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Retention in healthcare and health behavior remains a critical issue, contributing to inequitable distribution of intervention benefits. In diseases such as HIV, where half of the new infections occur among racial and sexual minorities, it is important that interventions do not enlarge pre-existing health disparities. To effectively combat this public health issue, it is crucial that we quantify the magnitude of racial/ethnic disparity in retention. Further, there is a need to identify mediating factors to this relationship to inform equitable intervention design. In the present study, we assess the racial/ethnic disparity in retention in a peer-led online behavioral intervention to increase HIV self-testing behavior and identify explanatory factors. The research used data collected from the Harnessing Online Peer Education (HOPE) HIV Study that included 899 primarily African American and Latinx men who have sex with men (MSM) in the United States. Results show that African American participants had higher lost-to-follow-up rates at 12-week follow-up compared to Latinx participants (11.1% and 5.8% respectively, Odds Ratio = 2.18, 95% confidence interval: 1.12 - 4.11, p = 0.02), which is substantially mediated by participants' self-rated health score (14.1% of the variation in the African American v.s. Latinx difference in lost-follow-up, p = 0.006). Thus, how MSM perceive their health may play an important role in their retention in HIV-related behavioral intervention programs and its racial/ethnic disparity.

Despite the availability of effective treatment, medications for opioid use disorder are underutilized due to a variety of practical, political, and psychological reasons. Digital inequalities, such as limited access to technology, skills to leverage the technology for desirable outcomes, and social resources, may be contributing to negative health outcomes. In addition, broader health literacy plays an integral part in the capacity of individuals to appraise opioid medication-related online information. This paper explores the role of digital inequalities in the uptake of treatment for opioid use disorder. Given the shift toward telemedicine and online counseling for substance use treatment as a consequence of the COVID-19 pandemic, more research into the digital inequalities faced by those who misuse opioids may provide insight into ways of engaging and encouraging this population to utilize treatment.

Human Immunodeficiency Virus (HIV) continues to be a significant public health problem, with ~1.2 million Americans living with HIV and ~14% unaware of their infection. The Centers for Disease Control and Prevention recommends that patients 13 to 64 years of age get screened for HIV at least once, and those with higher risk profiles screen at least annually. Unfortunately, screening rates are below recommendations for high-risk populations, leading to problems of delayed diagnosis. Novel technologies have been applied in HIV research to increase prevention, testing and treatment. Conversational agents, with potential for integrating artificial intelligence and natural language processing, may offer an opportunity to improve outreach to these high-risk populations. The feasibility, accessibility and acceptance of using conversational agents for HIV testing outreach is important to evaluate, especially amidst a global coronavirus disease 2019 pandemic when clinical services have been drastically affected. This viewpoint explores the application of a conversational agent in increasing HIV testing among high-risk populations.

IntroductionVaccine hesitancy is still rampant in the United States, including health care personnel. Vaccination of frontline essential workers (e.g. health care workers) is very important, especially during a pandemic. We tested the efficacy of a 4-week online, peer-led intervention (Harnessing Online Peer Education) to promote requests for COVID-19 vaccine information among essential workers.MethodsParticipants (N = 120) and peer leaders (N = 12) were recruited through online advertisements from July 23 to August 20, 2021. Eligibility criteria included: 18 years or older, U.S. resident, English speaker, part of phase 1a or 1 b of COVID-19 vaccine rollout (e.g. frontline essential workers), hadn't received a COVID-19 vaccine but able to receive one. This was a parallel assignment randomised trial. STATA was used to create a randomisation using a random number generator so that all possible assignments of participants and peer leaders to groups were equally likely. Participants were randomly assigned to intervention or control arms that consisted of two private, hidden Facebook groups, each with 30 participants. Peer leaders were randomly assigned to an intervention group, each with six peer leaders. Participants in the intervention arm were randomly assigned to three peer leaders. Participants were blinded after assignment. Peer leaders were tasked with reaching out to their assigned participants at least three times each week. Participants completed a baseline and a post intervention survey. The study is registered on ClinicalTrials.org under identifier NCT04376515 and is no longer recruiting. This work was supported by the NIAID under grant 5R01AI132030-05.ResultsA total of 101 participants analysed (50 intervention and 51 control). Six people in the intervention group and 0 people in the control group requested vaccine information. Ten people in the intervention group and six people in the control group provided proof of vaccination. The odds of requesting vaccine information in the intervention group was 13 times that in the control group (95% confidence interval: (1.5, 1772), p-value = 0.015). Thirty-seven participants in the intervention group and 31 in the control group were engaged at some point during the study.ConclusionsResults suggest peer-led online community groups may help to disseminate health information, aid public health efforts, and combat vaccine hesitancy. Key MessagesThe odds of requesting vaccine information was 13 times in the intervention group.Peer-led online communities may help to disseminate information and aid public health efforts to combat vaccine hesitancy.

The COVID-19 pandemic and its related policies (e.g., stay at home and social distancing orders) have increased people's use of digital technology, such as social media. Researchers have, in turn, utilized artificial intelligence to analyze social media data for public health surveillance. For example, through machine learning and natural language processing, they have monitored social media data to examine public knowledge and behavior. This paper explores the ethical considerations of using artificial intelligence to monitor social media to understand the public's perspectives and behaviors surrounding COVID-19, including potential risks and benefits of an AI-driven approach. Importantly, investigators and ethics committees have a role in ensuring that researchers adhere to ethical principles of respect for persons, beneficence, and justice in a way that moves science forward while ensuring public safety and confidence in the process.

Many preventive trials randomize individuals to intervention condition which is then delivered in a group setting. Other trials randomize higher levels, say organizations, and then use learning collaboratives comprised of multiple organizations to support improved implementation or sustainment. Other trials randomize or expand existing social networks and use key opinion leaders to deliver interventions through these networks. We use the term contextually driven to refer generally to such trials (traditionally referred to as clustering, where groups are formed either pre-randomization or post-randomization - i.e., a cluster-randomized trial), as these groupings or networks provide fixed or time-varying contexts that matter both theoretically and practically in the delivery of interventions. While such contextually driven trials can provide efficient and effective ways to deliver and evaluate prevention programs, they all require analytical procedures that take appropriate account of non-independence, something not always appreciated. Published analyses of many prevention trials have failed to take this into account. We discuss different types of contextually driven designs and then show that even small amounts of non-independence can inflate actual Type I error rates. This inflation leads to rejecting the null hypotheses too often, and erroneously leading us to conclude that there are significant differences between interventions when they do not exist. We describe a procedure to account for non-independence in the important case of a two-arm trial that randomizes units of individuals or organizations in both arms and then provides the active treatment in one arm through groups formed after assignment. We provide sample code in multiple programming languages to guide the analyst, distinguish diverse contextually driven designs, and summarize implications for multiple audiences.

This study aimed to examine the depression and anxiety among men of color (primarily African American and Latinx) who have sex with men after the lockdown due to the COVID-19 pandemic. Outcomes included 21-item Beck Depression Inventory (BDI), 7-item Generalized Anxiety Disorder (GAD), and a 10-item COVID-related anxiety measure using a modified H1N1-related anxiety question. Independent variables were food insecurity and belief in government efficiency. Data were analyzed by Regression models with random cluster effects. Food insecurity experiences were significantly associated with higher depression (p

PurposeAlcohol use among adolescents and emerging adults is an important public health issue requiring prevention approaches. Herein, we describe outcomes from a randomized controlled trial testing the efficacy of group-based social media interventions targeting risky drinking among youth.ProceduresUsing social media advertisements to screen potential participants, we recruited 955 youth (ages 16-24) reporting recent risky drinking. After completing a baseline assessment, participants were randomized to 8-week secret Facebook group conditions: Social Media Intervention + Incentives for engagement, Social Media Intervention only, and attention-placebo control. Electronic coaches trained in motivational interviewing facilitated interaction in intervention groups. Primary outcomes include past 3-month alcohol use and consequences over 3-, 6-, and 12-month follow-ups. Secondary outcomes include other drug use, consequences, and impaired driving. We also measured intervention engagement and acceptability.ResultsThe interventions were well-received, with significantly greater acceptability ratings and engagement in the SMI+I condition relative to other groups. In adjusted analyses, there were no significant differences between interventions and control on alcohol-related outcomes, with all groups showing reductions. Regarding secondary outcomes (70.4% used other drugs), compared to control, the incentivized group reduced other drug use, consequences, and cannabis-impaired driving; the non-incentivized group did not significantly differ from the control condition.ConclusionsAmong this predominantly poly-substance using sample, findings were mixed, with significant effects of the incentivized social media intervention on drug (but not alcohol) outcomes. Future studies are needed to further refine social media-delivered interventions to reduce alcohol and other drug use.Trial registrationClinicalTrials.gov NCT02809586; University of Michigan HUM#00102242.

Background: Deaths due to opioid overdose continue to rise in the United States. Despite availability of effective treatment for opioid use disorder, uptake is low among those who misuse opioids. Methods: This paper explores the role of misconception, stigma, and misinformation in influencing decisions to initiate medications for opioid use disorder among patients and providers. Conclusion: Misinformation about opioids has been prevalent among future healthcare providers and first responders as well as pharmaceutical companies, which may have implications for treatment. Among individuals with opioid use disorder, treatment uptake and adherence have been negatively affected by misconceptions about treatment efficacy and side effects, as well as stigma. We discuss the role of social media, education, and the community, in mitigating misinformation and addressing misconceptions about opioids and treatment options.

Approximately 1.2 million people are living with HIV, with many of them unaware of their infection. Pre-exposure prophylaxis (PrEP) is available to minimize transmission among those at high risk for infection, including men who have sex with men, people who inject drugs, and female sex workers. Despite its availability, there is low usage of PrEP. To address this problem, various digital tools have been examined in HIV research. Among those, conversational agents are still underused and their capacity warrants examination to reach at-risk populations. In this paper, we discuss the potential of conversational agents in increasing uptake of PrEP by addressing barriers experienced among those at high risk for infection.

ObjectiveWe sought to assess the effectiveness of using a peer-led online community to increase HIV self-testing among Latinx and African American men who have sex with men (MSM).DesignRandomized controlled trial.MethodsThroughout 6 waves, between February 18, 2017, and January 8, 2021, 900 HIV negative and/or serostatus unknown Los Angeles-based MSM (68.9% Latinx, 16.0% African American, and 7.4% White) participated in an online 12-week HIV prevention randomized controlled trial. A total of 79 trained role models (peer leaders) were randomly assigned to participants within clusters to build trust and deliver HIV testing information on Facebook groups. Participants in control groups were assigned to groups without peer leaders. Participants were not required to respond to peer leaders or to remain group members. Participants completed self-report assessments at baseline and 12-week follow-up and could receive a free HIV self-testing kit during the study period.ResultsCompared with control group participants, intervention group participants were significantly more likely to accept the offer for the HIV self-testing kit (intervention 130 of 450, 29%; control 102 of 450, 22.7%; odds ratio = 1.43, 95% confidence interval: 1.04 to 1.95, P = 0.03), report having taken an HIV self-test within the past 3 months (odds ratio = 1.47, 95% confidence interval: 1.01 to 2.13, P = 0.04), and report drinking fewer glasses of alcohol in an average week (P = 0.01). Effects seemed concentrated within later study waves. Study retention was greater than 93%.ConclusionsA peer-led online community seems to be an effective method of increasing HIV self-testing among MSM of color. We discuss the implications of the wave effects on public health research and policy.

The COVID-19 pandemic has led public health departments to issue several orders and recommendations to reduce COVID-19-related morbidity and mortality. However, for various reasons, including lack of ability to sufficiently monitor and influence behavior change, adherence to these health orders and recommendations has been suboptimal. Starting April 29, 2020, during the initial stay-at-home orders issued by various state governors, we conducted an intervention that sent online website and mobile application advertisements to people's mobile phones to encourage them to adhere to stay-at-home orders. Adherence to stay-at-home orders was monitored using individual-level cell phone mobility data, from April 29, 2020 through May 10, 2020. Mobile devices across 5 regions in the United States were randomly-assigned to either receive advertisements from our research team advising them to stay at home to stay safe (intervention group) or standard advertisements from other advertisers (control group). Compared to control group devices that received only standard corporate advertisements (i.e., did not receive public health advertisements to stay at home), the (intervention group) devices that received public health advertisements to stay at home demonstrated objectively-measured increased adherence to stay at home (i.e., smaller radius of gyration, average travel distance, and larger stay-at-home ratios). Results suggest that 1) it is feasible to use mobility data to assess efficacy of an online advertising intervention, and 2) online advertisements are a potentially effective method for increasing adherence to government/public health stay-at-home orders.

Same-day alcohol and cannabis use is relatively common in adolescents and young adults, constituting a higher-risk behavior relative to single-substance use. However, the association between quantity of alcohol and cannabis use on co-use days is understudied. We examined the association between the quantity of alcohol and same-day cannabis use with a multilevel regression analysis in a sample of youth (16-24 years old) with risky alcohol use. Participants reported one or more days of alcohol and cannabis over the past month (N = 468). Quantity of cannabis use was highest on heavy drinking days [M = 0.91 grams, SD = 0.68] followed by moderate drinking days (M = 0.78 grams, SD = 0.63), and lowest on days without alcohol use (M = 0.74 grams, SD = 0.64, p < 0.001). In multilevel modeling analyses, adjusted for clustering within individuals, greater quantity of drinking on a given day was associated with greater cannabis use (estimate = 0.03, p < 0.001). When using alcohol and cannabis on the same day, greater alcohol use was associated with greater cannabis use. Preventing days of heavy use of multiple substances, particularly among at-risk drinkers, may complement interventions addressing co-use generally to prevent substance-related consequences.

PurposeCannabis use is increasing among emerging adults (ages 18-25), necessitating the need for prevention interventions. Using a novel platform - social media - we developed an 8-week motivational interviewing and cognitive-behavioral intervention targeting cannabis use among emerging adults. Herein, we report on the feasibility and acceptability of the intervention in a pilot trial.ProceduresFor NCT04187989 we recruited N = 149 emerging adults who used cannabis (at least 3 times/week for the past month) using social media advertising. Their mean age was 21.0 years (SD = 2.2); 55.7% were female. Most were White (70.5%; 20.1% Black/African American, 9.4% Other races), with 20.1% identifying as Hispanic/Latinx. Participants were randomized to the 8-week intervention or an 8-week attention-placebo control condition, both delivered in secret Facebook groups by electronic health coaches (e-coaches). Follow-up assessments occurred at 3- and 6-months.ResultsThe intervention was well-received and follow-up rates were high; fidelity was good. Intervention participants rated e-coaches significantly higher in terms of helpfulness, warmth, etc., compared to control participants. Intervention participants were more likely to engage with and recommend the group. In terms of percentage reductions in cannabis outcomes, the intervention group evidenced absolute reductions over time in several measures of cannabis consumption across modalities. In an adjusted model, reductions in vaping days in the intervention group, relative to attention-control, reached statistical significance (p = .020, D =.40).ConclusionsThis social media intervention for emerging adults' cannabis use was feasible and acceptable in the target population warranting future testing in a fully powered trial.

Data from digital technologies are increasingly integrated in public health research. In April of 2020, we interviewed a subset of participants (N=25) who completed a survey approximately one month earlier (just prior to the declaration of the COVID-19 pandemic in the United States). Using the survey, we contacted and interviewed participants who had expressed their willingness or unwillingness to share digital data (e.g., from contact tracing apps) for use in public health. We followed a directed content analysis approach for the analysis of the interview data. Among participants who had reported being unwilling to share data, concerns about privacy, confidentiality, and the purpose of the research were cited. During the interviews, 76.9% of the participants who had previously indicated that they were unwilling to share their data, expressed willingness to share data in order to assist with COVID-19 prevention. Our results contribute to our understanding of people's perspectives on sharing personal data and of the way their perspectives can vary as a function of potential uses of their personal information (e.g., prevention of COVID-19).

BackgroundSocial media sites, dating apps, and information search sites have been used to reach individuals at high risk for HIV infection. However, it is not clear which platform is the most efficient in promoting home HIV self-testing, given that the users of various platforms may have different characteristics that impact their readiness for HIV testing.ObjectiveThis study aimed to compare the relative effectiveness of social media sites, dating apps, and information search sites in promoting HIV self-testing among minority men who have sex with men (MSM) at an increased risk of HIV infection. Test kit order rates were used as a proxy to evaluate promotion effectiveness. In addition, we assessed differences in characteristics between participants who ordered and did not order an HIV test kit.MethodsCulturally appropriate advertisements were placed on popular sites of three different platforms: social media sites (Facebook, Instagram), dating apps (Grindr, Jack'D), and information search sites (Google, Bing). Advertisements targeted young (18-30 years old) and minority (Black or Latinx) MSM at risk of HIV exposure. Recruitment occurred in 2 waves, with each wave running advertisements on 1 platform of each type over the same period. Participants completed a baseline survey assessing sexual or injection use behavior, substance use including alcohol, psychological readiness to test, attitudes toward HIV testing and treatment, and HIV-related stigma. Participants received an electronic code to order a free home-based HIV self-test kit. Follow-up assessments were conducted to assess HIV self-test kit use and uptake of pre-exposure prophylaxis (PrEP) at 14 and 60 days post enrollment.ResultsIn total, 271 participants were enrolled, and 254 were included in the final analysis. Among these 254 participants, 177 (69.7%) ordered a home HIV self-test kit. Most of the self-test kits were ordered by participants enrolled from dating apps. Due to waves with low enrollment, between wave statistical comparisons were not feasible. Within wave comparison revealed that Jack'D showed higher order rates (3.29 kits/day) compared to Instagram (0.34 kits/day) and Bing (0 kits/day). There were no associations among self-test kit ordering and HIV-related stigma, perceptions about HIV testing and treatment, and mistrust of medical organizations.ConclusionsOur findings show that using popular dating apps might be an efficient way to promote HIV self-testing. Stigma, perceptions about HIV testing and treatment, or mistrust of medical organizations may not affect order rates of HIV test kits promoted on the internet.Trial registrationClinicalTrials.gov NCT04155502; https://clinicaltrials.gov/ct2/show/NCT04155502.International registered report identifier (irrid)RR2-10.2196/20417.

Background: High-intensity drinking (HID; 8+ U.S. standard drinks for women, 10+ men) is initiated during adolescence/emerging adulthood, increasing risk for negative outcomes, including blackouts. We examined baseline data from a study of risky drinking youth to identify factors associated with HID. Methods: Risky drinkers (ages 16-24) were recruited online (positive 3-month AUDIT-C score) as part of a larger study to examine social media interventions for risky drinking. We used baseline survey data to examine HID in relation to demographics, substance use-related variables, and individual and social factors. Results: Among 931 risky drinkers, 29.8% reported past-month HID, and those with HID reported greater substance use and consequences. In multivariable analysis, HID was associated with male sex; greater social motives, impulsivity, and motivation; lower self-efficacy; and greater likelihood of not living with parents, drinking with important peers, and parental disapproval of posting drinking pictures. When examining age group interactions (16-20; 21-24), underage drinkers with high sensation-seeking scores and lower parental disapproval of posting drinking pictures on social media reported greater HID. Conclusions: Among risky drinking youth, male sex, social motives, impulsivity, higher motivation to and lower-self-efficacy to reduce drinking, living away from parents, more frequent drinking with important peers, and lower parental disapproval of posting drinking pictures on social media were positively associated with HID. Further, HID was associated with greater health consequences, underscoring the need for HID interventions. Such interventions may benefit from enhancing motivation and self-efficacy, particularly in social contexts, as well as increasing positive peer and leisure activities to reduce HID.

Digital data, including social media, wearable device data, electronic health records, and internet search data, are increasingly being integrated into public health research and policy. Because of the current issues around public distrust of science and other ethical issues in public health research, it is essential that researchers conduct ongoing studies assessing people's perceptions around and willingness to share digital data. This study aims to examine participants' social media use and comfort sharing their data with health researchers. One hundred and sixty-one participants with medical conditions were recruited through social media paid advertisements and referral from a website, and invited to complete surveys on social media use and ethical perspectives on data sharing. Eligibility criteria were adults 18 years old or older, living in the US, self-reported having been diagnosed by a physician with a medical condition, belonging to at least one social media platform, using social media at least twice a week, and owning a smartphone. Study participants were mostly female, White, and with a mean age of 36.31 years. More than one third of participants reported being very comfortable sharing electronic health data and social media data for personalized healthcare and to help others. Findings suggest that participants are very uncomfortable sharing their location and text message data with researchers, with primary concerns centered around loss of privacy, disclosing private information, and that friends, family, and others may find out that they shared text messages with researchers. We discuss the implications of this research before and after the COVID-19 pandemic, along with its potential implications for future collection of digital data for public health.

Digital health behavior change interventions (DHBCIs) offer users accessible support, yet their promise to improve health behaviors at scale has not been met. One reason for this unmet potential may be a failure to offer users support that is tailored to their personal characteristics and goals. We apply the concept of antifragility to propose how DHBCIs could be better designed to support diverse users' behavior change journeys. We first define antifragility as a feature of an individual's relationship to a particular challenge such that if one is antifragile to a challenge, one is well positioned to benefit from facing that challenge. Second, we introduce antifragile behavior change to describe behavior change processes that leverage person-specific antifragilities to maximize benefits and minimize risk in the behavior change process. While most existing behavior change models focus on improving one's motivation and ability to face challenges, antifragile behavior change complements these models by helping to select challenges that are most likely to produce desired outcomes. Next, we propose three principles by which DHBCIs can help users to develop antifragile behavior change strategies: providing personalized guidance, embracing variance and exploration in choosing behaviors, and prioritizing user agency. Finally, we offer an example of how a DHBCI could be designed to support antifragile behavior change.

Although rates of vaccination have increased worldwide, the rise in nonmedical exemptions for vaccination may have caused a resurgence of childhood vaccine-preventable diseases. Vaccine hesitancy plays an important role in the decreasing rates of vaccination and is considered by the World Health Organization as a top ten global threat to public health. Online vaccine misinformation is present in news outlets, websites, and social media, and its rapid and extensive dissemination is aided by artificial intelligence (AI). In combating online misinformation, public health experts, the medical community, and lay vaccination advocates can correct false statements using language that appeal to those who are undecided about vaccination. As the gatekeepers to online information, they can implement and enforce policy that limits or bans vaccine misinformation on their platforms. AI tools might also be used to address misinformation, but more research is needed before implementing this approach more broadly in health policy. This commentary examines the role that different online platforms appear to be playing in the spread of misinformation about vaccines. We also discuss the implications of online misinformation on attitudes about COVID-19 vaccine uptake and provide suggestions for ways to combat online misinformation.

There is a need for real-time and predictive data on alcohol use both broadly and specific to HIV. However, substance use and HIV data often suffer from lag times in reporting as they are typically measured from surveys, clinical case visits and other methods requiring extensive time for collection and analysis. Social big data might help to address this problem and be used to provide near real-time assessments of people's alcohol use and/or alcohol. This manuscript describes three types of social data sources (i.e., social media data, internet search data, and wearable device data) that might be used in surveillance of alcohol and HIV, and then discusses the implications and potential of implementing them as additional tools for public health surveillance.

The HIV epidemic continues to disproportionally affect marginalized populations. Digital tools, including global positioning system and ecologic momentary assessment, have been studied as methods for improving data collection and interventions among HIV-affected communities. Although people living with HIV and populations at high risk have found it acceptable to use digital technologies for HIV research, concerns over privacy and trust have also been expressed. This paper explores and describes the use of geolocation technology data (e.g., location-based social media) in HIV research as well as the ethical and implementation considerations that warrant examination prior to use. Transparent and clear language in consent forms might improve participant trust in the project and investigators' ability to keep participant data secure and private. With respect to institutional review boards, a committee member who is knowledgeable about digital technologies and consumer protections may offer guidance in assessing adequate protections in study protocols. As technology used in research continues to evolve, investigators and the research community must continue to examine the ethical challenges that emerge to address participant concerns.

Since the beginning of the HIV epidemic, there have been more than 75 million cases. Currently, there about 1.2 million living with HIV in the USA. Despite current testing recommendations, test rates continue to be suboptimal. Investigators have studied the use of digital technology to promote HIV testing, especially among high-risk populations. PURPOSE OF REVIEW: This non-systematic review provides an overview of the scientific research between 2015 and 2020 focused on the use of digital technology to bolster HIV testing and suggests novel technologies for exploration. RECENT FINDINGS: A total of 40 studies were included in the review that span a wide range of available technology. Studies effectively increased HIV testing among study participants. Generally, participants in the intervention/exposure groups had significantly higher rates of HIV test uptake compared to participants in the comparison groups at study follow-up. For a variety of reasons (e.g., differences in ways the technologies were used and study design), no digital tool clearly performed better than others, but each have the capacity to increase outreach and self-testing. An exploration of the potential use of nascent technologies is also discussed, as well as the authors' experiences using a number of these technologies in our research.

Cannabis-using youth are a large epidemiologic subgroup whose age and smoking-related risks underscore the importance of examining the impact of the COVID-19 pandemic in this population. Within a clinical trial (n = 36 received an intervention prior to data collection reported herein), we surveyed cannabis-using emerging adults (ages 18-25) about perceived COVID-19 impacts. Participants (n = 141) reporting weekly cannabis use (M = 18.6 use days in the past 30) were enrolled and completed online surveys as part of either their baseline or 3 month assessment. COVID-19-related measures included symptoms, substance use, mood, etc. Participants were 57% female (mean age = 21, standard deviation = 2.2), with 21% Hispanic/Latinx, 70% White, 20% Black/African American, and 10% of other races. Most participants (86%) reported quarantine/self-isolation (M = 59 days). Several had COVID-19 symptoms (16%), but none reported testing COVID-19 positive. Many respondents felt their cannabis use (35%-50%, across consumption methods) and negative emotions (e.g., loneliness, stress, and depression; 69.5%, 69.5%, and 61.8%, respectively) increased. They reported decreased in-person socialization (90.8%) and job losses (23.4%). Reports of increased cannabis smoking were associated with increased negative emotions. On an open-response item, employment/finances and social isolation were frequently named negative impacts (33.3% and 29.4%, respectively). Although cannabis-using emerging adults' reports of increases in cannabis use, coupled with mental health symptoms and social isolation, are concerning, the full impact of the pandemic on their health and well-being remains unknown. Future studies examining the relationship between social isolation, mental health, and cannabis use among young people are needed.

BackgroundNew data sources and analysis methods are urgently needed to improve opioid surveillance and prevent potential overdose. Social media data is one potential data source that might be used and integrated to address this issue. Objective: This study explored opioid-related topics discussed across geographical regions of varying population sizes to determine whether social media data might inform opioid surveillance. Methods: Between March 17th to July 17th, 2020, we collected tweets (N = 19,721) mentioning opioid-related keywords across seven cities within the United States. Results: Results found that opioid-related keywords were distributed as follows: New York (29%), Los Angeles (23%), Chicago (18%), Atlanta (18%), San Francisco (8%), Iowa (3%), and Orange County, CA (1%). We also found regional differences in the types of opioids and topics mentioned. Conclusions: Findings suggest the feasibility of using opioid-related social media data to inform surveillance efforts, as well as potential regional and time-varying differences in topics discussed.

ObjectivesCommunity popular opinion leaders have played a critical role in HIV prevention interventions. However, it is often difficult to identify these 'HIV influencers' who are qualified and willing to promote HIV campaigns, especially online, because social media influencers change frequently. We sought to use an iterative deep learning framework to automatically discover HIV-related online social influencers.Design and methodOut of 1.15 million Twitter users' data from March 2018 to March 2020, we extracted tweets from 1099 Twitter users who had mentioned the keywords 'HIV' or 'AIDS'. Two Twitter users determined to be 'online HIV influencers' based on their conversation topics and engagement were hand-picked by domain experts and used as a seed training dataset. We modelled social influence and discovered new potential influencers based on these seeds using a graph neural network model. We tested the model's precision and recall compared with other baseline model approaches. We validated the results through manual verification.ResultsThe model identified 23 new (manually verified) HIV-related influencers, including health and research organizations and local HIV advocates across the United States. Our proposed model achieved the highest accuracy/recall, with an average improvement of 38.5% over the other baseline models.ConclusionResults suggest that iterative deep learning models can be used to automatically identify new and changing key HIV-related influencers online. We discuss the implications and potential of HIV researchers/departments applying this approach across online big data (e.g. hundreds of millions of social media posts per day) to help promote HIV prevention campaigns to affected communities.

Despite widespread national, state, and local guidelines for COVID-19 prevention, including social distancing and mask orders, many people continue to not adhere to recommendations, including congregating in groups for non-essential activities, putting themselves and others at risk. A social psychological perspective can be used to understand reasons for lack of adherence to policies and methods for increasing adherence based on successes from other behavior change campaigns. This manuscript seeks to describe some of the social psychological research that may be relevant to COVID-19 prevention and behavior change, describe how these theories have been previously applied in various domains to change behavior, and provide examples of how these approaches might be similarly applied to control the pandemic. We provide concrete examples of actions that can be taken based on social psychological research that might help to increase adherence to COVID-19 recommendations and improve prevention and control of the virus.

The primary contributors to the opioid crisis continue to rapidly evolve both geographically and temporally, hampering the ability to halt the growing epidemic. To address this issue, we evaluated whether integration of near real-time social/behavioral (i.e., Google Trends) and traditional health care (i.e., Medicaid prescription drug utilization) data might predict geographic and longitudinal trends in opioid-related Emergency Department (ED) visits. From January 2005 through December 2015, we collected quarterly State Drug Utilization Data; opioid-related internet search terms/phrases; and opioid-related ED visit data. Modeling was conducted using least absolute shrinkage and selection operator (LASSO) regression prediction. Models combining Google and Medicaid variables were a better fit and more accurate (R2 values from 0.913 to 0.960, across states) than models using either data source alone. The combined model predicted sharp and state-specific changes in ED visits during the post 2013 transition from heroin to fentanyl. Models integrating internet search and drug utilization data might inform policy efforts about regional medical treatment preferences and needs.

Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible-exposed-infectious-recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number ≈ 1.0).

BackgroundInterdisciplinary approaches are needed to address complex societal problems, such as the opioid crisis. We sought to explore the feasibility and potential issues encountered in planning and implementing a 24-hour hackathon competition to bring together teams from law enforcement, public health, and data science to develop solutions to the opioid epidemic.MethodsWe enlisted an advisory board and planning committee, including key stakeholders (e.g., high-level representatives from government agencies) to plan the event. Teams completed an online registration form with questions about team composition. Each team captain completed a survey at the event describing prior experiences with their team, knowledge and interest around the opioid epidemic, and hackathon expectations.ResultsTwenty-nine teams (108 individuals) registered. 76% had a technical/engineering background. Participants were from industry (55%), academia (30%), public health/medicine (9%), and government/public policy (6%). Nineteen teams attended the event. Team captains were primarily 18-29 years of age, had moderate experience and interest in the opioid crisis, and had never attended an opioid-related event.ConclusionsIt is feasible to implement a 24-hour opioid-focused hackathon and recruit teams/participants from a broad range of disciplines. We discuss the solutions developed, barriers encountered, and insights gained throughout the planning and implementation process.

Unprecedented public engagement with social media has provided viable and culturally relevant platforms for application in sexual health interventions, yet there are concerns that methods for evaluating engagement in these interventions have not kept pace with their implementation. More recently, the rise of social media analytics (SMA) and online marketing has spawned the development of analytic tools that boast promise for such a task. In this paper, we review a sample of the most popular of these tools, paying particular attention to: (1) the social media platforms that can be analyzed; (2) analytic capabilities; and (3) measures of engagement. We follow this with a review of sexual health intervention studies that apply these tools in evaluation efforts. Our findings suggest that these tools have numerous analytic capabilities that would be useful for evaluating interventions more efficiently. However, in nearly all cases, the tools we reviewed alone would not be sufficient to fully grasp engagement dynamics, as they need to be complemented with additional tools for textual analysis and social network analysis. Therefore, we consider this fertile ground for future collaborations between software developers and behavioral health scientists to develop more comprehensive analytic platforms with applications for public health research.

Interventions are urgently needed to reduce prescription opioid misuse risk factors, including anxiety and concomitant use of sedatives. However, only a limited number of randomized controlled opioid intervention trials have been conducted. We sought to determine whether an online behavior change/support community, compared to a control Facebook group, could reduce anxiety and opioid misuse among chronic pain patients. 51 high-risk non-cancer chronic pain patients were randomly assigned to either a Harnessing Online Peer Education (HOPE) peer-led online behavior change intervention or a control group (no peer leaders) on Facebook for 12 weeks. Inclusion criteria were: 18 years or older, a UCLA Health System patient, prescribed an opioid for non-cancer chronic pain between 3 and 12 months ago, and a score of ≥9 on the Current Opioid Misuse Measure (COMM) and/or concomitant use of benzodiazepines. Participation in the online community was voluntary. Patients completed baseline and follow-up assessments on Generalized Anxiety Disorder screener (GAD-7), COMM, and frequency of social media discussions about pain and opioid use. Compared to control group participants, intervention participants showed a baseline-to-follow-up decrease in anxiety, and more frequently used social media to discuss pain, prescription opioid use, coping strategies, places to seek help, and alternative therapies for pain. Both groups showed a baseline to follow-up decrease in COMM score. Preliminary results support the use an online community interventions as a low-cost tool to decrease risk for prescription opioid misuse and its complications.

Understanding factors influencing risky drinking among emerging adults could enhance interventions to reduce adverse outcomes. As a motivational construct, the dualistic model of passion (i.e., obsessive passion: drinking is compelling and conflicts with other life activities/values; harmonious passion: drinking is an important, but not overwhelming, part of life) offers a novel explanation for persistent alcohol use. Yet, the dualistic model of passion has not yet been evaluated in this at-risk population. Therefore, we examined whether the variables proposed by the dualistic model of passion were associated with sex, binge-drinking, and alcohol-related consequences among young risky drinkers. We analyzed cross-sectional baseline data collected as part of an ongoing randomized controlled trial of emerging adults (n=327; M age=22.6, SD=1.1, range 21-24; 61% female; 76% White non-Hispanic) engaged in risky drinking (AUDIT-C score ≥ 4 females, ≥ 5 males) recruited nationally via social media advertisements. A path analysis revealed significant positive direct effects between obsessive passion and binge drinking and alcohol-related consequences, and significant negative direct effects between male sex and harmonious passion and binge drinking. There was also a positive direct effect between binge drinking and alcohol-related consequences. Male sex and obsessive passion were both indirectly related to alcohol-related consequences via binge drinking. Type of passion and male sex differentiates risky drinkers who binge drink and who experienced alcohol-related consequences. Future research should examine whether targeting obsessive passion reduces binge drinking and negative outcomes.

Influence measurement in social networks is vital to various real-world applications, such as online marketing and political campaigns. In this paper, we investigate the problem of measuring time-sensitive and topic-specific influence based on streaming texts and dynamic social networks. A user's influence can change rapidly in response to a new event and vary on different topics. For example, the political influence of Douglas Jones increased dramatically after winning the Alabama special election, and then rapidly decreased after the election week. During the same period, however, Douglas Jones' influence on sports remained low. Most existing approaches can only model the influence based on static social network structures and topic distributions. Furthermore, as popular social networking services embody many features to connect their users, multi-typed interactions make it hard to learn the roles that different interactions play when propagating information. To address these challenges, we propose a Time-sensitive and Topic-specific Influence Measurement (TTIM) method, to jointly model the streaming texts and dynamic social networks. We simulate the influence propagation process with a self-attention mechanism to learn the contributions of different interactions and track the influence dynamics with a matrix-adaptive long short-term memory. To the best of our knowledge, this is the first attempt to measure time-sensitive and topic-specific influence. Furthermore, the TTIM model can be easily adapted to supporting online learning which consumes constant training time on newly arrived data for each timestamp. We comprehensively evaluate the proposed TTIM model on five datasets from Twitter and Reddit. The experimental results demonstrate promising performance compared to the state-of-the-art social influence analysis models and the potential of TTIM in visualizing influence dynamics and topic distribution.

Purpose of reviewDespite tremendous potential for public health impact and continued investments in development and evaluation, it is rare for eHealth behavioral interventions to be implemented broadly in practice. Intervention developers may not be planning for implementation when designing technology-enabled interventions, thus creating greater challenges for real-world deployment following a research trial. To facilitate faster translation to practice, we aimed to provide researchers and developers with an implementation-focused approach and set of design considerations as they develop new eHealth programs.Recent findingsUsing the Accelerated Creation-to-Sustainment model as a lens, we examined challenges and successes experienced during the development and evaluation of four diverse eHealth HIV prevention programs for young men who have sex with men: Keep It Up!, Harnessing Online Peer Education, Guy2Guy, and HealthMindr. HIV is useful for studying eHealth implementation because of the substantial proliferation of diverse eHealth interventions with strong evidence of reach and efficacy and the responsiveness to rapid and radical disruptions in the field. Rather than locked-down products to be disseminated, eHealth interventions are complex sociotechnical systems that require continual optimization, vigilance to monitor and troubleshoot technological issues, and decision rules to refresh content and functionality while maintaining fidelity to core intervention principles. Platform choice and sociotechnical relationships (among end users, implementers, and the technology) heavily influence implementation needs and challenges. We present a checklist of critical implementation questions to address during intervention development. In the absence of a clear path forward for eHealth implementation, deliberate design of an eHealth intervention's service and technological components in tandem with their implementation plans is critical to mitigating barriers to widespread use. The design considerations presented can be used by developers, evaluators, reviewers, and funders to prioritize the pragmatic scalability of eHealth interventions in research.

Crime monitoring tools are needed for public health and law enforcement officials to deploy appropriate resources and develop targeted interventions. Social media, such as Twitter, has been shown to be a feasible tool for monitoring and predicting public health events such as disease outbreaks. Social media might also serve as a feasible tool for crime surveillance. In this study, we collected Twitter data between May and December 2012 and crime data for the years 2012 and 2013 in the United States. We examined the association between crime data and drug-related tweets. We found that tweets from 2012 were strongly associated with county-level crime data in both 2012 and 2013. This study presents preliminary evidence that social media data can be used to help predict future crimes. We discuss how future research can build upon this initial study to further examine the feasibility and effectiveness of this approach.

ObjectivesInternet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China.DesignWe identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016).ResultsSearch query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks.ConclusionsBaidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention.

BackgroundFor a number of fiscal and practical reasons, data on heroin use have been of poor quality, which has hampered the ability to halt the growing epidemic. Internet search data, such as those made available by Google Trends, have been used as a low-cost, real-time data source for monitoring and predicting a variety of public health outcomes. We aimed to determine whether data on opioid-related internet searches might predict future heroin-related admissions to emergency departments (ED).MethodsAcross nine metropolitan statistical areas (MSAs) in the United States, we obtained data on Google searches for prescription and non-prescription opioids, as well as Substance Abuse and Mental Health Services Administration (SAMHSA) data on heroin-related ED visits from 2004 to 2011. A linear mixed model assessed the relationship between opioid-related Internet searches and following year heroin-related visits, controlling for MSA GINI index and total number of ED visits.ResultsThe best-fitting model explained 72% of the variance in heroin-related ED visits. The final model included the search keywords "Avinza," "Brown Sugar," "China White," "Codeine," "Kadian," "Methadone," and "Oxymorphone." We found regional differences in where and how people searched for opioid-related information.ConclusionsInternet search-based modeling should be explored as a new source of insights for predicting heroin-related admissions. In geographic regions where no current heroin-related data exist, Internet search modeling might be a particularly valuable and inexpensive tool for estimating changing heroin use trends. We discuss the immediate implications for using this approach to assist in managing opioid-related morbidity and mortality in the United States.

Social media data can provide valuable information regarding people's behaviors and health outcomes. Previous studies have shown that social media data can be extracted to monitor and predict infectious disease outbreaks. These same approaches can be applied to other fields including physical activity research and forensic science. Social media data have the potential to provide real-time monitoring and prediction of physical activity level in a given region. This tool can be valuable to public health organizations as it can overcome the time lag in the reporting of physical activity epidemiology data faced by traditional research methods (e.g. surveys, observational studies). As a result, this tool could help public health organizations better mobilize and target physical activity interventions. The first part of this paper aims to describe current approaches (e.g. topic modeling, sentiment analysis and social network analysis) that could be used to analyze social media data to provide real-time monitoring of physical activity level. The second aim of this paper was to discuss ways to apply social media analysis to other fields such as forensic sciences and provide recommendations to further social media research.

BackgroundResearchers have suggested that social media and online search data might be used to monitor and predict syphilis and other sexually transmitted diseases. Because people at risk for syphilis might seek sexual health and risk-related information on the internet, we investigated associations between internet state-level search query data (e.g., Google Trends) and reported weekly syphilis cases.MethodsWe obtained weekly counts of reported primary and secondary syphilis for 50 states from 2012 to 2014 from the US Centers for Disease Control and Prevention. We collected weekly internet search query data regarding 25 risk-related keywords from 2012 to 2014 for 50 states using Google Trends. We joined 155 weeks of Google Trends data with 1-week lag to weekly syphilis data for a total of 7750 data points. Using the least absolute shrinkage and selection operator, we trained three linear mixed models on the first 10 weeks of each year. We validated models for 2012 and 2014 for the following 52 weeks and the 2014 model for the following 42 weeks.ResultsThe models, consisting of different sets of keyword predictors for each year, accurately predicted 144 weeks of primary and secondary syphilis counts for each state, with an overall average R of 0.9 and overall average root mean squared error of 4.9.ConclusionsWe used Google Trends search data from the prior week to predict cases of syphilis in the following weeks for each state. Further research could explore how search data could be integrated into public health monitoring systems.

Aims: Assess whether the Harnessing Online Peer Education (HOPE) social media-based support group can engage patients on opioids at risk for misuse/overdose to discuss risk reduction strategies. Methods: Fifty-one patients on chronic opioid therapy and risk factors for aberrant medication-taking behaviors were randomized to a HOPE intervention or control (Facebook) group. Results: Compared to control group participants, intervention participants had almost 10 times higher posting engagement (n = 411 posts versus 45; 73% versus 52% of participants). Participants discussed coping, pain, medication and non-medication treatments, and other opioid and addiction-related topics. Discussion: Results suggest that a HOPE online community might serve as an effective behavioral intervention tool among chronic pain patients on opioid therapy.

BackgroundFreshman experiences can greatly influence students' success. Traditional methods of monitoring the freshman experience, such as conducting surveys, can be resource intensive and time consuming. Social media, such as Twitter, enable users to share their daily experiences. Thus, it may be possible to use Twitter to monitor students' postsecondary experience.ObjectiveOur objectives were to (1) describe the proportion of content posted on Twitter by college students relating to academic studies, personal health, and social life throughout the semester; and (2) examine whether the proportion of content differed by demographics and during nonexam versus exam periods.MethodsBetween October 5 and December 11, 2015, we collected tweets from 170 freshmen attending the University of California Los Angeles, California, USA, aged 18 to 20 years. We categorized the tweets into topics related to academic, personal health, and social life using keyword searches. Mann-Whitney U and Kruskal-Wallis H tests examined whether the content posted differed by sex, ethnicity, and major. The Friedman test determined whether the total number of tweets and percentage of tweets related to academic studies, personal health, and social life differed between nonexam (weeks 1-8) and final exam (weeks 9 and 10) periods.ResultsParticipants posted 24,421 tweets during the fall semester. Academic-related tweets (n=3433, 14.06%) were the most prevalent during the entire semester, compared with tweets related to personal health (n=2483, 10.17%) and social life (n=1646, 6.74%). The proportion of academic-related tweets increased during final-exam compared with nonexam periods (mean rank 68.9, mean 18%, standard error (SE) 0.1% vs mean rank 80.7, mean 21%, SE 0.2%; Z=-2.1, P=.04). Meanwhile, the proportion of tweets related to social life decreased during final exams compared with nonexam periods (mean rank 70.2, mean 5.4%, SE 0.01% vs mean rank 81.8, mean 7.4%, SE 0.01%; Z=-4.8, P.05). However, during the final-exam periods, the percentage of academic tweets was significantly lower among African Americans than whites (χ24=15.1, P=.004). The percentages of tweets related to academic studies, personal health, and social life were not significantly different between areas of study during nonexam and exam periods (P>.05).ConclusionsThe results suggest that the number of tweets related to academic studies and social life fluctuates to reflect real-time events. Student's ethnicity influenced the proportion of academic-related tweets posted. The findings from this study provide valuable information on the types of information that could be extracted from social media data. This information can be valuable for school administrators and researchers to improve students' university experience.

BackgroundCommercial sex venues (CSV), bathhouses and sex clubs, have a long history of serving a high-risk population. In those facilities, patrons engage in multiple sexual encounters and often in high-risk sexual behaviors. Designing prevention interventions specifically for CSVs could be an effective way to increase testing and control HIV transmission.MethodsIn collaboration with the AIDS Healthcare Foundation (AHF), our team distributed free HIV self-test kits using vending machines located at two CSVs in Los Angeles, California. Test kit dispensing rate was monitored remotely. Patrons receiving a test kit were surveyed regarding their testing experience, test result and follow up. Linkage to care was offered to participants.ResultsDuring 18 months, 1,398 kits were dispensed. The survey was completed by 110 patrons (response rate =7.9%). Among those who reported that they used the test kit (n=96), 17 (17.7%) participants reported a first-time reactive HIV result. At the time of the survey, six participants with reactive results reported seeking confirmatory testing and linkage to care and four had initiated treatment. Two participants requested linkage-to-care assistance. Participants reported valuing the privacy and convenience of the vending machine but were skeptical on the accuracy of their result. The startup cost, including the purchase of two vending machines, was $10,000 and the recurring cost (monitoring, test kits, personnel) was $33.81 per kit vended.ConclusionsWhile survey response was low, our results demonstrate that an intervention using vending machines and HIV self-test kits in CSVs was acceptable, feasible, used by the CSV patrons and can help identify new HIV cases.

BackgroundA large and growing body of "big data" is generated by internet search engines, such as Google. Because people often search for information about public health and medical issues, researchers may be able to use search engine data to monitor and predict public health problems, such as HIV. We sought to assess the feasibility of using Google search data to analyze and predict new HIV diagnoses cases in the United States.Methods and findingsFrom 2007 to 2014, we collected search volume data on HIV-related Google search keywords across the United States. State-level new HIV diagnoses data were collected from the Centers for Disease Control and Prevention (CDC) and AIDSVu.org. We developed a negative binomial model to predict HIV cases using a subset of significant predictor keywords identified by LASSO. The Google search data were combined with state-level HIV case reports provided by the CDC. We use historical data to train the model and predict new HIV diagnoses from 2011 to 2014, with an average R2 value of 0.99 between predicted versus actual cases, and average root-mean-square error (RMSE) of 108.75.ConclusionsResults indicate that Google Trends is a feasible tool to predict new cases of HIV at the state level. We discuss the implications of integrating visualization maps and tools based on these models into public health and HIV monitoring and surveillance.