Infection tracking in travellers using a mobile app (ITIT): The pilot study.

BACKGROUND: Current surveillance of travellers' health captures only a small proportion of illness events. We aimed to evaluate the usability and feasibility of using an app to enable travellers to self-report illness. METHOD: This pilot study assesses a novel mobile application called Infection Tracking in Travellers (ITIT) that records travel-related symptoms with associated geolocation and weather data. Participants were recruited in three Swiss travel clinics between December 2021 and March 2022. A feedback survey was used to examine app ease of use, and data from the app was used to examine travel and illness patterns as a proof-of-concept for the larger ITIT study. RESULTS: Participants were recruited from Zürich, Basel, and Geneva, with 37 individuals completing a total of 394 questionnaires in 116 locations in Asia, Africa, the Americas, and Europe. Illness symptoms were reported by 41% of participants, 67% of which were respiratory. The post travel questionnaire showed that all participants found the app easy to use and 63% said they would recommend it to others. Several users provided suggestions for improved usability. CONCLUSION: The app fulfilled its function as a research tool linking infection symptoms with geolocation and climate data.

Travel behaviours and health outcomes during travel: Profiling destination-specific risks in a prospective mHealth cohort of Swiss travellers.

BACKGROUND: We used a mobile application to determine the incidence of health events and risk behaviours during travel by country and identify which health risks are significantly elevated during travel compared with at home. METHOD: TOURIST2 is a prospective cohort study of 1000 adult travellers from Switzerland to Thailand, India, China, Tanzania, Brazil and Peru, planning travel of ≤4 weeks between 09/2017 and 04/2019. The incidence rate ratio (IRR) in each country was calculated. RESULTS: All countries had significantly higher incidence of health events than at home. The most elevated symptoms were sunburn, itching from mosquitoes, and gastrointestinal disorders (e.g. vomiting, diarrhoea), corresponding with universally high food/drink risk behaviours. Peru had the highest incidence of both overall negative health events and severe health events (172.0/1000 travel-days). Traffic accidents were significantly higher in Peru (IRR: 2.4, 1.2, 4.7), although incidence of transportation risk was highest in India and Thailand. In Tanzania, incidence of negative mental health events was significantly lower than at home, although it was elevated in other countries. Sexual risk behaviours were high in Brazil. CONCLUSIONS: Our study improves the understanding of the non-infectious disease related health challenges travellers face and provides evidence for more personalised traveller support.

TOURIST2 - Tracking of urgent risks in swiss travellers to the 6 main travel destinations - Feasibility and ethical considerations of a smartphone application-based study.

BACKGROUND: The adoption of mHealth technology in travel medicine is a relatively new and unexplored field. We have further developed a TRAVEL application (app) for real-time data monitoring during travel. In this manuscript we report on the feasibility using this new app in a large and diverse cohort of travellers to three continents. METHODS: We enrolled 1000 participants from the travel clinics of Zurich and Basel, Switzerland, aged ≥18 years, travelling to Thailand, India, China, Tanzania, Brazil and Peru between 09/2017-01/2019. Participants included healthy travellers, individuals with pre-existing chronic diseases and elderly travellers (≥60 years). Participants completed an app-based daily survey on risk behaviours/health incidents pre-, during and after travel. Simultaneously, GPS locations were tightly collected and linked to environmental data. RESULTS: 793 (79%) travellers answered at least one questionnaire during their trip. Participants' median age was 34 years (range 18-84 years); 8% were aged ≥60 years; 55% female; 32% had pre-existing chronic diseases. Completion rates were similar in younger and elderly travellers and in those with and without pre-existing diseases. CONCLUSIONS: The use of a smartphone app is a feasible method for collecting behavioural and health data in elderly travellers and individuals with chronic diseases travelling to three continents.

Does the DOSPERT scale predict risk-taking behaviour during travel? A study using smartphones.

Background: Despite the continuing growth of international tourism, very little research has been done on the link between individual risk attitudes and health behaviours during travel. Our study uses a validated risk-taking questionnaire Domain-Specific Risk-Taking Scale (DOSPERT) and data from a smartphone application to study the association between pre-travel risk attitudes and the occurrence of behaviours during travel. Methods: A prospective cohort of travellers to Thailand used a smartphone application to answer a daily questionnaire about health behaviours and events. Prior to travel, participants completed the DOSPERT, a validated 30-item scale that assesses risk-taking and perception in five content domains: financial decisions, health/safety, recreational, ethical and social decisions. Multiple linear regression models were used to model the relationship between DOSPERT risk-taking subdomain score and health behaviour. Results: Of the 75 travellers that completed the study, 70 (93.3%) completed the DOSPERT pre-travel. Men, backpackers and young travellers reported a higher willingness to take recreational risks than women, luxury travellers and older travellers. Incidence of drug and alcohol risk behaviours during travel, itching from mosquitoes, smoking and failing to use a seatbelt in automobiles while at home were all significantly associated with an individual's score on the health and safety DOSPERT subdomain. Conclusions: In our study, individual scores on risk-taking in the health and safety subdomain of the DOSPERT questionnaire seem to be predictive of health behaviours both during travel and at home. By pairing new methods of data collection with questionnaires such as DOSPERT that identify key traveller characteristics to intervene on, travel medicine doctors will be able to provide more specialised health advice, ensuring that all travellers receive well-rounded advice about the full range of health challenges they will face during travel.

Streaming data from a smartphone application: A new approach to mapping health during travel.

BACKGROUND: New research methods offer opportunities to investigate the influence of environment on health during travel. Our study uses data from a smartphone application to describe spatial and environmental patterns in health among travellers. METHODS: A prospective cohort of travellers to Thailand used a smartphone application during their trips to 1) answer a daily questionnaire about health behaviours and events, and 2) collect streaming data on environment, itinerary, and weather. Incidence of health events was described by region and trip type. The relationship between environmental factors and health events was modelled using a logistic mixed model. RESULTS: The 75/101 (74.3%) travellers that completed the study answered 940 questionnaires, 796 (84.7%) of which were geolocated to Southeast Asia. Accidents occurred to 20.0% of participants and were mainly in the Thai islands, while self-rated "severe" mental health events (21.3%) were centred in Bangkok. The odds of a health event were higher in Chiang Mai (2.34, 95% CI: 1.08, 5.08) and on rainy days (1.86, 95% CI: 1.03, 3.36). CONCLUSIONS: Distinct patterns in spatial and environmental risk factors emerged in travellers to Thailand. Location based tracking could identify "hotspots" for health problems and update travel advice to target specific risk groups and regions.

The quantified self during travel: mapping health in a prospective cohort of travellers.

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.

Travel medicine and mHealth technology: a study using smartphones to collect health data during travel.

BACKGROUND: mHealth methodology such as smartphone applications offers new opportunities to capture the full range of health risks during travel in real time. Our study aims to widen the scope of travel health research in tropical and subtropical destinations by using a smartphone application to collect detailed information on health behaviours, clinical symptoms, accidents and environmental factors during travel. METHODS: We enrolled travel clinic clients in Zurich and Basel ≥18 years of age travelling to Thailand for <5 weeks. Sociodemographic, clinical and risk behaviour information was collected pre-travel. Participants were equipped with a smartphone and an application that (1) actively administers a daily self-report questionnaire on the health risks, behaviours and symptoms the traveller encountered, and (2) passively collects information on the traveller's location and environmental conditions by transformation of raw GPS data. RESULTS: A prospective cohort of 101 travellers planning travel to Thailand between January and June 2015 was recruited. Of the 101 enrolled travellers, 75 (74.3%) answered at least one questionnaire during travel, 10 (9.9%) had technical difficulties and 16 (15.8%) dropped out. Those who completed questionnaires were a median of 27.0 years old (range 18-57). Travellers filled out a median of 12.0 questionnaires during their trip (range 1-30), corresponding to a median completion rate of 85.0% days of travel. The typical example of a healthy female traveller shows that many and diverse health issues arise during a trip that clusters on certain days. The rich data on behaviour and local environment may be used to explain the occurrence and clustering of health issues. CONCLUSIONS: Use of a smartphone app to collect health information is technically feasible and acceptable amongst a traveller population, minimizes recall bias and greatly increases the quality and quantity of data collected during travel. mHealth technology shows great potential for innovation in travel medicine.

Saving Human Lives: What Complexity Science and Information Systems can Contribute.

We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.