The quantified self during travel: mapping health in a prospective cohort of travellers
Bibliographic record
Abstract
BACKGROUND: Travel medicine research has remained relatively unchanged in the face of rapid expansion of international travel and is unlikely to meet health challenges beyond infectious diseases. Our aim was to identify the range of health outcomes during travel using real-time monitoring and daily reporting of health behaviours and outcomes and identify traveller subgroups who may benefit from more targeted advice before and during travel. METHODS: We recruited a prospective cohort of travellers ≥ 18 years and planning travel to Thailand for <5 weeks from the travel clinics in Zurich and Basel (Switzerland). Participants answered demographic, clinical and risk behaviour questionnaires pre-travel and a daily health questionnaire each day during travel using a smartphone application. Environmental and location data were collected passively by GPS. Classification trees were used to identify predictors of health behaviour and outcomes during travel. RESULTS: Non-infectious disease events were relatively common, with 22.7% (17 out of 75 travellers) experiencing an accident, 40.0% ( n = 30) a wound or cut and 14.7% ( n = 11) a bite or lick from an animal. Mental health associated events were widely reported, with 80.0% ( n = 60) reporting lethargy, 34.7% ( n = 26) anxiety and 34.7% ( n = 26) feeling tense or irritable. Classification trees identified age, trip length, previous travel experience and having experienced a sports injury in the past year as the most important discriminatory variables for health threats. CONCLUSIONS: Our study offers a revolutionary look at an almost real-time timeline of health events and behaviours during travel using mHealth technology. Non-infectious disease related health issues were common in this cohort, despite being largely unaddressed in traditional travel medicine research and suggest a substantial potential for improving evidence-based travel medicine advice.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".