Frontiers in Digital Health

BackgroundThe rapid adoption of telehealth services for youth mental health care necessitates a comprehensive evaluation of its effectiveness. This study aimed to analyze the impact of telehealth on youth mental health outcomes using artificial intelligence techniques applied to large-scale public health data. MethodsWe conducted an AI-driven analysis of data from the National Survey on Drug Use and Health (NSDUH) and other SAMHSA datasets. Machine learning techniques, including Random Forest models, K-means clustering, and time series analysis, were employed to evaluate telehealth adoption patterns, predictors of effectiveness, and comparative outcomes with traditional in-person care. Natural language processing was used to analyze sentiment in user feedback. ResultsTelehealth adoption among youth increased significantly, with usage rising from 2.3 sessions per year in 2019 to 8.7 in 2022. Telehealth showed comparable effectiveness to in-person care for depressive disorders and superior effectiveness for anxiety disorders. Session frequency, age, and prior diagnosis were identified as key predictors of telehealth effectiveness. Four distinct user clusters were identified, with socioeconomic status and home environment strongly associated with positive outcomes. States with favorable reimbursement policies saw a 15% greater increase in youth telehealth utilization and a 7% greater improvement in mental health outcomes. ConclusionsTelehealth demonstrates significant potential in improving access to and effectiveness of mental health services for youth. However, addressing technological barriers and socioeconomic disparities is crucial to maximize its benefits.

Digital health tools may facilitate care continuum. However, enhancement of digital aid is imperative to prevent information gaps or redundancies, as well as to facilitate support of flexible care plans. The study presents Health Circuit, a digital health tool with an adaptive case management approach and analyses its healthcare impact, as well as its usability (SUS) and acceptability (NPS) by healthcare professionals and patients. In 2018-19, an initial prototype of Health Circuit was tested in a cluster randomized clinical pilot (n=100) in patients with high risk for hospitalization (Study I). In 2021, a pilot version of Health Circuit was evaluated in 104 high risk patients undergoing prehabilitation before major surgery (Study II). In study I, Health Circuit resulted in reduction of emergency room visits [4 (13%) vs 7 (44%)] and enhanced patients empowerment (p<0.0001) and showed good acceptability/usability scores (NPS 31 and SUS 54/100). In Study II, NPS scored 40 and SUS 85/100. The acceptance rate was also high (mean score of 8.4/10). Health Circuit showed potential for healthcare value generation, good both acceptability and usability despite being a prototype system, prompting the need for testing a completed system in real-world scenarios.

BackgroundConversational agents based on large language models (LLMs) have shown moderate efficacy in reducing depressive and anxiety symptoms. However, most existing evaluations lack methodological transparency, rely on closed-source models, and show limited standardization in performance and safety assessment. ObjectiveWe have two study objectives: (1) to develop an LLM-based conversational agent through system design analysis and initial functionality testing, and (2) to evaluate its safety and performance through standardized assessment in controlled simulated interactions focused on depression and anxiety of two LLMs (GPT-4o and Llama 3.1-8B). MethodsWe conducted a cross-sectional study in two phases. First, we developed a mental health platform integrating a conversational agent with functionalities including personalized context, pretrained therapeutic modules, self-assessment tools, and an emergency alert system. Second, we evaluated the agents responses in simulated interactions based on predefined user personas for each LLM. Four expert raters assessed 816 interaction pairs using a 5-criterion Likert scale evaluating tone, clarity, domain accuracy (correctness), robustness, completeness, boundaries, target language, and safety. In addition, we use performance metrics based on numerical criteria such as cost, response length, and number of tokens. Multiple linear regression models were used to compare LLM performance and assess metric interrelations. ResultsFirst, we developed a web-based mental health platform using a user-centered design, structured into frontend, backend, and database layers. The system integrates therapeutic chat (GPT-4o and Llama 3.1-8B), psychological assessments (PHQ-9, GAD-7), CBT-based tasks, and an emergency alert system. The platform supports secure user authentication, data encryption, multilingual access, and session tracking. Second, GPT-4o outperformed Llama 3.1-8B in both performance metrics based on numerical criteria and Likert scale criteria, generating longer and more lexically diverse responses, using more tokens, and scoring higher in clarity, robustness, completeness, boundaries, and target language. However, it incurred higher costs, with no significant differences in tone, accuracy, or safety. ConclusionOur study presents a conversational agent with multiple functionalities and shows that GPT-4o outperforms Llama 3.1-8B in performance, although at a higher cost. This platform could be used in future clinical trials or real-world implementation studies.

BackgroundCOVID-19 shifted Indonesian education to remote learning. The Gadjah Mada University (UGM) Faculty of Medicine Public Health and Nursing (UGM FMPHN) struggled with studying tumor images remotely due to resource shortages. In parallel, the University of Dundees (UoDs) OME team created OMERO for image management, inspiring UGM to use OMERO to develop GamaPath for web-based image viewing. This report details its implementation, effectiveness, and potential expansion to practical sessions and workshops in Indonesia. MethodsTeaching slides were scanned in Indonesia and imported into OMERO at UoD. UGM students, residents, and clinicians used GamaPath for anatomical pathology training for several sessions between 2022 and 2024. GamaPath was also used for national continuing education sessions for pathologists held through 2023. Experiences and evaluations were collected via online surveys and results were assessed by modified MARuL scores. ResultsThe UoD-UGM collaboration produced an application satisfying the initial need for remote teaching during COVID lockdown. However, after returning to in-person teaching, access to interactive training materials online was considered to be an essential part of effective instruction by students and faculty. The GamaPath application provided flexible access to interactive materials that enhanced the educational experience. Of 256 survey respondents, mean modified MARuL score for GamaPath web app was 40.92 (SD = 10.73) with a median of 41 (IQR = 34- 50). Among the 107 pathologists who used GamaPath for national continuing education, the majority gave it the highest possible rating for functionality, ease of use, and overall experience. ConclusionsUGMs FMPHN collaborated with UoD to create a digital pathology platform based on OMERO, improving image analysis and feedback for practical sessions and workshops using GamaPath.

ObjectiveVirtual patient cases are a scalable and engaging tool for training medical professionals. Strategies and frameworks for their implementation in teaching and training settings are few, technically complicated and/or expensive. We developed and evaluated open source and free virtual patient cases to test knowledge acquisition during an echocardiography training program for internal medicine trainees in Haiti. The objective of this paper is to describe the technical aspects of the GMENEcho virtual patient cases implementation and motivate similar work by resource-constrained teams. MethodsWe used an open source engine for text-based games (Twine) since it provides the necessary interaction mechanics and is usable out-of-the-box. The case code was written in SugarCube 2.30.0 notation and the tweego-generated .html file was hosted on Github Pages for continuous integration and deployment, making iterations by the clinical team seamless. Data from completed tests were reported back via email through a third party integration. ResultsThe technical work was completed in two weeks by a team member with a clinical background and minimal computer programming experience. The virtual patient cases were deployed for a pretest (November 2023) and a second time unaltered for a posttest (June 2024) after the interim hands-on and theoretical training had been completed. Qualitative feedback was positive or neutral. The overall score in the posttest was significantly higher with a large effect size (mean absolute improvement 15.26%, p < 0.001; Cohens d: 1.398), similarly to the diagnostic score (mean absolute difference 16.09%, p < 0.001; Cohens d: 1.402). Management performance missed statistical significance by a small margin. The System Usability Scale (SUS) score was 74.6 ("Excellent").There was reduced inter-trainee variability across metrics in the posttest, including the SUS score. DiscussionThis proof-of-concept methodology can be applied to create clinical patient cases for use within a class or a clinical training setting, through a friendly graphical user interface. A more complex software stack can allow for remote or larger scale implementations with additional features. ConclusionThe rapid development time and positive qualitative and quantitative feedback highlight the potential of this approach for clinical education in resource-constrained settings. It can serve as a template for more streamlined adaptations of case-based learning in diverse healthcare settings.

The growing use of large language models for health communication raises important questions about patient preferences, trust, and satisfaction with AI-generated content. We conducted a qualitative survey comparing patient perceptions of clinician versus AI answers to questions about Alzheimers disease and genetics. Twenty-six participants scored responses on relevance, trustworthiness, and coherence, and additionally selected a preferred response and provided free-text comments. We found that participants generally preferred AI responses over human-written ones and rated them higher on all evaluation axes. Qualitative analysis of free-text comments identified key factors influencing patient preferences, including clarity, verbosity, certainty, empathy, and numeracy, with patients showing diverse and sometimes contradictory preferences along these dimensions. These findings suggest the AI-generated responses were well-received by patients and may have a role in triaging patients with information-seeking queries. Future work should focus on dynamically identifying patient communication preferences and tailoring communication styles accordingly.

Background. General purpose generative AI (GenAI) chatbots are increasingly used by students for mental health support. Research on prevalence estimates vary widely, rarely link use to validated clinical measures, and have not been reported in a Canadian student population. We estimated the prevalence trends, patterns, perceived impact, and correlates of GenAI use for mental health support among Canadian university students. Methods. We analysed one year (May 2025 to April 2026) repeated cross-sectional data from the Canadian arm of the WHO World Mental Health International College Student survey (WMH-ICS) The primary outcome was past-year prevalence of GenAI use for mental health support. Specific use purposes, perceived impact, reasons for non-use, and future use intent were also analysed. Factors associated with GenAI use were assessed using modified Poisson regression. As a sensitivity analysis, an elastic-net penalised regression model was fitted to assess the robustness of findings to an alternative modelling approach. Results. The past-year prevalence of GenAI chatbot use for mental health support was 25.2% (95% CI: 22.7 - 27.9), with a lifetime prevalence of 30.2%. Use was mostly occasional and predominately for seeking mental health information, stress management, and emotional support/companionship. Students of Asian ethnicity, those with higher clinical burden, recent adverse life experiences, weaker social support, and prior digital help-seeking behaviours were more likely to use GenAI for mental health purposes. Conversely, 2SLGBTQ+ students and those with romantic partners were less likely. Nearly three-quarters (74.2%) of users perceived such use to have a positive impact on their mental health and emotional wellbeing. Non-users reported preference for human interaction, distrust of GenAI in mental health (67.4% each), and privacy/security concerns (50.3%). Non-use also reflected principled objections to AI, including ethical and environmental concerns, with most non-users indicating no future use intention. Conclusions. GenAI chatbot use for mental health support has become commonplace among Canadian university students and is concentrated among those with greater mental health needs and fewer social support resources. Although most users perceived these tools as beneficial, their clinical effectiveness and safety remain uncertain. Rigorous prospective studies are needed to determine whether perceived benefits translate into improved mental health outcomes and whether purpose-built GenAI mental health interventions offer greater clinical benefit and safety than general-purpose chatbots.

Patient Simulators 1.0 is a novel display interface that provides an alternative means of showing real-time changes in physiological variables derived from human patient simulators. It is designed for teachers and learners of the sciences for whom showing variables on simulated clinical monitors may be less relevant. It allows import of comma separated data sets, and their display using customisable gauges and trendlines. Data from variables not normally shown on simulated patient monitors, e.g. pH, and data from variables that are not produced during the simulation but can be derived, e.g. stroke volume, can be displayed. It also allows presentation of data produced during simulation sessions carried out elsewhere without the need for proprietary simulation software. This interface may assist in wider adoption of the use of simulated data in teaching the basic science underpinning health and disease.

Point-of-care (POC) blood testing enables rapid, decentralized diagnostics with transformative promise, yet its innovation landscape remains poorly mapped. To this end, we focused on features that we believe are key to make progress in areas of precision healthcare and predictive medicine, such as longitudinal data collection and data analytics integration. While no review can be complete, this work attempts to address this gap by analyzing 86 POC blood testing devices worldwide and proposing a unified framework to compare them across technology principles, diagnostic breadth, usability, regulatory pathway, deployment feasibility (via a custom index), and data/AI integration. Electrochemical biosensors were the single largest platform (29.1%), strongly associated with glucose testing ({chi}2=237.8, p<0.001), while spectroscopic and microfluidic systems remained niche due to higher costs and specialized requirements. Regulatory approval skewed toward moderate risk (44.2% FDA II; 27.4% IVDR C), while approval times lengthened with risk class (e.g., IVDR D {approx}540 days). A trade-off was observed between usability and panel breadth: tools for home or low-resource settings emphasize simplicity and affordability, whereas clinical systems expand diagnostic range at higher complexity and cost. Deployment feasibility scores favored handhelds, while benchtops were penalized by workflow and capital demands, and microfluidics by consumables. Innovation clusters in North America, Europe, and East Asia reinforce global leadership and disparities.

BackgroundUnderstanding user engagement with conversational agents (CAs) in mobile health apps is crucial for improving sustained usage. We analyzed CA interactions in a mobile health app to identify usage patterns and potential barriers. Materials and MethodsRetrospective data from 100,571 active users of the Albert Health app in 2022 were analyzed. Interactions with CA were categorized by demographics (gender and age), interaction type (health information, medication-related, clinical parameters, and non-clinical), and engagement method (text, voice). Descriptive statistics were used to identify trends and patterns in app usage. ResultsOut of the active users, 19,051 (18.9%) engaged with the CA. The majority were female (61%), with 43% aged 30-45 years and 23% older than 45 years. The analysis showed that 94.5% engaged in general health management, while 5.3% used disease-specific programs. Average usage per user was highest in cardiovascular and respiratory diseases. Interaction types varied, with health information and medication-related interactions. The varied messaging behavior suggests different user engagement levels, with some users seeking quick information and others engaging more deeply for health management. Engagement was high initially but decreased over time. DiscussionThis study provides insights into user interactions with a healthcare CA, highlighting a preference for general health management and diverse usage patterns. The significant number of single-session users indicates potential barriers to sustained engagement, highlighting the need for strategies to enhance user experience and retention. Future research should investigate the CAs performance, effectiveness and extend observations to broader healthcare contexts by using large language models.

Despite longitudinal and multidimensional collected data within registries, their routine exploitation for value-based care and outcome transparency remains limited by analytical complexity and heterogeneous expertise across centers. To address this gap, we developed an open-source and free web-based software which allows registry-based data analysis operational for evaluation of practices and quality system management applied to allogeneic hematopoietic cell transplant registry. It was built with Python and Dash framework to treat user formatted data. AlloGraph produces epidemiological summaries, survival analyses, and quality management indicators. Privacy protection is ensured by a Transport Layer Security protocol to a secure server where processing occurs in-memory, without data saving. AlloGraph was evaluated positively by 30 practitioners in 24 transplant centers, of whom 89% anticipated that AlloGraph would change their monitoring practice. AlloGraph represents a privacy-preserving and user-centered platform simplifying registry analysis for activity monitoring. This scalable model could be adapted to exploit real-world health databases.

A Wearable Monitoring System (WMS), comprising a chest patch, wrist-worn pulse oximeter, and arm-worn blood pressure device, was developed in preparation for a pilot Randomised Controlled Trial (RCT) on a UK surgical ward. The system was designed to support continuous physiological monitoring and early detection of deterioration. An initial prototype user interface was developed by the research team based on prior clinical experience and engineering knowledge. To ensure suitability for clinical practice, iterative user-centred refinement was undertaken through a series of clinician focus groups and wearability assessments. Six focus groups were conducted between November 2019 and May 2021 involving multidisciplinary healthcare professionals. Feedback from these sessions informed successive interface and system modifications. System development spanned the COVID-19 pandemic, during which the WMS was rapidly adapted and deployed to support clinical care on isolation wards. Feedback obtained during this period was incorporated into later versions of the system and provided a unique opportunity to examine changes in clinician priorities under pandemic conditions. Clinicians consistently prioritised alert visibility, alarm fatigue mitigation, parameter flexibility, and centralised monitoring. Notably, preferences regarding alert modality and access mechanisms evolved over time: early enthusiasm for mobile or smartphone-type devices shifted towards a preference for fixed, ward-based displays and audible alerts at the nurses station following pandemic deployment. Building on previous wearability testing in healthy volunteers, wearability testing using a validated questionnaire was completed by 169 patient participants during the RCT. The chest patch and pulse oximeter demonstrated high tolerability, whereas the blood pressure cuff showed poor wearability and was removed from the final system. These findings demonstrate the importance of iterative, clinician-led design for wearable WMS and highlight how extreme clinical contexts such as the COVID-19 pandemic can significantly reshape perceived requirements for safety-critical monitoring technologies.

Suicide, a serious public health concern affecting millions of individuals worldwide, refers to the intentional act of ending ones own life. Mental health issues such as depression, frustration, and hopelessness can directly or indirectly influence the emergence of suicidal thoughts. Early identification of these thoughts is crucial for timely diagnosis. In recent years, advances in artificial intelligence (AI) and natural language processing (NLP) have paved the way for revolutionizing mental health support and education. In this proof-of-concept study, we have created MindWatch, a cutting-edge tool that harnesses the power of AI-driven language models to serve as a valuable computer-aided system for the mental health professions to achieve two important goals such as early symptom detection, and personalized psychoeducation. We utilized ALBERT and Bio-Clinical BERT language models and fine-tuned them with the Reddit dataset to build the classifiers. We evaluated the performance of bi-LSTM, ALBERT, Bio-Clinical BERT, OpenAI GPT3.5 (via prompt engineering), and an ensembled voting classifier to detect suicide ideation. For personalized psychoeducation, we used the state-of-the-art Llama 2 foundation model leveraging prompt engineering. The tool is developed in the Amazon Web Service environment. All models performed exceptionally well, with accuracy and precision/recall greater than 92%. ALBERT performed better (AUC=.98) compared to the zero-shot classification accuracies obtained from OpenAI GPT3.5 Turbo (ChatGPT) on hidden datasets (AUC=.91). Furthermore, we observed that the inconclusiveness rate of the Llama 2 model is low while tested for few examples. This study emphasizes how transformer models can help provide customized psychoeducation to individuals dealing with mental health issues. By tailoring content to address their unique mental health conditions, treatment choices, and self-help resources, this approach empowers individuals to actively engage in their recovery journey. Additionally, these models have the potential to advance the automated detection of depressive disorders.

Alzheimers disease (AD) is increasing in prevalence, and early detection is essential for timely care. Clinical services face growing demand, leading to delays in diagnostic appointments and increasing the risk of disease progression before evaluation. This work examines artificial intelligence (AI) methods for assessing cognitive status from linguistic features. The proposed architecture uses small language models (SLMs) to analyze speech patterns, and its compact design allows deployment on mobile devices. Recent reasoning-focused models, including Deepseek-R1 and Llama, were evaluated for dementia classification. Multiple fine-tuning strategies were compared, and the best model achieved 91% accuracy and an F1 score. The findings show that AI systems built on SLMs can achieve performance comparable to large language models, indicating their potential as efficient tools that may support health care providers through accessible pre-clinical screening for AD.

ObjectivesDigital phenotyping methods present a scalable tool to realize the potential of personalized medicine. But underlying this potential is the need for digital phenotyping data to represent accurate and precise health measurements. This requires a focus on the data quality of digital phenotyping and assessing the nature of the smartphone data used to derive clinical and health-related features. DesignRetrospective cohorts. Representing the largest combined dataset of smartphone digital phenotyping, we report on the impact of sampling frequency, active engagement with the app, phone type (Android vs Apple), gender, and study protocol features may have on missingness / data quality. SettingmindLAMP smartphone app digital phenotyping studies run at BIDMC between May 2019 and March 2022 Participants1178 people who partook in mindLAMP studies Main outcome measuresRates of missing digital phenotyping data. ResultsMissingness from sensors in digital phenotyping is related to active user engagement with the app. There are small but notable differences in missingness between phone models and genders. Datasets with high degrees of missingness can generate incorrect behavioral features that may lead to faulty clinical interpretations. ConclusionsDigital phenotyping data quality is a moving target that requires ongoing technical and protocol efforts to minimize missingness. Adding run-in periods, education with hands-on support, and tools to easily monitor data coverage are all productive strategies studies can utilize today. Strengths and Limitations of this Study{circ} Methods are informed by a large sample of participants in digital phenotyping studies. {circ}Methods can be replicated by others given the open-source nature of the app and code. {circ}Methods are informed by only mindLAMP studies from one team which is a limitation.

Clinical reasoning is a fundamental skill in medical education that requires intensive faculty resources and deliberate practice. Here, we present the design and implementation of a novel adaptive Socratic tutor powered by large language models (LLMs) that facilitates case-based learning for medical trainees. Our system takes input from any structure of a published clinical case to create interactive and adaptive clinical scenarios where learners engage in realistic patient encounters with real-time feedback on their reasoning process. As a proof of concept, we demonstrate its use with the NEJM clinical pathological case series. This paper describes the architecture, knowledge representation, and educational design principles incorporated into our system, which we are releasing as an open-source tool for medical education. Our work demonstrates how LLMs can promote high-quality precision education in clinical reasoning and provide a structured assessment of the strengths and weaknesses of the learner.

Real-world adoption of closed-loop insulin delivery systems (CLIDS) in type 1 diabetes remains low, driven not by technical failure, but by diverse behavioral, psychosocial, and social barriers. We introduce ChatCLIDS, the first bench-mark to rigorously evaluate LLM-driven persuasive dialogue for health behavior change. Our framework features a library of expert-validated virtual patients, each with clinically grounded, heterogeneous profiles and realistic adoption barriers, and simulates multi-turn interactions with nurse agents equipped with a diverse set of evidence-based persuasive strategies. ChatCLIDS uniquely supports longitudinal counseling and adversarial social influence scenarios, enabling robust, multi-dimensional evaluation. Our findings reveal that while larger and more reflective LLMs adapt strategies over time, all models struggle to overcome resistance, especially under realistic social pressure. These results highlight critical limitations of current LLMs for behavior change, and offer a high-fidelity, scalable testbed for advancing trustworthy persuasive AI in healthcare and beyond. 1

This study investigates the integration of Voice AI into a locally hosted generative AI chatbot designed to function as a mental health assistant, with the goal of enabling intuitive, voice-based therapeutic interaction. Leveraging the Llama3.1 8B language model for privacy-preserving generation, the system combines Deepgrams Speech-to-Text API and OpenAIs Text-to-Speech API within a WebRTC-based framework to support low-latency, bi-directional communication. A custom pipeline facilitates real-time voice input and output, aiming to reduce barriers to engagement and foster a more natural conversational flow. Technical evaluation focuses on latency across short, long-form, and multi-turn dialogues, revealing response times within tolerable bounds for synchronous use. Prompt engineering and system prompt customization guide empathetic, context-aware responses in standard therapeutic scenarios, though limitations persist in handling edge cases. These findings suggest that locally hosted voice-enabled LLMs can support responsive, privacy-conscious mental health applications, with future work directed toward fine-tuning for high-risk interactions.

Objectives. To assess how digital inclusion factors and physical access barriers are associated with user trust in smartphone-based remote photoplethysmography (rPPG) hypertension screening, and to identify implications for digital health pol- icy, procurement and implementation in low-resource settings. Methods. Cross-sectional mixed-methods survey in five outpatient clinics in Kebbi State, northern Nigeria (N =287). Trust was measured using comfort, confidence and perceived usefulness Likert scales. Primary analyses used binary logistic models with HC3 robust standard errors; sensitivity analyses are reported in supplementary material. Free-text responses were thematically analysed. Results. Smartphone ownership was 51.2%; Transsion-brand devices comprised 56.5% of owners. Greater distance to a blood pressure facility was independently associated with lower perceived usefulness (OR 0.51, 95% CI 0.30-0.87; p=0.013) and lower comfort (OR 0.61, 0.37-0.98; p=0.042). Among owners, Transsion versus Samsung showed higher confidence odds (OR 3.82, 1.02-14.27; p=0.046). Qualitative themes supported the implementation interpretation: platform-fit and device speed requests among Transsion owners; connectivity and offline-first concerns among those with greater travel distance. No brand contrast achieved FDR-adjusted significance; brand findings are exploratory. Conclusions. Digital health policy and health technology assessment for smartphone-based screening should incorporate local device ecology, connectivity constraints, physical access burden and trust-calibration safeguards. Pre-implementation assessment of these factors is necessary for equitable and safe rPPG adoption in low-resource health systems.

Depression and anxiety are some of the most common mental health disorders in the world contributing to significant morbidity and mortality. Past treatments have focused primarily on treating depression and anxiety. However, there is an urgent need to detect chronic stress states and potentially intervene using just-in-time personalized interventions. Modern technology has revolutionized our ability to passively measure various biological and physiological signals. In our daily lives, we generate significant amounts of electronic data from our phones, wearable technology, watches, and even computers and cars. In this analysis, we focus on using wearable data from FitBit to passively predict daily mood states (e.g., sad/tense/anxious vs. happy). We use daily FitBit data from 38 participants and ~1200 days of data to predict mood states (e.g., sad/tense/anxious vs. happy) on a day-to-day basis using an elastic net regression machine learning algorithm. We were able to accurately predict these states using a cross-validated machine learning algorithm and identified features predictive of each of the mood states. In this proof-of-concept analysis, we show that predicting daily mood states is feasible and may help to not only detect daily mood states but also improve passive awareness and deliver just-in-time interventions.