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Validation of the ActivPAL Activity Monitor as a Measure of Walking at Pre-determined Slow Walking Speeds in a Healthy Population in a Controlled Setting

by Nadia Kanoun[1], Department of Physiotherapy, Queen Margaret University, Edinburgh

Abstract

Background

Physical activity is important for the maintenance of health in people of all ages, especially the elderly and patient populations. Interventions to increase physical activity require accurate measurement tools. Many objective monitors currently available have inadequate sensitivity to measure physical activity at the slow walking speeds characteristic of elderly and patient populations.


Objective

This study aimed to evaluate the criterion-related validity of the activPAL™ activity monitor to measure steps taken when walking at speeds of 0.45, 0.67, 0.90 and 1.33m.s1.


Participants

Convenience sample of 42 healthy individuals (nine males, 33 females; mean age 23.5±4.0 range 18-39) recruited from a population of students.


Methods

Validity of the activPAL™, measuring total steps taken, was compared to direct observation of steps taken using a hand-tally counter during a five minute period of treadmill walking at 0.45, 0.67, 0.90 and 1.33m.s-1.


Results

On average the activPAL™ underestimated steps taken by less than 1% at 0.67, 0.90 and 1.33m.s-1 and by 3.5% at 0.45m.s-1.


Conclusion

These results suggest that activPAL™ is a valid measure of steps taken when walking at 0.67, 0.90 and 1.33m.s-1. The activPAL™ could potentially be used to measure physical activity in populations who walk slowly. However, further investigation of activPAL™ validity at 0.45m.s-1 and in elderly and patient populations is needed.

Keywords: Validity, Physical Activity, Accelerometer, Slow Speed, ActivPAL™, Walking.

Introduction

Physical activity is important for the maintenance of health in people of all ages (Leenders et al., 2001; Surgeon General, 1996) and is especially important to the conservation of functional ability (Mernitz and McDermott, 2004; Steele et al., 2003), independence (Surgeon General, 1996) and quality of life in the elderly and people with chronic diseases (Mernitz and McDermott, 2004; Steele et al., 2003). Participation in regular physical activity reduces the risk of premature mortality from a wide variety of medical conditions (Chakravarthy et al., 2002; Surgeon General, 1996; Warburton et al., 2006) and has been linked to physical, psychological and social benefits in the elderly (WHO, 1997). Consequently, promotion of physical activity should be targeted at these individuals (Chodzko-Zajko, 1998).

Numerous recommendations exist that advocate participation in regular physical activity for its health benefits (Haskell et al., 2007; Mehr and Tatum, 2002; Nelson et al., 2007; NIH, 1996). Accurate and valid assessment of physical activity is therefore needed to assess compliance to these recommendations. Valid assessment of physical activity remains a challenge for researchers and practitioners and measurement of low intensity activity, characteristic of elderly and patient populations, is particularly problematic to accurately quantify (Tudor-Locke and Myers, 2001a).

A number of objective measuring devices are available, the most simple of which is the pedometer. Pedometers are inexpensive, easy to use and generally quantify physical activity in cumulative steps taken. Pedometers, such as the Yamax Digiwalker SW-200 (Yamax, Tokyo, Japan), have been used successfully in large scale interventions to increase physical activity among the general population (De Cocker et al., 2007). However, pedometers cannot discriminate between different walking intensities (Leenders et al., 2001; Tudor-Locke et al., 2002a; Tudor-Locke et al., 2002b); they fail to give an indication of when or for how long the individual was active (Beets et al., 2005); their accuracy may vary (McClung et al., 2000; Swartz et al., 2003); and their accuracy tends to be poor at slow speeds (e.g. 0.90m.s ˉ¹ and below) (Bassett et al., 1996; Bergman et al., 2008; Crouter et al., 2003; Cyarto et al., 2004; Grant et al., 2008; Karabulut et al., 2005; Ryan et al., 2006; Schneider et al., 2003). Although it has been reported that these inaccuracies are not important for the measurement of physical activity in the general population (Hendelman et al., 2000; Le Masurier and Tudor-Locke, 2003); they would be relevant to physical activity measurement in elderly and patient populations who often ambulate at these slow speeds due to limited mobility (Bergman et al., 2008; Bowden and Behrman, 2007; Cyarto et al., 2004; Haeuber et al., 2004; Melanson et al., 2004; Spenkelink et al., 2002; Steele et al., 2003).

Devices, such as the StepWatch3 (Cyma Corporation 6405 218th St. S.W., Suite 100, Mountlake Terrace, WA 98043-2180, USA), which is a newer version of the Step Activity Monitor (SAM), claim to have overcome some of these limitations by using more advanced accelerometer technology (Bergman et al., 2008; Bowden and Behrman, 2007; Coleman et al., 1999; Hartsell et al., 2002; Karabulut et al., 2005; Macko et al., 2002; Resnick et al., 2001; Shepherd et al., 1999). Although the StepWatch3 can give a time-based breakdown of step activity, it is unable to measure 'static work' (Steele et al., 2003), such as time spent sitting and standing, which are particularly important in physical activity measurement of a characteristically sedentary population, such as the elderly. One accelerometer that has the potential to overcome these limitations is the activPAL™ professional (PAL Technologies Ltd, Glasgow, Scotland).

The activPAL™ is a miniature electronic device, worn on the thigh, which can record data continuously for up to seven days. It is smaller and lighter than other accelerometers such as the StepWatch3 (Godfrey et al., 2008) and attaches securely to the skin, thus avoiding any swaying of the device independent of body movement. The activPAL™ and its accompanying manufacturer's hardware classifies activity in terms of the time spent sitting or lying, standing, stepping, number of steps taken, cadence and the amount of sit-to-stand and stand-to-sit transitions. These are features that no other comparable device offers. The validity of the posture classification elements of the activPAL™ have been established elsewhere (Godfrey et al., 2007; Grant et al., 2006; Ryan et al., 2008). ActivPAL™ has been shown to be a valid and reliable measure of steps taken at speeds of between 0.90m.s ˉ¹ and 1.56m.s ˉ¹ in a healthy population (Ryan et al., 2006), at self-selected speeds in a population of people with chronic low back pain (Ryan et al., 2008), at speeds between 0.67m.s ˉ¹ and 1.78m.s ˉ¹ in an elderly population (Grant et al., 2008) and has been used to investigate the differences in walking patterns between people with venous leg ulcers and health controls (Clarke-Moloney et al., 2007). However, the walking speed of elderly people, for example those living in assisted living and nursing homes, are reported to range between 0.29 and 1.07m.s ˉ¹ (Bergman et al., 2008; Cyarto et al., 2004). Furthermore, the walking speed in patient populations such as people with an incomplete spinal injury have been reported to range between 0.12 and 1.06m.s ˉ¹ (Bowden and Behrman, 2007). In spite of this, no data exists regarding the validity of the ActivPAL™ at speeds of less than 0.67m.s ˉ¹.

It was therefore the purpose of this study to assess the criterion-related validity of the activPAL™ to measure steps taken at slow walking speeds (0.45, 0.67, 0.90 and 1.33m.s ˉ¹), using direct observation as the criterion standard. It is hypothesised that the activPAL™ will be a valid measure of steps taken at slow speeds including 0.45m.s ˉ¹, which has not previously been investigated.

Methods

Participants

Previous literature with a similar study design was reviewed to inform the study sample size. Sample size ranged from 10 to 29 healthy participants (Bassett et al., 1996; Crouter et al., 2003; Hartsell et al., 2002; Karabulut et al., 2005; Le Masurier and Tudor-Locke, 2003; Le Masurier et al., 2004; Ryan et al., 2006; Schneider et al., 2003; Shepherd et al., 1999), except in one study which had 259 (Melanson et al., 2004). Male and female, able-bodied, healthy university student volunteers were recruited through notice board and email advertising with the aim of equalling or surpassing 30 participants. Any participant with a condition affecting their ability to walk was excluded. The final sample was composed of 42 participants, nine male, 33 female, aged 18 to 31 (See Table 1). The University Ethics Committee approved this study. Written informed consent was obtained from all participants prior to their participation in the study.
 

Sample

Mean ± SD(range)

Total (n=42)

Age (Years)

23.5 ± 4.0

(18.0-39.0)

Height (m)

1.76 ± 0.11

(1.63-2.01)

Mass (kg)

63.3 ± 11.7

(41-91)


Table 1:
sample characteristics


Instrument

The activPAL™ is a small device (13cm³; mass, 20g) which measures acceleration in the long axis of the thigh using a piezo-resistive accelerometer. ActivPAL™ detects steps using a combination of the amplitude of the measured acceleration and the shape of the signal over time. The activPAL™ has a sensitivity threshold of less than 0.01 ×g and is initialised to detect steps taken in 15 second epochs.


Protocol

The established protocol for assessment of criterion-related validity of a measure of steps taken (Bassett et al., 1996) was adapted to focus on slower walking speeds. Two speeds (0.9m.s ˉ¹ and 1.33m.s ˉ¹) were chosen from the established protocol to allow for comparison and two slower speeds were added (0.67m.s ˉ¹ and 0.45m.s ˉ¹). These were selected from speeds used in the literature (Beets et al., 2005; Grant et al., 2008; Karabulut et al., 2005; Melanson et al., 2004) and were deemed to cover a range of slow speeds.

The treadmill (Woodway ELG55) (Woodway GmbH, D-79576 Weil am Rhein) gradient was set to zero. Participants were asked to wear shorts and comfortable flat footwear, preferably trainers. Participants received basic standardised instructions for walking on the treadmill but were not given any instruction with regard to which foot to start or finish the trial with. This introduces a very small potential for error, however it was deemed too difficult for the participants to start and finish at a predetermined phase of gait whilst walking on a treadmill. The participants were however instructed to start and finish with both feet together.

An assessment of inter-rater reliability and concurrence between steps counted with a hand-tally counter (ENM, England) in real-time and steps counted with a hand-tally counter from video footage was undertaken in a pilot study. One participant was clipped to an emergency stop cord and allowed a familiarisation period where they experienced the slowest and fastest speeds used in the test before the test period began (this was also undertaken by each participant in the main part of the study). The participant then completed a five-minute walk on the treadmill at each of the four test speeds. This trial was videoed with a camera aimed at the participant's lower half as described in the literature (Le Masurier et al., 2004). The participant was observed by four raters simultaneously who counted steps taken using a hand-tally counter. The rater clicked the hand-tally counter once for each right heel strike and the final number was doubled to give the total number of steps. Steps taken ranged from 300 at the slowest speed to 564 at the fastest speed (see Table 2) and were counted identically by the four raters (statistical confirmation was therefore unnecessary). This high level of inter-rater reliability mimics that observed in similar studies (r=0.9999) (Hartsell et al., 2002). The four raters then reviewed the video and counted steps taken; again these were counted identically between raters and also when comparing real-time to video analysis (see Table 2). Based on this, only one rater was used to count observed steps taken and video analysis was not undertaken.
 

Speed

Rater 1

Real-time/video analysis

Rater 2

Real-time/video analysis

Rater 3

Real-time/video analysis

Rater 4

Real-time/video analysis

(m.sˉ¹)

(Steps)

(Steps)

(Steps)

(Steps)

0.45

300

300

300

300

0.67

382

382

382

382

0.90

466

466

466

466

1.33

564

564

564

564


Table 2:
number of steps counted by four raters, real time and from video analysis


In the main study, activPAL™ was attached anteriorly, on the mid-line of the right thigh, mid-way between the hip and the knee, using PALstickies™ (double-sided hydro-gel adhesive pads) and Micropore™ surgical tape (3M Healthcare, D-41453 Neuss Germany) (Figure 1). As described in the pilot study, each trial consisted of five minutes walking at the given speed. The activPAL™ was turned on, using a pen tip to depress the button, once the participant was in position on the treadmill; it was turned off, with the pen tip, immediately on completion of each five-minute trial.


Figure 1: activPAL™ in situ (source: Dr Marietta van der Linden) 

Figure 1: activPAL™ in situ (source: Dr Marietta van der Linden)


The treadmill was stopped between trials just long enough for a new activPAL™ session to be initiated and to record the hand-tally counter data. Once the participant had completed a trial at each of the four speeds their activPAL™ data was downloaded using the manufacturer's hardware and software (Version 5.5.6.11). The final output for the activPAL™ was recorded as total steps taken at each speed.


Data analysis

Analysis was carried out using the Statistical Package for Social Sciences (SPSS), version 13.0 for windows XP (SPSS, Inc, Chicago, IL), and Microsoft Office Excel 2003. All descriptive data are presented as means ± One Standard Deviation (SD) and (range).

Analysis of limits of agreement (Bland and Altman, 1986) was performed to explore the agreement between the number of observed steps and the number of steps indicated by the activPAL™. In Bland and Altman analyses percentage mean difference and upper and lower limits are calculated. The values obtained define the range within which most (approximately 95%) of the differences between the two measures will lie (Bland and Altman, 1986). The percentage mean difference is the difference between the number of steps counted by activPAL™ and the number of steps counted by direct observation using a hand-tally counter, divided by the average of total steps counted by the two measures. The upper and lower limits are the mean percentage difference plus or minus 1.96 times the standard deviation of the percentage difference. A positive percentage difference signifies an overestimation of steps taken by activPAL™ and a negative percentage difference signifies an underestimation of steps taken by activPAL™. No guidelines defining clinically important limits of agreement exist, however, if the differences within the obtained limits of agreement are not deemed to be clinically important, it can be assumed that the two measures can be used interchangeably (Bland and Altman, 1986).

The absolute percentage error ((absolute difference/observed steps) ×100, where absolute difference is the numerical difference between activPAL™ steps and observed steps, without regard to sign) was also calculated to facilitate comparison with existing literature.

Results

There was no data lost due to participant drop-out, equipment malfunction or missing/lost data. Participant compliance rate for wearing the activPAL™ was 100% which indicates that there were no acceptability issues with the monitor (de Vries et al., 2006). A summary of the performance of the sample is shown in Table 3. Bland and Altman limits of agreement demonstrated narrow limits of agreement between activPAL™ and observed number of steps, for speeds of 0.67m.sˉ¹ and above, but were much wider for the slowest walking speed (0.45m.sˉ¹). The negative difference scores show that activPAL™ tended to underestimate the number of steps taken. This bias was seen at all speeds and was increased when walking at 0.45m.sˉ¹ (See Table 3).
 

Number of steps Observed

Mean Difference

Mean Difference

Upper Limit

Lower Limit

Speed

(Mean±SD)

(+1.96SD)

(-1.96SD)

(m.sˉ¹)

(Steps)

(Steps)

(%)

(%)

(%)

0.45

312± 36

-10

-3.69

10.61

-17.99

0.67

393±34

-2

-0.59

0.16

-1.33

0.90

464±34

-2

-0.39

0.22

-1.01

1.33

561±27

-2

-0.37

0.76

-1.49


Table 3:
limits of agreement between activPAL™ and observed steps


The absolute percentage error is displayed in Table 4. The mean absolute percentage error was less than 1% and the maximum absolute percentage error found was 2.55% for speeds of 0.67m.sˉ¹ and above. However, at 0.45m.sˉ¹, the mean absolute percentage error was increased to 3.47%, while the maximum percentage error found was 29.70%.
 

Speed

Observed

Absolute Error

m.sˉ¹

Steps

mean ±SD

%

mean ±SD (Range)

0.45

312±36

3.47±6.30 (0-29.70)

0.67

393±34

0.59±0.38 (0-1.64)

0.90

464±34

0.45±0.21 (0-0.92)

1.33

561±27

0.49±0.47 (0-2.55)


Table 4:
activPAL™ absolute percentage error

Discussion

The purpose of this study was to assess the criterion-related validity of the activPAL™ to measure steps taken at slow speeds under controlled conditions. The results of this study confirm the conclusions of earlier research (Grant et al., 2008; Ryan et al., 2006) and suggests that the activPAL™ is a valid device for measuring steps taken at speeds of 0.67m.sˉ¹ and above, with a mean absolute percentage error of less than 1%.

A breakdown of the mean absolute percentage error by speed shows that the activPAL™ performed very similarly at 1.33m.sˉ¹ (0.49% compared with 0.53% (Ryan et al., 2006)), while at 0.9m.sˉ¹ the activPAL™ performed slightly better than previously reported (0.45% compared with 0.94% (Ryan et al. 2006)). The slight difference in mean absolute percentage error reported by the current study at 0.9 m.sˉ¹ may be attributed to the relatively large sample size (n=42), compared with just 20 participants in the previous study (Ryan et al., 2006). This will have reduced the effect of any outliers; therefore 0.45% is likely to be more representative of the true mean absolute percentage error. Unfortunately, since the mean absolute percentage error when walking at 0.67m.sˉ¹ in an elderly population is summarised as less than 1% (Grant et al., 2008), this cannot be directly compared to 0.59% in the current study.

The agreement data between the activPAL™ and direct observation when walking at 0.67 m.sˉ¹ in an elderly population was reported in steps (Mean difference= 2.6, upper limit 8.2, lower limit -3.1) (Grant et al., 2008) rather than as a percentage, therefore comparison with the current study cannot be made.

In the present study, the activPAL™ error was higher at 0.45m.sˉ¹, where the absolute percentage error was 3.47% and maximum percentage error was 29.70%. The mean percentage difference (3.69%) and limits of agreement (-18% to 10.6%) were also considerably wider for this speed. Results for 0.45m.sˉ¹ extend the findings of earlier research which showed accuracy was not affected by the speeds previously tested (Grant et al., 2008; Ryan et al., 2006), three of which were the same as the current study (0.67, 0.9 and 1.33m.sˉ¹).

The StepWatch3 has also been investigated at 0.45m.sˉ¹, along with the other speeds in the present study (Karabulut et al., 2005). Unlike the activPAL™, the StepWatch3 maintained an error of less than 1% for all speeds, from 0.45m.sˉ¹ to 1.78 m.sˉ¹. Two pedometers were also investigated in the study. Both the pedometers (New Lifestyles NL-2000 and the Digiwalker SW-701) were adversely affected by a reduction in walking speed. The percentage of actual steps measured was less than 20% for the New Lifestyles NL-2000 and less than 40% for the Digiwalker SW-701 when walking at speeds of 0.45m.sˉ¹. The StepWatch3 has been further investigated in populations who walk at a self-selected slow speeds and have altered gait patterns, such as the elderly living in assisted living facilities and people with an incomplete spinal cord injury (Bergman et al., 2008; Bowden and Behrman, 2007). In these populations, the error is seen to rise to approximately 3%. This is a similar error to that demonstrated by the activPAL™ at 0.45 m.sˉ¹.

The increased error at 0.45m.sˉ¹ may be due to a shortening of stride length and a smaller rise and fall in the centre of mass; this results in less defined acceleration patterns making them more difficult for the activPAL™ to detect. However, it may also be a result of the study participants finding it increasingly difficult to walk at an artificially slow speed and therefore walking with an altered gait pattern, perhaps mimicking the gait pattern which may be seen in the elderly or patient population. If the latter is the case, activPAL™ error when measuring the steps taken by elderly individuals and patient populations with altered gait patterns may be similar to the error exhibited at 0.45m.sˉ¹.

Proposed expected steps per day for both healthy and chronically ill elderly people (Tudor-Locke and Myers, 2001b) are useful to evaluate the effects of measurement error in practice. Since it is suggested that healthy older adults take between 6,000-8,500 steps per day and individuals with disabilities and chronic diseases take between 3,500-5,500 steps per day, activPAL™ error would be in the region of 60-85 and 35-55 steps per day respectively for speeds of 0.67m.sˉ¹ and above and 180-255 and 105-165 steps per day respectively for steps taken 0.45m.sˉ¹. Average errors of this size are unlikely to have any effect on choice of intervention or outcome. This is supported by literature which found that total steps taken post-intervention, aimed at increasing physical activity, namely walking, is likely to increase by greater than 1300 steps per day (Richardson et al., 2005; Stovitz et al., 2005; Tudor-Locke et al., 2004).

However, if further analysis is undertaken using the data obtained from the Bland and Altman analysis, the validity of the activPAL™ when walking at 0.45m.sˉ¹ is brought into question. Using the mean of the two estimated number of steps per day for healthy and chronically ill elderly people (7250 and 4500 steps per day, respectively); for a healthy elderly person walking at 0.45m.sˉ¹, the mean difference between measured and actual steps taken would be 268 steps, with lower limits of -1305 steps and upper limits of 769 steps and for a chronically ill elderly person walking at 0.45m.sˉ¹, the mean difference between measured and actual steps would be 166 steps, with lower limits of -810 steps and upper limits of 477 steps. In a small number of cases, i.e. when the difference is close to the lower limit, the error could mean that a clinically significant improvement in steps taken is missed. It could therefore be construed that the error displayed by the activPAL™ is acceptably small enough at speeds of 0.67m.sˉ¹ and above, to support the interchangeability of the activPAL™ with direct observation, the criterion (Mantha et al., 2000), however, further investigation is required to establish the validity of the activPAL™ at 0.45 m.sˉ¹.

The primary limitation of this study is that it was undertaken in a controlled environment with healthy participants on a treadmill (Steele et al., 2003). It is widely accepted that gait patterns may be altered by treadmill walking (Vogt et al., 2002; Warabi et al., 2005). Future research should focus on the free-living environment with sedentary populations, including the elderly and patient populations who walk at slow speeds (less than 0.67m.sˉ¹) and may have altered gait patterns, to investigate further the validity of the activPAL™.

Practical limitations such as unit cost and the need for computers applies to both the activPAL™ and other activity monitors with accelerometer technology, such as the StepWatch3. However, activPAL™ is a valid measure of steps taken and is also a valid and reliable measure of time spent in sedentary postures (Godfrey et al., 2007; Grant et al., 2006; Ryan et al., 2008), and provides information on time spent standing, sitting and the number of sit-to-stand and stand-to-sit transitions performed. These additional features provide a more complete measurement of an individual's intentional and incidental physical activity.

Conclusion

This study confirms the findings from previous literature (Grant et al., 2008; Ryan et al., 2006) and extends their findings to slower speeds (0.45m.s-1). The main conclusion of this study is that the activPALä is a valid measure of steps taken between 0.67m.s-1 and 1.33m.s-1 when assessed in a controlled environment in a healthy population, but requires further investigation to establish validity at 0.45m.s-1. The activPAL™ therefore proposes a potential solution for valid measurement of steps taken at slow walking speeds. However, further assessment of validity at 0.45m.s-1 and with elderly and patient populations under free-living conditions is warranted.


Acknowledgements

The author is indebted to Marietta van der Linden PhD who has acted as a scientific adviser throughout, and to Lauren Guthrie, Heather O'Brien and Elizabeth Wright who contributed to the initial study proposal and data collection.

Funding

The Queen Margaret University, School of Health Sciences funded this study and no financial support was received from any commercial company.

List of Illustrations

Figure 1: activPAL™ in situ (reproduced by kind permission of Marietta van der Linden PhD; the informed consent of the subject was obtained prior to publication)

List of Tables

Table 1: sample characteristics

Table 2: number of steps counted by four raters, real time and from video analysis

Table 3: limits of agreement between activPAL™ and observed steps

Table 4: activPAL™absolute percentage error

Competing Interests

The author declares no competing interests.

Notes

[1] Nadia Kanoun studied Physiotherapy at the Queen Margaret University in Edinburgh and currently works as a Physiotherapist at the Royal Blackburn Hospital, Lancashire.

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To cite this paper please use the following details: Kanoun, N. (2009), 'Validation of the ActivPAL Activity Monitor as a Measure of Walking at Pre-determined Slow Walking Speeds in a Healthy Population in a Controlled Setting', Reinvention: a Journal of Undergraduate Research, Volume 2, Issue 2, http://www.warwick.ac.uk/go/reinventionjournal/archive/volume2issue2/kanoun Date accessed [insert date]. If you cite this article or use it in any teaching or other related activities please let us know by e-mailing us at reinventionjournal@warwick.ac.uk