BACKGROUND: Suicide is a significant public health issue. Many risk prediction tools have been developed to estimate an individual\'s risk of suicide. Risk prediction models can go beyond individual risk assessment; one important application of risk prediction models is population health planning. Suicide is a result of the interaction among the risk and protective factors at the individual, health care system, and community levels. Thus, policy and decision makers can play an important role in suicide prevention. However, few prediction models for the population risk of suicide have been developed.
OBJECTIVE: This study aims to develop and validate prediction models for the population risk of suicide using health administrative data, considering individual-, health system-, and community-level predictors.
METHODS: We used a case-control study design to develop sex-specific risk prediction models for suicide, using the health administrative data in Quebec, Canada. The training data included all suicide cases (n=8899) that occurred from January 1, 2002, to December 31, 2010. The control group was a 1% random sample of living individuals in each year between January 1, 2002, and December 31, 2010 (n=645,590). Logistic regression was used to develop the prediction models based on individual-, health care system-, and community-level predictors. The developed model was converted into synthetic estimation models, which concerted the individual-level predictors into community-level predictors. The synthetic estimation models were directly applied to the validation data from January 1, 2011, to December 31, 2019. We assessed the performance of the synthetic estimation models with four indicators: the agreement between predicted and observed proportions of suicide, mean average error, root mean square error, and the proportion of correctly identified high-risk regions.
RESULTS: The sex-specific models based on individual data had good discrimination (male model: C=0.79; female model: C=0.85) and calibration (Brier score for male model 0.01; Brier score for female model 0.005). With the regression-based synthetic models applied in the validation data, the absolute differences between the synthetic risk estimates and observed suicide risk ranged from 0% to 0.001%. The root mean square errors were under 0.2. The synthetic estimation model for males correctly predicted 4 of 5 high-risk regions in 8 years, and the model for females correctly predicted 4 of 5 high-risk regions in 5 years.
CONCLUSIONS: Using linked health administrative databases, this study demonstrated the feasibility and the validity of developing prediction models for the population risk of suicide, incorporating individual-, health system-, and community-level variables. Synthetic estimation models built on routinely collected health administrative data can accurately predict the population risk of suicide. This effort can be enhanced by timely access to other critical information at the population level.

UNASSIGNED: Regular physical activity and exercise are fundamental components of a healthy lifestyle for youth living with type 1 diabetes (T1D). Yet, few youth living with T1D achieve the daily minimum recommended levels of physical activity. For all youth, regardless of their disease status, minutes of physical activity compete with other daily activities, including digital gaming. There is an emerging area of research exploring whether digital games could be displacing other physical activities and exercise among youth, though, to date, no studies have examined this question in the context of youth living with T1D.
UNASSIGNED: We examined characteristics of digital gaming versus nondigital gaming (other exercise) sessions and whether youth with T1D who play digital games (gamers) engaged in less other exercise than youth who do not (nongamers), using data from the Type 1 Diabetes Exercise Initiative Pediatric study.
UNASSIGNED: During a 10-day observation period, youth self-reported exercise sessions, digital gaming sessions, and insulin use. We also collected data from activity wearables, continuous glucose monitors, and insulin pumps (if available).
UNASSIGNED: The sample included 251 youths with T1D (age: mean 14, SD 2 y; self-reported glycated hemoglobin A1c level: mean 7.1%, SD 1.3%), of whom 105 (41.8%) were female. Youth logged 123 digital gaming sessions and 3658 other exercise (nondigital gaming) sessions during the 10-day observation period. Digital gaming sessions lasted longer, and youth had less changes in glucose and lower mean heart rates during these sessions than during other exercise sessions. Youth described a greater percentage of digital gaming sessions as low intensity (82/123, 66.7%) when compared to other exercise sessions (1104/3658, 30.2%). We had 31 youths with T1D who reported at least 1 digital gaming session (gamers) and 220 youths who reported no digital gaming (nongamers). Notably, gamers engaged in a mean of 86 (SD 43) minutes of other exercise per day, which was similar to the minutes of other exercise per day reported by nongamers (mean 80, SD 47 min).
UNASSIGNED: Digital gaming sessions were longer in duration, and youth had less changes in glucose and lower mean heart rates during these sessions when compared to other exercise sessions. Nevertheless, gamers reported similar levels of other exercise per day as nongamers, suggesting that digital gaming may not fully displace other exercise among youth with T1D.

BACKGROUND: Patients with hematological malignancies receiving hematopoietic cell transplantation (HCT) or chimeric antigen receptor (CAR) T-cell therapy are at risk of developing serious clinical complications after discharge.
OBJECTIVE: The aim of the TEL-HEMATO study was to improve our telehealth platform for the follow-up of patients undergoing HCT or CAR T-cell therapy during the first 3 months after discharge with the addition of wearable devices.
METHODS: Eleven patients who received autologous (n=2) or allogeneic (n=5) HCT or CAR T-cell therapy (n=4) for hematological malignancies were screened from November 2022 to July 2023. Two patients discontinued the study after enrollment. The telehealth platform consisted of the daily collection of vital signs, physical symptoms, and quality of life assessment up to 3 months after hospital discharge. Each patient received a clinically validated smartwatch (ScanWatch) and a digital thermometer, and a dedicated smartphone app was used to collect these data. Daily revision of the data was performed through a web-based platform by a hematologist or a nurse specialized in HCT and CAR T-cell therapy.
RESULTS: Vital signs measured through ScanWatch were successfully collected with medium/high adherence: heart rate was recorded in 8/9 (89%) patients, oxygen saturation and daily steps were recorded in 9/9 (100%) patients, and sleeping hours were recorded in 7/9 (78%) patients. However, temperature recorded manually by the patients was associated with lower compliance, which was recorded in 5/9 (55%) patients. Overall, 5/9 (55%) patients reported clinical symptoms in the app. Quality of life assessment was completed by 8/9 (89%) patients at study enrollment, which decreased to 3/9 (33%) at the end of the third month. Usability was considered acceptable through ratings provided on the System Usability Scale. However, technological issues were reported by the patients.
CONCLUSIONS: While the addition of wearable devices to a telehealth clinical platform could have potentially synergic benefits for HCT and CAR T-cell therapy patient monitoring, noncomplete automation of the platform and the absence of a dedicated telemedicine team still represent major limitations to be overcome. This is especially true in our real-life setting where the target population generally comprises patients of older age with a low digital education level.

OBJECTIVE: Adolescence is a crucial phase in a woman\'s life, as it signifies the beginning of her reproductive journey. During this time, there are notable variations in sexual development and a sense of caution that can present challenges for healthcare providers. The rationale for studying adolescent gynecological problems lies in the need to understand and address the unique reproductive health challenges faced by young girls. By investigating these issues, researchers and healthcare professionals can develop effective strategies for prevention, early detection, and treatment of gynecological conditions in adolescents. This knowledge is crucial for promoting the overall well-being and reproductive health of young girls, ensuring they receive appropriate care and support during this critical stage of development. This study focuses on identifying the most common gynecological issues in teenagers, exploring the root causes, examining the available treatment options, and understanding how they are managed at a tertiary care facility.  Methods: This cross-sectional observational study took place at a tertiary care center and focused on gynecological issues in adolescent females who sought care at the gynecology department from January 2016 to December 2022. The study participants were categorized into three groups: early adolescence (10-13 years), middle adolescence (14-16 years), and late adolescence (17-19 years) for analysis. Ethical approval was obtained for this hospital-based research, which involved examining, investigating, and treating the study participants.
RESULTS: Out of the 49,700 new female patients, 2000 (4.02%) fell within the specified age range. The average age of the participants was 16.87±2.16, and the majority of them sought help for menstrual issues (63.45%), followed by abdominal discomfort (15.6%) and vaginal discharge (7.2%). Menstrual disorders were the most common concern, with dysmenorrhea and puberty menorrhagia being prevalent issues. Abdominal pain was caused by various factors, such as urinary tract infections, ovarian tumors, pelvic inflammatory disease, endometriosis, and endometrial tuberculosis. The majority of cases were treated conservatively, with only a small percentage requiring surgical intervention.
CONCLUSIONS: The significance of early detection and intervention in addressing gynecological issues among adolescents is highlighted in the findings of this research, underscoring the necessity for specialized healthcare services that cater to the specific needs of this demographic. Adolescent gynecology plays a crucial role in safeguarding the reproductive health and overall well-being of young women, emphasizing the importance of seeking assistance proactively.

BACKGROUND: Valid assessment tools are needed when investigating adherence to national dietary and lifestyle guidelines.
OBJECTIVE: The relative validity of the new digital food frequency questionnaire, the DIGIKOST-FFQ, against 7-day weighed food records and activity sensors was investigated.
METHODS: In total, 77 participants were included in the validation study and completed the DIGIKOST-FFQ and the weighed food record, and of these, 56 (73%) also used the activity sensors. The DIGIKOST-FFQ estimates the intake of foods according to the Norwegian food-based dietary guidelines (FBDGs) in addition to lifestyle factors.
RESULTS: At the group level, the DIGIKOST-FFQ showed good validity in estimating intakes according to the Norwegian FBDG. The median differences were small and well below portion sizes for all foods except \"water\" (median difference 230 g/day). The DIGIKOST-FFQ was able to rank individual intakes for all foods (r=0.2-0.7). However, ranking estimates of vegetable intakes should be interpreted with caution. Between 69% and 88% of the participants were classified into the same or adjacent quartile for foods and between 71% and 82% for different activity intensities. The Bland-Altman plots showed acceptable agreements between DIGIKOST-FFQ and the reference methods. The absolute amount of time in \"moderate to vigorous intensity\" was underestimated with the DIGIKOST-FFQ. However, estimated time in \"moderate to vigorous intensity,\" \"vigorous intensity,\" and \"sedentary time\" showed acceptable correlations and good agreement between the methods. The DIGIKOST-FFQ was able to identify adherence to the Norwegian FBDG and physical activity recommendations.
CONCLUSIONS: The DIGIKOST-FFQ gave valid estimates of dietary intakes and was able to identify individuals with different degrees of adherence to the Norwegian FBDG and physical activity recommendations. Moderate physical activity was underreported, water was overreported, and vegetables showed poor correlation, which are important to consider when interpreting the data. Good agreement was observed between the methods in estimating dietary intakes and time in \"moderate to vigorous physical activity,\" \"sedentary time,\" and \"sleep.\"

UNASSIGNED: Medical history contributes approximately 80% to a diagnosis, although physical examinations and laboratory investigations increase a physician\'s confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis.
UNASSIGNED: This study explored the contribution of patient history to AI-assisted medical diagnoses and assessed the accuracy of ChatGPT in reaching a clinical diagnosis based on the medical history provided.
UNASSIGNED: Using clinical vignettes of 30 cases identified in The BMJ, we evaluated the accuracy of diagnoses generated by ChatGPT. We compared the diagnoses made by ChatGPT based solely on medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside history with the correct diagnoses.
UNASSIGNED: ChatGPT accurately diagnosed 76.6% (23/30) of the cases with only the medical history, consistent with previous research targeting physicians. We also found that this rate was 93.3% (28/30) when additional information was included.
UNASSIGNED: Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when using AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems.

BACKGROUND: Digital interventions are increasingly popular for the provision of nonpharmacological pain interventions, but few exist for adolescents with menstrual pain. User-centered design involves incorporating users across phases of digital health intervention design, development, and implementation and leads to improved user engagement and outcomes. A needs assessment is the first step of this approach.
OBJECTIVE: The goal of this study was to conduct a needs assessment to understand menstrual pain management needs and preferences and mindfulness experiences, preferences, and knowledge of adolescents with menstrual pain to inform the future development of an app for managing menstrual pain.
METHODS: We used an explanatory sequential mixed method design that included a survey followed by focus groups. Adolescents aged 13-17 years completed a survey (n=111) and participated in focus groups (n=16). Data were analyzed using descriptive statistics and thematic content analysis and synthesized to provide specific recommendations based on adolescent responses.
RESULTS: Adolescents (n=111) who completed the survey reported a moderate understanding of mindfulness and menstrual pain. Over three-quarters (n=87, 78%) of participants practiced some form of mindfulness and 87% (n=97) of survey participants used nonpharmacological pain management strategies. Teens had a moderate perception that mindfulness could help their menstrual pain (mean 4.51/10, SD 2.45, with higher scores suggesting more interest). Themes were generated related to mindfulness experiences, menstrual pain knowledge and experiences, and app functionality. These themes underscored adolescents\' need for continued support and flexible access to mindfulness activities; their awareness of multiple influences to pain, with potential for further education in this area; and the need for menstrual pain-specific content, along with content relevant to typical day-to-day experiences of adolescents.
CONCLUSIONS: Adolescents with menstrual pain have an interest in using a mindfulness app for pain but have unique needs that need to be addressed to ensure app engagement and relevance for this population. Concrete recommendations for future app development are provided.

BACKGROUND: Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records.
OBJECTIVE: This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak.
METHODS: Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children\'s hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras.
RESULTS: There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras.
CONCLUSIONS: This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

BACKGROUND: Although adolescents report high levels of stress, they report engaging in few stress management techniques. Consequently, developing effective and targeted programs to help address this transdiagnostic risk factor in adolescence is particularly important. Most stress management programs for adolescents are delivered within schools, and the evidence for these programs is mixed, suggesting a need for alternative options for stress management among adolescents.
OBJECTIVE: The aim of the study is to test the short-term effects of a self-guided digital mental health intervention (DMHI) designed for adolescents on perceived stress and rumination (ie, brooding).
METHODS: This was a 12-week, 2-arm decentralized randomized controlled trial of adolescents aged 13 to 17 years who presented with elevated levels of perceived stress and brooding. Participants were randomly assigned to engage with a self-guided DMHI (Happify for Teens) or to a waitlist control. Participants assigned to the intervention group were given access to the program for 12 weeks. Happify for Teens consists of various evidence-based activities drawn from therapeutic modalities such as cognitive behavioral therapy, positive psychology, and mindfulness, which are then organized into several programs targeting specific areas of concern (eg, Stress Buster 101). Participants in the waitlist control received access to this product for 12 weeks upon completing the study. Participants in both groups completed measures of perceived stress, brooding, optimism, sleep disturbance, and loneliness at baseline, 4 weeks, 8 weeks, and 12 weeks. Changes in outcomes between the intervention and waitlist control groups were assessed using repeated-measures multilevel models.
RESULTS: Of the 303 participants included in data analyses, 132 were assigned to the intervention and 171 to the waitlist. There were significantly greater improvements in the intervention condition for perceived stress (intervention: B=-1.50; 95% CI -1.82 to -1.19; P<.001 and control: B=-0.09; 95% CI -0.44 to 0.26; P=.61), brooding (intervention: B=-0.84; 95% CI -1.00 to -0.68; P<.001 and control: B=-0.30; 95% CI -0.47 to -0.12; P=.001), and loneliness (intervention: B=-0.96; 95% CI -1.2 to -0.73; P<.001 and control: B=-0.38; 95% CI: -0.64 to -0.12; P=.005) over the 12-week study period. Changes in optimism and sleep disturbance were not significantly different across groups (Ps≥.096).
CONCLUSIONS: Happify for Teens was effective at reducing perceived stress, rumination, and loneliness among adolescents over 12 weeks when compared to a waitlist control group. Our data reveal the potential benefits of DMHIs for adolescents, which may present a more scalable, destigmatized, and cost-effective alternative to school-based programs.
BACKGROUND: ClinicalTrials.gov NCT04567888; https://clinicaltrials.gov/ct2/show/NCT04567888.
UNASSIGNED: RR2-10.2196/25545.

BACKGROUND: Last Aid Courses (LAC) for adults have been established in 21 countries in Europe, Australia and America to improve the public discussion about death and dying and to empower people to participate in end-of-life care provision. In 2018, the first Last Aid Courses for kids and teens (LAC-KT) were introduced. The aim of the study was to explore the views and experiences of the course participants and Last Aid Course instructors on the LAC-KT.
METHODS: A mixed-methods approach was used. The views of the LAC-KT participants, aged 7 to 17 years, on the LAC-KT were collected using a questionnaire. In addition, the experiences of the Last Aid Course instructors were explored in focus group interviews.
RESULTS: The results show that 84% of the participants had experiences with death and dying and 91% found the LAC-KT helpful for everyone. The majority of the participants appreciate the opportunity to talk and learn about death, dying, grief and palliative care.
CONCLUSIONS: The LAC-KT is feasible, very well accepted and a welcome opportunity for exchanging and obtaining information about dying, grief and palliative care. The findings of the study indicate that the LAC-KT should be offered to all interested children and teenagers and included in the school curriculum.