Asthma continues to be the leading chronic condition among US adolescents. Despite medical advances, many adolescents face uncontrolled asthma mainly due to insufficient self-management skills. Mobile apps pose a promising adjunct to in-clinic asthma care. However, little is known about the usability and effectiveness of such technology. In all, 20 adolescents participated in a 3-month trial to test an asthma app tailored to their age. Qualitative data on adolescents’ experience with the app were inquired. Overall, participants thought the app was functional and user-friendly. The majority expressed that the app assisted them with asthma self-management through tracking of asthma status and text reminders to test their peak flow regularly. They indicated external factors that limited app use and suggested improvements to make the app more engaging and appealing to adolescents. The tested app provides a feasible means to assist adolescent in developing self-management skills, tracking disease status, and communicating with healthcare providers.
Background Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users’ demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. Objective We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. Methods This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users’ age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. Results The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. Conclusions We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users’ posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.
Objectives: Identifying which youth are experimenting with vaping could aid the development and evaluation of targeted media campaigns and research and surveillance activities. In this study, we sought to identify behavioral definitions that best differentiate youth experimenters and established vapers. Methods: We conducted an online survey with a non-probability sample of 1500 youth aged 13-17 who reported ever vaping. Based on recency and lifetime vaping, we constructed 12 definitions of experimenters versus more established or recent users. We examined how well each definition discriminated between experimenters and established/recent users based on correlates (eg, vaping dependence, harm perceptions) using multivariate tests of mean differences, controlling for multiple variables, and ratios of between- to within-group variance. Results: Four definitions best distinguished between experimenters and more established/recent users (ie, had greater Hotelling T2 statistics for the multivariate tests and higher ratios of between- to within-group variance). Three of these 4 identified experimenters as those with no past 30-day vaping. Conclusion: Ever vapers are not a monolithic group. Our results suggest that past 30-day use is an important criterion for distinguishing experimenters from more established users. Understanding nuances between user groups could lead to greater differentiation among ever vapers and aid campaign targeting.
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