Digital Information Technology Use and Patient Preferences for Internet-Based Health Education Modalities: Cross-Sectional Survey Study of Middle-Aged and Older Adults With Chronic Health Conditions
Bibliographic record
Abstract
BACKGROUND: Health information, patient education, and self-management (health information and advice, HIA) tools are increasingly being made available to adults with chronic health conditions through internet-based health and mobile health (mHealth) digital information technologies. However, there is limited information about patient preferences for using specific types of health information and advice resources and how preferences and usage differ by age group and education. OBJECTIVE: The objective of this study was to examine how use of digital information technologies and preferred methods for obtaining health information and advice varies by age group and education among middle-aged and older adults with chronic health conditions. METHODS: The study used cross-sectional survey data for 9005 Kaiser Permanente Northern California members aged 45 to 85 years who responded to a mailed and Web-based health survey conducted during 2014 and 2015 and indicated having at least 1 chronic health condition. Bivariate analyses and logistic regression models with weighted data were used to estimate and compare the prevalence of digital information technology use, past-year use of internet-based health information and advice resources, and preferences for using internet-based, mHealth, and traditional health information and advice modalities for adults aged 45 to 65 years, 66 to 75 years, and 76 to 85 years. RESULTS: The percentages of adults who used digital information technologies (computers, smartphones, internet, email, and apps), had obtained health information and advice from an internet-based resource in the past year, and who were interested in using internet-based and mHealth modalities for obtaining health information and advice declined with age. Within age group, prevalence of digital information technologies use and interest in internet-based and mHealth modalities was lower among adults with no college education versus college graduates. Differences in preferences for internet-based health information and advice modalities between the oldest and younger groups and those with lower versus higher education were substantially diminished when we restricted analyses to internet users. CONCLUSIONS: Health care providers and organizations serving middle-aged and older adults with chronic health conditions should not assume that patients, especially those who are older and less educated, want to engage with internet-based and mHealth resources. In addition, increasing the engagement of nonutilizers of digital devices and the internet with internet-based health information and advice and mHealth apps might require both instrumental (eg, providing digital information technology devices, internet, and skills training) and social support. As part of patient-centered care, it is important for providers to ascertain their patients' use of digital information technologies and preferences for obtaining health information and patient education rather than routinely referring them to internet-based resources. It is also important for health care providers and consumer health organizations to user test their Web-based resources to make sure they are easy for older and less educated adults to use and to make sure that it remains easy for adults with chronic conditions to obtain health information and patient education using offline resources.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".