Using Multigroup-Multiphase Latent State-Trait Models to Study Treatment-Induced Changes in Intra-Individual State Variability: An Application to Smokers\u27 Affect

Sometimes, researchers are interested in whether an intervention, experimental manipulation, or other treatment causes changes in intra-individual state variability. The authors show how multigroup-multiphase latent state-trait (MG-MP-LST) models can be used to examine treatment effects with regard to both mean differences and differences in state variability. The approach is illustrated based on a randomized controlled trial in which N = 338 smokers were randomly assigned to nicotine replacement therapy (NRT) vs. placebo prior to quitting smoking. We found that post quitting, smokers in both the NRT and placebo group had significantly reduced intra-individual affect state variability with respect to the affect items calm and contentrelative to the pre-quitting phase. This reduction in state variability did not differ between the NRT and placebo groups, indicating that quitting smoking may lead to a stabilization of individuals\u27 affect states regardless of whether or not individuals receive NRT

Equivalence of Electronic and Paper-and-Pencil Administration of Patient-Reported Outcome Measures: A Meta-Analytic Review

AbstractObjectivesPatient-reported outcomes (PROs; self-report assessments) are increasingly important in evaluating medical care and treatment efficacy. Electronic administration of PROs via computer is becoming widespread. This article reviews the literature addressing whether computer-administered tests are equivalent to their paper-and-pencil forms.MethodsMeta-analysis was used to synthesize 65 studies that directly assessed the equivalence of computer versus paper versions of PROs used in clinical trials. A total of 46 unique studies, evaluating 278 scales, provided sufficient detail to allow quantitative analysis.ResultsAmong 233 direct comparisons, the average mean difference between modes averaged 0.2% of the scale range (e.g., 0.02 points on a 10-point scale), and 93% were within ±5% of the scale range. Among 207 correlation coefficients between paper and computer instruments (typically intraclass correlation coefficients), the average weighted correlation was 0.90; 94% of correlations were at least 0.75. Because the cross-mode correlation (paper vs. computer) is also a test–retest correlation, with potential variation because of retest, we compared it to the within-mode (paper vs. paper) test–retest correlation. In four comparisons that evaluated both, the average cross-mode paper-to-computer correlation was almost identical to the within-mode correlation for readministration of a paper measure (0.88 vs. 0.91).ConclusionsExtensive evidence indicates that paper- and computer-administered PROs are equivalent

Association between smoking-related attentional bias and craving measured in the clinic and in the natural environment

Previous laboratory studies have investigated associations between attentional bias and craving, but ecological momentary assessment (EMA) may provide ecologically-valid data. This study examines whether clinic-measured attentional bias is associated with noticing smoking cues, attention to smoking, and craving assessed by EMA and whether EMA-assessed cues and attention to smoking are associated with craving in a secondary analysis of data from 100 cigarette smokers attempting cessation. Two weeks before quitting, participants completed attentional bias assessments on visual probe (VP) and Stroop tasks and completed random EMA-assessments for seven weeks thereafter. Participants completed 9,271 random assessments, averaging 3.3 prompts/day. Clinic-measured attentional bias was not associated with cues seen (VP: OR = 1.00, 95% CI = [0.99, 1.01]; Stroop: OR = 1.00, 95% CI [0.99, 1.00]), attention toward smoking (VP: OR = 1.00, 95% CI [0.99, 1.02]; Stroop: OR = 1.00, 95% CI [0.99, 1.00]), or craving (VP: OR = 1.00, 95% CI [0.99, 1.02]; Stroop: OR = 1.00, 95% CI [0.99, 1.01]). EMA responses to seeing a smoking cue (OR = 1.94, 95% CI [1.74, 2.16]) and attention toward smoking (OR = 3.69, 95% CI [3.42, 3.98]) were associated with craving. Internal reliability was higher for the Stroop (α = .75) than visual probe task (α = .20). In smokers attempting cessation, clinic measures of attentional bias do not predict noticing smoking cues, focus on smoking, or craving. However, associations exist between noticing smoking cues, attention toward smoking, and craving assessed in the moment, suggesting that attentional bias may not be a stable trait. (PsycINFO Database Recor

Analyzing latent state-trait and multiple-indicator latent growth curve models as multilevel structural equation models

Latent state-trait (LST) and latent growth curve (LGC) models are frequently used in the analysis of longitudinal data. Although it is well-known that standard single-indicator LGC models can be analyzed within either the structural equation modeling (SEM) or multilevel (ML; hierarchical linear modeling) frameworks, few researchers realize that LST and multivariate LGC models, which use multiple indicators at each time point, can also be specified as ML models. In the present paper, we demonstrate that using the ML-SEM rather than the SL-SEM framework to estimate the parameters of these models can be practical when the study involves (1) a large number of time points, (2) individually-varying times of observation, (3) unequally spaced time intervals, and/or (4) incomplete data. Despite the practical advantages of the ML-SEM approach under these circumstances, there are also some limitations that researchers should consider. We present an application to an ecological momentary assessment study (N = 158 youths with an average of 23.49 observations of positive mood per person) using the software Mplus (Muthén and Muthén, 1998–2012) and discuss advantages and disadvantages of using the ML-SEM approach to estimate the parameters of LST and multiple-indicator LGC models

Smoking patterns and stimulus control in intermittent and daily smokers

Intermittent smokers (ITS) - who smoke less than daily - comprise an increasing proportion of adult smokers. Their smoking patterns challenge theoretical models of smoking motivation, which emphasize regular and frequent smoking to maintain nicotine levels and avoid withdrawal, but yet have gone largely unexamined. We characterized smoking patterns among 212 ITS (smoking 4-27 days per month) compared to 194 daily smokers (DS; smoking 5-30 cigarettes daily) who monitored situational antecedents of smoking using ecological momentary assessment. Subjects recorded each cigarette on an electronic diary, and situational variables were assessed in a random subset (n = 21,539 smoking episodes); parallel assessments were obtained by beeping subjects at random when they were not smoking (n = 26,930 non-smoking occasions). Compared to DS, ITS' smoking was more strongly associated with being away from home, being in a bar, drinking alcohol, socializing, being with friends and acquaintances, and when others were smoking. Mood had only modest effects in either group. DS' and ITS' smoking were substantially and equally suppressed by smoking restrictions, although ITS more often cited self-imposed restrictions. ITS' smoking was consistently more associated with environmental cues and contexts, especially those associated with positive or "indulgent" smoking situations. Stimulus control may be an important influence in maintaining smoking and making quitting difficult among ITS. © 2014 Shiffman et al

Sweetened Drink and Snacking Cues in Adolescents. A Study Using Ecological Momentary Assessment

The objective of this study was to identify physical, social, and intrapersonal cues that were associated with the consumption of sweetened beverages and sweet and salty snacks among adolescents from lower SES neighborhoods. Students were recruited from high schools with a minimum level of 25% free or reduced cost lunches. Using ecological momentary assessment, participants (N=158) were trained to answer brief questionnaires on handheld PDA devices: (a) each time they ate or drank, (b) when prompted randomly, and (c) once each evening. Data were collected over 7days for each participant. Participants reported their location (e.g., school grounds, home), mood, social environment, activities (e.g., watching TV, texting), cravings, food cues (e.g., saw a snack), and food choices. Results showed that having unhealthy snacks or sweet drinks among adolescents was associated with being at school, being with friends, feeling lonely or bored, craving a drink or snack, and being exposed to food cues. Surprisingly, sweet drink consumption was associated with exercising. Watching TV was associated with consuming sweet snacks but not with salty snacks or sweet drinks. These findings identify important environmental and intrapersonal cues to poor snacking choices that may be applied to interventions designed to disrupt these food-related, cue-behavior linked habits

Attentional bias retraining in cigarette smokers attempting smoking cessation (ARTS): study protocol for a double bline randomised controlled trial

YesSmokers attend preferentially to cigarettes and other smoking-related cues in the environment, in what is known as an attentional bias. There is evidence that attentional bias may contribute to craving and failure to stop smoking. Attentional retraining procedures have been used in laboratory studies to train smokers to reduce attentional bias, although these procedures have not been applied in smoking cessation programmes. This trial will examine the efficacy of multiple sessions of attentional retraining on attentional bias, craving, and abstinence in smokers attempting cessation. This is a double-blind randomised controlled trial. Adult smokers attending a 7-session weekly stop smoking clinic will be randomised to either a modified visual probe task with attentional retraining or placebo training. Training will start 1 week prior to quit day and be given weekly for 5 sessions. Both groups will receive 21 mg transdermal nicotine patches for 8–12 weeks and withdrawal-orientated behavioural support for 7 sessions. Primary outcome measures are the change in attentional bias reaction time and urge to smoke on the Mood and Physical Symptoms Scale at 4 weeks post-quit. Secondary outcome measures include differences in withdrawal, time to first lapse and prolonged abstinence at 4 weeks post-quit, which will be biochemically validated at each clinic visit. Follow-up will take place at 8 weeks, 3 months and 6 months post-quit. This is the first randomised controlled trial of attentional retraining in smokers attempting cessation. This trial could provide proof of principle for a treatment aimed at a fundamental cause of addiction.National Institute for Health Research (NIHR) Doctoral Research Fellowship (DRF) awarded to RB (DRF-2009-02-15

Commuting and happiness: What ways feel best for what kinds of people?

Question: How happy we are, depends partly on how we live our life and part of our way of life is how we commute between home and work. In that context, we are faced with the question of how much time spent on commuting is optimal happiness wise and with what means of transportation we will feel best. Decisions about commuting are typically made as a side issue in job choice and there are indications that we are bad in predicting how such decisions will work out on our happiness in the long-run. For that reason, it is helpful to know how commuting has worked out on the happiness of other people and on people like you in particular. Earlier research: Several cross-sectional studies found lower happiness among long-distance commuters and among users of public transportation. Yet these differences could be due to selection effects, such as unhappy people opting more often for distant jobs without having a car. Still another limitation is that earlier research has focused on the average effect of commuting, rather than specifying what is optimal for whom. Method: Data of the Dutch ‘Happiness Indicator’ study was analyzed, in the context of which 5000 participants recorded what they had done in the previous day and how happy they had felt during these activities. This data allows comparison between how the same person feels at home and during commute, which eliminates selection effects. The number of participants is large enough to allow a split-up between different kinds of people, in particular among the many well-educated women who participated in this study. Results: People feel typically less happy when commuting than at home, and that the negative difference is largest when commuting with public transportation and smallest when commuting by bike. It is not per se the commuting time that causes happiness loss, but specific combinations of commuting time and commuting mode. Increasing commuting times can even lead to a gain in happiness for certain types of women, when the commute is by bike. Split-up by different kinds of people shows considerable differences, such as an optimal commute alone or even by public transport for some highly educated women. Optimal ways of commuting for different kinds of people are presented in a summary table, from which individuals can read what will fit them best. The differences illustrate that research focusing on average effects of happiness will not help individuals in making a more informed choice. Keywords: happiness, commuting, experience utility, informed choice, DRM