Improved subjective sleep quality after three months of balance learning in older adults

Abstract

Introduction

Around half of adults over the age of 60 experience sleep problems (Reid et al., 2006). The most common treatments for sleep disorders like insomnia are pharmacotherapy and cognitive behavioural therapy. Unfortunately, pharmacotherapy often leads to increased mortality and fall rates in older adults, whereas cognitive behavioural therapy is expensive and difficult to access (Patel et al., 2018). Hence, there is an urgent need for new effective and affordable treatments with reduced negative side effects. An important role for the initiation and maintenance of sleep is attributed to gamma-aminobutyric acid (GABA)-mediated inhibition (Saper et al., 2005). On one hand, it has been shown that GABA-mediated inhibition as well as GABA concentrations are lower in older compared to younger adults (Cuypers et al., 2018). On the other hand, balance training was shown to increase GABA-mediated inhibition in young (Taube et al., 2020) and older adults (Kuhn et al., 2023). Therefore, balance learning seems to be a promising treatment for older adults suffering from sleep problems. Furthermore, balance learning was shown to enhance functional connectivity (Ueta et al., 2022). In particular, functional connectivity in the sensorimotor cortex has been associated with better subjective sleep quality (Jiang et al., 2023). Therefore, we hypothesized that balance learning in older adults improves subjective sleep quality through an increase in GABA-mediated inhibition and sensorimotor network functional connectivity.

Methods

Forty healthy volunteers aged 64-81 years were randomly assigned to either follow a three-month balance learning intervention (minimum of 30 training sessions) or to continue with their daily routines. Thirty-six participants (18 in intervention group, 18 in control group) completed pre and post measurements and were included in the analysis. Before and after the three-month period, subjective sleep quality, balance performance, and neurophysiological and neuroimaging parameters were assessed. The Pittsburgh Sleep Quality Questionnaire (PSQI) was employed to evaluate subjective sleep quality in the preceding four weeks. Balance performance was assessed by determining the sway area in cm² during a twenty second balance task on the most difficult wobble board level the participant still succeeded at. Short- interval intracortical inhibition (SICI), a measure of the activity of inhibitory interneurons in the motor cortex, was measured using transcranial magnetic stimulations while the participants were balancing on the same wobble board as during the balance performance assessment, and during an afternoon nap. Furthermore, resting-state functional connectivity was assessed with functional magnetic resonance imaging. The PSQI total scores were not normally distributed and therefore square root- transformed prior to the statistical analysis. Differences between post measurements were analysed using analysis of covariance (ANCOVA) with pre values as a covariate. Post-hoc t-tests were applied to determine the direction of change. Correlations between improvements in balance performance and sleep quality and neurochemical and neurophysiological measures were calculated using Spearman correlation analysis.

Results

ANCOVA revealed a significant effect of group on balance performance (p = 0.025). Post-hoc tests showed a significant improvement in performance after balance learning, indicated by a mean decrease of sway area by 33% (p = 0.002), while there was no significant change in the control group (p = 0.365). Furthermore, increases in balance performance were significantly associated with increases in SICI during execution of a balance task (r = -0.54, p = 0.02). ANCOVA revealed a significant effect of group on PSQI total score (p = 0.04). Post-hoc tests showed a significant decrease in the balance group by 23% (p = 0.015), indicating better subjective sleep quality after balance learning, while there was no significant change in the control group (p = 0.72). Improved subjective sleep quality in the balance group showed a trend towards an association with increased SICI while participants were falling asleep (r = -0.59, p = 0.07). Furthermore, there was a significant effect of group on functional connectivity (p = 0.005). In the balance group, functional connectivity increased by 40% (p = 0.003), while there were no significant changes in the control group (p = 0.32; p = 0.34). Correlation analysis at the population level revealed significant correlations between SICI during a balance task and balance performance (r = -0.4, p = 0.02), between SICI while balancing and functional connectivity (r = 0.38, p = 0.04), and between functional connectivity and balance performance (r = -0.5, p = 0.006).

Discussion/Conclusion

After three months of balance learning, older adults did not only show an improved balance performance but also significant improvements in their subjective sleep quality. Moreover, functional connectivity was significantly enhanced after balance learning and was positively associated with changes in SICI during execution of a balance task. These findings may be explained by the idea that functional connectivity plays a crucial role in the functional use of GABA, modulating inhibition across brain regions. Furthermore, the concept of task-specificity of intracortical inhibition is endorsed by the finding that specifically participants who showed an increase in GABAergic inhibition while falling asleep improved their subjective sleep quality. In conclusion, balance learning did improve subjective sleep quality and changes in functional connectivity and GABAergic inhibition might be (part of) the underlying mechanisms driving this change.

References

Cuypers, K., Maes, C., & Swinnen, S. P. (2018). Aging and GABA. Aging, 10(6), 1186-1187. https://doi.org/10.18632/aging.101480

Jiang, C., Cai, S., & Zhang, L. (2023). Functional connectivity of white matter and its association with sleep quality. Nature and Science of Sleep, 15, 287-300. https://doi.org/10.2147/NSS.S406120

Kuhn, Y.-A., Bugnon, M., Egger, S., Lehmann, N., Taubert, M., & Taube, W. (2023). Age-related decline in GABAergic intracortical inhibition can be counteracted by long-term learning of balance skills [Manuscript submitted for publication]. Neurosciences and Movement Science, University of Fribourg.

Patel, D., Steinberg, J., & Patel, P. (2018). Insomnia in the elderly: A review. Journal of Clinical Sleep Medicine, 14(6), 1017-1024. https://doi.org/10.5664/jcsm.7172

Reid, K. J., Martinovich, Z., Finkel, S., Statsinger, J., Golden, R., Harter, K., & Zee, P. C. (2006). Sleep: A marker of physical and mental health in the elderly. The American Journal of Geriatric Psychiatry, 14(10), 860-866. https://doi.org/10.1097/01.JGP.0000206164.56404.ba

Saper, C. B., Scammell, T. E., & Lu, J. (2005). Hypothalamic regulation of sleep and circadian rhythms. Nature, 437(7063), 1257-1263. https://doi.org/10.1038/nature04284

Taube, W., Gollhofer, A., & Lauber, B. (2020). Training-, muscle- and task-specific up- and downregulation of cortical inhibitory processes. European Journal of Neuroscience, 51(6), 1428-1440. https://doi.org/10.1111/ejn.14538

Ueta, K., Mizuguchi, N., Sugiyama, T., Isaka, T., & Otomo, S. (2022). The Motor Engram of Functional Connectivity Generated by Acute Whole-Body Dynamic Balance Training. Medicine and Science in Sports and Exercise, 54(4), 598-608. https://doi.org/10.1249/MSS.0000000000002829