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Record W2974388509 · doi:10.1177/2327857919081029

Health Behavior Nudging Through Health Information Exposure and Information Search

2019· article· en· W2974388509 on OpenAlexaff
Jessie Chin, Ece Üreten, Catherine M. Burns

Bibliographic record

VenueProceedings of the International Symposium on Human Factors and Ergonomics in Health Care · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychologyHealth informationPublic healthThe InternetHealth Information National Trends SurveyInformation behaviorHealth behaviorApplied psychologySocial psychologyEnvironmental healthMedicineHealth careComputer scienceWorld Wide WebNursingPolitical science

Abstract

fetched live from OpenAlex

As the Internet has become one of the dominant sources of health information, online health information plays an important role for patients to acquire health knowledge and regulate their health behavior (European Commission, 2014). Researchers suggested different ways to nudge public health behavior through environments and policies (Marteau et al., 2011); few studies had explored the potential to use online health information environment to nudge health behavior. While there was established evidence showing the individual differences in online health information search behavior across the lifespan (e.g., Chin et al., 2009; Sharit et al., 2008), the current study was to examine the nudging effects on health behavior through online health information exposure and search. An online mixed-factor-design experiment was conducted on 136 adults across the lifespan (Mean age=49.79, SD=16.00). We examined two kinds of nudging routes, (1) health information exposure (manipulated by the experimenters), and (2) health information search (decided by the participants), on two kinds of health behaviors varying in the costs of taking these health behaviors. Target health behaviors included, (1) self-related health behavior: participants were asked to take a break for doing a stretch (low cost) or a walk (high cost) after long sitting; (2) self-unrelated health behavior: participants were asked to have researchers to donate to the rare disease association through writing down the date (low cost) or a 100-word endorsement article (high cost). In the experiment, each participant was assigned to read four topics (3 articles under each topic) and answer the questions after each health topic. The questions varied in difficulties, which participants could decide to answer the questions based on their own knowledge, their memory from reading, or searching the answers online. To manipulate health information exposure, half of the participants were assigned to read the online articles related to the target health behaviors (such as the harms of long sitting and the target rare disease). Participants were not disclosed about the study goals at the beginning. They were not told that the study goal was to examine whether they took the target health behaviors or not, but to examine how adults learn from online health information. To measure the actions of target health behaviors, for the self-related health behavior, after roughly 40 minutes of the study, participants were requested to take a break for 10 minutes. For the self-unrelated health behavior, at the end of the study, participants were asked whether they would like to show their support to a rare disease association. The manipulations in the costs of health behaviors were assigned in counterbalanced order. Logistic regressions were used to examine the effects of nudging routes and costs of actions on two kinds of target health behaviors. Results suggested that mere information exposure did not affect the likelihood to take the target health behaviors regardless of its relatedness to self-interests or costs of actions. Further, for self-related health behavior, adults were more likely to take actions after a more deliberate engagement with the information - through information search. For health behavior that was unrelated to self-interests, participants were more likely to take actions after they searched the information about this rare disease and only when the costs of actions were low. This study has shown the potentials and limitations of health nudging in different health behaviors, and has its implications on designing effective health nudging strategies on different health behaviors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.389
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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