BackgroundCanadian youth exhibit a number of risky behaviours, some of which are associated with overweight and obesity. The purpose of this study was to examine the prevalence of 15 modifiable risk behaviours in a large sample of Canadian youth, to identify underlying subgroups based on patterns of health behaviours, and to examine the association between identified subgroups and overweight/obesity.MethodsData from 18,587 grades 9–12 students in Year 1 (2012–13) of the COMPASS study and latent class analysis were used to identify patterns and clustering among 15 health behaviours (e.g., physical inactivity, sedentary behaviour, unhealthy eating, substance use). A logistic regression model examined the associations between these clusters and overweight/obesity status.ResultsFour distinct classes were identified: traditional school athletes, inactive screenagers, health conscious, and moderately active substance users. Each behavioural cluster demonstrated a distinct pattern of behaviours, some with a greater number of risk factors than others. Traditional school athletes (odds ratio (OR) 1.15, 95% CI 1.03–1.29), inactive screenagers (OR 1.33; 1.19–1.48), and moderately active substance users (OR 1.27; 1.14–1.43) were all significantly more likely to be overweight/obese compared to the health conscious group.ConclusionsFour distinct subpopulations of youth were identified based on their patterns of health and risk behaviours. The three clusters demonstrating poorer health behaviour were all at an increased risk of being overweight/obese compared to their somewhat healthier peers. Obesity-related public health interventions and health promotion efforts might be more effective if consideration is given to population segments with certain behavioural patterns, targeting subgroups at greatest risk of overweight or obesity.

Background Self-diagnosis is the process of diagnosing or identifying a medical condition in oneself. Artificially intelligent digital platforms for self-diagnosis are becoming widely available and are used by the general public; however, little is known about the body of knowledge surrounding this technology. Objective The objectives of this scoping review were to (1) systematically map the extent and nature of the literature and topic areas pertaining to digital platforms that use computerized algorithms to provide users with a list of potential diagnoses and (2) identify key knowledge gaps. Methods The following databases were searched: PubMed (Medline), Scopus, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers, Google Scholar, Open Grey, and ProQuest Dissertations and Theses. The search strategy was developed and refined with the assistance of a librarian and consisted of 3 main concepts: (1) self-diagnosis; (2) digital platforms; and (3) public or patients. The search generated 2536 articles from which 217 were duplicates. Following the Tricco et al 2018 checklist, 2 researchers screened the titles and abstracts (n=2316) and full texts (n=104), independently. A total of 19 articles were included for review, and data were retrieved following a data-charting form that was pretested by the research team. Results The included articles were mainly conducted in the United States (n=10) or the United Kingdom (n=4). Among the articles, topic areas included accuracy or correspondence with a doctor’s diagnosis (n=6), commentaries (n=2), regulation (n=3), sociological (n=2), user experience (n=2), theoretical (n=1), privacy and security (n=1), ethical (n=1), and design (n=1). Individuals who do not have access to health care and perceive to have a stigmatizing condition are more likely to use this technology. The accuracy of this technology varied substantially based on the disease examined and platform used. Women and those with higher education were more likely to choose the right diagnosis out of the potential list of diagnoses. Regulation of this technology is lacking in most parts of the world; however, they are currently under development. Conclusions There are prominent research gaps in the literature surrounding the use of artificially intelligent self-diagnosing digital platforms. Given the variety of digital platforms and the wide array of diseases they cover, measuring accuracy is cumbersome. More research is needed to understand the user experience and inform regulations.

BackgroundDiets of U.S. adolescents and adults do not meet recommendations, increasing risk of chronic disease. This study examined trajectories and predictors of eating behaviors in U.S. youth from age 16–20 years, and evaluated longitudinal associations of eating behaviors with weight outcomes.MethodsData come from the first four waves (years) of the NEXT Generation Health Study, a nationally representative cohort of U.S. students in 10th grade during the 2009–2010 school year (n = 2785). Annual surveys queried frequency of food group intake (times/day of fruit and vegetables, whole grains, sugar-sweetened soda, sweet and salty snacks), and meal practices (days/week of breakfast, family meals, fast food, and television during meals). Body mass index (BMI, kg/m2) was calculated from self-reported height and weight. Adjusted generalized estimating equations and linear mixed models with multiple imputation for missing data estimated eating behavior trajectories overall and by baseline weight status (normal weight = 5 ≤ BMI%ile < 85, overweight = 85 ≤ BMI%ile < 95, obese = BMI%ile ≥ 95), accounting for the complex sampling design. Separate GEE models estimated longitudinal associations of food group frequencies with meal practices and of BMI with eating behaviors.ResultsEating behaviors tracked strongly from wave 1–4 (residual intraclass correlation = 41 % - 51 %). Across all baseline weight categories, frequency of food group intake and meal practices decreased over time, except for fast food, which remained stable. Fruit/vegetable intake frequency was associated positively with family meals (β ± SE = 0.33 ± 0.05) and breakfast (0.18 ± 0.03), and inversely with fast food (−0.31 ± 0.04), while whole grain intake frequency was associated positively with family meals (0.07 ± 0.02), television meals (0.02 ± 0.009) and breakfast (0.04 ± 0.01). Soda and snacks were positively associated with television meals (0.08 ± 0.008 and 0.07 ± 0.009, respectively) and fast food (0.24 ± 0.02 and 0.20 ± 0.03, respectively), while soda was inversely associated with breakfast frequency (−0.05 ± 0.01). Time-varying BMI was unrelated to eating behaviors other than an inverse association with time-varying snacks (−0.33 ± 0.12).ConclusionsStrong tracking over time supports the importance of early establishment of health-promoting eating behaviors in U.S. adolescents. Findings suggest meal practices may be important intervention targets. Lack of evidence for hypothesized associations of BMI and eating behaviors indicates the need for research confirming these findings using more precise measures of dietary intake.Electronic supplementary materialThe online version of this article (doi:10.1186/s12966-015-0298-x) contains supplementary material, which is available to authorized users.

This study presents the predictive validity of the susceptibility construct for the use of tobacco cigarettes among secondary school students in Ontario, Canada. It also presents a novel use of the susceptibility construct for predicting the use of e-cigarettes, cigarillos or little cigars, cigars, hookah, and smokeless tobacco among secondary school students in Ontario, Canada.

ABSTRACT. Objective:The fi rst year after high school is a transitional year, with increased independence from parental supervision, contact with other independent youth, and exposure to new environments, all of which may infl uence substance use. This article reports longitudinal predictors of change in the prevalence of alcohol use and heavy episodic drinking among adolescents and environmental correlates (i.e., residence, college attendance, and work status) with drinking the year after high school. Method: A national sample of study participants (N = 2,659; 55% female) in the NEXT Generation Health Study were followed annually from 10th grade (Wave 1) to the year after high school (Wave 4). Longitudinal binary outcomes, including recent (30-day) drinking and two measures of heavy episodic drinking, were examined. Transition models with generalized estimating equations estimated the effect of previous drinking behaviors, social infl uences, and current residential status and activity (school and /or work) on drinking prevalence. Results: Drinking increased from 40.5% among high school seniors (Wave 3) to 53.5% in Wave 4 for 30-day use, and from 29.0% to 41.2% for heavy episodic drinking. Signifi cant predictors of 30-day drinking included previous drinking status (odds ratio [OR] = 5.48), peer drinking often (OR = 3.25), parental expectations (OR = 0.91), and current year living on campus (OR = 2.10). The same signifi cant predictors with similar magnitudes were found for both measures of heavy episodic drinking. Peer use did not interact with college attendance or residence. Conclusions: Predictors of drinking and heavy episodic drinking during the fi rst year after high school included being White, living on campus, previous drinking, lower parental expectations, and having peers who drink. (J. Stud. Alcohol Drugs, 77, 121-132, 2016) Received: April 14, 2015. Revision: July 31, 2015. This research (contract number HHSN275201200001I) was supported by the intramural research program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD); the National Heart, Lung and Blood Institute (NHLBI); the National Institute on Alcohol Abuse and Alcoholism (NIAAA); the Maternal and Child Health Bureau (MCHB) of the Health Resources and Services Administration (HRSA); and the National Institute on Drug Abuse (NIDA).*Correspondence may be sent to Bruce Simons-Morton at the Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6100 Executive Blvd., Room 7B13Q, MSC 7510, Bethesda MD 20892-7510, or via email at: mortonb@mail.nih.gov. T HE FIRST YEAR AFTER HIGH SCHOOL is transitional in numerous ways as many adolescents go to college, take jobs, and live away from home for the fi rst time. Greater independence may afford greater exposure to drinking and opportunities to drink (Arnett, 2005). Drinking prevalence during late adolescence and emerging adulthood is of particular int...

BackgroundYouth are engaging in multiple risky behaviours, increasing their risk of overweight, obesity, and related chronic diseases. The objective of this study was to examine the effect of engaging in unique clusters of unhealthy behaviours on youths’ body mass index (BMI) trajectories.MethodsThis study used a linked-longitudinal sample of Grades 9 and 10 students (13 to 17 years of age) participating in the COMPASS host study. Students reported obesity-related and other risky behaviours at baseline and height and weight (to derive BMI) at baseline (2012/2013) and annually for 2 years post-baseline (2013/14 and 2014/15). Students were grouped into behavioural clusters based on response probabilities. Linear mixed effects models, using BMI as a continuous outcome measure, were used to examine the effect of engaging in clusters of risky behaviours on BMI trajectories.ResultsThere were significant differences in BMI of the four behavioural clusters at baseline that remained consistent over time. Higher BMI values were found among youth classified at baseline to be Typical High School Athletes (β = 0.232 kg/m2, [confidence interval (CI): 0.03–0.50]), Inactive High Screen-User (β = 0.348 kg/m2, CI: 0.11–0.59) and Moderately Active Substance Users (β = 0.759 kg/m2, CI: 0.36–1.15) compared to students classified as Health Conscious. Despite these baseline differences, BMI appeared to increase across all behavioural clusters annually by the same amount (β = 0.6097 kg/m2, (CI) = 0.57–0.64).ConclusionsAlthough annual increases in BMI did not differ by behavioural clusters, membership in a particular behavioural cluster was associated with baseline BMI, and these differences remained consistent over time. Results indicate that intervening and modifying unhealthy behaviours earlier might have a greater impact than during adolescence. Health promotion strategies targeting the highest risk youth as they enter secondary school might be promising means to prevent or delay the onset of obesity.

Background Young adults often browse the internet for self-triage and diagnosis. More sophisticated digital platforms such as symptom checkers have recently become pervasive; however, little is known about their use. Objective The aim of this study was to understand young adults’ (18-34 years old) perspectives on the use of the Google search engine versus a symptom checker, as well as to identify the barriers and enablers for using a symptom checker for self-triage and self-diagnosis. Methods A qualitative descriptive case study research design was used. Semistructured interviews were conducted with 24 young adults enrolled in a university in Ontario, Canada. All participants were given a clinical vignette and were asked to use a symptom checker (WebMD Symptom Checker or Babylon Health) while thinking out loud, and were asked questions regarding their experience. Interviews were audio-recorded, transcribed, and imported into the NVivo software program. Inductive thematic analysis was conducted independently by two researchers. Results Using the Google search engine was perceived to be faster and more customizable (ie, ability to enter symptoms freely in the search engine) than a symptom checker; however, a symptom checker was perceived to be useful for a more personalized assessment. After having used a symptom checker, most of the participants believed that the platform needed improvement in the areas of accuracy, security and privacy, and medical jargon used. Given these limitations, most participants believed that symptom checkers could be more useful for self-triage than for self-diagnosis. Interestingly, more than half of the participants were not aware of symptom checkers prior to this study and most believed that this lack of awareness about the existence of symptom checkers hindered their use. Conclusions Awareness related to the existence of symptom checkers and their integration into the health care system are required to maximize benefits related to these platforms. Addressing the barriers identified in this study is likely to increase the acceptance and use of symptom checkers by young adults.

Public health practitioners should not overlook the benefits of enabling religious/spiritual practices among religious adults (i.e., offering ride programs could help isolated elders attend religious gatherings).