Implications of Personal Genomic Testing for Health Behaviors: The Case of Smoking
Bibliographic record
Abstract
INTRODUCTION: Direct-to-consumer personal genomic testing has the potential to influence health behaviors, including smoking. Critics of this testing highlight limited evidence to support positive behavioral benefits and caution that genomic results may provide false reassurance, leading to unhealthy behaviors. This study investigates interest in genetic risks of smoking-related diseases and changes in smoking behaviors among genomic testing consumers. METHODS: From 2012 to 2013, a longitudinal series of web surveys was conducted. A total of 1464 customers of 23andMe and Pathway Genomics completed a survey prior to viewing genomic test results, of which 1002 participants provided data on smoking behaviors 6 months after receiving results. RESULTS: At baseline, 64% of participants were never smokers, 29% were former smokers, and 7% were current smokers. Most baseline current smokers were very interested in genetic risk results for lung cancer (65%) and heart disease (72%). For lung cancer, this interest was significantly greater than former (50% very interested) and never smokers (37% very interested) (p < .0001). Even though participants were interested in smoking-related disease genetic risks, 96% reported the same smoking status at baseline and 6-month follow-up. Importantly, only 1% (n = 13/916) of former and never smokers became current smokers at 6 months and 22% (n = 14/64) of current smokers reported quitting. CONCLUSIONS: Overall, smokers show a high level of interest in genetic risks of smoking-related illnesses. The experience of receiving direct-to-consumer genomic health risks does not appear to have obvious harms related to smoking behaviors, with some potential benefits. IMPLICATIONS: In the setting of ongoing controversy surrounding direct-to-consumer genomic testing, this study provides evidence that consumers are interested in genetic risk results of smoking-related diseases. Receiving genomic testing results does not lead to smoking initiation among never smokers or reinitiation among former smokers and may be associated with a higher quit rate among current smokers at 6-month follow-up than the general population. These findings ease concerns that direct-to-consumer genomic testing could lead to false reassurance and unhealthy behaviors related to smoking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".