Health Behavior Nudging Through Health Information Exposure and Information Search
Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".