Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,737

The DOI matches its Crossref record.

The validity of self-reported smoking: a review and meta-analysis.

Open access
Patrick, D L, Cheadle, A, Thompson, D C, Diehr, P, Koepsell, T, Kinne, S·1994·American Journal of Public Health

Abstract

OBJECTIVES. The purpose of this study was to identify circumstances in which biochemical assessments of smoking produce systematically higher or lower estimates of smoking than self-reports. A secondary aim was to evaluate different statistical approaches to analyzing variation in validity estimates. METHODS. Literature searches and personal inquiries identified 26 published reports containing 51 comparisons between self-reported behavior and biochemical measures. The sensitivity and specificity of self-reports of smoking were calculated for each study as measures of accuracy. RESULTS. Sensitivity ranged from 6% to 100% (mean = 87.5%), and specificity ranged from 33% to 100% (mean = 89.2%). Interviewer-administered questionnaires, observational studies, reports by adults, and biochemical validation with cotinine plasma were associated with higher estimates of sensitivity and specificity. CONCLUSIONS. Self-reports of smoking are accurate in most studies. To improve accuracy, biochemical assessment, preferably with cotinine plasma, should be considered in intervention studies and student populations.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2105/ajph.84.7.1086

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 1,737

Metadata

Authors
Patrick, D. L., Cheadle, A., Thompson, D. C., Diehr, P., Koepsell, T., & Kinne, S.
Venue
American Journal of Public Health
Published
1994-07
Volume
84
Issue
7
Pages
1086-1093
ISSN
  • 0090-0036 (ISSN-L)
  • 1541-0048
OpenAlex
W2085191348
Cited by
1,737
References
0
FWCI
12.81
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The validity of self-reported smoking: a review and meta-analysis. https://app.cochat.ai/papers/10.2105/ajph.84.7.1086
The paper
Patrick, D. L., Cheadle, A., Thompson, D. C., Diehr, P., Koepsell, T., & Kinne, S. (1994). The validity of self-reported smoking: a review and meta-analysis. American Journal of Public Health. https://doi.org/10.2105/ajph.84.7.1086

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.