ECSS Paris 2023: OP-MH01
INTRODUCTION: Cardiorespiratory fitness (CRF), a strong predictor of overall health and longevity, typically declines with age, increasing the risk of chronic diseases in older adults.1 Home-based high-intensity interval training (HIIT) potentially offers a practical and accessible method to improve CRF. However, objectively measured adherence levels and subsequent effectiveness of home-based HIIT in older populations remain underexplored.2,3 Therefore, this study aimed to investigate adherence to a six-month home-based HIIT intervention in older adults, as well as assessing associations between various adherence metrics and the resultant changes in peak oxygen consumption (V̇O2peak). METHODS: 233 healthy older adults (60-84 years, 54% female) were randomised to six-month, thrice-weekly home-based HIIT (one circuit and two interval sessions) or a passive control group. Exercise sessions were monitored with a Polar watch and logbook for objective and subjective data, respectively, and guided by a personal coach. Adherence was assessed using frequency, intensity, and duration data, and was quantified using a novel method involving metabolic equivalents (METs).4 For each metric, adherence was expressed as percentage completion relative to what was planned. V̇O2peak was assessed using a modified Balke treadmill protocol to volitional exhaustion. General linear regression models (GLMs) assessed between-group differences in post-intervention V̇O2peak, with baseline V̇O2peak, age, sex, and country as covariates, and group as a fixed factor. For adherence-V̇O2peak associations, GLMs were used with age, sex, and country as covariates. RESULTS: The HIIT group achieved an average total of 11116±5455 MET-min (122% of the planned exercise volume). Participants completed 2.6±0.6 sessions per week (86% of planned) and spent 10.3±5.2 min per session at ≥80% of HRpeak (98% of planned), with an average session duration of 39.9±11.8 min (135% of planned). Between-group differences were observed in the pre-to-post intervention change in V̇O2peak (1.8 [1.2;2.3] mL/kg/min; effect size: 0.35). There was a positive association between adherence to frequency and intensity and percentage improvements in V̇O2peak (β=0.1 [0.0;0.2]; β=0.04 [0.00;0.07], respectively), but not duration or total MET-mins. CONCLUSION: The findings indicate that older adults can successfully adhere to and benefit from a home-based HIIT program, achieving clinically meaningful improvements in CRF over six months. Notably, while adherence levels are crucial in designing effective exercise interventions for this demographic, the findings indicate that exceeding prescribed exercise volumes does not necessarily lead to superior enhancements in CRF. References: 1. Kaminsky et al. Progress in cardiovascular diseases. 2019. 2. Gray et al. British Journal of Sports Medicine. 2016. 3. Stork et al. Health psychology review. 2017. 4. Nilsen et al. Medicine and science in sports and exercise. 2018. (www.fab-study.com)
Read CV Sindre H. FosstveitECSS Paris 2023: OP-MH01
INTRODUCTION: Around 50% of individuals aged 60 and older experience sleep problems, and current insomnia treatments are difficult to access or have adverse effects [1]. Thus, there is urgent need for alternatives. Gamma-aminobutyric acid (GABA)-mediated inhibition is crucial for sleep [2] and older adults exhibit lower GABA levels [3]. Interestingly, balance training has been shown to enhance GABA-mediated inhibition [4]. Thus, we hypothesised that balance training would improve sleep quality in older adults. METHODS: 60 volunteers (64-81 years old) were randomly assigned to a three-month balance intervention (> 30 sessions; BT), a three-month strength intervention (> 30 sessions; ST) or a control group, following their daily routines (CON). Before and after the three-month period, subjective sleep quality was evaluated with the Pittsburgh Sleep Quality Questionnaire (PSQI). Sleep efficiency was recorded with polysomnography (PSG) at the participant’s home. During an afternoon nap in the laboratory, short- interval intracortical inhibition (SICI), a measure of the activity of GABAergic inhibitory interneurons in the motor cortex, was assessed. GABA and lactate levels in the motor cortex were determined with magnetic resonance spectroscopy. Linear mixed-effects models (LME), followed by Bonferroni-corrected paired t- tests and cohen’s d effect sizes with 95% confidence intervals were calculated. RESULTS: LME indicated a significant time effect for PSQI scores (p=.016). BT improved subjective sleep quality by -1.35 score points (d=0.58, 95% CI [0.06, 1.09], p=.046), with no change in ST (p=1) and CON (p=1). Improved sleep scores in BT showed a trend towards a strong correlation with increased SICI at sleep onset (r =-0.59, p=.073). Furthermore, a time effect on GABA levels was revealed (p=.025). BT increased GABA levels by 22% (d=0.97, 95% CI [0.40, 1.55], p=.009), while ST (p=1) and CON (p=1) showed no change. Sleep efficiency demonstrated a significant interaction effect of group and time (p=.006): BT (p=.353) and CON (p=1) did not change significantly while ST increased sleep efficiency by 7% (d=0.71, 95% CI [0.22, 1.21], p=.022). Additionally, ST revealed a significant decrease in brain lactate level (p=.02). CONCLUSION: Older adults showed improved subjective sleep quality along with increased GABA levels after BT. Moreover, increased GABAergic inhibition at sleep onset was associated with improved sleep scores. These findings support the idea that BT counteracts hyperarousal in the elderly brain [5]. The observed decrease in brain lactate, a biomarker of sleep [6], suggests that ST improves sleep efficiency by improving lactate metabolism, as previously shown in animal models [7]. 1. Patel, D. et al., J Clin Sleep Med, 2018. 2. Saper, C.B. et al., Nature, 2005. 3. Cuypers, K. et al., Aging (Albany NY), 2018. 4. Taube, W. et al., Eur J Neurosc, 2020. 5. Riemann, D. et al., Sleep Med Rev, 2010. 6. Naylor, E. et al., Sleep, 2012. 7. Carroll, C.M. et al., bioRxiv, 2022.
Read CV Selin ScherrerECSS Paris 2023: OP-MH01
INTRODUCTION: Aging constitutes a multifaceted and inevitable process characterized by a gradual decline in physiological functions and an escalation in health risks. Despite a significant increase in life expectancy, the achievement of healthy aging has not kept pace accordingly. This emphasizes the importance of identifying biomarkers associated with healthy aging. Thus, this study aimed 1) to cluster the study population into healthy and unhealthy aging by using machine learning methodologies, 2) to identify straightforward predictive variables for these clusters, and 3) to analyze the relationship between clusters indicative of unhealthy aging and adverse health events and conditions. METHODS: A prospective cohort study that included 1852 older adults (>65 years) from the Toledo Study for Healthy Aging. A total of 366 variables (including physical, cognitive, and biochemical outcomes among others) were assessed using machine learning k-means clustering. Gradient boosting, area under the curve (AUC) values, and classification accuracy (CA) were used to evaluate the predictive capacity of healthy and unhealthy aging clusters. Logistic regression analyses adjusted for age, educational level, and comorbidities were used to assess the relationship between the healthy and unhealthy aging clusters and the risk of experiencing cognitive impairment, frailty, hospitalizations, and all-cause mortality over the subsequent 6 years. RESULTS: Two clusters of healthy (C1) and unhealthy aging (C2) were identified in both men (C1= 243; C2= 565) and women (C1= 342; C2= 702). Among the 366 variables tested, relative sit-to-stand power, loss of memory in the last two years, and habitual gait speed collectively exhibited excellent predictive capacity for healthy and unhealthy aging in both older men and women according to the AUC values (0.850 in men and 0.875 in women) and CA (0.819 in men and 0.792 in women). After 6 years of follow-up, older adults in the unhealthy aging cluster exhibited a higher risk of experiencing cognitive impairment [OR (95%CI) = 4.7 (1.8, 12.3) in men and 2.9 (1.3, 6.3) in women], frailty [OR (95%CI) = 2.8 (1.2, 6.8) in men and 7.1 (3.3, 15.4) in women], hospitalizations [OR (95%CI) = 1.8 (1.2, 2.6) in men and 2.0 (1.4 , 2.8) in women] and all-cause mortality [OR (95%CI) = 2.0 (1.4, 2.9) in men and 2.2 (1.5, 3.1) in women] compared to those older individuals in the healthy aging cluster. CONCLUSION: The machine-learning method identified that relative sit-to-stand power, memory loss, and habitual gait speed were strong predictors of healthy and unhealthy aging in older adults. Those older people within the unhealthy aging cluster exhibited a 2 to 7 times higher likelihood of experiencing an adverse event or condition compared to those in the healthy aging cluster after 6 years of follow-up, emphasizing the importance of assessing and monitoring these variables to ensure healthy aging.
Read CV Mikel García AguirreECSS Paris 2023: OP-MH01