A Preliminary Study Using Smartphone Accelerometers to Sense Gait Impairments Due to Alcohol Intoxication

Detection of Alcohol Intoxication Using Voice Features: A Controlled Laboratory Study

OBJECTIVE

Devices such as mobile phones and smart speakers could be useful to remotely identify voice alterations associated with alcohol intoxication that could be used to deliver just-in-time interventions, but data to support such approaches for the English language are lacking. In this controlled laboratory study, we compare how well English spectrographic voice features identify alcohol intoxication.

METHOD

A total of 18 participants (72% male, ages 21-62 years) read a randomly assigned tongue twister before drinking and each hour for up to 7 hours after drinking a weight-based dose of alcohol. Vocal segments were cleaned and split into 1-second windows. We built support vector machine models for detecting alcohol intoxication, defined as breath alcohol concentration > .08%, comparing the baseline voice spectrographic signature to each subsequent timepoint and examined accuracy with 95% confidence intervals (CIs).

RESULTS

Alcohol intoxication was predicted with an accuracy of 98% (95% CI [97.1, 98.6]); mean sensitivity = .98; specificity = .97; positive predictive value = .97; and negative predictive value = .98.

CONCLUSIONS

In this small, controlled laboratory study, voice spectrographic signatures collected from brief recorded English segments were useful in identifying alcohol intoxication. Larger studies using varied voice samples are needed to validate and expand models.

Gender differences in the effect of a 0.11% breath alcohol concentration on forward and backward gait

Alcohol contributes to a large number of diseases and health conditions related to injuries. The aim of our study was to evaluate gender differences in forward and backward gait when sober and at a breath alcohol concentration (BrAC) of 0.11%. Fifty females and fifty males participated in our study. The gait analysis was performed twice, when sober and after drinking a given amount of vodka mixed with orange juice. Under both conditions, participants were asked to walk forward and then backward on a Zebris platform. Multivariate analysis and the Mann-Whitney U test were used to compare the differences between genders when walking forward and backward. The Wilcoxon Signed Ranks test was used to compare the differences between 0.00% BrAC and 0.11% BrAC. Spearman's Rho was used to analyze the relationship between the AUDIT score, anthropometrical characteristics and the subjective score of drunkenness and gait parameters. The results show different strategies to improve stability during gait in women and men when intoxicated with alcohol. When intoxicated, males in forward gait increase their stability by increasing their foot rotation, while females increase their step width. A decrease in balance-related variables was observed in females when walking backward with a BrAC of 0.11%. Additionally, females tended to perform an increase in balance-related gait variables when subjectively feeling more drunk in both forward and backward gait. Different strategies to maintain stability during gait were observed in women and men. The results of our study show that alcohol intoxication has a greater impact on gait in females who tended to perform an increase in balance-related variables with an increase in their subjective score of drunkenness.

Using wearable technology to detect prescription opioid self-administration

ABSTRACT

Appropriate monitoring of opioid use in patients with pain conditions is paramount, yet it remains a very challenging task. The current work examined the use of a wearable sensor to detect self-administration of opioids after dental surgery using machine learning. Participants were recruited from an oral and maxillofacial surgery clinic. Participants were 46 adult patients (26 female) receiving opioids after dental surgery. Participants wore Empatica E4 sensors during the period they self-administered opioids. The E4 collected physiological parameters including accelerometer x-, y-, and z-axes, heart rate, and electrodermal activity. Four machine learning models provided validation accuracies greater than 80%, but the bagged-tree model provided the highest combination of validation accuracy (83.7%) and area under the receiver operating characteristic curve (0.92). The trained model had a validation sensitivity of 82%, a specificity of 85%, a positive predictive value of 85%, and a negative predictive value of 83%. A subsequent test of the trained model on withheld data had a sensitivity of 81%, a specificity of 88%, a positive predictive value of 87%, and a negative predictive value of 82%. Results from training and testing model of machine learning indicated that opioid self-administration could be identified with reasonable accuracy, leading to considerable possibilities of the use of wearable technology to advance prevention and treatment.

Acceptability, Validity, and Engagement With a Mobile App for Frequent, Continuous Multiyear Assessment of Youth Health Behaviors (mNCANDA): Mixed Methods Study

BACKGROUND

Longitudinal studies of many health behaviors often rely on infrequent self-report assessments. The measurement of psychoactive substance use among youth is expected to improve with more frequent mobile assessments, which can reduce recall bias. Researchers have used mobile devices for longitudinal research, but studies that last years and assess youth continuously at a fine-grained, temporal level (eg, weekly) are rare. A tailored mobile app (mNCANDA [mobile National Consortium on Alcohol and Neurodevelopment in Adolescence]) and a brief assessment protocol were designed specifically to provide a feasible platform to elicit responses to health behavior assessments in longitudinal studies, including NCANDA (National Consortium on Alcohol and Neurodevelopment in Adolescence).

OBJECTIVE

This study aimed to determine whether an acceptable mobile app system could provide repeatable and valid assessment of youth's health behaviors in different developmental stages over extended follow-up.

METHODS

Participants were recruited (n=534; aged 17-28 years) from a larger longitudinal study of neurodevelopment. Participants used mNCANDA to register reports of their behaviors for up to 18 months. Response rates as a function of time measured using mNCANDA and participant age were modeled using generalized estimating equations to evaluate response rate stability and age effects. Substance use reports captured using mNCANDA were compared with responses from standardized interviews to assess concurrent validity. Reactivity was assessed by evaluating patterns of change in substance use after participants initiated weekly reports via mNCANDA. Quantitative feedback about the app was obtained from the participants. Qualitative interviews were conducted with a subset of participants who used the app for at least one month to obtain feedback on user experience, user-derived explanations of some quantitative results, and suggestions for system improvements.

RESULTS

The mNCANDA protocol adherence was high (mean response rate 82%, SD 27%) and stable over time across all age groups. The median time to complete each assessment was 51 s (mean response time 1.14, SD 1.03 min). Comparisons between mNCANDA and interview self-reports on recent (previous 30 days) alcohol and cannabis use days demonstrate close agreement (eg, within 1 day of reported use) for most observations. Models used to identify reactivity failed to detect changes in substance use patterns subsequent to enrolling in mNCANDA app assessments (P>.39). Most participants (64/76, 84%) across the age range reported finding the mNCANDA system acceptable. Participants provided recommendations for improving the system (eg, tailoring signaling times).

CONCLUSIONS

mNCANDA provides a feasible, multi-year, continuous, fine-grained (eg, weekly) assessment of health behaviors designed to minimize respondent burden and provides acceptable regimes for long-term self-reporting of health behaviors. Fine-grained characterization of variability in behaviors over relatively long periods (eg, up to 18 months) may, through the use of mNCANDA, improve our understanding of the relationship between substance use exposure and neurocognitive development.

Ambulatory Assessment Methods to Examine Momentary State-Based Predictors of Opioid Use Behaviors

Purpose of Review

Addiction scientists have begun using ambulatory assessment methods—including ecological momentary assessment (EMA), experience sampling, and daily diaries—to collect real-time or near-real-time reports of participants’ internal states in their natural environments. The goal of this short review is to synthesize EMA findings from our research group, which has studied several hundred outpatients during treatment for opioid-use disorder (OUD). (We cite pertinent findings from other groups, but have not tried to be comprehensive.) One of our main goals in using EMA is to examine momentary changes in internal states that proximally predict, or concurrently mark, events such as lapses to opioid use.

Recent Findings

We summarize findings evaluating several classes of momentary markers or predictors (craving, stress, negative and positive moods, and physical pain/discomfort) of lapses and other states/behaviors. Craving and some negatively valenced mood states are concurrently and prospectively associated with lapses to opioid use during treatment. Craving is also concurrently and prospectively associated with momentary changes in stress and mood. Convincing evidence has not yet emerged for stress as a robust redictor of lapse to opioid use; it appears to be contributory, but neither necessary nor sufficient.

Summary

Ambulatory assessment can capture changes in internal states and drug-related behaviors in situ and at high temporal resolution. We recommend research strategies that may increase the clinical and prognostic utility of ambulatory assessment, including denser sampling (i.e., more assessments per day) and more attention to heterogeneity across people and across populations.