Digital mental health interventions as stand-alone vs. augmented treatment as usual.

BACKGROUND: Smartphone-based digital mental health interventions (DMHI) have been described as a purported solution to meet growing healthcare demands and lack of providers, but studies often don't account for whether patients are concurrently in another treatment modality. METHODS: This preregistered quasi-experimental intent-to-treat study with 354 patients enrolled in a therapist-supported DMHI examined the treatment effectiveness of the Meru Health Program (MHP) as a stand-alone treatment as compared to the MHP in combination with any other form of treatment, including (1) in-person therapy, (2) psychotropic medication use, and (3) in-person therapy and psychotropic medication use. RESULTS: Patients with higher baseline depressive and anxiety symptoms were more likely to self-select into multiple forms of treatment, an effect driven by patients in the MHP as adjunctive treatment to in-person therapy and psychotropic medication. Patients in combined treatments had significantly higher depressive and anxiety symptoms across treatment, but all treatment groups had similar decreasing depressive and anxiety symptom trajectories. Exploratory analyses revealed differential treatment outcomes across treatment combinations. Patients in the MHP in combination with another treatment had higher rates of major depressive episodes, psychiatric hospitalization, and attempted death by suicide at baseline. CONCLUSIONS: Patients with higher depressive and anxiety symptoms tend to self-select into using DMHI in addition to more traditional types of treatment, rather than as a stand-alone intervention, and have more severe clinical characteristics. The use the MHP alone was associated with improvement at a similar rate to those with higher baseline symptoms who are in traditional treatments and use MHP adjunctively.

Real-time heart rate variability biofeedback amplitude during a large-scale digital mental health intervention differed by age, gender, and mental and physical health.

Heart rate variability biofeedback (HRVB) is an efficacious treatment for depression and anxiety. However, translation to digital mental health interventions (DMHI) requires computing and providing real-time HRVB metrics in a personalized and user-friendly fashion. To address these gaps, this study validates a real-time HRVB feedback algorithm and characterizes the association of the main algorithmic summary metric-HRVB amplitude-with demographic, psychological, and health factors. We analyzed HRVB data from 5158 participants in a therapist-supported DMHI incorporating slow-paced breathing to treat depression or anxiety symptoms. A real-time feedback metric of HRVB amplitude and a gold-standard research metric of low-frequency (LF) power were computed for each session and then averaged within-participants over 2 weeks. We provide HRVB amplitude values, stratified by age and gender, and we characterize the multivariate associations of HRVB amplitude with demographic, psychological, and health factors. Real-time HRVB amplitude correlated strongly (r = .93, p < .001) with the LF power around the respiratory frequency (~0.1 Hz). Age was associated with a significant decline in HRVB (ß = -0.46, p < .001), which was steeper among men than women, adjusting for demographic, psychological, and health factors. Resting high- and low-frequency power, body mass index, hypertension, Asian race, depression symptoms, and trauma history were significantly associated with HRVB amplitude in multivariate analyses (p's < .01). Real-time HRVB amplitude correlates highly with a research gold-standard spectral metric, enabling automated biofeedback delivery as a potential treatment component of DMHIs. Moreover, we identify demographic, psychological, and health factors relevant to building an equitable, accurate, and personalized biofeedback user experience.

Therapist-supported digital mental health intervention for depressive symptoms: A randomized clinical trial.

Depression is a chronic and debilitating mental disorder. Despite the existence of several evidence-based treatments, many individuals suffering from depression face myriad structural barriers to accessing timely care which may be alleviated by digital mental health interventions (DMHI). Accordingly, this randomized clinical trial (ClinicalTrials.gov: NCT04738084) investigated the efficacy of a newer version of the therapist-supported and guided DMHI, the Meru Health Program (MHP), which was recently enhanced with heart rate variability biofeedback and lengthened from 8- to 12-weeks duration, among people with elevated depression symptoms (N = 100, mean age 37). Recruited participants were randomized to the MHP (n = 54) or a waitlist control (n = 46) condition for 12 weeks. The MHP group had greater decreases in depression symptoms compared to the waitlist control (d = -0.8). A larger proportion of participants in the MHP group reported a minimal clinically important difference (MCID) in depression symptoms than participants in the waitlist control group (39.1 % vs. 9.8 %, χ2(1) = 9.90, p = .002). Similar effects were demonstrated for anxiety symptoms, quality of life, insomnia, and resilience. The results confirm the utility of the enhanced MHP in reducing depression symptoms and associated health burdens.

Longitudinal outcomes of a therapist-supported digital mental health intervention for depression and anxiety symptoms: A retrospective cohort study.

PURPOSE: This study examined treatment outcomes (depression and anxiety symptoms) up to 24 months after completion of a therapist-supported digital mental health intervention (DMHI). METHODS: The sample consisted of 380 participants who participated in an eight-week DMHI from February 6, 2017 to May 20, 2019. Participants reported depression and anxiety symptoms at eight timepoints from baseline to 24 months. Mixed-effects modelling was used to investigate symptom changes over time. The proportion of participants meeting criteria for treatment response, clinically significant change, and remission of depression and anxiety symptoms were calculated, including proportions demonstrating each outcome sustained up to each timepoint. RESULTS: Multivariate analyses yielded statistically significant reductions in depression (ß = -5.40) and anxiety (ß = -3.31) symptoms from baseline to end of treatment (8 weeks). Symptom levels remained significantly reduced from baseline through 24 months. The proportion of participants meeting criteria for clinical treatment outcomes remained constant over 24 months, although there were linear decreases in the proportions experiencing sustained clinical outcomes. CONCLUSIONS: Treatment gains were made for depression and anxiety symptoms at the end of treatment and up to 24 months. Future studies should determine the feasibility of integrating post-treatment programmes into DMHIs to address symptom deterioration.

Preliminary Effectiveness of a Therapist-Supported Digital Mental Health Intervention in Reducing Suicidal Ideation.

Suicidal ideation (SI) is a significant public health concern with increasing prevalence. Therapist-supported digital mental health interventions (DMHI) are an emergent modality to address common mental health problems like depression and anxiety, although less is known about SI. This study examined SI trajectories among 778 patients who participated in a therapist-supported DMHI using multilevel models during and up to 6-months post-treatment. Estimates of associated suicide attempts and deaths by suicide were calculated using published data linking PHQ-9-assessed SI to records of suicide attempts and deaths by suicide. The proportion of participants reporting no SI significantly increased between baseline and end-of-treatment (78.02% to 91.00%). Effect sizes of SI changes between baseline and end-of-treatment, 3-month, and 6-month follow-ups were 0.33 (95%CI = 0.27-0.38), 0.32 (95%CI = 0.27-0.38), and 0.32 (95%CI = 0.27-0.38), respectively. Results also indicated an estimated 30.49% reduction (95%CI = 25.15%-35.13%) in suicide attempts and death by suicide across treatment. This study provides preliminary evidence of the effectiveness of a therapist-supported DMHI in reducing SI.

Longitudinal Patterns of Engagement and Clinical Outcomes: Results From a Therapist-Supported Digital Mental Health Intervention.

OBJECTIVE: Digital mental health interventions (DMHIs) are an effective treatment modality for common mental disorders like depression and anxiety; however, the role of intervention engagement as a longitudinal "dosing" factor is poorly understood in relation to clinical outcomes. METHODS: We studied 4978 participants in a 12-week therapist-supported DMHI (June 2020-December 2021), applying a longitudinal agglomerative hierarchical cluster analysis to the number of days per week of intervention engagement. The proportion of people demonstrating remission in depression and anxiety symptoms during the intervention was calculated for each cluster. Multivariable logistic regression models were fit to examine associations between the engagement clusters and symptom remission, adjusting for demographic and clinical characteristics. RESULTS: Based on clinical interpretability and stopping rules, four clusters were derived from the hierarchical cluster analysis (in descending order): a) sustained high engagers (45.0%), b) late disengagers (24.1%), c) early disengagers (22.5%), and d) immediate disengagers (8.4%). Bivariate and multivariate analyses supported a dose-response relationship between engagement and depression symptom remission, whereas the pattern was partially evident for anxiety symptom remission. In multivariable logistic regression models, older age groups, male participants, and Asians had increased odds of achieving depression and anxiety symptom remission, whereas higher odds of anxiety symptom remission were observed among gender-expansive individuals. CONCLUSIONS: Segmentation based on the frequency of engagement performs well in discerning timing of intervention disengagement and a dose-response relationship with clinical outcomes. The findings among the demographic subpopulations indicate that therapist-supported DMHIs may be effective in addressing mental health problems among patients who disproportionately experience stigma and structural barriers to care. Machine learning models can enable precision care by delineating how heterogeneous patterns of engagement over time relate to clinical outcomes. This empirical identification may help clinicians personalize and optimize interventions to prevent premature disengagement.

Residence in urban or rural counties in relation to opioid overdose mortality among Kentucky hospitalizations before and during the COVID-19 pandemic.

BACKGROUND: At the beginning of the opioid overdose epidemic, overdose mortality rates were higher in urban than in rural areas. We examined the association between residence in an urban or rural county and subsequent opioid overdose mortality in Kentucky, a state highly impacted by the opioid epidemic, and whether this was modified by the COVID-19 pandemic. METHODS: We captured hospitalizations in Kentucky from 2016 to 2020, involving an opioid using ICD-10-CM codes T40.0-T40.4 and T40.6. Patient's county was classified as urban or rural based on the NCHS Urban-Rural Classification Scheme. Multivariable logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of opioid overdose mortality, adjusted for demographics, hospitalization severity, and zip code SES. We assessed effect modification by the COVID-19 pandemic. RESULTS: Overall, patients living in urban counties had 46% higher odds of opioid overdose death than patients residing in rural counties (adjusted OR=1.46; 95% CI=1.22, 1.74). Before the pandemic, patients in urban counties had 63% increased odds of opioid overdose death (adjusted OR=1.63; 95% CI=1.34, 1.97); however, during the COVID-19 pandemic, patients in urban and rural counties became more similar in regard to opioid overdose mortality (adjusted OR=0.72; 95% CI=0.45, 1.16; p-value for interaction =0.02). CONCLUSION: Before the pandemic, living in urban counties was associated with higher opioid overdose mortality among Kentucky hospitalizations; however, during the COVID-19 pandemic, opioid overdose mortality in rural areas increased, approaching rates in urban areas. COVID-19 posed social, economic, and healthcare challenges that may be contributing to worsening mortality trends affecting both urban and rural patients.

Trajectories of depression symptoms in a therapist-supported digital mental health intervention: a repeated measures latent profile analysis.

PURPOSE: Major depression affects 10% of the US adult population annually, contributing to significant burden and impairment. Research indicates treatment response is a non-linear process characterized by combinations of gradual changes and abrupt shifts in depression symptoms, although less is known about differential trajectories of depression symptoms in therapist-supported digital mental health interventions (DMHI). METHODS: Repeated measures latent profile analysis was used to empirically identify differential trajectories based upon biweekly depression scores on the Patient Health Questionnaire-9 (PHQ-9) among patients engaging in a therapist-supported DMHI from January 2020 to July 2021. Multivariate associations between symptom trajectories with sociodemographics and clinical characteristics were examined with multinomial logistic regression. Minimal clinically important differences (MCID) were defined as a five-point change on the PHQ-9 from baseline to week 12. RESULTS: The final sample included 2192 patients aged 18 to 82 (mean = 39.1). Four distinct trajectories emerged that differed by symptom severity and trajectory of depression symptoms over 12 weeks. All trajectories demonstrated reductions in symptoms. Despite meeting MCID criteria, evidence of treatment resistance was found among the trajectory with the highest symptom severity. Chronicity of major depressive episodes and lifetime trauma exposures were ubiquitous across the trajectories in a multinomial logistic regression model. CONCLUSIONS: These data indicate that changes in depression symptoms during DMHI are heterogenous and non-linear, suggesting a need for precision care strategies to address treatment resistance and increase engagement. Future efforts should examine the effectiveness of trauma-informed treatment modules for DMHIs as well as protocols for continuation treatment and relapse prevention.

Eating Disorder Screening and Treatment in a Medically Underserved Southern State: Data Collected by a Legislative Eating Disorder Council and Implications for Statewide Assessment.

Healthcare providers play a key role in early identification of eating disorders (EDs), especially in underserved states where ED treatment resources are lacking. Currently, there is little known about ED screening and treatment practices in underserved states. The current study assessed current ED screening and treatment practices among healthcare providers in an underserved state using data collected by a government-formed state ED council. Healthcare providers (N = 242; n = 209 behavioral health providers; n = 33 medical providers) practicing in Kentucky completed a brief, anonymous survey on ED screening and treatment practices, comfort with screening for EDs, and interest in continued education. Over half of healthcare providers indicated screening for EDs, with the majority using a clinical interview. After identification of ED symptoms, providers reported a combination of treating in-house, referring out, or seeking consultation. In bivariate analyses, medical providers were significantly more likely than behavioral health providers to use a screening tool specifically designed for EDs. The majority of medical providers indicated that they received education about EDs and feel knowledgeable about ED screening tools, though most reported infrequent use of these screening tools in their practice. Nearly all behavioral health and medical providers expressed interest in continuing education on ED screening and treatment. These findings indicate a need for, and interest in, education on evidence-based ED screening and treatment resources in underserved states and demonstrate the utility of a state ED council to collect these data to inform future education and treatment strategies.

A Network Analysis Investigation of Disordered Eating Across Demographic and Developmental Subpopulations Using a National Epidemiological Sample of High School Students.

Disordered eating (DE) poses a large societal burden, yet limited research has examined DE from a developmental epidemiological perspective. It is important to consider how demographics influence DE symptoms to inform prevention and early intervention programs across diverse subpopulations. Therefore, we conducted network analyses using a large nationally representative epidemiological sample of high school students (Youth Risk Behavior Survey, United States; n = 59,582) to identify the most important symptoms and symptom relationships among six DE behaviors. We compared networks by sex, grade, and race to identify differences in symptom networks. Dieting for weight loss was highly central across networks. Networks significantly differed across sex, grade, and race. Our results suggest that dieting for weight loss may be an early intervention target for eating disorders, regardless of demographic and developmental factors. In addition, sex, race, and age should be accounted for when researching and developing prevention programs for DE and eating disorders. Public health officials, as well as mental health professionals, should present a more balanced message about dieting and weight loss to high school students to prevent the detrimental impact of DE on physical and mental health. Notably, this study is the first large, nationwide epidemiological sample using DE symptoms in network analysis.

Acceptability of a HIV self-testing program among people who use illicit drugs.

BACKGROUND: People who use illicit drugs (PWUD) remain at significantly elevated risk for HIV infection and continue to have very low testing rates. HIV self-testing (HIVST) has been shown to be acceptable among many high-risk populations, but less is known about PWUD. METHODS: From May-June 2021, a HIVST program was implemented at a syringe services program (SSP) in Louisville, Kentucky. PWUD were given the option to privately self-test at the SSP or take the test home and follow-up with study staff. Primary outcomes were acceptability, ease of use, usability, reasons for self-testing, testing location, frequency of future testing, and preference for future testing location. RESULTS: Among 230 study participants, 77% reported high acceptability (i.e., the HIVST kits made them feel much more able to keep track of their HIV status compared to standard testing methods). Virtually all (97.4%) reported the test kits were very easy to use. Problems while using the HIVST kits were rare (range 1.3-3.0%). The most common reasons for testing were a desire to know their status (85.2%), the test was free (37%), and the short duration for results (30.9%). Testing primarily occurred onsite (87.8%). The majority (83%) reported they would use the HIVST kits at least every six months if made available through the health department and would prefer to test at home (71.7%). Multivariate analyses found that awareness of and intention to use pre-exposure prophylaxis (PrEP) were significantly associated with high acceptability and testing onsite. CONCLUSION: Study participants found HIVST to be acceptable and very easy to use. The multivariate findings suggest HIVST interventions should be packaged with PrEP interventions and harm reduction programs.

Leveraging general risk surveillance to reduce suicide in early adolescents: Associations between suicidality, disordered eating, and other developmental risk factors.

OBJECTIVE: Suicide is a leading cause of death in early adolescents (i.e., children ages 11-14), underscoring the need for a more complex understanding of suicidality in youth. Syndemics framework posits that the overlap of multiple maladaptive behaviors (or risk factors) produces worse health outcomes compared to each behavior alone. The use of this framework in preventing suicide necessitates that identification of developmental risk factors that occur in tandem to suicide (e.g., disordered eating behaviors and substance use) may be important for intervening on those at greatest risk of suicide. METHOD: The present study uses latent class analysis (LCA) to evaluate the relationship between suicidality and disordered eating behaviors in early adolescence, and associations with other developmental risk factors in an epidemiological sample of middle school students (N = 3,811). RESULTS: Lifetime prevalence ranged from 6.8% to 18.2% for suicidality, and 5.3%-48.7% for disordered eating behavior. Our final model identified six independent classes, and each class had differential associations with several additional developmental risk factors. CONCLUSION: By pinpointing specific classes of individuals who may be at risk for multiple developmental risk factors, our results have important implications for public health intervention and prevention efforts for a wide range of adolescent risk behaviors, including suicidality.

A network examination of risky behaviours in a state-level and national epidemiological sample of high school students.

AIM: Engagement in risky behaviours, including substance use, disordered eating, suicidal behaviour, and peer victimization/violence, during adolescence is becoming increasingly prevalent. These risky behaviours are highly comorbid and associated with long-term consequences for health, relationships, and socioeconomic status, representing an important public health concern. Past research has primarily investigated risky behaviours in adolescence using latent variable models, which are based on assumptions that may limit insight into the complex reality of these behaviours. METHODS: The current study uses network analysis to examine adolescent substance use, disordered eating, suicide risk, and peer victimization/violence in a national (N = 29 008) and state-level (Kentucky; N = 3455) epidemiological dataset. We calculated central and bridge symptoms and compared network structure based on demographic factors (race, sex, grade) and sample (state vs. nation). RESULTS: The most central symptoms were suicidal ideation and attempts, stimulant drug use, and prescription drug misuse. The most central bridge symptoms were depression, methamphetamine use, peer violence, and suicide attempts. There were no differences in network structure between samples or across demographic factors in the Kentucky sample. There were differences in network structure across sex and race in the national dataset. CONCLUSIONS: These findings suggest stimulant use, suicidal ideation, depression, and peer violence may contribute to the high rates and co-occurrence of risky behaviours in adolescence. Based on network theory, these symptoms may represent important targets for intervention. Due to network differences, special considerations may be necessary to adapt such interventions to meet the needs of students from different backgrounds.

Consumer discernment of fentanyl in illicit opioids confirmed by fentanyl test strips: Lessons from a syringe services program in North Carolina.

BACKGROUND: The United States (U.S.) continues to witness an unprecedented increase in opioid overdose deaths driven by precipitous growth in the supply and use of illicitly-manufactured fentanyls (IMF). Fentanyl's growing market share of the illicit opioid supply in the U.S. has led to seismic shifts in the composition of the country's heroin supply. The growth in fentanyl supply has transformed illicit opioid markets once offering heroin with fairly consistent purity and potency to a supply overpopulated with fentanyl(s) of inconsistent and unpredictable potency. In response, people who inject drugs (PWID) have developed a number of sensory strategies to detect fentanyl in illicit opioids. The current study examined the accuracy of sensory discernment strategies by measuring study participants' descriptions of the last opioid injected and checked with a fentanyl test strip (FTS) by that test's positive/negative result. The primary objective was to determine associations between FTS results and descriptions of the illicit opioid's physical appearance and physiological effects. METHODS: Between September-October 2017, a total of 129 PWID were recruited from a syringe services program in Greensboro, North Carolina and completed an online survey about their most recent use of FTS. Participants were instructed to describe the appearance and effects associated with the most recent opioid they injected and tested with FTS. We conducted bivariate and multivariate analyses to determine differences in positive vs negative FTS results and the physical characteristics and physiological experiences reported. An exploratory analysis was also conducted to describe the types and bodily locations of unusual sensations experienced by PWID reporting positive FTS results. RESULTS: For physical characteristics, 32% reported that the drug was white before adding water and 38% reported the solution was clear after adding water. For physiological effects compared to heroin, 42% reported a stronger rush, 30% a shorter high, 30% a shorter time to the onset of withdrawal symptoms, and 42% experienced unusual sensations. In the multivariable model adjusting for demographics and polydrug correlates, white color of drug before adding water, stronger rush, shorter time to withdrawal, and unusual sensations were significantly associated with a positive FTS result. The most common unusual sensations were pins and needles (51%), warming of the head and face (35%), and lightheadedness (30%), and the most common locations where sensations occurred were face and neck (61%), arms/legs (54%), and chest (37%). CONCLUSION: We found positive FTS results were significantly associated with the physical characteristics and physiological effects described by PWID. Descriptions concerning physical appearance were consistent with law enforcement profiles of illicitly-manufactured fentanyl and physiological effects were concomitant with scientific and clinical medical literature on iatrogenic fentanyl use. Taken together, these findings suggest sensory strategies for detecting fentanyl in illicit opioids may be an effective risk reduction tool to help consumers navigate unpredictable markets more safely.

Characterizing psychiatric symptoms and neurocognitive functioning among substance-naïve early adolescents: Associations with sleep problems.

AIM: Evidence consistently links psychiatric symptoms, reduced neurocognitive functioning (NCF) and sleep problems to the initiation of a wide range of risk behaviours. Less is known, however, about the associations between sleep problems with psychiatric symptoms and NCF among early adolescents yet to engage in substance use. METHODS: The present study examined baseline data from an ongoing prospective study of 529 youth aged 10-12 years who completed a battery of instruments measuring symptom counts for four psychiatric disorders, performance on six tests of NCF and five types of sleep behaviour on week days. We used latent class analysis to classify the 473 substance-naïve youth into subtypes characterized by probabilistic patterns of psychiatric symptoms and poorer NCF. RESULTS: Four subtypes emerged: normative (24% of the sample); nonspecific mental health symptoms (27%); lower neurocognitive function (24%) and comorbid psychiatric symptoms and lower neurocognitive function (25%). In a multivariable latent regression model, three or more sleep arousals per night, sleep phase of two or more hours and sleep latency of 20 minutes or more were significantly associated with the two classes having higher symptom counts. Lack of family support was significantly associated with the two classes having lower neurocognitive function and comorbid psychiatric symptoms. CONCLUSIONS: The youth subtypes in this study provide an important baseline characterization to subsequently understand how these neuropsychiatric relationships may change when substance use and other risk behaviours develop during adolescence. Implications for preventing and treating sleep problems associated with psychiatric comorbidity and neurocognitive dysfunctions are discussed.

Fentanyl test strips as an opioid overdose prevention strategy: Findings from a syringe services program in the Southeastern United States.

BACKGROUND: In 2016, the number of overdose deaths involving illicitly-manufactured fentanyl (IMF) surpassed heroin and prescription opioid deaths in the United States for the first time, with IMF-involved overdose deaths increasing more than 500% across 10 states from 2013 to 2016. IMF is an extremely potent synthetic opioid that is regularly mixed with heroin and often sold to unwitting consumers. Community-based organizations have started to distribute fentanyl test strips (FTS) as a strategy to identify IMF in street purchased products. We investigated the association between FTS use and changes in drug use behavior and perceived overdose safety among a community-based sample of people who inject drugs (PWID) in the United States. METHODS: Between September-October 2017, a total of 125 PWID completed an online survey about their most recent FTS use in Greensboro, North Carolina. Our first outcome of interest included whether PWID engaged in any of the following changes in drug use behavior after using FTS: used less than usual, administered tester shot, pushed syringe plunger slower than usual, and snorted instead of injected. Our second outcome of interest was whether PWID felt that FTS use made them feel better able to protect themselves from overdose. We conducted bivariate and multivariate analyses to determine the association between FTS use and these two outcomes. RESULTS: Overall, 63% of the sample reported a positive FTS test result and 81% reported using FTS prior to consuming their drugs. For the outcomes, 43% reported a change in drug use behavior and 77% indicated increased perceived overdose safety by using FTS. In multivariable models adjusting for demographic and FTS correlates, PWID with a positive FTS test result had five times the odds of reporting changes in drug use behavior compared to those with a negative result. PWID who used the FTS after drug consumption were 70% less likely to report behavioral changes at subsequent drug consumption compared to those who used it before consumption. PWID who were not existing clients of the syringe services program had four times higher odds than existing clients to report increased overdose safety from using FTS. CONCLUSIONS: We found that using FTS and receiving a positive test result was associated with changes in drug use behavior and perceptions of overdose safety. FTS may represent an effective addition to current overdose prevention efforts when included with other evidence-based strategies to prevent opioid overdose and related harm.

Comparing medical and recreational cannabis use among employees: associations with health and work-related outcomes.

Nearly one in five US employees reports having used cannabis in the past year. As policies and laws regarding cannabis use rapidly change, concerns have arisen over employees' use of cannabis, for both medical and recreational purposes. While extant workplace research has not distinguished between types of cannabis users, other studies have found that medical users are clinically and socio-demographically different from non-medical users. This study utilized a sample of employed National Survey on Drug Use and Health (NSDUH) respondents to examine differences in workplace characteristics and health outcomes among employed medical, recreational, and mixed-use cannabis users. While some differences were initially seen when examining health and work-related outcomes between the groups, several changed after controlling for other important health-related factors. One key difference between the user groups is the higher percentages of medical and mixed-use cannabis users in the construction and mining industries. This study discusses future research needs, as well as practical implications for workers and employers.

Medical Decision-Making Processes and Online Behaviors Among Cannabis Dispensary Staff.

BACKGROUND: Most cannabis patients engage with dispensary staff, like budtenders, for medical advice on cannabis. Yet, little is known about these interactions and how the characteristics of budtenders affect these interactions. This study investigated demographics, workplace characteristics, medical decision-making, and online behaviors among a sample of budtenders. METHODS: Between June and September 2016, a cross-sectional Internet survey was administered to budtenders in the San Francisco Bay Area and Greater Los Angeles. A total of 158 budtenders fully responded to the survey. A series of comparisons were conducted to determine differences between trained and untrained budtenders. RESULTS: Among the 158 budtenders, 56% had received formal training to become a budtender. Several demographic differences were found between trained and untrained budtenders. For workplace characteristics, trained budtenders were more likely to report budtender as their primary job (74% vs 53%), practice more than 5 years (34% vs 11%), and receive sales commission (57% vs 16%). Trained budtenders were significantly less likely to perceive medical decision-making as very important (47% vs 68%) and have a patient-centered philosophy (77% vs 89%). Although trained budtenders had significantly lower Internet usage, they were significantly more likely to exchange information with patients through e-mail (58% vs 39%), text message (46% vs 30%), mobile app (33% vs 11%), video call (26% vs 3%), and social media (51% vs 23%). CONCLUSIONS: Budtenders who are formally trained exhibit significantly different patterns of interaction with medical cannabis patients. Future studies will use multivariate methods to better determine which factors independently influence interactions and how budtenders operate after the introduction of regulations under the newly passed Proposition 64 that permits recreational cannabis use in California.

Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California.

BACKGROUND: Twitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks. OBJECTIVES: This study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California. METHODS: From a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities. RESULTS: The modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles. CONCLUSIONS: The most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time.

Vaping Synthetic Cannabinoids: A Novel Preclinical Model of E-Cigarette Use in Mice.

Smoking is the most common route of administration for cannabis; however, vaping cannabis extracts and synthetic cannabinoids ("fake marijuana") in electronic cigarette devices has become increasingly popular. Yet, most animal models used to investigate biological mechanisms underlying cannabis use employ injection as the route of administration. This study evaluated a novel e-cigarette device that delivers aerosolized cannabinoids to mice. The effects of aerosolized and injected synthetic cannabinoids (CP 55,940, AB-CHMINACA, XLR-11, and JWH-018) in mice were compared in a battery of bioassays in which psychoactive cannabinoids produce characteristic effects. The most potent cannabinoids (CP 55,940 and AB-CHMINACA) produced the full cannabinoid profile (ie, hypothermia, hypolocomotion, and analgesia), regardless of the route of administration. In contrast, aerosolized JWH-018 and XLR-11 did not produce the full profile of cannabimimetic effects. Results of time course analysis for hypothermia showed that aerosol exposure to CP 55,940 and AB-CHMINACA produced faster onset of effects and shorter duration of action than injection. The ability to administer cannabinoids to rodents using the most common route of administration among humans provides a method for collecting preclinical data with enhanced translational relevance.