~ Department of
`Veterans Affairs
`
`Journal of Rehabilitation Research and Development
`Vol. 40, No. 5, September/October 2003, Supplement 2
`Pages 45–58
`
`Bodies in motion: Monitoring daily activity and exercise
`with motion sensors in people with chronic pulmonary disease
`
`Bonnie G. Steele, PhD, ARNP; Basia Belza, PhD, RN; Kevin Cain, PhD;
`Catherine Warms, PhD, ARNP, CRRN; Jeff Coppersmith, MS, CSCS; JoEllen Howard, BS, GCS
`Primary Care and Specialty Medicine Service, Health Services Research and Development Department, Department
`of Veterans Affairs (VA) Puget Sound Health Care System, Seattle Division; Department of Biobehavioral Nursing and
`Health Systems, University of Washington, Seattle, WA; Department of Biostatistics and Office of Nursing Research, School
`of Nursing, University of Washington, Seattle, WA
`
`Abstract—A primary goal of pulmonary rehabilitation is to
`improve health and life quality by encouraging participants to
`engage in exercise and to increase daily physical activity. The
`recent advent of motion sensors, including digital pedometers and
`accelerometers that measure motion as a continuous variable, have
`added precision to the measurement of free-living daily activity.
`Daily activity and exercise are variables of keen interest to propo-
`nents of the national health agenda, epidemiologists, clinical
`researchers, and rehabilitation interventionists. This paper summa-
`rizes issues related to conceptualizing and monitoring activity in
`the rehabilitation setting; reviews motion sensor methodology;
`compares motion-sensing devices; presents analysis issues and
`current and potential applications to the pulmonary rehabilitation
`setting; and gives practical applications and limitations.
`
`Key words: accelerometer, daily activity, exercise, pedometer,
`pulmonary disease, pulmonary rehabilitation.
`
`INTRODUCTION
`
`In chronic pulmonary disease, dyspnea and decondi-
`tioning profoundly constrain physical activity and are
`known to produce, over time, spiraling losses in global
`functioning and life quality. Pulmonary rehabilitation,
`which includes graded exercise, strength and flexibility
`training, and collaborative self-management education,
`improves physical functioning and life quality and is now
`considered an integral component of optimal care for per-
`
`sons with severe lung disease [1,2]. It is likely that the most
`salient benefits of pulmonary rehabilitation come through
`program-related improvement in the ability to carry out
`daily physical activities, and in particular, to undertake the
`ubiquitous behavior of walking. The measurement of free-
`living physical activity and walking has recently been
`found to be particularly suited to devices that measure
`motion, such as accelerometers, which can objectively
`quantify even low levels of physical activity as a continu-
`ous variable and can detect subtle incremental changes as a
`result of intervention [3]. This article provides an overview
`of the potential utility of motion sensors to measure physi-
`cal activity in persons with chronic pulmonary disease in
`the setting of pulmonary rehabilitation. We address
`the conceptualization of activity, exercise rehabilitation,
`motion sensing, comparison of motion sensors, method-
`ological and analysis issues, applications to pulmonary
`rehabilitation, and practical considerations and limitations.
`
`Abbreviations: COPD = chronic obstructive pulmonary dis-
`ease, ICC = intraclass correlation coefficient, VMU = vector
`magnitude units.
`This material was based on work supported by the Health
`Services Research and Development Merit Review NRI 98-
`194.
`Address all correspondence and requests for reprints to Bonnie G.
`Steele, PhD, ARNP; VA Puget Sound Health Care System (111-
`B), 1660 Columbian Way South, Seattle, WA 98108; 206-764-
`2496; fax: 206-768-5398; email: bonnie.steele@med.va.gov.
`
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`46
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`Journal of Rehabilitation Research and Development Vol. 40, No. 5, 2003, Supplement 2
`
`CONCEPTUAL DISTINCTIONS
`
`For purposes of clarity, a number of conceptual dis-
`tinctions should be made. First, exercise, such as those
`activities undertaken in a pulmonary rehabilitation pro-
`gram, is defined as the planned, structured, and repetitive
`bodily movement carried out to improve or maintain one
`or more aspects of physical fitness [4]. Daily physical
`activity, a variable only recently quantifiable, is the total-
`ity of voluntary movement, produced by skeletal muscles
`during everyday functioning [4]. Daily physical activity
`includes exercise. Because daily physical activity is both
`voluntary and community-based, the additional descrip-
`tor of “free-living” daily activity is often used. Tudor-
`Locke conceptualizes physical inactivity as a human
`behavior characterized by lack of participation in vigor-
`ous activities and minimal physical movement [5]. Per-
`sons who experience daily, incapacitating dyspnea due to
`chronic pulmonary illness fall readily into this group.
`According to Webster’s Dictionary [6], motion is
`defined as the act of moving the body or any of its parts;
`motion sensing is therefore the measurement of movement
`of the body, or in selected instances, depending on the
`location of the device, the movement of a body part, such
`as the arm or leg. While devices that measure motion of
`the body in toto or in one of its parts would seem to have
`strong face validity for characterizing daily physical activ-
`ity measurement, this issue is less than clear, particularly
`in persons with very low levels of activity. For example,
`the issue of sensing extraneous motion that is not associ-
`ated with voluntary movement and energy expenditure
`(arm movement without movement of the body, pendulous
`abdomen movement, and movement associated with car
`trips) may be responsible for considerable error variance.
`
`MEASURING PHYSICAL ACTIVITY WITH
`MOTION SENSORS
`
`Overview
`Motion sensors in current use include pedometers
`and accelerometers. These devices may be used for pur-
`poses of surveillance, clinical, research, and program
`evaluation [7].
`
`Surveillance
`Motion sensors have been used to characterize popula-
`tion-based activity levels for the purposes of monitoring
`
`national physical activity levels and evaluating the attain-
`ment of physical activity recommendations, both at an
`individual and a population level [8,9].
`
`Clinical Settings
`Clinical uses of motion sensors include measurement
`of the processes and outcomes of programs in which
`exercise enhancement and increased daily activity are
`variables of interest [10]. Much work has been conducted
`with pedometers as a means to motivate clinical groups
`to exercise, including people with diabetes, obesity, and
`congestive heart failure [11–13].
`
`Research and Program Evaluation
`Apart from the research substantiating the validity,
`reliability, and stability of these devices in specific
`groups and settings, motion sensors have been used to
`measure adherence to experimental exercise protocols
`and relationships between free-living physical activity
`and other key variables, such as functional capacity, self-
`efficacy for walking, and health status [14,15]. Acceler-
`ometers are particularly useful in providing objective
`feedback of ambulatory activity (dose quantification) to
`investigators and to study participants in exercise adher-
`ence research. This is especially important for pulmonary
`patients, for whom precise quantification of walking dur-
`ing daily living is essential because small improvements
`due to effective treatment can often produce large gains
`in overall functioning. Motion sensor technology may
`also be used to evaluate and improve the quality of reha-
`bilitation programs and program changes.
`
`Motion Sensor Methodology
`Traditional methods for measuring daily, free-living
`physical activity are imprecise and suffer from a number
`of problems. For example, methods that rely on self-
`report of activity and exercise, such as diaries and ques-
`tionnaires, are both time-consuming and unreliable, espe-
`cially for the elderly because they depend on memory
`[16,17]. Direct observation is time-intensive and intru-
`sive. Other more reliable methods, such as radioisotope
`techniques using doubly labeled water, are technologi-
`cally complex and expensive [18].
`A wide array of motion sensors exists that has the
`potential to more precisely measure free-living daily phys-
`ical activity in rehabilitation and other settings. The Table
`contains an overview of the types of devices, ranging from
`the simplest (least complex) to the most complex, with a
`
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`STEELE et al. Monitoring daily activity and exercise with motion sensors
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`47
`
`Table.
`Comparison of activity monitors available in United States.
`
`Type
`
`Pedometer/
`Step Counter
`
`Brand/
`Manufacturers/
`Price
`
`Characteristics
`and Features
`
`Yamax Digiwalker
`(Yamax Inc., Tokyo,
`Japan; New Lifestyles,
`Inc., Kansas City, MO)
`(most often used in
`research)
`$20–$30
`Many other brands,
`including Freestyle
`Pacer, Eddie Bauer,
`and Accusplit
`$19–$30
`
`Measures vertical
`accelerations at hip
`to count steps taken.
`Smaller than a pager,
`extremely light.
`LCD screen display.
`4 models with variable
`programmable functions:
`steps, distance, calories, time.
`Uses photo/electronic battery
`with life up to 3 years.
`Has safety strap to prevent loss.
`
`Physical
`Placement
`of Device
`
`Waist
`
`Strengths
`
`Limitations
`
`Must remain
`vertical.
`Wearer must record
`output if daily activity
`data required.
`
`Displays cumulative
`data continuously.
`Useful as
`a motivational tool.
`Easy to use
`and unobtrusive.
`Least cost of any
`option.
`Good measure
`of walking activity.
`
`Populations
`Used in
`Validation
`Studies
`
`Healthy adults
`
`StepWatch
`(Prosthetics Research
`Study, Seattle, WA)
`$3300 for monitor,
`computer interface
`dock, and
`communication
`software
`
`Measures step counts via
`a custom accelerometer
`with programmable filtering
`parameters adjusted for
`cadence and motion.
`Requires Mac computer,
`reader interface unit, and
`proprietary software.
`Pager-sized.
`
`Expensive.
`
`Ankle
`
`Displays walking
`activity as time series.
`Allows long-term
`continuous recording
`of ambulatory function.
`
`Adults with
`amputations
`Adults with chronic
`conditions affect-
`ing mobility
`
`Uniaxial
`Accelerometers
`
`Caltrac
`(Muscle Dynamics,
`Torrance, CA,
`$70–$90
`
`Actigraph
`(formerly CSA
`Actigraph)
`(MTI Health Services,
`Fort Walton Beach, FL)
`$1500 for monitor,
`interface unit,
`and software
`
`Measures vertical
`accelerations.
`Pager-sized.
`LCD screen display
`with updates every 2 min.
`Energy expenditure estimated
`by entering age, height,
`weight, and gender of wearer.
`Programmable modes for
`cycling and weight lifting.
`Runs on two AAA batteries.
`
`Measures vertical
`accelerations.
`Analog filters reject
`frequencies outside range
`of normal human movement.
`Slightly smaller than pager.
`Programmable; requires PC,
`reader interface unit, and
`proprietary software.
`Memory up to 256 k. Data
`collection up to 22 days.
`Uses coin cell battery.
`Mainly used in research.
`
`Waist
`
`Displays cumulative
`data continuously.
`Useful as
`a motivational tool.
`Low cost.
`
`No time-series data,
`cannot show patterns
`of activity.
`Wearer must record
`output if daily activity
`data required.
`
`Healthy adults
`Older adults
`Children
`
`Healthy adults
`Adults who use
`wheelchairs
`
`Discriminates
`change in speed
`but not grade.
`Higher cost.
`No feedback
`to wearer.
`
`Waist
`Wrist
`Ankle
`
`Collects time-series
`data; shows activity
`patterns.
`Output can be either
`activity counts
`or step counts.
`Count ranges for
`light, moderate, hard
`and very hard have
`been established.
`Calibration device
`available.
`Water resistant.
`
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`48
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`Journal of Rehabilitation Research and Development Vol. 40, No. 5, 2003, Supplement 2
`
`Table. (Continued)
`Comparison of activity monitors available in United States.
`
`Type
`
`Brand/
`Manufacturers/
`Price
`
`Characteristics
`and Features
`
`Multiaxial
`Accelerometers
`
`RT3 Triaxial
`Research Tracker
`(replaced Tritrac)
`(StayHealthy, Inc.,
`Monrovia, CA)
`$500 for monitor and
`docking station
`CTI Personal
`Calorie Tracker
`(available for
`personal/clinical use)
`$150
`
`Measures 3 planes (vertical,
`horizontal, and sagittal);
`records as vector magnitude
`units.
`Pager size.
`Requires PC, docking station,
`and proprietary software that is
`downloadable through web site.
`Data collection up to 21 days.
`Reports activity units
`and energy expenditure.
`Has event marker.
`Uses two AAA batteries.
`
`Mini-Motion Logger
`Actigraphs
`(Ambulatory Monitoring,
`Ardsley, NY)
`$500–$2000/unit
`+$1200–$2600 for
`interface unit
`and software
`
`Measures 3 planes.
`Analog filters reject
`frequencies outside range
`of normal human movement.
`Multiple models available
`from micro-mini (wristwatch
`size, less than 1 oz) to
`basic-mini (4×3 cm, 1.7 oz).
`Light sensor available.
`Multiple programmable
`parameters. Requires PC,
`reader interface unit
`and proprietary software.
`Memory size 32 to 128 k.
`Software programs for motor
`activity, sleep, and circadian
`rhythms.
`Data collection 16–30 days,
`depending on model.
`Lithium battery.
`
`Physical
`Placement
`of Device
`
`Waist
`
`Strengths
`
`Limitations
`
`Possible vibration
`artifact.
`No feedback
`to wearer.
`
`Sensitive to low
`levels of activity.
`Reflects intensity
`& frequency of
`activity.
`Collects time-series
`data; shows activity
`patterns.
`Output available as
`x, y, z axis plots, as
`well as a triaxial
`vector plot over time.
`Moderate cost.
`
`Populations
`Used in
`Validation
`Studies
`
`No studies
`using RT3.
`Tritrac:
`Young adults
`Older adults
`Adults with
`multiple sclerosis
`Adults with COPD
`Children
`
`Possible vibration
`artifact.
`Expensive.
`No feedback
`to wearer.
`
`Healthy adults
`Women following
`coronary bypass
`surgery
`Older adults
`
`Wrist
`
`Sensitive to low
`activity levels.
`Collects time-series
`data; shows activity
`patterns.
`Validated sleep
`estimation algorithm.
`Wrist placement is
`convenient and familiar.
`
`Actiwatch
`(MiniMitter Company,
`Inc., Bend, OR)
`$1075 per unit + $1850
`for reader and software
`
`Wrist
`
`Measures 3 planes
`(“omnidirectional”).
`Watch size, 17 g wt.
`Programmable epoch length.
`Requires PC, reader interface unit,
`and proprietary software.
`Memory size 16 to 64 k.
`Software programs for motor
`activity, sleep, and circadian
`rhythms. Downloaded data can
`be displayed both graphically
`as actograms and numerically
`as activity counts.
`Data collection up to 44 days.
`Lithium battery.
`
`Sensitive to low
`activity levels.
`Collects time-series
`data, shows activity
`patterns.
`Validated sleep
`estimation algorithm.
`Very small and light.
`Wrist placement is
`convenient and familiar.
`Waterproof.
`
`Possible vibration
`artifact.
`Expensive.
`No feedback
`to wearer.
`
`Adults with
`Alzheimer’s disease
`Adults with cancer
`Children and infants
`
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`STEELE et al. Monitoring daily activity and exercise with motion sensors
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`49
`
`similar continuum from the least to the most expensive.
`They also vary on continuums of sensitivity to motion and
`degree of information available to participants. Selection
`of a motion sensor requires consideration of the strengths
`and features of the motion sensing device and the amount
`and type of data required. Practical issues include cost of
`the device, comfort and ease of wearing the device, and
`the need for computers or other accessories.
`Reliability and validity of physical activity monitors
`are specific to the device, the population, and the activity
`behavior being studied. Accuracy/precision depends on
`how the device is constructed, as well as how it is used.
`Concurrent validity is most often established by assess-
`ing the degree of correlation with other activity measures
`(calorimetry, self-report, observation) or with indicators
`of known outcomes of activity (fitness, functional capac-
`ity, heart rate, VO2max). Characteristics of the population
`under study may affect the accuracy of motion sensors.
`For example, older adults with limited mobility may
`move so slowly that the motion is not detected by the
`sensor. Finally, the specific activity behaviors of the indi-
`viduals being monitored will affect the validity of activ-
`ity measurement. Energy expenditure during static work
`(work done without movement) will not be measured by
`motion sensing technology.
`As a measure of steps taken, electronic pedometers
`have demonstrated reasonable validity and high reliabil-
`ity. All pedometers tend to underestimate distance or
`steps for very slow walking [19,20]. This inaccuracy
`results from vertical movements at the hip being less pro-
`nounced at slow speeds and the sensor commonly failing
`to register some of them. A comparison of the accuracy
`of five electronic pedometers (Freestyle, Pacer, Eddie
`Bauer, Yamax, and Accusplit) for measuring distance
`walked found significant differences among models; the
`Yamax, Pacer, and Accusplit demonstrated the greatest
`accuracy [19]. The effects of walking speed were also
`examined, and the Yamax was found to be significantly
`more accurate than the Pacer and Eddie Bauer models at
`slow to moderate speeds. No significant differences were
`found at the fastest speed. We also assessed inter-unit
`reliability and found only the Yamax to be consistent
`between units. Other investigators have found similar
`variability among units due to differences in spring ten-
`sion [20]. Step counts measured by the Yamax pedometer
`correlated only modestly with self-reported energy
`expenditure (r = 0.34–0.49) [20].
`
`Studies exploring the validity of both uniaxial and
`multiaxial accelerometers as a measure of energy expen-
`diture have substantiated significant correlations between
`the two (0.66–0.96) [20–22]. A major issue in the use of
`accelerometry for physical activity measurement is that
`the unit of measure (activity count, or vector magnitude
`units [VMU]) is not standardized, and no direct transla-
`tion into energy expended exists. Several of the instru-
`ments include programs based on regression equations to
`calculate caloric expenditure; but differences in the accu-
`racy of the calibration equations, rather than differences
`in the monitors themselves, have been shown to contrib-
`ute to differences in recorded energy expenditure [23].
`For research purposes, we recommend that motion sensor
`data be analyzed as counts [20].
`Because uniaxial sensors track motion in the vertical
`plane only, they are not accurate for activities with static
`trunk movement, such as cycling and rowing [20]. The
`specific activities being performed also affect the accu-
`racy of accelerometry for measuring energy expenditure.
`For example, the Tritrac accelerometer has been shown
`to overestimate the energy expenditure of walking and
`jogging and to underestimate the energy expenditure of
`stair climbing, stationary cycling, and arm ergometry
`[24]. Similarly, another study comparing three acceler-
`ometers and a pedometer for prediction of energy expen-
`diture during moderate intensity activity suggested that
`all four motion sensing devices overpredicted energy
`expenditure during walking, but underpredicted energy
`expenditure in activities that included arm movement and
`static work [25].
`A major advantage of a triaxial sensor over a uniaxial
`sensor is that the instrument is more sensitive to light
`activities, such as slow walking. A disadvantage, how-
`ever, related to this greater sensitivity is that the device
`also becomes sensitive to vibrational artifact, recording
`background vibration (especially that related to being in a
`vehicle) as movement. Some manufacturers claim to set
`the device at a frequency response capable of capturing
`the range of human movement but to filter out rapid
`vibrations. These claims require researcher evaluation and
`can be tested by determining if measures obtained during
`vehicular transportation as a passenger differ significantly
`from measures obtained during quiet sitting [26].
`Accelerometers have been shown to be more sensitive
`in detecting activity differences in inactive populations and
`more sensitive at detecting short activity periods than recall
`measures [14,27–29]. Field evaluation studies comparing
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`Journal of Rehabilitation Research and Development Vol. 40, No. 5, 2003, Supplement 2
`
`accelerometers to self-reported activity have usually sug-
`gested that accelerometers underestimate the amount of
`vigorous activity and energy expended in activity [30,31].
`However, it is well known that self-report of activity is
`subject to recall bias. Multiple assessment devices (acceler-
`ometers and self-report measures) can be used together to
`improve the accuracy of activity profiles [25,32].
`Like waist-mounted triaxial accelerometers, wrist-
`worn accelerometers have been shown to differentiate
`light, moderate, and heavy activity levels, as well as to
`record differences in sedentary activities. They can more
`accurately represent activities that are underrated by
`waist-mounted devices, such as cycling and rowing, but
`may overrate activities requiring rapid hand movements
`(especially typing) [33].
`Several recent studies have compared the relative
`accuracy and validity of various motion sensing devices.
`Tudor-Locke and colleagues [34] compared CSA uniax-
`ial accelerometer counts to Yamax pedometer steps and
`found the two to be highly correlated (r = 0.74–0.86), but
`because of the CSA’s greater sensitivity, it tended to
`record more steps than the Yamax pedometer. Leenders
`et al. [31] compared a pedometer (Yamax) with a uniaxial
`(CSA) and a triaxial (RT3) accelerometer and found all
`three to be highly correlated (r = 0.84–0.93), with the
`highest correlations between the two accelerometers. The
`authors concluded that the accelerometers were compara-
`ble for assessing the amount and intensity of activity.
`They also suggested that the high correlations between
`the accelerometers and step count indicated that a large
`portion of physical activity was determined simply by
`measuring the number of steps taken each day.
`Welk and colleagues [23] evaluated the absolute and
`relative validity of three accelerometers (CSA, Tritrac,
`and Biotrainer) during choreographed lifestyle activities
`and treadmill activity. Correlations among the three
`devices were high for both treadmill (r = 0.86) and life-
`style activities (r = 0.70). Kochersberger et al. [35] simi-
`larly compared the Tritrac and Mini-Motion Logger
`Actigraph. Again, correlations between the two were
`high and significant (r = 0.77, p = 0.001).
`Individual physical activity is known to vary based
`on day of the week due to differences in work and leisure
`activity profiles. This variability may be less important in
`those who do not work a typical work week. Matthews et
`al. [36] measured day of the week effects in a large sam-
`ple of healthy adults using an accelerometer (CSA) and
`found that, to guarantee 80 percent reliability for measur-
`
`ing activity counts and time spent in moderate to vigor-
`ous activity, at least 3 to 4 days of monitoring were
`required, with at least 1 weekend day included. To reli-
`ably measure inactivity, these same authors recommend
`at least 7 days of monitoring.
`
`MOTION SENSORS IN PULMONARY
`REHABILITATION
`
`For most adults, the health benefits of an exercise
`program are believed to be dependent on the dose of
`physical activity undertaken. Dose is characterized by the
`frequency, duration, intensity, and type of activity.
`Although less is known about specific dose-response
`relationships, a positive association between training
`intensity and maximal oxygen consumption, muscle
`strength, and other exercise outcomes has been reported
`in the literature [37,38]. Motion sensors have the poten-
`tial to document more accurately the actual dose of a pre-
`scribed exercise regimen. They are especially useful
`because in most outpatient or home-based programs, only
`a small part of the program is supervised by program
`staff, who assess and record the quantity of exercise and
`the responses of participants. Since there are greater risks
`of injury and other health emergencies associated with
`higher intensity exercise, it is important to identify the
`optimal level of activity that produces the greatest health
`and immediate outcomes benefits while minimizing
`potential risks [39].
`More recently, digital pedometers and accelerometers
`have shown promise as adjuncts to reinforce exercise
`adherence by allowing self-monitoring, and as process and
`outcome measures for physical conditioning programs
`[23,28,35,40]. A number of studies support the validity,
`reliability, feasibility and clinical utility of accelerometers
`in the care of people with chronic respiratory disease.
`Preusser and Winningham first used a Caltrac accelerome-
`ter to measure four days of home activity in a group of
`17 patients with chronic obstructive pulmonary disease
`(COPD) and found significant correlations with inspiratory
`muscle strength (r = 0.66; p = 0.004) and inspiratory mus-
`cle endurance (r = 0.71; p = 0.001) [41]. In a study of
`47 outpatients who had COPD as they entered a pulmonary
`rehabilitation program, we determined that a triaxial accel-
`erometer, the Tritrac R3D, had excellent test-retest reliabil-
`ity during three standardized 6 min walk tests (intraclass
`correlation coefficient, rICC = 0.84). Pearson correlations
`
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`51
`
`between accelerometer-measured movement during walk-
`ing compared to walking distance varied from 0.84 to 0.95.
`During measurement periods of free-living activity over
`three days at home, the device had an rICC of 0.69, imply-
`ing good stability over longer periods of meas-urement.
`These data support the use of this device in meas-uring
`daily activity, even in very inactive populations [14].
`To determine concurrent validity of accelerometer
`measurement of daily activity under free-living conditions,
`we measured daily activity over three full days at home in
`63 outpatients with COPD who did not exercise regularly.
`We found significant correlations between accelerometer-
`measured daily activity and exercise capacity (maximal
`6 min walk, r = 0.60; p < 0.001), level of obstructive pul-
`monary disease (percentage of predicted forced expiratory
`volume in 1 s, r = 0.37; p < 0.01), dyspnea (Functional
`Status and Dyspnea Questionnaire, r = –0.29; p < 0.05)
`and activity self-efficacy (Activity Self-Efficacy Question-
`naire, r = 0.27; p < 0.05) and physical health status (SF-36,
`Physical Functioning Subscale, r = 0.40; p < 0.01). Multi-
`variate analysis demonstrated that the only predictor of
`physical activity was the 6 min walk test. Like other stud-
`ies, physical activity measured by accelerometer was not
`associated in this sample with self-report of functional sta-
`tus [15]. These findings indicate that the accelerometer has
`good concurrent validity with other indicators of function
`and above all, represents with precision walking behavior
`in chronic respiratory disease.
`The greater accuracy of accelerometer technology
`has excellent potential in documenting pulmonary reha-
`bilitation outcomes [2]. Walking is a key indicator of
`improvement in pulmonary rehabilitation programs and
`similar regimens aimed at reducing the consequences of
`physical inactivity. Although these devices poorly repre-
`sent strength training activities that do not involve much
`bodily movement, they can capture improvements in
`endurance and exercise capacity. We have found that the
`Tritrac R3D is a sensitive process measure of physical
`activity undertaken during a pulmonary rehabilitation
`program. In a study of 41 men and women with COPD
`enrolled in an 8 week outpatient pulmonary rehabilitation
`program, subjects underwent 5 days of daily activity mea-
`surement using the Tritrac R3D accelerometer. Physical
`activity, measured in vector magnitude units (VMU) per
`minute, was 87.4 + 38.8 (mean, standard deviation)
`before the start of the program; VMU per minute
`increased to 115.2, standard deviation + 59.4, during the
`final weeks of the program (p < 0.01) [10]. Figure 1 dem-
`
`150 --- --- - - - - -- -......
`
`125 -
`
`C 100 -
`
`E -=>
`
`~
`
`0
`
`Pre program
`
`Program
`
`Figure 1.
`Average daily activity (VMU/min) for 2 days before beginning
`pulmonary rehabilitation program (PRP) (preprogram) and 2 days
`while participating in PRP (program). Solid bars = Thursdays
`(exercise day); shaded bars = Fridays (nonexercise day).
`
`onstrates differences between a patient’s daily physical
`activity measured in VMU before starting a pulmonary
`rehabilitation program and on the same days of the week
`while attending the program. Significant differences are
`evident between exercise and nonexercise days.
`In addition to providing information about group per-
`formance, accelerometer measurement of daily physical
`activity provides a more precise indicator of individual
`performance during unsupervised exercise than previ-
`ously possible. Figure 2 includes individual tracings of
`free-living daily activity of one pulmonary rehabilitation
`participant and clearly differentiates walking behavior
`during his two 6 min walk tests, sitting activity, and free-
`living walking to his vehicle, as well as the movement
`contributed by riding home in his car. Physical activity
`measured during an observed pulmonary rehabilitation
`exercise session is represented in Figure 3 and includes
`VMU measurement while patients used the treadmill, a
`seated stepper (NuStep®), an arm ergometer, and free
`weights. Static exercise (free weights, seated stepper, and
`arm ergometer) is generally less well represented than
`moderately paced walking on the treadmill. Figure 4
`reflects two full days of activity accelerometer monitor-
`ing of one participant, comparing a day when he attended
`his pulmonary rehabilitation program with a day when he
`
`Exhibit 1041 page 7 of 14
`DENTAL IMAGING
`
`

`

`52
`
`Journal of Rehabilitation Research and Development Vol. 40, No. 5, 2003, Supplement 2
`
`2soo, , . - - - - - - - - - - - - - - - - - - - - - - - - - -- -- - - - - - - - -~
`
`! 6 min walk A
`
`Wallk to car
`
`6 min walk B
`
`1500, 1-- 1 - - - - - - , 1 - - - - - - - - - - - - - - l - - --l- - - -__.
`
`1000 -·l-----f--+------- - - -- - -+---
`
`Car trip
`-+-- - --1-- +--- - - - - - -- - -----1
`
`Sitting
`(questionnaire)
`
`0
`
`5
`
`10
`
`15
`
`2.0
`
`25
`
`30
`
`35 40
`
`45 50
`
`55
`
`60
`
`65
`
`70 75 80
`
`85
`
`90 95 100
`
`Min
`
`Figure 2.
`Vector magnitude unit (VMU) measurements of minute-by-minute tracings for pulmonary rehabilitation program patient in defined activities:
`performing 6 min walk tests, sitting while completing written questionnaire, walking to vehicle, and driving home following appointment.
`
`did not attend. The graphical display demonstrates more
`activity (in VMU) on the day he attended the exercise
`session.
`
`CHOOSING A MOTION SENSOR FOR
`PULMONARY REHABILITATION
`
`Accelerometer technology, with its relatively com-
`plex data collection and management methodology to
`study daily activity, begs the question of why a digital
`pedometer would not suffice for this purpose. Certainly,
`in most circumstances with healthy individuals, step-
`counting will readily document walking activity and, as
`noted earlier, is useful as an adjunct to reinforce walk-
`ing behavior and other exercise. A number of reasons
`support the use of accelerometer technology for persons
`with chronic pulmonary disease and others with dimin-
`ished physical functioning. First, even the most reliable
`
`digital pedometers are unable to accurately measure
`slow walking speeds under 2 mph, because of less-
`pronounced accelerations at the hip [19]. For this
`reason, pedometers will underrepresent much of the
`walking activity of these groups. For the same reason,
`pedometers will be less sensitive to small improve-
`ments in walking activity in inactive individuals. Field-
`monitoring of exercise and daily activity over time is
`also better undertaken with an accelerometer because of
`its longer memory and its lack of dependence on having
`subjects record a daily value or otherwise keep a sepa-
`rate record of the pedometer data. This may be particu-
`larly true in persons with chronic pulmonary disease
`because of their high, preexisting burden of self-
`management and other considerations relating to their
`severe, chronic illness. Finally, the capability to docu-
`ment both the duration and relative intensity of move-
`ment, e.g., during walking, provides a unique
`quantitative index of the vigor with which exercise or
`
`Exhibit 1041 page 8 of 14
`DENTAL IMAGING
`
`

`

`STEELE et al. Monitoring daily activity and exercise with motion sensors
`
`53
`
`2000 -..-- - - - - - - - - - -- -- - - - - - - - - - - - -- - - - - - - - - ---,
`
`1750 -1---------- - - - -- - -fl-- - - - - -- - - - - - - - - - - - - ------j
`
`Treadmill (2.5 mph)
`1500 +-- - - - - - - - - - - - -- --f-.-~- - - - -