High-Intensity Interval Training in Older Adults: a Scoping Review
Bibliographic record
Abstract
Abstract High-intensity interval training (HIIT) is an increasingly popular form of aerobic exercise which includes bouts of high-intensity exercise interspersed with periods of rest. The health benefits, risks, and optimal design of HIIT are still unclear. Further, most research on HIIT has been done in young and middle-aged adults, and as such, the tolerability and effects in senior populations are less well-known. The purpose of this scoping review was to characterize HIIT research that has been done in older adults including protocols, feasibility, and safety and to identify gaps in the current knowledge. Five databases were searched with variations of the terms, “high-intensity interval training” and “older adults” for experimental or quasi-experimental studies published in or after 2009. Studies were included if they had a treatment group with a mean age of 65 years or older who did HIIT, exclusively. Of 4644 papers identified, 69 met the inclusion criteria. The average duration of training was 7.9 (7.0) weeks (mean [SD]) and protocols ranged widely. The average sample size was 47.0 (65.2) subjects (mean [SD]). Healthy populations were the most studied group (n = 30), followed by subjects with cardiovascular (n = 12) or cardiac disease (n = 9), metabolic dysfunction (n = 8), and others (n = 10). The most common primary outcomes included changes in cardiorespiratory fitness (such as VO2peak) as well as feasibility and safety of the protocols as measured by the number of participant dropouts, adverse events, and compliance rate. HIIT protocols were diverse but were generally well-tolerated and may confer many health advantages to older adults. Larger studies and more research in clinical populations most representative of older adults are needed to further evaluate the clinical effects of HIIT in these groups.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".