RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population.

Purpose: To evaluate the diagnostic performance of a robotically aligned optical coherence tomography (RAOCT) system coupled with a deep learning model in detecting referable posterior segment pathology in OCT images of emergency department patients. Methods: A deep learning model, RobOCTNet, was trained and internally tested to classify OCT images as referable versus non-referable for ophthalmology consultation. For external testing, emergency department patients with signs or symptoms warranting evaluation of the posterior segment were imaged with RAOCT. RobOCTNet was used to classify the images. Model performance was evaluated against a reference standard based on clinical diagnosis and retina specialist OCT review. Results: We included 90,250 OCT images for training and 1489 images for internal testing. RobOCTNet achieved an area under the curve (AUC) of 1.00 (95% confidence interval [CI], 0.99-1.00) for detection of referable posterior segment pathology in the internal test set. For external testing, RAOCT was used to image 72 eyes of 38 emergency department patients. In this set, RobOCTNet had an AUC of 0.91 (95% CI, 0.82-0.97), a sensitivity of 95% (95% CI, 87%-100%), and a specificity of 76% (95% CI, 62%-91%). The model's performance was comparable to two human experts' performance. Conclusions: A robotically aligned OCT coupled with a deep learning model demonstrated high diagnostic performance in detecting referable posterior segment pathology in a cohort of emergency department patients. Translational Relevance: Robotically aligned OCT coupled with a deep learning model may have the potential to improve emergency department patient triage for ophthalmology referral.

Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year.

Importance: Autism detection early in childhood is critical to ensure that autistic children and their families have access to early behavioral support. Early correlates of autism documented in electronic health records (EHRs) during routine care could allow passive, predictive model-based monitoring to improve the accuracy of early detection. Objective: To quantify the predictive value of early autism detection models based on EHR data collected before age 1 year. Design, Setting, and Participants: This retrospective diagnostic study used EHR data from children seen within the Duke University Health System before age 30 days between January 2006 and December 2020. These data were used to train and evaluate L2-regularized Cox proportional hazards models predicting later autism diagnosis based on data collected from birth up to the time of prediction (ages 30-360 days). Statistical analyses were performed between August 1, 2020, and April 1, 2022. Main Outcomes and Measures: Prediction performance was quantified in terms of sensitivity, specificity, and positive predictive value (PPV) at clinically relevant model operating thresholds. Results: Data from 45 080 children, including 924 (1.5%) meeting autism criteria, were included in this study. Model-based autism detection at age 30 days achieved 45.5% sensitivity and 23.0% PPV at 90.0% specificity. Detection by age 360 days achieved 59.8% sensitivity and 17.6% PPV at 81.5% specificity and 38.8% sensitivity and 31.0% PPV at 94.3% specificity. Conclusions and Relevance: In this diagnostic study of an autism screening test, EHR-based autism detection achieved clinically meaningful accuracy by age 30 days, improving by age 1 year. This automated approach could be integrated with caregiver surveys to improve the accuracy of early autism screening.

Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.

Importance: Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies. Objective: To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques. Design, Setting, and Participants: Retrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study for Atherosclerosis (MESA), and Reasons for Geographical and Racial Differences in Stroke (REGARDS) studies (1983-2019) conducted in the US. The 62 482 participants included at baseline were at least 45 years of age and free of stroke or transient ischemic attack. Exposures: Published stroke-specific algorithms from Framingham and REGARDS (based on self-reported risk factors) as well as pooled cohort equations for atherosclerotic cardiovascular disease plus 2 newly developed machine learning algorithms. Main Outcomes and Measures: Models were designed to estimate the 10-year risk of new-onset stroke (ischemic or hemorrhagic). Discrimination concordance index (C index) and calibration ratios of expected vs observed event rates were assessed at 10 years. Analyses were conducted by race, sex, and age groups. Results: The combined study sample included 62 482 participants (median age, 61 years, 54% women, and 29% Black individuals). Discrimination C indexes were not significantly different for the 2 stroke-specific models (Framingham stroke, 0.72; 95% CI, 0.72-073; REGARDS self-report, 0.73; 95% CI, 0.72-0.74) vs the pooled cohort equations (0.72; 95% CI, 0.71-0.73): differences 0.01 or less (P values >.05) in the combined sample. Significant differences in discrimination were observed by race: the C indexes were 0.76 for all 3 models in White vs 0.69 in Black women (all P values <.001) and between 0.71 and 0.72 in White men and between 0.64 and 0.66 in Black men (all P values ≤.001). When stratified by age, model discrimination was better for younger (<60 years) vs older (≥60 years) adults for both Black and White individuals. The ratios of observed to expected 10-year stroke rates were closest to 1 for the REGARDS self-report model (1.05; 95% CI, 1.00-1.09) and indicated risk overestimation for Framingham stroke (0.86; 95% CI, 0.82-0.89) and pooled cohort equations (0.74; 95% CI, 0.71-0.77). Performance did not significantly improve when novel machine learning algorithms were applied. Conclusions and Relevance: In this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.

A Disease Identification Algorithm for Medical Crowdfunding Campaigns: Validation Study.

BACKGROUND: Web-based crowdfunding has become a popular method to raise money for medical expenses, and there is growing research interest in this topic. However, crowdfunding data are largely composed of unstructured text, thereby posing many challenges for researchers hoping to answer questions about specific medical conditions. Previous studies have used methods that either failed to address major challenges or were poorly scalable to large sample sizes. To enable further research on this emerging funding mechanism in health care, better methods are needed. OBJECTIVE: We sought to validate an algorithm for identifying 11 disease categories in web-based medical crowdfunding campaigns. We hypothesized that a disease identification algorithm combining a named entity recognition (NER) model and word search approach could identify disease categories with high precision and accuracy. Such an algorithm would facilitate further research using these data. METHODS: Web scraping was used to collect data on medical crowdfunding campaigns from GoFundMe (GoFundMe Inc). Using pretrained NER and entity resolution models from Spark NLP for Healthcare in combination with targeted keyword searches, we constructed an algorithm to identify conditions in the campaign descriptions, translate conditions to International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes, and predict the presence or absence of 11 disease categories in the campaigns. The classification performance of the algorithm was evaluated against 400 manually labeled campaigns. RESULTS: We collected data on 89,645 crowdfunding campaigns through web scraping. The interrater reliability for detecting the presence of broad disease categories in the campaign descriptions was high (Cohen κ: range 0.69-0.96). The NER and entity resolution models identified 6594 unique (276,020 total) ICD-10-CM codes among all of the crowdfunding campaigns in our sample. Through our word search, we identified 3261 additional campaigns for which a medical condition was not otherwise detected with the NER model. When averaged across all disease categories and weighted by the number of campaigns that mentioned each disease category, the algorithm demonstrated an overall precision of 0.83 (range 0.48-0.97), a recall of 0.77 (range 0.42-0.98), an F1 score of 0.78 (range 0.56-0.96), and an accuracy of 95% (range 90%-98%). CONCLUSIONS: A disease identification algorithm combining pretrained natural language processing models and ICD-10-CM code-based disease categorization was able to detect 11 disease categories in medical crowdfunding campaigns with high precision and accuracy.

Regularity and Timing of Sleep Patterns and Behavioral Health Among Adolescents.

OBJECTIVE: Sleep is vital to supporting adolescent behavioral health and functioning; however, sleep disturbances remain under-recognized and undertreated in many health care settings. One barrier is the complexity of sleep, which makes it difficult for providers to determine which aspects-beyond sleep duration-may be most important to assess and treat to support adolescent health. This study examined associations between 2 sleep indices (regularity and timing) and adolescent behavioral health and functioning over and above the impact of shortened/fragmented sleep. METHOD: Eighty-nine adolescents recruited from the community (mean age = 14.04, 45% female participants) completed 7 days/nights of actigraphy and, along with a parent/guardian, reported on behavioral health (internalizing and externalizing symptoms) and psychosocial functioning. Stepwise linear regressions examined associations between sleep timing and regularity and behavioral/functional outcomes after accounting for shortened/fragmented sleep. RESULTS: Delayed sleep timing was associated with greater self-reported internalizing (F[6,82] = 11.57, p = 0.001) and externalizing (F[6,82] = 11.12, p = 0.001) symptoms after accounting for shortened/fragmented sleep. Irregular sleep was associated with greater self-reported and parent-reported externalizing symptoms (self: F[7,81] = 6.55, p = 0.01; parent: F[7,80] = 6.20, p = 0.01) and lower psychosocial functioning (self: F[7,81] = 6.03, p = 0.02; parent: F[7,78] = 3.99, p < 0.05) after accounting for both shortened/fragmented sleep and delayed sleep timing. CONCLUSION: Sleep regularity and timing may be critical for understanding the risk of poor behavioral health and functional deficits among adolescents and as prevention and intervention targets. Future work should focus on developing and evaluating convenient, low-cost, and effective methods for addressing delayed and/or irregular adolescent sleep patterns in real-world health care settings.

Impact of daily caffeine intake and timing on electroencephalogram-measured sleep in adolescents.

STUDY OBJECTIVES: Caffeine use is ubiquitous among adolescents and may be harmful to sleep, with downstream implications for health and development. Research has been limited by self-reported and/or aggregated measures of sleep and caffeine collected at a single time point. This study examines bidirectional associations between daily caffeine consumption and electroencephalogram-measured sleep among adolescents and explores whether these relationships depend on timing of caffeine use. METHODS: Ninety-eight adolescents aged 11-17 (mean =14.38, standard deviation = 1.77; 50% female) participated in 7 consecutive nights of at-home sleep electroencephalography and completed a daily diary querying morning, afternoon, and evening caffeine use. Linear mixed-effects regressions examined relationships between caffeine consumption and total sleep time, sleep-onset latency, sleep efficiency, wake after sleep onset, and time spent in sleep stages. Impact of sleep indices on next-day caffeine use was also examined. RESULTS: Increased total caffeine consumption was associated was increased sleep-onset latency (ß = .13; 95% CI = .06, .21; P < .001) and reduced total sleep time (ß = -.17; 95% confidence interval [CI] = -.31, -.02; P = .02), sleep efficiency (ß = -1.59; 95% CI = -2.51, -.67; P < .001), and rapid eye movement sleep (ß = -.12; 95% CI = -.19, -.05; P < .001). Findings were driven by afternoon and evening caffeine consumption. Reduced sleep efficiency was associated with increased afternoon caffeine intake the following day (ß = -.006; 95% CI = -.012, -.001; P = .01). CONCLUSIONS: Caffeine consumption, especially afternoon and evening use, impacts several aspects of adolescent sleep health. In contrast, most sleep indicators did not affect next-day caffeine use, suggesting multiple drivers of adolescent caffeine consumption. Federal mandates requiring caffeine content labeling and behavioral interventions focused on reducing caffeine intake may support adolescent sleep health. CITATION: Lunsford-Avery JR, Kollins SH, Kansagra S, Wang KW, Engelhard MM. Impact of daily caffeine intake and timing on electroencephalogram-measured sleep in adolescents. J Clin Sleep Med. 2022;18(3):877-884.

Prediction of Smoking Risk From Repeated Sampling of Environmental Images: Model Validation.

BACKGROUND: Viewing their habitual smoking environments increases smokers' craving and smoking behaviors in laboratory settings. A deep learning approach can differentiate between habitual smoking versus nonsmoking environments, suggesting that it may be possible to predict environment-associated smoking risk from continuously acquired images of smokers' daily environments. OBJECTIVE: In this study, we aim to predict environment-associated risk from continuously acquired images of smokers' daily environments. We also aim to understand how model performance varies by location type, as reported by participants. METHODS: Smokers from Durham, North Carolina and surrounding areas completed ecological momentary assessments both immediately after smoking and at randomly selected times throughout the day for 2 weeks. At each assessment, participants took a picture of their current environment and completed a questionnaire on smoking, craving, and the environmental setting. A convolutional neural network-based model was trained to predict smoking, craving, whether smoking was permitted in the current environment and whether the participant was outside based on images of participants' daily environments, the time since their last cigarette, and baseline data on daily smoking habits. Prediction performance, quantified using the area under the receiver operating characteristic curve (AUC) and average precision (AP), was assessed for out-of-sample prediction as well as personalized models trained on images from days 1 to 10. The models were optimized for mobile devices and implemented as a smartphone app. RESULTS: A total of 48 participants completed the study, and 8008 images were acquired. The personalized models were highly effective in predicting smoking risk (AUC=0.827; AP=0.882), craving (AUC=0.837; AP=0.798), whether smoking was permitted in the current environment (AUC=0.932; AP=0.981), and whether the participant was outside (AUC=0.977; AP=0.956). The out-of-sample models were also effective in predicting smoking risk (AUC=0.723; AP=0.785), whether smoking was permitted in the current environment (AUC=0.815; AP=0.937), and whether the participant was outside (AUC=0.949; AP=0.922); however, they were not effective in predicting craving (AUC=0.522; AP=0.427). Omitting image features reduced AUC by over 0.1 when predicting all outcomes except craving. Prediction of smoking was more effective for participants whose self-reported location type was more variable (Spearman ρ=0.48; P=.001). CONCLUSIONS: Images of daily environments can be used to effectively predict smoking risk. Model personalization, achieved by incorporating information about daily smoking habits and training on participant-specific images, further improves prediction performance. Environment-associated smoking risk can be assessed in real time on a mobile device and can be incorporated into device-based smoking cessation interventions.

Identifying neural signatures of tobacco retail outlet exposure: Preliminary validation of a "community neuroscience" paradigm.

An extensive epidemiological literature indicates that increased exposure to tobacco retail outlets (TROs) places never smokers at greater risk for smoking uptake and current smokers at greater risk for increased consumption and smoking relapse. Yet research into the mechanisms underlying this effect has been limited. This preliminary study represents the first effort to examine the neurobiological consequences of exposure to personally relevant TROs among both smokers (n = 17) and nonsmokers (n = 17). Individuals carried a global positioning system (GPS) tracker for 2 weeks. Traces were used to identify TROs and control outlets that fell inside and outside their ideographically defined activity space. Participants underwent functional MRI (fMRI) scanning during which they were presented with images of these storefronts, along with similar store images from a different county and rated their familiarity with these stores. The main effect of activity space was additive with a Smoking status × Store type interaction, resulting in smokers exhibiting greater neural activation to TROs falling inside activity space within the parahippocampus, precuneus, medial prefrontal cortex, and dorsal anterior insula. A similar pattern was observed for familiarity ratings. Together, these preliminary findings suggest that the otherwise distinct neural systems involved in self-orientation/self-relevance and smoking motivation may act in concert and underlie TRO influence on smoking behavior. This study also offers a novel methodological framework for evaluating the influence of community features on neural activity that can be readily adapted to study other health behaviors.

Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study.

BACKGROUND: Adolescence is an important life stage for the development of healthy behaviors, which have a long-lasting impact on health across the lifespan. Sleep undergoes significant changes during adolescence and is linked to physical and psychiatric health; however, sleep is rarely assessed in routine health care settings. Wearable sleep electroencephalogram (EEG) devices may represent user-friendly methods for assessing sleep among adolescents, but no studies to date have examined the feasibility and acceptability of sleep EEG wearables in this age group. OBJECTIVE: The goal of the research was to investigate the feasibility and acceptability of sleep EEG wearable devices among adolescents aged 11 to 17 years. METHODS: A total of 104 adolescents aged 11 to 17 years participated in 7 days of at-home sleep recording using a self-administered wearable sleep EEG device (Zmachine Insight+, General Sleep Corporation) as well as a wristworn actigraph. Feasibility was assessed as the number of full nights of successful recording completed by adolescents, and acceptability was measured by the wearable acceptability survey for sleep. Feasibility and acceptability were assessed separately for the sleep EEG device and wristworn actigraph. RESULTS: A total of 94.2% (98/104) of adolescents successfully recorded at least 1 night of data using the sleep EEG device (mean number of nights 5.42; SD 1.71; median 6, mode 7). A total of 81.6% (84/103) rated the comfort of the device as falling in the comfortable to mildly uncomfortable range while awake. A total of 40.8% (42/103) reported typical sleep while using the device, while 39.8% (41/103) indicated minimal to mild device-related sleep disturbances. A minority (32/104, 30.8%) indicated changes in their sleep position due to device use, and very few (11/103, 10.7%) expressed dissatisfaction with their experience with the device. A similar pattern was observed for the wristworn actigraph device. CONCLUSIONS: Wearable sleep EEG appears to represent a feasible, acceptable method for sleep assessment among adolescents and may have utility for assessing and treating sleep disturbances at a population level. Future studies with adolescents should evaluate strategies for further improving usability of such devices, assess relationships between sleep EEG-derived metrics and health outcomes, and investigate methods for incorporating data from these devices into emerging digital interventions and applications. TRIAL REGISTRATION: ClinicalTrials.gov NCT03843762; https://clinicaltrials.gov/ct2/show/NCT03843762.

Health system utilization before age 1 among children later diagnosed with autism or ADHD.

Children with autism spectrum disorder (ASD) or attention deficit hyperactivity disorder (ADHD) have 2-3 times increased healthcare utilization and annual costs once diagnosed, but little is known about their utilization patterns early in life. Quantifying their early health system utilization could uncover condition-specific health trajectories to facilitate earlier detection and intervention. Patients born 10/1/2006-10/1/2016 with ≥ 2 well-child visits within the Duke University Health System before age 1 were grouped as ASD, ADHD, ASD + ADHD, or No Diagnosis using retrospective billing codes. An additional comparison group was defined by later upper respiratory infection diagnosis. Adjusted odds ratios (AOR) for hospital admissions, procedures, emergency department (ED) visits, and outpatient clinic encounters before age 1 were compared between groups via logistic regression models. Length of hospital encounters were compared between groups via Mann-Whitney U test. In total, 29,929 patients met study criteria (ASD N = 343; ADHD N = 1175; ASD + ADHD N = 140). ASD was associated with increased procedures (AOR = 1.5, p < 0.001), including intubation and ventilation (AOR = 2.4, p < 0.001); and outpatient specialty care, including physical therapy (AOR = 3.5, p < 0.001) and ophthalmology (AOR = 3.1, p < 0.001). ADHD was associated with increased procedures (AOR = 1.41, p < 0.001), including blood transfusion (AOR = 4.7, p < 0.001); hospital admission (AOR = 1.60, p < 0.001); and ED visits (AOR = 1.58, p < 0.001). Median length of stay was increased after birth in ASD (+ 6.5 h, p < 0.001) and ADHD (+ 3.8 h, p < 0.001), and after non-birth admission in ADHD (+ 1.1 d, p < 0.001) and ASD + ADHD (+ 2.4 d, p = 0.003). Each condition was associated with increased health system utilization and distinctive patterns of utilization before age 1. Recognizing these patterns may contribute to earlier detection and intervention.

Digital envirotyping: quantifying environmental determinants of health and behavior.

Digital phenotyping efforts have used wearable devices to connect a rich array of physiologic data to health outcomes or behaviors of interest. The environmental context surrounding these phenomena has received less attention, yet is critically needed to understand their antecedents and deliver context-appropriate interventions. The coupling of improved smart eyewear with deep learning represents a technological turning point, one that calls for more comprehensive, ambitious study of environments and health.

Author Correction: Validation of the Sleep Regularity Index in Older Adults and Associations with Cardiometabolic Risk.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Sleep/Wake Regularity Associated with Default Mode Network Structure among Healthy Adolescents and Young Adults.

Sleep deprivation and disorders are linked to reduced DMN connectivity. Less is known about how naturalistic sleep patterns - specifically sleep irregularity - relate to the DMN, particularly among adolescents and young adults. Additionally, no studies have utilized graph theory analysis to clarify whether sleep-related decreases in connectivity reflect global or local DMN changes. Twenty-five healthy adolescents and young adults (age range = 12-22; mean = 18.08; SD = 2.64, 56% female) completed 7 days of actigraphy and resting-state fMRI. Sleep regularity was captured by the Sleep Regularity Index (SRI) and the relationship between the SRI and DMN was examined using graph theory analysis. Analogous analyses explored relationships between the SRI and additional resting-state networks. Greater sleep regularity related to decreased path length (increased network connectivity) in DMN regions, particularly the right and left lateral parietal lobule, and the Language Network, including the left inferior frontal gyrus and the left posterior superior frontal gyrus. Findings were robust to covariates including sex and age. Sleep and DMN function may be tightly linked during adolescence and young adulthood, and reduced DMN connectivity may reflect local changes within the network. Future studies should assess how this relationship impacts cognitive development and neuropsychiatric outcomes in this age group.

Review of Popularity and Quality Standards of Opioid-Related Smartphone Apps.

PURPOSE OF REVIEW: Opioid misuse, addiction, and related harm is a global crisis that affects public health and social and economic welfare. Many of the strategies being used to combat the opioid crisis could benefit from improved access and dissemination, such as that afforded by smartphone apps. The goal of this study was to characterize the purpose, audience, quality and popularity of opioid-related smartphone apps. Using web scraping, available information from 619 opioid-related apps (e.g., popularity metrics) was downloaded from Google Play, and 59 apps met criteria for review. The apps were additionally coded for quality by two raters using an 8-item screener for the American Psychiatric Association App Evaluation Model. FINDINGS: Sixty one percent of apps targeted patients, 29% providers, 8% the general community, and 2% healthcare trainees. Regarding app purpose, 49% addressed treatment, 27% prevention, and 24% overdose. Only one app met all criteria on the screener for quality, and there was no association between a total score calculated for the screener and measures of app popularity (e.g., star ratings; R2=0.10, p=0.19). SUMMARY: Opioid-related apps available for consumers addressed key stakeholders (patients, providers, community) and were consistent with strategies to address the opioid crisis (prevention, treatment, overdose). However, there was little evidence that available opioid-related apps meet basic quality standards, and no relationship was found between app quality and popularity. This review was conducted at the level of consumer decision-making (i.e., the app store), where only a handful of opioid-related apps met quality standards enough to warrant a more detailed evaluation of the app before recommendation for use. Because smartphone apps could be a critical tool to increase access to and utilization of opioid prevention, treatment, and recovery services, further development and testing is sorely needed.

Identifying Smoking Environments From Images of Daily Life With Deep Learning.

Importance: Environments associated with smoking increase a smoker's craving to smoke and may provoke lapses during a quit attempt. Identifying smoking risk environments from images of a smoker's daily life provides a basis for environment-based interventions. Objective: To apply a deep learning approach to the clinically relevant identification of smoking environments among settings that smokers encounter in daily life. Design, Setting, and Participants: In this cross-sectional study, 4902 images of smoking (n = 2457) and nonsmoking (n = 2445) locations were photographed by 169 smokers from Durham, North Carolina, and Pittsburgh, Pennsylvania, areas from 2010 to 2016. These images were used to develop a probabilistic classifier to predict the location type (smoking or nonsmoking location), thus relating objects and settings in daily environments to established smoking patterns. The classifier combines a deep convolutional neural network with an interpretable logistic regression model and was trained and evaluated via nested cross-validation with participant-wise partitions (ie, out-of-sample prediction). To contextualize model performance, images taken by 25 randomly selected participants were also classified by smoking cessation experts. As secondary validation, craving levels reported by participants when viewing unfamiliar environments were compared with the model's predictions. Data analysis was performed from September 2017 to May 2018. Main Outcomes and Measures: Classifier performance (accuracy and area under the receiver operating characteristic curve [AUC]), comparison with 4 smoking cessation experts, contribution of objects and settings to smoking environment status (standardized model coefficients), and correlation with participant-reported craving. Results: Of 169 participants, 106 (62.7%) were from Durham (53 [50.0%] female; mean [SD] age, 41.4 [12.0] years) and 63 (37.3%) were from Pittsburgh (31 [51.7%] female; mean [SD] age, 35.2 [13.8] years). A total of 4902 images were available for analysis, including 3386 from Durham (mean [SD], 31.9 [1.3] images per participant) and 1516 from Pittsburgh (mean [SD], 24.1 [0.5] images per participant). Images were evenly split between the 2 classes, with 2457 smoking images (50.1%) and 2445 nonsmoking images (49.9%). The final model discriminated smoking vs nonsmoking environments with a mean (SD) AUC of 0.840 (0.024) (accuracy [SD], 76.5% [1.6%]). A model trained only with images from Durham participants effectively classified images from Pittsburgh participants (AUC, 0.757; accuracy, 69.2%), and a model trained only with images from Pittsburgh participants effectively classified images from Durham participants (AUC, 0.821; accuracy, 75.0%), suggesting good generalizability between geographic areas. Only 1 expert's performance was a statistically significant improvement compared with the classifier (α = .05). Median self-reported craving was significantly correlated with model-predicted smoking environment status (ρ = 0.894; P = .003). Conclusions and Relevance: In this study, features of daily environments predicted smoking vs nonsmoking status consistently across participants. The findings suggest that a deep learning approach can identify environments associated with smoking, can predict the probability that any image of daily life represents a smoking environment, and can potentially trigger environment-based interventions. This work demonstrates a framework for predicting how daily environments may influence target behaviors or symptoms that may have broad applications in mental and physical health.

The Many Channels of Screen Media Technology in ADHD: a Paradigm for Quantifying Distinct Risks and Potential Benefits.

PURPOSE OF REVIEW: Individuals with attention-deficit hyperactivity disorder (ADHD) may be unusually sensitive to screen media technology (SMT), from television to mobile devices. Although an association between ADHD and SMT use has been confirmed, its importance is uncertain partly due to variability in the way SMT has been conceptualized and measured. Here, we identify distinct, quantifiable dimensions of SMT use and review possible links to ADHD to facilitate more precise, reproducible investigation. RECENT FINDINGS: Display characteristics, media multitasking, device notifications, SMT addiction, and media content all may uniquely impact the ADHD phenotype. Each can be investigated with a digital health approach and counteracted with device-based interventions. Novel digital therapeutics for ADHD demonstrate that specific forms of SMT can also have positive effects. Further study should quantify how distinct dimensions of SMT use relate to ADHD. SMT devices themselves can serve as a self-monitoring study platform and deliver digital interventions.

Validation of the Sleep Regularity Index in Older Adults and Associations with Cardiometabolic Risk.

Sleep disturbances, including insufficient sleep duration and circadian misalignment, confer risk for cardiometabolic disease. Less is known about the association between the regularity of sleep/wake schedules and cardiometabolic risk. This study evaluated the external validity of a new metric, the Sleep Regularity Index (SRI), among older adults (n = 1978; mean age 68.7 ± 9.2), as well as relationships between the SRI and cardiometabolic risk using data from the Multi-Ethnic Study of Atherosclerosis (MESA). Results indicated that sleep irregularity was associated with delayed sleep timing, increased daytime sleep and sleepiness, and reduced light exposure, but was independent of sleep duration. Greater sleep irregularity was also correlated with 10-year risk of cardiovascular disease and greater obesity, hypertension, fasting glucose, hemoglobin A1C, and diabetes status. Finally, greater sleep irregularity was associated with increased perceived stress and depression, psychiatric factors integrally tied to cardiometabolic disease. These results suggest that the SRI is a useful measure of sleep regularity in older adults. Additionally, sleep irregularity may represent a target for early identification and prevention of cardiometabolic disease. Future studies may clarify the causal direction of these effects, mechanisms underlying links between sleep irregularity and cardiometabolic risk, and the utility of sleep interventions in reducing cardiometabolic risk.

Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis.

Gait impairment in multiple sclerosis (MS) can result from muscle weakness, physical fatigue, lack of coordination, and other symptoms. Walking speed, as measured by a number of clinician-administered walking tests, is the primary measure of gait impairment used by clinical researchers, but inertial gait features from body-worn sensors have been proven to add clinical value. This paper seeks to understand and differentiate the physiological significance of four such features with proven value in MS to facilitate adoption by clinical researchers and incorporation in gait monitoring and analysis systems. In addition, this information can be used to select features that might be appropriate in other forms of disability. Two of the four features are computed using the dynamic time warping (DTW) algorithm: The "DTW Score" is based on the usual DTW distance, and the "Warp Score" is based on the warping length. The third feature, based on kernel density estimation (KDE), is the "KDE Peak" value. Finally, the "Causality Index" is based on the phase slope index between inertial signals from different body parts. Relationships between these measures and the aforementioned gait-related symptoms are determined by applying factor analysis to three common, clinical walking outcomes, then correlating the inertial measures as well as walking speed to each extracted factor. Statistically significant differences in correlation coefficients to the three extracted clinical factors support their distinct physiological meaning and suggest they may have complimentary roles in the analysis of MS-related walking disability.