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Record W4383303147 · doi:10.4212/cjhp.3261

Older Adults’ Use of and Interest in Technology and Applications for Health Management: A Survey Study

2023· article· en· W4383303147 on OpenAlexaffvenue
Ashley Sproul, Jonathan Stevens, Jacqueline Richard

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsHorizon Health NetworkSaint John Regional Hospital
Fundersnot available
KeywordsAge groupsmHealthMedicineMobile phonePhoneGerontologyPublic healthFamily medicinePsychologyDemographyNursingComputer science

Abstract

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Background: Older adults face challenges with managing their medications, obtaining health education, and accessing health services. Mobile health (mHealth), defined as any medical or public health practice facilitated through mobile devices, could help to overcome these difficulties. Objectives: To determine what technologies and apps are in current use by older adults, to explore the types of technologies and apps that may be of interest to people in this age group, to explore concerns about technologies, and to examine any age-related differences. Methods: Adults 60 years of age or older were invited to complete a 35-item electronic survey, in either French or English, which was distributed through social media and by email from organizations working with older adults. The survey was conducted in mid-2020. Results: A total of 266 respondents completed some or all of the survey. Most participants had a mobile phone (229/243, 94.2%), and approximately one-third (78/222, 35.1%) had used a health-related app in the previous 12 months; this level of usage was consistent across age groups. Most respondents were interested in using an app to improve their health (171/225, 76.0%), with variation by age: highest among those 60–64 years of age (82/95, 86.3%), lower among those 80 years or older (40/52, 76.9%), and lowest among those 65–69 years of age (6/14, 42.9%). Most older adults were interested in using an app to ask questions of pharmacists (161/219, 73.5%) and to review their medications (154/218, 70.6%). Participants’ mHealth concerns focused on costs, disclosure of personal information, effectiveness, usability, and endorsement by health care providers. The study limitations included challenges related to electronic recruitment and survey distribution, as well as a high representation of participants with postsecondary education. Conclusions: These findings suggest that a substantial proportion of older adults are already using and are interested in using mHealth for health information, to ask questions, and/or to review their medications with a health care team member. RÉSUMÉ Contexte : Les personnes âgées sont confrontées à des difficultés pour gérer leurs médicaments, s’informer sur la santé et accéder aux services de santé. Les applications de « santé mobile », définies comme toute pratique médicale ou de santé publique facilitée par des appareils mobiles, pourraient aider à surmonter ces difficultés. Objectifs : Déterminer quelles technologies et applications sont actuellement utilisées par les aînés; examiner celles qui pourraient être intéressantes dans cette tranche d’âge; étudier les préoccupations concernant les technologies et examiner les différences liées à l’âge. Méthodes : Des adultes d’au moins 60 ans ont été invités à répondre à un sondage électronique comprenant 35 questions en français ou en anglais. Ce sondage, mené à la mi-2020, a été diffusé par des organismes travaillant avec des aînés via les médias sociaux et par courriel. Résultats : Au total, 266 participants y ont répondu en partie ou en totalité. La plupart des répondants avaient un téléphone portable (229/243, 94,2 %) et environ un tiers (78/222, 35,1 %) avaient utilisé une application liée à la santé au cours des 12 derniers mois; ce taux d’utilisation était constant tous groupes d’âge confondus. La plupart des répondants souhaitaient utiliser une application pour améliorer leur santé (171/225, 76,0 %), avec des variations du taux d’utilisation selon l’âge : le plus élevé chez les 60 à 64 ans (82/95, 86,3 %), un peu moins chez les 80 ans ou plus (40/52, 76,9 %), et le plus bas chez les 65 à 69 ans (6/14, 42,9 %). La plupart des personnes âgées souhaitent utiliser une application pour poser des questions aux pharmaciens (161/219, 73,5 %) et pour s’informer sur leurs médicaments (154/218, 70,6 %). Les préoccupations des participants en matière de « santé mobile » portaient sur les coûts, la divulgation d’informations personnelles, l’efficacité, la convivialité et l’approbation par les prestataires de soins de santé. On notera, parmi les limites de l’étude, les défis liés au recrutement électronique et à la distribution électronique des sondages, ainsi qu’une forte représentation de participants ayant fait des études postsecondaires. Conclusions : Ces résultats portent à croire qu’une proportion importante d’adultes âgés utilisent déjà la technologie de « santé mobile » et souhaitent l’utiliser pour obtenir des informations sur la santé, poser des questions et/ou s’informer sur leurs médicaments auprès d’un membre de l’équipe de soins de santé.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.114
GPT teacher head0.450
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), 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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Citations22
Published2023
Admission routes2
Has abstractyes

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