Ecological Momentary Assessment of Alcohol Consumption and Its Concordance with Transdermal Alcohol Detection and Timeline Follow-Back Self-report Among Adults Experiencing Homelessness

Mobile Health Interventions for Substance Use Disorders

Substance use disorders (SUDs) have an enormous negative impact on individuals, families, and society as a whole. Most individuals with SUDs do not receive treatment because of the limited availability of treatment providers, costs, inflexible work schedules, required treatment-related time commitments, and other hurdles. A paradigm shift in the provision of SUD treatments is currently underway. Indeed, with rapid technological advances, novel mobile health (mHealth) interventions can now be downloaded and accessed by those that need them anytime and anywhere. Nevertheless, the development and evaluation process for mHealth interventions for SUDs is still in its infancy. This review provides a critical appraisal of the significant literature in the field of mHealth interventions for SUDs with a particular emphasis on interventions for understudied and underserved populations. We also discuss the mHealth intervention development process, intervention optimization, and important remaining questions.

Smartphone-Based Survey and Message Compliance in Adults Initially Unready to Quit Smoking: Secondary Analysis of a Randomized Controlled Trial

BACKGROUND

Efficacy of smartphone-based interventions depends on intervention content quality and level of exposure to that content. Smartphone-based survey completion rates tend to decline over time; however, few studies have identified variables that predict this decline over longer-term interventions (eg, 26 weeks).

OBJECTIVE

This study aims to identify predictors of survey completion and message viewing over time within a 26-week smoking cessation trial.

METHODS

This study examined data from a 3-group pilot randomized controlled trial of adults who smoke (N=152) and were not ready to quit smoking within the next 30 days. For 182 days, two intervention groups received smartphone-based morning and evening messages based on current readiness to quit smoking. The control group received 2 daily messages unrelated to smoking. All participants were prompted to complete 26 weekly smartphone-based surveys that assessed smoking behavior, quit attempts, and readiness to quit. Compliance was operationalized as percentages of weekly surveys completed and daily messages viewed. Linear regression and mixed-effects models were used to identify predictors (eg, intervention group, age, and sex) of weekly survey completion and daily message viewing and decline in compliance over time.

RESULTS

The sample (mean age 50, SD 12.5, range 19-75 years; mean years of education 13.3, SD 1.6, range 10-20 years) was 67.8% (n=103) female, 74.3% (n=113) White, 77% (n=117) urban, and 52.6% (n=80) unemployed, and 61.2% (n=93) had mental health diagnoses. On average, participants completed 18.3 (71.8%) out of 25.5 prompted weekly surveys and viewed 207.3 (60.6%) out of 345.1 presented messages (31,503/52,460 total). Age was positively associated with overall weekly survey completion (P=.003) and daily message viewing (P=.02). Mixed-effects models indicated a decline in survey completion from 77% (114/148) in the first week of the intervention to 56% (84/150) in the last week of the intervention (P<.001), which was significantly moderated by age, sex, ethnicity, municipality (ie, rural/urban), and employment status. Similarly, message viewing declined from 72.3% (1533/2120) in the first week of the intervention to 44.6% (868/1946) in the last week of the intervention (P<.001). This decline in message viewing was significantly moderated by age, sex, municipality, employment status, and education.

CONCLUSIONS

This study demonstrated the feasibility of a 26-week smartphone-based smoking cessation intervention. Study results identified subgroups that displayed accelerated rates in the decline of survey completion and message viewing. Future research should identify ways to maintain high levels of interaction with mobile health interventions that span long intervention periods, especially among subgroups that have demonstrated declining rates of intervention engagement over time.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03405129; https://clinicaltrials.gov/ct2/show/NCT03405129.

Detecting and Understanding Social Influence During Drinking Situations: Protocol for a Bluetooth-Based Sensor Feasibility and Acceptability Study

BACKGROUND

High-risk alcohol consumption among young adults frequently occurs in the presence of peers who are also drinking. A high-risk drinking situation may consist of particular social network members who have a primary association with drinking. Fine-grained approaches such as ecological momentary assessment (EMA) are growing in popularity for studying real-time social influence, but studies using these approaches exclusively rely on participant self-report. Passive indicators of peer presence using Bluetooth-based technology to detect real-time interactions have the potential to assist in the development of just-in-time interventions.

OBJECTIVE

This study seeks to examine the feasibility and acceptability of using a Bluetooth-based sensor and smartphone app to measure social contact in real-world drinking situations.

METHODS

Young adults (N=20) who drink heavily and report social drinking will be recruited from the community to participate in a 3-week EMA study. Using a social network interview, index participants will identify and recruit 3 of their friends to carry a Bluetooth beacon. Participants will complete a series of EMA reports on their own personal Android devices including random reports; morning reports; first-drink reports; and signal-contingent reports, which are triggered following the detection of a beacon carried by a peer participant. EMA will assess alcohol use and characteristics of the social environment, including who is nearby and who is drinking. For items about peer proximity and peer drinking, a customized peer list will be presented to participants. Feedback about the study protocol will be ascertained through weekly contact with both index and peer participants, followed by a qualitative interview at the end of the study. We will examine the feasibility and acceptability of recruitment, enrollment of participants and peers, and retention. Feasibility will be determined using indexes of eligibility, enrollment, and recruitment. Acceptability will be determined through participant enrollment and retention, protocol compliance, and participant-reported measures of acceptability. Feasibility and acceptability for peer participants will be informed by enrollment rates, latency to enrollment, compliance with carrying the beacon, and self-reported reasons for compliance or noncompliance with beacon procedures. Finally, EMA data about peer proximity and peer drinking will support the validity of the peer selection process.

RESULTS

Participant recruitment began in February 2023, and enrollment was completed in December 2023. Results will be reported in 2025.

CONCLUSIONS

The protocol allows us to examine the feasibility and acceptability of a Bluetooth-based sensor for the detection of social contact between index participants and their friends, including social interactions during real-world drinking situations. Data from this study will inform just-in-time adaptive interventions seeking to address drinking in the natural environment by providing personalized feedback about a high-risk social context and alerting an individual that they are in a potentially unsafe situation.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50650.

A qualitative exploration of the experiences of transdermal alcohol sensor devices amongst people in receipt of treatment for alcohol use disorder

Introduction

Transdermal alcohol sensors (TAS) have the potential to be used as a clinical tool in alcohol treatment, but there is limited research with individuals with alcohol dependence using TAS. Our study is a qualitative evaluation of the views of people attending alcohol treatment and their experiences of wearing the BACtrack Skyn, within alcohol services in South London.

Methods

Participants with alcohol dependence wore a BACtrack Skyn TAS for one week and met with the researcher every two days, for a total of four meetings (for example: Monday, Wednesday, Friday, and Monday). In the final meeting, a post-wear survey (on their physical, social and comfort experience of the TAS) and semi-structured interview were completed. The Technology Acceptance Model (TAM) informed the topic guide and data analysis.

Results

Adults (N = 16) receiving alcohol treatment were recruited. Three core topics guided analysis: perceived usefulness, perceived ease of use and attitudes towards use. Participants found the TAS easy to wear and felt positive about its appearance and comfort. The only challenges reported were side effects, mostly skin irritation. The main two perceived uses were 1) TAS working as a drinking deterrent and 2) reducing daily breathalyser visits during detox.

Conclusion

Findings support the use of TAS amongst alcohol service users. Wearing the TAS for one week was acceptable and feasible for objective alcohol concentration measurement. Participants reported high perceived ease of use and usefulness of the Skyn in the context of alcohol treatment. These results are encouraging for the use of TAS in clinical settings.

Alcohol consumption and dependence risk among male and female Veterans: Trajectories and predictors

BACKGROUND

With few exceptions, previously conducted research on hazardous drinking among Veterans has employed samples in which the majority of participants identify as male. In addition, past studies have solely focused on alcohol consumption, rather than associated risk for dependence. In this study, we expanded upon the extant literature by investigating sex differences in trajectories and predictors of change in alcohol consumption and dependence risk among post-9/11 Veterans.

METHODS

A national sample of 1649 Veterans (50.0% female) were recruited in a five-wave longitudinal study that followed Veterans for up to 16 years after deployment. We used growth curve modeling to investigate trajectories of change in alcohol consumption and dependence risk among men and women Veterans. We examined predictors of growth, including demographics, support and resources, psychiatric symptoms, and trauma exposure.

RESULTS

Among male Veterans, alcohol consumption and dependence risk remained stagnant, which is in contrast to past work using non-Veteran samples. For female Veterans, consumption exhibited initial reductions that decelerated, and dependence risk reduced at a continuous rate. PTSD diagnosis was a significant predictor of individual differences in growth for men. Psychiatric symptoms (i.e., PTSD diagnosis, probable depression diagnosis, suicidal ideation) and psychosocial functioning were significant predictors of decreasing alcohol use for women.

CONCLUSIONS

Results highlight important sex differences in patterns and predictors of change in alcohol consumption and dependence risk among post-9/11 Veterans. Findings are discussed in relation to screening for hazardous alcohol use and intervention strategies in this at-risk population.

Predicting quantity of cannabis smoked in daily life: An exploratory study using machine learning

BACKGROUND

Cannabis use is prevalent in the United States and is associated with a host of negative consequences. Importantly, a robust indicator of negative consequences is the amount of cannabis consumed.

METHODS

Data were obtained from fifty-two adult, regular cannabis flower users (3+ times per week) recruited from the community; participants completed multiple ecological momentary assessment (EMA) surveys each day for 14 days. In this exploratory study, we used various machine learning algorithms to build models to predict the amount of cannabis smoked since participants' last report including forty-three EMA measures of mood, impulsivity, pain, alcohol use, cigarette use, craving, cannabis potency, cannabis use motivation, subjective effects of cannabis, social context, and location in daily life.

RESULTS

Our best-fitting model (Gradient Boosted Trees; 71.15% accuracy, 72.46% precision) found that affects, subjective effects of cannabis, and cannabis use motives were among the best predictors of cannabis use amount in daily life. The social context of being with others, and particularly with a partner or friend, was moderately weighted in the final prediction model, but contextual items reflecting location were not strongly weighted in the final prediction model, the one exception being not at work.

CONCLUSIONS

Machine learning approaches can help identify additional environmental and psychological phenomena that may be clinically-relevant to cannabis use.

Applying a Social Determinants of Health Framework to Guide Digital Innovations That Reduce Disparities in Chronic Disease

ABSTRACT

Chronic diseases are among the top causes of global death, disability, and health care expenditure. Digital health interventions (e.g., patient support delivered via technologies such as smartphones, wearables, videoconferencing, social media, and virtual reality) may prevent and mitigate chronic disease by facilitating accessible, personalized care. Although these tools have promise to reach historically marginalized groups, who are disproportionately affected by chronic disease, evidence suggests that digital health interventions could unintentionally exacerbate health inequities. This commentary outlines opportunities to harness recent advancements in technology and research design to drive equitable digital health intervention development and implementation. We apply "calls to action" from the World Health Organization Commission on Social Determinants of Health conceptual framework to the development of new, and refinement of existing, digital health interventions that aim to prevent or treat chronic disease by targeting intermediary, social, and/or structural determinants of health. Three mirrored "calls to action" are thus proposed for digital health research: a) develop, implement, and evaluate multilevel, context-specific digital health interventions; b) engage in intersectoral partnerships to advance digital health equity and social equity more broadly; and c) include and empower historically marginalized groups to develop, implement, and access digital health interventions. Using these "action items," we review several technological and methodological innovations for designing, evaluating, and implementing digital health interventions that have greater potential to reduce health inequities. We also enumerate possible challenges to conducting this work, including leading interdisciplinary collaborations, diversifying the scientific workforce, building trustworthy community relationships, and evolving health care and digital infrastructures.

Retrospective and Real-time Measures of the Quantity of E-cigarette Use: An Ecological Momentary Assessment Study

INTRODUCTION

Quantifying e-cigarette use is challenging because of the wide variety of products and the lack of a clear, objective demarcation of a use event. This study aimed to characterize the difference between retrospective and real-time measures of the quantity of e-cigarette use and identify the covariates that may account for discrepancies between the two types of measures.

METHODS

This study analyzed data from 401 college student e-cigarette users in Indiana and Texas who responded to a web survey (retrospective) and 7-day ecological momentary assessments (EMA) (real-time) on their e-cigarette use behavior, dependence symptomatology, e-cigarette product characteristics, and use contexts from Fall 2019 to Fall 2021. Generalized linear mixed models were used to model the real-time measures of quantity offset by the retrospective average quantity.

RESULTS

Although the number of times using e-cigarettes per day seems to be applicable to both retrospective and real-time measures, the number reported via EMA was 8.5 times the retrospective report. E-cigarette users with higher e-cigarette primary dependence motives tended to report more daily nicotine consumption via EMA than their retrospective reports (ie, perceived average consumption). Other covariates that were associated with discrepancies between real-time and retrospective reports included gender, nicotine concentration, using a menthol- or fruit-flavored product, co-use with alcohol, and being with others when vaping.

CONCLUSIONS

The study found extreme under-reporting of e-cigarette consumption on retrospective surveys. Important covariates identified to be associated with higher than average consumption may be considered as potential targets for future vaping interventions.

IMPLICATIONS

This is the first study that characterizes the direction and magnitude of the difference between retrospective and real-time measures of the quantity of e-cigarette use among young adults-the population most likely to use e-cigarettes. An average retrospective account of vaping events per day may significantly underestimate e-cigarette use frequency among young adults. The lack of insight into the degree of consumption among users with heavy primary dependence motives illustrates the importance of incorporating self-monitoring into cessation interventions.

Agreement between momentary and retrospective reports of cannabis use and alcohol use: Comparison of ecological momentary assessment and timeline followback indices

OBJECTIVE

This study compares three methods of cannabis and of alcohol use assessment in a sample of regular cannabis users: (a) ecological momentary assessment (EMA) repeated momentary surveys aggregated to the daily level, (b) EMA morning reports (MR) where participants reported on their total use from the previous day, and (c) retrospective timeline followback (TLFB) interviews covering the same period of time as the EMA portion of the study. We assessed the overall correspondence between these methods in terms of cannabis and alcohol use occasions and also investigated predictors of agreement between methods.

METHOD

Forty-nine individuals aged 18-50 (Mage = 24.49, 49% female, 84% White) who reported regular cannabis use completed a 14-day EMA study. At the end of the EMA period, participants returned to the laboratory to complete a TLFB (administered via computer) corresponding to the same dates of the EMA period.

RESULTS

Daily aggregated EMA and TLFB reports showed a low to modest agreement for both alcohol and cannabis use. Overall, agreement between EMA and MR was better than agreement between EMA and TLFB, likely because less retrospection is required when only reporting on behavior from the previous day. Quantity and frequency of use differentially predicted agreement across reporting methods when assessing alcohol compared to cannabis. When reporting cannabis use, but not alcohol use, individuals who used more demonstrated higher agreement between EMA and TLFB.

CONCLUSIONS

Results suggest that retrospective reporting methods assessing alcohol and cannabis should not be considered a direct "substitute" for momentary or daily assessments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The effect of the COVID-19 pandemic on substance use patterns and physiological dysregulation in pregnant and postpartum women

BACKGROUND

The SARS-CoV-2/COVID-19 pandemic has been associated with increased stress levels and higher alcohol use, including in pregnant and postpartum women. In the general population, alcohol use is associated with dysregulation in the autonomic nervous system (ANS), which is indexed by heart rate variability (HRV). The objectives of this study were to: (1) characterize changes in substance use during the SARS-CoV-2/COVID-19 pandemic via a baseline self-report survey followed by mobile ecological momentary assessment (mEMA) of substance use; and (2) examine the associations between momentary substance use and ambulatory HRV measures in pregnant and postpartum women.

METHODS

Pregnant and postpartum women were identified from the ENRICH-2 prospective cohort study. Participants were administered a baseline structured phone interview that included the Coronavirus Perinatal Experiences (COPE) survey and ascertained the prevalence of substance use. Over a 14-day period, momentary substance use was assessed three times daily, and HRV measurements were captured via wearable electronics. Associations between momentary substance use and HRV measures (root mean square of successive differences [RMSSD] and low frequency/high frequency [LF/HF] ratio) were examined using a mixed effects model that included within-subject (WS) and between-subject (BS) effects and adjusted for pregnancy status and participant age.

RESULTS

The sample included 49 pregnant and 22 postpartum women. From a combination of a baseline and 14-day mEMA surveys, 21.2% reported alcohol use, 16.9% reported marijuana use, and 8.5% reported nicotine use. WS effects for momentary alcohol use were associated with the RMSSD (β = -0.14; p = 0.005) and LF/HF ratio (β = 0.14; p = 0.01) when controlling for pregnancy status and maternal age. No significant associations were observed between HRV measures and instances of marijuana or nicotine use.

CONCLUSIONS

These findings highlight the negative effect of the SARS-CoV-2/COVID-19 pandemic on the psychological health of pregnant and postpartum women associated with substance use, and in turn, ANS dysregulation, which potentially puts some women at risk of developing a substance use disorder.

Correspondence between alcohol use measured by a wrist-worn alcohol biosensor and self-report via ecological momentary assessment over a 2-week period

BACKGROUND

Transdermal alcohol biosensors measure alcohol use continuously, passively, and non-invasively. There is little field research on the Skyn biosensor, a new-generation, wrist-worn transdermal alcohol biosensor, and little evaluation of its sensitivity and specificity and the day-level correspondence between transdermal alcohol concentration (TAC) and number of self-reported drinks.

METHODS

Participants (N = 36; 61% male, M age  = 34.3) wore the Skyn biosensor and completed ecological momentary assessment (EMA) surveys about their alcohol use over 2 weeks. A total of 497 days of biosensor and EMA data were collected. Skyn-measured drinking episodes were defined by TAC > 5 μg/L. Skyn data were compared to self-reported drinking to calculate sensitivity and specificity (for drinking day vs. nondrinking day). Generalized estimating equations models were used to evaluate the correspondence between TAC features (peak TAC and TAC-area under the curve (AUC)) and number of drinks. Individual-level factors (sex, age, race/ethnicity, body mass index, human immunodeficiency virus status, and hazardous drinking) were examined to explore associations with TAC controlling for number of drinks.

RESULTS

Using a minimum TAC threshold of 5 μg/L plus coder review, the biosensor had sensitivity of 54.7% and specificity of 94.6% for distinguishing drinking from nondrinking days. Without coder review, the sensitivity was 78.1% and the specificity was 55.2%. Peak TAC (β = 0.92, p < 0.0001) and TAC-AUC (β = 1.60, p < 0.0001) were significantly associated with number of drinks. Females had significantly higher TAC levels than males for the same number of drinks.

CONCLUSIONS

Skyn-derived TAC can be used to measure alcohol use under naturalistic drinking conditions, additional research is needed to accurately identify drinking episodes based on Skyn TAC readings.

Validation of serotonin transporter mRNA as a quantitative biomarker of heavy drinking and its comparison to ethyl glucuronide/ethyl sulfate: A randomized, double-blind, crossover trial

BACKGROUND

The serotonin transporter (SERT) mRNA was previously reported to be a quantitative and pathophysiology-based biomarker of heavy drinking in 5HTTLPR:LL genotype-carriers treated with ondansetron. Here, we validated the potential use of SERT mRNA for quantitative prediction of recent alcohol consumption (in the absence of treatment) and compared it with the known biomarkers ethyl glucuronide (EtG) and ethyl sulfate (EtS).

METHODS

Binge drinking men and women of European ancestry aged 21 to 65 years were enrolled in a 12-day, in-patient, randomized, double-blind, crossover study, where they were administered three beverage doses (placebo, 0.5 g/kg [0.4 g/kg] ethanol, and 1 g/kg [0.9 g/kg] ethanol for men [women]) individually in three 4-day periods (experiments), separated by minimum 7-day washout period. Diet, sleep, and physical activity were controlled throughout the inpatient experiments. Twenty-nine participants were randomized to receive beverage doses counterbalancing the sequence of treatment and gender within subgroups stratified by SERT genotypes 5HTTLPR:LL+rs25531:AA (LA LA ) versus 5HTTLPR:LS/SS. Peripheral venous blood was collected daily for (1) quantification of SERT mRNA (the primary outcome measure) using qRT-PCR and (2) plasma EtG and EtS levels using tandem mass-spectrometry.

RESULTS

The association between administered beverage dose and SERT mRNA from completers of at least one 4-day experiment (N = 18) assessed by a linear mixed model was not statistically significant. Significant positive associations were found with beverage dose and plasma EtG, EtS and EtG/EtS ratio (β = 5.8, SE = 1.2, p < 0.0001; β = 1.3, SE = 0.6, p = 0.023; and β = 3.0, SE = 0.7, p < 0.0001, respectively; the C-statistics for discriminating outcomes were 0.97, 0.8, and 0.92, respectively). Additionally, we observed a sequence effect with a greater placebo effect on SERT mRNA when it was administered during the first experiment (p = 0.0009), but not on EtG/EtS measures.

CONCLUSION

The findings do not validate the use of SERT as a biomarker of heavy drinking. Larger and more innovative studies addressing the effects of placebo, race, gender, and response to treatment with serotonergic agents are needed to fully assess the utility of SERT as a biomarker of heavy and binge drinking.

Development and preliminary effectiveness of a smartphone-based, just-in-time adaptive intervention for adults with alcohol misuse who are experiencing homelessness

BACKGROUND

Adults experiencing homelessness have much higher rates of alcohol misuse than housed individuals. This study describes the development and preliminary effectiveness of a smartphone-based, just-in-time adaptive intervention (JITAI) to reduce alcohol use among adults experiencing homelessness.

METHODS

We conducted a pilot trial (N = 41; mean age [SD] = 45.2 [11.5]; 19.5% women) of the Smart-T Alcohol JITAI where participants completed brief ecological momentary assessments (EMAs) each day, received personalized treatment messages following each EMA, and accessed on-demand intervention content for 4 weeks. The prediction algorithm and treatment messages were developed based on an independent but similar sample as part of the trial. We examined three drinking outcomes: daily drinking (yes/no), drinks per day, and heavy episodic drinking, controlling for scores on the Alcohol Use Disorders Identification Test (AUDIT) at baseline, age, and sex using quadratic growth curve models.

RESULTS

Over the 4-week period, participants showed a decline in all alcohol use outcomes. Participants also reported high levels of satisfaction with the JITAI.

CONCLUSIONS

Use of the Smart-T Alcohol JITAI was well received and provided encouraging evidence that it may reduce any drinking, drinks per day, and heavy episodic drinking among adults experiencing homelessness.

Assessing alcohol use in situ: Correlates of self-report vs. objective alcohol consumption

Associations between self-report and objective measurement of young adult alcohol use are weakened by excessive consumption levels; therefore, associations between correlates of alcohol use and consumption likely also differ by alcohol measurement. This study examined the extent to which correlates of heavy drinking measured via self-report are also indicators of heavy drinking measured objectively. Data were collected from 164 bar patrons (54% male; 73% White, 12% Black, 15% Other; 15% Hispanic) as they exited the bar. Participants completed an intercept survey including self-reported measures of drinking, demographics, and social-environmental factors. A breath alcohol concentration (BrAC) reading was also obtained using a handheld breathalyzer device. Correlations between two self-reported outcomes, number of drinks consumed prior to and at the bar, and BrAC were significant among those in the lowest quartile of BrAC readings, but largely non-significant at moderate and high BrAC levels. Intention to get drunk that night was a robust predictor of alcohol consumption across self-reported outcomes and BrAC. Social factors (presence of drinking peers, witnessing drunk others) were predictive of self-reported alcohol use but not BrAC. AUDIT-C score was the only additional alcohol behavior predictive of objectively measured alcohol use. Self-reported outcomes and BrAC, as well as their association with key correlates, diverge at high levels of intoxication, when preventive intervention is most needed. Implications for further research and alcohol prevention practice are discussed.

Ecological Momentary Assessment: A Systematic Review of Validity Research

Ecological momentary assessment (EMA) is a self-report method that involves intensive longitudinal assessment of behavior and environmental conditions during everyday activities. EMA has been used extensively in health and clinical psychology to investigate a variety of health behaviors, including substance use, eating, medication adherence, sleep, and physical activity. However, it has not been widely implemented in behavior analytic research. This is likely an example of the empirically based skepticism with which behavioral scientists view self-report measures. We reviewed studies comparing electronic, mobile EMA (mEMA) to more objective measures of health behavior to explore the validity of mEMA as a measurement tool, and to identify procedures and factors that may promote the accuracy of mEMA. We identified 32 studies that compared mEMA to more objective measures of health behavior or environmental events (e.g., biochemical measures or automated devices such as accelerometers). Results showed that the correspondence rates varied considerably across individuals, behavior, and studies (agreement rates ranged from 1.8%-100%), and no unifying variables could be identified across the studies that found high correspondence. The findings suggest that mEMA can be an accurate measurement tool, but further research should be conducted to identify procedures and variables that promote accurate responding.

Transdermal sensor features correlate with ecological momentary assessment drinking reports and predict alcohol-related consequences in young adults' natural settings

BACKGROUND

Wearable transdermal alcohol concentration (TAC) sensors allow passive monitoring of alcohol concentration in natural settings and measurement of multiple features from drinking episodes, including peak intoxication level, speed of intoxication (absorption rate) and elimination, and duration. These passively collected features extend commonly used self-reported drink counts and may facilitate the prediction of alcohol-related consequences in natural settings, aiding risk stratification and prevention efforts.

METHOD

A total of 222 young adults aged 21-29 (M age = 22.3, 64% female, 79% non-Hispanic white, 84% undergraduates) who regularly drink heavily participated in a 5-day study that included the ecological momentary assessment (EMA) of alcohol consumption (daily morning reports and participant-initiated episodic EMA sequences) and the wearing of TAC sensors (SCRAM-CAM anklets). The analytic sample contained 218 participants and 1274 days (including 554 self-reported drinking days). Five features-area under the curve (AUC), peak TAC, rise rate (rate of absorption), fall rate (rate of elimination), and duration-were extracted from TAC-positive trajectories for each drinking day. Day- and person-level associations of TAC features with drink counts (morning and episodic EMA) and alcohol-related consequences were tested using multilevel modeling.

RESULTS

TAC features were strongly associated with morning drink reports (r = 0.6-0.7) but only moderately associated with episodic EMA drink counts (r = 0.3-0.5) at both day and person levels. Higher peaks, larger AUCs, faster rise rates, and faster fall rates were significantly predictive of day-level alcohol-related consequences after adjusting for both morning and episodic EMA drink counts in separate models. Person means of TAC features added little above daily scores to the prediction of alcohol-related consequences.

CONCLUSIONS

These results support the utility of TAC sensors in studies of alcohol misuse among young adults in natural settings and outline the specific TAC features that contribute to the day-level prediction of alcohol-related consequences. TAC sensors provide a passive option for obtaining valid and unique information predictive of drinking risk in natural settings.

Using machine learning to identify predictors of imminent drinking and create tailored messages for at-risk drinkers experiencing homelessness

Adults experiencing homelessness are more likely to have an alcohol use disorder compared to adults in the general population. Although shelter-based treatments are common, completion rates tend to be poor, suggesting a need for more effective approaches that are tailored to this understudied and underserved population. One barrier to developing more effective treatments is the limited knowledge of the triggers of alcohol use among homeless adults. This paper describes the use of ecological momentary assessment (EMA) to identify predictors of “imminent drinking” (i.e., drinking within the next 4 h), among a sample of adults experiencing homelessness and receiving health services at a homeless shelter. A total of 78 mostly male (84.6%) adults experiencing homelessness (mean age = 46.6) who reported hazardous drinking completed up to five EMAs per day over 4 weeks (a total of 4557 completed EMAs). The study used machine learning techniques to create a drinking risk algorithm that predicted 82% of imminent drinking episodes within 4 h of the first drink of the day, and correctly identified 76% of nondrinking episodes. The algorithm included the following 7 predictors of imminent drinking: urge to drink, having alcohol easily available, feeling confident that alcohol would improve mood, feeling depressed, lower commitment to being alcohol free, not interacting with someone drinking alcohol, and being indoors. The research team used the results to develop intervention content (e.g., brief tailored messages) that will be delivered when imminent drinking is detected in an upcoming intervention phase. Specifically, we created three theoretically grounded message tracks focused on urge/craving, social/availability, and negative affect/mood, which are further tailored to a participant’s current drinking goal (i.e., stay sober, drink less, no goal) to support positive change. To our knowledge, this is the first study to develop tailored intervention messages based on likelihood of imminent drinking, current drinking triggers, and drinking goals among adults experiencing homelessness.