The Effectiveness Of Digital Interventions To Increase Preventive Care Uptake In Older Adults: A Systematic Review
Bibliographic record
Abstract
Abstract The coming decade will witness a substantial increase in older adult populations, contributing to a surge in healthcare utilization and costs driven by rising chronic diseases. Addressing these challenges necessitates strategies to promote healthy aging, reduce chronic diseases, and enhance quality of life among older adults. One proactive approach is to encourage older individuals to follow recommended preventive care and immunization schedules. Despite the increasing number of recommended immunizations for older adults, completion rates remain alarmingly low, particularly for pneumococcal and shingles vaccines. What is unknown is how the use of digital interventions can be appropriately leveraged to address this gap in older adult health services utilization. Following PRISMA guidelines, this systematic review aimed to assess the range and effectiveness of digital interventions to improve preventive uptake among older adults. Twenty-seven studies (23 randomized clinical trials and 4 quasi-experimental) were included. The preventive care services targeted in interventions included colorectal cancer screening (n = 14), breast cancer screening (n = 2), influenza vaccination (n = 5), combined vaccinations (n = 2), pneumococcal vaccination (n = 2), herpes zoster vaccination (n = 1), and COVID-19 vaccination (n = 1). Interventions primarily leveraged automated digital communication, combined digital and printed outreach, tailored telephone communication, and supplemented in-person promotion. Studies consistently demonstrated an increase in preventive care uptake post intervention. However, including digital elements did not consistently show a significant improvement over control groups or standard care. These findings emphasize the importance and effectiveness of a combination of approaches in enhancing preventive care uptake among older adults, thus contributing to improved public health outcomes in aging populations.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".