Int.J. Behav. Med. DOI 10.1007/s12529-015-9492-0
Effects of Aquatic Exercise on Sleep in Older Adults with Mild Sleep Impairment: a Randomized Controlled Trial Li-Jung Chen 1 & Kenneth R. Fox 2 & Po-Wen Ku 3 & Yi-Wen Chang 1
# International Society of Behavioral Medicine 2015
Abstract Background Exercise has been found to be associated with improved sleep quality. However, most of the evidence is based on resistance exercise, walking, or gym-based aerobic activity. Purpose This study aimed to examine the effects of an 8week aquatic exercise program on objectively measured sleep parameters among older adults with mild sleep impairment. Methods A total of 67 eligible older adults with sleep impairment were selected and randomized to exercise and control groups, and 63 participants completed the study. The program involved 2×60-min sessions of aquatic exercise for 8 weeks. Participants wore wrist actigraphs to assess seven parameters of sleep for 1 week before and after the intervention. Mixeddesign analysis of variance (ANOVA) was used to assess the differences between groups in each of the sleep parameters. Results No significant group differences on demographic variables, life satisfaction, percentage of body fat, fitness, seated blood pressure, and any parameter of sleep were found at baseline. Significant group×time interaction effects were found in sleep onset latency, F(1,58)=6.921, p=.011, partial eta squared=.011, and in sleep efficiency, F(1, 61)=16.909, p3 times in the classes (n=4)
Post-test (week 9)
Post-test (week 9)
(Completed n=29)
(Completed n=34)
Fig. 1 Flow chart of the study participants
Variable means and percentages were calculated for exercise and control groups. The differences in scores on all variables at baseline between the two groups were tested for significance using t test or χ2 test. A mixed-design repeated-measures ANOVA was used to examine the pre-post-intervention changes. The between-subjects factor was group, with two levels, intervention group and control group. The withinsubjects variable was changed over time with an ANOVA for each of the sleep parameters, with two levels, pretest and posttest. Simple main effects were evaluated if a significant interaction resulted. All data are presented as mean (standard error of the mean). A p value of less than .05 was considered as statistically significant. All the analyses were performed with IBM SPSS statistics 20.
Measures of Sleep Sleep actigraphy uses accelerometry to monitor body motion to assess various parameters of sleep. Participants were asked to wear the ActiGraph (wActiSleep, Pensacola, FL, USA) on the wrist of the nondominant hand for 1 week at pretest and posttest. ActiLife software version 6 was used to analyze the sleep data with the Cole-Kripke algorithm, providing seven indices of sleep quality (sleep onset latency, sleep efficiency, total sleep time (TST), wake after sleep onset (WASO), total activity counts, and number and length of awakenings). Sleep onset latency was the first minute that the algorithm scored Basleep.^ Sleep efficiency was the number of sleep minutes divided by the total number of minutes a subject was in bed. TST was the total number of minutes scored as asleep. WASO was the total number of minutes a subject was awake after sleep onset occurred. Awakenings referred to the number of different awakening episodes. The length of awakening was the average length in minutes of all awakening episodes. Activity counts were the total actigraphy counts summed together for the entire sleep period [37]. Participants were also asked to keep a sleep diary to record the time they went to bed and got up in the morning, in order to coordinate with the actigraphic records.
Physical Fitness Physical fitness was assessed by the 8-ft up-and-go test and the 2-min step test. The 8-ft up-and-go test was used to assess mobility and requires the participant to get up from a seated position, walk 8 ft, turn, and return to the seated position. The
Results Participants were aged 65.7 years (0.7) and had a mean BMI of 25.1 kg/m2 (0.4), SBP of 130.3 mmHg (2.7), and DBP of 76.9 mmHg (2.6). Overall, the results showed that the exercise and control groups were not significantly different in age, sex, smoking behavior, alcohol consumption, living status, income source, or life satisfaction (Table 1). There were also no significant group differences in percentage of body fat, fitness tests (mobility and aerobic endurance), and seated blood pressure. Table 2 shows the outcomes of the ANOVA analyses including the means of the seven sleep index across time for each group. At baseline, there were no significant group differences for any parameter of sleep. Results of ANOVA analyses showed that there was a significant group difference in sleep latency, F(1,58) = 7.352, p = .009, partial eta squared=.112, which was qualified by a significant time× group interaction, F(1,58) = 6.921, p = .011, partial eta squared=.011. To break down the interaction, the simple effect of time within each group was tested. There was a significant increase in sleep latency from pretest to posttest for the control group, F(1,33) = 10.838, p = .002, partial eta squared=.247. A significant difference in sleep latency between the intervention and control groups at posttest was also seen, F(1,61)=13.712, p
Effects of Aquatic Exercise on Sleep in Older Adults with Mild Sleep Impairment: a Randomized Controlled Trial Li-Jung Chen 1 & Kenneth R. Fox 2 & Po-Wen Ku 3 & Yi-Wen Chang 1
# International Society of Behavioral Medicine 2015
Abstract Background Exercise has been found to be associated with improved sleep quality. However, most of the evidence is based on resistance exercise, walking, or gym-based aerobic activity. Purpose This study aimed to examine the effects of an 8week aquatic exercise program on objectively measured sleep parameters among older adults with mild sleep impairment. Methods A total of 67 eligible older adults with sleep impairment were selected and randomized to exercise and control groups, and 63 participants completed the study. The program involved 2×60-min sessions of aquatic exercise for 8 weeks. Participants wore wrist actigraphs to assess seven parameters of sleep for 1 week before and after the intervention. Mixeddesign analysis of variance (ANOVA) was used to assess the differences between groups in each of the sleep parameters. Results No significant group differences on demographic variables, life satisfaction, percentage of body fat, fitness, seated blood pressure, and any parameter of sleep were found at baseline. Significant group×time interaction effects were found in sleep onset latency, F(1,58)=6.921, p=.011, partial eta squared=.011, and in sleep efficiency, F(1, 61)=16.909, p3 times in the classes (n=4)
Post-test (week 9)
Post-test (week 9)
(Completed n=29)
(Completed n=34)
Fig. 1 Flow chart of the study participants
Variable means and percentages were calculated for exercise and control groups. The differences in scores on all variables at baseline between the two groups were tested for significance using t test or χ2 test. A mixed-design repeated-measures ANOVA was used to examine the pre-post-intervention changes. The between-subjects factor was group, with two levels, intervention group and control group. The withinsubjects variable was changed over time with an ANOVA for each of the sleep parameters, with two levels, pretest and posttest. Simple main effects were evaluated if a significant interaction resulted. All data are presented as mean (standard error of the mean). A p value of less than .05 was considered as statistically significant. All the analyses were performed with IBM SPSS statistics 20.
Measures of Sleep Sleep actigraphy uses accelerometry to monitor body motion to assess various parameters of sleep. Participants were asked to wear the ActiGraph (wActiSleep, Pensacola, FL, USA) on the wrist of the nondominant hand for 1 week at pretest and posttest. ActiLife software version 6 was used to analyze the sleep data with the Cole-Kripke algorithm, providing seven indices of sleep quality (sleep onset latency, sleep efficiency, total sleep time (TST), wake after sleep onset (WASO), total activity counts, and number and length of awakenings). Sleep onset latency was the first minute that the algorithm scored Basleep.^ Sleep efficiency was the number of sleep minutes divided by the total number of minutes a subject was in bed. TST was the total number of minutes scored as asleep. WASO was the total number of minutes a subject was awake after sleep onset occurred. Awakenings referred to the number of different awakening episodes. The length of awakening was the average length in minutes of all awakening episodes. Activity counts were the total actigraphy counts summed together for the entire sleep period [37]. Participants were also asked to keep a sleep diary to record the time they went to bed and got up in the morning, in order to coordinate with the actigraphic records.
Physical Fitness Physical fitness was assessed by the 8-ft up-and-go test and the 2-min step test. The 8-ft up-and-go test was used to assess mobility and requires the participant to get up from a seated position, walk 8 ft, turn, and return to the seated position. The
Results Participants were aged 65.7 years (0.7) and had a mean BMI of 25.1 kg/m2 (0.4), SBP of 130.3 mmHg (2.7), and DBP of 76.9 mmHg (2.6). Overall, the results showed that the exercise and control groups were not significantly different in age, sex, smoking behavior, alcohol consumption, living status, income source, or life satisfaction (Table 1). There were also no significant group differences in percentage of body fat, fitness tests (mobility and aerobic endurance), and seated blood pressure. Table 2 shows the outcomes of the ANOVA analyses including the means of the seven sleep index across time for each group. At baseline, there were no significant group differences for any parameter of sleep. Results of ANOVA analyses showed that there was a significant group difference in sleep latency, F(1,58) = 7.352, p = .009, partial eta squared=.112, which was qualified by a significant time× group interaction, F(1,58) = 6.921, p = .011, partial eta squared=.011. To break down the interaction, the simple effect of time within each group was tested. There was a significant increase in sleep latency from pretest to posttest for the control group, F(1,33) = 10.838, p = .002, partial eta squared=.247. A significant difference in sleep latency between the intervention and control groups at posttest was also seen, F(1,61)=13.712, p
