Smart technology vs. face-to-face physical activity interventions in older adults: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to determine the effect of physical activity interventions delivered via smart technology compared with face-to-face interventions for improving physical activity and physical function in older adults. INTRODUCTION: Physical activity is a modifiable risk factor for multiple noncommunicable diseases and reduces the risk of premature mortality. Despite this, one in four adults does not meet recommended levels of physical activity. This pattern of inactivity increases with age. Smart technology, such as wearables, tablets, or laptops, is one solution for improving physical activity. Research has shown that different smart technology solutions can increase physical activity in older adults. While individual studies support smart technology to increase physical activity, there are no systematic reviews comparing the effects of smart technology with traditional face-to-face physical activity interventions. INCLUSION CRITERIA: We will include randomized controlled trials of physical activity interventions delivered via smart technology (eg, wearables, tablets, computers) compared with face-to-face (ie, in person) interventions for community-dwelling older adults aged 60 years or older. METHODS: We will search four databases (AMED, CINAHL, Embase, MEDLINE) from inception for relevant studies. All abstracts and full texts will be screened independently and in duplicate. Risk of bias, data extraction, and quality assessment will be completed in the same manner. If possible, a meta-analysis will be performed of the primary outcomes of physical activity, physical function, and adherence rate. Subgroup analyses will be conducted by type of physical activity, and type of smart technology, where possible. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42020135232.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".