The Effect of Information and Communication Technologies Utilization Patterns on Self-Rated Health
Bibliographic record
Abstract
Background: An increasing number of older adults are using information and communication technologies (ICTs), and ICTs have become a major resource for older adults for improving health-related quality of life. ICTs possibly help older adults seek health information online, collaborate with other users in their decision making process, and receive social support. Although there have been some studies highlighting positive associations between ICT utilization and health, there is still limited knowledge about various patterns of ICT utilization among older adults. Objective: This study aims to extend the empirical evidence regarding the patterns of older adults’ ICT utilization, investigate different attributes across various ICT utilization patterns, and further examine how these patterns influence self-rated health. Methods: Data came from the 2012 and 2014 Health and Retirement Study, a nationally representative sample of Americans aged 51 and older. Our sample was restricted to individuals who responded to a special survey about technology use asked only to a subsample of 2012 interviews (N=1504). Latent class analysis was used to identify ICT utilization patterns based on ICT utilization variables: (1) communication-related utilization, including use of email, social networking sites, online video call, instant messenger, and smartphones; (2) finance-related utilization, such as online bill payment and online banking; (3) health-related utilization, including exercise equipment, exercise videos, online wellness programs, online health information, health monitoring devices, and Wii Fit; and (4) entertainment-related utilization, including e-readers/tablets, mp3 players, online streaming media, and video game. Ordinary least squares regressions were used to examine the effects of ICT utilization patterns on self-rated health at follow-up as compared to baseline. Results: Four ICT utilization patterns were identified: multifarious (n=90: high level of ICT utilization across most variables), e-commerce-oriented (n=147: high level of finance-related utilization), fundamental (n=280: email and online search focused utilization), and minimal users (n=552: low level of ICT utilization across most variables). We found that multifarious users were younger, more often female, married, and had higher education and income levels and better physical and mental health than other groups. Minimal users were more likely to be older, non-white, and single, and more likely to have lower level of education and income and poor physical and mental health. Regression models showed multifarious users were most likely to have better self-rated health, and minimal users tended to have the worst self-rated health over time, even after controlling for sociodemographic attributes and health conditions. E-commerce-oriented users were more likely to have better self-rated health than fundamental users. Conclusions: This study identified clearly different ICT utilization patterns among older adults and demonstrated positive effects of ICT utilization on health among older adults. Improving access to ICTs and ICT education programs will help to improve health outcomes of older adults, but the effects of different ICT utilization patterns need to be highlighted in future studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".