Modeling mood variation and covariation among adolescent smokers: application of a bivariate location-scale mixed-effects model

Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study

Background

For adolescents living with type 1 diabetes (T1D), completion of multiple daily self-management tasks, such as monitoring blood glucose and administering insulin, can be challenging because of psychosocial and contextual barriers. These barriers are hard to assess accurately and specifically by using traditional retrospective recall. Ecological momentary assessment (EMA) uses mobile technologies to assess the contexts, subjective experiences, and psychosocial processes that surround self-management decision-making in daily life. However, the rich data generated via EMA have not been frequently examined in T1D or integrated with machine learning analytic approaches.

Objective

The goal of this study is to develop a machine learning algorithm to predict the risk of missed self-management in young adults with T1D. To achieve this goal, we train and compare a number of machine learning models through a learned filtering architecture to explore the extent to which EMA data were associated with the completion of two self-management behaviors: mealtime self-monitoring of blood glucose (SMBG) and insulin administration.

Methods

We analyzed data from a randomized controlled pilot study using machine learning–based filtering architecture to investigate whether novel information related to contextual, psychosocial, and time-related factors (ie, time of day) relate to self-management. We combined EMA-collected contextual and insulin variables via the MyDay mobile app with Bluetooth blood glucose data to construct machine learning classifiers that predicted the 2 self-management behaviors of interest.

Results

With 1231 day-level SMBG frequency counts for 45 participants, demographic variables and time-related variables were able to predict whether daily SMBG was below the clinical threshold of 4 times a day. Using the 1869 data points derived from app-based EMA data of 31 participants, our learned filtering architecture method was able to infer nonadherence events with high accuracy and precision. Although the recall score is low, there is high confidence that the nonadherence events identified by the model are truly nonadherent.

Conclusions

Combining EMA data with machine learning methods showed promise in the relationship with risk for nonadherence. The next steps include collecting larger data sets that would more effectively power a classifier that can be deployed to infer individual behavior. Improvements in individual self-management insights, behavioral risk predictions, enhanced clinical decision-making, and just-in-time patient support in diabetes could result from this type of approach.

The Emotion Dynamics Conundrum in Developmental Psychopathology: Similarities, Distinctions, and Adaptiveness of Affective Variability and Socioaffective Flexibility

A recent emphasis in developmental psychopathology research has been on emotion dynamics, or how emotional experience changes over time in response to context, and how those emotion dynamics affect psychosocial functioning. Two prominent emotion dynamics constructs have emerged in the developmental psychopathology literature: affective variability and socioaffective flexibility. Affective variability is most often measured using momentary methods (e.g., EMA) and is theorized to reflect reactivity and regulation in response to context, whereas socioaffective flexibility is typically measured in the context of parent-child interactions and theorized as the ability to move effectively through a range of affective states. Notably, affective variability is considered broadly maladaptive; however, socioaffective flexibility is theorized to be fundamentally adaptive. Despite these diametric views on adaptability, these two constructs share an underlying dependency on non-effortful emotion change in response to context, which raises questions about whether these constructs are, at their core, more similar than dissimilar. This review examined the literatures on affective variability and socioaffective flexibility in child and adolescent samples, examining associations with psychosocial and clinical correlates, as well as conceptual and methodological similarities and distinctions. Findings indicate that despite considerable theoretical overlap, there are sufficient differences-albeit largely methodological-that justify continuing to treat these constructs as distinct, most notably the influence of parents in socioaffective flexibility. The review closes with several recommendations for future study targeted at further clarifying the distinctions (or lack thereof) between affective variability and socioaffective flexibility.

A mixed-effects location scale model for time-to-event data: A smoking behavior application

In general, mixed-effects location scale models (MELS) allow assessment of within-person and between-person variability with time-to-event data for outcomes that follow a normal or ordinal distribution. In this article, we extend the mixed-effects location scale model to time-to-event data in relation to smoking data. Better understanding of the time-graded within-person variability of factors involved in nicotine dependence can be helpful to researchers in their efforts to fine-tune smoking cessation programs. We illustrate the MELS model with data on time to first cigarette measured every day for 7 days in smokers randomized to two groups: a) those asked to keep smoking, or b) those asked to stop. Our results show that some individuals remain very stable in their time to first cigarette over the week, while others show variable patterns. The stable individuals smoked every day, did not smoke immediately upon waking, and were all in the group asked to keep smoking. Conversely, the variable individuals had at least one day during which they did not smoke, other days during which they smoked within the first 5 min of waking, and they were almost all in the group asked to quit smoking. These findings suggested that MELS have the potential to provide insights on how people try to stop smoking. More importantly, this model can be applied to other clinically important outcomes such as time to relapse in a range of cessation programs.

Ecological momentary assessment of various tobacco product use among young adults

INTRODUCTION

Young adults are at high risk for using traditional and novel tobacco products. However, little is known about daily/weekly use patterns or psychosocial triggers for using various tobacco products.

METHODS

This ecological momentary assessment (EMA) study examined timing, tobacco cravings, affect, social context, and other substance use (alcohol, marijuana) in relation to use of cigarettes, electronic nicotine delivery systems (ENDS), and any tobacco product (i.e., cigarettes, ENDS, cigars, hookah), respectively. We also examined interactions between these predictors, sex, and race/ethnicity. From a longitudinal study of 3418 18-25 year-olds from seven Georgia colleges/universities, we recruited 72 reporting current tobacco use to participate in the 21-day EMA study; 43 participated, of which 31 completed ≥66% assessments and were analyzed. Cravings, affect, social context, and substance use were assessed daily across four four-hour windows.

RESULTS

Of the 31 participants, average age was 21.10 years (SD = 1.95), 45.2% were female, and 71.0% non-Hispanic White; 71.0% used cigarettes, 58.1% ENDS, 38.7% cigars, and 25.8% hookah (25.6% used one product, 46.5% two, 27.9% ≥ three). Predictors of cigarette use included higher anxiety, greater odds of marijuana and alcohol use, and higher boredom levels among women. Predictors of ENDS use included being non-White and greater odds of marijuana use, as well as higher tobacco cravings among women and higher boredom among men. Predictors of any tobacco product use included being non-White, higher boredom levels, and greater odds of marijuana and alcohol use.

CONCLUSIONS

Distinct interventions may be needed to address use of differing tobacco products among young adults.

Nicotine dependence, internalizing symptoms, mood variability and daily tobacco use among young adult smokers

INTRODUCTION

Cigarette use among young adults continues to rise. As young adults transition to college and assume other adult roles and responsibilities, they are at risk for the development of mental health problems and for the progression of substance use problems. Previous studies suggest that individual differences in negative and positive mood contribute to cigarette use in established college-aged smokers, but less is known whether fluctuations in mood influence daily cigarette use, controlling for trait levels of internalizing symptoms and nicotine dependence.

METHODS

Data for this study came from a sample of college students (N=39, 59% female, mean age 20.4years) who reported regular cigarette use and participated in a 21-day ecological momentary assessment (EMA) study assessing within-individual variation in cigarette use and mood.

RESULTS

A three-level hierarchical linear model accounting for the structure of 1896 occasions of cigarette use nested within days and individuals indicated that within-individual variability in positive mood was associated with cigarette use at each occasion, after taking into account baseline levels of nicotine dependence and internalizing problems.

CONCLUSIONS

Daily shifts in positive moods are importantly associated with consuming cigarettes throughout the day.

Robotic Total Knee Arthroplasty: Surgical Assistant for a Customized Normal Kinematic Knee

Although current total knee arthroplasty (TKA) is considered a highly successful surgical procedure, patients undergoing TKA can still experience substantial functional impairment and increased revision rates as compared with those undergoing total hip arthroplasty. Robotic-assisted surgery has been available clinically for almost 15 years and was developed, in part, to address these concerns. Robotic-assisted surgery aims to improve TKA by enhancing the surgeon's ability to optimize soft tissue balancing, reproduce alignment, and restore normal knee kinematics. Current systems include a robotic arm with a variety of different navigation systems with active, semi-active, or passive control. Semi-active systems have become the dominant strategy, providing a haptic window through which the surgeon consistently prepares a TKA based on preoperative planning. A review of previous designs and clinical studies demonstrates that these robotic systems decrease variability and increase precision, primarily with the mechanical axis and restoration of the joint line. Future design objectives include precise planning and consistent intraoperative execution. Preoperative planning, intraoperative sensors, augmenting surgical instrumentation, and biomimetic surfaces will be used to re-create the 4-bar linkage system in the knee. Implants will be placed so that the knee functions with a medial pivot, lateral rollback, screw home mechanism, and patellar femoral tracking. Soft tissue balancing will become more than equalizing the flexion and extension gaps and will match the kinematics to a normal knee. Together, coupled with advanced knee designs, they may be the key to a patient stating, "My knee feels like my natural knee." [Orthopedics. 2016; 39(5):e822-e827.].

Examining the Variability of Sleep Patterns during Treatment for Chronic Insomnia: Application of a Location-Scale Mixed Model

STUDY OBJECTIVES

The purpose of this study was to introduce a novel statistical technique called the location-scale mixed model that can be used to analyze the mean level and intra-individual variability (IIV) using longitudinal sleep data.

METHODS

We applied the location-scale mixed model to examine changes from baseline in sleep efficiency on data collected from 54 participants with chronic insomnia who were randomized to an 8-week Mindfulness-Based Stress Reduction (MBSR; n = 19), an 8-week Mindfulness-Based Therapy for Insomnia (MBTI; n = 19), or an 8-week self-monitoring control (SM; n = 16). Sleep efficiency was derived from daily sleep diaries collected at baseline (days 1-7), early treatment (days 8-21), late treatment (days 22-63), and post week (days 64-70). The behavioral components (sleep restriction, stimulus control) were delivered during late treatment in MBTI.

RESULTS

For MBSR and MBTI, the pre-to-post change in mean levels of sleep efficiency were significantly larger than the change in mean levels for the SM control, but the change in IIV was not significantly different. During early and late treatment, MBSR showed a larger increase in mean levels of sleep efficiency and a larger decrease in IIV relative to the SM control. At late treatment, MBTI had a larger increase in the mean level of sleep efficiency compared to SM, but the IIV was not significantly different.

CONCLUSIONS

The location-scale mixed model provides a two-dimensional analysis on the mean and IIV using longitudinal sleep diary data with the potential to reveal insights into treatment mechanisms and outcomes.

Progression of nicotine dependence, mood level, and mood variability in adolescent smokers

Mood processes are theorized to play a role in the initiation and progression of smoking behavior. Available work using real-time assessments in samples of young smokers, including several reports from the Social and Emotional Contexts of Adolescent Smoking Patterns (SECASP) study, has indicated that smoking events acutely improve mood and that escalating smoking frequency may stabilize mood. However, prior analyses have not specifically evaluated within-person change in nicotine dependence, which is conceptually distinguishable from frequent smoking and may be associated with unique mood consequences. The current investigation addressed this question using data from 329 adolescent SECASP participants (9th or 10th grade at recruitment) who contributed mood reports via ecological momentary assessment in up to four 1-week bursts over the course of 24 months. Mixed-effects location scale analyses revealed that within-person increases in scores on the Nicotine Dependence Syndrome Scale were associated with elevations in negative mood level and increased variability of both positive and negative moods. These effects remained when within-person changes in smoking frequency were covaried and were not fully attributable to a subgroup of youth who rapidly escalated their smoking frequency over time. The findings indicate that adolescents tend to show increasing levels of positive mood states, decreasing levels of negative mood, and diminishing mood variability between ages 16 to 18, but progression of nicotine dependence may counteract some of these developmental gains. Emergence of withdrawal symptoms is a likely explanation for the adverse mood effects associated with dependence progression. (PsycINFO Database Record

Bayesian mixed-effects location and scale models for multivariate longitudinal outcomes: an application to ecological momentary assessment data

In the statistical literature, the methods to understand the relationship of explanatory variables on each individual outcome variable are well developed and widely applied. However, in most health-related studies given the technological advancement and sophisticated methods of obtaining and storing data, a need to perform joint analysis of multivariate outcomes while explaining the impact of predictors simultaneously and accounting for all the correlations is in high demand. In this manuscript, we propose a generalized approach within a Bayesian framework that models the changes in the variation in terms of explanatory variables and captures the correlations between the multivariate continuous outcomes by the inclusion of random effects at both the location and scale levels. We describe the use of a spherical transformation for the correlations between the random location and scale effects in order to apply separation strategy for prior elicitation while ensuring positive semi-definiteness of the covariance matrix. We present the details of our approach using an example from an ecological momentary assessment study on adolescents.

Modeling mood variation and covariation among adolescent smokers: application of a bivariate location-scale mixed-effects model

INTRODUCTION

Ecological momentary assessments (EMAs) are useful for understanding both between- and within-subject dynamic changes in smoking and mood. Modeling 2 moods (positive affect [PA] and negative affect [NA], PA and NA) simultaneously will better enable researchers to explore the association between mood variables and what influences them at both the momentary and subject level.

METHODS

The EMA component of a natural history study of adolescent smoking was analyzed with a bivariate location-scale mixed-effects model. The proposed model separately estimates the between- and within-subject variances and jointly models the 2 mood constructs. A total of 461 adolescents completed the baseline EMA wave, which resulted in 14,105 random prompts. Smoking level, represented by the number of smoking events on EMA, entered the model as 2 predictors: one that compared nonsmokers during the EMA week to 1-cigarette smokers, and the second one that estimated the effect of smoking level on mood among smokers.

RESULTS

Results suggest that nonsmokers had more consistent positive and negative moods compared to 1-cigarette smokers. Among those who smoked, both moods were more consistent at higher smoking levels. The effects of smoking level were greater for NA than for PA. The within-subject association between mood constructs was negative and strongest among 1-cigarette smokers; the within-subject association between positive and negative moods was negatively associated with smoking.

CONCLUSIONS

Mood variation and association between mood constructs varied across smoking levels. The most infrequent smokers were characterized with more inconsistent moods, whereas mood was more consistent for subjects with higher smoking levels.