Leveraging machine learning for effective child maltreatment prevention: A case study of home visiting service assessments.

BACKGROUND: Early identification of children and families who may benefit from support is crucial for implementing strategies that can prevent the onset of child maltreatment. Predictive risk modeling (PRM) may offer valuable and efficient enhancements to existing risk assessment techniques. OBJECTIVE: To evaluate the PRM's effectiveness against the existing assessment tool in identifying children and families needing home visiting services. PARTICIPANTS AND SETTING: Children born in hospitals affiliated with the Bridges Maternal Child Health Network in Orange County, California, from 2011 to 2016 (N = 132,216). METHODS: We developed a PRM tool by integrating a machine learning algorithm with a linked dataset of birth records and child protection system (CPS) records. To align with the existing assessment tool (baseline model), we limited the predicting features to the information used by the existing tool. The need for home visiting services was measured by substantiated maltreatment allegation reported during the first three years of the child's life. RESULTS: Of the children born in Bridges Network hospitals between 2011 and 2016, 2.7 % experienced substantiated maltreatment allegations by the age of three. Within the top 30 % of children with high-risk scores, the PRM tool outperformed the baseline model, accurately identifying 75.3 %-84.1 % of all children who would experience maltreatment substantiation, surpassing the baseline model's performance of 46.2 %. CONCLUSIONS: Our study underscores the potential of PRM in enhancing the risk assessment tool used by a prevention program in a child welfare center in California. The findings provide valuable insights to practitioners interested in utilizing data for PRM development, highlighting the potential of machine learning algorithms to generate accurate predictions and inform targeted preventive services.

Effectively communicating with local policymakers: a randomized trial of policy brief dissemination to address obesity.

Introduction: Evidence-based policies are a powerful tool for impacting health and addressing obesity. Effectively communicating evidence to policymakers is critical to ensure evidence is incorporated into policies. While all public health is local, limited knowledge exists regarding effective approaches for improving local policymakers' uptake of evidence-based policies. Methods: Local policymakers were randomized to view one of four versions of a policy brief (usual care, narrative, risk-framing, and narrative/risk-framing combination). They then answered a brief survey including questions about their impressions of the brief, their likelihood of using it, and how they determine legislative priorities. Results: Responses from 331 participants indicated that a majority rated local data (92%), constituent needs/opinions (92%), and cost-effectiveness data (89%) as important or very important in determining what issues they work on. The majority of respondents agreed or strongly agreed that briefs were understandable (87%), believable (77%), and held their attention (74%) with no brief version rated significantly higher than the others. Across the four types of briefs, 42% indicated they were likely to use the brief. Logistic regression models showed that those indicating that local data were important in determining what they work on were over seven times more likely to use the policy brief than those indicating that local data were less important in determining what they work on (aOR = 7.39, 95% CI = 1.86,52.57). Discussion: Among local policymakers in this study there was no dominant format or type of policy brief; all brief types were rated similarly highly. This highlights the importance of carefully crafting clear, succinct, credible, and understandable policy briefs, using different formats depending on communication objectives. Participants indicated a strong preference for receiving materials incorporating local data. To ensure maximum effect, every effort should be made to include data relevant to a policymaker's local area in policy communications.

Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions.

BACKGROUND: The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. MAIN TEXT: This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. CONCLUSIONS: Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.

Influence of Pokémon GO on Physical Activity and Psychosocial Well-Being in Children and Adolescents: Systematic Review.

BACKGROUND: Pokémon GO, an augmented reality game with widespread popularity, can potentially influence players' physical activity (PA) levels and psychosocial well-being. OBJECTIVE: This review aims to systematically examine the scientific evidence regarding the impact of Pokémon GO on PA and psychosocial well-being in children and adolescents. METHODS: Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework, we conducted keyword and reference searches in the PubMed, CINAHL, Web of Science, and Scopus databases. We performed title and abstract screening, full-text review, evidence synthesis, and identified research gaps. RESULTS: Our review included 10 studies that explored the effect of Pokémon GO on PA or psychosocial well-being among children and adolescents. These studies used diverse designs across multiple countries and regions. Pokémon GO use measures encompassed frequency, experience, adherence, and motivation. PA assessment methods ranged from self-reported questionnaires to technology-based evaluations and qualitative approaches. Psychosocial well-being measures included emotional intelligence, personal well-being, self-control, emotionality, and sociability. In general, the estimated impact of Pokémon GO on PA was positive, with gaming elements and engagement correlating with increased PA levels. However, the effect on psychosocial well-being presented mixed results, with positive associations for sociability but a complex relationship involving well-being and internet gaming disorder. The limitations of these studies comprised the absence of randomized controlled trials, heterogeneity in study designs and outcome measures, and potential confounding bias. CONCLUSIONS: Overall, Pokémon GO tends to positively affect PA levels, while the impact on psychosocial well-being remains complex and requires further investigation. Future research should investigate the mechanisms connecting Pokémon GO use with PA and psychosocial well-being and the potential risks of excessive gameplay. These findings can help inform public health interventions to harness gaming technologies for promoting PA and enhancing well-being among the younger generation. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42023412032; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=412032.

Sentiment Analysis of Tweets on Menu Labeling Regulations in the US.

Menu labeling regulations in the United States mandate chain restaurants to display calorie information for standard menu items, intending to facilitate healthy dietary choices and address obesity concerns. For this study, we utilized machine learning techniques to conduct a novel sentiment analysis of public opinions regarding menu labeling regulations, drawing on Twitter data from 2008 to 2022. Tweets were collected through a systematic search strategy and annotated as positive, negative, neutral, or news. Our temporal analysis revealed that tweeting peaked around major policy announcements, with a majority categorized as neutral or news-related. The prevalence of news tweets declined after 2017, as neutral views became more common over time. Deep neural network models like RoBERTa achieved strong performance (92% accuracy) in classifying sentiments. Key predictors of tweet sentiments identified by the random forest model included the author's followers and tweeting activity. Despite limitations such as Twitter's demographic biases, our analysis provides unique insights into the evolution of perceptions on the regulations since their inception, including the recent rise in negative sentiment. It underscores social media's utility for continuously monitoring public attitudes to inform health policy development, execution, and refinement.

Artificial Intelligence Applications to Public Health Nutrition.

Public health nutrition occupies a paramount position in the overarching domains of health promotion and disease prevention, setting itself apart from nutritional investigations concentrated at the individual level [...].

Impact of soda tax on beverage price, sale, purchase, and consumption in the US: a systematic review and meta-analysis of natural experiments.

Background: As a primary source of added sugars in the US diet, sugar-sweetened beverage (SSB) consumption is presumed to contribute to obesity prevalence and poor oral health. We systematically synthesized and quantified evidence from US-based natural experiments concerning the impact of SSB taxes on beverage prices, sales, purchases, and consumption. Methods: A keyword and reference search was performed in PubMed, Web of Science, Cochrane Library, Scopus, and EconLit from the inception of an electronic bibliographic database to Oct 31, 2022. Meta-analysis was conducted to estimate the pooled effect of soda taxes on SSB consumption, prices, passthrough rate, and purchases. Results: Twenty-six natural experiments, all adopting a difference-in-differences approach, were included. Studies assessed soda taxes in Berkeley, Oakland, and San Francisco in California, Philadelphia in Pennsylvania, Boulder in Colorado, Seattle in Washington, and Cook County in Illinois. Tax rates ranged from 1 to 2 ¢/oz. The imposition of the soda tax was associated with a 1.06 ¢/oz. (95% confidence interval [CI] = 0.90, 1.22) increase in SSB prices and a 27.3% (95% CI = 19.3, 35.4%) decrease in SSB purchases. The soda tax passthrough rate was 79.7% (95% CI = 65.8, 93.6%). A 1 ¢/oz. increase in soda tax rate was associated with increased prices of SSBs by 0.84 ¢/oz (95% CI = 0.33, 1.35). Conclusion: Soda taxes could be effective policy leverage to nudge people toward purchasing and consuming fewer SSBs. Future research should examine evidence-based classifications of SSBs, targeted use of revenues generated by taxes to reduce health and income disparities, and the feasibility of redesigning the soda tax to improve efficiency.

A scoping review of methodologies for applying artificial intelligence to physical activity interventions.

BACKGROUND: This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence (AI) applications in physical activity (PA) interventions; introduce them to prevalent machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms; and encourage the adoption of AI methodologies. METHODS: A scoping review was performed in PubMed, Web of Science, Cochrane Library, and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes. AI methodologies were summarized and categorized to identify synergies, patterns, and trends informing future research. Additionally, a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application. RESULTS: The review included 24 studies that met the predetermined eligibility criteria. AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes. Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data. Comparisons of different AI models yielded mixed results, likely due to model performance being highly dependent on the dataset and task. An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed, addressing complex human-machine communication, behavior modification, and decision-making tasks. Six key areas for future AI adoption in PA interventions emerged: personalized PA interventions, real-time monitoring and adaptation, integration of multimodal data sources, evaluation of intervention effectiveness, expanding access to PA interventions, and predicting and preventing injuries. CONCLUSION: The scoping review highlights the potential of AI methodologies for advancing PA interventions. As the field progresses, staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being.

Assessing the longitudinal relationship between whole egg consumption and cognition in Chinese older adults.

BACKGROUND: Given China's fast-growing aging population, cognitive decline is a leading public health concern. Eggs are an affordable food rich in several shortfall nutrients that may benefit cognitive health. AIM: This study assessed the longitudinal relationship between whole egg consumption and cognition among older adults in China. METHODS: Individual-level data of 4737 Chinese adults 55+ years came from the China Health and Nutrition Survey (CHNS) 1997-2006 waves. Daily egg consumption was measured using 3-day 24-h dietary recalls. Cognitive functioning was assessed with immediate and delayed recall of a 10-word list, counting backward from 20, and serial 7 subtraction. Multivariate mixed-effects regressions were performed to estimate the longitudinal associations between daily whole egg consumption and cognitive functioning in older Chinese adults. RESULTS: Approximately 46% of CHNS participants were whole egg consumers, and their daily intake averaged 47.4 g. The overall cognitive functioning test scores, separate scores for cognitive functioning subdomains, and the prevalence of cognitive impairment at the baseline were modestly higher among whole egg consumers than nonconsumers. However, after adjusting for individual characteristics, multivariate mixed-effects regressions did not find daily whole egg consumption to be associated with cognitive functioning among CHNS participants. By contrast, several demographic and socioeconomic factors, such as age, education attainment, and health insurance coverage, were found to correlate with older Chinese adults' cognition. CONCLUSION: This study has measurement and design limitations. Future research should investigate the causal impact of habitual egg intake on different domains of cognition using experimental designs with an extended follow-up period.

Healthy Diet Promotion through Evidence-Informed Policies.

The Special Issue entitled "The Impact of Policy and Food Environment on Food Purchase and Dietary Behavior" comprises 13 articles that collectively provide valuable insights into the complex interplay between policy, food environment, and individual food purchase and consumption [...].

Building Machine Learning Models to Correct Self-Reported Anthropometric Measures.

Monitoring population obesity risk primarily depends on self-reported anthropometric data prone to recall error and bias. This study developed machine learning (ML) models to correct self-reported height and weight and estimate obesity prevalence in US adults. Individual-level data from 50 274 adults were retrieved from the National Health and Nutrition Examination Survey (NHANES) 1999-2020 waves. Large, statistically significant differences between self-reported and objectively measured anthropometric data were present. Using their self-reported counterparts, we applied 9 ML models to predict objectively measured height, weight, and body mass index. Model performances were assessed using root-mean-square error. Adopting the best performing models reduced the discrepancy between self-reported and objectively measured sample average height by 22.08%, weight by 2.02%, body mass index by 11.14%, and obesity prevalence by 99.52%. The difference between predicted (36.05%) and objectively measured obesity prevalence (36.03%) was statistically nonsignificant. The models may be used to reliably estimate obesity prevalence in US adults using data from population health surveys.

Choline Intake and Cognitive Function Among U.S. Older Adults.

Choline is an essential nutrient affects brain development in early life. However, evidence is lacking regarding its potential neuroprotective effects in later life from community-based cohorts. This study assessed the relationship between choline intake and cognitive functioning in a sample of older adults 60 years + from the National Health and Nutrition Examination Survey 2011-2012 and 2013-2014 waves (n = 2,796). Choline intake was assessed using two nonconsecutive 24-hour dietary recalls. Cognitive assessments included immediate and delayed word recalls, Animal Fluency, and Digit Symbol Substitution Test. The average daily dietary choline intake was 307.5 mg, and the total intake (including intake from dietary supplements) was 330.9 mg, both below the Adequate Intake level. Neither dietary OR = 0.94, 95% CI (0.75, 1.17) nor total choline intake OR = 0.87, 95% CI (0.70, 1.09) was associated with changes in cognitive test scores. Further investigation adopting longitudinal or experimental designs may shed light on the issue.

Sentiment Analysis of Tweets on Soda Taxes.

CONTEXT: As a primary source of added sugars, sugar-sweetened beverage (SSB) consumption may contribute to the obesity epidemic. A soda tax is an excise tax charged on selling SSBs to reduce consumption. Currently, 8 cities/counties in the United States have imposed soda taxes. OBJECTIVE: This study assessed people's sentiments toward soda taxes in the United States based on social media posts on Twitter. DESIGN: We designed a search algorithm to systematically identify and collect soda tax-related tweets posted on Twitter. We built deep neural network models to classify tweets by sentiments. SETTING: Computer modeling. PARTICIPANTS: Approximately 370 000 soda tax-related tweets posted on Twitter from January 1, 2015, to April 16, 2022. MAIN OUTCOME MEASURE: Sentiment associated with a tweet. RESULTS: Public attention paid to soda taxes, indicated by the number of tweets posted annually, peaked in 2016, but has declined considerably ever since. The decreasing prevalence of tweets quoting soda tax-related news without revealing sentiments coincided with the rapid increase in tweets expressing a neutral sentiment toward soda taxes. The prevalence of tweets expressing a negative sentiment rose steadily from 2015 to 2019 and then slightly leveled off, whereas that of tweets expressing a positive sentiment remained unchanged. Excluding news-quoting tweets, tweets with neutral, negative, and positive sentiments occupied roughly 56%, 29%, and 15%, respectively, during 2015-2022. The authors' total number of tweets posted, followers, and retweets predicted tweet sentiment. The finalized neural network model achieved an accuracy of 88% and an F1 score of 0.87 in predicting tweet sentiments in the test set. CONCLUSIONS: Despite its potential to shape public opinion and catalyze social changes, social media remains an underutilized source of information to inform government decision making. Social media sentiment analysis may inform the design, implementation, and modification of soda tax policies to gain social support while minimizing confusion and misinterpretation.

Using Mobile Phone Data to Assess Socio-Economic Disparities in Unhealthy Food Reliance during the COVID-19 Pandemic.

Background: Although COVID-19 has disproportionately affected socio-economically vulnerable populations, research on its impact on socio-economic disparities in unhealthy food reliance remains scarce. Methods: This study uses mobile phone data to evaluate the impact of COVID-19 on socio-economic disparities in reliance on convenience stores and fast food. Reliance is defined in terms of the proportion of visits to convenience stores out of the total visits to both convenience and grocery stores, and the proportion of visits to fast food restaurants out of the total visits to both fast food and full-service restaurants. Visits to each type of food outlet at the county level were traced and aggregated using mobile phone data before being analyzed with socio-economic demographics and COVID-19 incidence data. Results: Our findings suggest that a new COVID-19 case per 1,000 population decreased a county's odds of relying on convenience stores by 3.41% and increased its odds of fast food reliance by 0.72%. As a county's COVID-19 incidence rate rises by an additional case per 1,000 population, the odds of relying on convenience stores increased by 0.01%, 0.02%, and 0.06% for each additional percentage of Hispanics, college-educated residents, and every additional year in median age, respectively. For fast food reliance, as a county's COVID-19 incidence rate increases by one case per 1,000 population, the odds decreased by 0.003% for every additional percentage of Hispanics but increased by 0.02% for every additional year in the county's median age. Conclusion: These results complement existing literature to promote equitable food environments.

Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies.

BACKGROUND: Obesity is a leading cause of preventable death worldwide. Artificial intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has become an indispensable tool in obesity research. OBJECTIVE: This scoping review aimed to provide researchers and practitioners with an overview of the AI applications to obesity research, familiarize them with popular ML and DL models, and facilitate the adoption of AI applications. METHODS: We conducted a scoping review in PubMed and Web of Science on the applications of AI to measure, predict, and treat obesity. We summarized and categorized the AI methodologies used in the hope of identifying synergies, patterns, and trends to inform future investigations. We also provided a high-level, beginner-friendly introduction to the core methodologies to facilitate the dissemination and adoption of various AI techniques. RESULTS: We identified 46 studies that used diverse ML and DL models to assess obesity-related outcomes. The studies found AI models helpful in detecting clinically meaningful patterns of obesity or relationships between specific covariates and weight outcomes. The majority (18/22, 82%) of the studies comparing AI models with conventional statistical approaches found that the AI models achieved higher prediction accuracy on test data. Some (5/46, 11%) of the studies comparing the performances of different AI models revealed mixed results, indicating the high contingency of model performance on the data set and task it was applied to. An accelerating trend of adopting state-of-the-art DL models over standard ML models was observed to address challenging computer vision and natural language processing tasks. We concisely introduced the popular ML and DL models and summarized their specific applications in the studies included in the review. CONCLUSIONS: This study reviewed AI-related methodologies adopted in the obesity literature, particularly ML and DL models applied to tabular, image, and text data. The review also discussed emerging trends such as multimodal or multitask AI models, synthetic data generation, and human-in-the-loop that may witness increasing applications in obesity research.

The Safety and Efficacy of Clonidine in Hemodialysis Patients: A Systematic Review and Meta-Analysis.

BACKGROUND: Clonidine is a frequently prescribed long-term antihypertensive medication in hemodialysis (HD) patients in the USA, but its safety and efficacy has not been clearly established in the HD population. OBJECTIVE: To evaluate, we conducted a systematic review and meta-analysis on the safety and efficacy of clonidine in HD patients. METHODS: Keyword search of "clonidine" and "dialysis" was conducted through April 2021 in PubMed, Cochrane Library, Web of Science, Scopus, and ClinicalTrials.gov databases. Inclusion criteria were as follows - study design: randomized controlled trials, cohort studies, prospective studies, retrospective studies, or case series; subjects: adult HD patients; main outcome: blood pressure (BP) and safety; language: English; and article type: peer-reviewed publications. Studies that examined the effects of clonidine in populations other than adult HD patients were excluded. Meta-analysis was performed on BP reduction outcomes. RESULTS: Eight studies met the inclusion criteria for the systematic review, including prospective pre-post studies (2), double-blind controlled trial (1), single-blinded placebo-controlled trial (1), crossover open-label clinical trial (1), retrospective analysis (1), and case report series (2). Three studies included in the meta-analysis ranged from 2 to 12 weeks duration, with a collective sample size of 24 (ages 12-77 years). Risk of bias, assessed using the ROBINS-1 tool, was high for all included studies. Significant adverse effects reported included hypotension, light-headedness, drowsiness, dry mouth, rebound hypertension, and contact dermatitis from patch application. Short-term clonidine use was associated with significant improvement in systolic BP (pooled effect: -12.985 mm Hg, 95% CI [-7.878, -18.092], p < 0.001), while changes in diastolic BP were not statistically significant (-11.119 mm Hg, 95% CI [-22.725, 0.487], p = 0.060). No data currently support the long-term efficacy of clonidine in HD patients. This study was unfunded and was developed using PRISMA guidelines and registered on PROSPERO (CRD42018112042). CONCLUSIONS: There is no evidence supporting the long-term use of clonidine in the HD population and a significant side-effect profile. There is low-quality evidence demonstrating the efficacy of clonidine in lowering BP in HD patients in short-term use, but significant safety concerns remain. Fluid removal strategies and other antihypertensives should be used over clonidine for long-term BP control in the HD population.

Development of a Risk Score to Predict Sudden Infant Death Syndrome.

Sudden Infant Death Syndrome (SIDS) is the third leading cause of death among infants younger than one year of age. Effective SIDS prediction models have yet to be developed. Hence, we developed a risk score for SIDS, testing contemporary factors including infant exposure to passive smoke, circumcision, and sleep position along with known risk factors based on 291 SIDS and 242 healthy control infants. The data were retrieved from death certificates, parent interviews, and medical records collected between 1989−1992, prior to the Back to Sleep Campaign. Multivariable logistic regression models were performed to develop a risk score model. Our finalized risk score model included: (i) breastfeeding duration (OR = 13.85, p < 0.001); (ii) family history of SIDS (OR = 4.31, p < 0.001); (iii) low birth weight (OR = 2.74, p = 0.003); (iv) exposure to passive smoking (OR = 2.64, p < 0.001); (v) maternal anemia during pregnancy (OR = 2.07, p = 0.03); and (vi) maternal age <25 years (OR = 1.77, p = 0.01). The area under the curve for the overall model was 0.79, and the sensitivity and specificity were 79% and 63%, respectively. Once this risk score is further validated it could ultimately help physicians identify the high risk infants and counsel parents about modifiable risk factors that are most predictive of SIDS.

We got nuts! use deep neural networks to classify images of common edible nuts.

BACKGROUND: Nuts are nutrient-dense foods that contribute to healthier eating. Food image datasets enable artificial intelligence (AI) powered diet-tracking apps to help people monitor daily eating patterns. AIM: This study aimed to create an image dataset of commonly consumed nut types and use it to build an AI computer vision model to automate nut type classification tasks. METHODS: iPhone 11 was used to take photos of 11 nut types-almond, brazil nut, cashew, chestnut, hazelnut, macadamia, peanut, pecan, pine nut, pistachio, and walnut. The dataset contains 2200 images, 200 per nut type. The dataset was randomly split into the training (60% or 1320 images), validation (20% or 440 images), and test sets (20% or 440 images). A neural network model was constructed and trained using transfer learning and other computer vision techniques-data augmentation, mixup, normalization, label smoothing, and learning rate optimization. RESULTS: The trained neural network model correctly predicted 338 out of 440 images (40 per nut type) in the validation set, achieving 99.55% accuracy. Moreover, the model classified the 440 images in the test set with 100% accuracy. CONCLUSION: This study built a nut image dataset and used it to train a neural network model to classify images by nut type. The model achieved near-perfect accuracy on the validation and test sets, demonstrating the feasibility of automating nut type classification using smartphone photos. Being made open-source, the dataset and model can assist the development of diet-tracking apps that facilitate users' adoption and adherence to a healthy diet.

Parenting styles in relation to childhood obesity, smoking and drinking: A gene-environment interaction study.

BACKGROUND: The present study assessed gene-environment interactions linking maternal parenting styles to childhood obesity and alcohol and tobacco use. METHODS: Data were retrieved from the first wave of the German Twin Family Panel. Participants comprised three birth cohorts, aged 5, 11 and 17 years, with approximately 500 pairs of same-sex monozygotic twins and 500 pairs of same-sex dizygotic twins per cohort. Self-reported parenting styles were measured in five dimensions: emotional warmth, psychological control, negative communication, monitoring and inconsistent parenting. Outcome variables included children's body mass index z-score (BMIz) and smoking and alcohol drinking frequency. Gene-environment interaction models were used to assess how parenting styles might moderate genetic and environmental influences on BMIz and smoking and drinking behaviours. RESULTS: A positive interaction of genetic effects with psychological control was found for BMIz at age 5 years, indicating that genetic influences on BMIz increased with psychological control. No interaction effect was found for BMIz at ages 11 and 17 years. Regarding adolescent smoking, positive interaction between genetic effects and negative communication was found, indicating that genetic influences on smoking increased with negative communication. There was no significant moderating effect of parenting styles on adolescent drinking. CONCLUSIONS: The present study found preliminary evidence indicating that parenting styles moderated genetic and environmental impacts on body weight status and smoking. Moderation effects of parenting on BMIz were observed only at a very young age. The moderating effects of parenting influenced adolescent smoking but not drinking.

Determinants of School Diabetes Care Services and Quality Through the Lens of the School Nurse: A Qualitative Analysis.

Schools play a critical role in students' diabetes management and ensure their safety and well-being. We conducted key informant interviews with 11 school nurses in Missouri to assess determinants for diabetes care implementation. Five themes and 29 subthemes were identified concerning school nurses, schools, external stakeholders, government, and the COVID-19 pandemic. A social-ecological model was developed to elucidate each level's barriers, facilitators, and resources, and their interplay. School nurses should lead diabetes management, emergency planning, and health education for students/staff. Multiple gray areas existed regarding school nurses' specific roles/responsibilities. Lacking funding, insurance, and communication with parents/physicians further challenged diabetes care.