RESEARCH ARTICLES
Performance on a Functional Motor Task Is Enhanced by Sleep in Middle-Aged and Older Adults Alham Al-Sharman, PT, PhD, and Catherine F. Siengsukon, PT, PhD
Background and Purpose: Although sleep has been shown to enhance motor skill learning, it remains unclear whether sleep enhances learning of a functional motor task in middle-aged and older individuals. The purpose of this study was to examine whether sleep enhances motor learning of a functional motor task in middle-aged and older adults. Methods: Twenty middle-aged and 20 older individuals were randomly assigned to either the sleep condition or the no-sleep condition. Participants in the sleep condition practiced a novel walking task in the evening, and returned the following morning for retesting. Participants in the no-sleep condition practiced the walking task in the morning and returned the same day in the evening for a retest. Outcome measures included time around the walking path and spatiotemporal gait parameters. Results: Only the middle-aged and older adults in the sleep condition demonstrated significant off-line improvement in performance, measured as a decline in time to walk around the novel path and improvement in spatiotemporal gait parameters. The middle-aged and older adults in the no-sleep condition failed to demonstrate off-line improvements in performance of this functional task. Conclusions: This is the first study to provide evidence that sleep facilitates learning a clinically relevant functional motor task in middleaged and older adults. Because many neurologic conditions occur in the middle-aged and older adults and sleep issues are very prevalent in many neurologic conditions, it is imperative that physical therapists consider sleep as a factor that may impact motor learning and recovery in these individuals. Video Abstract available. (See Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A73) for more insights from the authors.
Department of Physical Therapy and Rehabilitation Science, University of Kansas Medical Center, Kansas City, Kansas. This work was funded in part by a Kanas Partners in Progress grant awarded to C.S. Portions of data contained in this article were presented as a poster presentation by A.A. at CSM 2012. None of the data contained in this article has been submitted for publication. The authors declare no conflicts of interest. Supplemental digital content is available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal’s Web site (www.jnpt.org). Correspondence: Catherine F. Siengsukon, PT, PhD, University of Kansas Medical Center, 3901 Rainbow Blvd, Mail Stop 2002, Kansas City, KS 66160 ([email protected]). C 2014 Neurology Section, APTA. Copyright ISSN: 1557-0576/14/3803-0161 DOI: 10.1097/NPT.0000000000000048
Key words: functional task, middle-age, off-learning, older adults, sleep (JNPT 2014;38: 161–169)
INTRODUCTION
S
leep has an important role in motor learning and memory consolidation in young, healthy individuals1-6 as well as in people with neurologic injury,7 including stroke.8,9 Memory consolidation refers to a process in which motor skill performance is either stabilized or enhanced “off-line” without further practice.10-12 Although sleep has been demonstrated to have an important role in motor learning and memory consolidation in young individuals,13-17 research examining sleepdependent off-line motor learning in older adults offers mixed conclusions.8-10,18-21 Older adults fail to benefit from sleep to enhance their performance on a continuous tracking task,8,9 and on explicit and implicit versions of a serial reaction time task,21 but did demonstrate an improvement in performance after sleep on a pursuit rotor task,20 and stabilization of performance on a sequential finger-tapping task.19 Because of multiple tasks and methodological differences, it is very difficult to elucidate the reasons behind such conflicting results. In addition to the mixed conclusions about the role of sleep in off-line motor skill learning in older adults, only one study by Wilson et al18 has assessed sleep-dependent off-line motor learning in middle-aged individuals. In this study, middle-aged adults demonstrated sleep-dependent offline motor learning on a serial reaction time task.18 However, the magnitude of sleep-dependent improvement in performance in middle-aged adults was less, compared with young individuals. Considering that sleep architecture begins to change during midlife, it is important to study how these changes affect the process of sleep-dependent off-line learning in this age group. More studies are needed to examine the role of sleep in motor learning in middle-aged adults. Neurologic conditions such as multiple sclerosis, traumatic brain injury, Alzheimer disease, Parkinson disease, and stroke are conditions that typically occur in middle-aged and older adults. Because individuals with neurologic conditions must often learn new or relearn old motor skills, the impact of sleep on motor learning in middle-aged and older adults is of particular interest for physical therapists who practice in a neurologic setting. Furthermore, sleep disorders and sleep issues frequently occur in people with neurologic conditions, which may also impact their ability to learn motor skills
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and could potentially impact their response to rehabilitation interventions. A common feature of the previous studies that assessed sleep-dependent off-line motor learning in middle-aged and older adults and individuals with neurologic injury is that all these studies used simple fine motor tasks conducted on a computer. Most of the tasks performed in everyday life such as walking, driving, performing transfers, and going up and down stairs are more complex and require coordination between different body parts to be performed successfully. It is currently unknown whether the findings from the fine motor tasks generalize to a functionally relevant gross motor task that has important implications for everyday activities and for rehabilitation. Recently, we have found that sleep enhances learning a functional motor task in young adults.22 However, it remains unknown how sleep impacts motor learning of a functional motor task in middle-aged or older adults. Also, although 2 studies demonstrate that individuals with chronic stroke experience enhanced motor skill learning after sleep on a simple computer-based task,8,9 age was not considered nor was a functional task used. In young adults, it has been found that complex motor tasks undergo a larger magnitude of sleep-dependent off-line performance enhancement when compared with simple motor tasks.23−25 For example, Kuriyama et al23 have assessed the role of sleep in off-line motor learning for different complexity levels of a finger-tapping task and found that sleep provides the greatest improvement in performance for the most difficult version of this task. However, this complex task is still a computerbased motor task that has limited implications for daily life. Interestingly, Wilhelm et al26 found that when the young participants expected to be retested at the retention session, sleep resulted in the largest degree of performance improvement on a sequential finger-tapping task. The authors26 suggest that sleep provides more benefits for memories that are relevant for an “individual’s future.” Therefore, it can be proposed that sleep-dependent off-line motor learning in middle-aged and older adults might be maximized by choosing a task that is more complex and relevant to an individual’s life. This current study seeks to examine the role of sleep in learning a functional motor task in middle-aged and older adults. We hypothesized that performance on the functional motor task would improve off-line after a period of sleep compared with performance after a similar period of wakefulness in middle-aged and older individuals.
METHODS Participants
Twenty middle-aged (48 ± 3.7 years of age) and 20 older (70.4 ± 3.8 years of age) adults participated in this study. Individuals who had untreated sleep disorders including sleep apnea and restless leg syndrome, uncontrolled depression, a history of psychiatric or neurologic disorders, any orthopedic problems or gait deviations that made performing the study task difficult, or scored below 26 on the MiniMental Status Examination27 were excluded. We also excluded participants who worked a night shift, were regular nappers, or who were identified as extreme evening or morning-type
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persons by the Morningness-Eveningness Questionnaire.28 This study was approved by the Institutional Review Board at the University of Kansas Medical Center. Written informed consent was obtained from all participants before participation in the study. Participants were recruited from the University of Kansas Medical Center and the local community. For 12 hours before and during the study, participants were instructed not to engage in strenuous activities or consume caffeine, alcohol, or recreational drugs. To assess sleep quality, participants were asked to complete the Pittsburgh Sleep Quality Index29 and to maintain a daily sleep log for a week before testing. To assess participants’ level of sleepiness before practice and retention testing, participants completed the Stanford Sleepiness Scale.30 Because of the association between depression and sleep issues, depression was assessed using the Beck Depression Inventory.31 To assess the level of functional mobility before practice and retention testing, the Timed Up and Go test32,33 was performed. Other demographic information including age, sex, height, weight, and medical history was collected from participants.
Functional Motor Task The task used in this study to assess sleep-dependent offline motor skill learning was a novel walking task. This task is identical to the task used in previous work demonstrating the role of sleep in learning a functional motor task in young adults.22 The pathway for the walking task was an irregular elliptical pathway, 29.57 m (97 ft) long and 0.46 m (1.5 ft) wide, marked by yellow tape. Each participant was instructed to walk safely around the walking path as quickly and accurately as possible, avoiding stepping on or over the colored tape. To assess spatiotemporal gait parameters, a gait-mat (Gait-Mat II system E.Q., Inc, Chalfont, PA), 3.87 m (12.7 ft) long, was embedded within the walking path. A single, straight line was marked on the gait-mat, and participants were instructed to walk on the line with one foot directly in front of the other across the gait-mat as quickly and accurately as possible. Participants were also instructed to perform a mental cognitive task (count backward by 7s starting from a randomly selected number between 293 and 299) while walking around the pathway. Counting backward serves as a valid representation of walking in natural settings.34,35
Procedure Participants in the middle-aged and older age groups were randomized by a random number table after agreeing to participate in the study into 2 conditions: the sleep condition and the no-sleep condition. Participants in the sleep condition practiced the novel walking task in the evening (between 7 and 8 PM) and underwent retention testing the next morning (between 7 and 8 AM). Participants in the no-sleep condition practiced the walking task in the morning (between 7 and 8 AM) and underwent retention testing in the evening of the same day (between 7 and 8 PM). During the practice session, participants completed 6 blocks of walking around the path (1 baseline block and 5 practice blocks). In the baseline block, participants were instructed to walk without performing the cognitive calculation to become familiar with the path. For the 5 blocks of practice, participants walked around the path while C 2014 Neurology Section, APTA
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performing the cognitive calculation. Rests between practice blocks were allowed if needed. The retention test consisted of one block of walking around the path with cognitive calculations followed by another block of walking without performing the cognitive calculation. Each block in practice and retention consisted of 5 iterations of walking around the path. In total, participants completed 8 blocks (40 times around the path) of the novel walking task. To collect sleep outcome measures for the sleep group and to ensure the no-sleep group did not sleep in-between practice and retention testing, a subgroup of participants in each age group were asked to wear an actigraph (model Actiwatch 2; Phillips Respironics, Andover, MA) to monitor activity levels and sleep patterns on their dominant wrist between practice and retention testing. Actigraph data were collected for 12 participants in the older age group (6 participants in each condition) and 16 participants in the middle-aged group (8 participants in each condition). Actigraphs were not used on all participants because of limited availability of the devices. Actigraph data were summated over 15-s epoch and converted to digital activity counts at 60 Hz. Sleep measures were assessed using Actiware Software Package (version 5.57, Phillips Respironics, Andover, MA).
Outcome Measures The time required to walk around the path was the main outcome measure of interest and was recorded for each block using a stopwatch. Tandem velocity, tandem step length, and tandem step time were also collected using the gait-mat software. These spatiotemporal gait parameters have been used to assess gait performance as well as risk of fall, fear of falling, and community ambulation ability.36,37 For each of the outcome measures, individual data were averaged by condition to represent performance for blocks 2 to 6 during acquisition practice and a delayed retention test. Sleep measures including total sleep time, sleep latency (ie, time between lights off and sleep onset), sleep efficiency (ie, total sleep time/time in bed—from lights off to lights on—× 100), and the number of awakenings were objectively assessed for the sleep conditions in each age group, using the actigraph data.
Statistical Analyses For each of the age groups, one-way analyses of variance (ANOVAs) were used to assess differences between characteristics of the sleep and no-sleep conditions. A 2-factor— condition (sleep, no-sleep) × block (2, 3, 4, 5, 6)—repeatedmeasures ANOVA was used to assess performance acquisition during practice in older and middle-aged groups. Time around the path, tandem velocity, tandem step length, and tandem step time were used as dependent variables. Off-line learning was assessed using a 2-factor—condition (sleep, no-sleep) × block (last practice block, first retention block)—repeated-measures ANOVA with time around the path, tandem velocity, tandem step length, and tandem step time as dependent variables for each age group. Significant interactions were explored using one-way ANOVAs using off-line learning scores as the dependent variables. Off-line learning scores were calculated by subtracting first retention block scores from last practice scores for each of the outcome measures for both the sleep and
no-sleep conditions. Cohen’s d effect sizes (ES) were calculated for the main outcome measure in both age groups to examine whether the differences between the sleep and nosleep conditions were clinically meaningful.38
RESULTS Subject Characteristics For the 2 middle-aged and 2 older age groups, no differences were found between the sleep and no-sleep conditions in any of the following measures: age, amount of sleep the week before practice, height, and weight. Furthermore, no differences were found between the sleep and no-sleep conditions in terms of sleep quality, depression, and functional mobility level at practice or at retention testing, or the level of sleepiness at practice or at retention testing. The sleep and no-sleep characteristics for both middle-aged and older adults are shown in Table 1. The actigraphic data indicated that for both age groups, none of the participants monitored in the no-sleep condition slept between the practice session and retention testing. The actigraphic data for middle-aged adults indicated that participants in the sleep condition had a mean of 7.5 hours of total sleep time, a sleep latency of 10.7 minutes, 87.4% sleep efficiency, and 18.6 awakenings (Table 2). The actigraphic data for older adults showed that participants in the sleep condition demonstrated a mean of 7.6 hours of total sleep time, a sleep latency of 6.5 minutes, 86.3% sleep efficiency, and 24.8 awakenings (Table 2).
Acquisition Performance Middle-Aged Participants Middle-aged participants in the sleep and no-sleep conditions showed improvement in performance during the practice session as demonstrated by a significant main effect of block for all of the outcome measures (time around the walking path, F4,72 = 9.17; P < 0.001; tandem velocity, F4,72 = 16.42; P < 0.001; tandem step length, F4,72 = 8.30; P < 0.001; tandem step time, F4,72 = 4.30; P = 0.003 [Figures 1A, 1B, 1C, and 1D, respectively]). The main effect of condition for all outcome measures indicated no significant difference between the sleep and no-sleep conditions in the extent of improvement across the practice session (time around the walking path, F1,18 = 0.64; P = 0.44; tandem velocity, F1,18 = 0.02; P = 0.88; tandem step length, F1,18 = 0.03; P = 0. 80; tandem step time, F1,18 = 2.20; P = 0.15). Thus, the sleep and no-sleep conditions performed similarly at the practice session. The interactions between block and condition were not significant for any of the outcome measures (F4,72 = 0.78; P = 0.53, time around the walking path; F4,72 = 0.93; P = 0.45; tandem velocity; F4,72 = 0.92; P = 0.46, tandem step length; F4,72 = 0.52; P = 0.72, tandem step time).
Older Adult Participants Training on the novel walking task resulted in performance improvement in both the sleep and no-sleep older adults as indicated by the main effect of block for each outcome variable (time around the walking path, F4,72 = 13.04; P < 0.001; tandem velocity, F4,72 = 35.27; P < 0.001; tandem step length,
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Abbreviations: avg. sleep, average amount of sleep participants had the week before testing demonstrated by sleep log; BDI, Beck Depression Inventory; MMSE, Mini-Mental Status Examination; PSQI, Pittsburgh Sleep Quality Index; SSS prac, Stanford Sleepiness Scale taken at practice session; SSS ret, Stanford Sleepiness Scale taken at retention session; TUG prac, Timed Up and Go taken at practice session; TUG ret, Timed Up and Go taken at retention session. a Data are means (standard errors of the mean).
2.00 (0.38) 4.00 (0.76) 0.14 9.2 (0.13) 9.1 (0.18) 0.70 3.3 (0.33) 4.1 (0.45) 0.30 1.7 (0.18) 1.6 (0.18) 0.80 29.8 (0.09) 29.6 (0.16) 0.44
1.5 (0.16) 1.6 (0.13) 0.75
7.30 (0.11) 7.49 (0.09) 0.68
170.2 (8.47) 157.6 (7.92) 0.40
171.3 (2.64) 167.2 (2.64) 0.42
9.3 (0.16) 9.1 (0.20) 0.69
3.3 (0.65) 2.3 (0.36) 0.23 8.7 (0.11) 8.6 (0.18) 0.57 8.8 (0.09) 8.4 (0.16) 0.27 3.2 (0.42) 2.5 (0.31) 0.37 2.0 (0.18) 2.2 (0.23) 0.63 29.8 (0.09) 29.7 (0.11) 0.63
2.2 (0.09) 2.5 (0.18) 0.50
7.5 (0.16) 7.6 (0.18) 0.80
168.0 (8.45) 178.5 (4.54) 0.45
171.1 (2.33) 172.5 (2.26) 0.78
TUG Ret PSQI Avg. Sleep SSS Ret SSS Prac MMSE Age
Middle-aged adults Sleep 49.6 (0.80) No-sleep 47.0 (0.76) P value 0.11 Older adults Sleep 69.1 (0.42) No-sleep 71.6 (0.42) P value 0.12
Groups
Table 1. Descriptive Information for Middle-Aged and Older Participantsa
Weight
Height
TUG Prac
BDI
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F4,72 = 20.88; P < 0.001; tandem step time, F4,72 = 2.50; P = 0.047 [Figures 2A, 2B, 2C, and 2D, respectively]). The extent of improvement in performance revealed no differences between the sleep and no-sleep conditions at the practice session, demonstrated by a lack of condition main effect (time around the walking path, F1,18 = 0.37; P = 0.54; tandem velocity, F1,18 = 1.02; P = 0.33; tandem step length, F1,18 = 0.52; P = 0.48; and tandem step time, F1,18 = 2.11; P = 0.16). No interactions were found between block and condition for any of the outcome measures (time around the walking path, F4,72 = 1.6; P = 0.18; tandem velocity, F4,72 = 1.02; P = 0.33; tandem step length, F4,72 = 0.51; P = 0.48; tandem step time, F4,72 = 0.46; P = 0.76).
Off-Line Learning Middle-Aged Participants Interactions between condition and block were significant for all of the outcome measures. To explore these interactions, one-way ANOVAs were used. The results indicated that participants in the sleep condition significantly improved performance during the off-line period compared with participants in the no-sleep condition, as indicated by a decrease in the time required to walk around the path from the last practice block to the retention block (F1,18 = 54.06; P < 0.001; Figure 3A), an increase in tandem velocity (F1,18 = 10.43; P < 0.005; Figure 3B), an increase in tandem step length (F1,18 = 8.9; P = 0.008; Figure 3C), and a decrease in step time (F1,18 = 44.77; P < 0.001; Figure 3D). The effect size for the no-sleep condition was small and negative (ES = -.12), where as the effect size for the sleep condition was small and positive (ES = .29).
Older Participants Results for older adults were similar to those found for middle-aged adults. Significant condition–block interactions were found for all outcome measures. Compared with the nosleep condition, the results for one-way ANOVAs indicated that participants in the sleep condition significantly improved performance during the off-line period, as indicated by a decrease in time required to walk around the path at the retention block compared with time needed at the last practice block (F1,18 = 17.10; P = 0.001; Figure 4A), an increase in tandem velocity (F1,18 = 17.90; P < 0.001, Figure 4B), an increase in tandem step length (F1,18 = 7.50; P = 0.013; Figure 4C), and a significant decrease in step time (F1,18 = 4.6; P < 0.046; Figure 4D). The effect size for the no-sleep condition was small and negative (ES = -.04) where as the effect size for the sleep condition was small and positive (ES = .07).
DISCUSSION This study demonstrates that middle-aged and older adults improved performance on a functional motor task after a night of sleep but not after a period of wakefulness. In both age groups, only participants who slept after practicing the novel walking task demonstrated a significant decrease in the time around the walking path, an increase in tandem velocity, an increase in tandem step length, and a decrease in tandem step time. The effect size (for the middle-aged adults, and for C 2014 Neurology Section, APTA
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Table 2. Actigraph Data for Middle-Aged and Older Adultsa Sleep Measures (Actigraph Data) Middle-aged (8 participants) Older adults (6 participants)
Total Sleep Time, hrs
Sleep Latency, min
Sleep Efficiency, %
Awakenings Number
7.46 (0.78) 7.57 (0.6)
10.65 (7.52) 6.47 (1.3)
87.42 (3.5) 86.3 (3.4)
18.62 (4.5) 24.8 (13.8)
Abbreviations: awakenings number, number of awakes during the night of sleep between practice and retention testing for the sleep group; sleep efficiency, time of sleep/total time in bed × 100; sleep latency, time between bed time and sleep onset; total sleep time, time between sleep onset and final awake time. a Data are means (standard deviation).
Figure 1. Performance across the training blocks (block 2-6) and after 12 hours of sleep or wakefulness (retention block) for the middle-aged group demonstrated by time around the walking path (A), tandem velocity across the gait-mat (B), tandem step length across the gait-mat (C), and tandem step time across the gait-mat (D). Error bars are standard errors of the mean.
the older adults) for the main outcome variable, time to walk around the path, suggests a real and meaningful difference between the amounts of change in the time required to walk around the path (main outcome measure) for individuals who slept compared with those who stayed awake during the time between the last practice block and the retention block. Our results build on previous work indicating older adults benefit from sleep to enhance learning of motor tasks. This study supports the findings of Tucker et al19 and Peters et al20 in showing sleep-dependent enhancement in older adults. Furthermore, the results of this current study support the findings of Wilson et al18 that sleep enhances motor performance in middle-aged individuals. Our work expands on these previ-
ous studies by showing the importance of sleep in learning a functional gross motor task in middle-aged and older adults. Emerging evidence suggests that the role of sleep in off-line learning is dependent on task characteristics.11,25 This may explain the lack of consistency between our findings and the results from earlier studies by Siengsukon and Boyd,8,9 Spencer et al,21 and Wilson et al18 who found older adults failed to demonstrate sleep-dependent motor skill learning. Compared with motor tasks that were used in previous studies, the functional walking task used in the current study is more complex. Our walking task requires whole-body performance, coordination between different body parts, and response to environmental stimuli (narrow path way and transition from
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Figure 2. Performance across the training blocks (block 2-6) and after 12 hours of sleep or wakefulness (retention block) for older adults demonstrated by time around the walking path (A), tandem velocity across the gait-mat (B), tandem step length across the gait-mat (C), and tandem step time across the gait-mat (D). Error bars are standard errors of the mean.
standard gait to tandem gait). Furthermore, performing a mental task while walking is considered a very demanding task.39 The result of this study extends the findings that sleep selectively enhances learning of tasks that are more complex in young adults23,25 to middle-aged and older adults. Furthermore, our findings agree with the conclusion drawn from a study by Wilhelm et al26 who found that, in young adults, sleep benefits learning tasks that are significant to the individual’s future behavior. Therefore, we believe that sleep enhances learning the functional motor task, presented in this study, because it is more complex and relates to an individual’s behavior as compared with motor tasks previously used.
Limitations One limitation of this study is that we cannot completely rule out that the time of day may have influenced the results. It is unlikely, however, that a time-of-day effect could have caused the results shown here. In both age groups, participants in the sleep and no-sleep conditions practiced this novel task and reached a similar level of performance at the end of the training session, regardless of whether the practice took place in the evening (ie, sleep condition) or in the morning (ie, no-sleep condition). Visual inspection of Figures 2A, 2B,
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and 2C may suggest a potential difference in performance at practice between the sleep and no-sleep older adult groups, but there were no significant differences between groups, perhaps because of the large variability in performance of the older individuals. However, in both the middle-aged and older adults, the level of sleepiness and the level of functional mobility revealed no differences between the sleep and no-sleep conditions at practice or at retention testing regardless of the time of day the testing occurred. We did not directly assess fatigue in this study; in a study by Walker et al,15 participants in a no-sleep condition rested the tested extremity between practice and retention testing to reduce potential fatigue and found no benefit in rest. In combination, this suggests that in both age groups, delayed off-line improvement in performance observed in the sleep but not in the no-sleep conditions was a result of the consolidation process that occurs during sleep and not the circadian rhythm or time-of-day effect. The enhancement of performance on a functional motor task in healthy middle-aged and older adults by sleep has important implications for neurologic physical therapists. Many neurologic conditions, including multiple sclerosis, traumatic brain injury, Alzheimer disease, Parkinson disease, and stroke, occur primarily in middle-aged and older adults. Furthermore, C 2014 Neurology Section, APTA
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Figure 3. Off-line learning scores or change on performance on the novel walking task between the last practice block and the retention testing for the sleep and no-sleep groups in the middle-aged adults demonstrated by time around the walking path (A), tandem velocity across the gait-mat (B), tandem step length across the gait-mat (C), tandem step time across the gait-mat (D). Error bars are standard errors of the mean. **P < 0.001; *P < 0.05.
functional impairments are very common in these neurologic conditions. Physical therapists working with individuals with a neurologic condition should consider sleep as a potential factor that may impact motor learning and the potential to respond to interventions. Future studies are imperative to elucidate how sleep may impact motor learning, particularly motor learning of functional tasks, in individuals with various neurologic conditions. Sleep issues are very common in many neurologic conditions, including approximately 50% of people with multiple sclerosis,40-43 50% of people with traumatic brain injury,44-46 36% to 54% of people with Alzheimer disease,47,48 up to 90% of people with Parkinson disease,49,50 and more than 50% of individuals after acute stroke.51-53 Sleep disorders and sleep issues in people with neurologic conditions may be due to the impact of the condition on brain structures that regulate sleep54-58 and may also be due to secondary factors such as pain or depression.59-62 Despite the high prevalence of sleep disturbances in individuals with neurologic conditions, sleep disorders and sleep disturbances often go unrecognized by medical professionals, which may have serious consequences
for patients’ health and may adversely affect their prognosis. Sleep disturbances have been associated with increased risk of mortality, cardiac disease, obesity, and diabetes,63-65 and can contribute to depression, pain, and fatigue.40,66,67 Studies also show that poor sleep quality and daytime sleepiness have been associated with a reduction in several quality of life indices, including physical function, psychological well-being, self-care and activities of daily living, work ability, and interpersonal relationships.43,44,68,69 Future studies are needed to determine how sleep disorders and sleep issues impact motor learning and ultimately function and participation in people with neurologic conditions.
CONCLUSIONS In summary, this study provides the first evidence that sleep enhances learning of a complex functional motor skill that is similar to motor skills commonly practiced in rehabilitation of middle-aged and older adults. Effective plans need to be considered by clinicians to maximize the role of sleep on motor learning in this population. Emphasis should be placed
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Figure 4. Off-line learning scores or change on performance on the novel walking task between the last practice block and the retention testing for the sleep and no-sleep groups in the older adults demonstrated by time around the walking path (A), tandem velocity across the gait-mat (B), tandem step length across the gait-mat (C), tandem step time across the gait-mat (D). Error bars are standard errors of the mean. **P < 0.001; *P < 0.05.
on addressing sleep disorders and on ensuring adequate sleep for individuals participating in physical rehabilitation. REFERENCES 1. Walker MP, Stickgold R. Sleep, memory, and plasticity. Annu Rev Psychol. 2005;57(12):2.1-12.28. 2. Walker MP, Stickgold R. Sleep-dependent learning and memory consolidation. Neuron. 2004;44(1):121-133. 3. Peigneux P, Laureys S, Delbeuck X, Maquet P. Sleeping brain, learning brain. The role of sleep for memory systems. Neuroreport. 2001;12(18):A111-A124. 4. Stickgold R, Walker MP. Sleep and memory: the ongoing debate. Sleep. 2005;28(10):1225-1227. 5. Stickgold R. Sleep-dependent memory consolidation. Nature. 2005; 437(7063):1272-1278. 6. Stickgold R, Walker MP. Memory consolidation and reconsolidation: what is the role of sleep? Trends Neurosci. 2005;28(8):408-415. 7. Gomez Beldarrain M, Astorgano AG, Gonzalez AB, Garcia-Monco JC. Sleep improves sequential motor learning and performance in patients with prefrontal lobe lesions. Clin Neurol Neurosurg. 2008;110(3):245-252. 8. Siengsukon C, Boyd LA. Sleep enhances off-line spatial and temporal motor learning after stroke. Neurorehabilitation Neural Repair. 2009;23(4):327-335.
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9. Siengsukon CF, Boyd LA. Sleep to learn after stroke: implicit and explicit off-line motor learning. Neurosci Lett. 2009;451(1):1-5. 10. Nemeth D, Janacsek K, Londe Z, Ullman MT, Howard DV, Howard JH. Sleep has no critical role in implicit motor sequence learning in young and old adults. Exp Brain Res. 2010;201(2):351-358. 11. Doyon J, Korman M, Morin A, et al. Contribution of night and day sleep vs. simple passage of time to the consolidation of motor sequence and visuomotor adaptation learning. Exp Brain Res. 2009;195(1): 15-26. 12. Ungerleider LG, Doyon J, Karni A. Imaging brain plasticity during motor skill learning. Neurobiol Learn Mem. 2002;78(3):553-564. 13. Walker MP, Stickgold R. It’s practice, with sleep, that makes perfect: implications of sleep-dependent learning and plasticity for skill performance. Clin Sports Med. 2005;24(2):301-17, ix. 14. Walker MP, Brakefield T, Seidman J, Morgan A, Hobson JA, Stickgold R. Sleep and the time course of motor skill learning. Learn Mem. 2003;10(4):275-284. 15. Walker MP, Brakefield T, Morgan A, Hobson JA, Stickgold R. Practice with sleep makes perfect: sleep-dependent motor skill learning. Neuron. 2002;35(1):205-211. 16. Fischer S, Hallschmid M, Elsner AL, Born J. Sleep forms memory for finger skills. Proc Natl Acad Sci U S A. 2002;99(18):11987-11991. 17. Dijk DJ, Archer SN. PERIOD3, circadian phenotypes, and sleep homeostasis. Sleep Med Rev. 2010;14(3):151-160.
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18. Wilson JK, Baran B, Pace-Schott EF, Ivry RB, Spencer RM. Sleep modulates word-pair learning but not motor sequence learning in healthy older adults. Neurobiol Aging. 2012;33(5):991-1000. 19. Tucker M, McKinley S, Stickgold R. Sleep optimizes motor skill in older adults. J Am Geriatr Soci. 2011;59(4):603-609. 20. Peters KR, Ray L, Smith V, Smith C. Changes in the density of stage 2 sleep spindles following motor learning in young and older adults. J Sleep Res. 2008;17(1):23-33. 21. Spencer RM, Gouw AM, Ivry RB. Age-related decline of sleep-dependent consolidation. Learn Mem. 2007;14(7):480-484. 22. Al-Sharman A, Siengsukon CF. Sleep enhances learning of a functional motor task in young adults. Phys Ther. 2013;93(12):1625-1635. 23. Kuriyama K, Stickgold R, Walker MP. Sleep-dependent learning and motor-skill complexity. Learn Mem. 2004;11(6):705-713. 24. Brawn TP, Fenn KM, Nusbaum HC, Margoliash D. Consolidation of sensorimotor learning during sleep. Learn Mem. 2009;15:815819. 25. Siengsukon CF, Al-Sharman A. Sleep promotes off-line enhancement of an explicitly learned discrete but not an explicitly learned continuous task. Nature Sci Sleep. 2011;3:39-46. 26. Wilhelm I, Diekelmann S, Molzow I, Ayoub A, Molle M, Born J. Sleep selectively enhances memory expected to be of future relevance. J Neurosci. 2011;31(5):1563-1569. 27. Crum RM, Anthony JC, Bassett SS, Folstein MF. Population-based norms for the Mini-Mental State Examination by age and educational level. JAMA. 1993;269(18):2386-2391. 28. Mongrain V, Lavoie S, Selmaoui B, Paquet J, Dumont M. Phase relationships between sleep-wake cycle and underlying circadian rhythms in Morningness-Eveningness. J Biol Rhythms. 2004;19(3):248257. 29. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193-213. 30. Hoddes E, Zarcone V, Smythe H, Phillips R, Dement WC. Quantification of sleepiness: a new approach. Psychophysiology. 1973;10(4):431-436. 31. Beck A, Steer R, Brown G. BDI- Fastscreen for Medical Patients: Manual. San Antiono, TX: Harcourt Assessment/The Psychological Corporation; 2000. 32. Creel GL, Light KE, Thigpen MT. Concurrent and construct validity of scores on the Timed Movement Battery. Phys Ther. 2001;81(2): 789-798. 33. Podsiadlo D, Richardson S. The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142148. 34. Holtzer R, Mahoney JR, Izzetoglu M, Izzetoglu K, Onaral B, Verghese J. fNIRS study of walking and walking while talking in young and old individuals. J Gerontol A Biol Sci Med Sci. 2011;66(8):879-887. 35. Beauchet O, Dubost V, Herrmann FR, Kressig RW. Stride-to-stride variability while backward counting among healthy young adults. J Neuroengineering Rehabil. 2005;2:26. 36. Patterson KK, Nadkarni NK, Black SE, McIlroy WE. Gait symmetry and velocity differ in their relationship to age. Gait Posture. 2012;35(4):590594. 37. Hsiao-Wecksler ET, Robinovitch SN. The effect of step length on young and elderly women’s ability to recover balance. Clin Biomech. 2007;22(5):574-580. 38. Thomas JR, Lockbaum MR, Landers DM, He C. Planning significant and meaningful research in exercise science: estimating sample size. Res Q Exerc Sport. 1997;68(1):33-43. 39. Hall CD, Echt KV, Wolf SL, Rogers WA. Cognitive and motor mechanisms underlying older adults’ ability to divide attention while walking. Phys Ther. 2011;91(7):1039-1050. 40. Bamer AM, Johnson KL, Amtmann D, Kraft GH. Prevalence of sleep problems in individuals with multiple sclerosis. Mult Scler. 2008;14(8):1127-1130. 41. Fleming WE, Pollak CP. Sleep disorders in multiple sclerosis. Semin Neurol. 2005;25(1):64-68. 42. Kaynak H, Altintas A, Kaynak D, et al. Fatigue and sleep disturbance in multiple sclerosis. Eur J Neurol. 2006;13(12):1333-1339. 43. Merlino G, Fratticci L, Lenchig C, et al. Prevalence of “ poor sleep” among patients with multiple sclerosis: an independent predictor of mental and physical status. Sleep Med. 2009;10(1):26-34.
44. Castriotta RJ, Wilde MC, Lai JM, Atanasov S, Masel BE, Kuna ST. Prevalence and consequences of sleep disorders in traumatic brain injury. J Clin Sleep Med. 2007;3(4):349-356. 45. Mahmood O, Rapport LJ, Hanks RA, Fichtenberg NL. Neuropsychological performance and sleep disturbance following traumatic brain injury. J Head Trauma Rehabil. 2004;19(5):378-390. 46. Datta SG, Pillai SV, Rao SL, Kovoor JM, Chandramouli BA. Postconcussion syndrome: correlation of neuropsychological deficits, structural lesions on magnetic resonance imaging and symptoms. Neurol India. 2009;57(5):594-598. 47. Garcia-Alberca JM, Lara JP, Cruz B, Garrido V, Gris E, Barbancho MA. Sleep disturbances in Alzheimer’s disease are associated with neuropsychiatric symptoms and antidementia treatment. J Nerv Mental Dis. 2013;201(3):251-257. 48. Hart DJ, Craig D, Compton SA, et al. A retrospective study of the behavioural and psychological symptoms of mid and late phase Alzheimer’s disease. Int J Geriatr Psychiatry. 2003;18(11):1037-1042. 49. Kurtis MM, Rodriguez-Blazquez C, Martinez-Martin P, Group E. Relationship between sleep disorders and other non-motor symptoms in Parkinson’s disease. Parkinsonism Relat Disord. 2013;19(12):1152-1155. 50. Zoccolella S, Savarese M, Lamberti P, Manni R, Pacchetti C, Logroscino G. Sleep disorders and the natural history of Parkinson’s disease: the contribution of epidemiological studies. Sleep Med Rev. 2011;15(1):4150. 51. Bassetti CL. Sleep and stroke. Semin Neurol. 2005;25(1):19-32. 52. Elwood P, Hack M, Pickering J, Hughes J, Gallacher J. Sleep disturbance, stroke, and heart disease events: evidence from the Caerphilly cohort. J Epidemiol Community Health. 2006;60(1):69-73. 53. Johnson KG, Johnson DC. Frequency of sleep apnea in stroke and TIA patients: a meta-analysis. J Clin Sleep Med. 2010;6(2):131-137. 54. Plazzi G, Montagna P. Remitting REM sleep behavior disorder as the initial sign of multiple sclerosis. Sleep Med. 2002;3(5):437-439. 55. Limousin N, Dehais C, Gout O, Heran F, Oudiette D, Arnulf I. A brainstem inflammatory lesion causing REM sleep behavior disorder and sleepwalking (parasomnia overlap disorder). Sleep Med. 2009;10(9):1059-1062. 56. Iranzo A, Santamaria J, Tolosa E. The clinical and pathophysiological relevance of REM sleep behavior disorder in neurodegenerative diseases. Sleep Med Rev. 2009;13(6):385-401. 57. Kohl AD, Wylie GR, Genova HM, Hillary FG, Deluca J. The neural correlates of cognitive fatigue in traumatic brain injury using functional MRI. Brain Inj. 2009;23(5):420-432. 58. Kempf J, Werth E, Kaiser PR, Bassetti CL, Baumann CR. Sleep-wake disturbances 3 years after traumatic brain injury. J Neurol Neurosurg Psychiatry. 2010;81(12):1402-1405. 59. Zeitzer JM, Friedman L, O’Hara R. Insomnia in the context of traumatic brain injury. J Rehabil Res Dev. 2009;46(6):827-836. 60. Fichtenberg NL, Millis SR, Mann NR, Zafonte RD, Millard AE. Factors associated with insomnia among post-acute traumatic brain injury survivors. Brain Inj. 2000;14(7):659-667. 61. Zahariou A, Karamouti M, Karagiannis G, Papaioannou P. Maximal bladder capacity is a positive predictor of response to desmopressin treatment in patients with MS and nocturia. Int Urol Nephrol. 2008;40(1):65-69. 62. Clark CM, Fleming JA, Li D, Oger J, Klonoff H, Paty D. Sleep disturbance, depression, and lesion site in patients with multiple sclerosis. Arch Neurol. 1992;49(6):641-643. 63. Kripke DF, Garfinkel L, Wingard DL, Klauber MR, Marler MR. Mortality associated with sleep duration and insomnia. Arch Gen Psychiatry. 2002;59(2):131-136. 64. Patel SR, Redline S. Two epidemics: are we getting fatter as we sleep less? Sleep. 2004;27(4):602-603. 65. Ayas NT, White DP, Al-Delaimy WK, et al. A prospective study of selfreported sleep duration and incident diabetes in women. Diabetes Care. 2003;26(2):380-384. 66. Stanton BR, Barnes F, Silber E. Sleep and fatigue in multiple sclerosis. Mult Scler. 2006;12:481-486. 67. Watson NF, Viola-Saltzman M. Sleep and comorbid neurologic disorders. Continuum. 2013;19(1 Sleep Disorders):148-169. 68. Motl RW, Arnett PA, Smith MM, Barwick FH, Ahlstrom B, Stover EJ. Worsening of symptoms is associated with lower physical activity levels in individuals with multiple sclerosis. Mult Scler. 2008;14(1):140-142. 69. Hossain JL, Shapiro CM. The prevalence, cost implications, and management of sleep disorders: an overview. Sleep Breathing. 2002;6(2):85-102.
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Performance on a Functional Motor Task Is Enhanced by Sleep in Middle-Aged and Older Adults Alham Al-Sharman, PT, PhD, and Catherine F. Siengsukon, PT, PhD
Background and Purpose: Although sleep has been shown to enhance motor skill learning, it remains unclear whether sleep enhances learning of a functional motor task in middle-aged and older individuals. The purpose of this study was to examine whether sleep enhances motor learning of a functional motor task in middle-aged and older adults. Methods: Twenty middle-aged and 20 older individuals were randomly assigned to either the sleep condition or the no-sleep condition. Participants in the sleep condition practiced a novel walking task in the evening, and returned the following morning for retesting. Participants in the no-sleep condition practiced the walking task in the morning and returned the same day in the evening for a retest. Outcome measures included time around the walking path and spatiotemporal gait parameters. Results: Only the middle-aged and older adults in the sleep condition demonstrated significant off-line improvement in performance, measured as a decline in time to walk around the novel path and improvement in spatiotemporal gait parameters. The middle-aged and older adults in the no-sleep condition failed to demonstrate off-line improvements in performance of this functional task. Conclusions: This is the first study to provide evidence that sleep facilitates learning a clinically relevant functional motor task in middleaged and older adults. Because many neurologic conditions occur in the middle-aged and older adults and sleep issues are very prevalent in many neurologic conditions, it is imperative that physical therapists consider sleep as a factor that may impact motor learning and recovery in these individuals. Video Abstract available. (See Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A73) for more insights from the authors.
Department of Physical Therapy and Rehabilitation Science, University of Kansas Medical Center, Kansas City, Kansas. This work was funded in part by a Kanas Partners in Progress grant awarded to C.S. Portions of data contained in this article were presented as a poster presentation by A.A. at CSM 2012. None of the data contained in this article has been submitted for publication. The authors declare no conflicts of interest. Supplemental digital content is available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal’s Web site (www.jnpt.org). Correspondence: Catherine F. Siengsukon, PT, PhD, University of Kansas Medical Center, 3901 Rainbow Blvd, Mail Stop 2002, Kansas City, KS 66160 ([email protected]). C 2014 Neurology Section, APTA. Copyright ISSN: 1557-0576/14/3803-0161 DOI: 10.1097/NPT.0000000000000048
Key words: functional task, middle-age, off-learning, older adults, sleep (JNPT 2014;38: 161–169)
INTRODUCTION
S
leep has an important role in motor learning and memory consolidation in young, healthy individuals1-6 as well as in people with neurologic injury,7 including stroke.8,9 Memory consolidation refers to a process in which motor skill performance is either stabilized or enhanced “off-line” without further practice.10-12 Although sleep has been demonstrated to have an important role in motor learning and memory consolidation in young individuals,13-17 research examining sleepdependent off-line motor learning in older adults offers mixed conclusions.8-10,18-21 Older adults fail to benefit from sleep to enhance their performance on a continuous tracking task,8,9 and on explicit and implicit versions of a serial reaction time task,21 but did demonstrate an improvement in performance after sleep on a pursuit rotor task,20 and stabilization of performance on a sequential finger-tapping task.19 Because of multiple tasks and methodological differences, it is very difficult to elucidate the reasons behind such conflicting results. In addition to the mixed conclusions about the role of sleep in off-line motor skill learning in older adults, only one study by Wilson et al18 has assessed sleep-dependent off-line motor learning in middle-aged individuals. In this study, middle-aged adults demonstrated sleep-dependent offline motor learning on a serial reaction time task.18 However, the magnitude of sleep-dependent improvement in performance in middle-aged adults was less, compared with young individuals. Considering that sleep architecture begins to change during midlife, it is important to study how these changes affect the process of sleep-dependent off-line learning in this age group. More studies are needed to examine the role of sleep in motor learning in middle-aged adults. Neurologic conditions such as multiple sclerosis, traumatic brain injury, Alzheimer disease, Parkinson disease, and stroke are conditions that typically occur in middle-aged and older adults. Because individuals with neurologic conditions must often learn new or relearn old motor skills, the impact of sleep on motor learning in middle-aged and older adults is of particular interest for physical therapists who practice in a neurologic setting. Furthermore, sleep disorders and sleep issues frequently occur in people with neurologic conditions, which may also impact their ability to learn motor skills
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and could potentially impact their response to rehabilitation interventions. A common feature of the previous studies that assessed sleep-dependent off-line motor learning in middle-aged and older adults and individuals with neurologic injury is that all these studies used simple fine motor tasks conducted on a computer. Most of the tasks performed in everyday life such as walking, driving, performing transfers, and going up and down stairs are more complex and require coordination between different body parts to be performed successfully. It is currently unknown whether the findings from the fine motor tasks generalize to a functionally relevant gross motor task that has important implications for everyday activities and for rehabilitation. Recently, we have found that sleep enhances learning a functional motor task in young adults.22 However, it remains unknown how sleep impacts motor learning of a functional motor task in middle-aged or older adults. Also, although 2 studies demonstrate that individuals with chronic stroke experience enhanced motor skill learning after sleep on a simple computer-based task,8,9 age was not considered nor was a functional task used. In young adults, it has been found that complex motor tasks undergo a larger magnitude of sleep-dependent off-line performance enhancement when compared with simple motor tasks.23−25 For example, Kuriyama et al23 have assessed the role of sleep in off-line motor learning for different complexity levels of a finger-tapping task and found that sleep provides the greatest improvement in performance for the most difficult version of this task. However, this complex task is still a computerbased motor task that has limited implications for daily life. Interestingly, Wilhelm et al26 found that when the young participants expected to be retested at the retention session, sleep resulted in the largest degree of performance improvement on a sequential finger-tapping task. The authors26 suggest that sleep provides more benefits for memories that are relevant for an “individual’s future.” Therefore, it can be proposed that sleep-dependent off-line motor learning in middle-aged and older adults might be maximized by choosing a task that is more complex and relevant to an individual’s life. This current study seeks to examine the role of sleep in learning a functional motor task in middle-aged and older adults. We hypothesized that performance on the functional motor task would improve off-line after a period of sleep compared with performance after a similar period of wakefulness in middle-aged and older individuals.
METHODS Participants
Twenty middle-aged (48 ± 3.7 years of age) and 20 older (70.4 ± 3.8 years of age) adults participated in this study. Individuals who had untreated sleep disorders including sleep apnea and restless leg syndrome, uncontrolled depression, a history of psychiatric or neurologic disorders, any orthopedic problems or gait deviations that made performing the study task difficult, or scored below 26 on the MiniMental Status Examination27 were excluded. We also excluded participants who worked a night shift, were regular nappers, or who were identified as extreme evening or morning-type
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persons by the Morningness-Eveningness Questionnaire.28 This study was approved by the Institutional Review Board at the University of Kansas Medical Center. Written informed consent was obtained from all participants before participation in the study. Participants were recruited from the University of Kansas Medical Center and the local community. For 12 hours before and during the study, participants were instructed not to engage in strenuous activities or consume caffeine, alcohol, or recreational drugs. To assess sleep quality, participants were asked to complete the Pittsburgh Sleep Quality Index29 and to maintain a daily sleep log for a week before testing. To assess participants’ level of sleepiness before practice and retention testing, participants completed the Stanford Sleepiness Scale.30 Because of the association between depression and sleep issues, depression was assessed using the Beck Depression Inventory.31 To assess the level of functional mobility before practice and retention testing, the Timed Up and Go test32,33 was performed. Other demographic information including age, sex, height, weight, and medical history was collected from participants.
Functional Motor Task The task used in this study to assess sleep-dependent offline motor skill learning was a novel walking task. This task is identical to the task used in previous work demonstrating the role of sleep in learning a functional motor task in young adults.22 The pathway for the walking task was an irregular elliptical pathway, 29.57 m (97 ft) long and 0.46 m (1.5 ft) wide, marked by yellow tape. Each participant was instructed to walk safely around the walking path as quickly and accurately as possible, avoiding stepping on or over the colored tape. To assess spatiotemporal gait parameters, a gait-mat (Gait-Mat II system E.Q., Inc, Chalfont, PA), 3.87 m (12.7 ft) long, was embedded within the walking path. A single, straight line was marked on the gait-mat, and participants were instructed to walk on the line with one foot directly in front of the other across the gait-mat as quickly and accurately as possible. Participants were also instructed to perform a mental cognitive task (count backward by 7s starting from a randomly selected number between 293 and 299) while walking around the pathway. Counting backward serves as a valid representation of walking in natural settings.34,35
Procedure Participants in the middle-aged and older age groups were randomized by a random number table after agreeing to participate in the study into 2 conditions: the sleep condition and the no-sleep condition. Participants in the sleep condition practiced the novel walking task in the evening (between 7 and 8 PM) and underwent retention testing the next morning (between 7 and 8 AM). Participants in the no-sleep condition practiced the walking task in the morning (between 7 and 8 AM) and underwent retention testing in the evening of the same day (between 7 and 8 PM). During the practice session, participants completed 6 blocks of walking around the path (1 baseline block and 5 practice blocks). In the baseline block, participants were instructed to walk without performing the cognitive calculation to become familiar with the path. For the 5 blocks of practice, participants walked around the path while C 2014 Neurology Section, APTA
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performing the cognitive calculation. Rests between practice blocks were allowed if needed. The retention test consisted of one block of walking around the path with cognitive calculations followed by another block of walking without performing the cognitive calculation. Each block in practice and retention consisted of 5 iterations of walking around the path. In total, participants completed 8 blocks (40 times around the path) of the novel walking task. To collect sleep outcome measures for the sleep group and to ensure the no-sleep group did not sleep in-between practice and retention testing, a subgroup of participants in each age group were asked to wear an actigraph (model Actiwatch 2; Phillips Respironics, Andover, MA) to monitor activity levels and sleep patterns on their dominant wrist between practice and retention testing. Actigraph data were collected for 12 participants in the older age group (6 participants in each condition) and 16 participants in the middle-aged group (8 participants in each condition). Actigraphs were not used on all participants because of limited availability of the devices. Actigraph data were summated over 15-s epoch and converted to digital activity counts at 60 Hz. Sleep measures were assessed using Actiware Software Package (version 5.57, Phillips Respironics, Andover, MA).
Outcome Measures The time required to walk around the path was the main outcome measure of interest and was recorded for each block using a stopwatch. Tandem velocity, tandem step length, and tandem step time were also collected using the gait-mat software. These spatiotemporal gait parameters have been used to assess gait performance as well as risk of fall, fear of falling, and community ambulation ability.36,37 For each of the outcome measures, individual data were averaged by condition to represent performance for blocks 2 to 6 during acquisition practice and a delayed retention test. Sleep measures including total sleep time, sleep latency (ie, time between lights off and sleep onset), sleep efficiency (ie, total sleep time/time in bed—from lights off to lights on—× 100), and the number of awakenings were objectively assessed for the sleep conditions in each age group, using the actigraph data.
Statistical Analyses For each of the age groups, one-way analyses of variance (ANOVAs) were used to assess differences between characteristics of the sleep and no-sleep conditions. A 2-factor— condition (sleep, no-sleep) × block (2, 3, 4, 5, 6)—repeatedmeasures ANOVA was used to assess performance acquisition during practice in older and middle-aged groups. Time around the path, tandem velocity, tandem step length, and tandem step time were used as dependent variables. Off-line learning was assessed using a 2-factor—condition (sleep, no-sleep) × block (last practice block, first retention block)—repeated-measures ANOVA with time around the path, tandem velocity, tandem step length, and tandem step time as dependent variables for each age group. Significant interactions were explored using one-way ANOVAs using off-line learning scores as the dependent variables. Off-line learning scores were calculated by subtracting first retention block scores from last practice scores for each of the outcome measures for both the sleep and
no-sleep conditions. Cohen’s d effect sizes (ES) were calculated for the main outcome measure in both age groups to examine whether the differences between the sleep and nosleep conditions were clinically meaningful.38
RESULTS Subject Characteristics For the 2 middle-aged and 2 older age groups, no differences were found between the sleep and no-sleep conditions in any of the following measures: age, amount of sleep the week before practice, height, and weight. Furthermore, no differences were found between the sleep and no-sleep conditions in terms of sleep quality, depression, and functional mobility level at practice or at retention testing, or the level of sleepiness at practice or at retention testing. The sleep and no-sleep characteristics for both middle-aged and older adults are shown in Table 1. The actigraphic data indicated that for both age groups, none of the participants monitored in the no-sleep condition slept between the practice session and retention testing. The actigraphic data for middle-aged adults indicated that participants in the sleep condition had a mean of 7.5 hours of total sleep time, a sleep latency of 10.7 minutes, 87.4% sleep efficiency, and 18.6 awakenings (Table 2). The actigraphic data for older adults showed that participants in the sleep condition demonstrated a mean of 7.6 hours of total sleep time, a sleep latency of 6.5 minutes, 86.3% sleep efficiency, and 24.8 awakenings (Table 2).
Acquisition Performance Middle-Aged Participants Middle-aged participants in the sleep and no-sleep conditions showed improvement in performance during the practice session as demonstrated by a significant main effect of block for all of the outcome measures (time around the walking path, F4,72 = 9.17; P < 0.001; tandem velocity, F4,72 = 16.42; P < 0.001; tandem step length, F4,72 = 8.30; P < 0.001; tandem step time, F4,72 = 4.30; P = 0.003 [Figures 1A, 1B, 1C, and 1D, respectively]). The main effect of condition for all outcome measures indicated no significant difference between the sleep and no-sleep conditions in the extent of improvement across the practice session (time around the walking path, F1,18 = 0.64; P = 0.44; tandem velocity, F1,18 = 0.02; P = 0.88; tandem step length, F1,18 = 0.03; P = 0. 80; tandem step time, F1,18 = 2.20; P = 0.15). Thus, the sleep and no-sleep conditions performed similarly at the practice session. The interactions between block and condition were not significant for any of the outcome measures (F4,72 = 0.78; P = 0.53, time around the walking path; F4,72 = 0.93; P = 0.45; tandem velocity; F4,72 = 0.92; P = 0.46, tandem step length; F4,72 = 0.52; P = 0.72, tandem step time).
Older Adult Participants Training on the novel walking task resulted in performance improvement in both the sleep and no-sleep older adults as indicated by the main effect of block for each outcome variable (time around the walking path, F4,72 = 13.04; P < 0.001; tandem velocity, F4,72 = 35.27; P < 0.001; tandem step length,
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Abbreviations: avg. sleep, average amount of sleep participants had the week before testing demonstrated by sleep log; BDI, Beck Depression Inventory; MMSE, Mini-Mental Status Examination; PSQI, Pittsburgh Sleep Quality Index; SSS prac, Stanford Sleepiness Scale taken at practice session; SSS ret, Stanford Sleepiness Scale taken at retention session; TUG prac, Timed Up and Go taken at practice session; TUG ret, Timed Up and Go taken at retention session. a Data are means (standard errors of the mean).
2.00 (0.38) 4.00 (0.76) 0.14 9.2 (0.13) 9.1 (0.18) 0.70 3.3 (0.33) 4.1 (0.45) 0.30 1.7 (0.18) 1.6 (0.18) 0.80 29.8 (0.09) 29.6 (0.16) 0.44
1.5 (0.16) 1.6 (0.13) 0.75
7.30 (0.11) 7.49 (0.09) 0.68
170.2 (8.47) 157.6 (7.92) 0.40
171.3 (2.64) 167.2 (2.64) 0.42
9.3 (0.16) 9.1 (0.20) 0.69
3.3 (0.65) 2.3 (0.36) 0.23 8.7 (0.11) 8.6 (0.18) 0.57 8.8 (0.09) 8.4 (0.16) 0.27 3.2 (0.42) 2.5 (0.31) 0.37 2.0 (0.18) 2.2 (0.23) 0.63 29.8 (0.09) 29.7 (0.11) 0.63
2.2 (0.09) 2.5 (0.18) 0.50
7.5 (0.16) 7.6 (0.18) 0.80
168.0 (8.45) 178.5 (4.54) 0.45
171.1 (2.33) 172.5 (2.26) 0.78
TUG Ret PSQI Avg. Sleep SSS Ret SSS Prac MMSE Age
Middle-aged adults Sleep 49.6 (0.80) No-sleep 47.0 (0.76) P value 0.11 Older adults Sleep 69.1 (0.42) No-sleep 71.6 (0.42) P value 0.12
Groups
Table 1. Descriptive Information for Middle-Aged and Older Participantsa
Weight
Height
TUG Prac
BDI
Al-Sharman and Siengsukon
F4,72 = 20.88; P < 0.001; tandem step time, F4,72 = 2.50; P = 0.047 [Figures 2A, 2B, 2C, and 2D, respectively]). The extent of improvement in performance revealed no differences between the sleep and no-sleep conditions at the practice session, demonstrated by a lack of condition main effect (time around the walking path, F1,18 = 0.37; P = 0.54; tandem velocity, F1,18 = 1.02; P = 0.33; tandem step length, F1,18 = 0.52; P = 0.48; and tandem step time, F1,18 = 2.11; P = 0.16). No interactions were found between block and condition for any of the outcome measures (time around the walking path, F4,72 = 1.6; P = 0.18; tandem velocity, F4,72 = 1.02; P = 0.33; tandem step length, F4,72 = 0.51; P = 0.48; tandem step time, F4,72 = 0.46; P = 0.76).
Off-Line Learning Middle-Aged Participants Interactions between condition and block were significant for all of the outcome measures. To explore these interactions, one-way ANOVAs were used. The results indicated that participants in the sleep condition significantly improved performance during the off-line period compared with participants in the no-sleep condition, as indicated by a decrease in the time required to walk around the path from the last practice block to the retention block (F1,18 = 54.06; P < 0.001; Figure 3A), an increase in tandem velocity (F1,18 = 10.43; P < 0.005; Figure 3B), an increase in tandem step length (F1,18 = 8.9; P = 0.008; Figure 3C), and a decrease in step time (F1,18 = 44.77; P < 0.001; Figure 3D). The effect size for the no-sleep condition was small and negative (ES = -.12), where as the effect size for the sleep condition was small and positive (ES = .29).
Older Participants Results for older adults were similar to those found for middle-aged adults. Significant condition–block interactions were found for all outcome measures. Compared with the nosleep condition, the results for one-way ANOVAs indicated that participants in the sleep condition significantly improved performance during the off-line period, as indicated by a decrease in time required to walk around the path at the retention block compared with time needed at the last practice block (F1,18 = 17.10; P = 0.001; Figure 4A), an increase in tandem velocity (F1,18 = 17.90; P < 0.001, Figure 4B), an increase in tandem step length (F1,18 = 7.50; P = 0.013; Figure 4C), and a significant decrease in step time (F1,18 = 4.6; P < 0.046; Figure 4D). The effect size for the no-sleep condition was small and negative (ES = -.04) where as the effect size for the sleep condition was small and positive (ES = .07).
DISCUSSION This study demonstrates that middle-aged and older adults improved performance on a functional motor task after a night of sleep but not after a period of wakefulness. In both age groups, only participants who slept after practicing the novel walking task demonstrated a significant decrease in the time around the walking path, an increase in tandem velocity, an increase in tandem step length, and a decrease in tandem step time. The effect size (for the middle-aged adults, and for C 2014 Neurology Section, APTA
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Table 2. Actigraph Data for Middle-Aged and Older Adultsa Sleep Measures (Actigraph Data) Middle-aged (8 participants) Older adults (6 participants)
Total Sleep Time, hrs
Sleep Latency, min
Sleep Efficiency, %
Awakenings Number
7.46 (0.78) 7.57 (0.6)
10.65 (7.52) 6.47 (1.3)
87.42 (3.5) 86.3 (3.4)
18.62 (4.5) 24.8 (13.8)
Abbreviations: awakenings number, number of awakes during the night of sleep between practice and retention testing for the sleep group; sleep efficiency, time of sleep/total time in bed × 100; sleep latency, time between bed time and sleep onset; total sleep time, time between sleep onset and final awake time. a Data are means (standard deviation).
Figure 1. Performance across the training blocks (block 2-6) and after 12 hours of sleep or wakefulness (retention block) for the middle-aged group demonstrated by time around the walking path (A), tandem velocity across the gait-mat (B), tandem step length across the gait-mat (C), and tandem step time across the gait-mat (D). Error bars are standard errors of the mean.
the older adults) for the main outcome variable, time to walk around the path, suggests a real and meaningful difference between the amounts of change in the time required to walk around the path (main outcome measure) for individuals who slept compared with those who stayed awake during the time between the last practice block and the retention block. Our results build on previous work indicating older adults benefit from sleep to enhance learning of motor tasks. This study supports the findings of Tucker et al19 and Peters et al20 in showing sleep-dependent enhancement in older adults. Furthermore, the results of this current study support the findings of Wilson et al18 that sleep enhances motor performance in middle-aged individuals. Our work expands on these previ-
ous studies by showing the importance of sleep in learning a functional gross motor task in middle-aged and older adults. Emerging evidence suggests that the role of sleep in off-line learning is dependent on task characteristics.11,25 This may explain the lack of consistency between our findings and the results from earlier studies by Siengsukon and Boyd,8,9 Spencer et al,21 and Wilson et al18 who found older adults failed to demonstrate sleep-dependent motor skill learning. Compared with motor tasks that were used in previous studies, the functional walking task used in the current study is more complex. Our walking task requires whole-body performance, coordination between different body parts, and response to environmental stimuli (narrow path way and transition from
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Figure 2. Performance across the training blocks (block 2-6) and after 12 hours of sleep or wakefulness (retention block) for older adults demonstrated by time around the walking path (A), tandem velocity across the gait-mat (B), tandem step length across the gait-mat (C), and tandem step time across the gait-mat (D). Error bars are standard errors of the mean.
standard gait to tandem gait). Furthermore, performing a mental task while walking is considered a very demanding task.39 The result of this study extends the findings that sleep selectively enhances learning of tasks that are more complex in young adults23,25 to middle-aged and older adults. Furthermore, our findings agree with the conclusion drawn from a study by Wilhelm et al26 who found that, in young adults, sleep benefits learning tasks that are significant to the individual’s future behavior. Therefore, we believe that sleep enhances learning the functional motor task, presented in this study, because it is more complex and relates to an individual’s behavior as compared with motor tasks previously used.
Limitations One limitation of this study is that we cannot completely rule out that the time of day may have influenced the results. It is unlikely, however, that a time-of-day effect could have caused the results shown here. In both age groups, participants in the sleep and no-sleep conditions practiced this novel task and reached a similar level of performance at the end of the training session, regardless of whether the practice took place in the evening (ie, sleep condition) or in the morning (ie, no-sleep condition). Visual inspection of Figures 2A, 2B,
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and 2C may suggest a potential difference in performance at practice between the sleep and no-sleep older adult groups, but there were no significant differences between groups, perhaps because of the large variability in performance of the older individuals. However, in both the middle-aged and older adults, the level of sleepiness and the level of functional mobility revealed no differences between the sleep and no-sleep conditions at practice or at retention testing regardless of the time of day the testing occurred. We did not directly assess fatigue in this study; in a study by Walker et al,15 participants in a no-sleep condition rested the tested extremity between practice and retention testing to reduce potential fatigue and found no benefit in rest. In combination, this suggests that in both age groups, delayed off-line improvement in performance observed in the sleep but not in the no-sleep conditions was a result of the consolidation process that occurs during sleep and not the circadian rhythm or time-of-day effect. The enhancement of performance on a functional motor task in healthy middle-aged and older adults by sleep has important implications for neurologic physical therapists. Many neurologic conditions, including multiple sclerosis, traumatic brain injury, Alzheimer disease, Parkinson disease, and stroke, occur primarily in middle-aged and older adults. Furthermore, C 2014 Neurology Section, APTA
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Sleep Enhances Learning a Functional Motor Task in Middle-Aged and Older Adults
Figure 3. Off-line learning scores or change on performance on the novel walking task between the last practice block and the retention testing for the sleep and no-sleep groups in the middle-aged adults demonstrated by time around the walking path (A), tandem velocity across the gait-mat (B), tandem step length across the gait-mat (C), tandem step time across the gait-mat (D). Error bars are standard errors of the mean. **P < 0.001; *P < 0.05.
functional impairments are very common in these neurologic conditions. Physical therapists working with individuals with a neurologic condition should consider sleep as a potential factor that may impact motor learning and the potential to respond to interventions. Future studies are imperative to elucidate how sleep may impact motor learning, particularly motor learning of functional tasks, in individuals with various neurologic conditions. Sleep issues are very common in many neurologic conditions, including approximately 50% of people with multiple sclerosis,40-43 50% of people with traumatic brain injury,44-46 36% to 54% of people with Alzheimer disease,47,48 up to 90% of people with Parkinson disease,49,50 and more than 50% of individuals after acute stroke.51-53 Sleep disorders and sleep issues in people with neurologic conditions may be due to the impact of the condition on brain structures that regulate sleep54-58 and may also be due to secondary factors such as pain or depression.59-62 Despite the high prevalence of sleep disturbances in individuals with neurologic conditions, sleep disorders and sleep disturbances often go unrecognized by medical professionals, which may have serious consequences
for patients’ health and may adversely affect their prognosis. Sleep disturbances have been associated with increased risk of mortality, cardiac disease, obesity, and diabetes,63-65 and can contribute to depression, pain, and fatigue.40,66,67 Studies also show that poor sleep quality and daytime sleepiness have been associated with a reduction in several quality of life indices, including physical function, psychological well-being, self-care and activities of daily living, work ability, and interpersonal relationships.43,44,68,69 Future studies are needed to determine how sleep disorders and sleep issues impact motor learning and ultimately function and participation in people with neurologic conditions.
CONCLUSIONS In summary, this study provides the first evidence that sleep enhances learning of a complex functional motor skill that is similar to motor skills commonly practiced in rehabilitation of middle-aged and older adults. Effective plans need to be considered by clinicians to maximize the role of sleep on motor learning in this population. Emphasis should be placed
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Figure 4. Off-line learning scores or change on performance on the novel walking task between the last practice block and the retention testing for the sleep and no-sleep groups in the older adults demonstrated by time around the walking path (A), tandem velocity across the gait-mat (B), tandem step length across the gait-mat (C), tandem step time across the gait-mat (D). Error bars are standard errors of the mean. **P < 0.001; *P < 0.05.
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