Category: Statistics

JMIR Res Protoc. 2025 Jan 30;14:e64745. doi: 10.2196/64745.

ABSTRACT

BACKGROUND: Transitional-aged youth have a high burden of mental health difficulties in Canada, with Indigenous youth, in particular, experiencing additional circumstances that challenge their well-being. Mobile health (mHealth) approaches hold promise for supporting individuals in areas with less access to services such as Northern Ontario.

OBJECTIVE: The primary objective of this study is to evaluate the effectiveness of the JoyPop app in increasing emotion regulation skills for Indigenous transitional-aged youth (aged 18-25 years) on a waitlist for mental health services when compared with usual practice (UP). The secondary objectives are to (1) evaluate the impact of the app on general mental health symptoms and treatment readiness and (2) evaluate whether using the app is associated with a reduction in the use (and therefore cost) of other services while one is waiting for mental health services.

METHODS: The study is a pragmatic, parallel-arm randomized controlled superiority trial design spanning a 4-week period. All participants will receive UP, which involves waitlist monitoring practices at the study site, which includes regular check-in phone calls to obtain any updates regarding functioning. Participants will be allocated to the intervention (JoyPop+UP) or control (UP) condition in a 1:1 ratio using stratified block randomization. Participants will complete self-report measures of emotion regulation (primary outcome), mental health, treatment readiness, and service use during 3 assessments (baseline, second [after 2 weeks], and third [after 4 weeks]). Descriptive statistics pertaining to baseline variables and app usage will be reported. Linear mixed modeling will be used to analyze change in outcomes over time as a function of condition assignment, while a cost-consequence analysis will be used to evaluate the association between app use and service use.

RESULTS: Recruitment began September 1, 2023, and is ongoing. In total, 2 participants have completed the study.

CONCLUSIONS: This study will assess whether the JoyPop app is effective for Indigenous transitional-aged youth on a waitlist for mental health services. Positive findings may support the integration of the app into mental health services as a waitlist management tool.

TRIAL REGISTRATION: ClinicalTrials.gov NCT05991154; https://clinicaltrials.gov/study/NCT05991154.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64745.

PMID:39883939 | DOI:10.2196/64745

J Med Internet Res. 2025 Jan 30;27:e64749. doi: 10.2196/64749.

ABSTRACT

BACKGROUND: Few studies have explored the relationship between macronutrient intake and sleep outcomes using daily data from mobile apps.

OBJECTIVE: This cross-sectional study aimed to examine the associations between macronutrients, dietary components, and sleep parameters, considering their interdependencies.

METHODS: We analyzed data from 4825 users of the Pokémon Sleep and Asken smartphone apps, each used for at least 7 days to record objective sleep parameters and dietary components, respectively. Multivariable regression explored the associations between quartiles of macronutrients (protein; carbohydrate; and total fat, including saturated, monounsaturated, and polyunsaturated fats), dietary components (sodium, potassium, dietary fiber, and sodium-to-potassium ratio), and sleep variables (total sleep time [TST], sleep latency [SL], and percentage of wakefulness after sleep onset [%WASO]). The lowest intake group was the reference. Compositional data analysis accounted for macronutrient interdependencies. Models were adjusted for age, sex, and BMI.

RESULTS: Greater protein intake was associated with longer TST in the third (+0.17, 95% CI 0.09-0.26 h) and fourth (+0.18, 95% CI 0.09-0.27 h) quartiles. In contrast, greater fat intake was linked to shorter TST in the third (-0.11, 95% CI -0.20 to -0.27 h) and fourth (-0.16, 95% CI -0.25 to -0.07 h) quartiles. Greater carbohydrate intake was associated with shorter %WASO in the third (-0.82%, 95% CI -1.37% to -0.26%) and fourth (-0.57%, 95% CI -1.13% to -0.01%) quartiles, while greater fat intake was linked to longer %WASO in the fourth quartile (+0.62%, 95% CI 0.06%-1.18%). Dietary fiber intake correlated with longer TST and shorter SL. A greater sodium-to-potassium ratio was associated with shorter TST in the third (-0.11, 95% CI -0.20 to -0.02 h) and fourth (-0.19, 95% CI -0.28 to -0.10 h) quartiles; longer SL in the second (+1.03, 95% CI 0.08-1.98 min) and fourth (+1.50, 95% CI 0.53-2.47 min) quartiles; and longer %WASO in the fourth quartile (0.71%, 95% CI 0.15%-1.28%). Compositional data analysis, involving 6% changes in macronutrient proportions, showed that greater protein intake was associated with an elevated TST (+0.27, 95% CI 0.18-0.35 h), while greater monounsaturated fat intake was associated with a longer SL (+4.6, 95% CI 1.93-7.34 min) and a larger %WASO (+2.2%, 95% CI 0.63%-3.78%). In contrast, greater polyunsaturated fat intake was associated with a reduced TST (-0.22, 95% CI -0.39 to -0.05 h), a shorter SL (-4.7, 95% CI to 6.58 to -2.86 min), and a shorter %WASO (+2.0%, 95% CI -3.08% to -0.92%).

CONCLUSIONS: Greater protein and fiber intake were associated with longer TST, while greater fat intake and sodium-to-potassium ratios were linked to shorter TST and longer WASO. Increasing protein intake in place of other nutrients was associated with longer TST, while higher polyunsaturated fat intake improved SL and reduced WASO.

PMID:39883933 | DOI:10.2196/64749

Am J Drug Alcohol Abuse. 2025 Jan 30:1-11. doi: 10.1080/00952990.2024.2417820. Online ahead of print.

ABSTRACT

Background: While social support benefits those in treatment for opioid use disorder, it is unclear how social support impacts patient outcomes.Objectives: This study examines how support person attitudes toward buprenorphine and their communication about substance use are associated with the well-being of patients receiving buprenorphine treatment.Methods: We analyzed cross-sectional baseline data from 219 buprenorphine patients (40% female) and their support persons (72% female). Patients were recruited from five community health centers and asked to nominate a support person. Patient outcomes included symptoms of depression, anxiety, impairment due to substance use, and perceived social support. Support persons predictors included their attitudes toward buprenorphine from four statements (e.g. “Buprenorphine is just replacing one drug for another”) and communication using two items (e.g. comfort and effectiveness discussing substance use).Results: More stigmatizing attitudes, such as believing patients should quit on their own without medication, were associated with increased patient substance use-related impairment (F = 4.53, p = .01). Effective communication was associated with lower patient depression (F = 10.15, p < .001), anxiety (F = 4.73, p = .001), lower impairment (F = 6.46, p < .001), and higher perceived social support (F = 3.68, p = .007).Conclusions: This study highlights how support person attitudes and communication dynamics significantly affect the mental health and impairment of individuals receiving buprenorphine treatment. Interventions that reduce stigma and promote effective communication between patients and their loved ones could enhance treatment outcomes and overall well-being among patients with OUD.

PMID:39883925 | DOI:10.1080/00952990.2024.2417820

J Med Internet Res. 2025 Jan 30;27:e58760. doi: 10.2196/58760.

ABSTRACT

BACKGROUND: Primary intracranial germ cell tumors (iGCTs) are highly malignant brain tumors that predominantly occur in children and adolescents, with an incidence rate ranking third among primary brain tumors in East Asia (8%-15%). Due to their insidious onset and impact on critical functional areas of the brain, these tumors often result in irreversible abnormalities in growth and development, as well as cognitive and motor impairments in affected children. Therefore, early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.

OBJECTIVE: This study aimed to investigate the application of facial recognition technology in the early detection of iGCTs in children and adolescents. Early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.

METHODS: A multicenter, phased approach was adopted for the development and validation of a deep learning model, GVisageNet, dedicated to the screening of midline brain tumors from normal controls (NCs) and iGCTs from other midline brain tumors. The study comprised the collection and division of datasets into training (n=847, iGCTs=358, NCs=300, other midline brain tumors=189) and testing (n=212, iGCTs=79, NCs=70, other midline brain tumors=63), with an additional independent validation dataset (n=336, iGCTs=130, NCs=100, other midline brain tumors=106) sourced from 4 medical institutions. A regression model using clinically relevant, statistically significant data was developed and combined with GVisageNet outputs to create a hybrid model. This integration sought to assess the incremental value of clinical data. The model’s predictive mechanisms were explored through correlation analyses with endocrine indicators and stratified evaluations based on the degree of hypothalamic-pituitary-target axis damage. Performance metrics included area under the curve (AUC), accuracy, sensitivity, and specificity.

RESULTS: On the independent validation dataset, GVisageNet achieved an AUC of 0.938 (P<.01) in distinguishing midline brain tumors from NCs. Further, GVisageNet demonstrated significant diagnostic capability in distinguishing iGCTs from the other midline brain tumors, achieving an AUC of 0.739, which is superior to the regression model alone (AUC=0.632, P<.001) but less than the hybrid model (AUC=0.789, P=.04). Significant correlations were found between the GVisageNet’s outputs and 7 endocrine indicators. Performance varied with hypothalamic-pituitary-target axis damage, indicating a further understanding of the working mechanism of GVisageNet.

CONCLUSIONS: GVisageNet, capable of high accuracy both independently and with clinical data, shows substantial potential for early iGCTs detection, highlighting the importance of combining deep learning with clinical insights for personalized health care.

PMID:39883924 | DOI:10.2196/58760

JMIR Infodemiology. 2025 Jan 30;5:e58539. doi: 10.2196/58539.

ABSTRACT

BACKGROUND: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises. However, previous studies on the early warning capabilities of geosocial media data have largely been constrained by coarse spatial resolutions or short temporal scopes, with limited understanding of how local political beliefs may influence these capabilities.

OBJECTIVE: This study aimed to assess how the epidemiological early warning capabilities of geosocial media posts for COVID-19 vary over time and across US counties with differing political beliefs.

METHODS: We classified US counties into 3 political clusters, democrat, republican, and swing counties, based on voting data from the last 6 federal election cycles. In these clusters, we analyzed the early warning capabilities of geosocial media posts across 6 consecutive COVID-19 waves (February 2020-April 2022). We specifically examined the temporal lag between geosocial media signals and surges in COVID-19 cases, measuring both the number of days by which the geosocial media signals preceded the surges in COVID-19 cases (temporal lag) and the correlation between their respective time series.

RESULTS: The early warning capabilities of geosocial media data differed across political clusters and COVID-19 waves. On average, geosocial media posts preceded COVID-19 cases by 21 days in republican counties compared with 14.6 days in democrat counties and 24.2 days in swing counties. In general, geosocial media posts were preceding COVID-19 cases in 5 out of 6 waves across all political clusters. However, we observed a decrease over time in the number of days that posts preceded COVID-19 cases, particularly in democrat and republican counties. Furthermore, a decline in signal strength and the impact of trending topics presented challenges for the reliability of the early warning signals.

CONCLUSIONS: This study provides valuable insights into the strengths and limitations of geosocial media data as an epidemiological early warning tool, particularly highlighting how they can change across county-level political clusters. Thus, these findings indicate that future geosocial media based epidemiological early warning systems might benefit from accounting for political beliefs. In addition, the impact of declining geosocial media signal strength over time and the role of trending topics for signal reliability in early warning systems need to be assessed in future research.

PMID:39883923 | DOI:10.2196/58539

JMIR Res Protoc. 2025 Jan 30;14:e58031. doi: 10.2196/58031.

ABSTRACT

BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition emerging in early childhood, characterized by core features such as sociocommunicative deficits and repetitive, rigid behaviors, interests, and activities. In addition to these, disruptive behaviors (DB), including aggression, self-injury, and severe tantrums, are frequently observed in pediatric patients with ASD. The atypical antipsychotics risperidone and aripiprazole, currently the only Food and Drug Administration-approved treatments for severe DB in patients with ASD, often encounter therapeutic failure or intolerance. Given this, exploring pharmacological alternatives for more effective management of DB associated with ASD is essential. Clozapine, noted for its unique antiaggressive effects in schizophrenia and in various treatment-resistant neuropsychiatric disorders, independent from its antipsychotic efficacy, remains underexplored in youths with ASD facing severe and persistent DB.

OBJECTIVE: This study aimed to evaluate the efficacy, tolerability, and safety of clozapine for treatment-resistant DB in youths with ASD.

METHODS: This is a prospective, single-center, noncontrolled, open-label trial. After a cross-titration phase, 31 patients with ASD aged 10-17 years and with treatment-resistant DB received a flexible dosage regimen of clozapine (up to 600 mg/day) for 12 weeks. Standardized instruments were applied before, during, and after the treatment, and rigorous clinical monitoring was performed weekly. The primary outcome was assessed using the Irritability Subscale of the Aberrant Behavior Checklist. Other efficacy measures include the Clinical Global Impression Severity and Improvement, the Swanson, Nolan, and Pelham questionnaire-IV, the Childhood Autism Rating Scale, and the Vineland Adaptive Behavior Scale. Safety and tolerability measures comprised adverse events, vital signs, electrocardiography, laboratory tests, physical measurements, and extrapyramidal symptoms with the Simpsons-Angus Scale. Statistical analysis will include chi-square tests with Monte Carlo simulation for categorical variables, paired t tests or Wilcoxon tests for continuous variables, and multivariate linear mixed models to evaluate the primary outcome, adjusting for confounders.

RESULTS: Recruitment commenced in February 2023. Data collection was concluded by April 2024, with analysis ongoing. This article presents the protocol of the initially planned study to provide a detailed methodological description. The results of this trial will be published in a future paper.

CONCLUSIONS: The urgent need for effective pharmacological therapies in mitigating treatment-resistant DB in pediatric patients with ASD underscores the importance of this research. Our study represents the first open-label trial to explore the anti-aggressive effects of clozapine in this specific demographic, marking a pioneering step in clinical investigation. Adopting a pragmatic approach, this trial protocol aims to mirror real-world clinical settings, thereby enhancing the applicability and relevance of our findings. The preliminary nature of future results from this research has the potential to pave the way for more robust studies and emphasize the need for continued innovation in ASD treatment.

TRIAL REGISTRATION: Brazilian Clinical Trials Registry RBR-54j3726; https://ensaiosclinicos.gov.br/rg/RBR-54j3726.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58031.

PMID:39883920 | DOI:10.2196/58031

JMIR Aging. 2025 Jan 30;8:e66104. doi: 10.2196/66104.

ABSTRACT

BACKGROUND: The utility of aging metrics that incorporate cognitive and physical function is not fully understood.

OBJECTIVE: We aim to compare the predictive capacities of 3 distinct aging metrics-motoric cognitive risk syndrome (MCR), physio-cognitive decline syndrome (PCDS), and cognitive frailty (CF)-for incident dementia and all-cause mortality among community-dwelling older adults.

METHODS: We used longitudinal data from waves 10-15 of the Health and Retirement Study. Cox proportional hazards regression analysis was employed to evaluate the effects of MCR, PCDS, and CF on incident all-cause dementia and mortality, controlling for socioeconomic and lifestyle factors, as well as medical comorbidities. Discrimination analysis was conducted to assess and compare the predictive accuracy of the 3 aging metrics.

RESULTS: A total of 2367 older individuals aged 65 years and older, with no baseline prevalence of dementia or disability, were ultimately included. The prevalence rates of MCR, PCDS, and CF were 5.4%, 6.3%, and 1.3%, respectively. Over a decade-long follow-up period, 341 cases of dementia and 573 deaths were recorded. All 3 metrics were predictive of incident all-cause dementia and mortality when adjusting for multiple confounders, with variations in the strength of their associations (incident dementia: MCR odds ratio [OR] 1.90, 95% CI 1.30-2.78; CF 5.06, 95% CI 2.87-8.92; PCDS 3.35, 95% CI 2.44-4.58; mortality: MCR 1.60, 95% CI 1.17-2.19; CF 3.26, 95% CI 1.99-5.33; and PCDS 1.58, 95% CI 1.17-2.13). The C-index indicated that PCDS and MCR had the highest discriminatory accuracy for all-cause dementia and mortality, respectively.

CONCLUSIONS: Despite the inherent differences among the aging metrics that integrate cognitive and physical functions, they consistently identified risks of dementia and mortality. This underscores the importance of implementing targeted preventive strategies and intervention programs based on these metrics to enhance the overall quality of life and reduce premature deaths in aging populations.

PMID:39883919 | DOI:10.2196/66104

J Med Internet Res. 2025 Jan 30;27:e66896. doi: 10.2196/66896.

ABSTRACT

BACKGROUND: Large language model (LLM) artificial intelligence chatbots using generative language can offer smoking cessation information and advice. However, little is known about the reliability of the information provided to users.

OBJECTIVE: This study aims to examine whether 3 ChatGPT chatbots-the World Health Organization’s Sarah, BeFreeGPT, and BasicGPT-provide reliable information on how to quit smoking.

METHODS: A list of quit smoking queries was generated from frequent quit smoking searches on Google related to “how to quit smoking” (n=12). Each query was given to each chatbot, and responses were analyzed for their adherence to an index developed from the US Preventive Services Task Force public health guidelines for quitting smoking and counseling principles. Responses were independently coded by 2 reviewers, and differences were resolved by a third coder.

RESULTS: Across chatbots and queries, on average, chatbot responses were rated as being adherent to 57.1% of the items on the adherence index. Sarah’s adherence (72.2%) was significantly higher than BeFreeGPT (50%) and BasicGPT (47.8%; P<.001). The majority of chatbot responses had clear language (97.3%) and included a recommendation to seek out professional counseling (80.3%). About half of the responses included the recommendation to consider using nicotine replacement therapy (52.7%), the recommendation to seek out social support from friends and family (55.6%), and information on how to deal with cravings when quitting smoking (44.4%). The least common was information about considering the use of non-nicotine replacement therapy prescription drugs (14.1%). Finally, some types of misinformation were present in 22% of responses. Specific queries that were most challenging for the chatbots included queries on “how to quit smoking cold turkey,” “…with vapes,” “…with gummies,” “…with a necklace,” and “…with hypnosis.” All chatbots showed resilience to adversarial attacks that were intended to derail the conversation.

CONCLUSIONS: LLM chatbots varied in their adherence to quit-smoking guidelines and counseling principles. While chatbots reliably provided some types of information, they omitted other types, as well as occasionally provided misinformation, especially for queries about less evidence-based methods of quitting. LLM chatbot instructions can be revised to compensate for these weaknesses.

PMID:39883917 | DOI:10.2196/66896

JMIR Med Educ. 2025 Jan 30;11:e63065. doi: 10.2196/63065.

ABSTRACT

BACKGROUND: There has been a rise in the popularity of ChatGPT and other chat-based artificial intelligence (AI) apps in medical education. Despite data being available from other parts of the world, there is a significant lack of information on this topic in medical education and research, particularly in Saudi Arabia.

OBJECTIVE: The primary objective of the study was to examine the familiarity, usage patterns, and attitudes of Alfaisal University medical students toward ChatGPT and other chat-based AI apps in medical education.

METHODS: This was a cross-sectional study conducted from October 8, 2023, through November 22, 2023. A questionnaire was distributed through social media channels to medical students at Alfaisal University who were 18 years or older. Current Alfaisal University medical students in years 1 through 6, of both genders, were exclusively targeted by the questionnaire. The study was approved by Alfaisal University Institutional Review Board. A χ2 test was conducted to assess the relationships between gender, year of study, familiarity, and reasons for usage.

RESULTS: A total of 293 responses were received, of which 95 (32.4%) were from men and 198 (67.6%) were from women. There were 236 (80.5%) responses from preclinical students and 57 (19.5%) from clinical students, respectively. Overall, males (n=93, 97.9%) showed more familiarity with ChatGPT compared to females (n=180, 90.09%; P=.03). Additionally, males also used Google Bard and Microsoft Bing ChatGPT more than females (P<.001). Clinical-year students used ChatGPT significantly more for general writing purposes compared to preclinical students (P=.005). Additionally, 136 (46.4%) students believed that using ChatGPT and other chat-based AI apps for coursework was ethical, 86 (29.4%) were neutral, and 71 (24.2%) considered it unethical (all Ps>.05).

CONCLUSIONS: Familiarity with and usage of ChatGPT and other chat-based AI apps were common among the students of Alfaisal University. The usage patterns of these apps differ between males and females and between preclinical and clinical-year students.

PMID:39883912 | DOI:10.2196/63065

JCO Glob Oncol. 2025 Jan;11:e2400339. doi: 10.1200/GO-24-00339. Epub 2025 Jan 30.

ABSTRACT

The evolution of radiation therapy in Uganda has been a journey marked by significant milestones and persistent challenges. Since the inception of radiotherapy services in 1988-1989, there has been a concerted effort to enhance cancer treatment services. The early years were characterized by foundational developments, such as the installation of the first teletherapy units, low-dose-rate brachytherapy units, and conventional simulators, and the recognition of radiation oncologists and medical physicist professionals laid the groundwork for radiotherapy treatment modalities. With more support from the International Atomic Energy Agency, the acquisition of dosimetry equipment, treatment planning systems, and additional professional training signaled a new era in the fight against cancer. As we entered the second decade of the millennium, the Uganda Cancer Institute (UCI) witnessed a progression in sophisticated radiotherapy services, including high-dose-rate brachytherapy, initiation of intensity modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT), and use of artificial intelligence. These advancements improved the efficiency/precision of treatments and the time patients spent undergoing therapy. Around the second decade of radiotherapy services, about 600 new patients with cancer were annually treated compared with about 2,600 in 2023. Currently, an average of 1,440 brachytherapy insertions are done annually compared with 300 insertions for the first 20 years. Despite the technological strides, the UCI faced numerous obstacles, including limited equipment, knowledge gaps in appropriate tumor/organs at risk segmentations, treatment planning, and protocols. However, international support and collaboration efforts have led to significant improvement in the precision and effectiveness of treatments. Currently, about 51% of all patients are treated with image-guided techniques-IMRT/VMAT (42%) and three-dimensional conformal radiation treatment (10%). The Government has commenced the decentralization of radiotherapy services to other regions. This review can be a learning lesson for the more than 25 countries in Africa and other low-middle-income countries globally that do not have access to radiotherapy and/or are in the process of starting such facilities.

PMID:39883898 | DOI:10.1200/GO-24-00339