Journal of Sleep Disorders: Treatment and CareISSN: 2325-9639

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Research Article, J Sleep Disor Treat Care Vol: 3 Issue: 1

Subjective and Objective Sleep Measures in Older People with a History of Falls

Frances A Batchelor1*, Susan B Williams1, Briony Dow1,2, Xiaoping Lin1, Vanessa Wilkinson3, Karen Borschmann1, Melissa A Russell2, Kate E Crowley3, Keith D Hill1,4 and David J Berlowitz2,3
1National Ageing Research Institute, Parkville, Victoria, Australia
2University of Melbourne, Parkville, Victoria, Australia
3Institute for Breathing and Sleep, Heidelberg, Victoria, Australia
4Curtin University, Perth, Western Australia
Corresponding author : Frances A Batchelor
National Ageing Research Institute, PO Box 2127, The Royal Melbourne Hospital, Parkville, Victoria 3050, Australia
Tel: +61 3 8387 2383; Fax: + 61 3 8387 2153
E-mail: [email protected]
Received: October 31, 2013 Accepted: December 05, 2013 Published: December 09, 2013
Citation: Batchelor FA, Williams SB, Dow B, Lin X, Wilkinson V, et al. (2014) Subjective and Objective Sleep Measures in Older People with a History of Falls. J Sleep Disor: Treat Care 3:1. doi:10.4172/2325-9639.1000128

Abstract

Subjective and Objective Sleep Measures in Older People with a History of Falls

Study background: Falls are common in older people, with approximately 40% of those aged 80 and over falling each year. Sleep difficulties in older people have been recognized as a risk factor for falls. Most clinical and research information on sleep in older people, particularly in older fallers has been self-reported. Therefore, the objectives of this study were to describe the level and type of sleep problems in community-dwelling older people who have a history of falls, and to investigate whether sleep difficulties are associated with falls and falls risk. Methods: We undertook a cross-sectional observational study of objective and subjective sleep falls risk and falls history. Thirty-five community-dwelling Veterans or war widow/ers who had fallen at least once in the previous year were recruited. Objective inlaboratory polysomnography and in-home assessment of subjective sleep and falls risk profile were assessed.

Keywords:

Keywords

Ageing; Accidental Falls; Polysomnography; Sleep Disorders

Abbreviations

AHI: Apnea-Hypopnea Index; AQoL: Assessment of Quality of Life scale; BNSQ: Basic Nordic Sleep Questionnaire; ESS: Epworth Sleepiness Scale; FROP-Com: Falls Risk for Older People in the Community; KSS: Karolinska Sleep Scale; OSA: Obstructive Sleep Apnea; PLM: Periodic Leg Movement Index; PSG: Polysomnography

Introduction

Falls are a common problem for older people. Each year approximately 30% of those aged 65 years and older sustain a fall [1,2]. This increases with age, with 40% of community-dwelling people aged 80 and over falling each year [3]. Falls can have serious consequences including injury, fear of falling, physical and functional decline, residential care admission and depression. There are many well established risk factors for falls including balance impairment, decreased strength, polypharmacy, and visual problems [4]. Recently, several studies have found an association between sleep and falls in older people [5-8]. For example, people experiencing daytime sleepiness and poor sleep efficiency (less than 70% of time in bed spent sleeping) were found to have significantly increased odds of falling compared to those without these difficulties [6,7]. Additionally, it has been found that daytime napping is independently associated with falls risk in older women (after adjustment for age and other co-variates) [7]. Potential mechanisms for the association between sleep disorders and falls include decreased reaction time [9], and the effect of sleep deprivation or fragmentation on postural control [10], cognitive function and motor performance [11]. Despite the associations between falls and sleep difficulties, most research has focused on subjective sleep [12]. Although some studies have utilized actigraphy to quantify activity during sleep [6], no published studies examining the associations between sleep and falls have reported objective sleep measures obtained using polysomnography. In order to better understand the relationship between falls and sleep, evaluation of sleep using polysomnography in addition to subjective sleep assessment is required. The objectives of this exploratory study were to include both objective laboratory-based polysomnography and subjective information in the assessment of a group of active older fallers to describe sleep problems, and to evaluate the association between falls risk and specific sleep difficulties.

Materials and Methods

Study design and participants
This study was a cross-sectional observational study. Thirty-five World War II Veterans, war widow/ers or members of the Veteran community (mean age 83 years), who were community dwelling and who had fallen at least once in the previous year were recruited. Other eligibility criteria included being able to walk independently with or without an aid. People were excluded if they had cognitive impairment (defined by an Abbreviated Mental Test Score [13] <7), were unable to follow simple instructions or communicate in English, or if they lived more than 100 kilometers from the sleep laboratory. Participants were recruited through Veteran community newsletters, National Ageing Research Institute newsletters and presentations by project staff at Veteran clubs and groups.
Data collection
The project was approved by the relevant Ethics Committee. Informed consent was obtained for all participants. Data were collected in two assessments: the first was conducted in the participant’s home and the second, in-laboratory polysomnography (PSG, Compumedics™ E-Series, Abbotsford, Australia), occurred approximately two weeks later. All polysomnography studies were sleep staged, arousal and respiratory scored by a trained sleep scientist according to standard criteria [14-16]. The home-based assessment consisted of subjective questionnaires and objective measurements designed to measure sleep complaints, falls risk, function and quality of life. This included: information to describe the demographics and health of the sample [age, health conditions, living arrangements, falls history, medications (including sedative use)], body mass index, cognitive status (Abbreviated Mental Test Score [13]), depression (Geriatric Depression Scale–Short Form [17]), physical activity level (Human Activity Profile [18], mobility (Timed Up and Go) [19], reaction time [20], and falls risk (Falls Risk for Older People in the Community–FROP-Com [21]). The FROP-Com is a tool which assesses multiple falls risk factors and classifies falls risk. Apart from assessing a range of physical and functional falls risk factors, it specifically incorporates the use of sedating medications into calculation of falls risk. Generic quality of life was measured using the Assessment of Quality of Life Instrument (AQoL version 1) [22]. Subjective sleep was evaluated using the Epworth Sleepiness Scale (ESS) [23], Karolinska Sleep Scale (KSS) [24], and the Basic Nordic Sleep Questionnaire (BNSQ) [25].
Data analysis
Subjective and objective sleep: Responses from the subjective sleep questionnaires and the AQoL were grouped according to the following categories: overall sleep rating (BNSQ6, AQoL13), nighttime sleep difficulties (BNSQ1, 2, 3, 4, 5) daytime sleepiness (BNSQ9, 10, 11, KSS, ESS), daytime napping (BNSQ15) and snoring (BNSQ16, 17, 18, 19). Subjective hours of actual and ideal sleep were obtained from the BNSQ12 and 20. From the PSG data, five summary statistics were generated: Apnea-Hypopnea Index (AHI), Periodic Leg Movement Index (PLM), sleep efficiency (proportion of time spent asleep of the total time available for sleep after lights out), number of awakenings after sleep onset, and sleep latency (time taken to fall asleep after lights out).
Comparisons according to falls and falls risk: Comparisons of subjective and objective sleep were made between single (one fall) and multiple fallers (2 or more falls) based on self-report for the preceding 12 months, and falls risk based on the FROP-Com. The separation between single and multiple fallers was based on previous findings that multiple fallers differ in their falls risk profile and physiologically [26]. Low to moderate falls risk was defined as a FROP-Com score ≤ 18 and high falls risk as a FROP-Com score of ≥ 19 [21].
The characteristics of participants were described using means and Confidence Intervals (CI) for normally distributed variables, medians and Interquartile Ranges (IQR) for non-normally distributed variables, and percentages for categorical variables. For the normally distributed variables independent t-tests were used to investigate group differences and for those not normally distributed, the Mann– Whitney U test was used.
Because this study was exploratory in nature, and insufficient data was available on which to base an estimate, no sample size calculation was made.

Results

Participants
Thirty-five people (21 women, 14 men) with a mean age of 82.8 (95% CI: 81.5–84.1) were recruited for the study (Table 1). Participants had a median number of two falls (IQR 2) in the preceding year, with the majority reporting multiple falls (n=22, 62.9%). The mean FROPCom score was 15.2 (95% CI: 13.3-17.1), with over half having lowmoderate falls risk (n=24, 68.6%) (Table 1).
Table 1: Characteristics of study participants, n=35.
Subjective sleep
Overall sleep quality: The majority of participants reported that they sleep well or rather well (n=19, 54.3%), five participants (14.3%) felt that they sleep neither well nor badly and 11 (31.4%) participants reported sleeping rather badly or badly (BNSQ6). When overall sleep quality was assessed using the AQoL13, 12 (34.3%) people reporting sleeping without difficulty most of the time, six people (17.1%) reporting interruptions to sleep some of the time but able to go back to sleep without difficulty, and for eight people (22.9%), although their sleep was interrupted most nights, they could also go back to sleep without difficulty. Nine people (25.7%) reported being awake most of the night.
Subjective Sleep duration: The mean estimated actual and ideal hours of sleep per night were 6.5 (95%CI: 5.8-7.1), ranging from 2 to 9 hours and 7.5 hours (95% CI: 7.0-7.9), ranging from 3 to 11 hours respectively.
Night-time sleep difficulties: Difficulty falling asleep on a daily or almost daily based was reported by six (17.1%) of participants. Five participants (14.3%) reported difficulty falling asleep on 3-5 days per week, four (11.4%) on 1-2 days per week, and six (17.1%) less than once per month. Fourteen people (40.0%) reported they did not difficulty falling asleep or less than once per month (Table 2). The median time taken to fall asleep on work-days was reported as 25 minutes (IQR 50, range 0-360 minutes). During free time, median time to sleep was 11.3 minutes (IQR 36, range 0–120 minutes).
Table 2: Subjective sleep.
The majority of participants (n=23, 65.7%) reported very frequent (daily/almost daily) night-time waking. There was a spread of responses across the BNSQ5 categories (early waking without going back to sleep) (Table 2).
Daytime sleepiness: Participants reported low levels of daytime sleepiness. Seventy-one percent (n=25) reported that they did not experience excessive daytime sleepiness or less than once/month or week (BNSQ9). Similar low levels were seen for the tendency to fall asleep during the day, either while undertaking activity (BNSQ10) or during free time (BNSQ11) (Table 2).
The median KSS score (day-time sleepiness) was 3 (IQR 2) and the ESS score (likelihood of dozing off in different situations) was 5 (IQR 6), reflecting low levels of sleepiness.
Daytime napping: Thirteen (37.1%) participants reported that they never or rarely had a daytime nap and six (17.1%) had a daytime nap less than once/week. Nine (25.7%) napped on 1-2 days/week, 2 (5.7%) on 3-5 days/week, and five (14.3%) reported napping daily/ almost daily. The median reported nap time (n=21) was 45 minutes (IQR 42.5), ranging from 0–210 minutes.
Snoring/Obstructive sleep apnea: The majority of participants (n=16, 45.7%) reported that they did not snore while asleep or snored less than once/month. Three (8.6%) people reported snoring less than once/week, and three (8.6%) on 1-2 days/week. Nine people (25.7%) reported very frequent snoring (daily/almost daily). The frequency of snoring was unknown for four participants (11.4%). The majority of people (23, 65.7%) reported no breathing pauses during sleep (or less than once/month) (Table 2).
Objective sleep: The mean AHI was 32.9 (95%CI: 25.8–40.1) and the median PLM index was 17.3 (IQR 4). The average sleep efficiency (proportion of time spent asleep relative to total time spent in bed) was 64% (95%CI: 58.5–69.2) and the median time taken to fall asleep (sleep latency) was 24.5 minutes (IQR 38). The median number of awakenings after sleep onset for the whole sample was 23 (IQR 7).
Relationships between sleep variables, falls history and falls risk
Falls history: Participants with a history of multiple falls had significantly higher state sleepiness (median 3.0, IQR 1.5) as measured by the KSS than those with a history of single falls (median 1.0, IQR 2.0) (p=0.02). There was no significant difference in trait sleepiness as measured by the ESS in those with multiple falls (median 6.0, IQR 7.1) compared to those with single falls (median 4.0, IQR 4.0) (p=0.10) (Table 3).
Table 3: Sleep difficulties by history of falls and falls risk.
Similarly, there was no difference in AHI and sleep efficiency between participants who had two or more falls in the past 12 months compared with participants who had only one fall (Table 3).
Falls risk: Participants with high falls risk (FROP-Com score ≥ 19) had significantly higher scores on the ESS, KSS, BNQ9, and PLM index, indicating higher trait and state sleepiness, more daytime sleepiness and more lower limb movements while asleep. There were no differences in sleep apnea and efficiency in those with high versus low to moderate falls risk (Table 3).

Discussion

This exploratory study is one of the first studies to use polysomnography for objective sleep assessment in a population of older fallers. We found a concerning level of sleep problems in this sample, with a high frequency of objective sleep difficulties. Although there are little normative values for this age group, 60% of participants could be classified as having severe Obstructive Sleep Apnea (OSA) [16]. Participants had a high number of awakenings after sleep onset and sleep efficiency was poor across the whole sample (average of 64%). Of note was that no participant was assessed as having “normal” sleep on PSG. Despite this, less than one third of participants felt that they slept badly or very badly, and only two participants reported waking at least five times per night even though the median number of awakenings was found to be 23 when measured using PSG.
The objective data indicates that this group experienced substantial sleep disturbances and it is possible that poor sleep may have played a role in their falls history. Previous research has shown that poor sleep efficiency (where less than 70% of time in bed is spent sleeping) increased the odds of falling 1.36-fold [7]. Decreased reaction time [9], decreased mood, cognitive function and motor performance [11] due to inadequate and interrupted sleep may be possible mechanisms for an association between sleep and falls. Further research comparing the results of polysomnography of a group of older fallers and a group of age/gender-matched healthy older non-fallers would be useful.
We confirmed an association between falls history and daytime sleepiness [27], and importantly found those at high risk of falling to have both more daytime sleepiness (higher ESS, KSS and BNQ9 scores), and a significantly higher rate of periodic leg movements (PLM index) than those with low-moderate falls risk (Table 3). Although the ESS was significantly higher in the high falls risk group, the median score of nine in this sample is lower than the cut-off of around ten considered indicative of normality [28]. Similar data have previously been observed in sleep apnea in older people, where both the average ESS and the proportion of people with an ESS>10 (excessive daytime sleepiness) was significantly lower in older people than in middle-aged or young people with the same degree of sleep apnea [29]. These results suggest that with older people perhaps an equivalent physiological sleep insult does not result in equivalent subjective sleepiness.
There appeared to be more differentiation in sleep-related variables when participants were grouped according to falls risk rather than falls history. This probably reflects the multi-dimensional nature of the falls risk assessment undertaken encompassing aspects of physical activity, mobility and balance.
The interpretation of our findings is limited by the paucity of normative data for this age group. Further research is required to better establish normal values for OSA severity, PLM index, sleep efficiency and latency as well as for the number of awakenings after sleep onset, particularly given the low scores on scales of daytime sleepiness. The relationship between falls risk and sleep difficulties offers potential possibilities for testing interventions aimed at improving sleep and potentially reducing falls. In addition, further research with a larger sample is required to determine the associations between subjective and objective measures.
Despite the small sample size, the study results have important clinical implications. Health professionals should consider asking older people who have reported falls in the previous 12 months about their sleep. Subjective reporting will likely underestimate objective abnormalities (as shown in this study), and health professionals should consider implementing sleep hygiene recommendations in fallers even if they subjectively report sleep is of little concern to them. This could be done in conjunction with addressing identified falls risk factors using evidence-based interventions. Where other risk factors for sleep problems are present, referral for comprehensive polysomnography may be appropriate. Additionally, clinicians assessing the sleep of older people should consider referral for comprehensive falls risk assessment particularly for those who report ESS scores close to nine or above.
In conclusion, this research highlights that older people who have recently fallen experience high levels of sleep difficulties. This preliminary study supports the need for clinicians and researchers to consider objective assessment of sleep in those falling and not to rely solely on subjective information.

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