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Psychometric Evaluation of the Timeline Followback for Exercise among College Students.

ABSTRACT: OBJECTIVES: Two separate studies assessed the psychometric properties of a retrospective behavioral measure adapted for exercise called the Timeline Followback for Exercise (TLFB-E). Study one examined criterion, convergent, and predictive validity. Study two examined test-retest reliability. METHODS: Study one participants (N = 66) were college students 20.0 ± 1.4yr. Validity of frequency, intensity, time, and type (FITT) of exercise as assessed on the TLFB-E was examined using Pearson r correlations with accelerometers, weekly exercise contracts between participants and researchers, question four of the College Alumni Questionnaire, and a health-related physical fitness battery. Study two participants were a different sample (N = 40) of college students 18.63 ± 1.0yr. Pearson r correlations determined reliability of the TLFB-E for exercise frequency, intensity, and time between two interviews separated by one month. Kappa statistic determined reliability of the TLFB-E for type of exercise. RESULTS: The TLFB-E displayed evidence of criterion validity when compared to accelerometers (r = .35 to .39) and evidence of convergent validity when compared to weekly exercise contracts (r = .65 to .80) and question four of the College Alumni Questionnaire (r = .06 to .75). The TLFB-E displayed evidence of modest to adequate test-retest reliability (r = .79 to .97) for exercise frequency, intensity, and time and moderate Kappa (k = .49) for exercise type. CONCLUSIONS: The TLFB-E produces evidence of reliable and valid scores among college students and improves upon other self-report, retrospective questionnaires by enabling daily collection of exercise FITT over a specified time period.

SUBMITTER: Panza GA

PROVIDER: S-EPMC3403727 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Psychometric Evaluation of the Timeline Followback for Exercise among College Students.

Panza Gregory A GA Weinstock Jeremiah J Ash Garrett I GI Pescatello Linda S LS

Psychology of sport and exercise 20120620 6

OBJECTIVES: Two separate studies assessed the psychometric properties of a retrospective behavioral measure adapted for exercise called the Timeline Followback for Exercise (TLFB-E). Study one examined criterion, convergent, and predictive validity. Study two examined test-retest reliability. METHODS: Study one participants (N = 66) were college students 20.0 ± 1.4yr. Validity of frequency, intensity, time, and type (FITT) of exercise as assessed on the TLFB-E was examined using Pearson r corre ...[more]

PMID: 22844226

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Project description:IntroductionIn smoking cessation clinical trials, timeline followback (TLFB) interviews are widely used to track daily cigarette consumption. However, there are no standard tools for calculating abstinence based on TLFB data. Individual research groups have to develop their own calculation tools, which is not only time- and resource-consuming but might also lead to variability in the data processing and calculation procedures.Aims and methodsTo address these issues, we developed a novel open-source Python package named abstcal to calculate abstinence using TLFB data. This package provides data verification, duplicate and outlier detection, missing-data imputation, integration of biochemical verification data, and calculation of a variety of definitions of abstinence, including continuous, point-prevalence, and prolonged abstinence.ResultsWe verified the accuracy of the calculator using data derived from a clinical smoking cessation study. To improve the package's accessibility, we have made it available as a free web app.ConclusionsThe abstcal package is a reliable abstinence calculator with open-source access, providing a shared validated online tool to the addiction research field. We expect that this open-source abstinence calculation tool will improve the rigor and reproducibility of smoking and addiction research by standardizing TLFB-based abstinence calculation.ImplicationsAbstinence calculation is an essential task in any smoking intervention study. However, there have not been standard open-source tools available to the researchers. This commentary describes a Python-based package called abstcal that can calculate abstinence from TLFB data, a common methodology to collect smoking consumption data in research settings. The package supports the calculation of point-prevalence, prolonged, and continuous abstinence. Importantly, the package has a web app interface that allows researchers to use the tool without any coding experience. This tool will facilitate smoking research by providing a standardized and easy-to-use abstinence calculation tool.

Dataset Information

Psychometric Evaluation of the Timeline Followback for Exercise among College Students.

Publications

Psychometric Evaluation of the Timeline Followback for Exercise among College Students.

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