heart rate variability (HRV) galvanic skin response (GSR) respiratory sinus arrhythmia (RSA) whole-body vibration (WBV) beats per minute (bpm) photoplethysmography (PPG) Declarations Ethics approval and consent to participate This study was approved by the Committee for the Protection of Human Subjects (CPHS), affiliated with POLA Chemical Industries, Inc. (Kanagawa, Japan), which served as the internal ethics review board for this research (Approval Nos. 2021F78, 2021F83, and 2022F7). This study was conducted according to the Ethical Guidelines for Medical and Health Research Involving Human Subjects (Ministry of Education, Culture, Sports, Science, and Technology and Ministry of Health, Labour and Welfare, 2014, revised in 2017) and the Declaration of Helsinki (revised in 2013). Subjects received a sufficient briefing of the objective and content of the present study and signed a written informed consent form. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available for reasons of sensitivity but are available from the corresponding author on reasonable request. Competing interests T.M. and T.K. are employees of the POLA Chemical Industries, which partially funded this study. S.S. and J.O. have no conflicts of interest. Funding This research was supported by grants from the FemTech support service demonstration project of the Ministry of Economy, Trade, and Industry of Japan, the Innovative Research Program on Suicide Countermeasures (Grant no.: JPSCIRS20220301), and the POLA Chemical Industries. Authors’ contributions T.M.: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing. T.K.: Formal Analysis, Investigation, Methodology, Validation, Writing – review and editing. S.S.: Investigation, Writing – review and editing. J.O.: Funding Acquisition, Investigation, Supervision, Writing – review and editing. 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Effectiveness of digital mental health interventions: a systematic review and meta-analysis. Lancet Digit. Health 6, e123–e135 (2024). https://doi.org/10.1016/S2589-7500(23)00256-9 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7644071","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":525785382,"identity":"aa93ade4-102b-498e-9d1d-31149642d049","order_by":0,"name":"Tomomi Kato","email":"","orcid":"","institution":"POLA Chemical Industries, Inc","correspondingAuthor":false,"prefix":"","firstName":"Tomomi","middleName":"","lastName":"Kato","suffix":""},{"id":525785383,"identity":"da25ccd3-edbe-4f51-a8e1-3cd9afa2367d","order_by":1,"name":"Tomonori Motokawa","email":"","orcid":"","institution":"POLA Chemical Industries, 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1","display":"","copyAsset":false,"role":"figure","size":652641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of previous digital mental health applications.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis figure summarizes previous studies of digital mental health applications. Most rely mainly on subjective self-reports such as the WHO-Five Well-being Index (WHO-5), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9) to evaluate psychological states, while only a few incorporate physiological indicators. The present study uniquely integrates both subjective and objective measures, underscoring its novelty.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7644071/v1/921f66e036241bd91db08eea.png"},{"id":93797719,"identity":"5abe8c60-3f93-4cbc-be75-e6f4408738e1","added_by":"auto","created_at":"2025-10-17 16:03:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":880657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eApplication Workflow.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFacial video input (Photoplethysmography, PPG analysis) was used to estimate the heart rate, particularly focusing on changes in cheek color. Brief self-perception questions about facial appearance were administered to complement the estimation of psychological states. Based on these data, a tailored mood-regulation program was selected from over 200 options. The intervention emphasized heart rate information: vibration and music started at the user’s heart rate and gradually slowed to 50 bpm. When indicated, audio guidance such as mindfulness, self-compassion, or poetic voice reading was integrated.\u003c/p\u003e","description":"","filename":"Figure2workflow.png","url":"https://assets-eu.researchsquare.com/files/rs-7644071/v1/6bfa964e3bdffc2b427381a4.png"},{"id":93796972,"identity":"8af5627f-c821-4c01-925c-5f82ce816d0b","added_by":"auto","created_at":"2025-10-17 15:55:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":258387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects on stress assessed by subjective surveys (Phase 1).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChanges in validated scales (DASS-21, Athens Insomnia Scale, PANAS, and Chalder Fatigue Scale) before and after one month of application use, compared with the control group. Significant improvements in depression, anxiety, insomnia, and mood were observed only in the application group, with additional benefits for highly fatigued participants.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7644071/v1/73d8ebeb2d038e31400f9d32.png"},{"id":93796969,"identity":"14c7c4c2-82f2-4239-9e87-b3b588ea21b1","added_by":"auto","created_at":"2025-10-17 15:55:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94915,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects on stress assessed by salivary cortisol (Phase 2).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoxplots represent the distribution of salivary cortisol concentrations, indicating mean, median, maximum, 3rd quartile, 1st quartile, and minimum. The accompanying table provides the descriptive statistics corresponding to the plotted data. Saliva samples were collected immediately after waking under standardized conditions, and cortisol significantly decreased in the application group but remained unchanged in the control group.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7644071/v1/ed43428be3740fa80558b93f.png"},{"id":93796977,"identity":"27f88799-ef85-44fa-9abd-6ca7315d34e6","added_by":"auto","created_at":"2025-10-17 15:55:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":142069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVerification of feasibility of social retention (Phase 3).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants’ perceptions of the application after one week of use. (a) 67% reported relaxation of mind and body, (b) 46% reported improved sleep, and (c) 73% expressed an intention to continue using the application.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7644071/v1/7723479211bc13fe69dae57e.png"},{"id":106960069,"identity":"159eecdd-384c-4aa2-915b-f5be6dc5ce4b","added_by":"auto","created_at":"2026-04-15 09:18:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3074515,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7644071/v1/0b8018ac-1810-4441-82d2-c8778168f389.pdf"},{"id":93796968,"identity":"8949544f-1f44-40d2-ad39-11e12ac243f5","added_by":"auto","created_at":"2025-10-17 15:55:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17773,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7644071/v1/f26584f661dedae0a4f50d2b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eToward robust and sustainable digital mental health: Real-world validation of an AI-enabled vibro-acoustic smartphone application for stress management\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eMental illness is a highly prevalent and complex public health issue. The total number of people with any mental health disorder reached 792\u0026nbsp;million in 2017 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Moreover, according to the World Health Organization, the number of people with common mental disorders (CMD), such as mild to severe depression and anxiety, is globally increasing over time [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The burden of these conditions highlights the urgent need for scalable, accessible, and cost-effective mental health interventions.\u003c/p\u003e\u003cp\u003eIn this context, mobile health (mHealth) applications have rapidly emerged as potential tools for promoting mental well-being and reducing symptoms of depression and anxiety. However, evidence on their efficacy remains mixed. For example, while many digital interventions show promise, evidence on the efficacy of mHealth applications in reducing depressive symptoms in pregnant and postpartum women remains inconclusive [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This reflects a broader challenge in digital mental health research: although smartphone applications are widely available and heavily promoted, the evaluation of their clinical effectiveness and the establishment of robust validation frameworks are still at an early stage.\u003c/p\u003e\u003cp\u003ePrevious studies have explored various types of digital mental health apps, including the Foundations app for well-being [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the Flourishing App targeting workplace women [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], app-based mindfulness programs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and commercial programs such as Meru Health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. More recently, a randomized controlled trial demonstrated that a digital, data-driven intervention significantly reduced depressive and generalized anxiety symptoms in adults [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Other approaches include mindfulness-based stress reduction (MBSR) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] programs incorporating subjective and limited physiological indicators and data-driven cognitive behavioral therapy (CBT)-based digital therapeutics [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although these interventions demonstrate some improvements in subjective outcomes\u0026mdash;such as stress, anxiety, or sleep quality\u0026mdash;most remain limited to self-reported measures, with little integration of objective biomarkers or large-scale real-world validation.\u003c/p\u003e\u003cp\u003eThe potential of mobile health technologies to support psychiatric practice has been widely recognized [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, questions remain regarding which applications are clinically useful and how their efficacy should be evaluated [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While biofeedback-based smartphone applications have shown promise in reducing stress [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], few have integrated both subjective assessments and objective biomarkers in real-world conditions.\u003c/p\u003e\u003cp\u003eThe primary objective of this study was to evaluate the usefulness of an AI-enabled vibro-acoustic smartphone application originally developed to support daily well-being. Although the application was designed as a general wellness tool, its core functions\u0026mdash;including the estimation of psychological symptoms such as depression, anxiety, and fatigue, and the provision of tailored mood-improvement programs based on these estimations\u0026mdash;suggest potential applications in supporting psychiatric care.\u003c/p\u003e\u003cp\u003eSpecifically, we aimed to test whether the application could provide evidence-based benefits that extend beyond temporary relaxation, demonstrating its value as a self-monitoring and self-care tool for mental health. If proven effective, such an application could enable individuals to perform regular self-checks of their mental state, engage in personalized self-care interventions, and, when necessary, be guided more smoothly to professional psychiatric services. In this way, the application could function as both a preventive strategy for maintaining well-being and a supportive pathway to clinical care.\u003c/p\u003e\u003cp\u003eThe primary objective of this study was to evaluate the usefulness of an AI-enabled vibro-acoustic smartphone application originally developed to support daily well-being. Although the application was designed as a general wellness tool, its core functions\u0026mdash;including the estimation of psychological symptoms such as depression, anxiety, and fatigue, and the provision of tailored mood-improvement programs based on these estimations\u0026mdash;suggest potential applications in supporting psychiatric care.\u003c/p\u003e\u003cp\u003eSpecifically, we aimed to test whether the application could provide evidence-based benefits that extend beyond temporary relaxation, demonstrating its value as a self-monitoring and self-care tool for mental health. If proven effective, such an application could enable individuals to perform regular self-checks of their mental state, engage in personalized self-care interventions, and, when necessary, be guided more smoothly to professional psychiatric services. In this way, the application could function as both a preventive strategy for maintaining well-being and a supportive pathway to clinical care.\u003c/p\u003e\u003cp\u003eBased on previous research demonstrating the potential of biofeedback and vibro-acoustic stimulation for stress reduction [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], we formulated the following hypotheses:\u003c/p\u003e\u003cp\u003e1. Subjective outcomes (Phase 1):\u003c/p\u003e\u003cp\u003eAfter one month of application use, participants in the application group would show significant reductions in depression, anxiety, stress, and fatigue, as well as improvements in insomnia and mood scores, compared to the control group.\u003c/p\u003e\u003cp\u003e2. Objective outcomes (Phase 2):\u003c/p\u003e\u003cp\u003e After one month of application use, participants in the application group would exhibit a significant reduction in salivary cortisol levels compared to baseline, whereas no such change would be observed in the control group.\u003c/p\u003e\u003cp\u003e3. User perception and retention (Phase 3): After one week of application use, a majority of participants would report perceived relaxation benefits, improvements in sleep, and a positive intention to continue using the application.\u003c/p\u003e\u003cp\u003eTogether, these hypotheses reflect the assumption that the application would demonstrate stability of outcomes over time, robustness across different measurement methods (subjective scales and biological markers), and sustainability through favorable user acceptance in real-world conditions.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes these previous studies and highlights how most digital mental health applications are limited to subjective assessments, underscoring the novelty of our study in combining subjective and objective measures.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eApplication Design\u003c/h2\u003e\u003cp\u003eThe AI-enabled vibro-acoustic smartphone application used in this study has been described in detail previously [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the application design, which analyzes facial color changes in the cheek area from facial videos to estimate heart rate. This method employs photoplethysmography (PPG), capturing subtle skin color changes caused by blood flow, and has been validated in previous studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Based on this physiological data and brief self-perception questions about facial appearance, the system delivers individualized content comprising synchronized vibration and music, with tempo gradually decreasing from the user\u0026rsquo;s heart rate to 50 beats per minute. When indicated, voice guidance modules (e.g., mindfulness, self-compassion, or poetic voice reading) are integrated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStress reduction intervention\u003c/h3\u003e\n\u003cp\u003eBased on the psychological stress level, a personalized stress reduction strategy is implemented, centered on tactile and auditory tempo modulation through two primary mechanisms:\u003c/p\u003e\n\u003ch3\u003eTactile Tempo Change (Vibration)\u003c/h3\u003e\n\u003cp\u003eThe vibration starts at the same tempo as the user's heart rate and gradually slows to 50 bpm, inducing relaxation through slow rhythmic modulation.\u003c/p\u003e\n\u003ch3\u003eAuditory Tempo Change (Music)\u003c/h3\u003e\n\u003cp\u003eWhen the vibration tempo drops to 60 bpm, the music begins and its tempo gradually slows to synchronize with the ongoing vibration. The type of music is adapted to the user's stress level:Levels 8\u0026ndash;10 (low stress): Calm ambient music.\u003c/p\u003e\u003cp\u003eLevels 4\u0026ndash;7: Ambient music with subtle natural sounds (waves, wind, river, rain, birdsong).\u003c/p\u003e\u003cp\u003eLevels 1\u0026ndash;3 (high stress): A mindfulness voice guide is added to the music to actively promote relaxation.\u003c/p\u003e\u003cp\u003eThis intervention is supported by scientific findings demonstrating that gradually decreasing music tempo enhances parasympathetic nervous system activity, reduces cortisol levels, and lowers subjective stress ratings [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, music has been shown to facilitate stress recovery by promoting physiological relaxation and reducing stress-related biomarkers [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, tactile vibration stimulation has been reported to regulate autonomic nervous system responses and contribute to stress reduction [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A preliminary study has confirmed the effectiveness of combining these techniques [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eSupplementary Voice Guidance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhile vibration and music form the core intervention, voice guidance serves as a supplementary feature, particularly for users experiencing severe psychological distress (Levels 1\u0026ndash;3). Depending on the user\u0026apos;s condition, one of seven mindfulness guides is activated and seamlessly integrated with the music. This voice guidance, featuring relaxation exercises, breathing techniques, and present-moment awareness, provides additional support for emotional regulation and relaxation. While not a primary component of the intervention, it enhances user engagement by fostering connection and trust through human voice interaction.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cem\u003ePsychological Assessments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate subjective psychological outcomes, we used validated self-report questionnaires widely applied in Japanese populations:\u003c/p\u003e\n\u003cp\u003eDepression Anxiety Stress Scales\u0026ndash;21 (DASS-21): A brief measure assessing depression, anxiety, and stress, validated in both clinical and community samples [21].\u003c/p\u003e\n\u003cp\u003eAthens Insomnia Scale (AIS): An 8-item tool for quantifying insomnia symptoms. The Japanese version has shown good reliability and validity [22,23].\u003c/p\u003e\n\u003cp\u003ePositive and Negative Affect Schedule (PANAS): Measures mood in terms of positive and negative affect. The Japanese version demonstrated comparable factor structure and validity [24,25].\u003c/p\u003e\n\u003cp\u003eChalder Fatigue Scale (CFS): Widely used for assessing fatigue. The Japanese version was validated among university students [26,27]. A cut-off score of \u0026ge;15 was used to identify participants with severe fatigue, based on population norms [28,29].\u003c/p\u003e\n\u003ch3\u003eSalivary Cortisol Analysis\u003c/h3\u003e\n\u003cp\u003eIn addition to psychological measures, salivary cortisol was collected as an objective biomarker of stress.\u003c/p\u003e\u003cp\u003eRationale: Cortisol is a well-established index of hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis activity and stress response [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e Sampling procedure: Participants were instructed to refrain from eating or drinking (except water) after 9 PM and to collect saliva samples immediately after awakening to minimize diurnal variation.\u003c/p\u003e\u003cp\u003eStorage and analysis: Samples were frozen at \u0026minus;\u0026thinsp;20\u0026deg;C and transported to the laboratory for analysis. Cortisol concentrations were determined using enzyme-linked immunosorbent assay (ELISA), following standardized protocols for salivary biomarker assessment [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eJapanese context: Prior work has validated the use of salivary cortisol for stress research in Japanese populations [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLimitations: In line with expert consensus guidelines [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], the cortisol awakening response (CAR) measured across multiple consecutive days is recommended as the gold standard for evaluating HPA axis reactivity. However, due to practical and logistical constraints, this study assessed cortisol only once upon awakening. While this single-sample approach has been applied in prior stress research [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], it provides a limited picture of HPA axis dynamics. Future studies should therefore adopt multi-day CAR protocols to increase robustness and generalizability.\u003c/p\u003e\n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eThis prospective study, conducted in Tokyo, Japan, from 2021 to 2023, enrolled Japanese men and women aged 20\u0026ndash;60 years. Participants were recruited through a testing company\u0026rsquo;s database of registered members, enabling the efficient recruitment of individuals with diverse characteristics. Participants with serious cardiovascular, hepatic, renal, respiratory, endocrine, or metabolic disorders; those with a medical history of such conditions; and those taking medications that could affect the autonomic nervous system were excluded, as were patients deemed unsuitable by the investigator.\u003c/p\u003e\u003cp\u003e The study was approved by the Committee for the Protection of Human Subjects (CPHS) of POLA Chemical Industries, Inc., Kanagawa, Japan (Approval # 2021F78, 2021F83, and 2022F7) and was conducted according to the Ethical Guidelines for Medical and Health Research Involving Human Subjects (Japan\u0026rsquo;s Ministry of Education, Culture, Sports, Science, and Technology and Ministry of Health, Labour and Welfare, 2014, revised in 2017) and the Declaration of Helsinki (revised in 2013). All participants received a detailed explanation of the study\u0026rsquo;s objectives and procedures and provided written informed consent.\u003c/p\u003e\u003cp\u003eThis study was divided into three phases, each focusing on a different aspect of the application\u0026rsquo;s effectiveness in stress management (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study used a combination of subjective surveys and biological analyses to provide a holistic evaluation of the application\u0026rsquo;s impact on stress across multiple methods and timeframes [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\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\u003e Study design and participant characteristics across the three phases.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudy type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOutcomes / Scales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDuration of application use\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eParticipants (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSex distribution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (years)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubjective survey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDASS-21 (Depression, Anxiety, Stress); AIS; PANAS (Positive/Negative Affect); CFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFemale only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObjective survey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSalivary cortisol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMale\u0026thinsp;+\u0026thinsp;Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e44.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuestionnaire survey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRelaxation, Sleep, Intention to continue use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e862 Male, 625 Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e39.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003ePhase 1 evaluated subjective psychological outcomes using validated self-report scales, including the Depression, Anxiety, and Stress Scales\u0026ndash;21 (DASS-21), Athens Insomnia Scale (AIS), Positive and Negative Affect Schedule (PANAS), and Chalder Fatigue Scale (CFS).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003ePhase 2 assessed objective physiological stress using salivary cortisol as a biomarker under standardized sampling conditions.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003ePhase 3 investigated user acceptability and feasibility through a large-scale questionnaire survey.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eThis multi-phase approach provides complementary subjective, physiological, and usability data, ensuring stability and robustness of the findings, and supporting the sustainability of real-world application.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eValues represent the number of participants, sex distribution, and mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for each phase.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePhase 1: Subjective Stress Improvement\u003c/h2\u003e\u003cp\u003ePhase 1 focused on measuring subjective stress and mood improvement using seven validated scales: Depression, Anxiety, Stress, Insomnia, Positive Mood, Negative Mood, and Fatigue. The research question aimed to determine whether the application would reduce subjective stress, improve mood, and alleviate insomnia within one month. It was hypothesized that the application group would show significant reductions in depression, anxiety, stress, and fatigue as well as improvements in mood and insomnia scores, compared to the control group. The independent variable was the use of the application (yes/no; application group vs. control group), while the dependent variables were scores on validated scales such as the DASS-21 for psychological stress [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], AIS for sleep quality [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], PANAS for mood [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and CFS for energy levels [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeventy-six female participants, aged 25\u0026ndash;35 years, were randomized in a 2:1 ratio (application: control). This unequal allocation was adopted to maximize the statistical power for detecting changes in the application group while still maintaining a sufficient control group for comparison, given the practical constraints of recruitment and resources. Unequal randomization ratios have been frequently employed in early-stage intervention studies where the primary focus is on characterizing effects within the intervention group [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eParticipants completed surveys before and after the intervention, providing data on psychological stress, sleep quality, mood, and energy levels. A subgroup with high fatigue (initial CFS score\u0026thinsp;\u0026ge;\u0026thinsp;15) was identified and classified as the high-fatigue group. A previous study published in PLOS ONE [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] reported that the mean CFS score of doctors following an overnight on-call duty was 18.4 (SD\u0026thinsp;=\u0026thinsp;6.6), indicating a significant increase in fatigue levels due to work-related stress. Based on this, we set the threshold at 15 in our study, considering the standard deviation and the range at which fatigue becomes more pronounced. Furthermore, Cella and Chalder [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] reported that the mean CFS score in the general population is 14.2 (SD\u0026thinsp;=\u0026thinsp;4.6). Since a score of 15 or higher exceeds the normal fatigue range, this threshold provides a reasonable basis for identifying participants with substantial fatigue in our analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePhase 2: Objective Stress Improvement\u003c/h2\u003e\u003cp\u003ePhase 2 assessed objective biological markers of stress, particularly salivary cortisol levels. Cortisol was chosen for this study because it is a well-established and widely recognized biomarker for stress, providing a robust foundation for evaluating the effectiveness of the application. The research question sought to determine whether the application would reduce salivary cortisol levels after one month of use. It was hypothesized that the application group would show a significant reduction in salivary cortisol levels compared to baseline. The independent variable was the use of the application (yes/no; application group vs. control group), whereas the dependent variable was the change in salivary cortisol levels. Ninety-three male and female participants, aged 30\u0026ndash;59 years, were assessed for changes in cortisol levels. Saliva samples were collected immediately after waking up to measure morning cortisol levels and to minimize diurnal variations. Participants were instructed to follow specific procedures, including avoiding excessive exercise, finishing eating by 9 PM, and refraining from food and drink (except water) until sample collection. Saliva samples were frozen and transported for analysis by Macromill Inc.\u003c/p\u003e\u003cp\u003eAlthough significant improvements were observed in the application group, it should be noted that these findings are based on a single morning cortisol measurement. As highlighted by methodological guidelines [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], single-point assessments may not capture day-to-day variability in HPA axis reactivity. Accordingly, future research should implement multi-day sampling of the cortisol awakening response to provide more comprehensive biomarker validation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePhase 3: User Perception and Retention\u003c/h2\u003e\u003cp\u003ePhase 3 evaluated user perceptions of the application\u0026rsquo;s effectiveness and the likelihood of continued use after one week. The research question aimed to determine whether users perceived the application as effective, and whether they intended to continue using it. It was hypothesized that the majority of users would report feeling relaxed and experiencing improved sleep, and they would express a positive intention to continue using the application. The independent variable was the use of the application for one week, and the dependent variables were responses to a questionnaire assessing relaxation, sleep improvement, and intention to continue using the app. In total. 1,487 participants (862 males and 625 females, aged 20\u0026ndash;59 years) completed the questionnaire after using the application for one week. The survey included questions about whether the participants felt relaxed in their minds and bodies, found it easier to fall asleep, and intended to continue using the application. The collected data provided insights into immediate user satisfaction and the potential for long-term integration of the application into daily life.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eParticipant Selection and Study Size Justification\u003c/h2\u003e\u003cp\u003eParticipants in Phases 1 and 2 were pre-screened using a 5-point questionnaire to exclude those with mild stress levels, with those scoring between 2 and 5 qualifying for inclusion. In Phase 3, no pre-screening was conducted, as it aimed to assess general user acceptance and intention to continue using the app, regardless of initial stress levels. In all phases, participants downloaded and used the application independently in their everyday environments for one month (Phases 1 and 2) or one week (Phase 3). The sample size for each phase was determined based on available resources, while ensuring statistical validity. Phases 1 and 2 were designed as smaller-scale experimental studies to assess the psychological and biological effects of the application in detail, whereas Phase 3 employed a large-scale survey to evaluate broader user experiences and acceptance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData were summarized as mean and standard errors (SE). The pre- and post-application values of the stress parameters were compared using the Wilcoxon signed-rank sum tests. This non-parametric test was chosen because several outcome measures did not meet the assumptions of normality and because the sample sizes in Phases 1 and 2 were relatively small, making Wilcoxon more robust than parametric alternatives under these conditions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Bonferroni correction was applied for multiple testing corrections wherever applicable. Missing values were minimal (\u0026lt;\u0026thinsp;5%) and were handled using listwise deletion. Given the small proportion and the absence of systematic patterns of missingness, this approach was considered acceptable and unlikely to bias the results [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStatistical analysis was performed using JMP\u0026reg; 17 (SAS Institute Japan) with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the participants\u0026rsquo; general characteristics. The average age of the participants was 30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 years in Phase 1, 44.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7 years in Phase 2, and 39.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6 years in Phase 3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003ePhase 1: Subjective Stress Improvement\u003c/h2\u003e\u003cp\u003eAfter one month, subjective stress values remained unchanged in the control group, whereas the application-using group exhibited significant improvements in depression, anxiety, stress, insomnia, and positive/negative mood scales. No change was observed in the Fatigue Scale in either group; however, analyses of the highly fatigued subgroup revealed a significant improvement only in the application-using group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For detailed numerical values, see Supplementary Table\u0026nbsp;1 (subjective outcomes).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePhase 2: Objective Stress Improvement\u003c/h2\u003e\u003cp\u003eAfter one month, the application group exhibited significant improvements in salivary cortisol levels, but the control group values remained unchanged (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For detailed numerical values, see Supplementary Table\u0026nbsp;2 (objective outcomes).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003ePhase 3: User Perception and Retention\u003c/h2\u003e\u003cp\u003eAfter one week of application use, 67% of the participants reported that their therapy was effective in relaxing both the body and mind, while 46% felt that it had contributed to improved sleep. Additionally, 73% of the participants expressed a positive intention to continue with therapy, indicating a desire for either daily or occasional use (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we evaluated a vibro-acoustic smartphone application using a three-phase design. The application was associated with improvements in depressive, anxiety, stress, insomnia, and mood outcomes (Phase 1), as well as reductions in salivary cortisol levels (Phase 2), and favorable user perceptions of relaxation, sleep, and continued use (Phase 3). Together, these findings suggest that the application may contribute to psychological well-being and stress reduction in real-world settings.\u003c/p\u003e\u003cp\u003eDigital mental health applications are increasingly common, but relatively few have been validated through rigorous clinical or biomarker-based studies. Prior evidence has been inconsistent, with many interventions lacking standardized assessments or objective outcomes. The present study adds to the literature by combining validated self-report scales, a biological stress marker, and feasibility evaluation in a unified framework. This multi-layered approach provides encouraging initial evidence to support the potential role of digital interventions in psychiatry.\u003c/p\u003e\u003cp\u003eThe results are broadly consistent with the themes of this collection. Indicators of stability were observed through convergent improvements across subjective and objective outcomes. Robustness was partially supported by the multi-phase design, although the modest sample sizes and limited biomarker assessment warrant caution. Sustainability was suggested by user engagement and willingness to continue, highlighting the potential for long-term integration of such tools into daily life. However, these outcomes should be interpreted as preliminary and require replication in larger and more diverse samples.\u003c/p\u003e\u003cp\u003eImportantly, while this study provides initial evidence, several limitations restrict the strength of conclusions. First, the use of a single morning salivary cortisol measurement limits the ability to capture daily variability in hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis activity. Future studies should implement multi-day cortisol awakening response (CAR) protocols, as recommended by expert consensus guidelines [ref]. Second, the relatively small sample sizes in Phases 1 and 2 reduce statistical power and generalizability. Third, the short duration of follow-up prevents conclusions regarding long-term sustainability. Finally, the study population was limited to Japanese participants, and further research is needed to examine cross-cultural generalizability.\u003c/p\u003e\u003cp\u003eFrom a psychiatric perspective, these findings have potential implications. Patients are often exposed to unregulated wellness apps that lack evidence and may delay appropriate care. By contrast, the present application was examined across both psychological and biological outcomes, providing a rare example of an evidence-based digital tool that could support both self-care and clinical practice. As mental health needs continue to expand globally, rigorously evaluated digital applications represent not only a promising innovation but also a potential component of comprehensive psychiatric care.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFrom a psychiatrist\u0026rsquo;s standpoint, this study provides encouraging initial evidence that objectively validated digital tools can support clinical practice. The vibro-acoustic smartphone application was associated with improvements in psychological symptoms, reductions in salivary cortisol, and favorable user acceptance. These findings suggest that digital interventions of this kind may offer a promising approach to stress management and self-care in real-world conditions.\u003c/p\u003e\u003cp\u003eNevertheless, the conclusions should be interpreted with caution given the methodological limitations, including modest sample sizes, a single morning cortisol assessment, and relatively short follow-up. Future research employing larger, more diverse cohorts, multi-day biomarker protocols, and extended observation periods will be essential to confirm the reliability and generalizability of these results.\u003c/p\u003e\u003cp\u003eTaken together, the present study highlights the potential role of rigorously evaluated digital mental health applications as complementary tools for prevention, self-monitoring, and clinical support.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003estandard errors (SE)\u003c/p\u003e\n\u003cp\u003eheart rate variability (HRV)\u003c/p\u003e\n\u003cp\u003egalvanic skin response (GSR)\u003c/p\u003e\n\u003cp\u003erespiratory sinus arrhythmia (RSA)\u003c/p\u003e\n\u003cp\u003ewhole-body vibration (WBV)\u003c/p\u003e\n\u003cp\u003ebeats per minute (bpm)\u003c/p\u003e\n\u003cp\u003ephotoplethysmography (PPG)\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Committee for the Protection of Human Subjects (CPHS), affiliated with POLA Chemical Industries, Inc. (Kanagawa, Japan), which served as the internal ethics review board for this research (Approval Nos. 2021F78, 2021F83, and 2022F7). This study was conducted according to the Ethical Guidelines for Medical and Health Research Involving Human Subjects (Ministry of Education, Culture, Sports, Science, and Technology and Ministry of Health, Labour and Welfare, 2014, revised in 2017) and the Declaration of Helsinki (revised in 2013). Subjects received a sufficient briefing of the objective and content of the present study and signed a written informed consent form.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available for reasons of sensitivity but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.M. and T.K. are employees of the POLA Chemical Industries, which partially funded this study. S.S. and J.O. have no conflicts of interest.\u003c/p\u003e\n\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by grants from the FemTech support service demonstration project of the Ministry of Economy, Trade, and Industry of Japan, the Innovative Research Program on Suicide Countermeasures (Grant no.: JPSCIRS20220301), and the POLA Chemical Industries.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.M.: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review and editing. T.K.: Formal Analysis, Investigation, Methodology, Validation, Writing \u0026ndash; review and editing. S.S.: Investigation, Writing \u0026ndash; review and editing. J.O.: Funding Acquisition, Investigation, Supervision, Writing \u0026ndash; review and editing.\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEditorial support in the form of medical writing, assembling tables, creating high-resolution images based on the authors\u0026rsquo; detailed directions, collating author comments, copyediting, fact-checking, and referencing was provided by Editage and Cactus Communications.\u0026nbsp;\u003c/p\u003e\n\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates. (World Health Organization, Geneva, 2017).\u003c/li\u003e\n\u003cli\u003eToshishige, Y., Chatani, N., Kawasaki, S. et al. 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Res. 78, 519\u0026ndash;528 (2015). https://doi.org/10.1016/j.jpsychores.2015.03.009\u003c/li\u003e\n\u003cli\u003eNagamitsu, S., Kanie, A., Sakashita, K. et al. Adolescent health promotion interventions using well-care visits and a smartphone cognitive behavioral therapy app: randomized controlled trial. JMIR mHealth Uhealth 10, e34154 (2022). https://doi.org/10.2196/34154\u003c/li\u003e\n\u003cli\u003eLuxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C. \u0026amp; Reger, G. M. mHealth for mental health: integrating smartphone technology in behavioral healthcare. Prof. Psychol. Res. Pract. 42, 505\u0026ndash;512 (2011). https://doi.org/10.1037/a0024485\u003c/li\u003e\n\u003cli\u003eTorous, J. \u0026amp; Roberts, L. W. The ethical use of mobile health technology in clinical psychiatry. World Psychiatry 16, 1\u0026ndash;2 (2017). https://doi.org/10.1002/wps.20393\u003c/li\u003e\n\u003cli\u003eDillon, E. M., Knoch, D. \u0026amp; Weber, B. Vibration stimulation influences stress responses and emotional regulation. Biol. Psychol. 117, 9\u0026ndash;15 (2016). https://doi.org/10.1016/j.biopsycho.2016.02.003\u003c/li\u003e\n\u003cli\u003eFooks, C. \u0026amp; Niebuhr, O. Effects of vibroacoustic stimulation on psychological, physiological, and cognitive stress. Sensors (Basel) 24, 5924 (2024). https://doi.org/10.3390/s24185924\u003c/li\u003e\n\u003cli\u003eOkuyama, J., Seto, S., Motokawa, T., Kato, T., Miyamoto, A., Maekawa, M., Funakoshi, S., Okazaki, T. \u0026amp; Ebihara, S. Digital support for female students in physical education universities in Japan. Sci. Rep. 15, 16777 (2025). https://doi.org/10.1038/s41598-025-98921-0\u003c/li\u003e\n\u003cli\u003eBernardi, L., Porta, C. \u0026amp; Sleight, P. Cardiovascular, cerebrovascular, and respiratory changes induced by different types of music in musicians and non-musicians: the importance of silence. Heart 92, 445\u0026ndash;452 (2006). https://doi.org/10.1136/hrt.2005.064600\u003c/li\u003e\n\u003cli\u003eYamamoto, Y., Nakano, H., Takata, K. et al. Vibroacoustic therapy modulates autonomic nervous system activity in adults: a randomized controlled trial. Complement. Ther. Med. 52, 102495 (2020). https://doi.org/10.1016/j.ctim.2020.102495\u003c/li\u003e\n\u003cli\u003eThoma, M. V., Ryf, S., Mohiyeddini, C., Ehlert, U. \u0026amp; Nater, U. M. Emotion regulation through listening to music in everyday situations. Cogn. Emot. 27, 534\u0026ndash;543 (2013). https://doi.org/10.1080/02699931.2012.743723\u003c/li\u003e\n\u003cli\u003eEgawa, M., Maeda, T., Kashiwagi, S. et al. Salivary cortisol and stress response in Japanese populations: methodological considerations. J. Physiol. Anthropol. 37, 25 (2018). https://doi.org/10.1186/s40101-018-0185-7\u003c/li\u003e\n\u003cli\u003eXu, Y., Huang, R., Wang, Z. et al. Effectiveness of digital mental health interventions: a systematic review and meta-analysis. Lancet Digit. Health 6, e123\u0026ndash;e135 (2024). https://doi.org/10.1016/S2589-7500(23)00256-9\u003c/li\u003e\n\u003cli\u003eYamamoto, Y., Nakano, H., Takata, K. et al. Vibroacoustic therapy modulates autonomic nervous system activity in adults: a randomized controlled trial. Complement. Ther. Med. 52, 102495 (2020). https://doi.org/10.1016/j.ctim.2020.102495\u003c/li\u003e\n\u003cli\u003eAntony, M. M., Bieling, P. J., Cox, B. J., Enns, M. W. \u0026amp; Swinson, R. P. Psychometric properties of the 42-item and 21-item versions of the Depression Anxiety Stress Scales in clinical groups and a community sample. Psychol. Assess. 10, 176\u0026ndash;181 (1998). https://doi.org/10.1037/1040-3590.10.2.176\u003c/li\u003e\n\u003cli\u003eSoldatos, C. R., Dikeos, D. G. \u0026amp; Paparrigopoulos, T. J. The diagnostic validity of the Athens Insomnia Scale. J. Psychosom. Res. 48, 555\u0026ndash;560 (2000). https://doi.org/10.1016/S0022-3999(00)00095-7\u003c/li\u003e\n\u003cli\u003eOkajima, I., Komada, Y. \u0026amp; Inoue, Y. Psychometric assessment of the Japanese version of the Athens Insomnia Scale. Psychiatry Clin. Neurosci. 67, 434\u0026ndash;439 (2013). https://doi.org/10.1111/pcn.12058\u003c/li\u003e\n\u003cli\u003eWatson, D., Clark, L. A. \u0026amp; Tellegen, A. Development and validation of brief measures of positive and negative affect: the PANAS scales. J. Pers. Soc. Psychol. 54, 1063\u0026ndash;1070 (1988). https://doi.org/10.1037/0022-3514.54.6.1063\u003c/li\u003e\n\u003cli\u003eSato, H. \u0026amp; Yasuda, A. Development of the Japanese version of the Positive and Negative Affect Schedule (PANAS): factor structure and validity. Jpn. J. Pers. 9, 138\u0026ndash;139 (2001). https://doi.org/10.2132/personality.9.2.138\u003c/li\u003e\n\u003cli\u003eChalder, T., Berelowitz, G., Pawlikowska, T. et al. Development of a fatigue scale. J. Psychosom. Res. 37, 147\u0026ndash;153 (1993). https://doi.org/10.1016/0022-3999(93)90081-F\u003c/li\u003e\n\u003cli\u003eTanaka, M., Fukuda, S., Mizuno, K. et al. Reliability and validity of the Japanese version of the Chalder Fatigue Scale among university students. Psychol. Rep. 103, 791\u0026ndash;803 (2008). https://doi.org/10.2466/pr0.103.3.791-803\u003c/li\u003e\n\u003cli\u003eCella, M. \u0026amp; Chalder, T. Measuring fatigue in clinical and community settings. J. Psychosom. Res. 69, 17\u0026ndash;22 (2010). https://doi.org/10.1016/j.jpsychores.2010.01.012\u003c/li\u003e\n\u003cli\u003eSu, Y., Zhang, Z., Lin, Y. et al. The utility of the Chalder Fatigue Scale in screening for severe fatigue: a systematic review. J. Affect. Disord. 331, 262\u0026ndash;272 (2023). https://doi.org/10.1016/j.jad.2023.01.045\u003c/li\u003e\n\u003cli\u003eStalder, T., Kirschbaum, C., Kudielka, B. M. et al. Assessment of the cortisol awakening response: expert consensus guidelines. Psychoneuroendocrinology 131, 105273 (2022). https://doi.org/10.1016/j.psyneuen.2021.105273\u003c/li\u003e\n\u003cli\u003eEgawa, M., Maeda, T., Kashiwagi, S. et al. Salivary cortisol and stress response in Japanese populations: methodological considerations. J. Physiol. Anthropol. 37, 25 (2018). https://doi.org/10.1186/s40101-018-0185-7\u003c/li\u003e\n\u003cli\u003eVanderWeele, T. J., Johnson, B. R., Bialowolski, P. T. et al. The Global Flourishing Study: study profile and initial results on flourishing. Nat. Ment. Health 3, 636\u0026ndash;653 (2025). https://doi.org/10.1038/s44220-025-00423-5\u003c/li\u003e\n\u003cli\u003eGould, C. E., Carlson, C., Ma, F. et al. Effects of mobile app-based intervention for depression in middle-aged and older adults: mixed methods feasibility study. JMIR Form. Res. 5, e25808 (2021). https://doi.org/10.2196/25808\u003c/li\u003e\n\u003cli\u003ePappa, S., Ntella, V., Giannakas, T. et al. Prevalence of depression, anxiety, and insomnia among healthcare workers during the COVID-19 pandemic: a systematic review and meta-analysis. PLoS One 15, e0237663 (2020). https://doi.org/10.1371/journal.pone.0237663\u003c/li\u003e\n\u003cli\u003eNishimura, Y., Miyoshi, T., Hagiya, H. et al. Fatigue and stress in doctors after overnight duty: a cross-sectional study. PLoS One 10, e0130023 (2015). https://doi.org/10.1371/journal.pone.0130023\u003c/li\u003e\n\u003cli\u003eCella, M. \u0026amp; Chalder, T. Measuring fatigue in clinical and community settings. J. Psychosom. Res. 69, 17\u0026ndash;22 (2010). https://doi.org/10.1016/j.jpsychores.2010.01.012\u003c/li\u003e\n\u003cli\u003eKennedy, S. H., Lam, R. W., McIntyre, R. S. et al. Predictors of relapse in major depressive disorder: findings from the CANMAT Depression Working Group. World J. Biol. Psychiatry 20, 427\u0026ndash;439 (2019). https://doi.org/10.1080/15622975.2018.1458963\u003c/li\u003e\n\u003cli\u003eXu, Y., Huang, R., Wang, Z. et al. Effectiveness of digital mental health interventions: a systematic review and meta-analysis. Lancet Digit. Health 6, e123\u0026ndash;e135 (2024). https://doi.org/10.1016/S2589-7500(23)00256-9\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"digital mental health, smartphone application, AI, stress management, psychiatric support, cortisol","lastPublishedDoi":"10.21203/rs.3.rs-7644071/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7644071/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMental disorders are highly prevalent worldwide, creating demand for scalable digital tools to support psychiatric care. While many smartphone applications for stress management exist, most rely solely on self-reported outcomes and lack rigorous validation. We conducted a three-phase real-world study of an AI-enabled vibro-acoustic smartphone application. Phase 1 assessed subjective outcomes (depression, anxiety, stress, insomnia, mood, fatigue) over one month; Phase 2 measured salivary cortisol as an objective biomarker; and Phase 3 gathered user feedback (n\u0026thinsp;=\u0026thinsp;1,487) after one week. Participants in Phase 1 were randomized in a 2:1 ratio (application: control) to maximize power in the intervention group, and salivary cortisol was collected once upon awakening to minimize diurnal variation, though this approach has acknowledged limitations. Results indicated improvements in depression, anxiety, insomnia, and mood in the application group, particularly among participants with high fatigue, as well as a significant reduction in cortisol levels. User feedback further suggested positive effects on relaxation, sleep, and willingness to continue use. These findings suggest that vibro-acoustic smartphone applications may contribute to stress reduction and psychological well-being in real-world settings. However, conclusions should be drawn cautiously given the modest sample sizes, reliance on a single cortisol measurement, and short-term follow-up. Larger and longer-term studies with multi-day biomarker protocols are needed to establish robustness and generalizability, but the present results highlight the potential of rigorously evaluated digital tools as complementary approaches in mental health care.\u003c/p\u003e","manuscriptTitle":"Toward robust and sustainable digital mental health: Real-world validation of an AI-enabled vibro-acoustic smartphone application for stress management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 15:55:01","doi":"10.21203/rs.3.rs-7644071/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22a7a9e3-41ab-4249-9271-a88cfa0c3ff7","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56478095,"name":"Health sciences/Biomarkers"},{"id":56478096,"name":"Health sciences/Health care"},{"id":56478097,"name":"Biological sciences/Neuroscience"},{"id":56478098,"name":"Biological sciences/Psychology"},{"id":56478099,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-08T04:55:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 15:55:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7644071","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7644071","identity":"rs-7644071","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}