Yung-Chieh Ching

BACKGROUND Repeated assessment can describe anhedonia over time, but interpretable temporal signals require evaluation under realistic missingness and participant burden. Moving averages compare recent experience with longer-term patterns without assuming that a smoothed trajectory is a validated clinical warning. OBJECTIVE This second-year study will evaluate the feasibility and temporal information of 7-, 14-, and 30-day moving averages in a cross-device anhedonia monitoring platform, extending the published Part I methodological protocol. METHODS This prospective observational study in Taiwan targets 202 participants: 101 psychiatric outpatients and 101 university students. A separate implementation cohort comprises 30 hospitalized service members. Participants undertake 24 weeks of ecological momentary assessment using a current-state adaptation of the Snaith-Hamilton Pleasure Scale, targeting 3 participant-initiated assessments per day with 5-hour spacing. Optional evening LINE reminders depend on noncompletion, while participants can view descriptive weather and trend feedback. One daily mean will support calendar-defined moving averages. Three crossover pairs will be compared in 100 northern participants, with a selected rule evaluated without retuning in 102 central participants. The target is a first observed sustained threshold-state transition within the subsequent 14 days. Evaluation includes computational availability, event capture, false-signal burden, lead time, and comparison with the current score. Missingness sensitivities and continuous level, slope, and instability features are secondary analyses. RESULTS The funded project began on August 1, 2026. As of September 22, 2026, recruitment is ongoing and no outcome analyses have been conducted. The grant work plan schedules recruitment through March 2027 and analysis activities during April-July 2027. Final analysis will follow completion of the relevant 24-week observation periods; enrollment continuing through March would extend final follow-up to September 2027. CONCLUSIONS The study will establish the achievable observation density and assess whether moving-average signals add temporal information beyond current anhedonia severity. Forecasting within the same symptom series will be interpreted separately from independently verified clinical deterioration or clinical benefit.

BACKGROUND Nursing students need opportunities to recognize and manage emotion while communicating with patients in psychologically complex situations. A two-cycle teaching project combined game-based activities with AI virtual-human cases in a psychiatric nursing course in Taiwan. The study was previously published as a registered-report protocol. OBJECTIVE This study evaluated changes in emotional intelligence across two action-research cycles, described students' learning experiences, and integrated quantitative, qualitative, measurement, and implementation evidence. METHODS This nonrandomized mixed methods action research study used a single-group pretest-posttest design in two 18-week course cycles. The Adult Emotional Intelligence Scale (AEIS) was administered at pretest and posttest. Paired data were analyzed with Wilcoxon signed-rank tests because total-score change was nonnormal; standardized paired mean differences (Cohen dz) and linear mixed models were also reported. Sensitivity analyses addressed duplicate responses, missingness, multiple imputation, baseline-adjusted cycle comparisons, and floor or ceiling effects. End-of-course reflections underwent inductive thematic analysis. A joint display compared data streams. Three experts reviewed virtual-human scenario content separately from AEIS reliability analysis. RESULTS Of 57 students with pretest data, 56 had matched pretest-posttest AEIS data. AEIS total scores increased from mean 52.96 (SD 9.70) to 60.29 (SD 10.48; mean change 7.32; Cohen dz=0.80, 95% CI 0.50-1.10; P<.001). Each domain had higher posttest scores, and all overall paired Wilcoxon tests remained P<.001 after Holm adjustment for the four domain tests. The time-by-cycle interaction for AEIS total was not statistically significant (F1,54.80=1.09; P=.300), so the study did not detect a difference in score change between cycles. Five themes in 59 reflection narratives described students’ professional development trajectory: discovering the self, reframing the helping mindset, understanding others through empathy, growing through uncertainty, and becoming the nurse they aspire to be. AEIS total internal consistency was alpha=.914 at pretest and alpha=.933 at posttest. For virtual-human scenarios, expert-review S-CVI/Ave was .92 for content suitability and .92 for clinical interaction fidelity. CONCLUSIONS Course participation was associated with higher self-reported emotional intelligence in two cohorts. Reflection narratives described discovering the self, reframing the helping mindset, understanding others through empathy, growing through uncertainty, and becoming the nurse they aspire to be. The noncontrolled design, small second cycle, and absence of objective competence or longitudinal outcomes require cautious interpretation and motivate a controlled, multisite evaluation. INTERNATIONAL REGISTERED REPORT RR2-10.2196/80290

Background: Anhedonia is a core symptom of depressive disorders. Although group-level cutoff points for the Snaith–Hamilton Pleasure Scale (SHAPS) are established, real-time, individual-level monitoring remains limited. Ecological momentary assessment (EMA) enables high-frequency sampling, and simple moving averages (SMA; 7/14/30 days) offer an interpretable way to smooth daily signals and surface early-warning trends. Objective: The aim of this study was to develop and validate a cross-device platform that collects EMA-based SHAPS responses and visualizes SMA trends for individual anhedonia monitoring. This part I protocol focuses on platform design, feasibility, usability, data quality, and initial analytical validity to inform subsequent evaluation. Methods: We will conduct a single-arm methodological pilot in approximately 24 adults, recruited from an outpatient psychiatry clinic (clinical cohort) and a university setting (nonclinical cohort). After baseline assessment, participants will complete 30 days of daily EMA-SHAPS. Primary outcomes are feasibility (adherence, missingness patterns, timing fidelity) and usability/face validity (eg, System Usability Scale, qualitative feedback). We will also describe expert content validity ratings for key visual elements and exploratory analytic checks of within-person variability and the descriptive behavior of 7/14/30-day moving averages; these analyses are not powered for confirmatory hypothesis testing. Results: This protocol specifies the design–validate sequence, recruitment procedures, and predefined adherence and missing-data rules for computing moving averages. Recruitment for the year-1 feasibility pilot is scheduled to begin in March 2026 following institutional review board approval (approval 114006; approval date: June 10, 2025), with feasibility metrics, usability indices, and parameter estimates used to refine alert logic and power calculations for a planned part II evaluation. Conclusions: Combining EMA-SHAPS with SMA-based trend visualizations may provide an interpretable way to summarize daily anhedonia signals and generate candidate early-warning indicators. This methodological pilot will deliver feasibility, usability, and preliminary analytic parameters needed to support a subsequent controlled evaluation of the platform’s clinical utility. International Registered Report Identifier (IRRID): PRR1-10.2196/84024

BACKGROUND Mental health apps are widely promoted to support assessment, self-management, and recovery. Yet their real-world value for people with lived experience depends on how apps are encountered, trusted, and sustained (or abandoned) in everyday life—contexts that quantitative trials alone rarely capture. OBJECTIVE To synthesize qualitative evidence on how individuals with lived experience of mental illness use mental health–related mobile apps and to develop a line-of-argument model explaining variability in engagement, perceived benefit, and burden across settings. METHODS We conducted a qualitative evidence synthesis using meta-ethnography (Noblit & Hare) and reported per eMERGe guidance. We searched CINAHL for English-language qualitative studies on mental health app use (2019–2024) and complemented this with backward/forward citation checking. Two reviewers independently screened records, extracted first-order (participant) and second-order (author) constructs, and appraised study quality using NICE qualitative standards. Through reciprocal translation and synthesis of translations, we generated third-order interpretations and an integrative line-of-argument. RESULTS Six studies from the United States, England, and the Netherlands were included, spanning apps targeting depression/anxiety, bipolar disorder, serious mental illness, and suicide prevention. Four cross-cutting themes emerged. (1) Apps as adjuncts, not replacements: they scaffold self-regulation between clinical contacts but are often set aside during crises. (2) Self-management and empowerment: mood/symptom tracking, prompts, and psychoeducation can strengthen insight and agency when tailored and meaningful. (3) Privacy, data governance, and trust: opaque data flows, surveillance concerns, and uncertainty about escalation pathways constrain disclosure and sustained use. (4) Design, usability, and discontinuation: low cognitive load, reliability, and personalization facilitate integration into routines; glitches, advertising, and burdensome data entry drive attrition. These dynamics shaped whether app use translated into improved day-to-day functioning and therapeutic conversations. CONCLUSIONS Mental health apps can extend person-centered care when embedded within trusted therapeutic relationships and usable ecosystems. Implementation should prioritize co-design with people with lived experience, transparent and choice-rich data governance (eg, clear data routes, consent options, and review plans), and design choices that minimize cognitive load—especially during distress. Clinicians and services should agree on what to track, how to review app data, and when to escalate, treating apps as shared tools rather than stand-alone solutions. Further work should standardize reporting of privacy practices and engagement trajectories and evaluate models that integrate app data into routine care without increasing burden for users or clinicians.

BACKGROUND Anhedonia is a core symptom of depressive disorders. Although group-level cutoff points for the Snaith–Hamilton Pleasure Scale (SHAPS) are established, real-time, individual-level monitoring remains limited. Ecological momentary assessment (EMA) enables high-frequency sampling, and simple moving averages (SMA; 7/14/30 days) offer an interpretable way to smooth daily signals and surface early-warning trends. OBJECTIVE The aim of this study was to develop and validate a cross-device platform that collects EMA-based SHAPS responses and visualizes SMA trends for individual anhedonia monitoring. This part I protocol focuses on platform design, feasibility, usability, data quality, and initial analytical validity to inform subsequent evaluation. METHODS We will conduct a single-arm methodological pilot in approximately 24 adults, recruited from an outpatient psychiatry clinic (clinical cohort) and a university setting (nonclinical cohort). After baseline assessment, participants will complete 30 days of daily EMA-SHAPS. Primary outcomes are feasibility (adherence, missingness patterns, timing fidelity) and usability/face validity (eg, System Usability Scale, qualitative feedback). We will also describe expert content validity ratings for key visual elements and exploratory analytic checks of within-person variability and the descriptive behavior of 7/14/30-day moving averages; these analyses are not powered for confirmatory hypothesis testing. RESULTS This protocol specifies the design–validate sequence, recruitment procedures, and predefined adherence and missing-data rules for computing moving averages. Recruitment for the year-1 feasibility pilot is scheduled to begin in March 2026 following institutional review board approval (approval 114006; approval date: June 10, 2025), with feasibility metrics, usability indices, and parameter estimates used to refine alert logic and power calculations for a planned part II evaluation. CONCLUSIONS Combining EMA-SHAPS with SMA-based trend visualizations may provide an interpretable way to summarize daily anhedonia signals and generate candidate early-warning indicators. This methodological pilot will deliver feasibility, usability, and preliminary analytic parameters needed to support a subsequent controlled evaluation of the platform’s clinical utility. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/84024

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Mental health issues among Taiwanese university students are increasingly prevalent, with 20–25% experiencing psychological distress and suicide risk becoming a major concern. Nursing students often lack structured training in self-awareness and crisis management. Current psychiatric nursing education provides limited practice on suicide-related topics, leading to anxiety and uncertainty. Integrating generative AI and game-based learning offers a promising solution to enhance emotional intelligence and crisis response skills in a safe, interactive setting. Objective: This study will use action research to evaluate an innovative teaching model combining AI virtual patients and game-based learning to improve nursing students’ emotional intelligence, crisis management competence, and learning engagement, while offering evidence for curriculum innovation. Methods: A two-cycle action research design will be conducted with nursing students in a psychiatric nursing course. The course will be incorporating card-based games for self-awareness and AI virtual patients for repeating practice in suicide risk assessment and empathetic communication. A one-group pretest-posttest design with the Adult Emotional IntelligeThe project will develop five AI virtual patient modules and aims to improve students’ emotional awareness, self-understanding, and crisis response skills. Results will support future curriculum design integrating AI and game-based learning in nursing education.nce Scale (AEIS) and qualitative data from reflections and classroom observations will be used. Results: The project will develop five AI virtual patient modules and aims to improve students’ emotional awareness, self-understanding, and crisis response skills. Results will support future curriculum design integrating AI and game-based learning in nursing education. Conclusions: This study proposes a novel teaching model integrating game-based learning and AI Virtual Human simulations to enhance emotional intelligence and crisis management skills in nursing students. Using action research and the AEIS, the intervention will be evaluated and refined. The findings may inform future AI-driven approaches in competency-based psychiatric nursing education.