Efficacy Of Limb And Neck Cooling For Reducing Thermal And Cardiovascular Strain In Older Adults
Bibliographic record
Abstract
Low-cost cooling interventions are being increasingly recommended as sustainable alternatives to air conditioning during heat waves. These include interventions exploiting the thermal properties of water, such as lower limb immersion and neck cooling, which carry the added benefit that they can be used in low resource settings or during power outages. However, there is currently a lack of empirical evidence to support their efficacy for limiting physiological strain in heat-vulnerable older adults (e.g., in aged care homes). PURPOSE: In this randomized trial (NCT05601713), we evaluated the effects of limb immersion with and without neck cooling on core temperature, cardiovascular strain, and hydration status in older adults during heat exposure. METHODS: Eight older adults (4 women, 72 (SD 5) yrs) completed 3 randomized 6-hour exposures to 38 °C and 35% relative humidity. Trials differed in the cooling strategy employed: no cooling (CTRL), lower limb (mid-calf) immersion in 20 °C water (LIMB), or limb immersion with a damp towel (20 °C) placed around the neck (LIMB+NECK). Cooling was applied intermittently (20 min off, 40 min on). Core temperature (rectal), heart rate, and systolic blood pressure were monitored throughout. Hourly fluid consumption and cumulative fluid loss (% body mass change) were also recorded. Mixed model estimated marginal means were compared between conditions. RESULTS: After 6 hours of heat exposure, core temperature was not different between CTRL (37.9 (0.4)°C) and either LIMB or LIMB+NECK (mean diff and 95% CI vs CTRL: both 0.0 °C [-0.1, 0.2], P ≥ 0.69). By contrast, heart rate was reduced 7 bpm [4, 10] and 5 bpm [2, 8] from CTRL (68 (10) bpm) in LIMB and LIMB+NECK, respectively (P ≤ 0.003). No between-condition differences in systolic pressure were detected (P ≥ 0.37; 115 (10) mm Hg). Compared to CTRL, fluid consumption was reduced 53 mL/hour [9, 96] in LIMB and 71 mL/hour [28, 115] LIMB+NECK (P ≤ 0.018). There were no differences in the change in body mass (P ≥ 0.09; -0.5 (0.9)% body mass). CONCLUSION: Our preliminary data suggest that lower limb immersion with or without neck cooling does not reduce core temperature in older adults resting in the heat. We did, however, observe small but statistically significant reductions in heart rate and improvements in fluid maintenance. FUNDING: Health Canada
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".