Using Smartphone Survey Data and Machine Learning to Identify Situational and Contextual Risk Factors for HIV Risk Behavior Among Men Who Have Sex with Men Who Are Not on PrEP

Sexual health in the era of artificial intelligence: a scoping review of the literature

INTRODUCTION

Artificial Intelligence (AI) has witnessed significant growth in the field of medicine, leveraging machine learning, artificial neuron networks, and large language models. These technologies are effective in disease diagnosis, education, and prevention, while raising ethical concerns and potential challenges. However, their utility in sexual medicine remains relatively unexplored.

OBJECTIVE

We aim to provide a comprehensive summary of the status of AI in the field of sexual medicine.

METHODS

A comprehensive search was conducted using MeSH keywords, including "artificial intelligence," "sexual medicine," "sexual health," and "machine learning." Two investigators screened articles for eligibility within the PubMed and MEDLINE databases, with conflicts resolved by a third reviewer. Articles in English language that reported on AI in sexual medicine and health were included. A total of 69 full-text articles were systematically analyzed based on predefined inclusion criteria. Data extraction included information on article characteristics, study design, assessment methods, and outcomes.

RESULTS

The initial search yielded 905 articles relevant to AI in sexual medicine. Upon assessing the full texts of 121 articles for eligibility, 52 studies unrelated to AI in sexual health were excluded, resulting in 69 articles for systematic review. The analysis revealed AI's accuracy in preventing, diagnosing, and decision-making in sexually transmitted diseases. AI also demonstrated the ability to diagnose and offer precise treatment plans for male and female sexual dysfunction and infertility, accurately predict sex from bone and teeth imaging, and correctly predict and diagnose sexual orientation and relationship issues. AI emerged as a promising modality with significant implications for the future of sexual medicine.

CONCLUSIONS

Further research is essential to unlock the potential of AI in sexual medicine. AI presents advantages such as accessibility, user-friendliness, confidentiality, and a preferred source of sexual health information. However, it still lags human healthcare providers in terms of compassion and clinical expertise.

HIV-associated neurocognitive disorders (HAND): Optimal diagnosis, antiviral therapy, pharmacological treatment, management, and future scopes

In the context of HIV infection, HIV-associated neurocognitive disorders (HAND) have become a serious concern. An extensive summary of the diagnosis, care, and mental health consequences related to HAND is given in this article. The diagnosis of HAND entails a multimodal approach that includes neuroimaging, cognition tests, and clinical examinations. Numerous screening instruments and standardized evaluations have been created to support the early identification and tracking of HAND. Appropriate actions and individualized treatment plans are made possible by prompt diagnosis. A multidisciplinary approach is used in the treatment of HAND, aiming to address various elements of cognitive impairment. The main stream of treatment is still antiretroviral medication (ART), which successfully lowers viral loads and stops further neurocognitive deterioration. Adjunctive treatments are essential for treating cognitive symptoms and improving overall quality of life. These therapies include cognitive rehabilitation, pharmaceutical interventions, and psychological support. Our knowledge of the pathophysiology of HAND has improved with the identification of the inflammatory milieu and persistent viral persistence in the central nervous system (CNS), which has also aided in the creation of biomarkers for CNS illness. Although biomarkers show inflammation, neuronal damage, and monocyte activity, their clinical use is still restricted. Although new techniques to treating HAND have been developed as a result of a better knowledge of pathogenic processes, the best course of action is still unknown.

Explainable artificial intelligence and domain adaptation for predicting HIV infection with graph neural networks

OBJECTIVE

Investigation of explainable deep learning methods for graph neural networks to predict HIV infections with social network information and performing domain adaptation to evaluate model transferability across different datasets.

METHODS

Network data from two cohorts of younger sexual minority men (SMM) from two U.S. cities (Chicago, IL, and Houston, TX) were collected between 2014 and 2016. Feature importance from graph attention network (GAT) models were determined using GNNExplainer. Domain adaptation was performed to examine model transferability from one city dataset to the other dataset, training with 100% of the source dataset with 30% of the target dataset and prediction on the remaining 70% from the target dataset.

RESULTS

Domain adaptation showed the ability of GAT to improve prediction over training with single city datasets. Feature importance analysis with GAT models in single city training indicated similar features across different cities, reinforcing potential application of GAT models in predicting HIV infections through domain adaptation.

CONCLUSION

GAT models can be used to address the data sparsity issue in HIV study populations. They are powerful tools for predicting individual risk of HIV that can be further explored for better understanding of HIV transmission.

Behind Prep Decisions: Understanding User Patterns and Discontinuation Factors in Real-World

This study aimed to characterize the epidemiological aspects of PrEP use and barriers to accessing this prophylaxis. This cross-sectional study was conducted between January 2021 and April 2022, encompassing 140 PrEP users treated at the Testing and Counseling Center (CTA) in Campo Grande, Mato Grosso do Sul. Data on sociodemographic characteristics and factors associated with PrEP discontinuation were obtained using a standardized questionnaire. Most PrEP users were cisgender men (92.00%), predominantly white (51.00%), over 30 years of age (56.50%), homosexual-oriented (76.50%), and had a minimum of 12 years of education (77.50%). Approximately 60.00% admitted to inconsistent condom use in recent sexual encounters, primarily involving anal intercourse. Approximately 88.00% perceived themselves as at risk of contracting STIs in the upcoming year. Regarding new presentation forms, 54.00% indicated a willingness to use "on-demand PrEP," and 92.00% expressed interest in using "injectable PrEP." After 6 months of follow-up, 43.60% (95.00% CI: 35.50-52.00) discontinued PrEP use, primarily due to changes in sexual behavior (38.30%) and difficulties accessing healthcare services (21.28%). This study underscores the need to involve diverse key populations and highlights the significance of PrEP as an ongoing monitoring strategy for HIV/STI prevention in addition to the importance of incorporating new formulations such as daily oral PrEP into the Brazilian National Health System (SUS).

Identifying Best Practices for Increasing HIV Pre-exposure Prophylaxis (PrEP) Use and Persistence in the United States: A Systematic Review

A qualitative systematic review was conducted to evaluate pre-exposure prophylaxis (PrEP) interventions, describe characteristics of best practices for increasing PrEP use and persistence, and explore research gaps based on current PrEP interventions. We searched CDC's Prevention Research Synthesis (PRS) Project's cumulative HIV database (includes CINAHL, EMBASE, Global Health, MEDLINE, PsycInfo, and Sociological Abstracts) to identify PrEP intervention studies conducted in the U.S., published between 2000 and 2022 (last searched January 2023). Eligibility criteria include studies that evaluated PrEP interventions for persons testing negative for HIV infection, or for healthcare providers who prescribed PrEP; included comparisons between groups or pre/post; and reported at least one relevant PrEP outcome. Each eligible intervention was evaluated on the quality of study design, implementation, analysis, and strength of evidence (PROSPERO registration number: CRD42021256460). Of the 26 eligible interventions, the majority were focused on men who have sex with men (n = 18) and reported PrEP adherence outcomes (n = 12). Nine interventions met the criteria for Best Practices (i.e., evidence-based interventions, evidence-informed interventions). Five were digital health interventions while two implemented individual counseling, one offered motivational interviewing, and one provided integrated medical care with a PrEP peer navigator. Longer intervention periods may provide more time for intervention exposure to facilitate behavioral change, and engaging the community when developing, designing and implementing interventions may be key for effectiveness. For digital health interventions, two-way messaging may help participants feel supported. Research gaps included a lack of Best Practices for several populations (e.g., Black persons, Hispanic/Latino persons, persons who inject drugs, and women of color) and evidence for various intervention strategies (e.g., interventions for promoting provider's PrEP prescription behavior, peer support). These findings call for more collaborative work with communities to develop interventions that work and implement and disseminate Best Practices for increasing PrEP use and persistence in communities.

Emotional Reactions to High-Risk Sex among Sexual Minority Men: Exploring Potential Opportunities for Just-In-Time Intervention

Rates of HIV and other sexually transmitted infections (STIs) are high among sexual minority men (SMM). A large body of research has explored determinants of HIV/STI risk behavior, but few studies have explored emotional consequences of these events. Understanding the valence, timing, and strength of emotional reactions to sexual risk could inform use of specific behavior change techniques in interventions (such as anticipated regret) and identify new opportunities for intervention, including just-in-time interventions. We analyzed data from an ecological momentary assessment (EMA) study of 100 HIV-negative/unknown-status SMM to understand patterns of positive affect, negative affect, shame, and stress in the 24 hours after sex. Mixed-effects models showed that the probability of negative affect was higher in the hours following condomless anal sex (CAS) with high-risk partners during which SMM reported being under the influence of alcohol or drugs (A/D involved CAS), versus all other types of sex events (OR = 0.92, SE = 0.03, p = .017). The probability of shame was also higher after A/D-involved CAS, versus other sex events (OR = 1.14, SE = 0.07, p = .035). Findings suggest that the hours following A/D-involved CAS events may be an opportune time to intervene to help SMM avoid similarly aversive experiences in the future.

Predicting the HIV/AIDS Knowledge among the Adolescent and Young Adult Population in Peru: Application of Quasi-Binomial Logistic Regression and Machine Learning Algorithms

Inadequate knowledge is one of the principal obstacles for preventing HIV/AIDS spread. Worldwide, it is reported that adolescents and young people have a higher vulnerability of being infected. Thus, the need to understand youths' knowledge towards HIV/AIDS becomes crucial. This study aimed to identify the determinants and develop a predictive model to estimate HIV/AIDS knowledge among this target population in Peru. Data from the 2019 DHS Survey were used. The software RStudio and RapidMiner were used for quasi-binomial logistic regression and computational model building, respectively. Five classification algorithms were considered for model development and their performance was assessed using accuracy, sensitivity, specificity, FPR, FNR, Cohen's kappa, F1 score and AUC. The results revealed an association between 14 socio-demographic, economic and health factors and HIV/AIDS knowledge. The accuracy levels were estimated between 59.47 and 64.30%, with the random forest model showing the best performance (64.30%). Additionally, the best classifier showed that the gender of the respondent, area of residence, wealth index, region of residence, interviewee's age, highest educational level, ethnic self-perception, having heard about HIV/AIDS in the past, the performance of an HIV/AIDS screening test and mass media access have a major influence on HIV/AIDS knowledge prediction. The results suggest the usefulness of the associations found and the random forest model as a predictor of knowledge of HIV/AIDS and may aid policy makers to guide and reinforce the planning and implementation of healthcare strategies.

External validity of analogue sexual risk laboratory data

This article presents data on the external validity of an alcohol administration study of sexual decision-making in men who have sex with men (MSM) ages 21-50. Men (N = 135) randomized to alcohol (blood alcohol concentration [BAC] = .075%) or water control conditions reported intentions to engage in condomless anal intercourse (CAI) in response to video vignettes. Following the experiment participants provided 6 weeks of experience sampling method (ESM) data assessing intoxication, sexual arousal, partner relationship, and sexual behavior. Laboratory CAI intentions were hypothesized to predict future CAI behavior, and associations were hypothesized to be conditional upon sexual arousal and intoxication contextual factors as well as laboratory beverage condition. The hypotheses were partially supported. CAI intentions were correlated with subject proportions of days engaging in CAI (r = .29). A multilevel analysis indicated, on average, CAI intention predicted increased probability of CAI versus anal intercourse with a condom (relative risk ratio [RRR] = 1.43). There was mixed evidence of CAI intentions effects being conditional upon laboratory condition as well as arousal and intoxication contextual factors. Graphs of conditional marginal effects identified regions of significance. Effects of CAI intention for men in the alcohol condition on the CAI versus No Sex contrast were significant when sexual arousal was elevated. CAI intentions for men in the water control condition predicted a higher probability of CAI versus anal intercourse with a condom when intoxication was moderately elevated and/or arousal moderately low. The results support the external validity of alcohol administration experiments of sexual decision-making among MSM and, reciprocally, provide support for the validity of ESM assessment of sexual behavior. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Determinants and prediction of Chlamydia trachomatis re-testing and re-infection within 1 year among heterosexuals with chlamydia attending a sexual health clinic

Background

Chlamydia trachomatis (chlamydia) is one of the most common sexually transmitted infections (STI) globally, and re-infections are common. Current Australian guidelines recommend re-testing for chlamydia 3 months after treatment to identify possible re-infection. Patient-delivered partner therapy (PDPT) has been proposed to control chlamydia re-infection among heterosexuals. We aimed to identify determinants and the prediction of chlamydia re-testing and re-infection within 1 year among heterosexuals with chlamydia to identify potential PDPT candidates.

Methods

Our baseline data included 5,806 heterosexuals with chlamydia aged ≥18 years and 2,070 re-tested for chlamydia within 1 year of their chlamydia diagnosis at the Melbourne Sexual Health Center from January 2, 2015, to May 15, 2020. We used routinely collected electronic health record (EHR) variables and machine-learning models to predict chlamydia re-testing and re-infection events. We also used logistic regression to investigate factors associated with chlamydia re-testing and re-infection.

Results

About 2,070 (36%) of 5,806 heterosexuals with chlamydia were re-tested for chlamydia within 1 year. Among those retested, 307 (15%) were re-infected. Multivariable logistic regression analysis showed that older age (≥35 years old), female, living with HIV, being a current sex worker, patient-delivered partner therapy users, and higher numbers of sex partners were associated with an increased chlamydia re-testing within 1 year. Multivariable logistic regression analysis also showed that younger age (18-24 years), male gender, and living with HIV were associated with an increased chlamydia re-infection within 1 year. The XGBoost model was the best model for predicting chlamydia re-testing and re-infection within 1 year among heterosexuals with chlamydia; however, machine learning approaches and these self-reported answers from clients did not provide a good predictive value (AUC < 60.0%).

Conclusion

The low rate of chlamydia re-testing and high rate of chlamydia re-infection among heterosexuals with chlamydia highlights the need for further interventions. Better targeting of individuals more likely to be re-infected is needed to optimize the provision of PDPT and encourage the test of re-infection at 3 months.

Effects of Alcohol Intoxication on Sexual Decision-Making among Men Who Have Sex with Men (MSM): Alcohol's Influences on Self-Control Processes

This experiment tested mechanisms linking alcohol intoxication and analogue determinants of condomless anal intercourse (CAI) in a sample of 257 men who have sex with men (MSM). The two mechanisms tested were implicit approach biases toward CAI stimuli and executive working memory. Participants were randomized to 3 conditions (water control, placebo, or alcohol) and following beverage administration completed a working memory task, an Approach Avoidance Task of sexual vs. condom stimuli, and two video role-play vignettes of high-risk sexual scenarios. Sexual arousal and CAI intentions were assessed by self-report, and behavioral skills and risk exposure were derived from participants' role-play behavior. Estimation of four path models showed that the hypothesized mechanisms were supported for the CAI intention outcome, but the findings for the skills and risk exposure outcome were mixed. Implications for development and enhancement of HIV prevention interventions were discussed.

Addressing Health Inequities in Digital Clinical Trials: A Review of Challenges and Solutions From the Field of HIV Research

Clinical trials are considered the gold standard for establishing efficacy of health interventions, thus determining which interventions are brought to scale in health care and public health programs. Digital clinical trials, broadly defined as trials that have partial to full integration of technology across implementation, interventions, and/or data collection, are valued for increased efficiencies as well as testing of digitally delivered interventions. Although recent reviews have described the advantages and disadvantages of and provided recommendations for improving scientific rigor in the conduct of digital clinical trials, few to none have investigated how digital clinical trials address the digital divide, whether they are equitably accessible, and if trial outcomes are potentially beneficial only to those with optimal and consistent access to technology. Human immunodeficiency virus (HIV), among other health conditions, disproportionately affects socially and economically marginalized populations, raising questions of whether interventions found to be efficacious in digital clinical trials and subsequently brought to scale will sufficiently and consistently reach and provide benefit to these populations. We reviewed examples from HIV research from across geographic settings to describe how digital clinical trials can either reproduce or mitigate health inequities via the design and implementation of the digital clinical trials and, ultimately, the programs that result. We discuss how digital clinical trials can be intentionally designed to prevent inequities, monitor ongoing access and utilization, and assess for differential impacts among subgroups with diverse technology access and use. These findings can be generalized to many other health fields and are practical considerations for donors, investigators, reviewers, and ethics committees engaged in digital clinical trials.

Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages

Timely and regular testing for HIV and sexually transmitted infections (STI) is important for controlling HIV and STI (HIV/STI) among men who have sex with men (MSM). We established multiple machine learning models (e.g., logistic regression, lasso regression, ridge regression, elastic net regression, support vector machine, k-nearest neighbour, naïve bayes, random forest, gradient boosting machine, XGBoost, and multi-layer perceptron) to predict timely (i.e., within 30 days) clinic attendance and HIV/STI testing uptake after receiving a reminder message via short message service (SMS) or email). Our study used 3044 clinic consultations among MSM within 12 months after receiving an email or SMS reminder at the Melbourne Sexual Health Centre between April 11, 2019, and April 30, 2020. About 29.5% [899/3044] were timely clinic attendance post reminder messages, and 84.6% [761/899] had HIV/STI testing. The XGBoost model performed best in predicting timely clinic attendance [mean [SD] AUC 62.8% (3.2%); F1 score 70.8% (1.2%)]. The elastic net regression model performed best in predicting HIV/STI testing within 30 days [AUC 82.7% (6.3%); F1 score 85.3% (1.8%)]. The machine learning approach is helpful in predicting timely clinic attendance and HIV/STI re-testing. Our predictive models could be incorporated into clinic websites to inform sexual health care or follow-up service.

Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review

SARS-CoV-2 (COVID-19) has been one of the worst global health crises in the 21st century. The currently available rollout vaccines are not 100% effective for COVID-19 due to the evolving nature of the virus. There is a real need for a concerted effort to fight the virus, and research from diverse fields must contribute. Artificial intelligence-based approaches have proven to be significantly effective in every branch of our daily lives, including healthcare and medical domains. During the early days of this pandemic, artificial intelligence (AI) was utilized in the fight against this virus outbreak and it has played a major role in containing the spread of the virus. It provided innovative opportunities to speed up the development of disease interventions. Several methods, models, AI-based devices, robotics, and technologies have been proposed and utilized for diverse tasks such as surveillance, spread prediction, peak time prediction, classification, hospitalization, healthcare management, heath system capacity, etc. This paper attempts to provide a quick, concise, and precise survey of the state-of-the-art AI-based techniques, technologies, and datasets used in fighting COVID-19. Several domains, including forecasting, surveillance, dynamic times series forecasting, spread prediction, genomics, compute vision, peak time prediction, the classification of medical imaging-including CT and X-ray and how they can be processed-and biological data (genome and protein sequences) have been investigated. An overview of the open-access computational resources and platforms is given and their useful tools are pointed out. The paper presents the potential research areas in AI and will thus encourage researchers to contribute to fighting against the virus and aid global health by slowing down the spread of the virus. This will be a significant contribution to help minimize the high death rate across the globe.

Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa

INTRODUCTION

High yield HIV testing strategies are critical to reach epidemic control in high prevalence and low-resource settings such as East and Southern Africa. In this study, we aimed to predict the HIV status of individuals living in Angola, Burundi, Ethiopia, Lesotho, Malawi, Mozambique, Namibia, Rwanda, Zambia and Zimbabwe with the highest precision and sensitivity for different policy targets and constraints based on a minimal set of socio-behavioural characteristics.

METHODS

We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). The algorithms were trained and validated on 80% of the data, and tested on the remaining 20%. We compared the predictions based on the F1 score, the harmonic mean of sensitivity and positive predictive value (PPV), and we assessed the generalization of our models by testing them against an independent left-out country. The best performing algorithm was trained on a minimal subset of variables which were identified as the most predictive, and used to 1) identify 95% of people living with HIV (PLHIV) while maximising precision and 2) identify groups of individuals by adjusting the probability threshold of being HIV positive (90% in our scenario) for achieving specific testing strategies.

RESULTS

Overall 55,151 males and 69,626 females were included in the analysis. The gradient boosting trees algorithm performed best in predicting HIV status with a mean F1 score of 76.8% [95% confidence interval (CI) 76.0%-77.6%] for males (vs [CI 67.8%-70.6%] for SVM) and 78.8% [CI 78.2%-79.4%] for females (vs [CI 73.4%-75.8%] for SVM). Among the ten most predictive variables for each sex, nine were identical: longitude, latitude and, altitude of place of residence, current age, age of most recent partner, total lifetime number of sexual partners, years lived in current place of residence, condom use during last intercourse and, wealth index. Only age at first sex for male (ranked 10th) and Rohrer's index for female (ranked 6th) were not similar for both sexes. Our large-scale scenario, which consisted in identifying 95% of all PLHIV, would have required testing 49.4% of males and 48.1% of females while achieving a precision of 15.4% for males and 22.7% for females. For the second scenario, only 4.6% of males and 6.0% of females would have had to be tested to find 55.7% of all males and 50.5% of all females living with HIV.

CONCLUSIONS

We trained a gradient boosting trees algorithm to find 95% of PLHIV with a precision twice higher than with general population testing by using only a limited number of socio-behavioural characteristics. We also successfully identified people at high risk of infection who may be offered pre-exposure prophylaxis or voluntary medical male circumcision. These findings can inform the implementation of new high-yield HIV tests and help develop very precise strategies based on low-resource settings constraints.

Daily Associations Among Alcohol Intoxication, Partner Familiarity, Participant Effortful Control, Urgency, and PrEP Uptake on Sexual Behavior in Men Who Have Sex with Men

The purpose of this study was to examine the effects of alcohol intoxication and its interaction with contextual or situation (partner familiarity) and individual differences variables (effortful control, urgency, and whether taking pre-exposure prophylaxis (PrEP) medication) on sexual behaviors in men who have sex with men (MSM), a subgroup for whom HIV continues to be a major public health problem in the U.S. The participants were 236 men recruited from two northeastern U.S. cities and aged 21-50 years, M = 27.8). These men participated in a 6-week (two 3-week sampling bursts) experience sampling method (ESM) study. The ESM data were collected via use of software installed on the participant's own or study-provided mobile phone. Individual differences variables were measured by participants' completing questionnaires measuring effortful control and urgency, and the participant's self-report of whether he was currently taking PrEP. The ESM data pertained to sexual behavior as well as situation variables of familiarity of relevant sexual partners and number of standard alcohol drinks consumed. The results generally were consistent with hypotheses, as alcohol intoxication showed a curvilinear relation to the occurrence of condomless anal intercourse. Furthermore, the likelihood of occurrence of condomless anal sex increased with increased familiarity of the sexual partner. Similarly, taking PrEP increased the likelihood of occurrence of condomless anal sex. At the same time, alcohol's effects were moderated by all three individual differences variables as expected, but the prediction that partner familiarity would moderate alcohol's effects on the occurrence of condomless sex was not supported. Clinical implications of the findings center on the application of the data to HIV prevention programs toward inclusion of more empirically supported, nuanced information on the relation between acute alcohol intoxication and sexual behavior. Directions for further research address the need for additional testing and refinement of a person × situation approach to alcohol and sexual behavior. Furthermore, it is argued that it is important to refine further the concept of sexual risk in the context of taking PrEP and to conduct more detailed, multivariate studies of the relation between taking PrEP and patterns of sexual behavior.

Ecological Momentary Assessment and mHealth Interventions Among Men Who Have Sex With Men: Scoping Review

BACKGROUND

Ecological momentary assessment (EMA) is a research design that allows for the measurement of nearly instantaneous experiences within the participant's natural environment. Using EMA can help improve recall bias, ecological validity, and patient engagement while enhancing personalization and the ubiquity of interventions. People that can benefit from the use of EMA are men who have sex with men (MSM). Previous EMA studies have been successful in capturing patterns of depression, anxiety, substance use, and risky sexual behavior. These findings are directly relevant to MSM, who have high rates of each of these psychological and behavioral outcomes. Although there is a driving force behind the growing literature surrounding EMAs among MSM, no synthesizing reviews yet exist.

OBJECTIVE

The aims of this study were to (1) synthesize the literature across fields on how EMA methods have been used among MSM, (2) better understand the feasibility and acceptability of EMA interventions among MSM, and (3) inform designs for future research studies on best evidence-based practices for EMA interventions.

METHODS

Based on 4 library databases, we conducted a scoping review of EMAs used within interventions among MSM. The eligibility criteria included peer-reviewed studies conducted in the United States and the use of EMA methodology in an intervention for MSM. Modeling after the Centers for Disease Control and Prevention's Compendium of Evidence-Based Interventions as the framework, we applied a typology that used 8 distinct review criteria, for example, sample size, design of the intervention, random assignment, design of the follow-up investigation, rate of retention, and rate of engagement.

RESULTS

Our results (k=15, N=952) indicated a range of sample sizes; the smallest sample size was 12, while the largest sample size was 120. Of the 15 studies, 7 (47%) focused on outcomes related to substance use or outcomes related to psychological experiences. Of the 15 studies, 5 (33%) implemented an EMA intervention across 30 days. Of the 15 studies, 2 studies (13%) used random assignment, and 2 studies (13%) had quasi-experimental designs. Of the 15 studies, 10 studies (67%) reported acceptable retention rates greater than 70%. The outcomes that had event-contingent prompts (ie, prompts after engaging in substance use) were not as effective in engaging participants, with overall engagement rates as low as 37%.

CONCLUSIONS

Our systematic scoping review indicates strong evidence that the EMA methodology is both feasible and acceptable at high rates among MSM, especially, when examining psychological and behavioral outcomes such as negative or positive affect, risky sexual behavior, or substance use. Further research on optimal designs of EMA interventions for MSM is warranted.

A Systematic Review of Recent Methodological Approaches for Using Ecological Momentary Assessment to Examine Outcomes in U.S. Based HIV Research

PURPOSE OF REVIEW

In recent years, researchers have been adopting and using ecological momentary assessment (EMA) methods via technology devices for real-time measurement of exposures and outcomes in HIV research. To assess and critically evaluate how EMA methods are currently being used in HIV research, we systematically reviewed recent published literature (October 2017-October 2019) and searched select conference databases for 2018 and 2019.

RECENT FINDINGS

Our searches identified 8 published articles that used EMA via smartphone app, a handheld Personal Digital Assistant, and web-based survey programs for real-time measurement of HIV-related exposures and outcomes in behavioral research. Overall trends include use of EMA and technology devices to address substance use, HIV primary prevention (e.g., condom use and preexposure prophylaxis), and HIV treatment (medication adherence). This review supports the use of EMA methods in HIV research and recommends that researchers use EMA methods to measure psychosocial factors and social contexts and with Black and Latinx samples of gay and bisexual men, transgender women, and cisgendered women to reflect current HIV disparities in the U.S.A.

Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic

PURPOSE OF REVIEW

We review applications of artificial intelligence (AI), including machine learning (ML), in the field of HIV prevention.

RECENT FINDINGS

ML approaches have been used to identify potential candidates for preexposure prophylaxis (PrEP) in healthcare settings in the USA and Denmark and in a population-based research setting in Eastern Africa. Although still in the proof-of-concept stage, other applications include ML with smartphone-collected and social media data to promote real-time HIV risk reduction, virtual reality tools to facilitate HIV serodisclosure, and chatbots for HIV education. ML has also been used for causal inference in HIV prevention studies. ML has strong potential to improve delivery of PrEP, with this approach moving from development to implementation. Development and evaluation of AI and ML strategies for HIV prevention may benefit from an implementation science approach, including qualitative assessments with end users, and should be developed and evaluated with attention to equity.