Social media photos of substance use and their relationship to attitudes and behaviors among ethnic and racial minority emerging adult men living in low-income areas

Young Adults' Exposure to and Engagement With Tobacco-Related Social Media Content and Subsequent Tobacco Use

INTRODUCTION

Nicotine/tobacco social media content may increase young people's risk for use. This study examined prospective associations between exposure to and engagement with nicotine/tobacco-related social media content and nicotine/tobacco use among young adults.

AIMS AND METHODS

Young adults (N = 2080) originally recruited from Southern California high schools for a prospective cohort study reported frequency of viewing and posting nicotine/tobacco content on four social media platforms (Facebook, Instagram, Twitter, and YouTube). Participants also reported frequency of seeing nicotine/tobacco posts from friends, seeing nicotine/tobacco posts from influencers or celebrities, and liking nicotine/tobacco posts. Within subsamples of nicotine/tobacco never users (n = 794), past users (n = 897) and current users (n = 389), analyses examined associations of baseline (May-October 2020) social media content exposure and engagement with follow-up (January-June 2021) tobacco use initiation (among never users), resumption (among past users), and continuation (among current users), adjusting for sociodemographic and socioenvironmental characteristics.

RESULTS

Never users who saw nicotine/tobacco posts from friends (Adjusted Odds Ratio [AOR] = 2.91 [95% Confidence Interval [CI]: 1.46, 5.82], p = .002) or from celebrities or influencers (AOR = 2.77 [1.32, 5.84], p = .007) were significantly more likely to initiate nicotine/tobacco use than their peers. Among past users, posting nicotine/tobacco content at baseline was associated with use resumption (AOR = 1.77 [1.12, 2.80], p = .014). Content exposure and engagement were not associated with nicotine/tobacco use continuation among current users (p-values > .05).

CONCLUSIONS

Seeing nicotine/tobacco posts from friends, influencers, or celebrities was associated with greater odds of tobacco use initiation, but not resumption or continuation, 6 months later. Young adults with past nicotine/tobacco use who post about nicotine/tobacco may be at elevated risk for resuming use.

IMPLICATIONS

Young adults with exposure to nicotine/tobacco social media content were more likely than their peers to initiate nicotine/tobacco use 6 months later. Past nicotine/tobacco users who reported posting about nicotine/tobacco on social media at baseline were more likely than their peers to resume nicotine/tobacco use. Among young adults with current nicotine/tobacco use at baseline, social media activity did not predict odds of nicotine/tobacco use continuation at follow-up. Nicotine/tobacco content on social media should be restricted to reduce young people's chances of nicotine/tobacco use initiation or resumption.

Development and validation of the Alcoholic Beverage Identification Deep Learning Algorithm version 2 for quantifying alcohol exposure in electronic images

BACKGROUND

Seeing alcohol in media has been demonstrated to increase alcohol craving, impulsive decision-making, and hazardous drinking. Due to the exponential growth of (social) media use it is important to develop algorithms to quantify alcohol exposure efficiently in electronic images. In this article, we describe the development of an improved version of the Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA), called ABIDLA2.

METHODS

ABIDLA2 was trained on 191,286 images downloaded from Google Image Search results (based on search terms) and Bing Image Search results. In Task-1, ABIDLA2 identified images as containing one of eight beverage categories (beer/cider cup, beer/cider bottle, beer/cider can, wine, champagne, cocktails, whiskey/cognac/brandy, other images). In Task-2, ABIDLA2 made a binary classification between images containing an "alcoholic beverage" or "other". An ablation study was performed to determine which techniques improved algorithm performance.

RESULTS

ABIDLA2 was most accurate in identifying Whiskey/Cognac/Brandy (88.1%) followed by Beer/Cider Can (80.5%), Beer/Cider Bottle (78.3%), and Wine (77.8%). Its overall accuracy was 77.0% (Task-1) and 87.7% (Task-2). Even the identification of the least accurate beverage category (Champagne, 64.5%) was more than five times higher than random chance (12.5% = 1/8 categories). The implementation of balanced data sampler to address class skewness and the use of self-training to make use of a large, secondary, weakly labeled dataset particularly improved overall algorithm performance.

CONCLUSION

With extended capabilities and a higher accuracy, ABIDLA2 outperforms its predecessor and enables the screening of any kind of electronic media rapidly to estimate the quantity of alcohol exposure. Quantifying alcohol exposure automatically through algorithms like ABIDLA2 is important because viewing images of alcoholic beverages in media tends to increase alcohol consumption and related harms.

Digital phenotyping in psychiatry: A scoping review

BACKGROUND

Digital phenotyping has been defined as the moment-by-moment assessment of an illness state through digital means, promising objective, quantifiable data on psychiatric patients' conditions, and could potentially improve diagnosis and management of mental illness. As it is a rapidly growing field, it is to be expected that new literature is being published frequently.

OBJECTIVE

We conducted this scoping review to assess the current state of literature on digital phenotyping and offer some discussion on the current trends and future direction of this area of research.

METHODS

We searched four databases, PubMed, Ovid MEDLINE, PsycINFO and Web of Science, from inception to August 25th, 2021. We included studies written in English that 1) investigated or applied their findings to diagnose psychiatric disorders and 2) utilized passive sensing for management or diagnosis. Protocols were excluded. A narrative synthesis approach was used, due to the heterogeneity and variability in outcomes and outcome types reported.

RESULTS

Of 10506 unique records identified, we included a total of 107 articles. The number of published studies has increased over tenfold from 2 in 2014 to 28 in 2020, illustrating the field's rapid growth. However, a significant proportion of these (49% of all studies and 87% of primary studies) were proof of concept, pilot or correlational studies examining digital phenotyping's potential. Most (62%) of the primary studies published evaluated individuals with depression (21%), BD (18%) and SZ (23%) (Appendix 1).

CONCLUSION

There is promise shown in certain domains of data and their clinical relevance, which have yet to be fully elucidated. A consensus has yet to be reached on the best methods of data collection and processing, and more multidisciplinary collaboration between physicians and other fields is needed to unlock the full potential of digital phenotyping and allow for statistically powerful clinical trials to prove clinical utility.