Reinstating Over-Pouring Behavior: Importance of the Imagined Drinking Context.

BACKGROUND: College students significantly over-pour more than a standard drink in a free-pour simulated alcohol-pouring task. Due to this effect, it is likely that much of the self-report alcohol consumption data incorrectly or underreport actual alcohol consumption. Objective: We sought to determine factors that influence over-pouring. Specifically, in two studies we sought to determine the effect of different factors on the amount of fluid subjects pour in a simulated alcohol-pouring task. Methods: Data were collected from 217 undergraduate students (105 subjects in study 1 and 112 different subjects in study 2). In study one, subjects were asked to pour what they consider to be a standard beer for themselves and an unfamiliar peer. In study two subjects were instructed to pour a beer for themself and the experimenter as if they were at an off-campus party. Results: In study one, we found that size of the cup used to pour into significantly impacted the amount of fluid poured. In addition, subjects poured significantly less for themselves than the unfamiliar peer. In study two, the imagined context in which subjects poured significantly increased the amount of fluid poured demonstrating the importance of imagined contextual cues on alcohol use behavior. Conclusions/Importance: Imagined drinking context, presence of an unfamiliar peer, and cup size affect the amount of fluid poured in a simulated alcohol free pour task. Given the various factors that impact free pouring in college students, self-report alcohol data should be considered with caution.

Estimating the intervention effect in calibration substudies.

Exposure assessment is often subject to measurement errors. We consider here the analysis of studies aimed at reducing exposure to potential health hazards, in which exposure is the outcome variable. In these studies, the intervention effect may be estimated using either biomarkers or self-report data, but it is not common to combine these measures of exposure. Bias in the self-reported measures of exposure is a well-known fact; however, only few studies attempt to correct it. Recently, Keogh et al addressed this problem, presenting a model for measurement error in this setting and investigating how self-report and biomarker data can be combined. Keogh et al find the maximum likelihood estimate for the intervention effect in their model via direct numerical maximization of the likelihood. Here, we exploit an alternative presentation of the model that leads us to a closed formula for the MLE and also for its variance, when the number of biomarker replicates is the same for all subjects in the substudy. The variance formula enables efficient design of such intervention studies. When the number of biomarker replicates is not constant, our approach can be used along with the EM-algorithm to quickly compute the MLE. We compare the MLE to Buonaccorsi's method (Buonaccorsi, 1996) and find that they have similar efficiency when most subjects have biomarker data, but that the MLE has clear advantages when only a small fraction of subjects has biomarker data. This conclusion extends the findings of Keogh et al (2016) and has practical importance for efficiently designing studies.

Information bias of social gradients in sickness absence: a comparison of self-report data in the Norwegian Mother and Child Cohort Study (MoBa) and data in national registries.

BACKGROUND: Measurement error in self-report questionnaires is a common source of bias in epidemiologic studies. The study aim was to assess information bias of the educational gradient in sickness absence among participants in the Norwegian Mother and Child Cohort Study (MoBa), comparing self-report data with national register data. METHODS: MoBa is a national prospective cohort study. The present study included 49,637 participants, born 1967-1976, who gave birth 2000-2009. The highest completed education level was recorded in categories and as educational years. Sickness absence was defined as one or more spell lasting more than 16 days between pregnancy weeks 13 and 30. We computed sickness absence risk in mid-pregnancy in strata of education level. Associations between completed educational years and sickness absence were estimated as risk differences in binomial regression and compared between self-report and register data. In additional analyses, we aimed to explain discrepancies between estimates from the two data sources. RESULTS: The overall registry-based sickness absence risk was 0.478 and decreased for increasingly higher education in a consistent fashion, yielding an additive risk difference in association with one additional education year of - 0.032 (95% confidence interval - 0.035 to - 0.030). The self-report risk was lower (0.307) with a corresponding risk difference of only - 0.013 (95% confidence interval - 0.015 to - 0.011). The main explanation of the lower risk difference in the self-report data was a tendency for mothers in low education categories to omit reporting sickness absence in the questionnaire. CONCLUSIONS: A plausible explanation for the biased self-report association is complexity of the sickness absence question and a resulting educational gradient in non-response. As shown for sickness absence in mid-pregnancy in the present study, national registries could be a preferred alternative to self-report questionnaires.

Using Cognitive Interviews to Adapt Interpersonal Violence Measures for Use With Middle School Youth.

Experiences of interpersonal violence are common among youth. Starting prevention programming early (e.g., middle school) may be beneficial for primary prevention. Evaluating whether such programs are effective often requires collecting self-report data from youth, but many existing measures have been developed for high school and college-aged youth. This study aimed to assess adolescents' comprehension of self-report survey items on interpersonal violence with middle school youth. We conducted virtual cognitive interviews with 15 youth in grades 6 to 8. A content analysis was used to identify patterns and to classify the nature and type of comprehension issues youth experienced. Nearly all students found most questions clear and understandable. We identified the following comprehension issues: (1) uncertainty with how the intent of a perpetrator factored into a victim's experience (e.g., distinguishing the difference between joking and bullying, or intentional versus unintentional behavior); (2) lack of familiarity with certain expressions of sexualized violence (e.g., "sexual looks") or sex-related terminology (e.g., intercourse); and (3) narrow interpretations of question prompts (e.g., interpreting "forced" as physically forced, not psychologically coerced). Students suggested including language describing dating relationships, types of social media platforms where cyber abuse takes place, and additional examples alongside items to enhance relevance and clarity. Survey questions to measure interpersonal violence may need to be adapted for use among middle school youth. Our findings highlight potential considerations for improving the measurement of interpersonal violence in this age group.

First-interview response patterns of intensive longitudinal psychological and health data.

Self-report data are essential in health psychology research where an individual's perception is critical to understanding one's health and psychological status. Intensive data collection over time, including daily diary assessments, is necessary in understanding within- and between-person variability in health and psychological processes over time. An "initial elevation or latent decline" (IELD) effect, inherent of self-report data, is increasingly acknowledged in the social psychology literature, but awareness of this effect in health psychology research is lacking, particularly in studies that emphasize within- and between-person variability in self-reports. The IELD effect is a pattern in which responses tend to be more extreme at the initial interview relative to subsequent responses. This paper illustrates the impact of IELD in applications of mixed-effects models based on observational self-reports and concludes that researchers take such effects into account in data analysis or in the research designing phase to help mitigate such effects.

Understanding risky driving behaviors among young novice drivers in Nigeria: A latent class analysis coupled with association rule mining approach.

Traffic crashes are significant public health concern in Nigeria, particularly among young drivers. The study aims to explore the underlying pattern of risky driving behaviors and the associations with demographic factors among young drivers in Nigeria. A combined approach of Latent Class Analysis (LCA) and Association Rule Mining is applied to the dataset comprising responses from 684 young drivers who complete the "Behavior of Young Novice Drivers Scale" (BYND) questionnaires. The LCA identifies four distinct classes of drivers based on the risky behavior profiles: Reckless-Speedsters, Cautious Drivers, Distracted Multitaskers, and Emotion-impacted Drivers. Association rule mining further connects these driver classes to demographic and driving history variables, uncovering intriguing insights. Reckless-Speedsters predominantly consist of young males who engage in riskier driving behaviors, including exceeding speed limits and disregarding traffic rules. Conversely, Cautious Drivers, also predominantly young males, exhibit a safer driving profile marked by rule adherence and a notably lower crash rate. Distracted Multitaskers, sharing a demographic profile with Cautious Drivers, diverge significantly due to their higher crash involvement, hinting at a propensity for distracted driving practices. Lastly, Emotion-Impacted Drivers, primarily comprising young employed males, display behaviors influenced by emotions, shorter driving distances, and prior unsupervised driving experience. Most of the behaviors are attributed to inadequate traffic control, absence of traffic signs in most of the roads, preferential treatment, and lack of strict law enforcement in the country. The findings hold substantial implications for road safety interventions in Nigeria, urging targeted approaches to address the unique challenges presented by each driver class. With acknowledging the study limitations and advocating for future research in objective measures and emotion-behavior interactions, the comprehensive approach provides a robust foundation for enhancing road safety in the Nigerian context.

The relationship between gambling behaviour and gambling-related harm: A data fusion approach using open banking data.

BACKGROUND AND AIMS: UK-based gambling policymakers have proposed affordability checks starting at monthly losses of £125. The present study combines open banking data with self-reports of the Problem Gambling Severity Index (PGSI) and other relevant information to explore the harm profiles of people who gamble at different levels of electronic gambling behaviour. DESIGN, SETTING AND PARTICIPANTS: This was a data fusion study in which participants consented to share their bank data via an open banking application programming interface (API) and who also completed relevant self-report items. Hierarchical hurdle models were used to predict being an at-risk gambler (PGSI > 0) and being a 'higher-risk' gambler (higher PGSI scores among those with non-zero scores) using four specifications of electronic gambling behaviour (net-spend, outgoing expenditure, incoming withdrawals, interaction model combining expenditure and withdrawals), and by adding self-reported data across two additional steps. The study took place in the United Kingdom. Participants were past-year people who gamble (n = 424), recruited via Prolific. MEASUREMENTS: Self-report measures were used of gambling-related harm (PGSI), depression [Patient Health Questionnaire 9 (PHQ-9)], age and gender; bank-recorded measures of income and electronic gambling behaviour. FINDINGS: Unharmed gamblers had an average monthly gambling net-spend of £16.41, compared with £208.91 among highest-risk gamblers (PGSI ≥ 5). Being an at-risk gambler (PGSI > 0) was predicted significantly by all four types of gambling behaviour throughout all three steps [1.08 ≤ odds ratios (ORs) ≤ 2.92; Ps < 0.001), with only outgoing expenditure being significant in the interaction model (2.26 ≤ ORs ≤ 2.81; Ps < 0.001). Higher PHQ-9 scores also predicted at-risk gambling in steps 2-3 (1.09 ≤ ORs ≤ 1.10; Ps < 0.001), as did lower age (0.95 ≤ ORs ≤ 0.96; Ps < 0.001) and male gender identity in step 3 (2.51 ≤ ORs ≤ 2.95; Ps < 0.001). Being a higher-risk gambler was predicted significantly by gambling behaviour only in the expenditure-only (1.16 ≤ ORs ≤ 1.17; Ps ≤ 0.048) and withdrawal-only (1.08 ≤ ORs ≤ 1.09; Ps ≤ 0.004) models, and was not predicted by income (0.98 ≤ ORs ≤ 1.14; Ps ≥ 0.601), age (0.98 ≤ ORs ≤ 0.99; Ps ≥ 0.143) or male gender identity (1.07 ≤ ORs ≤ 1.15; Ps ≥ 0.472). CONCLUSION: The UK government's proposed affordability checks for gamblers should rarely affect people who are not experiencing gambling-related harm. At-risk gambling is predicted well by different types of gambling behaviour. Novel insights about gambling can be generated by fusing self-reported and objective data.

The convergent effects of primary school physical activity, sleep, and recreational screen time on cognition and academic performance in grade 9.

Lab-based experiments and randomized controlled trials consistently demonstrate improvements in youth cognition following physical activity (PA), while cross-sectional studies suggest that sedentary behavior (especially recreational screen time [RST]) and poor sleep are inversely related to cognition. However, little is known about how these 24-h movement behaviors-sleep, PA, and sedentary behavior-converge to affect youth cognition. Therefore, the purpose of this study is to test the associations between childhood 24-h movement behaviors and adolescent cognition using a longitudinal design and examine moderating effects of each behavior. This study utilized structural equation modeling with data from the NICHD Study of Early Child Care and Youth Development (N = 1,364, 52% female, 80% White). Independent variables-sleep, RST, and PA-were collected in grade 5. Dependent variables of cognitive and academic performance were collected at grade 9, including the Stroop task, Woodcock-Johnson, and Tower of London. Grade 5 PA was inversely associated with grade 9 cognition, but this relationship was no longer significant once grade 5 cognition was controlled for in analyses. Grade 5 sleep was positively related to grade 9 cognition, whether baseline cognition was controlled for or not. Finally, grade 5 RST was inversely related to cognition and academic performance, regardless of whether baseline values were controlled. Moderation analyses showed the relationship between grade 5 RST and grade 9 cognition was moderated by grade 5 PA, while the relationship between grade 5 PA and grade 9 cognition was moderated by grade 5 sleep. In each case, more PA and sleep blunted the negative relationships. These findings extend evidence that greater sleep promotes cognition and greater RST impairs cognition, by affirming these relationships over a longer period. They extend the evidence by demonstrating that the longitudinal relationship between individual 24-h movement behavior and cognition is moderated by other behaviors.

Very Early Cognitive Screening and Self-Reported Feeling of Fatigue Three Months After Stroke.

Stroke is a major cause of disability and the second leading cause of death worldwide. Post-stroke fatigue has been reported as one of the most limiting symptoms after a stroke. Early identification of risk factors for developing post-stroke fatigue is important for providing timely rehabilitation. A correlation has been found between fatigue and cognitive impairment after stroke, but 2 months after stroke at the earliest. In the present study, we examined whether cognitive function screening using the Montreal Cognitive Assessment (MoCA) very early after stroke could explain fatigue 3 months after stroke. A total of 311 stroke patients admitted to a comprehensive stroke unit in Sweden between 2011 and 2016 were included in this longitudinal study. Cognition was screened within 2 days after admission to the stroke unit. Data on self-reported feeling of fatigue were retrieved from Riksstroke's 3-month follow-up form. The data were analyzed using binary logistic regression. We found that the cognitive function in an acute phase after stroke could not explain self-reported feeling of fatigue in a later stage. The correlation between cognitive impairment and fatigue that has been reported may be detectable no earlier than the subacute phase of stroke. As previous studies have shown that functional outcome, severity of stroke, and sex also correlate with fatigue after stroke, we controlled for these variables in our analysis. In line with previous studies, we found that female patients had higher odds of experiencing fatigue. This is something that health care professionals should be aware of when working with stroke patients.

Child maltreatment data: A summary of progress, prospects and challenges.

BACKGROUND: In 1996, the ISPCAN Working Group on Child Maltreatment Data (ISPCAN-WGCMD) was established to provide an international forum in which individuals, who deal with child maltreatment data in their respective professional roles, can share concerns and solutions. OBJECTIVE: This commentary describes some of the key features and the status of child maltreatment related data collection addressed by the ISPCAN-WGCMD. METHODS: Different types of data collection methods including self-report, sentinel, and administrative data designs are described as well as how they address different needs for information to help understand child maltreatment and systems of prevention and intervention. RESULTS: While still lacking in many parts of the world, access to child maltreatment data has become much more widespread, and in many places a very sophisticated undertaking. CONCLUSION: The ISPCAN-WGCMD has been an important forum for supporting the continued development and improvement in the global effort to understand and combat child maltreatment thus contributing to the long term goals of the UN Convention on the Rights of the Child. Nevertheless, based on what has been learned, even greater efforts are required to improve data in order to effectively combat child maltreatment.

The hidden complexity of measuring number of chronic conditions using administrative and self-report data: A short report.

OBJECTIVE: To examine agreement between administrative and self-reported data on the number of and constituent chronic conditions (CCs) used to measure multimorbidity. STUDY DESIGN AND SETTING: Cross-sectional self-reported survey data from four Canadian Community Health Survey waves were linked to administrative data for residents of Ontario, Canada. Agreement for each of 12 CCs was assessed using kappa (κ) statistics. For the overall number of CCs, perfect agreement was defined as agreement on both the number and constituent CCs. Jackknife methods were used to assess the impact of individual CCs on perfect agreement. RESULTS: The level of chance-adjusted agreement between self-report and administrative data for individual CCs varied widely, from κ = 5.5% (inflammatory bowel disease) to κ = 77.5% (diabetes), and there was no clear pattern on whether using administrative data or self-reported data led to higher prevalence estimates. Only 26.9% of participants had perfect agreement on the number and constituent CCs; 10.6% agreed on the number but not constituent CCs. The impact of each CC on perfect agreement depended on both the level of agreement and the prevalence of the individual CC. CONCLUSION: Our results show that measuring agreement on multimorbidity is more complex than for individual CCs and that even small levels of individual condition disagreement can have a large impact on the agreement on the number of CCs.

Measuring multimorbidity series. An overlooked complexity - Comparison of self-report vs. administrative data in community-living adults: Paper 3. Agreement across data sources and implications for estimating associations with health service use.

OBJECTIVE: The objective of this study is to describe agreement between administrative and self-report data on the number and type of chronic conditions (CCs) and determine whether associations between CC count and health service use differ by data source. STUDY DESIGN AND SETTING: We linked Canadian Community Health Survey and administrative data for a cohort of adults aged 45+ years in Ontario and identified 12 CCs from both data sources. Agreement was described by count and constituent CCs. We estimated associations between CC count (self-report and administrative data) and health service use (administrative data only) over 1 year. RESULTS: Among 71,317 adults, 26.9% showed agreement on both count and constituent CCs but agreement declined with increasing CCs. Health service use increased with CC count but the association was stronger when CCs were measured with administrative data. For example, when measured with administrative data, the odds of a general practitioner visit for 5+ CCs vs. none was 20.3 (95% CI 20.0-20.5) but when using self-report data, the estimate was 8.0 (95% CI 7.8-8.2). CONCLUSION: Agreement on the number of CCs was low and resulted in different estimates on the association with health service use, illustrating the challenges in CC measurement and the ability to interpret the effects on outcomes.

Identifying Rheumatoid Arthritis Cases within the Quebec Health Administrative Database.

OBJECTIVE: Our objective was to calculate rheumatoid arthritis (RA) point prevalence estimates in the CARTaGENE cohort, as well as to estimate the sensitivity and specificity of our ascertainment approach, using physician billing data. We investigated the effects of using varying observation windows in the Régie de l'assurance maladie du Québec (RAMQ) health services administrative databases, alone or in combination with self-reported diagnoses and drugs. METHODS: We studied subjects enrolled in the CARTaGENE cohort, which recruited 19,995 participants from 4 metropolitan regions in Québec from August 2009 to October 2010. A series of Bayesian latent class models were developed to assess the effects of 3 factors: the number of years of billing data, the addition of self-reported information on RA diagnoses and drugs, and the adjustment for misclassification error. RESULTS: The 3-year 2010 point prevalence estimate among cohort members aged 40-69 years, using physician billing plus self-report, adjusting for misclassification error in each source, was 0.9% [95% credible interval (CrI) 0.7-1.2] with RAMQ sensitivity of 84.0% (95% CrI 74.0-93.7) and a specificity of 99.8% (95% CrI 99.6-100.0). Our results show variations in the prevalence point estimates related to all 3 factors investigated. CONCLUSION: Our study illustrates that multiple data sources identify more RA cases and thus a higher prevalence estimate. RA point prevalence estimates using billing data are lower if fewer years of data are used.

Case Study: Converting Paper-based Case Report Forms to an Electronic Format (e-CRF) with ACASI Self-Report Integration.

This paper will discuss the integration of electronic Case Report Forms (e-CRFs) into an already existing Android-based Audio Computer-Assisted Self-Interview (ACASI) software solution that was developed for a public health project in Kampala, Uganda, the technical outcome results, and lessons learned that may be useful to other projects requiring or considering such a technology solution. The developed product can function without a connection to the Internet and allows for synchronizing collected data once connectivity is possible. Previously, only paper-based CRFs were utilized at the Uganda project site. A subset or select group of CRFs were targeted for integration with ACASI in order to test feasibility and success. Survey volume, error rate, and acceptance of the system, as well as the operational and technical design of the solution, will be discussed.

Exploring the perils of cross-national comparisons of drug prevalence: The effect of survey modality.

BACKGROUND: There is significant interest in comparing countries on many different indicators of social problems and policies. Cross-national comparisons of drug prevalence and policies are often hampered by differences in the approach used to reach respondents and the methods used to obtain information in national surveys. The paper explores how much these differences could affect cross-country comparisons. METHODS: This study reports prevalence of drug use according to the most recent national household survey and then adjusts estimates as if all national surveys used the same methodology. The analysis focuses on European countries for which the European Monitoring Centre for Drugs and Drug Addiction reports data, the United States, Canada, and Australia. Adjustment factors are based on US data. FINDINGS: Adjusting for modality differences appears likely to modestly affect the rankings of countries by prevalence, but to an extent that could be important for comparisons. For example, general population surveys suggest that the US had some of the highest cannabis and cocaine prevalence rates circa 2012, but this is partially driven by the use of a modality known to produce higher prevalence estimates. This analysis shows that country rankings are partly an artifact of the mode of interview used in national general population surveys. CONCLUSIONS: Our preliminary efforts suggest that cross-national prevalence comparisons, policy analyses and, other projects such as estimating the global burden of disease could be improved by adjusting estimates from drug use surveys for differences in modality. Research is needed to create more authoritative adjustment factors.

Course of self-reported symptoms of 342 outpatients receiving medium- versus long-term psychodynamic psychotherapy.

BACKGROUND: The course of self-reported symptoms during medium- versus long-term psychodynamic psychotherapy has rarely been documented for outpatient settings. This observational study describes routine practice of ambulatory treatment in Germany and explores self-reported symptoms of a broad patient sample undergoing one (medium-term) versus two years (long-term) of psychodynamic psychotherapy. METHODS: Over four and a half years, longitudinal self-report symptom data were collected from 342 outpatients as part of a standardized documentation system. Self-report data were compared between patients receiving either medium-term or long-term psychodynamic psychotherapy. RESULTS: Routine care significantly decreased disease burden as reported by patients by small to medium effect sizes (ES) for depression (ES = 0.58), anxiety (ES = 0.49), obsessive-compulsive disorder (ES = 0.54), somatoform disorder (ES = 0.32), eating disorder (ES = 0.38). The majority of patients completed treatment after one year and showed medium-size changes. For a subgroup of patients with depressive and/or obsessive-compulsive disorder symptoms for whom two years of therapy were deemed necessary, additional benefits were reported during the second year of treatment (ES = 0.61 and ES 0.47, respectively). CONCLUSIONS: Our findings suggest that both medium- and long-term psychodynamic psychotherapy decrease self-reported disease burden of patients with depression, anxiety, obsessive-compulsive, somatoform and/or eating disorders. For a subgroup of patients, additional benefits were gained in the second year of treatment.

Comparison of the estimated prevalence of mood and/or anxiety disorders in Canada between self-report and administrative data.

BACKGROUND: To compare trends in the estimated prevalence of mood and/or anxiety disorders identified from two data sources (self-report and administrative). Reviewing, synthesising and interpreting data from these two sources will help identify potential factors that underlie the observed estimates and inform public health action. METHOD: We used self-reported, diagnosed mood and/or anxiety disorder cases from the Canadian Community Health Survey (CCHS) across a 5-year span (from 2003 to 2009) to estimate the prevalence among the Canadian population aged ≥15 years. We also estimated the prevalence of mood and/or anxiety disorders using the Canadian Chronic Disease Surveillance System (CCDSS), which identified cases using ICD-9/-10-CA codes from physician billing claims and hospital discharge records during the same time period. The prevalence rates for mood and/or anxiety disorders were compared across the CCHS and CCDSS by age and sex for all available years of data from 2003 to 2009. Summary rates were age-standardised to the Canadian population as of 1 October 1991. RESULTS: In 2009, the prevalence of mood and/or anxiety disorders was 9.4% using self-reported data v. 11.3% using administrative data. Prevalence rates obtained from administrative data were consistently higher than those from self-report for both men and women. However, due to an increase in the prevalence of self-reported cases, these differences decreased over time (rate ratios for both sexes: 1.6-1.2). Prevalence estimates were consistently higher among females compared with males irrespective of data source. While differences in the prevalence estimates between the two data sources were evident across all age groups, the reduction of these differences was greater among adolescent, young and middle-aged adults compared with those 70 years and older. CONCLUSIONS: The overall narrowing of differences over time reflects a convergence of information regarding the prevalence of mood and/or anxiety disorders trends between self-report and administrative data sources. While the administrative data-based prevalences remained relatively stable, the self-reported prevalences increased over time. These observations may reflect positive societal changes in the perceptions of mental health (declining stigma) and/or increasing mental health literacy. Additional research using non-ecological data is required to further our understanding of the observed findings and trends, including a data linkage exercise permitting a comparison of prevalence estimates and population characteristics from these two data sources both separately and merged.

Avatar Web-Based Self-Report Survey System Technology for Public Health Research: Technical Outcome Results and Lessons Learned.

This paper reports on a specific Web-based self-report data collection system that was developed for a public health research study in the United States. Our focus is on technical outcome results and lessons learned that may be useful to other projects requiring such a solution. The system was accessible from any device that had a browser that supported HTML5. Report findings include: which hardware devices, Web browsers, and operating systems were used; the rate of survey completion; and key considerations for employing Web-based surveys in a clinical trial setting.

Misreporting in a randomized clinical trial for smoking cessation in adolescents.

INTRODUCTION: Misreporting smoking behavior is common among younger smokers participating in clinical trials for smoking cessation. This study focused on the prevalence of and factors associated with adolescent misreporting of smoking behaviors within the context of a randomized clinical trial for smoking cessation. METHODS: Adolescent smokers (N=129) participated in a randomized clinical trial that compared two brief interventions for smoking cessation. Following the final (6-month) follow-up, a confidential, self-administered exit questionnaire examined the extent to which participants admitted to having misreported smoking quantity, frequency and/or consequences during the study. Factors associated with under- and over-reporting were compared to accurate-reporting. RESULTS: One in 4 adolescent smokers (25.6%) admitted to under-reporting during the study and 14.7% admitted to over-reporting; 10.9% of the adolescents admitted to both under- and over-reporting. Rates of admitted misreporting did not differ between treatment conditions or recruitment site. Compared to accurate-reporting, under- and over-reporting were significantly associated with home smoking environment and the belief among adolescents that the baseline interviewer wanted them to report smoking more or less than they actually smoked. Compared to accurate reporters, over-reporters were more likely to be non-White and to report being concerned with the confidentiality of their responses. CONCLUSIONS: A post-study confidential debriefing questionnaire can be a useful tool for estimating rates of misreporting and examining whether potential differences in misreporting might bias the interpretation of treatment effects. Future studies are needed to thoroughly examine potentially addressable reasons that adolescents misreport their smoking behavior and to develop methods for reducing misreporting.