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Unobtrusive assessment of activity patterns associated with mildcognitive impairment

Tamara L. Hayesa,b,*, Francena Abendrothb,c, Andre Adamid, Misha Pavela,b,Tracy A. Zitzelbergerb,c, Jeffrey A. Kayea,b,c

aDepartment of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USAbOregon Center for Aging and Technology, Oregon Health & Science University, Portland, OR, USA

cDepartment of Neurology, Oregon Health & Science University, Portland, OR, USAdDepartment of Computer Science, Universidade de Caxias do Sul, RS, Brazil

bstract Background: Timely detection of early cognitive impairment is difficult. Measures taken in theclinic reflect a single snapshot of performance that might be confounded by the increased variabilitytypical in aging and disease. We evaluated the use of continuous, long-term, and unobtrusivein-home monitoring to assess neurologic function in healthy and cognitively impaired elders.Methods: Fourteen older adults 65 years and older living independently in the community weremonitored in their homes by using an unobtrusive sensor system. Measures of walking speed andamount of activity in the home were obtained. Wavelet analysis was used to examine variance inactivity at multiple time scales.Results: More than 108,000 person-hours of continuous activity data were collected during periodsas long as 418 days (mean, 315 � 82 days). The coefficient of variation in the median walking speedwas twice as high in the mild cognitive impairment (MCI) group (0.147 � 0.074) as compared withthe healthy group (0.079 � 0.027; t11 � 2.266, P � .03). Furthermore, the 24-hour wavelet variancewas greater in the MCI group (MCI, 4.07 � 0.14; healthy elderly, 3.79 � 0.23; F � 7.58, P � .008),indicating that the day-to-day pattern of activity of subjects in the MCI group was more variable thanthat of the cognitively healthy controls.Conclusions: The results not only demonstrate the feasibility of these methods but also suggestclear potential advantages to this new methodology. This approach might provide an improvedmeans of detecting the earliest transition to MCI compared with conventional episodic testing in aclinic environment.© 2008 The Alzheimer’s Association. All rights reserved.

eywords: Assessment of cognitive disorders/dementia; MCI (mild cognitive impairment); Cognitive aging; Technology

Alzheimer’s & Dementia 4 (2008) 395–405

and aging; In-home assessment

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. Background

Early detection of cognitive decline preceding the onsetf dementia or functional impairment is important for manyeasons [1,2]. Cognitive changes in the elderly might havemmediately remediable causes such as medication compli-ations or unsuspected medical illnesses. Failure to recog-

*Corresponding author: Tel.: 503-418-9315; Fax: 503-418-9311.

cE-mail address: hayesta@ohsu.edu

552-5260/08/$ – see front matter © 2008 The Alzheimer’s Association. All righoi:10.1016/j.jalz.2008.07.004

ize some of these causes in a timely manner might lead torreversible damage. Mild cognitive decline can also be anarly indicator of dementia, and timely recognition of cog-itive impairment provides an opportunity to focus on strat-gies for treatment, compensation, and coping [3,4] andight allow an individual to maintain greater independence

han would otherwise be the case. In addition, early recog-ition is an opportunity for those with irreversible decline toroactively plan for their future and avoid being forced into

risis management.

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Unfortunately, early detection of cognitive impair-ent is problematic, because patients might be unaware

f their impairment, or if noted, uncomfortable discuss-ng their concerns. In addition, cognitive testing is not aoutine part of an elder’s visit to their physician. Fully0% of people age 75 or older seeing a primary careractitioner have no diagnosis or evaluation of theiremory complaint [5,6], and in fact, even the patient or

amily report memory problems in only a small percent-ge of cases in which the patient has clinically detectibleognitive impairment [7]. Thus, alternative means ofdentifying early cognitive decline are needed.

Further confounding our ability to detect cognitiveecline early are the facts that both cognitive and motorunctional measures in the elderly become increasinglyore variable as people age [8 –10], and that this vari-

bility increases differentially in Alzheimer’s diseaseAD) and other neurologic disorders [11]. For example,ariability in mobility measures such as walking speed ortride length have been shown to increase with age andven more so in AD and other dementias [8,10]. Mea-ures taken in the clinic reflect a single snapshot oferformance that might be confounded by such variabil-ty; identification of a decline might require many visitsver months or years to obtain an accurate picture of truehanges. As can be seen in Fig. 1, test scores taken duringperiodic clinic visits (left panel) might show changes inerformance that do not reflect true change but are in-tead simply reflective of normal variability for eachubject. The true trend is clearly apparent when manyore measures are available. We hypothesized that mea-

ures taken consecutively in an elder’s home on a fre-uent or continuous basis would provide a much bettericture of true functional performance. Not only woulduch an approach allow better understanding of normalaily variability for an individual, but change in theariability itself could herald cognitive decline.

Clearly, frequent or continuous measures of cognitiveunction would be difficult to collect by using conven-ional time- and location-restricted methods. However,otor measures such as walking speed and movement-

ig. 1. Problem with infrequent measurements. Left panel depicts testcores taken during a standard clinic visit, taken at 6-month intervals, forpatients. Right panel depicts how continuous assessment could reveal a

ery different picture.

elated activity might be better suited for continuous t

easurement because they are part of an individual’sormal daily functioning. It is becoming increasinglyvident that motor and activity measures, which are im-ortant measures of functional ability in the elderly12–14], are also correlated with cognitive function [15].ven measures as simple as gait speed or timed walkingave been shown to be independent predictors of cogni-ive impairment [16 –20]. However, the precise relation-hip between motor and cognitive function in aging andognitive decline is not well-understood, and further re-earch is needed to better understand this relationship.

Recent research has shown that intraindividual variabil-ty in motor measures such as walking tasks correlates withognitive performance [8,21]. These studies have exam-ned frequent (eg, biweekly) clinic-based measures suchs timed walking and have suggested that the short-termariance (week-to-week) in motor measures might be aensitive indicator of cognitive health. These latter stud-es underscore the value of assessing intraindividual vari-bility through more frequent measures. However, con-ucting frequent clinic-based assessments is impracticalnd labor-intensive. Alternatively, the collection of mea-ures of motor activity gathered in the home on a con-inuous basis can be done unobtrusively and automati-ally, without requiring the presence or involvement of aealth care provider. Furthermore, measures gathered inhe home might be more representative of an individual’sormal daily functioning.

To determine the feasibility of using continuous mea-urement of motor activity for early detection of cogni-ive decline, we carried out a cross-sectional study inhich we gathered measures of walking speed and of

otal movement within the home by using unobtrusiven-home technology. Two groups of community-dwellinglders were compared, those with mild cognitive loss andhose who were cognitively healthy.

. Methods

.1. Subjects

All subjects provided informed consent to participate.rotocol and consent forms were approved by the Oregonealth and Science University Institutional Review Board.ourteen older adults (age, 89.3 � 3.7 years) were recruitedrom ongoing studies at the National Institute on Aging–ayton Aging and Alzheimer’s Disease Center (LAADC)

OHSU IRB #1487). All participants were ambulatory adultsged 65 years or older, living independently and alone in theommunity. Subjects were clinically assessed during regularAADC visits by using a standardized battery of tests consist-

ng of neurologic and psychometric assessments including testsf motor performance. Neurologic tests of motor functiononsisted of Tinetti gait and balance scales [22], finger tapping,

imed one-leg standing, the motor portion of the Unified Par-

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397T.L. Hayes et al. / Alzheimer’s & Dementia 4 (2008) 395–405

inson’s Rating Scale [23], and timed walk [17]. Subjects werelassified into one of two groups on the basis of their scores onhe global Clinical Dementia Rating (CDR) scale [24] and the

ini-Mental State Examination (MMSE) [25]: a healthy groupn � 7; CDR, 0; MMSE, �24) and a mild cognitive im-airment (MCI) group (n � 7; CDR, 0.5; MMSE, �24). Allubjects were continuously monitored for at least 6 months.emographic and functional characteristics of each group

re shown in Table 1.

.2. Procedures

To collect continuous activity data, an unobtrusive activityssessment system was installed in the home of each partici-ant. The system used X-10 motion sensors and contact sen-ors to monitor activity in the home as well as comings andoings from the home. X10 is an international and open in-ustry standard for communication among electronic devicessed for home automation. The sensors transmit data at 310Hz, and the original protocol was designed to use household

lectrical wiring as a carrier for the digital signal. Unfortu-ately, this approach introduces significant noise into the sig-al; fluctuations in the power level (for example, caused by aefrigerator compressor turning on) can introduce false posi-ives and extra bits into the data stream. As a result, past studieshat used household wiring to carry the signal experiencedignificant levels of false positives and false negatives. A moreobust approach, which was used in this study, is to collect theensor firings by using a wireless transceiver (WGL 800; WGL

Associates, San Antonio, TX) connected directly to a com-uter installed in the subject’s homes. Collisions caused byultiple sensors firing and other sources of 310-MHz noiseight result in garbled data (representing false negatives, be-

able 1emographic and functional characteristics of participants

D MCI/Norm Sex Rms Educ Age MMSE

2† M M 8 17 82.7 273‡ M F 8 12 91.0 254 M M 9 14 92.2 265 M F 4 12 90.6 258† M M 2 16 88.3 284 M F 7 14 89.5 285 M F 7 16 84.8 251 N F 6 18 91.9 296 N M 9 16 82.5 277 N M 8 16 94.3 279 N F 4 16 88.4 280 N F 3 12 90.9 281† N F 2 12 88.4 282† N F 3 20 93.9 28

Abbreviations: Group N, healthy elderly; group M, those with MCI; Rmseasure; TINgait, Tinetti gait measure; (I)ADL, (Instrumental) Activities* P � .05, significant differences between groups adjusted for multiple† Subjects 2, 8, 11, and 12 were excluded from walking time analysis b

alking speed.‡ Subject 3 excluded from walking speed and 6-month analyses becaus

ause the original signals are lost); these types of errors were d

uantified by tracking the number of invalid signals received.n our previous studies, the data loss as a result of these errorsas 1.9% [26]. Although false positives are theoretically pos-

ible (simultaneous modification of 12 bits as a result of noiser collision could result in the creation of a new valid sensorode), we used sensor codes with little similarity to furthereduce the likelihood of this rare event.

Three configurations of sensors were used to gather databout movement and activity in the home. First, passivenfrared pyroelectric motion sensors (MS16A, X10.com)ere placed in every room at locations expected to pick up

he participant’s movements restricted to that room. Theseensors fire once every 6 seconds as long as movement isetected. Because these sensors are sensitive to changes ineat sources, sedentary activities such as reading might notause the sensors to fire, whereas activities involving arm,ody, or leg movements (folding laundry, making meals, usinghe bathroom, moving between rooms) will result in regularring. Clearly, differentiating from continuous motion involv-

ng mild exertion (such as folding laundry) and high exertionctivities (such as jogging on a treadmill) is not possible withhese sensors. However, the sensors do capture well differ-nces in daily activities typical of this population. Second,agnetic contact sensors (DS10A, �10.com) were placed on

ach door of the home to track visitors and absences from theome. Third, to estimate walking speed, motion sensors with aestricted field of view were installed along a hallway so theyould fire only when someone passed directly in front of them

restricted to �4 degrees field of view, or about �6.5 cm at aistance of 90 cm from the sensor). All data were time-tamped at the computer and uploaded nightly via automated

R* ADL IADL ModifiedUPDRS TINbal TINgait

0 0 2 1 02 3 1 11 10 1 2 6 31 1 n/a 12 41 0 3 4 00 0 5 7 10 2 3 9 20 0 1 3 00 0 5 n/a n/a0 0 2 5 10 0 4 1 01 0 7 2 00 0 1 2 00 0 0 2 1

er of rooms in the home; Educ, years of education; TINbal, Tinetti balancey Living.risons.f inappropriate geometry of their homes for unobtrusive measurement of

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.3. Analysis

.3.1. Data preparationSensor data were first cleaned to remove redundancy

each sensor sends five identical signals each time it detectsotion to reduce the potential loss of data as a result of

imultaneous transmission of multiple sensors). Then, daysn which multiple people were in the home or in whichxcessive data errors occurred were removed from the anal-sis. Periods in which more than one person was likely to beresent in the home were identified by examining doorpenings and activity in the home, and days in which theseeriods occurred were excluded from the analysis. In addi-ion, because collisions and errors in the data can influenceur estimates of daily counts, we excluded those days inhich the numbers of such errors were extreme outliers.pecifically, days in which the percentage of data errors wasore than three standard deviations above the mean for that

ome were excluded from the analysis. Finally, full days inhich the subject was away from home were also excluded

rom analysis.Paired comparisons were made by using a t test unless

therwise indicated. All data cleaning and analyses wereerformed with Matlab standard, wavelet, and statisticalackages (The Mathworks Inc, Natick, MA).

.4. Estimates of walking times

Walking times were estimated for each subject as de-cribed previously [26]. Briefly, three restricted-field mo-ion sensors were placed along a hall or path of frequencyraffic in the home. If the restricted-field sensors are s1, s2,nd s3, then each time the sensors fired in the order (s1,s2,s3)r (s3,s2,s1) without another intervening sensor firing, theubject was considered to have walked along the restricted-eld path, and the difference in firing times, dt � |s1 � s3|,as calculated as the observed sample of the walking time.he walking times for each subject were normalized to aistance of 1 meter. Walking times more than three standardeviations from the norm for this age group were excludedrom analysis, because in some homes there was a cupboardr other distraction along the sensor line at which subjectsould stop, resulting in very long (and nonrepresentative)alking times. The median walking time was then calcu-

ated for each 1-week period in which at least seven mea-ures were taken. The median walking time was used be-ause it is more robust to outliers, and because we werearticularly interested in typical walking times for eachubject. Walking times were compared across groups bysing data from the 26 contiguous weeks of monitoringommon to all subjects (January to June). The coefficient ofariation of the median walking times was also calculatedver the 26 intervals and compared across subjects by usingt test. One MCI subject (S3) fractured her femur near the

tart of the study and was not ambulatory for an extended

eriod during the study; her data were excluded from this c

nalysis. In four other homes (S2, S8, S11, S12) there wasot a hall in the home that restricted the motion along theensor line. Therefore, it was possible to approach the sen-or line from other angles, which might have impacted theeasurement of walking speed. Therefore, we analyzed the

ata both with and without these additional homes.Previous work has shown that the elderly have decreased

obility and activity during the latter part of the day [27].e hypothesized that our subjects would walk more

lowly in the late afternoon and evening, with the con-equence that our data would show increased walkingimes during the latter half of the day. We were alsonterested in whether this increase would be greater forubjects with MCI. To this end, the observed data werenalyzed for each subject by comparing median walkingpeed during the period 6 AM to 3 PM (morning) with thoseuring the period 3 PM to 6 AM (evening) during the entireonitoring period, using a Bonferroni correction [28] for

omparisons of multiple outcome measures. In addition,e compared the difference between the evening andorning walking times between groups.

.5. Estimates of daily activity

As previously mentioned, our activity measures reflecthe amount that a subject moves throughout the home.lthough some subjects were active outside the home, ourata are restricted to in-home activity. To compare in-homectivity levels across subjects, we determined the number ofensor firings during all periods in which the subject wasome by using door openings and lack of activity in theome to identify when the subject was out of the home. Welso calculated the mean number of outings per day and theverage time spent outside the home per day for eachubject. Daily activity counts were normalized to the num-er of firings per minute on the basis of the time the subjectas in the home. The coefficient of variation of activity was

hen calculated for each subject during the first 26 monitor-ng weeks during which the subject was home and the dataere valid.In addition to these estimates of daily activity, wavelet

nalysis (a common method of decomposing a time-varyingignal at multiple resolutions [see Appendix]) was used toxamine differences in 24-hour activity variance over timeor each group of subjects [29,30]. Specifically, the dataere divided into six consecutive 4-week periods, andavelet analysis was done for each subject during eacheriod. This transformation of the data produces a set ofavelet coefficients corresponding to specific time scales,

ncluding 3 hours, 6 hours, 12 hours, and 24 hours. Theariance in these coefficients during each 4-week periodherefore provides a measure of the amount of variance inctivity at each of those time scales. The wavelet variance attime scale of 24 hours (reflecting daily variance) was

ompared during the periods and between groups by using

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mixed model repeated-measures analysis of variance toxamine trends in the variance over time [31].

. Results

More than 108,000 person-hours of continuous activ-ty data were collected during this study. These data wereollected unobtrusively for up to 418 days (mean, 315 �2 days), without requiring the subjects to wear anyevices or to interact with research staff during the mon-toring period.

.1. Data cleanup and errors

The number of days of data removed from analysis as aesult of a disproportionate amount of collisions and datarror ranged from zero to 27 but did not differ betweenroups (MCI, 10.3 � 9.3 days removed; healthy, 7.8 � 6.4ays removed). For the remaining days, the amount of errors a percentage of total daily activity was 1.1% � 2.3%cross all homes (range, 0.1% to 8.5%). However, themount of error was much greater in two of the homes (S15,.3% and S8, 85%). In S15 the collisions were due toverlap of the field of view of two sensors in the den; in thisome, the subject spent about 5% of her time in the denitting at the computer, and thus total overall activity for thisubject was likely only slightly underestimated. In the casef S8, the subject lived in a mid-sized recreational vehicle,nd the data errors appeared to be due to the placement ofhe antenna receiving the data. Thus, some data from each

able 2ean walking times and activity levels for all subjects during a 6-month

ubject Walking time

Mean (sec/m)* CoV

1 1.37 0.0716 1.29 0.0587 1.36 0.0589 2.10 0.0490 2.69 0.1111 1.50 0.1162 1.99 0.089ealthy 1.76 � .52 0.079 � 0.0272 0.83 0.0953§ N/A N/A4 1.85 0.1535 1.21 0.2908 2.09 0.1364 2.48 0.1157 1.74 0.090CI 1.70 � .60 0.147 � 0.074

* Mean walking times are reported as the mean of the median weeklyverage sensor firings per minute during periods in which the subject was

† Coefficient of variation is for the average sensor firings.‡ The 24-hour wavelet variance is an average of the six consecutive 4-w§ Walking speed data for subject 3 was excluded because of insufficien

ensor were lost, and total activity was certainly underesti- e

ated. However, in this home the distribution of collisionshroughout the day was evenly distributed.

.2. Comparison of subjects by conventional measures

All subjects were ambulatory and had normal motorunction as assessed clinically and measured by the motorunction scales. There were no significant differences be-ween the groups in modified unified Parkinson’s diseaseating scale (UPDRS) (healthy, 1.9 � 2.3; MCI, 1.9 � 2.0;

� .87) or the clinic-measured 9-m timed walk (healthy,4.5 � 3.7 seconds; MCI, 15.6 � 5.5 seconds; P � .78).owever, the MCI group had higher Tinetti gait (healthy,.33 � 0.52; MCI, 1.6 � 1.5; P � .08) and Tinetti balancecores (healthy, 2.5 � 1.4; MCI, 7.1 � 3.9; P � .02).

.3. Comparison of walking speed

Comparison of week-to-week normalized walking timestime to walk 1 m) and the week-to-week coefficient of vari-tion for the walking times revealed no differences in medianr mean walking times of participants in the MCI group asompared with the healthy elderly group (healthy elderly,.76 � 0.52 sec/m versus MCI, 1.70 � 0.60 sec/m; t11 �0.194, P � .43). In addition, median walking times did nothange during the 6-month monitoring period for either group.owever, the coefficient of variation in the median walking

imes was twice as high in the MCI group (0.147 � 0.74) asompared with the healthy group (0.079 � 0.027; t11 � 2.266,� .03). As is apparent from Table 2, the homes without halls

id not differ from those with halls. Analysis of the data

activity Mean 24-h waveletvariance‡

(counts/min)* CoV†

0.54 3.890.37 4.080.40 4.000.18 3.820.69 3.440.39 3.730.56 3.55

0.15 0.45 � 0.027 3.79 � 0.230.64 4.300.45 4.000.39 4.160.39 4.080.36 3.930.36 3.910.49 4.12

0.14 0.44 � 0.012 4.07 � 0.14

time, normalized to seconds/meter. Mean activity levels are reported inalone.

easures.This subject broke her leg during the third week of the study.

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400 T.L. Hayes et al. / Alzheimer’s & Dementia 4 (2008) 395–405

alking times between groups (t7 � 0.142, P � .45) and aignificantly greater coefficient of variation in median walkingimes for the MCI group as compared with the healthy groupt7 � 2.257, P � .03). Not surprisingly, both the medianalking times and the coefficient of variation in walking timesere correlated with the Tinetti balance scores (medians: r �0.45; coefficient of variation: r � 0.75).

We also looked at the morning and evening walkingpeeds for each subject. Figure 2 shows the median morningnd evening walking times for each subject during the-month period. Most of the MCI subjects had longer walk-ng times in the evening, as compared with the healthyontrols. The difference between evening and morningalking times was significantly greater for the MCI subjects

han the healthy elderly (MCI, 0.31 � 0.08 sec/m versusealthy elderly, 0.057 � 0.01 sec/m; t7 � 1.19, P � .05).

.4. Comparison of measures of daily activity

Table 2 summarizes the activity counts, which reflectntensity of activity in the home, for all subjects. Figure 3hows typical activity for each subject during the monitor-ng period. These graphs show the considerable differencescross subjects in the variability in their activity. The aster-sks in this figure indicate those subjects with the greatestariance in their daily activity. In general, MCI subjectsere somewhat more active than their healthy counterparts,

lthough this difference was not statistically significantMCI, 0.85 � 0.14 counts/min; healthy, 0.64 � 0.16; t12 �.03, P � .16). In one case (S7), a cognitively healthyubject also had high day-to-day variance in their dailyctivity measure. This subject had severe visual and hearingmpairments and seldom left his home; however, it is notlear how this might have influenced his activity in theome. MCI subjects were also less likely to go out of the

ig. 2. Walking times in the morning and evening, averaged over 6 months,or each subject. Numbers indicate the subject IDs. Across all subjects, theean walking times were longer in the evening than in the morning, with

ine of the 12 subjects showing some slowing later in the day. Compari-ons across all walking times in individuals showed that this slowingreflected in longer walking times) was significantly greater in 7 subjects.sterisks indicate those subjects whose walking time was significantly

ncreased in the evening.

ome than the healthy elders (MCI, 0.96 � 0.67 outings per m

ay; healthy, 2.03 � 1.43 outings per day) and spent lessime on average out of the home each day (MCI, 62.0 �0.9 minutes per day; healthy, 198.0 � 142.3 minutes peray), but the differences between individuals were muchreater than those between groups. There was also no dif-erence in the coefficient of variation of the daily activityetween the groups.

In contrast, wavelet analysis, which allowed us to exam-ne the variance at different time scales, showed significantifferences between the groups. The wavelet variance wasigher in the MCI group than in the healthy elderly group atll time scales (Fig. 4A) and, in particular, at the 24-hourime scale. A repeated-measures analysis of variance of4-hour wavelet variance, calculated for six consecutive-week periods, showed a significant difference betweenroups (Fig. 4B; MCI, 4.07 � 0.14; healthy elderly, 3.79 �.23; F1,60 � 7.58, P � .008) but no effect over timeeriods. This indicated that the day-to-day pattern of activ-ty of subjects in the MCI group was more variable than thatf the cognitively healthy control group. Because the coef-cient of variation of the daily activity measure during thentire 6-month period did not differ, this greater variabilityt specific time scales suggests that the patterns of varianceiffer between the groups, with the healthy elders showingmore consistent pattern of activity throughout the day.

. Discussion

The results presented here provide a first look at contin-ous motor and activity measures derived from the normalaily activities of community-dwelling elders. By continu-usly monitoring elders by using unobtrusive wireless tech-ologies in the home, we have been able to identify a set ofctivity parameters that might differentiate individuals withCI from their healthy counterparts. The use of unobtrusive

n-home technologies allowed us to observe activity param-ters of individuals during an extended period of time with-ut interfering with their daily activities. Thus, unlike brief,eriodic clinical visits, our measures factor in the naturalaily variability in an individual’s health, mood, and energyevel. This objective, continuous documentation of dailyctivity affords us insights into differences in activity levelsetween MCI and healthy elderly that could not be easilydentified with a typical cross-sectional study design.

The normalized mean walking times for both groups, 1.8ec/m (0.56 m/sec), were somewhat longer than what haseen reported in the literature [17,32], where healthy elderlyypically walk about 0.7 to 1.2 m/sec (equivalent to 0.85 �.4 sec/m) in timed walk tests in the clinic. We hypothesizehat the desire to perform well during a clinical testingituation leads subjects to walk faster than their normalaily pace. In contrast, because we obtained continuouseasures during a period of months, our data reflected a

ore natural walking pace. Characteristics of natural mo-

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401T.L. Hayes et al. / Alzheimer’s & Dementia 4 (2008) 395–405

ility observed over time might reflect systematic variationshat would otherwise be interpreted as random effects.

Although one might anticipate longer walking times inhe MCI group, this was not the case. Rather, our datandicated that the MCI subjects slowed more in the eveninghan did their healthy counterparts, and the amount of slow-ng was significantly greater for the MCI subjects as com-ared with the healthy group. This greater slowing rate inhe data might reflect increased difficulty in performingotor tasks when tired. Because of our sample size, weere unable to use group comparisons of “slow walkers”

nd “fast walkers,” as has been done in previous studies thatuggest that motor slowing (including bradykinesia, gait

ig. 3. Daily activity levels for each subject, calculated with a 7-day movirdinate shows activity levels, in 1,000s of sensor firings. The measure istandard deviation above and below the mean. Not all subjects were monits scaled differently as a result of a greater variance in the daily activity leeaks typically correspond to periods in which the subject had an overnig

isturbance, and reduced gait speed or timed walking) i

ight be predictive of the eventual onset of dementia [16–8,20,33]. What is of key importance in assessing motorlowing is not group performance, but rather how muchlowing one experiences relative to one’s own baseline. Inur study, we did not see significant slowing in any of theubjects during the 6-month period. However, the greateroefficient of variation in walking times in the MCI subjectss compared with healthy controls, coupled with theirreater slowing in the evenings, suggests that variability inhis motor measure might be more strongly correlated withheir cognitive impairments than absolute walking speed.

Of even more interest than the differences in walkingpeed were the differences in the wavelet variance of activ-

age. The abscissa shows the number of days since start of monitoring; thember of sensor firings per day. The horizontal dashed lines indicate onethe same period of time. Asterisks indicate subjects for whom the Y-axisissing data indicate absences from home (eg, subjects S7 and s12); sharpt in the home (eg, S1, S2).

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402 T.L. Hayes et al. / Alzheimer’s & Dementia 4 (2008) 395–405

ariability in the wavelet variance was smaller in the MCIroup, although the wavelet variance itself was greater at allime scales. This suggests that the wavelet variance mightncrease as early cognitive decline occurs. More precisepecification of trajectories of change in these putativearkers of MCI requires confirmation during a longitudinal

tudy. However, this study does demonstrate the ability toollect measures that are sensitive to differences between

ig. 4. (A) Box plots of the log variance in the wavelet representation of aolid boxes are healthy subjects. (B) Plot of the mean of the 24-hour waiamonds are MCI subjects; open boxes are healthy subjects.

CI and healthy elderly, as well as to track changes in these t

easures over time. Thus, this approach has the potential toe a valuable tool for assessing longitudinal change.

There were several technological limitations of the cur-ent study. The primary drawback of the activity assessmentystem we used for this study is that it cannot reliablyetermine when multiple people are present in the home.lthough we selected subjects for the study who lived alone

nd excluded intervals in which multiple people were de-

evels across subjects for six time scales. Dashed boxes are MCI subjects;riance across MCI and healthy subjects for 6 consecutive months. Solid

ctivity lvelet va

ermined to be present in the home, to be widely useful an

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403T.L. Hayes et al. / Alzheimer’s & Dementia 4 (2008) 395–405

ctivity assessment system must necessarily be equally ef-ective in multi-person homes. Although subjects couldaintain a log of visitors, this would be excessively bur-

ensome during long periods, and thus a technological so-ution is needed. A number of technologies have been pro-osed to mitigate this problem [34–37]; however, becausehe participants must wear tags, the system is no longernobtrusive. We are actively investigating unobtrusive al-ernatives to simultaneously tracking multiple individuals inhe home. In addition, more sophisticated statistical ap-roaches, such as the use of Bayesian networks to modelctivities, might uncover measures that differentiate theroups more definitively. Also, because the measurement ofalking speed was necessarily restricted to the times duringhich subjects walked down the hall, this approach has

imited usefulness in small apartments that lack such a hall.e are now developing new approaches for deriving walk-

ng speed data from the other motion sensors distributedround the home [38].

Previous studies of activity in the home have used wrist-orn actigraphs to quantify movement in the home [39,40].he present study differs from the use of actigraphy in twoajor ways. First, our approach allows continuous moni-

oring over months or even years, without requiring com-liance by the subject (to wear the actigraph). Second, then-home sensors provide information about location in theome, as well as capture all types of movements (includingalking speed) rather than just arm movements, and there-

ore these data might be of particular value in interpretingatterns of activity in the home.

Recently, there have been a number of laboratory-basednd case study examples of using motion sensors for mon-toring acute changes in activity and movement in the home41–43]. However, to the best of our knowledge this is therst case-control study of in-home activity patterns exam-

ning the potential clinical relevance of continuous activityssessment. The results strongly suggest that continuousssessment of activity patterns in the home, and in particularariance in daily activity, might provide a useful earlyarker of MCI. It is highly likely that individuals begin to

djust their behavior and adapt coping mechanisms longefore their cognitive decline results in apparent functionaloss or even before an individual recognizes a memoryroblem. Continuous assessment of in-home activity wouldllow detection of a change in the consistency of an indi-idual’s daily activity patterns that could provide an earlyarning system for the onset of such cognitive problems,

ven before the individual was aware of a problem them-elves. Furthermore, the combination of information aboutovement consistency changes and information such as

otal recent activity and the proportion of time spent in theedroom versus other areas of the home could providearkers of other illnesses including depression, movement

isorders, mild stroke, cardiac decompensation, or occult

nfection. Thus, the results of the current study suggest that

nobtrusive, continuous in-home assessment provides aromising new tool for the early detection of clinicallyelevant changes not only affecting cognition but for aumber of other neurologic and general medical conditions.

cknowledgments

The authors gratefully acknowledge the staff of the Lay-on Aging and Alzheimer’s Disease Center for their help inecruiting participants for this study and Brad Stenger foris technical assistance in designing and deploying the sys-ems used in this study. This work was funded by a pilotrant from the National Institute on Aging (P30 AG08017).r Kaye’s time was partially supported by a Merit Reviewrant, Office of Research and Development, Department ofeterans Affairs.

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ppendix

avelet analysis of motion sensor activity

Wavelet analysis is a method of decomposing a time-arying signal at multiple resolutions [1], which has beensed extensively in the analysis of biomedical signals, in-luding analysis of gait and walking behaviors [2–4]. In thisupplement, we explain the motivation behind our use ofavelets for studying our activity data and provide addi-

ional detail on the approach we used.The raw activity data consist of a series of times at which

motion sensor fired somewhere in the subject’s home.uring periods in which the subject is very active, these

ime points will be very close together, because the sensorsight fire as frequently as 10 times per minute. (The sensors

hemselves have about a 6-second refractory period, andherefore in the presence of continuous motion, a singleensor will fire at most once every 6 seconds.) Thus, theumber of firings per minute provides an estimate of thectivity level of the subject.

However, the pattern of firing might provide addi-ional insights. For example, if the subject were veryctive in the mornings (doing chores) and very sedentaryn the afternoon (reading or napping), the data wouldhow a periodic pattern on a 24-hour cycle. If the subjectended to work in “bursts” such that they would have aurry of activity followed by a rest period, the data would

how a periodic pattern at a much shorter time scale,

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405T.L. Hayes et al. / Alzheimer’s & Dementia 4 (2008) 395–405

erhaps hourly. Clearly, the patterns of activity are nothis simple or consistent; however, by using a techniquehat examines periodicity in the data, we can get insightsnto the subject’s patterns of behavior. Wavelet analysiss a multi-resolutional technique that allows decomposi-ion of a time signal into frequency components at mul-iple time scales through appropriate low-pass and high-ass filtering of the signal. Like Fourier transforms, theavelet transforms decompose the signal into componentarts. In the case of Fourier transforms, the componentarts are sine waves of different frequencies, and the

Fig A1.

ourier coefficients represent the contribution of each

requency to the signal. In the case of wavelet transforms,he component parts are scaled wavelet functions, and theoefficients correspond to the contribution of the wavelett each scaling level.

Wavelet functions are composed of a scaling function(x) (the low-pass filter) and a wavelet function �(x) (theigh-pass filter). In our study, we used a coiflet with 10anishing moments. This is a compact, biorthogonal, nearlyymmetric wavelet that bears good similarity to the struc-ure of the signal we were analyzing (Fig. A1).

In this study, we calculated the total number of sensorrings for each consecutive 11.25-minute interval (128 inter-als per day) and used discrete wavelet analysis (coif5 waveletasis) to filter this time series at time scales of 1.5 hours, 3ours, 6 hours, 12 hours, and 24 hours. Wavelet variance wasalculated at each time scale and compared across groups. Themount of variance at a particular time scale (24 hours) reflectshe consistency of the periodic pattern over time (across weeksr months).

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[1] Vidakovic B. Statistical modeling by wavelets. New York: JohnWiley & Sons; 1999.

[2] Sekine M, Tamura T, Akay M, Fujimoto T, Togawa T, Fukui Y.Discrimination of walking patterns using wavelet-based fractal anal-ysis. IEEE Trans Neural Syst Rehabil Eng 2002;10:188–96.

[3] Najafi B, Aminian K, Paraschiv-Ionescu A, Loew F, Bula CJ, RobertP. Ambulatory system for human motion analysis using a kinematicsensor: monitoring of daily physical activity in the elderly. Biomed-ical Engineering, IEEE Transactions 2003;50:711–23.

[4] Aldroubi A, Unser M. Wavelets in medicine and biology. Boca

Raton, FL: CRC Press; 1996.