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Decreased nocturnal awakenings in young adults performing bikram yoga: a low-constraint home sleep monitoring study.

Kudesia RS, Bianchi MT - ISRN Neurol (2012)

Bottom Line: Consistent with prior work, transition probability analysis was a more sensitive index of sleep architecture changes than standard metrics.We conclude that objective home sleep monitoring is feasible in a low-constraint, real-world study design.Further studies on patients with insomnia will determine whether the results generalize or not.

View Article: PubMed Central - PubMed

Affiliation: Sleep Division, Neurology Department, Massachusetts General Hospital, Wang 720, Boston, MA 02114, USA.

ABSTRACT
This pilot study evaluated the impact of Bikram Yoga on subjective and objective sleep parameters. We compared subjective (diary) and objective (headband sleep monitor) sleep measures on yoga versus nonyoga days during a 14-day period. Subjects (n = 13) were not constrained regarding yoga-practice days, other exercise, caffeine, alcohol, or naps. These activities did not segregate by choice of yoga days. Standard sleep metrics were unaffected by yoga, including sleep latency, total sleep time, and percentage of time spent in rapid eye movement (REM), light non-REM, deep non-REM, or wake after sleep onset (WASO). Consistent with prior work, transition probability analysis was a more sensitive index of sleep architecture changes than standard metrics. Specifically, Bikram Yoga was associated with significantly faster return to sleep after nocturnal awakenings. We conclude that objective home sleep monitoring is feasible in a low-constraint, real-world study design. Further studies on patients with insomnia will determine whether the results generalize or not.

No MeSH data available.


Related in: MedlinePlus

Sleep-wake architecture. (a) The percentage of time spent in wake after sleep onset (W), REM, light NR (L-NR), and deep NR (D-NR) sleep are shown in box and whisker plots (median, 25–75% quartiles, and 95% confidence interval whiskers; mean indicated by plus sign). The values of each sleep-wake stage for nonyoga nights (open boxes) were not different than nonyoga nights (gray boxes). (b) Survival curves for bouts of wake after sleep onset (WASO) were significantly different for nonyoga (black line) and yoga (gray line) nights. The survival curves show the normalized relative frequency of observing bouts of WASO of different durations. The inset shows the absolute number of awakenings per hour of sleep for nonyoga (open) and yoga (gray) nights, which were not different. (c–e) There were no differences in the survival curves between yoga and nonyoga nights for REM, L-NR, or D-NR sleep-stage-bout distributions.
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fig2: Sleep-wake architecture. (a) The percentage of time spent in wake after sleep onset (W), REM, light NR (L-NR), and deep NR (D-NR) sleep are shown in box and whisker plots (median, 25–75% quartiles, and 95% confidence interval whiskers; mean indicated by plus sign). The values of each sleep-wake stage for nonyoga nights (open boxes) were not different than nonyoga nights (gray boxes). (b) Survival curves for bouts of wake after sleep onset (WASO) were significantly different for nonyoga (black line) and yoga (gray line) nights. The survival curves show the normalized relative frequency of observing bouts of WASO of different durations. The inset shows the absolute number of awakenings per hour of sleep for nonyoga (open) and yoga (gray) nights, which were not different. (c–e) There were no differences in the survival curves between yoga and nonyoga nights for REM, L-NR, or D-NR sleep-stage-bout distributions.

Mentions: Standard analysis of sleep architecture involves calculating the percentage of time spent in each sleep-wake stage. In this study, we found that the percentage of time spent in wake, light non-REM, deep non-REM, or REM sleep did not differ based on Bikram performance (ANOVA, with Bonferroni correction, P > 0.05) (Figure 2(a)). This was not surprising, given that (1) this is a baseline healthy population without significant sleep complaints and (2) sleep-stage percentage is an insensitive metric of fragmentation, such as that caused by sleep apnea [9–11].


Decreased nocturnal awakenings in young adults performing bikram yoga: a low-constraint home sleep monitoring study.

Kudesia RS, Bianchi MT - ISRN Neurol (2012)

Sleep-wake architecture. (a) The percentage of time spent in wake after sleep onset (W), REM, light NR (L-NR), and deep NR (D-NR) sleep are shown in box and whisker plots (median, 25–75% quartiles, and 95% confidence interval whiskers; mean indicated by plus sign). The values of each sleep-wake stage for nonyoga nights (open boxes) were not different than nonyoga nights (gray boxes). (b) Survival curves for bouts of wake after sleep onset (WASO) were significantly different for nonyoga (black line) and yoga (gray line) nights. The survival curves show the normalized relative frequency of observing bouts of WASO of different durations. The inset shows the absolute number of awakenings per hour of sleep for nonyoga (open) and yoga (gray) nights, which were not different. (c–e) There were no differences in the survival curves between yoga and nonyoga nights for REM, L-NR, or D-NR sleep-stage-bout distributions.
© Copyright Policy - open-access
Related In: Results  -  Collection

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fig2: Sleep-wake architecture. (a) The percentage of time spent in wake after sleep onset (W), REM, light NR (L-NR), and deep NR (D-NR) sleep are shown in box and whisker plots (median, 25–75% quartiles, and 95% confidence interval whiskers; mean indicated by plus sign). The values of each sleep-wake stage for nonyoga nights (open boxes) were not different than nonyoga nights (gray boxes). (b) Survival curves for bouts of wake after sleep onset (WASO) were significantly different for nonyoga (black line) and yoga (gray line) nights. The survival curves show the normalized relative frequency of observing bouts of WASO of different durations. The inset shows the absolute number of awakenings per hour of sleep for nonyoga (open) and yoga (gray) nights, which were not different. (c–e) There were no differences in the survival curves between yoga and nonyoga nights for REM, L-NR, or D-NR sleep-stage-bout distributions.
Mentions: Standard analysis of sleep architecture involves calculating the percentage of time spent in each sleep-wake stage. In this study, we found that the percentage of time spent in wake, light non-REM, deep non-REM, or REM sleep did not differ based on Bikram performance (ANOVA, with Bonferroni correction, P > 0.05) (Figure 2(a)). This was not surprising, given that (1) this is a baseline healthy population without significant sleep complaints and (2) sleep-stage percentage is an insensitive metric of fragmentation, such as that caused by sleep apnea [9–11].

Bottom Line: Consistent with prior work, transition probability analysis was a more sensitive index of sleep architecture changes than standard metrics.We conclude that objective home sleep monitoring is feasible in a low-constraint, real-world study design.Further studies on patients with insomnia will determine whether the results generalize or not.

View Article: PubMed Central - PubMed

Affiliation: Sleep Division, Neurology Department, Massachusetts General Hospital, Wang 720, Boston, MA 02114, USA.

ABSTRACT
This pilot study evaluated the impact of Bikram Yoga on subjective and objective sleep parameters. We compared subjective (diary) and objective (headband sleep monitor) sleep measures on yoga versus nonyoga days during a 14-day period. Subjects (n = 13) were not constrained regarding yoga-practice days, other exercise, caffeine, alcohol, or naps. These activities did not segregate by choice of yoga days. Standard sleep metrics were unaffected by yoga, including sleep latency, total sleep time, and percentage of time spent in rapid eye movement (REM), light non-REM, deep non-REM, or wake after sleep onset (WASO). Consistent with prior work, transition probability analysis was a more sensitive index of sleep architecture changes than standard metrics. Specifically, Bikram Yoga was associated with significantly faster return to sleep after nocturnal awakenings. We conclude that objective home sleep monitoring is feasible in a low-constraint, real-world study design. Further studies on patients with insomnia will determine whether the results generalize or not.

No MeSH data available.


Related in: MedlinePlus