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Chatbots increase business productivity by handling customer conversations instead of human agents. Similar rationale applies to use chatbots in the healthcare sector, especially for health coaches who converse with clients. Chatbots are nascent in healthcare. Study findings have been mixed in terms of engagement and their impact on outcomes. Questions remain as to chatbot acceptability with coaches and other providers; studies have focused on clients. To clarify perceived benefits of chatbots in HIV interventions we conducted virtual focus groups with 13 research staff, eight community advisory board members, and seven young adults who were HIV intervention trial participants (clients). Our HIV healthcare context is important. Clients represent a promising age demographic for chatbot uptake. They are a marginalized population warranting consideration to avoid technology that limits healthcare access. Focus group participants expressed the value of chatbots for HIV research staff and clients. Staff discussed how chatbot functions, such as automated appointment scheduling and service referrals, could reduce workloads while clients discussed the after-hours convenience of these functions. Participants also emphasized that chatbots should provide relatable conversation, reliable functionality, and would not be appropriate for all clients. Our findings underscore the need to further examine appropriate chatbot functionality in HIV interventions. [ABSTRACT FROM AUTHOR]

Introduction: People living with type 2 diabetes who experience homelessness face a myriad of barriers to engaging in diabetes self-care behaviors that lead to premature complications and death. This is exacerbated by high rates of comorbid mental illness, substance use disorder, and other physical health problems. Despite strong evidence to support lay health coach and behavioral activation, little research has effectively engaged people living with type 2 diabetes who had experienced homelessness (DH). Methods: We used community engaged research and incremental behavioral treatment development to design the Diabetes HOmeless MEdication Support (DHOMES) program, a one-on-one, 3 month, coaching intervention to improve medication adherence and psychological wellness for DH. We present results of our pilot randomized trial (with baseline, 3 mo., 6 mo. assessments) comparing DHOMES to enhanced usual care (EUC; brief diabetes education session and routine care; NCT05258630). Participants were English-speaking adults with type 2 diabetes, current/recent (<24 mo.) homelessness, and an HbA1c‗7.5%. We focused on feasibility (recruitment, retention, engagement) and acceptability (Client Satisfaction Questionnaire, CSQ-8). Our primary clinical outcome was glycemic control (HbA1c) and primary behavioral outcome was medication adherence. Secondary outcomes included psychological wellness and diabetes self-care. Results: Thirty-six eligible participants enrolled, 18 in each arm. Most participants identified as Black males, had high rates of co-morbidities, and lived in subsidized housing. We retained 100% of participants at 3-months, and 94% at 6-months. Participants reported high satisfaction (mean CSQ-8 scores=28.64 [SD 3.94] of 32). HbA1c reduced to clinically significant levels in both groups, but we found no between group differences. Mean blood pressure improved more in D-HOMES than EUC between baseline and 6 mo. with between group mean differences of systolic -19.5 mmHg (p=0.030) and diastolic blood pressure -11.1 mmHg (p=0.049). We found no significant between group differences in other secondary outcomes. Conclusion: We effectively recruited and retained DH over 6 months. Data support that the D-HOMES intervention was acceptable and feasible. We observe preliminary blood pressure improvement favoring D-HOMES that were statistically and clinically significant. D-HOMES warrants testing in a fully powered trial which could inform future high quality behavioral trials to promote health equity. [ABSTRACT FROM AUTHOR]

Psychological researchers often use standard linear regression to identify relevant predictors of an outcome of interest, but challenges emerge with incomplete data and growing numbers of candidate predictors. Regularization methods like the LASSO can reduce the risk of overfitting, increase model interpretability, and improve prediction in future samples; however, handling missing data when using regularization-based variable selection methods is complicated. Using listwise deletion or an ad hoc imputation strategy to deal with missing data when using regularization methods can lead to loss of precision, substantial bias, and a reduction in predictive ability. In this tutorial, we describe three approaches for fitting a LASSO when using multiple imputation to handle missing data and illustrate how to implement these approaches in practice with an applied example. We discuss implications of each approach and describe additional research that would help solidify recommendations for best practices. [ABSTRACT FROM AUTHOR]

Health-science researchers often measure psychological constructs using multi-item scales and encounter missing items on some participants. Multiple imputation (MI) has emerged as an alternative to ad-hoc methods (e.g., mean substitution) for handling incomplete data on multi-item scales, appealingly reflecting available information while accounting for uncertainty due to missing values in a unified inferential framework. However, MI can be implemented in a variety of ways. When the number of variables to impute gets large, some strategies yield unstable estimates of quantities of interest while others are not technically feasible to implement. These considerations raise pragmatic questions about the extent to which ad-hoc procedures would yield statistical properties that are competitive with theoretically motivated methods. Drawing on an HIV study where depression and anxiety symptoms are measured with multi-item scales, this empirical investigation contrasts ad-hoc methods for handling missing items with various MI implementations that differ as to whether imputation is at the item-level or scale-level and how auxiliary variables are incorporated. While the findings are consistent with previous reports favoring item-level imputation when feasible to implement, we found only subtle differences in statistical properties across procedures, suggesting that weaknesses of ad-hoc procedures may be muted when missing data percentages are modest. [ABSTRACT FROM AUTHOR]

Objectives. We examined the efficacy of the Healthy Living Program in reducing risky sexual behavior and substance use among adults with HIV infection who were marginally housed (i.e., homeless at some point over a 37-month period). Methods. We had previously conducted a randomized controlled trial with 936 adults living with HIV infection. In that study, 3 intervention modules of 5 sessions each addressed different goals: reducing risky sexual acts and drug use, improving the quality of life, and adhering to healthful behaviors. Participants were interviewed at baseline and at 5, 10, 15, 20, and 25 months; 746 completed 4 or more assessments. In this study, we analyzed sexual behavior and drug use outcomes for the 35% (n=270 of 767) of participants who were considered marginally housed. Results. Among the marginally housed participants, there were significantly greater reductions in unprotected risky sexual acts, the number of sexual partners of HIV negative or unknown serostatus, alcohol or marijuana use, and hard drug use among the intervention group than among the control group. Conclusions. Intensive, skill-focused intervention programs may improve the lives of marginally housed adults living with HIV infection. doi: 10.2105/AJ PH. 2007.121186)

Youth at-risk for HIV are also at-risk for mental health disorders and psychiatric hospitalization. Understanding the association between engagement in HIV prevention, concurrent risk behaviors, and psychiatric hospitalization may lead to improvements in integrated prevention and mental health treatment efforts. Youth at-risk for HIV, aged 14–24 years old, predominantly Black/African American and Latinx (75%) were recruited through youth-serving clinics and community sites in Los Angeles (n = 839) and New Orleans (n = 647). We compared youth with and without histories of psychiatric hospitalization on engagement in HIV prevention, concurrent risk behaviors, and demographic characteristics. We examined predictors of hospitalization using multiple imputations for missing data. Hospitalized youth (30%) were more involved in HIV programs, but were less likely to use PrEP/PEP or condoms than non-hospitalized youth. The odds of hospitalization were higher for transgender/gender nonconforming youth relative to cisgender youth; the OR was increased after adjustment for concurrent risk behaviors. Hospitalization was associated with homelessness, trauma, incarceration, substance use, and involvement in substance abuse treatment programs. There is a continuing need to integrate the diagnosis and treatment of mental health disorders into HIV prevention programs to better address multiple challenges faced by vulnerable youth. [ABSTRACT FROM AUTHOR]

We tested the hypothesis that a computerized intervention would be as efficacious as an in-person, small-group intervention in reducing sexual risk behaviors. The sexual behavior of high-risk adolescents in 3 intervention conditions was examined: (1) computer based, (2) small groups, and (3) control. Adolescents in the computerized intervention were significantly less likely to engage in sexual activity and reported significantly fewer partners. For some youths, computers are a viable way to deliver prevention information and promote skill development. doi:10.2105/AJPH.2005.072652

Background: Workplace wellness programs (WWP) offer physiological and psychological benefits to employees and financial and productivity benefits to employers. However, the COVID-19 pandemic has prevented in-person sessions and has required WWP's to transition to online platforms. The purpose of this brief report was to assess the preliminary feasibility, acceptability, and efficacy of a mobile version of the Bruin Health Improvement Program (BHIP mobile) WWP. Methods: Participants virtually attended (i.e., via Zoom) twice weekly physical activity sessions and a once weekly nutrition seminar for 10-weeks with the option of a 30-minute one-on-one consultation with a registered dietician. Demographics, anthropometric indices, stress, muscular endurance, and aerobic fitness were assessed at baseline and follow-up. All analyses were conducted in SPSS v. 27. Results: Twenty-seven participants (96% female) enrolled and 13 (100% female) completed the 10-week program. There were significant reductions in bodyweight (p <.01) and body mass index (p <.02) but not stress (p >.05), and significant increases in muscular endurance (p <.01) but not aerobic fitness (p >.05). Overall, BHIP mobile appears to be acceptable to participants but logistical concerns such as inconsistent internet connection was noted as potential downfalls. Conclusions/Applications to Practice: Improvements in health outcome among completers of a mobile WWP were promising. Completer feedback highlighted program strengths as the flexibility and comfort of attending exercise sessions and nutrition classes from home. Future versions of the program will allocate resources to improve completion and expand appeal for men and women. [ABSTRACT FROM AUTHOR]

Background Violence against women by intimate partners remains unacceptably common worldwide. The evidence base for the assumed psychological impacts of intimate partner violence (IPV) is derived primarily from studies conducted in high-income countries. A recently published systematic review identified 13 studies linking IPV to incident depression, none of which were conducted in sub-Saharan Africa. To address this gap in the literature, we analyzed longitudinal data collected during the course of a 3-y cluster-randomized trial with the aim of estimating the association between IPV and depression symptom severity. Methods and Findings We conducted a secondary analysis of population-based, longitudinal data collected from 1,238 pregnant women during a 3-y cluster-randomized trial of a home visiting intervention in Cape Town, South Africa. Surveys were conducted at baseline, 6 mo, 18 mo, and 36 mo (85% retention). The primary explanatory variable of interest was exposure to four types of physical IPV in the past year. Depression symptom severity was measured using the Xhosa version of the ten-item Edinburgh Postnatal Depression Scale. In a pooled cross-sectional multivariable regression model adjusting for potentially confounding time-fixed and time-varying covariates, lagged IPV intensity had a statistically significant association with depression symptom severity (regression coefficient b = 1.04; 95% CI, 0.61-1.47), with estimates from a quantile regression model showing greater adverse impacts at the upper end of the conditional depression distribution. Fitting a fixed effects regression model accounting for all time-invariant confounding (e.g., history of childhood sexual abuse) yielded similar findings (b = 1.54; 95% CI, 1.13-1.96). The magnitudes of the coefficients indicated that a one-standard-deviation increase in IPV intensity was associated with a 12.3% relative increase in depression symptom severity over the same time period. The most important limitations of our study include exposure assessment that lacked measurement of sexual violence, which could have caused us to underestimate the severity of exposure; the extended latency period in the lagged analysis, which could have caused us to underestimate the strength of the association; and outcome assessment that was limited to the use of a screening instrument for depression symptom severity. Conclusions In this secondary analysis of data from a population-based, 3-y cluster-randomized controlled trial, IPV had a statistically significant association with depression symptom severity. The estimated associations were relatively large in magnitude, consistent with findings from high-income countries, and robust to potential confounding by time-invariant factors. Intensive health sector responses to reduce IPV and improve women's mental health should be explored., Introduction Violence against women by intimate partners remains unacceptably common worldwide [15], particularly in South Africa, where violence against women occurs at rates that are among the highest in the [...]

Machine learning creates new opportunities to design digital health interventions for youth at risk for acquiring HIV (YARH), capitalizing on YARH's health information seeking on the internet. To date, researchers have focused on descriptive analyses that associate individual factors with health-seeking behaviors, without estimating of the strength of these predictive models. We developed predictive models by applying machine learning methods (i.e., elastic net and lasso regression models) to YARH's self-reports of internet use. The YARH were aged 14–24 years old (N = 1287) from Los Angeles and New Orleans. Models were fit to three binary indicators of YARH's lifetime internet searches for general health, sexual and reproductive health (SRH), and social service information. YARH responses regarding internet health information seeking were fed into machine learning models with potential predictor variables based on findings from previous research, including sociodemographic characteristics, sexual and gender minority identity, healthcare access and engagement, sexual behavior, substance use, and mental health. About half of the YARH reported seeking general health and SRH information and 26% sought social service information. Areas under the ROC curve (≥.75) indicated strong predictive models and results were consistent with the existing literature. For example, higher education and sexual minority identification was associated with seeking general health, SRH, and social service information. New findings also emerged. Cisgender identity versus transgender and non-binary identities was associated with lower odds of general health, SRH, and social service information seeking. Experiencing intimate partner violence was associated with higher odds of seeking general health, SRH, and social service information. Findings demonstrate the ability to develop predictive models to inform targeted health information dissemination strategies but underscore the need to better understand health disparities that can be operationalized as predictors in machine learning algorithms. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic illuminated the benefits of telemedicine. Self-collected specimens are a promising alternative to clinician-collected specimens when in-person testing is not feasible. In this study, we assessed the adequacy of self-collected pharyngeal and rectal specimens for the detection of Chlamydia trachomatis and Neisseria gonorrhoeae among individuals undergoing chlamydia and gonorrhea screening. Methods: We used data from a large cohort study that included male and female adolescents between the ages of 12–24 years. We considered self-collected specimens adequate for clinical use if the human synthase gene (a control target of the assay) was detected in the specimen. Results: In total, 2,458 specimens were included in the analysis. The human synthase gene was detected in 99.2% (2,439/2,458) of all self-collected specimens, 99.5% (1,108/1,114) of the pharyngeal specimens, and 99.0% (1,331/1,344) of the rectal specimens. Conclusion: Self-collected pharyngeal and rectal specimens demonstrated a very high proportion of human gene presence, suggesting that self-collection was accurate. A limitation of this study is that the sample adequacy control detects the presence or absence of the human hydroxymethylbilane synthase gene, but it does not indicate the specific anatomic origin of the human hydroxymethylbilane synthase gene. Self-collected specimens may be an appropriate alternative to clinician-collected specimens. [ABSTRACT FROM AUTHOR]