Glacial retreat in recent decades has exposed unstable slopes and allowed deep water to extend beneath some of those slopes. Slope failure at the terminus of Tyndall Glacier on 17 October 2015 sent ...180 million tons of rock into Taan Fiord, Alaska. The resulting tsunami reached elevations as high as 193 m, one of the highest tsunami runups ever documented worldwide. Precursory deformation began decades before failure, and the event left a distinct sedimentary record, showing that geologic evidence can help understand past occurrences of similar events, and might provide forewarning. The event was detected within hours through automated seismological techniques, which also estimated the mass and direction of the slide - all of which were later confirmed by remote sensing. Our field observations provide a benchmark for modeling landslide and tsunami hazards. Inverse and forward modeling can provide the framework of a detailed understanding of the geologic and hazards implications of similar events. Our results call attention to an indirect effect of climate change that is increasing the frequency and magnitude of natural hazards near glaciated mountains.
Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted-weekly, daily, or even many times a day. The ...microrandomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI. The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise.
Translational AbstractWith the development of smartphone and wearable sensors, we have unprecedented opportunity to use mobile devices to facilitate healthy behavior change. Mobile health interventions, such as push notifications containing helpful suggestions have the potential to make an impact as people go about their day-to-day lives. However, delivering too many push notifications or delivering these notifications at the wrong time could be irritating and burdensome, making the intervention less effective. Therefore, it is crucial to find out when, in what context, and what intervention content to deliver to each person to make the intervention the most effective. In this paper we review the microrandomized trial (MRT), a study design that can be used to improve mobile health interventions by answering the above questions. In an MRT, each person is repeatedly randomized to receive or not receive an intervention, often hundreds of thousands of times throughout the trial. We review the key elements of MRTs and provide three case studies of real-world MRTs in various application realms including physical activity and substance abuse. We also provide an accessible review of data analysis methods for MRTs.
Individuals in stressful work environments often experience mental health issues, such as depression. Reducing depression rates is difficult because of persistently stressful work environments and ...inadequate time or resources to access traditional mental health care services. Mobile health (mHealth) interventions provide an opportunity to deliver real-time interventions in the real world. In addition, the delivery times of interventions can be based on real-time data collected with a mobile device. To date, data and analyses informing the timing of delivery of mHealth interventions are generally lacking.
This study aimed to investigate when to provide mHealth interventions to individuals in stressful work environments to improve their behavior and mental health. The mHealth interventions targeted 3 categories of behavior: mood, activity, and sleep. The interventions aimed to improve 3 different outcomes: weekly mood (assessed through a daily survey), weekly step count, and weekly sleep time. We explored when these interventions were most effective, based on previous mood, step, and sleep scores.
We conducted a 6-month micro-randomized trial on 1565 medical interns. Medical internship, during the first year of physician residency training, is highly stressful, resulting in depression rates several folds higher than those of the general population. Every week, interns were randomly assigned to receive push notifications related to a particular category (mood, activity, sleep, or no notifications). Every day, we collected interns' daily mood valence, sleep, and step data. We assessed the causal effect moderation by the previous week's mood, steps, and sleep. Specifically, we examined changes in the effect of notifications containing mood, activity, and sleep messages based on the previous week's mood, step, and sleep scores. Moderation was assessed with a weighted and centered least-squares estimator.
We found that the previous week's mood negatively moderated the effect of notifications on the current week's mood with an estimated moderation of -0.052 (P=.001). That is, notifications had a better impact on mood when the studied interns had a low mood in the previous week. Similarly, we found that the previous week's step count negatively moderated the effect of activity notifications on the current week's step count, with an estimated moderation of -0.039 (P=.01) and that the previous week's sleep negatively moderated the effect of sleep notifications on the current week's sleep with an estimated moderation of -0.075 (P<.001). For all three of these moderators, we estimated that the treatment effect was positive (beneficial) when the moderator was low, and negative (harmful) when the moderator was high.
These findings suggest that an individual's current state meaningfully influences their receptivity to mHealth interventions for mental health. Timing interventions to match an individual's state may be critical to maximizing the efficacy of interventions.
ClinicalTrials.gov NCT03972293; http://clinicaltrials.gov/ct2/show/NCT03972293.
Precipitants of alcohol use transitions can differ from generalized risk factors. We extend prior research by predicting transitions in alcohol use disorder (AUD) during adolescence and emerging ...adulthood.
From 12/2009-9/2011, research assistants recruited 599 drug-using youth age 14-24 from Level-1 Emergency Department in Flint, Michigan. Youth were assessed at baseline and four biannual follow-ups, including a MINI Neuropsychiatric interview to diagnose AUD (abuse/dependence). We modeled AUD transitions using continuous time Markov Chains with transition probabilities modulated by validated measures of demographics, anxiety/depression symptoms, cannabis use, peer drinking, parental drinking, and violence exposure. Separate models were fit for underage (<21) and those of legal drinking age.
We observed 2,024 pairs of consecutive AUD states, including 264 transitions (119 No-AUD→AUD; 145 AUD→No-AUD); 194 (32.4%) individuals were diagnosed with AUD at ≥1 assessment. Among age 14-20, peer drinking increased AUD onset (No-AUD→AUD transition) rates (Hazard ratio-HR = 1.70; 95%CI: 1.13,2.54), parental drinking lowered AUD remission (AUD→No-AUD transition) rates (HR = 0.53; 95%CI: 0.29,0.97), and cannabis use severity both hastened AUD onset (HR = 1.18; 95%CI: 1.06,1.32) and slowed AUD remission (HR = 0.85; 95%CI: 0.76,0.95). Among age 21-24, anxiety/depression symptoms both increased AUD onset rates (HR = 1.35; 95%CI: 1.13,1.60) and decreased AUD remission rates (HR = 0.74; 95%CI: 0.63,0.88). Friend drinking hastened AUD onset (HR = 1.18, 95%CI: 1.05,1.33), and slowed AUD remission (HR = 0.84; 95%CI: 0.75,0.95). Community violence exposure slowed AUD remission (HR = 0.69, 95%CI: 0.48,0.99). In both age groups, males had >2x the AUD onset rate of females, but there were no sex differences in AUD remission rates. Limitations, most notably that this study occurred at a single site, are discussed.
Social influences broadly predicted AUD transitions in both age groups. Transitions among younger youth were predicted by cannabis use, while those among older youth were predicted more by internalizing symptoms and stress exposure (e.g., community violence). Our results suggest age-specific AUD etiology, and contrasts between prevention and treatment strategies.
To characterize youth seeking care for assault injuries, the context of violence, and previous emergency department (ED) service utilization to inform ED-based injury prevention.
A consecutive sample ...of youth (14-24) presenting to an urban ED with an assault injury completed a survey of partner violence, gun/knife victimization, gang membership, and context of the fight.
A total of 925 youth entered the ED with an assault injury; 718 completed the survey (15.4% refused); 730 comparison youth were sampled. The fights leading to the ED visit occurred at home (37.6%) or on streets (30.4%), and were commonly with a known person (68.3%). Fights were caused by issues of territory (23.3%) and retaliation (8.9%); 20.8% of youth reported substance use before the fight. The assault-injured group reported more peer/partner violence and more gun experiences. Assault-injured youth reported higher past ED utilization for assault (odds ratio OR: 2.16) or mental health reasons (OR: 7.98). Regression analysis found the assault-injured youth had more frequent weapon use (OR: 1.25) and substance misuse (OR: 1.41).
Assault-injured youth seeking ED care report higher levels of previous violence, weapon experience, and substance use compared with a comparison group seeking care for other complaints. Almost 10% of assault-injured youth had another fight-related ED visit in the previous year, and ~5% had an ED visit for mental health. Most fights were with people known to them and for well-defined reasons, and were therefore likely preventable. The ED is a critical time to interact with youth to prevent future morbidity.
Objective Preventing sexual violence among college students is a public health priority. This paper was catalyzed by a summit convened in 2018 to review the state of the science on campus sexual ...violence prevention. We summarize key risk and vulnerability factors and campus-based interventions, and provide directions for future research pertaining to campus sexual violence. Results and Conclusions: Although studies have identified risk factors for campus sexual violence, longitudinal research is needed to examine time-varying risk factors across social ecological levels (individual, relationship, campus context/broader community and culture) and data are particularly needed to identify protective factors. In terms of prevention, promising individual and relational level interventions exist, including active bystander, resistance, and gender transformative approaches; however, further evidence-based interventions are needed, particularly at the community-level, with attention to vulnerability factors and inclusion for marginalized students.
Emerging adults’ (EAs; ages 18–25) perceived risk of cannabis-related harms has decreased in recent decades, potentially contributing to their high prevalence of cannabis consumption. With the ...changing cannabis policy and product landscape, it is critical to understand perceived risk related to different consumption methods (e.g., smoking, dabbing). We examined differences in cannabis risk perceptions by method and consumption patterns.
EAs recruited from an emergency department (N=359, 71.3% female, 53.5% Black) completed assessments on individual characteristics, cannabis/other substance use, and perceived risk of cannabis-related harm for four different methods (smoking, vaping, dabbing, ingestion) and two use frequencies (occasional, regular). Analyses examined associations between variables of interest and three mutually exclusive groups: no cannabis use, smoking-only, and multiple/other methods.
Forty-two percent of EAs reported no past 3-month cannabis use, 22.8% reported smoking only, and 35.1% reported consumption via multiple/other methods. Among all participants, the methods and frequency with the largest number of EAs endorsing any perceived risk from cannabis were dabbing and vaping cannabis regularly; smoking occasionally had the smallest number of EAs endorsing perceived risk. A greater proportion of EAs in the no use group viewed vaping cannabis regularly as having the most risk (63.6%), whereas the largest proportion of EAs in the smoking-only (64.6%) and multiple/other methods (47.2%) groups perceived dabbing regularly as having the most risk.
This work shows that EAs vary in perceptions of risk across methods of cannabis use and can inform potential directions for public health and policy efforts.
•Cannabis risk perceptions vary by cannabis consumption patterns and method.•Emerging adults (EAs) perceived smoking cannabis occasionally with less risk.•Dabbing and vaping cannabis regularly were perceived as having greater risk.•EAs who consumed cannabis via multiple/other methods reported less perceived cannabis risk.
The risk for firearm violence among high-risk youth after treatment for an assault is unknown.
In this 2-year prospective cohort study, data were analyzed from a consecutive sample of 14- to ...24-year-olds with drug use in the past 6 months seeking assault-injury care (AIG) at an urban level 1 emergency department (ED) compared with a proportionally sampled comparison group (CG) of drug-using nonassaulted youth. Validated measures were administered at baseline and follow-up (6, 12, 18, 24 months).
A total of 349 AIG and 250 CG youth were followed for 24 months. During the follow-up period, 59% of the AIG reported firearm violence, a 40% higher risk than was observed among the CG (59.0% vs. 42.5%; relative risk RR = 1.39). Among those reporting firearm violence, 31.7% reported aggression, and 96.4% reported victimization, including 19 firearm injuries requiring medical care and 2 homicides. The majority with firearm violence (63.5%) reported at least 1 event within the first 6 months. Poisson regression identified baseline predictors of firearm violence, including male gender (RR = 1.51), African American race (RR = 1.26), assault-injury (RR = 1.35), firearm possession (RR = 1.23), attitudes favoring retaliation (RR = 1.03), posttraumatic stress disorder (RR = 1.39), and a drug use disorder (RR = 1.22).
High-risk youth presenting to urban EDs for assault have elevated rates of subsequent firearm violence. Interventions at an index visit addressing substance use, mental health needs, retaliatory attitudes, and firearm possession may help decrease firearm violence among urban youth.
Background and aims
Research from cohorts of individuals with recreational cannabis use indicates that cannabis withdrawal symptoms are reported by more than 40% of those using regularly. Withdrawal ...symptoms are not well understood in those who use cannabis for medical purposes. Therefore, we prospectively examined the stability of withdrawal symptoms in individuals using cannabis to manage chronic pain.
Design, Setting, Participants
Using latent class analysis (LCA) we examined baseline cannabis withdrawal to derive symptom profiles. Then, using latent transition analysis (LTA) we examined the longitudinal course of withdrawal symptoms across the time points. Exploratory analyses examined demographic and clinical characteristics predictive of withdrawal class and transitioning to more or fewer withdrawal symptoms over time.
A cohort of 527 adults with chronic pain seeking medical cannabis certification or re‐certification was recruited between February 2014 and June 2015. Participants were recruited from medical cannabis clinic waiting rooms in Michigan, USA. Participants were predominantly white (82%) and 49% identified as male, with an average age of 45.6 years (standard deviation = 12.8).
Measurements
Baseline, 12‐month and 24‐month assessments of withdrawal symptoms using the Marijuana Withdrawal Checklist–revised.
Findings
A three‐class LCA model including a mild (41%), moderate (34%) and severe (25%) symptom class parsimoniously represented withdrawal symptoms experienced by people using medical cannabis. Stability of withdrawal symptoms using a three‐class LTA at 12 and 24 months ranged from 0.58 to 0.87, with the most stability in the mild withdrawal class. Younger age predicted greater severity and worsening of withdrawal over time.
Conclusions
Adults with chronic pain seeking medical cannabis certification or re‐certification appear to experience mild to severe withdrawal symptoms. Withdrawal symptoms tend to be stable over a 2‐year period, but younger age is predictive of worse symptoms and of an escalating withdrawal trajectory.
PDF
