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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 59:M1050-M1055 (2004)
© 2004 The Gerontological Society of America

The Timing of Activity Rhythms in Patients With Dementia Is Related to Survival

Phil Gehrman1, Matthew Marler2,3, Jennifer L. Martin1, Tamar Shochat2, Jody Corey-Bloom3,4 and Sonia Ancoli-Israel2,3,

1 SDSU/UCSD Joint Doctoral Program in Clinical Psychology, University of California, San Diego.
2 Department of Psychiatry, University of California, San Diego.
3 Veterans Affairs San Diego Healthcare System, California.
4 Department of Neurology, University of California, San Diego.

Address correspondence to Sonia Ancoli-Israel, PhD, VASDHS, Department of Psychiatry (116A), 3350 La Jolla Village Drive, San Diego, CA 92161. E-mail: sancoliisrael{at}ucsd.edu


    Abstract
 Top
 Abstract
 Methods
 Results
 Discussion
 References
 
Background. Older adults with dementia often have disruptions in circadian rhythms, including disruptions of the rest–activity rhythm. These disruptions are a product of internal neuronal activity and external environmental influences, both of which are deficient in dementia. However, the consequences of disturbed rhythms are unknown. This study examined the relationship between rest–activity rhythms and death in patients with dementia.

Methods. The authors recruited 149 older adults with dementia (104 women; mean age, 84.1 years) from nursing homes. Activity was recorded with wrist actigraphs from each participant for 3 days. Survival was determined by examining public death records. Cox proportional hazards models were used to determine which aspects of rest–activity rhythms were related to survival.

Results. The timing of each participant's rest–activity rhythm compared with a sample of persons without dementia was related to survival, such that those who more closely resembled the persons without dementia lived longer.

Conclusions. Although rest–activity rhythms as a whole were not related to survival, the timing of the rhythm was. Patients with dementia appear to develop an abnormal timing of their rhythms, which is predictive of shorter survival. It may be possible to intervene with these patients to correct the timing of their rhythms and possibly prolong their lives.


MOST forms of dementia, particularly Alzheimer's disease, are characterized by a progressive deterioration in all areas of functioning and, ultimately, shorter survival time. It remains unclear, however, to what degree increased mortality risk is the result of the dementing illness itself or is a consequence of the many comorbid conditions associated with dementia. In all likelihood, there is a complex interaction among all these factors. An untangling of this intricate web of relationships may further our understanding of the ways in which dementia affects health and well-being and may open the door to novel treatments that may ameliorate some of the symptoms of dementia, slow its progression, and possibly prolong life.

One of the disturbances associated with dementia is a disruption of the regular daily cycles that most physiologic processes follow (that is, circadian rhythms). Circadian rhythms are generated endogenously by the suprachiasmatic nuclei of the hypothalamus. Studies have found a reduction in the overall volume of the suprachiasmatic nuclei in patients with dementia (1) and a decrease at the molecular level in the rhythmicity of the suprachiasmatic nuclei neurons (2). In animal studies, suprachiasmatic nuclei lesions produced nocturnal wakefulness and daytime napping, similar to the changes seen in dementia (3).

Circadian rhythms are also strongly influenced by environmental factors, such as exposure to bright light, physical activity, and regular social interaction. These Zeitgebers ("time givers") help to synchronize circadian rhythms with the external environment, coordinating the appropriate timing of physiologic functions. Patients with dementia often live in an environment that is deficient in Zeitgebers. They may receive very little exposure to bright light (4), the strongest Zeitgeber, with more severely demented patients receiving the lowest exposure (5). Patients with dementia also have low levels of physical activity and few regular social interactions (6), which can also negatively affect circadian rhythms. The environment is even further devoid of Zeitgebers for those residing in nursing homes.

One of the most apparent manifestations of irregular circadian rhythms is the disrupted sleep–wake cycle. Patients with dementia in nursing homes rarely spend a full hour awake during the day or a full hour asleep at night (7,8). These patients can be very difficult to care for because they are often awake during the night when their caregiver is trying to sleep. In fact, nighttime wakening has been found to be among the primary reasons for institutionalization of patients with dementia (9).

Several studies have found a difference in the timing of circadian rhythms of body temperature (10) and rest–activity (11) in patients with dementia compared with older adults who do not have dementia. However, little is known about the health-related consequences of this altered timing. Circadian rhythm disruption alters the temporal organization of sleep, which can lead to an overall decrease in both sleep quantity and quality. A considerable body of evidence has documented the impairments in cognitive, affective, and physical domains that can result from insufficient sleep. Studies in animals have shown that disturbed circadian rhythms are associated with shorter survival (12,13).

In the only study in humans, Bliwise and colleagues (14) measured body temperature rhythms in nursing home residents with dementia and found that those temperature rhythms that were out of phase with what is typical for healthy adults had shorter survival times.

In the current study, we evaluated the relationship between rest–activity rhythms and survival in patients with dementia of the Alzheimer's type. We hypothesized that patients with greater rhythm disturbances would have shorter survival times.


    METHODS
 Top
 Abstract
 Methods
 Results
 Discussion
 References
 
Participants
We collected the data for these analyses in two separate studies that evaluated treatments for agitation, sleep, and circadian rhythms in institutionalized older adults with a diagnosis of dementia and who resided in the San Diego, California, area. We combined the data from these studies, yielding a total of 187 participants. Data were incomplete or missing for 38 participants, and thus they were dropped from the analyses (12 with missing results of the Mini-Mental State Examination (MMSE) and 26 with missing actigraphy findings). The mean age of the remaining 149 participants (104 women, 45 men) was 84.1 years (standard deviation [SD], 7.8; age range, 60 to 100 years).

We identified potential participants from all residents in the nursing homes, and we contacted their legal guardian to obtain written informed consent. Participants gave verbal assent, as did their primary care physicians. The University of California, San Diego Investigational Review Board approved the study.

We excluded patients if they had had a severe stroke or a history of a primary psychiatric disorder preceding the suspected onset of their dementia. For the second study only, participants were examined by a board-certified neurologist using the NINCDS-ADRDA criteria (15) and were also excluded if they had clinically significant visual impairment as determined from an ophthalmic examination. We abstracted medical information from the participants' medical charts. We assessed the severity of dementia at the beginning of the study using the MMSE (16). The mean MMSE score for this combined sample was 8.5 (SD, 7.6; range, 0 to 27). Most participants had severe dementia (i.e., MMSE < 18).

Apparatus
To assess rest–activity rhythms, we collected information using an Actillume Monitor (Ambulatory Monitoring, Ardsley, NY). The Actillume is a small device similar to a watch that is worn on the nondominant wrist. It contained a piezoelectric linear accelerometer that could detect even small body movements of 0.003 g or greater. It also included a photometric transducer that was sensitive to light from <0.01 lux to >100,000 lux, with measurements approximately log linear from a range below moonlight to the brightest summer day at noon, a microprocessor, and 32 K memory chip for data storage. The Actillume recorded activity levels every 10 seconds and then stored the activity level for each minute. Actigraphy is an accepted technique for the assessment of sleep and activity levels in this population (17,18). Participants wore the Actillume recorder for 72 continuous hours. The recorder was removed only for showers.

Mortality Data
We searched San Diego county public records for information on survival of the participants. For those who had died, we ascertained the date, time, and cause of death from death certificates. For all participants for whom no death certificate was on record, we contacted the institution where they were examined to determine whether they were still alive. We found death certificates for 121 participants (81.2%). Nine (6%) participants were confirmed alive and 19 (12.8%) were lost to follow-up.

Data Analyses
We computed measures of circadian rhythmicity from the activity data. We did not use the traditional cosinor model because others have noted that activity data often assume a shape that is more similar to a square wave than to a cosine curve in patients with Alzheimer's disease (19). To avoid this problem, we used an extension of the traditional cosinor model (20). This model allows the shape of the rhythm to conform to a square wave at one extreme and a saw-toothed wave at the other. The parameters used were mesor (mean), acrophase (timing of the peak), amplitude (height of the peak), alpha (width of the peak), and beta (steepness of the rhythm). We estimated the parameters of the extended cosinor model using the nonlinear least-squares procedure in Statistical Analysis Software (Cary, NC). An F-statistic was computed as a measure of the overall fit of the model and included as a possible predictor, with higher F-values indicating stronger rhythms.

Finally, to compare these results with the survival results found by Bliwise and colleagues (14), we obtained acrophase data on 66 healthy (physically and mentally) older adults without dementia (mean age, 67.1 years; SD, 5.4; age range, 60 to 75 years) from the laboratory of our colleague, Dr. Shawn Youngstedt (written personal communication, July 2002). The data on the healthy elderly had been collected with the same equipment used in the current study, thus keeping the measurement technique consistent. This allowed us to compute the absolute value of the difference between the acrophase of the participants with dementia and a "healthy" acrophase, which was referred to as the acrophase deviation. The healthy acrophase was defined as the mean acrophase (12:59 PM) computed from the rest–activity rhythms of the healthy older adults. This acrophase deviation was used as an additional measure of activity rhythms such that higher values indicated greater deviation from a sample of persons without dementia.

We used Cox proportional hazards models to evaluate the relationship between activity rhythm parameters and survival. Because acrophase is a circular variable, it may not behave in the same way as linear variables. We studied the scatterplots of relationships with acrophase and analysis of residuals based on linear models, and we concluded that acrophase analyses were appropriately modeled with linear statistics. We defined survival as the time in years that patients lived after participating in the original study. For the 9 participants who were still alive, we used right censoring. We dropped participants lost to follow-up from all analyses because they each contributed only one data point. To control for the influence of potential confounding variables, we included in the model the following covariates: age, sex, dementia severity (MMSE), ambulatory status (wheelchair bound, ambulatory with assistance, fully ambulatory), medical burden (total number of medical diagnoses listed in their chart), use of sedating medications (sedative-hypnotics, tranquilizers, and antihistamines), depression rating as measured by the Cornell Depression Scale, agitation ratings as assessed with the Cohen-Mansfield Agitation Inventory, and light exposure (average minutes per day over 1000 lux). We entered all rhythm parameters and covariates into the model, and we used a stepwise selection procedure to determine which variables were significant. We used a.10 level of statistical significance for all covariates and a.05 level for all rhythm parameters. We used Blom normal scores transformation of the activity rhythm parameters because of nonnormal distributions.


    RESULTS
 Top
 Abstract
 Methods
 Results
 Discussion
 References
 
Table 1 lists the descriptive statistics of the activity rhythm parameters. Figure 1 shows the distribution of acrophase deviation values. Many participants were clustered around the acrophase of the comparison group of patients without dementia, but there was a considerable range of values. Approximately two thirds of the participants had acrophase that fell later in the day (negative acrophase deviation) than the comparison group. Figure 2 shows the average modeled activity rhythm.


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Table 1. Parameters Obtained From Extended Cosinor Model (N = 150).

 


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Figure 1. The distribution of acrophase deviation values computed as a "healthy" acrophase minus the participant's acrophase. Negative values indicate that the participant's acrophase fell later in the day than that of the comparison group

 


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Figure 2. The average modeled activity rhythm is graphed with labels showing the rhythm parameters. The nearly square-wave shape of the rhythm is typical of the activity rhythms because of the sudden onset of activity on waking in the morning and the sudden cessation of activity when going to bed at night

 
The mean survival time was 2.3 years (SD, 2.0; range, 0.05 to 10.4 years). The mean time between participation in the study and follow-up was 6.7 years (SD, 3.4; range, 1.8 to 12.2 years). Figure 3 shows the distribution of survival times. Longer survival was associated with having a higher MMSE score (i.e., being more cognitively intact) ({chi}2(1) = 16.94, p <.001), female sex ({chi}2(1) = 7.10, p =.008), and being ambulatory compared with being confined to a wheelchair ({chi}2(1) = 16.12, ≤.001). No other covariates were statistically significant predictors of survival.



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Figure 3. The distribution of survival times for participants with dementia once they were included in the study

 
The acrophase deviation ({chi}2(1) = 9.01, p =.003) and beta parameter ({chi}2(1) = 4.88, p =.027) were associated with survival. Patients with lower acrophase deviation (i.e., acrophase closer to that of the patients without dementia) and those with a steeper rhythm (higher beta) had longer survival times. Figure 4 and Table 2 show the survival times for those in the upper 10%, middle 80%, and lower 10% of values for the acrophase deviation. No other circadian variables were significantly related to survival.



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Figure 4. Survival curves for values of the acrophase deviation from the mean of 14:07. Lines represent deviations at the 10th (0:10), 50th (1:41), and 90th (6:47) percentiles. Having an acrophase closer to a "normal" time was associated with longer survival

 

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Table 2. Descriptive Statistics of Survival Based on Acrophase Deviation. The Sample Was Divided Into Percentile Groups (Lower 10%, Middle 80%, Upper 10%) in Order to Facilitate Comparison Across Different Values of the Acrophase Deviation. The Further the Participant's Acrophase Was From That of the Nondemented Sample, the Shorter Their Survival.

 

    DISCUSSION
 Top
 Abstract
 Methods
 Results
 Discussion
 References
 
We studied the relationship between rest–activity rhythms and death in older adults with dementia. Survival was related to the magnitude of the difference between the participant's acrophase and the mean acrophase of a sample of older adults without dementia and to the steepness of rhythms. These results suggest that having an acrophase with "healthy" timing is associated with longer survival.

One strength of these analyses was the ability to determine which specific aspects of circadian rhythmicity were related to survival time. The F-statistic was not associated with survival, so overall circadian rhythmicity did not affect survival time. Rather, the relative timing was most important. Circadian rhythms maintain a delicate synchronization among various physiologic processes and maintain appropriate timing with the environment. Perhaps the acrophase deviation provided a measure of the degree to which a participant was synchronized. Those with large deviations were in a state of desynchronization, which portended an earlier death.

This result is similar to that found by Bliwise and colleagues (14) in the only other study that evaluated the relationship between rhythmicity and survival. They found that the minimum of the body temperature rhythm was related to survival time such that patients with "normal" timing of their temperature minimum (between 2:00 AM and 5:00 AM) lived longer than did those with "abnormal" timing. The underlying reason for this relationship is not clear and will need to be studied further.

These results raise the question of the direction of causation. Does the timing of rhythms have health-related consequences that contribute to shorter survival, is abnormal timing merely an indicator of poor health, or both? We could not answer these questions in the current study. The next logical step would be to perform detailed longitudinal studies, but even then the question of causation may be difficult to answer. If poor circadian rhythms play a causal role, an altered acrophase may be indicative of a breakdown in the temporal organization both within and among various physiologic and behavioral rhythms. This state of internal desynchony may be detrimental to health and shorten life, as suggested by Samis (21). As described earlier, a breakdown of rhythms could result from neuronal loss in the intrinsic pacemaker, an environment with diminished time cues, a failure of the entrainment process, or any combination of these.

If the latter hypothesis is more accurate and alterations in the timing of rest–activity rhythms are the results of other health-related processes, the mechanisms of this process may take the same route. The most likely scenario is that rhythms and health are in a state of constant interaction. Disturbance of one may affect the other in a reciprocal manner that produces a positive feedback loop, leading to a downward spiral of both rhythmicity and health. The initial source of dysfunction may vary from person to person but result in the same outcome.

The beta parameter, which indicates the steepness of the rhythm, was also related to survival. The meaning of a steep rhythm is less clear than the meaning of the rhythm acrophase. It may represent the speed with which a person can get out of bed and get going in the morning and also transition into sleep at night. This could presumably reflect overall health and mobility. It is therefore not surprising that persons who could transition more quickly lived longer. Patients with physical ailments may have had greater difficulty getting in and out of bed, which was reflected in a flatter rhythm, which was predictive of shorter survival time.

The results of this study have important clinical implications. If the timing of rest–activity rhythms can affect health, it would be important to intervene with patients with dementia to help them maintain a "healthy" acrophase earlier in the day. To the extent that deteriorating rhythms are the result of impaired suprachiasmatic nuclei function, directly intervening at the neuronal level may not be possible. However, promising evidence suggests that environmental interventions may be effective for restoring some degree of rhythmicity in this population. Several investigators have evaluated the efficacy of timed bright light exposure in patients with dementia (22–26). Although results have been mixed, there is clear evidence that bright light can be an effective intervention. Interestingly, one of the effects that bright light had in these studies was to change the timing of activity rhythms (22,24).

One difficulty with light treatment would be knowing when to begin the intervention. It may be difficult to assess a patient's circadian rhythms to know whether they have a "healthy" or "unhealthy" timing. Although rest–activity rhythms can be monitored with relatively little equipment, this may place an extra burden on caregivers or nursing staff and would require special training to score and interpret the results.

Perhaps a different line of intervention, that of using the patient's regular daily schedule, may be able to circumvent this difficulty. There has been growing interest in the effects of social rhythms on physical and emotional health (27). Nursing homes, although they often provide relatively little daily stimulation, have the benefit of highly structured environments. Meals are served at the same time each day, and there is some degree of control of the patients' bedtimes and waking times. It may be possible to take advantage of these regular social rhythms to provide important entrainment cues (28). As part of this environmental intervention, ambient light could also be increased for all patients to improve bright light exposure.

Conclusion
The quality of rest–activity rhythms does appear to play an important role in dementia such that patients with disturbed rhythms have shorter survival times. Interventions aimed at improving rest–activity rhythms may improve the sleep and activity rhythms of patients with dementia and ultimately lead to longer survival times. Future studies will need to examine the outcomes associated with treatment to determine whether, in fact, the quality and length of life improve.


    Acknowledgments
 
The authors thank the administration, staff, and patients at the nursing homes participating in this study and Drs. Ruth Pat-Horenczyk, Donald Connor, Leah Levi, and all the UCSD staff and student volunteers who spent countless hours with the patients. Shawn Youngstedt, PhD, provided comparison data.

Supported by National Institute on Aging grants AG08415 and AG15391, National Cancer Institute grant CA85264, the Department of Veterans Affairs VISN-22 Mental Illness Research, Education and Clinical Center (MIRECC), and the Research Service of the Veterans Affairs San Diego Healthcare System.

Received November 8, 2002

Accepted March 6, 2003


    References
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 Abstract
 Methods
 Results
 Discussion
 References
 

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