IntroductionThe therapist-guided, webcam-based OMPRIS treatment for internet use disorder (IUD) has demonstrated efficacy in prior studies. Research has also shown that comorbidity, motivation for change, and therapeutic alliance influence treatment outcomes in general. In a secondary analysis of the registered randomised controlled trial (RCT) of OMPRIS, we tested whether these psychological factors relate to IUD symptom severity (IUDSS) and inter-individual differences.MethodsParticipants of the OMPRIS study (N = 180, 136 men [75.6%], 41 women [22.8%], 3 others [1.7%]) Mage 31.78 years, SD = 12.19, range = [16; 84]) were tested at six measurement times (pretreatment—2 years after). Primary outcome: IUDSS. Independent variables: therapeutic alliance, depression, anxiety, and motivation for change. We used multiple imputation for missing data, mixed-effects models adjusted for sociodemographics and IUD subtype, and cluster analyses for hypothesis testing.ResultsLinear mixed-effects models showed that therapist-rated alliance (negative), depression (positive), anxiety (positive), recognition (positive), and taking steps (negative) were associated with IUDSS, whereas ambivalence, patient-rated alliance, sociodemographics, and IUD subtype were not. Exploratory clustering of individual IUDSS slopes identified three treatment-response groups (Good, Low, and High). These clusters differed in baseline severity, comorbidity, and motivation. High Responders showed the highest recognition level (strongest psychological correlate), reflecting reduced denial and greater readiness for change.DiscussionThis study identified psychological factors associated with IUDSS trajectory. Findings underscore that comorbidities can delay strong treatment success. In general, inter-individual differences in treatment response should be considered by therapists.Clinical Trial Registrationhttps://drks.de/search/en/trial/DRKS00019925/details, identifier DRKS00019925.
BackgroundGeneral-purpose large language models (LLMs) have become a popular source of oral health information. However, the patient education materials (PEMs) generated by these models often pose readability challenges. This study aimed to: (1) compare the readability of PEMs generated by six AI chatbots in response to ten frequently asked questions on pediatric oral health; and (2) determine whether the readability of these texts meets the level recommended by the American Medical Association (AMA).MethodsTen sample questions on pediatric oral health were compiled. Each question was submitted once to each of six AI chatbots (GPT-5 Mini, Gemini 3 Flash, Claude Haiku 4.5, DeepSeek-V3, Kimi K2.5, and Qwen3-Max), generating a corpus of 60 texts in a blocked (repeated-measures) design. Six readability indices — Flesch Reading Ease (FRE), Flesch–Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman–Liau Index (CLI), Simple Measure of Gobbledygook (SMOG), and Automated Readability Index (ARI) — were calculated for every text. Each index was analysed separately using the Friedman test, with the ten questions as blocks and the six models as conditions, followed by Dunn's multiple comparison test. Kendall's coefficient of concordance (W) was calculated to quantify the consistency of model ranking across questions.ResultsDeepSeek-V3 showed the highest readability (easiest texts) and Gemini 3 Flash the second highest, whereas Claude Haiku 4.5 showed the lowest readability across all six indices; GPT-5 Mini, Qwen3-Max, and Kimi K2.5 occupied intermediate positions. All six Friedman tests were statistically significant (χ2(5) = 31.77–35.94, all p < 0.0001), and Kendall's W ranged from 0.635 to 0.719, indicating that the relative ranking of the six models was broadly consistent across the ten questions. Against the AMA-recommended sixth-grade reading level, all models exceeded the recommended difficulty on every index; even against a more lenient seventh-grade threshold, only the ARI median of DeepSeek-V3 (6.5) fell below 7.ConclusionsThe PEMs generated by the six LLMs were written at a reading level above the AMA recommendation, and reading difficulty differed systematically among models. Readability should be explicitly controlled through prompt design, model optimisation, and professional review before such materials are used in pediatric oral health education.
Type 2 diabetes mellitus is highly prevalent and frequently undiagnosed, yet HbA1c testing depends on blood sampling and laboratory infrastructure, limiting its use for low-friction dysglycemia screening. Remote photoplethysmography (rPPG) records facial color signals with a standard camera and has been proposed as a digital biomarker platform for cardiometabolic risk. We tested whether facial rPPG color time series carry information about laboratory HbA1c beyond simple comparators. In a prospective single-center study at Singapore General Hospital, 254 participants had both a usable facial recording and a laboratory HbA1c. Each recording was reduced to the mean red, green and blue values of six facial regions over 8.53 s. NervoHbA1c, a 4.27-million-parameter convolutional model with demographic inputs, was evaluated by pooled 5-fold cross-validation repeated three times, leave-one-cohort-out evaluation, a learning curve and testing on two independent smartphone cohorts, against mean, median and demographics-only comparators fitted within each training split. Within the development cohort, 16 models spanning 0–4.27 million fitted parameters were compared, including frozen-encoder linear read-outs and conventional signal-processing features. Pooled over 254 participants, NervoHbA1c had a mean absolute error (MAE) of 1.081% (95% CI 0.973–1.193), against 1.230% for the median baseline, 1.237% for demographics-only and 1.254% for the mean baseline (Nadeau–Bengio p = 0.008–0.042), and its predictions correlated with laboratory HbA1c (r = 0.505). It had the lowest error in every leave-one-cohort-out split, and its error fell steadily as the training set grew. In the development cohort, error decreased with model capacity: every lower-capacity model had higher error than the full model (MAE 0.732% against 0.844% for the mean baseline). A signal-only model was modestly correlated with HbA1c (r = 0.368). The published checkpoint, trained on 53 webcam participants, did not transfer to the smartphone cohorts (SGH-6 MAE 1.478% against 1.205% for demographics-only), a gap attributable to the change of acquisition device (webcam against smartphone AUROC 1.000). Short facial rPPG recordings therefore carry modest but recoverable information about HbA1c beyond demographics when the training data match the acquisition device. Participant-level evaluation, cross-cohort testing and simple comparators are needed before camera-based digital biomarkers advance toward clinical use, and a larger full-precision cohort is planned.
BackgroundSmoking remains a leading cause of preventable morbidity and mortality worldwide. Digital interventions have shown promise in addressing the limitations of traditional smoking cessation approaches. Gamification is increasingly being integrated into these interventions to enhance engagement. However, engagement may decline over time, and the gamification elements used across interventions have not been systematically characterized. The Octalysis Framework provides a structured approach to classifying these elements according to their underlying motivational drives.ObjectiveThis systematic review aimed to identify, classify, and describe the gamification elements used in digital smoking cessation interventions using the Octalysis Framework, and to summarize reported engagement-related outcomes to inform the design of future interventions.MethodsWe followed PRISMA guidelines. Studies of gamified digital smoking cessation interventions were eligible, whereas studies of related but distinct concepts, including serious games, were excluded. MEDLINE, PsycINFO, Embase, CINAHL, Web of Science, Epistemonikos, and Scopus were searched from inception through April 16, 2026. Two reviewers independently screened studies and extracted data. Risk of bias was assessed using design-appropriate JBI tools. Elements were grouped and counted at the intervention level, classified using the Octalysis Framework, and narratively synthesized because of heterogeneity.ResultsThirteen studies describing nine predominantly smartphone-based interventions met the inclusion criteria. Across the interventions, 56 elements were mapped to all eight core drives. Development and Accomplishment contained the most elements, whereas Unpredictability and Curiosity contained the fewest. Engagement-related findings were heterogeneous and often favorable. Formal cessation outcomes were reported in seven studies and were mixed. Only three studies explicitly reported using a formal gamification framework or taxonomy.ConclusionsThis review provides a focused, framework-based synthesis of gamification elements used in digital smoking cessation interventions. By identifying how these elements have been applied across interventions, it offers a structured basis for future intervention design. The findings may inform the selection of theory-informed gamification strategies; however, their effects on sustained engagement and smoking cessation outcomes require further evaluation.RegistrationNot registered.
BackgroundYoung people in low- and middle-income countries face social and structural stressors that increase vulnerability to mental health difficulties, while access to formal mental health services remains limited. Digital mental health interventions may expand access to support, but their design requires understanding young people’ lived experiences, coping strategies, and help-seeking behaviors.ObjectiveThis study explored young people's experiences of mental health challenges, coping strategies, and help-seeking behaviors in Kenyan communities to inform the design of youth-centered digital mental health interventions.MethodsWe conducted an exploratory qualitative study using 12 focus group discussions with 72 young people aged 18–24 years in urban (Kibra), peri-urban (Kikuyu Town), and rural (Nachu) Kenya. Participants were purposively sampled by gender, education, and geography. Discussions explored mental health experiences, coping, support-seeking, barriers to care, and perspectives on digital mental health tools. Audio-recorded discussions were transcribed verbatim and analyzed using thematic analysis. Reporting followed COREQ guidelines.ResultsSix themes emerged: (1) causes of mental distress, including academic pressure, financial hardship, and uncertainty about the future; (2) emotional distress, including sadness, anxiety, loneliness, and overwhelm; (3) stigma and barriers to professional help-seeking; (4) reliance on informal support from friends, family, teachers, and religious leaders; (5) coping strategies, including sports, music, social interaction, and spiritual practices; and (6) openness to digital mental health support. Participants valued digital platforms that offered anonymity, trustworthy information, and culturally relevant content.ConclusionsYoung people in Kenyan communities face multiple stressors and barriers to formal mental health care. Youth-centered digital mental health platforms may offer accessible and acceptable support when designed to be confidential, culturally relevant, and linked to existing community support systems.
