Dataset Information

Military service absences and family members' mental health: A timeline followback assessment.

ABSTRACT: Although military service, and particularly absence due to deployment, has been linked to risk for depression and anxiety among some spouses and children of active duty service members, there is limited research to explain the heterogeneity in family members' reactions to military service stressors. The current investigation introduces the Timeline Followback Military Family Interview (TFMFI) as a clinically useful strategy to collect detailed time-linked information about the service member's absences. Two dimensions of parent absence--the extent to which absences coincide with important family events and cumulative time absent--were tested as potential risks to family members' mental health. Data from 70 mother-adolescent pairs revealed that the number of important family events missed by the service member was linked to elevated youth symptoms of depression, even when accounting for the number of deployments and cumulative duration of the service member's absence. However, youth who reported more frequent contact with the service member during absences were buffered from the effects of extensive absence. Mothers' symptoms were associated with the cumulative duration of the service members' time away, but not with family events missed by the service member. These results identify circumstances that increase the risk for mental health symptoms associated with military family life. The TFMFI provides an interview-based strategy for clinicians wishing to understand military family members' lived experience during periods of service-member absence.

SUBMITTER: Rodriguez AJ

PROVIDER: S-EPMC4573828 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Military service absences and family members' mental health: A timeline followback assessment.

Rodriguez Aubrey J AJ Margolin Gayla G

Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43) 20150801 4

Although military service, and particularly absence due to deployment, has been linked to risk for depression and anxiety among some spouses and children of active duty service members, there is limited research to explain the heterogeneity in family members' reactions to military service stressors. The current investigation introduces the Timeline Followback Military Family Interview (TFMFI) as a clinically useful strategy to collect detailed time-linked information about the service member's a ...[more]

PMID: 26075736

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Project description:Prenatal alcohol exposure (PAE) has been linked to poor pregnancy outcomes, yet there is no recognized standard for PAE assessment, and the specific effects of quantity, frequency, and timing remain largely unknown. The Safe Passage Study was designed to investigate the role of PAE in a continuum of poor peri- and postnatal outcomes. The objective of this manuscript is to describe the rationale for, and feasibility of, modifications to the traditional Timeline Followback (TLFB) for collecting PAE information in a large cohort of pregnant women. Participants from the Northern Plains region (in the United States) and Cape Town, South Africa, were followed prospectively using a modified 30-day TLFB interview, administered up to five times, to obtain detailed PAE information. Required modifications for our population included capturing information regarding sharing, type/brand, container size, and duration, in order to accurately record the amount of alcohol consumed. PAE status was defined for 99.9% of the 11,892 enrolled pregnancies at least once during pregnancy and for 92% across all trimesters. Of 53,823 drinks reported, 98% had all items necessary for standard drink computation. Sharing was reported for 74% of drinks in Cape Town, South Africa and for 10% in the Northern Plains. Compared to referent values from the traditional TLFB, 74% and 67% of drinks had different alcohol-by-volume and container size, respectively. Furthermore, a statistically significant difference was found between the number of containers reported and the number of standard drinks computed, using information from the modified TLFB. This is the first study of this size to wholly encompass all of these changes into a single measure in order to more accurately calculate daily consumption and assess patterns over time. The methods used to collect PAE information and create alcohol exposure measures likely increased the accuracy of standard drinks reported and could be generalized to other populations.

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

Military service absences and family members' mental health: A timeline followback assessment.

Publications

Military service absences and family members' mental health: A timeline followback assessment.

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