HIGH-INTENSITY INTERVAL TRAINING AND MODERATE-INTENSITY CONTINUOUS TRAINING EFFECTS IN OBESE OLDER ADULTS
Bibliographic record
Abstract
Background: Aging is associated with obesity, which is related to functional capacity decline. Even if resistance or aerobic trainings (moderate-intensity continuous trainings: MICT) counteract these phenomena, the majority of older adults are sedentary and one of the main reported barriers is lack of time. However, high-intensity interval training (HIIT) with half of time seems to induce health benefits. Objective: To verify if HIIT leads to higher improvements on functional capacity and body composition than MICT in obese older adults. Methods: Seventy-two sedentary obese (fat mass[FM]: Men>27%, Women>35%) older adults (>60yrs) were randomized in two groups: 1)HIIT (n=36; women(18)/men(18)) or 2)MICT (n=36; women(17)/men(19)). Participants followed a 12-week intervention (3times/week): elliptical HIIT program (30min/session; cycle:30sec>85%-Borg scale>17 and 90sec~65%-Borg scale:13–16) of maximal heart-rate; or a treadmill MICT (60min/session; ~65–75%-Borg scale:13–16) of maximal heart-rate. Body composition (FM and lean mass); fast (Timed Up-and-Go[TUG]) and self-paced (4-meter walk test[4-mWT]) walking speed, aerobic capacity (6-min walk test[6MWT]) and sit-to-stand test were measured. Results: At baseline, groups were similar regarding age, BMI, body composition, functional capacity, lifestyle habits and adherence (HIIT=93.9 ± 6.6% vs. MICT=94.5 ± 9.2%). A within-group effect for functional tests (p<.001); gynoïd FM loss (DXA;p=.008) and subcutaneous FM loss (pQCT;p=.001) was observed. Post-intervention, HIIT resulted in greater improvements in functional capacities (sit-to-stand, p=.008; 4-mWT,p=.05; TUG,p<.001; 6MWT,p<.001) than MICT group. Conclusion: Although of a shorter duration, our results indicate that HIIT in obese older adults is more efficient to improve functional capacities than MCIT. Our results also indicate that HIIT is as effective as MICT for FM loss.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".