e-Health interventions for healthy aging: a systematic review
Bibliographic record
Abstract
BACKGROUND: Healthy aging (HA) is a contemporary challenge for population health worldwide. Electronic health (e-Health) interventions have the potential to support empowerment and education of adults aged 50 and over. OBJECTIVES: To summarize evidence on the effectiveness of e-Health interventions on HA and explore how specific e-Health interventions and their characteristics effectively impact HA. METHODS: A systematic review was conducted based on the Cochrane Collaboration methods including any experimental study design published in French, Dutch, Spanish, and English from 2000 to 2018. RESULTS: Fourteen studies comparing various e-Health interventions to multiple components controls were included. The target population, type of interventions, and outcomes measured were very heterogeneous across studies; thus, a meta-analysis was not possible. However, effect estimates indicate that e-Health interventions could improve physical activity. Positive effects were also found for other healthy behaviors (e.g., healthy eating), psychological outcomes (e.g., memory), and clinical parameters (e.g., blood pressure). Given the low certainty of the evidence related to most outcomes, these results should be interpreted cautiously. CONCLUSIONS: This systematic review found limited evidence supporting the effectiveness of e-Health interventions, although the majority of studies show positive effects of these interventions for improving physical activity in older adults. Thus, better quality evidence is needed regarding the effects of e-Health on the physiological, psychological, and social dimensions of HA. SYSTEMATIC REVIEW REGISTRATION: The review protocol was registered in PROSPERO (registration number: CRD42016033163).
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 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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".