Technology and Health Care 22 (2014) 199–208 DOI 10.3233/THC-140817 IOS Press

199

Evaluation of active video games intensity: Comparison between accelerometer-based predictions and indirect calorimetric measurements Julien Tripettea,∗, Takafumi Andob , Haruka Murakamia, Kenta Yamamotoc , Kazunori Ohkawarad , Shigeho Tanakab and Motohiko Miyachia a Department

of Health Promotion and Exercise, National Institute of Health and Nutrition, Tokyo, Japan b Department of Nutritional Sciences, National Institute of Health and Nutrition, Tokyo, Japan c Department of Integrative Physiology, University of North Texas Health Science Centre, TX, USA d Faculty of Informatics and Engineering, University of Electro-Communications, Tokyo, Japan

Received 6 March 2014 Accepted 14 April 2014 Abstract. BACKGROUND: Several active video game (AVG) intervention studies failed in showing an increase in physical activity by using accelerometry measurements. OBJECTIVE: To test the validity of accelerometry for monitoring AVG playing intensity. METHOD: Twenty-two adults performed 80 activities included in the Wii Sports and Wii Fit Plus series. The energy expenditure (EE) and subsequent MET values were measured by indirect calorimetry using metabolic chambers. Subjects wore an accelerometer-based monitor displaying MET values. For each activity, METs values obtained from indirect calorimetry and accelerometry were compared. Each activity was classified as light or moderate to vigorous physical activity (LPA: < 3METs or MVPA:  3METs) for the two methods. RESULTS: AVG intensities have been slightly but significantly underestimated by the acceleromater-based monitor compared to the indirect calorimetry (2.5 ± 1.0 instead of 2.7 ± 0.9 METs). Fourty percent of activities have been significantly misestimated, and 20% have been misclassified. CONCLUSION: Those results point out the potential bias of accelerometry measurements for evaluating AVG intensities. Because average AVG intensity lays at the boundary between LPA and MVPA classes, misclassifications can frequently occur. Accelerometry data should be interpreted with caution in intervention studies using AVG. Keywords: Exergame, accelerometry, metabolic chamber, energy expenditure, metabolic equivalent

∗ Corresponding author: Julien Tripette, Department of Health Promotion and Exercise, National Institute of Health and Nutrition, Tokyo, Japan. Tel.: +81 3 3203 8061; Fax: +81 3 3203 1731; E-mail: [email protected].

c 2014 – IOS Press and the authors. All rights reserved 0928-7329/14/$27.50 

200

J. Tripette et al. / Active video games and accelerometry

1. Introduction During the past decade, the video game industry has started to commercialize a new kind of games able to track player movements and translate them into gaming commands. The opportunity to use these active video games (AVG, or “exergames”) to help inactive people to increase their level of physical activity (PA) has been addressed by several authors [1]. Many studies used indirect calorimetric methods to evaluate AVG energy expenditure (EE) and indeed rated them as either light or moderate to vigorous PA (LPA, < 3METs and MVPA,  3METs) [1,2]. However, intervention studies interested in this new type of games did not find any significant increment of LPA or MVPA in AVG players [3–5]. Those observations are surprising since several trials also reported beneficial effects on body composition [5–7]. One may hypothesize that accelerometry data collected in these studies might have not been accurate. AVG indeed present a very specific pattern of physical exercise (cf. tempo, nature and amplitude of movements, limited displacements, upper body solicitation, etc.), and the validity of accelerometry techniques to evaluate their intensity has never been tested. Moreover, most AVG intensities are located at the border of LPA and MVPA categories [2,8], which could induce some misclassifications of activities and subsequent misinterpretation of accelerometry data collected during AVG intervention. The present study aimed at testing the validity of accelerometry-based methods for evaluating AVG playing intensity. We used a recently developed tri-axial accelerometer-based monitor (Activity Style Pro HJA-350IT, Omron Healthcare, Kyoto, Japan) to estimate the intensities of a wide range of AVG. The displayed MET values were compared with metabolic chamber measurements. It may be hypothesized that the accelerometry method would induce a significant misestimating of MET values. The results would help to appreciate the validity of accelerometry data in past and future studies focused on AVG. 2. Methods 2.1. Protocol outline The energy expenditure and metabolic equivalent (METs) of Wii Fit and Wii Sports games (Nintendo Inc.) have been systematically evaluated at the National Institute of Health and Nutrition using metabolic chambers. Those two games are constituted by a wide number of different activities (N = 80). METs have also been evaluated by using tri-axial accelerometer-based activity monitor (Active Style Pro HJA350IT, Omron Healthcare, Kyoto, Japan). Subjects wore an accelerometer-based monitor while being in the metabolic chamber. The whole experiment was split in 4 non-successive experimental days. Thus, 21, 21, 20 and 18 activities were completed respectively at day 1, 2, 3 and 4). Twenty-two subjects (Females: 10, Males: 12; body mass index: 19.7 ± 1.8 and 25.1 ± 2.6 kg.m−2, respectively) aged from 25 to 45 years old participated in the study. Subjects did not declare any chronic diseases or physical activity habits and were therefore considered healthy and inactive. Seven of the 12 male subjects were overweight (body mass index over 25 kg.m−2 ). The study protocol was approved by the ethical committee of the National Institute of Health and Nutrition (NIHN). 2.2. Metabolic chamber measurements The indirect calorimetric measurements were performed using two open-circuit indirect metabolic chambers (15.000 and 20.000 L) equipped with a TV, a video game console, a table and a chair. Temperature was controlled at 25◦ C and relative humidity was set at 55%. The oxygen and carbon dioxide

J. Tripette et al. / Active video games and accelerometry

201

concentrations of the air supply and exhaust were measured by mass spectrometry (ARCO-1000A-CH, Arco System, Kashiwa, Japan). The flow rate exhausted from the chamber was measured by pneumotachography (FLB1; Arco System). The oxygen consumption and carbon dioxide production were determined from the flow rate of exhausted air and the concentration of the inlet and outlet air of the chamber. Values were displayed and recorded every 12 seconds. EE was determined using Weir’s equation [9]. Each AVG activity was continued for at least 8 minutes. MET values were calculated from resting EE and steady-state EE reached during the last 5 minutes of each activity. More details are provided elsewhere [8]. 2.3. Accelerometry measurements Accelerometry measurements were performed with a tri-axial monitor (Activity Style Pro HJA-350IT, Omron Healthcare, Kyoto, Japan). The latter is implemented with a multi-regression data processing algorithm able to discriminate between sedentary (i.e. lying, sitting or standing with no movement), non-locomotive or locomotive activities to accurately estimate intensities [10]. This algorithm processes filtered and unfiltered acceleration data (ACCfil and ACCunfil ), and relies on the following decision tree: If ACCf il < 29.9 mG, Sedentary activity: MET = 0.8823 + 0.0351∗ACCfil If ACCfil > 29.9 mG, Then if ACCunfil /ACCfil > 1.16 Non-locomotive activity: MET = 1.3435 + 0.0196∗ACCfil Else if ACCunfil /ACCfil < 1.16 Locomotive activity: MET = 1.1128 + 0.0086∗ACCfil The algorithm has been described in detail elsewhere [11], and has been reported to have a high level of performance especially for the evaluation of lower intensity activities [10,11]. The monitor records MET values on a 10-second epoch length. As for the metabolic chamber, MET values were recorded for the 5 last minutes of each activity. In order to discuss the response of the multiregression model algorithm to AVGs, the percentage of data assigned to the non-locomotive regression line is also described in this paper (Table 1). 2.4. Data analysis In order to facilitate data interpretation, the 80 tested AVG activities were categorized according to the followings: (1) Stand-up activities engaging lower-body movements only (N = 20); (2) Stand-up activities engaging upper-body movements mainly (N = 12); (3) Stand-up activities engaging both lower- and upper-body movements (N = 27); (4) Sitting or plank activities with movements (N = 5); (5) Postural activities with no movement (N = 16). Accelerometry data and metabolic chamber MET results were compared following the suggestions of Welk et al. [12]. First, the overall difference for all activities together between the two methods was tested using a paired t-test. Then, the analysis was conducted at the activity level (i.e. individual values have been averaged for each activity resulting in one data point). Multiple comparisons were done for each activity and activity category ((1) to (5), see the above description) using a two-way ANOVA with repeated measures. The words “underestimation” and “overestimation” were used to describe a significant difference between the two methods. Then, for each method, AVG were classified as light (1.6 to 2.9 METs), moderate to vigorous (3.0 to 5.9 METs), or vigorous (6 METs) physical activities

202

J. Tripette et al. / Active video games and accelerometry Table 1 Estimation of AVG intensities using an accelerometer-based monitor and comparison with indirect calorimetry

All activities (N = 80)

Averaged METs Averaged Number of UnderOverMisAcceleration (indirect METs activities in which estimations estimations classifications data assigned to calorimetry) (accelerometry accelerometry non-locomotive estimations) and calorimetric regression methods line (%) significantly differ 2.7 ± 0.9 2.5 ± 1.0∗ 35/80 (44%) 30/80 (38%) 5/80 (6%) 16/80 (21%) 70 ± 9 (p = 0.001)

(1) Stand-up & lower-body movements (N = 20)

2.7 ± 0.9

2.9 ± 1.5

7/20 (35%)

3/20 (15%) 4/20 (20%) 5/20 (25%)

73 ± 10

(2) Stand-up & upper-body movements (N = 12)

2.8 ± 0.8

2.4 ± 0.6∗ (p = 0.005)

4/12 (33%)

4/1 (33%)

(3) Stand-up & both lower- and upper-body movements (N = 27)

3.2 ± 0.8

2.7 ± 0.5∗ (p = 0.0003)

10/27 (37%)

(4) Sitting/ plank activities (N = 5)

3.0 ± 0.6

2.6 ± 0.8

4/5 (80%)

(5) Postural activities (N = 16)

2.0 ± 0.4

1.6 ± 0.2∗ (p < 0.0001)

10/16 (62%)



0/12 (0%)

3/12 (25%)

73 ± 10

10/27 (37%) 0/27 (0%)

6/27 (26%)

47 ± 12

1/5 (20%)

2/5 (40%)

97 ± 3

10/16 (62%) 0/16 (0%)

0/16 (0%)

93 ± 5

3/5 (60%)

Significantly different from metabolic chamber estimation.

(detailed data not showed). Indirect calorimetric measurements were considered as the reference, and accelerometry estimations resulting in misclassification (e.g. LPA instead of MVPA) were counted. To estimate the overall bias and how the accelerometer-based monitor is measuring METs differently depending on AVG intensities, we realized a Bland-Altman-like plot at the activity level (cf. Fig. 1, x: METs values measured by the metabolic chamber, y: accelerometry values – metabolic chamber values). Results are presented as mean ± SD. Level of significance was set at p < 0.05. 3. Results The average intensity of the 80 tested activities was 2.7 ± 0.9 METs (1.2 ± 0.3 to 5.6 ± 1.1) when measured with metabolic chambers, and 2.5 ± 1.0 when measured with accelerometer-based monitor. The difference between the two methods was significant. Multiple comparisons indicate significant difference between accelerometry and metabolic chamber measurements for 35 of the 80 activities (Table 2). Thirty of these 35 activities where underestimated by the accelerometer-based monitor (mean underestimation: 0.7 ± 0.4 METs), while the remaining five activities were overestimated (mean overestimation: 1.5 ± 0.9 METs). As showed in the table 1, the categories of activities using upper-body movements (cf. category 2 and 3) as well as the category of postural activities (cf. category 5) were significantly underestimated by the accelerometer-based monitor.

J. Tripette et al. / Active video games and accelerometry

203

Fig. 1. Relationship between AVG intensities (metabolic chamber values, x axis) and the difference between accelerometer-based and metabolic chamber measurements (ΔMETs, y axis) according to the category of each activity ♦: stand-up activities engaging lower-body movements only; : stand-up activities engaging upper-body movement mainly; : Stand-up activities engaging both lower- and upper-body movements; ×: activities realized in sitting and plank position;  postural activities. Linear regression equations are displayed for (A) categories of activity that present a trend toward underestimation when the intensity increases (cf. , , ×, ), and (B) category of activity that present a trend toward overestimation when the intensity increases (cf. ♦).

The accelerometry METs estimation resulted in the misclassification of 16 activities (20% of all tested activities), with 11 of them (15%) being ranked as LPA instead of MVPA. The misclassification rate varied from 0 to 40 percent (cf.categories 5 and 4, respectively) depending on the category/nature of AVG. Significant accelerometry misestimations are more likely to occur for higher intensity activities (cf. Fig. 1). Two trends have been noted. First, activities using lower-body only, tend to be overestimated by the accelerometer-based monitor when the intensity increases. Second, activities engaging upper-body movements, as well as sitting, plank and postural activities, tend to be underestimated by accelerometry when the intensity increases. 4. Discussion In the present study, the average AVG intensity have been slightly but significantly underestimated by the acceleromater-based monitor compared to the indirect calorimetry. Among the 80 tested AVG activities, 35 (i.e. 44%) have been significantly misestimated, and 16 (i.e. 20%) have been misclassified (cf. Tables 1 and 2). Those results point out the potential bias of accelerometry measurements for evaluating AVG intensities and categorizing them as either LPA or MVPA. 4.1. Estimation of AVG intensity using accelerometry methods When measured by indirect calorimetry, the overall level of reported METs (2.7 ± 0.9 METs) is in accordance with the literature [2], and confirm that AVG can be considered as LPA, with some activ-

204

J. Tripette et al. / Active video games and accelerometry Table 2 Accelerometry vs. indirect calorimetry for all activities Activities

Indirect calorimetric Accelerometry measurements measurements Category 1 : Stand-up & lower-body movements (N = 20) Ski jump 2.2 ± 0.4 1.9 ± 0.4 Table tilt 1.8 ± 0.6 1.6 ± 0.3 Penguin slide 2.2 ± 0.4 2.5 ± 0.2 Ski slalom 2.2 ± 0.4 2.1 ± 0.3 Snowboard slalom 2.4 ± 0.6 2.1 ± 0.3 Soccer head 2.2 ± 0.5 2.3 ± 0.3 Lunge 2.8 ± 0.8 2.2 ± 0.2 Single leg extension 3.2 ± 0.7 2.4 ± 0.2 Basic run 5.1 ± 1.0 8.1 ± 1.5 Table tilt plus 1.4 ± 0.3 1.7 ± 0.4 Tilt city 1.7 ± 0.4 1.8 ± 0.2 Segway circuit 1.9 ± 0.4 1.5 ± 0.2 Perfect 10 2.4 ± 0.4 2.7 ± 0.3 Skateboard arena 2.9 ± 0.6 2.7 ± 0.4 Obstacle course 3.2 ± 0.4 3.4 ± 0.7 Island cycling 2.7 ± 0.4 3.3 ± 0.7 Running plus 4.0 ± 0.6 6.1 ± 1.8 Trampoline 3.9 ± 1.0 3.4 ± 0.7 Athletic 3.5 ± 0.9 3.8 ± 0.7 Orienteering 2.7 ± 0.5 3.3 ± 1.0 Category 2: Stand-up & upper-body movements (N = 12) Balance bubble 1.8 ± 0.3 1.7 ± 0.1 Triceps extension 1.7 ± 0.2 1.6 ± 0.2 Torso twist 2.1 ± 0.4 1.7 ± 0.1 Hula hoop 4.3 ± 1.2 3.0 ± 0.5 Balance bubble plus 1.8 ± 0.4 1.6 ± 0.2 Driving range 3.0 ± 0.4 2.5 ± 0.3 Golf 2.0 ± 0.3 2.0 ± 0.2 Bowling 2.7 ± 0.5 2.6 ± 0.4 Baseball 3.0 ± 0.6 2.9 ± 0.4 Tennis 3.0 ± 0.7 2.7 ± 0.4 Boxing 4.1 ± 0.8 3.4 ± 0.7 Squash 3.3 ± 0.7 3.2 ± 0.4 Category 3: Stand-up & both lower- and upper-body movements (N = 27) Tightrope walk 2.0 ± 0.4 2.3 ± 0.3 Rowing squat 3.6 ± 0.7 2.5 ± 0.2 Single leg twist 3.1 ± 0.5 2.2 ± 0.2 Sideway leg lift 2.6 ± 0.6 2.2 ± 0.3 Single arm stand 5.6 ± 1.0 3.4 ± 0.7 Dance 2.2 ± 0.6 1.9 ± 0.3 Free step 3.3 ± 0.6 2.7 ± 0.4 Basic step 3.0 ± 0.4 3.9 ± 0.7 Rhythm boxing 3.9 ± 0.7 2.8 ± 0.6 Advanced step 3.9 ± 0.6 3.4 ± 0.5 Snowball fight 1.9 ± 0.4 1.9 ± 0.3 Big top juggling 2.0 ± 0.7 1.9 ± 0.2 Bird’s eye bull’s eye 2.7 ± 0.4 2.3 ± 0.4 Rhythm parade 3.1 ± 0.3 3.1 ± 0.3 Rhythm kung-fu 2.9 ± 0.5 2.6 ± 0.3 Diving 2.7 ± 0.4 2.8 ± 0.5

Under- / over-estimations

↓ ↓ ↑

Misclassifications

MVPA → LPA MVPA → VPA



↑ ↑

LPA → MVPA MVPA → VPA



LPA → MVPA

↓ ↓ ↓



↓ ↓

MVPA → LPA MVPA → LPA MVPA → LPA

MVPA → LPA MVPA → LPA

↓ ↓

MVPA → LPA



MVPA → LPA



J. Tripette et al. / Active video games and accelerometry

205

Table 2, continued Activities Water gun Waiter Climbing Hula dance Hajimete dance Jazz dance Rock dance Nagara dance Salsa dance Flamenco Hip hop dance

Indirect calorimetric measurements 2.8 ± 0.6 2.8 ± 0.5 3.0 ± 0.9 2.2 ± 0.4 2.5 ± 0.3 3.6 ± 0.7 3.5 ± 0.8 3.6 ± 0.4 3.7 ± 0.9 4.0 ± 6.0 4.7 ± 1.3

Category 4: Sitting/ plank activities (N = 5) Arm and leg lift 3.0 ± 0.7 Plank 2.5 ± 0.4 Push-up & side plank 4.0 ± 1.3 Jack knife 2.8 ± 0.8 Rowing 3.0 ± 0.9 Category 5: Postural activities (N = 16) Luge 2.3 ± 0.5 Lotus focus 1.3 ± 0.4 Deep breathing 1.2 ± 0.3 Spinal twist 1.6 ± 0.4 Cobra 2.1 ± 0.5 Bridge 1.7 ± 0.5 Shoulder stand 2.2 ± 0.6 Half moon 1.8 ± 0.3 Warrior 2.1 ± 0.3 Tree 2.3 ± 0.4 Sun salutation 2.2 ± 0.5 Standing knee 2.2 ± 0.3 Palm tree 1.8 ± 0.3 Chair 2.1 ± 0.3 Triangle 2.2 ± 0.4 Downward facing dog 2.3 ± 0.6 Total 2.7 ± 0.9

Accelerometry measurements 2.6 ± 0.5 2.6 ± 0.6 3.0 ± 0.9 2.2 ± 0.3 2.8 ± 0.6 2.7 ± 0.3 2.9 ± 0.5 3.2 ± 0.4 3.5 ± 0.6 3.4 ± 0.5 4.2 ± 1.1

Under- / over-estimations

Misclassifications

↓ ↓ ↓

MVPA → LPA MVPA → LPA

2.0 ± 0.2 1.8 ± 0.2 2.6 ± 0.3 2.0 ± 0.3 4.2 ± 0.7

↓ ↓ ↓ ↓ ↑



2.2 ± 0.2 1.1 ± 0.2 1.2 ± 0.1 1.5 ± 0.1 1.6 ± 0.2 1.5 ± 0.1 1.7 ± 0.1 1.4 ± 0.1 1.7 ± 0.2 1.7 ± 0.2 1.6 ± 0.1 1.7 ± 0.1 1.6 ± 0.2 1.7 ± 0.1 2.0 ± 0.1 1.7 ± 0.1



2.5 ± 1.0



MVPA → LPA LPA → MVPA

↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓

LPA: light-intensity physical activity; MVPA: moderate to vigorous physical activity; VPA: vigorous physical activity; ↓ significant underestimation (p < 0.05); ↑ significant overestimation (p < 0.05).

ities categorized as MVPA. The accelerometry estimation was significantly lower (2.5 ± 1.0). While the described 0.2 METs difference could be viewed as acceptable (especially in the absence of other monitoring techniques for studies performed in naturalistic conditions), the following detailed analysis might lead to different conclusions. Indeed, on the basis of the observed average METs, the accelerometry measurements induced 5 significant overestimations and 35 significant underestimations among the 80 activities. Not surprisingly, postural and sitting/plank activities had a higher rate of underestimation (respectively 62% and 60% of the activities), while standing-up activities engaging lower-body movements only, have been underestimated in 15% of the case only (Table 1). In the latter category, the accelerometry method also overestimated 20% of the activities. According to the Fig. 1, the most vigorous activities are prone to a higher rate of misestimation (either under- or overestimation). In half of the cases, the observed misestimation

206

J. Tripette et al. / Active video games and accelerometry

also induced a misclassification of the activity. Most of these misclassifications are MVPA activities classified as LPA by the accelerometry method. Overestimations can be explained by the specificity of the OMRON algorithm, which for a same level of acceleration tends to attribute higher MET values for non-locomotive activities compared to locomotive ones [11]. In the present study, 4 of the 5 overestimations were observed in the first category of activities (i.e., standing-up AVGs with lower body movement only, cf. Table 1). While these 4 activities do not produce effective displacement of the player, they do require real steps (i.e. stationary running motion; see the following activities: “basic run”, “running plus”, “island cycling” and “orienteering”, Table 2), and subsequently produce effective high level of acceleration. They are by nature very close to locomotive activities, and should have been classified as such to avoid any overestimation. Instead, a significant percentage of acceleration data have been assigned to the non-locomotive regression line, as indicated by the Table 1. Because the non-locomotive activities’ regression line presents a higher intercept and a more important slope compared to its locomotive activities’ counterpart (cf. method section), the assignment of locomotion-like activity to this line may have been responsible for the observed overestimations. However, overestimations have been observed in 5 activities only, and the recurrent underestimations of AVG intensities appear to be a bigger concern. In the present study 30 of the 80 have been significantly underestimated (cf. Tables 1 and 2). Unfortunately, this result reflects the invariable limitation of waist-worn monitors to evaluate intensity for activities involving upper-body movements (categories 2 and 3), activities that are accomplished in complex postures (category 4), or postural activities (category 5). While this limitation can be intuitively pointed for some traditionnal sport activities requiring low displacements or upper-limb movements, the present study emphasizes its impact in the specific case of AVG. 4.2. Practical implications The fisrt implication of the present work regards the interpretation of accelerometry data in studies that use AVG as an intervention tool. Because the average AVG intensity (2.7 ± 0.9 METs in the present study) is located just at the boundary between LPA and MVPA category, slight underestimations can conduct to a misclassification of some MVPA activities in the LPA category. Recurrent underestimations might explain why some AVG intervention studies were able to observe an improvement in body composition without any increase in the time spent in accelerometry-measured MVPA; and why other studies failed to observe higher amount of MVPA in children undergoing a AVG intervention compared to their control counterpart [3–5]. The real effect of AVG might actually have been blunt. The question to know whether AVG interventions are able to influence positively the risks factors for obesity is still highly debated [13–15]. To avoid any misinterpretation, readers may have to consider that a significant part of the effective MVPA could have been underestimated and misclassified as LPA. To balance this accelerometry-induced errors in AVG intensity estimations, the performance of the selected accelerometer-based activity monitor or algorithm should be tested specifically for AVG prior to the intervention study. Alternative PA monitors might also be considered. For instance, some monitors able to combine accelerometry data with heart-rate information would be less lower-limb movementsdependent and could be therefore more suitable to measure AVG-related level of PA [16]. Finally, the present results could be of interest for the video game industry that now commercializes accelerometer-based monitors compatible with video game consoles. With regards to the present results, further algorithmic developments are clearly necessary to provide an accurate estimation of gamers EE.

J. Tripette et al. / Active video games and accelerometry

207

4.3. Limitations One limitation of the present study is that the results might not be extended to other type of accelerometer-based monitors. Nevertheless, because the tested accelerometer-based monitor tends to attribute relatively high MET values for non-locomotive activities [11], and because an important part of acceleration data has been assigned to the non-locomotive regression line (73 ± 10, 73 ± 10, 47 ± 12, 97 ± 3, 93 ± 5 % respectively for each category of activity, cf. Table 1), we believe that the issue presented herein would have been the same (if not worse) with most of other commercialized monitor using a single linear regression model. A second limitation is that the present study only tested Wii Fit and Wii Sports series. However, those two popular games include activities that are played in various postures, present different tempos, engage either or both upper- and lower-body, require various movement amplitudes, and use different kinds of peripherals (balance board, hand remote controls, mini-screen. . . ). We therefore believe that the wide range of tested activities (N = 80) could be representative of the current AVG offer. Finally, the present work included inactive adults who did not developed significant chronic disease yet (except slight overweight for some of the male subjects). Because of specific energy requirements and gaming behaviors, and different energy-generating processes, indirect calorimetric measurements might have been different in obese people. Although one recent study showed no influence of the weight status on AVG-related EE for a range of Wii-based activities [17], it could be meaningful to repeat the present protocol in various populations (including younger and older people, as well as subjects with significant metabolic disorders) in order to improve the usage of accelerometry measurements in AVG studies.

5. Conclusion In summary, the present study described a slight but statistically significant overall underestimation of AVG intensity by the accelerometry method, resulting in some activity misclassification (as LPA instead MVPA mainly). Given the low magnitude of the difference with metabolic chamber measurements (0.2 METs), authors should determine case by case whether this difference could induce a significant bias in the data interpretation. The present results point out the necessity for further algorithmic developments in order to provide an accurate estimation of AVG players’ EE.

Acknowledgments Authors thank the subjects for participating in the study, as well as Ryoko Kawakami, Noriko Ishiguro Tanaka, and Hiroko Kogure for helping in the management of the protocol. Julien Tripette is supported by the Fonds de Recherche du Québec – Santé and the Japanese Society for the Promotion of Science.

Conflict of interest The study has been partially found by Nintendo Inc. However, Nintendo staffs have not participated in the design of the study, neither in the interpretation or discussion of results. Authors therefore declare no competing or financial interest, and are alone responsible for the content and writing of the paper.

208

J. Tripette et al. / Active video games and accelerometry

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]

Peng W, Crouse JC, Lin J-H. Using Active Video Games for Physical Activity Promotion A Systematic Review of the Current State of Research. Health Education & Behavior. 2013; 40(2): 171-92. doi: 10.1177/1090198112444956. Peng W, Lin J-H, Crouse J. Is playing exergames really exercising? A meta-analysis of energy expenditure in active video games. Cyberpsychology, Behavior, and Social Networking. 2011; 14(11): 681-8. doi: 10.1089/cyber.2010.0578. Baranowski T, Abdelsamad D, Baranowski J, O’Connor TM, Thompson D, Barnett A, et al. Impact of an active video game on healthy children’s physical activity. Pediatrics. 2012; 129(3): e636-e42.doi: 10.1542/peds.2011-2050. Graves LE, Ridgers ND, Atkinson G, Stratton G. The effect of active video gaming on children’s physical activity, behavior preferences and body composition. Pediatric Exercise Science. 2010; 22(4). Maddison R, Foley L, Mhurchu CN, Jiang Y, Jull A, Prapavessis H, et al. Effects of active video games on body composition: A randomized controlled trial. The American Journal of Clinical Nutrition. 2011; 94(1): 156-63. doi: 10.3945/ajcn.110.009142. Mhurchu CN, Maddison R, Jiang Y, Jull A, Prapavessis H, Rodgers A. Couch potatoes to jumping beans: A pilot study of the effect of active video games on physical activity in children. International Journal of Behavioral Nutrition and Physical Activity. 2008; 5(1): 8. doi: 10.1186/1479-5868-5-8. Trout J, Zamora K. Dance Dance Revolution: A physiological look at an interactive arcade game. International Council for Health, Physical Education, Recreation, Sport, and Dance Journal of Research. 2008; 3(1): 67-72. Miyachi M, Yamamoto K, Ohkawara K, Tanaka S. METs in adults while playing active video games: A metabolic chamber study. Medicine and Science in Sports and Exercise. 2010; 42(6): 1149-53. doi: 10.1249/MSS.0b013e3181c51c78. Weir JdV. New methods for calculating metabolic rate with special reference to protein metabolism. The Journal of Physiology. 1949; 109(1-2): 1. Oshima Y, Kawaguchi K, Tanaka S, Ohkawara K, Hikihara Y, Ishikawa-Takata K, et al. Classifying household and locomotive activities using a triaxial accelerometer. Gait & Posture. 2010; 31(3): 370-4. doi: 10.1016/j.gaitpost.2010.01.005. Ohkawara K, Oshima Y, Hikihara Y, Ishikawa-Takata K, Tabata I, Tanaka S. Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm. British Journal of Nutrition. 2011; 105(11): 1681-91. doi: 10.1017/S0007114510005441. Welk GJ, McClain J, Ainsworth BE. Protocols for evaluating equivalency of accelerometry-based activity monitors. Medicine and Science in Sports and Exercise. 2012; 44(1 Suppl 1): S39-49. doi: 10.1249/MSS.0b013e3182399d8f. Bauman A, Macniven R. Are active video games useful in increasing physical activity and addressing obesity in children? JAMA Pediatrics. 2013; 167(7): 676-7. doi: 10.1001/jamapediatrics.2013.2418. Chaput JP, LeBlanc AG, Goldfield GS, Tremblay MS. Are active video games useful in increasing physical activity and addressing obesity in children? JAMA Pediatrics. 2013; 167(7): 677-8. doi: 10.1001/jamapediatrics.2013.2424. Smallwood SR, Morris MM, Fallows SJ, Buckley JP. Are active video games useful in increasing physical activity and addressing obesity in children?–Reply. JAMA Pediatrics. 2013; 167(7): 678. doi: 10.1001/jamapediatrics.2013.2421. Villars C, Bergouignan A, Dugas J, Antoun E, Schoeller DA, Roth H, et al. Validity of combining heart rate and uniaxial acceleration to measure free-living physical activity energy expenditure in young men. Journal of Applied Physiology. 2012; 113(11): 1763-71. doi: 10.1152/japplphysiol.01413.2011. Lyons EJ, Tate DF, Komoski SE, Carr PM, Ward DS. Novel approaches to obesity prevention: Effect of game enjoyment and game type on energy expenditure in active vide games. Journal of Diabetes Science and Technology. 2012; 6(4): 839-47.

Copyright of Technology & Health Care is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

Evaluation of active video games intensity: comparison between accelerometer-based predictions and indirect calorimetric measurements.

Several active video game (AVG) intervention studies failed in showing an increase in physical activity by using accelerometry measurements...
218KB Sizes 0 Downloads 3 Views