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Record W2791800245 · doi:10.2196/mhealth.9131

Exploring Digital Health Use and Opinions of University Students: Field Survey Study

2018· article· en· W2791800245 on OpenAlexvenueno aff
Ilaria Montagni, Tanguy Cariou, Tiphaine Feuillet, Emmanuel Langlois, Christophe Tzourio

Bibliographic record

VenueJMIR mhealth and uhealth · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersInstitut pour la Recherche en Santé Publique
KeywordsMental healthPsychological interventionDigital healthHealth promotionMedical educationPsychologyMedicineApplied psychologyHealth careGerontologyPublic healthNursingPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: During university, students face some potentially serious health risks, and their lifestyle can have a direct effect on health and health behaviors later in life. Concurrently, university students are digital natives having easy access to the internet and new technologies. Digital health interventions offer promising new opportunities for health promotion, disease prevention, and care in this specific population. The description of the current use of and opinions on digital health among university students can inform future digital health strategies and interventions within university settings. OBJECTIVE: The aim of this exploratory study was to report on university students' use and opinions regarding information and communication technologies for health and well-being, taking into account sociodemographic and self-rated general and mental health correlates. METHODS: This field survey was conducted from March to April 2017. An informed consent form and a paper questionnaire were given to students aged 18 to 24 years in 4 university campuses in Bordeaux, France. The survey was formulated in 3 sections: (1) sociodemographic characteristics and self-rated general and mental health, (2) information about the use of digital health, and (3) opinions about digital health. Data were analyzed using descriptive statistics and tests of independence. RESULTS: A total of 59.8% (303/507 females) students completed the questionnaire. Concerning digital health use, 34.9% (174/498) had at least 1 health app mostly for physical activity (49.4%, 86/174) and general health monitoring (41.4%, 72/174,), but only 3.9% (20/507) of students had a wearable device. Almost all (94.8%, 450/476) had searched for Web-based health-related information at least once in the last 12 months. The most sought health-related topics were nutrition (68.1%, 324/476); pain and illnesses (64.5%, 307/476); and stress, anxiety, or depression (51.1%, 243/476). Although Wikipedia (79.7%, 357/448) and general health websites (349/448, 77.9%) were the most consulted sources, students considered institutional or official websites as the most credible sources (309/335, 92.2%). There were significant differences in digital health use by gender, field, and year of study. No statistically significant association was found between digital health use and self-rated general and mental health status. Concerning opinions on digital health, although 94.1% (475/505) of students estimated that today's digital health cannot replace traditional health services and medical consultations, 44.6% (207/464) of students declared that this could be possible in the future, provided that digital health interventions are promoted by institutional or official entities. CONCLUSIONS: University students are largely using the internet for health information seeking, but using less mobile health apps and very few wearable devices. Our data suggest that digital health has the potential for improving health and well-being at the university, especially if digital health interventions take into account students' profiles, interests, and needs.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
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 head0.317
GPT teacher head0.497
Teacher spread0.181 · 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.

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".

Quick stats

Citations117
Published2018
Admission routes1
Has abstractyes

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