MétaCan
Menu
Back to cohort
Record W4399470889 · doi:10.2196/53384

Internet Use and Higher-Level Functional Capacity Decline Suppression in Japanese Older Adults With Low Education: JAGES 2016-2019 Longitudinal Study

2024· article· en· W4399470889 on OpenAlexvenueno aff
Atsuko Tajika, Atsushi Nakagomi, Yasuhiro Miyaguni, Chie Koga, Katsunori Kondo, Toshiyuki Ojima

Bibliographic record

VenueJMIR Aging · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceNational Center for Geriatrics and Gerontology8020 Promotion FoundationJapan Science and Technology AgencyChiba Foundation for Health Promotion and Disease PreventionJapan Health Promotion and Fitness FoundationSasakawa Sports FoundationMeiji Yasuda Life Foundation of Health and WelfareJapan Agency for Medical Research and Development
KeywordsGerontologyPsychologyThe InternetMedicineComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Higher-level functional capacity (HLFC) is crucial for the independent living of older adults. While internet use positively impacts the health of older adults, its effect on HLFC and how this effect varies with educational attainment remains uncertain. OBJECTIVE: This longitudinal study aimed to investigate whether internet use could mitigate the risk of HLFC decline and if this benefit extends to older adults with lower levels of education. METHODS: The data were sourced from the Japan Gerontological Evaluation Study (JAGES), encompassing 8050 community-dwelling adults aged 65 years and older from 2016 to 2019. The study focused on those who remained self-sufficient from 2016 to 2019, identifying participants with independent HLFC in 2016. The Tokyo Metropolitan Institute of Gerontology Index of Competence defined HLFC operationally, consisting of 3 subscales, namely instrumental activities of daily living, intellectual activity, and social role. The primary variable was the frequency of internet use in 2016; participants who reported using the internet were classified as internet users, while those who answered "No" were identified as nonusers. The study compared the effects of internet use on HLFC decline across educational levels of ≤9 years, 10-12 years, and ≥13 years using Poisson regression analysis adjusted for robust SE to calculate the risk ratio (RR) and 95% CI for HLFC decline in 2019. RESULTS: After adjusting for demographic and health condition risk factors, internet use was significantly linked to a decreased risk of HLFC decline in older adults over 3 years, including those with lower educational levels. Internet users with ≤9 years of educational attainment experienced a suppressed decline in the total score (RR 0.57, 95% CI 0.43-0.76; P<.001); instrumental activities of daily living (RR 0.58, 95% CI 0.38-0.91; P=.02), intellectual activity (RR 0.60, 95% CI 0.41-0.89; P=.01), and social role (RR 0.74, 95% CI 0.56-0.97; P=.03) compared with nonusers. Participants with 10-12 years of education showed suppression rates of 0.78 (95% CI 0.63-0.98; P=.03), 0.59 (95% CI 0.39-0.90; P=.01), 0.91 (95% CI 0.63-1.31; P=.61), and 0.82 (95% CI 0.68-1.00; P=.05), respectively, and those with ≥13 years displayed suppression rates of 0.65 (95% CI 0.51-0.85; P=.001), 0.55 (95% CI 0.36-0.83; P=.01), 0.64 (95% CI 0.37-1.10; P=.11), and 0.83 (95% CI 0.64-1.08; P=.17), respectively. CONCLUSIONS: These findings indicate that internet use supports the maintenance of HLFC independence in older adults with higher education and those with lower educational levels. Encouraging internet use among older adults with lower levels of education through future policies could help narrow functional health disparities associated with educational attainment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.070
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.313
Teacher spread0.273 · 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.

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJMIR AgingSame topicTechnology Use by Older AdultsFrench-language works237,207