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Examensarbete i fysioterapi, 30 hp

CAN ACTIVPAL

REPLACE ACTIGRAPH WHEN MEASURING

PHYSICAL ACTIVITY ON ADULTS IN A FREE

LIVING

ENVIRONMENT?

Johan Sunesson

(2)

Magisterprogrammet i fysioterapi 60hp

Titel: Can ActivPAL replace ActiGraph when measuring physical activity on adults in a free living environment?

År: 2018

Författare: Johan Sunesson;

JohanSune@gmail.com Handledare: RPT, PhD, Ann Sörlin, Institutionen för samhällsmedicin och rehabilitering, Umeå universitet, Ann.Sorlin@umu.se

RPT, MSc, Frida Bergman, Institutionen för folkhälsa och klinisk medicin, Umeå universitet, Frida.Bergman@umu.se

Nyckelord: Accelerometer, Samtidig validitet, Kadens, Gång, MVPA, ADL

Sammanfattning: Introduktion I takt med en ökad kunskap om de positiva hälsoeffekterna av fysisk aktivitet (FA) ökar även intresset av att objektivt mäta FA i vardagliga miljöer. ActiGraph är den mest använda accelerometern för att mäta FA, medan ActivPAL anses vara en tillförlitlig accelerometer i avseende att mäta stillasittande beteende. Syfte Syftet med denna studie var att undersöka möjligheterna att mäta måttlig till högintensiv fysisk aktivitet (MVPA) med ActivPAL istället för ActiGraph. Metod Data från 79 överviktiga kontorsarbetare som bar ActiGraph och ActivPAL valdes ut för analys. Alla aktiviteter i ActivPAL med en kadens på 90 steg per minut (spm) eller mer som pågick i minst 30 sekunder extraherades och matchades mot samma aktivitet i ActiGraph. Överensstämmelsen mellan de båda mätarna undersöktes för att se om dessa mätare klassificerade aktiviteter likvärdigt. ActivPAL klassificerade MVPA som aktiviteter med en kadens på 100 spm eller mer, och ActiGraph som aktiviteter med 3208 aktivitetsmarkeringar per minut (cpm) eller mer. Resultat En korrelation på r=0,326 (p<0,001) sågs mellan ActivPAL och ActiGraph tillsammans med Cohen’s kappa på K=0.14, en procentuell överensstämmelse på 60,7 % och en sensitivitet på 61,5 % med ActiGraph som nämnare vilket gav ett positivt prediktivt värde (PPV) på 84,3% för ActivPAL. Korrelationen påverkades inte av deltagarnas ålder eller BMI. Ingen korrelation upptäcktes mellan tid spenderad i MVPA mellan mätarna. Slutsats ActivPAL kan inte ersätta ActiGraph för att mäta MVPA i vardagsmiljö hos överviktiga vuxna kontorsarbetare.

(3)

Master Programme in Physiotherapy 60 credits

Title: Can ActivPAL replace ActiGraph when measuring physical activity

on adults in a free living environment? Year: 2018

Author: Johan Sunesson;

JohanSune@gmail.com

Tutor: RPT, PhD, Ann Sörlin, Department of Community Medicine and Rehabilitation, Umeå university, Ann.sorlin@umu.se

RPT, MSc, Frida Bergman, Department of Public Health and Clinical Medicine, Umeå university, Frida.Bergman@umu.se

Keywords: Accelerometer, Concurrent validity, Walking, Cadence, MVPA, ADL

Abstract: Introduction With an increasing knowledge of the health benefits from physical activity (PA) the interest in objectively measuring PA in free living environment has increased. ActiGraph is the most commonly used accelerometer to objectively measure PA, while ActivPAL is considered gold standard when it comes to measuring sedentary behavior. Aims The aim of this study was to investigate if ActivPAL could be used to measure Moderate to Vigorous Physical Activity (MVPA) instead of ActiGraph. Methods Data from 79 overweight office workers carrying the ActivPAL and ActiGraph device simultaneously were analyzed. All activities with a cadence of 90 steps per minute (spm) or more lasting for at least 30 seconds from one day from ActivPAL data was extracted and compared to the corresponding activity from ActiGraph. An activity was classified as MVPA by using the cut points of 100 spm for ActivPAL and 3208 activity-counts per minute (cpm) for ActiGraph using vector magnitude (VM). Results A correlation of r=0.326 (p<0.001) was seen between ActiGraph and ActivPAL with a Cohen’s kappa of K=0.14, a percentage agreement of 60.7%, a sensitivity of 61.5% with ActiGraph as denominator and a positive predictive value (PPV) of 84.3% for ActivPAL. Neither age nor BMI affected the association between the estimates by these devices. There was no correlation for time spent in MVPA between devices. Conclusion Cadence from ActivPAL cannot replace ActiGraph to measure MVPA in a free living environment in overweight adults.

List of abbreviations

AUC Area under the curve BMI Body mass index PA Physical activity LPA Light physical activity METs Metabolic equivalent tasks

MVPA Moderate to vigorous physical activity PPV Positive predictive value

ROC Receiver operator characteristic SB Sedentary behavior

VM Vector magnitude

(4)

Introduction

Physical activity (PA) is one of the greatest factors influencing public health (1, 2). A study by Moore et al. (3) found that being physically active could increase the life expectancy with up to 4.7 years compared to an inactive lifestyle. The higher the activity level the greater was the association to increased life expectancy. To define different levels of PA, the metabolic equivalent task (MET) is commonly used. 1 MET represent the average energy expenditure of a resting person. A value between 1.5 and 3 METs indicate light physical activity (LPA) and values of 3 METs or above indicate moderate to vigorous physical activity (MVPA) (4). Methods to measure PA vary from subjective questionnaires to direct observations. One common method to measure PA is to use accelerometers, which are small devices carried on the body to measure body movements. Several accelerometers have shown to be reliable tools for measuring PA (5) and many studies now use some sort of accelerometer to measure free-living PA (5-8). Two of the most common devices used to objectively measure PA and sedentary behaviour (SB) are the ActiGraph and ActivPAL.

ActiGraph

ActiGraph is a small accelerometer device commonly worn above the hip with an elastic belt around the waist. The device measures in counts, which is a combination of the frequency and intensity of an acceleration, which can then be summarized throughout a time interval (epoch), normally either at 1, 15 or 60 seconds. Based on different cut points, a certain amount of counts during the given time interval determines the intensity of an activity and thereby categorizes it as either SB, LPA or MVPA.

Earlier ActiGraph models used only the vertical axis to measure movement but lately it is possible to measure anteroposterior and mediolateral movements as well, and to combine all these directional movements to create a vector magnitude (VM) counts value. The possibility to measure with VM has increased the accuracy of ActiGraph compared to the use of vertical axis only (9-11). Unfortunately, the cut-points previously recommended for the vertical axis cannot be used for VM (9). Several studies have tried to define a relevant MVPA cut-point for ActiGraph using VM, with the results varying from 2504 to 3360 for 60 s epochs (7, 11-13).

Most commonly the 60 second epoch is used for ActiGraph and the most frequently used

MVPA cut-point for VM in ActiGraph is 2691 developed by Sasaki et al. (8, 12). A more

recent study by Santos-Lozano et al. (13) observed that adequate cut-points differs with

age, and therefore developed new cut-points for different age groups. A validation of their

MVPA cut-point on 3208 for adults against Sasaki’s 2691 showed the 3208 to be more

accurate (bias at -0.01 instead of Sasaki’s -0.73) compared to the gold standard indirect

calorimetry measured with oxygen uptake (13).

(5)

ActivPAL

ActivPAL is a small accelerometer device worn on the anterior middle thigh. It uses a built-in inclinometer to measure body position (leg position) and ambulatory activities and can thus differentiate between sitting/lying, standing and walking. A study by Grant et al. (14) states that ActivPAL has a good to excellent interdevice reliability (ICC = 0.79- 0.99), an excellent validity regarding ActivPAL’s comparison with direct observation in a controlled environment (>97%), and an acceptable to excellent validity in active daily living (63.7-99.5%). Other studies have also confirmed that ActivPAL is a valid and reliable device to measure body position, SB and PA in people compared to direct observation (15-17). Furthermore, ActivPAL has the capability to count steps and measure cadence (step rate) since all the data is timestamped to the closest decimal of a second. A study by Ryan et al. (18) validated ActivPAL’s possibility to measure cadence, and showed a difference of less than 2% compared to direct observation regarding step cadence during free walking. Other studies also confirm that ActivPAL could be a valid device for measuring cadence in walking (19, 20). When it comes to measuring PA of higher intensities, however, some studies have found ActivPAL to be limited (10, 21-23).

According to Montoye et al. (21) the ActivPAL seems to underestimate time spent in MVPA and overestimate time in LPA compared to indirect calorimetry.

Comparison

When comparing the two devices to one another, the ActivPAL is the superior device for

measuring SB (10, 15, 16) despite ActiGraph measuring with VM (9, 10). ActiGraph on the

other hand is the superior device for measuring higher intensities of PA (24). The

accuracy of ActiGraph increases with higher PA intensities in the same time as the

ActivPAL accuracy decreases with increased PA intensity, compared to direct observation

or indirect calorimetry (10, 24). Thus, many studies choose to only measure SB with

ActivPAL and use other devices, normally ActiGraph, for the assessment of PA (25-28),

and to get a greater view over peoples PA habits and SB, the seemingly best way is to use

both devices simultaneously (25, 26). However, a cadence of 100 steps per minute (spm)

with ActivPAL represents an energy expenditure of 3 METs (29-31), thus providing a

possible way to measure MVPA also with ActivPAL, although with individual factors such

as age, height and BMI possibly affecting the results (31-33). A possibility to measure

both SB and higher levels of PA with one single device would facilitate the conduction of

several studies measuring a greater spectrum of peoples’ activity level and reduce both

resources and time for the making of these studies. However, to our knowledge the

possibility of using cadence measured with ActivPAL to determine higher PA intensities

in free living environments has not yet been tested against other devices such as

ActiGraph. If ActivPAL shows to be as good as ActiGraph in measuring MVPA it can be

argued that using ActivPAL is enough for measuring both SB and PA. ActiGraph would

thus not contribute with any more information and would thereby be redundant.

(6)

Aim

The aim of this study was to examine the possibility to measure MVPA in a free living environment with ActivPAL, using its cadence meter to determine PA intensity, in comparison to the otherwise most commonly used accelerometer for measuring PA. Our hypothesis is that ActivPAL using cadence with a cut-point of 100 spm will be a valid device with a correlation over 0.7 and an percentage agreement on 90% or more compared to ActiGraph using VM and the cut-point 3208 cpm created by Santos-Lozano (13) when measuring MVPA.

Scientific questions:

Correlation

If the cadence value increases in ActivPAL, will the ActiGraph counts value increase in the same proportion?

Does age or a high BMI affect the level of correlation between ActiGraph and ActivPAL?

Agreement

Does ActivPAL and ActiGraph categorise activity intensities in the same way?

What ActivPAL cadence cut-point value for MVPA in a free living environment does 3208 cpm in ActiGraph represent?

Metod

Design

This is a secondary data analysis looking at the concurrent validity of ActivPAL using cadence to measure MVPA compared to ActiGraph using VM. The material used in this study was previously collected from the Inphact-study by Bergman et al. (25).

The Inphact-study

PA and SB was measured in 80 overweight or obese office workers. Half of the

participants were randomized to an intervention group given a treadmill workstation at

their height adjustable work desk, encouraged to walk on it at a slow self-selected walking

pace for at least one hour a day. The other half of the participants were randomized to the

control group. The study went on for 13 months measuring PA and SB using ActiGraph

and ActivPAL on 5 occasions. The measurements were conducted at baseline, 2 months, 6

months, 10 months and 13 months. Participants aged between 39-67 years, with a body

mass index (BMI) of 24-40 kg/m

2

and with work tasks that were mainly sedentary were

(7)

included in the study. In total, their study had 2590 days of data collected where ActiGraph and ActivPAL were worn simultaneously.

Procedure

In the Inphact-study, the participants were instructed to wear the ActivPAL device

fastened to the anterior mid-line of the right thigh using Mepore surgical dressing and the ActiGraph device around the waist above the right hip with an elastic belt. ActivPAL was asked to be carried 24 hours a day for 7 days and only removed during water based activities. ActiGraph was asked to be carried during all waking hours for 14 days, the first 7 simultaneously as ActivPAL, but also removed during water based activities. The

difference in time carried is due to the limitations of battery time in each device. Raw data from ActiGraph was collected at 30Hz. Participants were instructed to give an

approximate sleep time and declare when they usually went to bed at night and woke up in the morning. Additionally, participants were asked to report any period of non-wear time during the day which then was removed from the data set. This was categorized as non-wear time in ActivPAL and removed from the analyses. With ActiGraph, non-wear time was categorized and removed according to the recommendations by Migueles et al.

(8) following a modified version of the Choi algorithm, with 60 minutes of consecutive zero counts, no spike tolerance, and a small window length of 1 minute as a definition of non-wear time using vector magnitude.

Measurement

This current study analysed one day from the baseline measurement from each participant. Some participants did not wear both accelerometers for each day of the assessment period. Some also forgot taking them on in the morning, or took them off early in the evening, leaving some days with a limited amount of data available to analyse.

Selection of data

The days used in this analysis were chosen manually to get the days were both devices

were worn for a full day and showed a high amount of PA. Days of 900 minutes of wear

time or more was considered as full wear time, and of these the day that showed the most

PA-time was used. If no day had 900 minutes of wear time or more from one or both of

the devices, the day with the highest wear time was used. Any day of the week could be

used. From each day, all walking activities in ActivPAL with a cadence over 90 spm and a

duration of more than 30 seconds was extracted and matched to the corresponding

activity in ActiGraph. Activities with a cadence of 100 spm or more was categorized as

MVPA by ActivPAL. To be categorised as an MVPA activity by ActiGraph a VM counts at

3208 cpm or more was needed.

(8)

To extract raw data from ActiGraph their own software ActiLife (version 6.13.3) was used.

Data from ActivPAL was extracted with an excel macro (HSC PAL analysis software v2.19s). Data from ActivPAL is time stamped to the closest 0.1 second and updates every time a new activity occurs (change in body position or initiating or ending of movement).

ActiGraph data was analysed at 1 s epochs to enable a more exact time match between ActivPAL and ActiGraph. Activities were extracted with activPAL as the denominator. The cadence from ActivPAL is a mean from the amounts of steps taken during the activity divided by the activity duration. For ActiGraph, the amount of counts in the activity corresponding to ActivPAL data was divided by the duration of the activity in seconds and then multiplied with 60 to get a value equivalent to counts per minute. Activities with an ActiGraph value lower than 1000 cpm was considered a missing value and the activity was removed from the analysis.

Data Analysis

Correlation

All analyses in the study were carried out using SPSS (Statistical Package for the Social Sciences) version 24 for Mac by IBM (International Business Machine). Correlation between ActiGraph cpm values and ActivPAL spm values was measured using two tailed Pearson’s correlation coefficient. As in the study by Dowd et al. (34) an r value of >0.70 was considered a high correlation between the devices output.

To investigate whether BMI affected the correlation between ActivPAL and ActiGraph all participants with a BMI over 30 was marked in the scatter plot for visual inspection. The same procedure was carried out regarding age, with all participants of 52 years or older (mean age in the study population) to control for age bias. A separate correlation analysis was conducted for each sub group of BMI and age and compared to the whole group correlation. A difference in correlation between the specific BMI or age group of 0.1 or more compared to the other part of the group or the whole group correlation would be counted as a considerable difference.

Agreement

Difference between ActiGraph’s and ActivPAL’s MVPA classification was calculated using Cohen’s kappa together with percentage agreement, sensitivity and positive predictive value (PPV) with following equations:

% agreement:

No. of events where ActiGraph = ActivPAL in PA intensity classification

Total number of events • 100

(9)

Sensitivity:

No. of events where ActiGraph and ActivPAL is MVPA No. of events when ActiGraph is MVPA • 100

PPV:

No. of events where ActiGraph and ActivPAL is MVPA No. of events when ActivPAL is MVPA • 100

ActiGraph is the denominator of sensitivity and ActivPAL for PPV since ActiGraph is the more commonly used device to measure MVPA compared to ActivPAL. The sensitivity was calculated to show the probability for if ActivPAL had estimated an activity correct compared to ActiGraph. The PPV value indicates the probability for an activity categorised as MVPA by ActivPAL also being categorised as MVPA by Actiraph.

An estimation of which spm value in ActivPAL that best represent 3208 cpm in ActiGraph was conducted using a Receiver Operating Characteristic (ROC)-analysis (35). In this analysis, ActiGraph was the denominator, and the ActivPAL activity cadence was therefor changed to 50 to increase the amount of activities included, since some activities with a lower cadence than 90 in ActivPAL still generated an ActiGraph cpm value over 3208.

Youden’s index was calculated to find what ActivPAL spm value that best agreed with AcriGraph’s 3208 cpm. The calculation was made by making summary of the sensitivity and specificity values for each specific ActivPAL spm value. The spm value with the highest combined sensitivity and specificity was considered to best represent 3208 cpm.

Additionally, Area Under the Curve (AUC) calculations were conducted.

Ethics

Ethical approval for the Inphact study (25) was granted by the Regional Ethical Review

Board (2013/338-31), Umeå, Sweden. This study will only us coded ID numbers for the

participants, not risking to spread any critical information, and will therefore not need

any additional ethical approval.

(10)

Results

In total, 1649 activities with a cadence of >90 spm and duration of 30+

seconds was extracted and analysed from 79 participants eligible with baseline data (see table 1). The amount of activities with a cadence of 90 spm or more lasting for 30 seconds or more that the participants performed ranged from 2-78 activities per day. There was a large variation in cadence for ActivPAL and cpm for ActiGraph between the activities (see table 1).

Correlation

A significant (p<0.001) correlation of r=0.326 was seen between ActiGraph and ActivPAL (figure 1). Figure 1 shows a high variety between the values from ActiGraph that correspond to certain ActivPAL cadences, indicating that the characteristics of ActivPAL does not follow the characteristics of ActiGraph.

The group distribution for BMI showed that 507 activities were performed by people with an BMI over 30 and 1142 by people with BMI lower than 30. For age, 765 activities were performed by people older than 52 years old, and 884 activities by people younger than 52 years old. The correlation when adjusted for age (fig. 2) or BMI (fig. 3) ranges from 0.318 (<52 years) to 0.345 (>52 years), and 0.331 (<30 kg/m

2

) to 0.334 (>30 kg/m

2

), with p<0,001 for all these correlations.

Values

Participants (n) 79

Sex (n, F/M) 35/44

Events analysed (n) 1649

Age (years; mean ± sd) 51,81 ± 6,29 BMI (kg/m

2

; mean ± sd) 29,07 ± 3,26 Events/day (n; mean ± sd) 21,10 ± 12,22

ActivPAL cadence (spm; mean ± sd)

103,60 ± 9,98

ActiGraph counts (cpm; mean ± sd)

4096,46 ± 1256,77

Table 1. Characteristics of the study population

(11)

Figure 1. Scatter plot showing the distribution of the intensity from all activities analysed from ActivPAL and ActiGraph

Figure 2. Scatter plot arranged after age, blue dots

<52 years old, green dots

>52 years old.

Figure 3. Scatter plot arranged after BMI, blue dots <30 kg/m

2

, green dots >30 kg/m

2

.

ActivPAL cadence (spm)

Ac ti G ra p h c o u n ts ( cp m )

ActivPAL cadence (spm) ActivPAL cadence (spm)

ActiGraph counts (cpm)

ActiGraph counts (cpm)

(12)

Agreement

Of the 1649 activities analysed 701 activities had a cadence lower than 100 spm, of these, 500 had an cpm value of 3208 or higher (table 2). The agreement calculated between devices was as following: Cohen’s kappa (K=0.14), percentages agreement (60.70%), sensitivity (61.54%) and PPV (84.34%).

Table 2. Diagram of activities classified as moderate-to-vigorous physical activity with the ActivPAL and ActiGraph, respectively.

AP MVPA AP not MVPA

AG MVPA 800 500 Total AG:

1300

AG not MVPA

148 201

Total AP: 948 Total events

1649

For the ROC analysis, 3741 activities with a cadence of >50 spm was used. The highest Youden’s index value received (1.330) was with an ActivPAL spm value at 86.6 (sensitivity 0.645, specificity 0.685). The AUC value received from ActiGraph with a VM cut point of 3208 cpm compared to ActivPAL was 0.719 (figure 4).

Figure 4. AUC of the ActiGraph VM cut point of 3208 cpm compared to spm measured by ActivPAL

.

AP=ActivPAL AG=ActiGraph MVPA =Moderate to Vigorous

Activity

(13)

Discussion

The purpose of this study was to investigate the possibility to use only one accelerometer for the measurement of MVPA in a free-living environment by looking at the concurrent validity between ActivPAL and AcitGraph. Our findings show a highly significant but low correlation between ActivPAL and ActiGraph, which indicates that the characteristics of ActivPAL is not the same as for ActiGraph. Furthermore, a value from Cohen’s kappa of

<0.2 indicate a poor agreement, which was the case in our study (K=0.14). This result is consistent with other studies that also shows a difference in how ActivPAL and ActiGraph measures PA (10, 24). The percentage agreement between ActivPAL and ActiGraph was low (60.7 %), categorising activities in the same way approximately 60.7 % of the time. As indicated by the sensitivity ActivPAL detects about 61.5 % of ActiGraphs MVPA activities and, according to PPV, has an 84.3 % chance that the activity is correctly classified when categorized as MVPA by ActivPAL compared to ActiGraph. The low agreement between devices might have many explanations. For the activPAL, 701 of the 1649 activities extracted had a cadence between 90-100 spm. Despite that many studies consider 100 spm to be a suitable cadence cut point for MVPA when walking, there are some individual differences and a variation in results between studies and populations (30). Ryan et al.

(18) found ActivPAL to have a high accuracy (less than 1% difference to direct observations) for step counting and cadence measure in outdoor walking. However, the results in this study indicate that the step count in ActivPAL agrees poorly with the counts in ActiGraph, indicating that one or both devices measures incorrectly. The accuracy of ActiGraph’s cut-point 3208 cpm has a fairly low accuracy compared to indirect calorimetry (52% sensitivity, 22% specificity) (13). The low accuracy for ActiGraph cut points according to Santos Lozano et al. and the low agreement between devices in this study further indicates that ActivPAL cannot replace ActiGraph while expecting similar results.

The population used in this study was overweight or obese adults. Tudor-Locke et al. (31) states that BMI and age is two of the greatest factors influencing energy expenditure while walking, and other studies also confirm that obese people tend to have a greater sway when walking (30). However, in this study neither BMI nor age had an effect on the outcome of the results and hence the population chosen does not seem to generate any bias to the result. On the other hand, there was quite a narrow age and BMI range in our study population. A greater range in age or BMI might show a larger difference in results.

Additionally, other studies commonly compare time spent in different activity levels when

comparing ActiGraph and ActivPAL (14, 17). Lyden et al. (17) found ActivPAL to have an

excellent agreement (ICC >0.98) with direct observations regarding time classification in

(14)

different activity intensities. However, after screening the days from 10 randomly chosen participants we could not see any correlation between time spent in MVPA between ActiGraph and ActivPAL.

Thus, despite that the accelerometers are made to measure the same thing and carried simultaneously on the same person they are not categorizing MVPA in the same way and an increased cadence in ActivPAL does not always mean an increased counts-value for ActiGraph and vice versa. Therefore, it cannot be stated that ActivPAL can replace ActiGraph for measuring PA and we still recommend that both devices or other trustworthy methods should be used when measuring the whole spectra of people’s activity levels.

Methodological discussion

Since the walking cadence equivalent to 3 METs varies some for the individuals (30) the value 90 spm was used instead of 100 spm to limit the risk of missing data and to increase the amount of activities extracted. The time duration of 30 s was used for limiting the impact of sudden or uncontrolled movements (such as a stumble) in the activity but to still receive a higher amount of activities compared to only using activities of 60s or higher, which otherwise would have been preferred since we were using cadence and 60 s epoch cut-points for ActiGraph.

The choice of using ActiGraph’s VM cut point made by Santos-Lozano et al. (13) on 3208

cpm for MVPA instead of the otherwise more commonly used cut point of 2691 cpm by

Sasaki et al. (12) was due to a more age equivalent population in Santos-Lozanos study

compared to ours. Santos-Lozano stated that the relevant cut points differ with age and

that their cut point of 3208 was more accurate to middle aged adults compared to

Sasaki’s on 2691. Furthermore, Arvidsson et al. (36) considers both of these cut-points to

be too low and the commonly used filters within ActiGraph to remove some extremely

high activities value to be too narrow. The findings by Arvidsson et al. would therefore

support the use of Santos-Lozano’s cut point since it has a higher value than Sasaki’s,

even though neither one of the cut points might be adequate. This is also supported by the

results from this study were the ROC-analysis shows that 3208 represents a cadence of

86.6 spm indicating that either 3208 is a low cut point, 100 spm is a high cut point or that

one or both of the devices are not reliable. Most probably it might be a combination of

more than one errors that combined give a large misleading result. The VM cut points

used for ActiGraph are developed on treadmills with indirect calorimetry as the gold

standard criterion measurement. Walking on a treadmill is not completely comparable to

walking in a free living environment and a given speed on a treadmill often generates a

lower MET value than the same speed in free walking, possibly due to different

(15)

locomotion or reduction of head wind (37). This difference might also affect the agreement between devices in this study since we measure PA in free living environment and not on treadmills meaning that the ActiGraph values might be higher than expected and the cut point therefore too low.

The result obtained from ActiGraph might differ depending on which epoch lengths with associated cut point that have been used (38). In a study by Logan et al. (39) they found that time spent in LPA increased when the epoch length was increased, with a difference in LPA of more than 200 minutes between 1 s epochs and 60 s epochs. On the other hand, in a study by Powell et al. (7) the difference between cut points from 15 s epochs and 60 s epochs was usually about one to four , indicating a close to linear difference. In our study ActiGraph data was recorded in 1 s epochs to enable the transfer of the exact time of the activity compared to ActivPAL. The cut point used was, however, for 60 second epochs to make it comparable to ActivPAL’s spm. 60 s epoch and its cut points is to our knowledge the most commonly used epoch length and cut points for ActiGraph using VM in adults (8) and was therefore considered suitable for this study.

A limitation with cut points and longer epochs is that a majority of the time in an epoch could be MVPA but with some time in LPA or SB, which will lower the categorization from MVPA to a lower intensity. The same applies for the opposite, where a low PA level could be classified as high if a small part of the time was conducted in a high PA intensity.

This could give a misleading result of time spent in different PA levels. Additionally, the cut points by Sasaki et al (12) or Santos-Lozano (13) were created on a treadmill with a steady state walking for several minutes. This means that these cut points and longer epochs can be difficult to interpret in free living environment where many activities might not last for a whole minute even though the categorization will be made for that time.

When looking at PA habits among the population the most relevant environment for

doing so is in a free living environment. Laboratory environments may affect a person’s

behaviour making the results from these studies being less generalizable for the common

population. However, making studies in free living environments without affecting a

person’s behaviour but to still be able to accurately measure their behaviour may be

difficult. Compared to direct observations and oxygen masks, accelerometers do not risk

interfering with a person’s normal behaviour as much together with being a more cost

effective method (5, 8). Accelerometers can therefore be a suitable device when it comes

to assessing PA in free living environments, even though it may not be as accurate as the

indirect calorimetry measured with oxygen masks. Considering the risk of the PA-

measuring device to affect the participant’s behaviour, ActivPAL most likely constitutes a

(16)

smaller risk compared to ActiGraph since it is a smaller device, carried underneath the clothes and during night time (34).

Strengths and Limitations

The results in this study and the conclusions drawn only apply if assumed that ActiGraph is measuring correctly compared to reality, which cannot be confirmed since there is no comparison to gold standard in this study and therefore it is hard to state how well these results match the results from other studies using gold standard as criterion measure. The lack of a gold standard criterion measure may be the greatest limitation in this study.

Neither direct observation, indirect calorimetry or any other gold standard criterion method was used in this study and it is therefore not possible to state which device that is more accurate compared to reality. The results from this study can only state if ActivPAL measures PA similar to ActiGraph or if it does not. There are no reports of exactly what activities that has been conducted and even though the majority of the activities are most likely walking it is not known if there is a certain type of activity that might affect the results in any way.

Further limitations are the absence of more detailed calculation of the agreement between ActiGraph and ActivPAL. Limits of agreement by Bland and Altman (40) is commonly used to calculate possible bias to the correlation. However, in this study the great difference in scale proportion between ActiGraph and ActivPAL made limits of agreement not applicable since a scale conversion could generate other biases, mainly the risk of analysing the interpretation of the scale conversion rather than the actual agreement between the devices. However, by a visual look on the scatter plot and the distribution of events on an individual basis we could see that all individuals had events spread out widely, and that similar ActivPAL cadence values could generate a great difference in ActiGraph values, and vice versa. This together with the low correlation and agreement indicates that there is most likely no bias that would change the results of this study namely and thereby not the conclusions drawn either.

Another limitation can be the handling of our data. The events analysed are based on data from ActivPAL and then time matched to ActiGraph. Since the devices were carried simultaneously the start and stop of the activities agreed well most of the times between the devices, but could sometimes differ to some extent, which could be a possible bias and reason for a lower correlation.

Strengths with this study is the high number of activities analysed which gives a high

power and a great chance of finding reliable differences or similarities between the

devices. In other studies validating the use of ActivPAL or ActiGraph, the data collection

(17)

ranged from 10-50 people wearing the monitors for 6 min to 5 days depending on the structure of the study (11, 12, 14-16, 18, 22, 23, 41). Since we received a high amount of activities from the 79 days analysed any additional days to analyse was not considered necessary to increase the power further. Also, the activities being performed in an unsupervised free living environment is also considered a strength for the generalisation and clinical relevance of the study. Another strength is that a mean value from absolute counts and spm values for the activities analysed was used instead of following certain epochs in ActiGraph. By creating events according to the start and stop of specific activities the data analysed is more likely to be homogenous and since the activities is generated from ActivPAL who categorises activities also according to leg position and not only by accelerations the events is most likely consisting of walking only.

Recommendations for future studies

Criterion validity studies looking at ActivPAL’s possibility to use cadence to measure MVPA in free living environment compared to a gold standard such as direct observations or indirect calorimetry is needed.

More studies validating ActiGraph and its VM cut points for MVPA is needed, preferably in free living environment. The existing cut points show a very low agreement with gold standard criterion measure but the device is still commonly used. To keep and increase the confidence in the device it is important to clearly validate it with trustworthy methods in all aspects of use.

Furthermore, it could be interesting to further look at the possibility of using only ActiGraph attached to the thigh instead of ActivPAL to measure both SB and PA (34, Treacy 41, 42).

Ethical consideration

Regarding the ethics in the INPHACT-study, different aspects can be discussed when it

comes to putting a treadmill in an office where others may walk by, especially the head of

the company. Since these treadmills make some sound, it is often obvious if someone use

the treadmill for the requested amount of time or not. This may indicate if the person is

devoted to his/her tasks, has a good discipline or is interested in their health. These

aspects might not be all positive for the participants and may affect their work position

negatively, and is therefore important to take in ethic consideration. Some people may be

seen as lazy, while others might put more pressure on themselves to perform and look

good, making the treadmills cause more stress and unhealthy behaviour than being a new

positive element in the working environment as intended. Similar aspects can be drawn

(18)

being assessed in any way, people become aware of and tend to change their behaviour.

That is also one of the reasons why the accelerometers were worn for several days, so that this bias would be reduced. But just like the treadmills, wearing the accelerometers may be a stress factor for some people, feeling supervised and once again feel the need to perform. In general, influencing people to become more active is a good and healthy thing, but increasing their stress level on the other hand has opposite effects. Also, wearing these instruments all day, knowing that someone can see all ones’ activities during the day, could make someone feel uncomfortable. All participants agreed to participate in the study, with the chance to cancel their participation at any time without any reason, and all data is coded and cannot be connected with a specific person. But this may still be a negative aspect of accelerometer use for the individuals.

CONCLUSION

There is a significant difference in how ActiGraph and ActivPAL categorises activities as

MVPA or not. Cadence measured with ActivPAL cannot be used instead of ActiGraph on

adults to measure MVPA in a free-living environment. Taking the low validity of VM cut

points for ActiGraph in consideration, the conclusions drawn from the results in this

study should be interpreted with caution. The analyses in this study are based on an

assumption that ActiGraph measures correctly which is not likely correct for all subjects

and might therefore give an inaccurate picture of the reality. Further studies comparing

these accelerometers to a gold standard measurement as criterion measure in free living

environments is needed. This study contributes with further proof for the need of using

multiple accelerometers, or improving the existing ones, when assessing activities in

people’s daily living.

(19)

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