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Open Access Study protocol

Rationale, design and methods for a randomised and controlled trial to investigate whether home access to electronic games decreases children's physical activity

Leon M Straker15*, Rebecca A Abbott2, Jan P Piek35, Clare M Pollock35, Peter S Davies4 and Anne J Smith15

Author Affiliations

1 School of Physiotherapy, Curtin University of Technology, Perth, Australia

2 School of Human Movement Studies, The University of Queensland, Brisbane, Australia

3 School of Psychology, Curtin University of Technology, Perth, Australia

4 Children's Nutrition Research Centre, The University of Queensland, Brisbane, Australia

5 Curtin Health Innovation Research Institute, Curtin University of Technology, Perth, Australia

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BMC Public Health 2009, 9:212  doi:10.1186/1471-2458-9-212


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2458/9/212


Received:10 June 2009
Accepted:29 June 2009
Published:29 June 2009

© 2009 Straker et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Many children are reported to have insufficient physical activity (PA) placing them at greater risk of poor health outcomes. Participating in sedentary activities such as playing electronic games is widely believed to contribute to less PA. However there is no experimental evidence that playing electronic games reduces PA. There is also no evidence regarding the effect of different types of electronic games (traditional sedentary electronic games versus new active input electronic games) on PA. Further, there is a poor understanding about how characteristics of children may moderate the impact of electronic game access on PA and about what leisure activities are displaced when children play electronic games. Given that many children play electronic games, a better understanding of the effect of electronic game use on PA is critical to inform child health policy and intervention.

Methods

This randomised and controlled trial will examine whether PA is decreased by access to electronic games and whether any effect is dependent on the type of game input or the child's characteristics. Children aged 10–12 years (N = 72, 36 females) will be recruited and randomised to a balanced ordering of 'no electronic games', 'traditional' electronic games and 'active' electronic games. Each child will participate in each condition for 8 weeks, and be assessed prior to participation and at the end of each condition. The primary outcome is PA, assessed by Actical accelerometers worn for 7 days on the wrist and hip. Energy expenditure will be assessed by the doubly labelled water technique and motor coordination, adiposity, self-confidence, attitudes to technology and PA and leisure activities will also be assessed. A sample of 72 will provide a power of > 0.9 for detecting a 15 mins difference in PA (sd = 30 mins).

Discussion

This is the first such trial and will provide critical information to understand whether access to electronic games affects children's PA. Given the vital importance of adequate PA to a healthy start to life and establishing patterns which may track into adulthood, this project can inform interventions which could have a profound impact on the long term health of children.

Trial registration

This trial is registered in the Australia and New Zealand Clinical Trials Registry (ACTRN 12609000279224).

Background

Nationally and internationally, promoting physical activity (PA) has become a major health priority. In the USA, Healthy 2010 has PA ranked as a leading health indicator [1] and the Department of Health in the UK have recently launched their "At least five a week" PA call for action [2]. In Australia, the Strategic Inter-Government forum on Physical Activity and Health was established in 1999 as the collaborative body to coordinate a national approach in supporting health-promoting PA in Australia. The resultant "Be Active Australia" framework was endorsed in 2005. Increasing PA is now a priority issue of state governments across Australia (eg http://www.beactive.wa.gov.au/ webcite and http://www.goforyourlife.vic.gov.au/ webcite) with Queensland even calling 2008 "The year of Physical Activity" http://statements.cabinet.qld.gov.au/ webcite.

PA in adulthood is an important inverse risk factor for the major causes of mortality and morbidity including heart disease, stroke, cancer, musculoskeletal disorders, depression, obesity and diabetes [3]. Insufficient PA contributes a major international health burden [4-6] with Australian direct health care costs estimated at more than $400 million p.a. [7]. In Australia, insufficient PA has been estimated to account for 8,000 deaths per year and is the 4th leading cause of premature death, after obesity, tobacco and hypertension [8].

Whilst the data are inconclusive as to whether PA levels in children track through to adulthood [9], there is evidence to suggest that inactivity tracks [10]. Furthermore, lower levels of PA in childhood have been linked in the short-term with increased levels of obesity, poorer skeletal health [11], and poorer psychosocial well-being [12]. Whilst there is an absence of definitive data providing a causal link between PA and health, Biddle et al. conclude that the evidence suggests that PA in childhood has beneficial effects on cardiovascular disease, obesity, psychosocial outcomes, type II diabetes and osteoporosis [13].

In response to the growing awareness of the health benefits of PA, developed countries have established specific guidelines for PA by children [14-18], with Australia releasing their own in 2004 [19]. In a state-wide survey of 3,691 Queensland children we recently found that nine in ten children, on self-report, failed to meet PA guidelines of accumulating at least 60 minutes of moderate to vigorous activity every day [20]. Other surveys have reported smaller though still substantial proportions of Australian children are insufficiently active [21], with the state-wide WA study finding 1 in 4 high school boys and 1 in 3 high school girls reporting no PA [22]. Sufficient PA is clearly a critical aspect of getting a healthy start to life, yet many Australian children are not sufficiently physically active.

Increasing use of screen based media (SBM) is widely blamed for the perceived reduction of childhood levels of PA [23-25]. SBM exposure includes watching the television (TV), using computers and playing electronic games. TV viewing by children includes watching programs on free to air and pay TV and watching VHS/DVD videos. Computer use by school children includes searching for information on CD ROMs or the Internet, preparing documents and presentations, literacy/numeracy/problem solving activities, email and chat communication. Electronic games are played on computers, dedicated hand held devices (such as Nintendo DS and PSP) and consoles viewed on TV (such as PlayStation, Wii and Xbox).

Nearly all children in Australia now use SBM. Australian Bureau of Statistics' figures show 98% of school aged children watch TV, 95% use computers and 71% play electronic games (compared to 75% who read for leisure) [26]. Children's exposure to SBM starts at an early age. We recently reported that by 5 years of age over half of Western Australian children are using a computer [27]. SBM use is not only very prevalent, but daily doses are now substantial. Marshall et al.'s meta analysis of studies from affluent countries found 130 mins mean daily TV viewing, 34 mins mean computer use and 40 mins mean electronic game playing [28]. Our state-wide Queensland figures are similar, with daily mean daylight SBM times ranging from 83–123 mins depending on child age and sex [20]. Overall, media exposure of children (including reading comics and books, listening to radios and watching TV) appears to have been fairly stable over several decades [13]. However the use of computers and electronic games has doubled recently [29]. Children clearly have significant, and increasing, exposure to computers and electronic games.

Accompanying the increase in computer and electronic game use has been a concern about possible detrimental effects on children's health and development [29]. In a recent review [30], we reported that the available evidence suggested computer use targeted on learning areas is associated with enhanced academic achievement, but that electronic game playing has a negative effect on school achievement. We also found that game-related discourse may provide a stimulus for children's social development, although there are concerns about the potential negative effects of violence in electronic games.

Research on the impact of computer use on children's physical development has focused on postures during computer use at school, use of laptop computers and the impact of workstation design on posture and muscle activity [31]. This research has suggested potential musculoskeletal problems associated with prolonged and constrained postures and repetitive small movements. There is also some evidence that use of computers and electronic games can improve fine motor skills but decrease gross motor skills. The negative impact on gross motor development may be due to displacement of other childhood leisure activities which provide energy expenditure and critical practice of gross motor tasks [30].

The Australian guidelines for PA for children and adolescents [19], as well as prescribing 60 minutes of moderate to vigorous activity per day, recommend that not more than 2 hours per day, in daylight hours, is spent on SBM for entertainment purposes. This is defined as TV viewing, computer and video games and using a computer for non home-work purposes. The discussion paper [25] commissioned by the Australian government to facilitate the development of these guidelines recommended that 'children should avoid extended periods of inactivity through participation in sedentary activities such as TV, computer and video game usage and 'surfing the internet'. This aligns with the national guidelines from Canada to reduce 'non-active' time spent on electronic media by 30 minutes a day. The only other published recommendation referring to electronic media are those from the USA, which advocate that children watch less than 2 hours of TV per day [14,16], and recommend that children should avoid extended periods of inactivity. Our recent state-wide survey found 24% of Queensland children exceeded the national SBM guidelines, with the highest proportion (40%) reported by high school males [20]. Similarly, Hesketh et al. reported that by late elementary school, more than 80% of Victorian children did not meet the national guideline [32].

Whilst it is clear that children's use of SBM is substantial and growing, and that their use is often greater than national guidelines, the relationship between SBM use and PA is not clear. It has been suggested that TV viewing, computer use and electronic game playing may well have different relationships with PA or obesity and should therefore be investigated independently [33-35].

The majority of research investigating links between SBM exposure and PA has focused on TV viewing. There is extensive evidence linking TV viewing to increased body fatness (e.g. [36]), however, little that shows an effect on PA, except where TV viewing is contingent on activity. Whilst logic might suggest that TV viewing is related to less PA, the evidence to date for this relationship is surprisingly weak. The early evidence has not been consistently replicated. For example, Vandewater et al. and Burke et al. found no relationship between TV viewing and PA [37,34].

In a review of 39 studies in children and youth, Marshall et al. concluded that the relationship between TV viewing and PA is small (r = -.129) and negative [23], and this has been supported by similar reviews. Motl et al. recently reported a stronger relationship between naturally occurring fluctuations in TV viewing and PA [24]. However, only one RCT [38] has evaluated the effect of an intervention to reduce TV viewing on increasing PA. Whilst the study was able to demonstrate a reduction in body fatness, there was no effect on overall PA and it has not been replicated.

Whilst increased computer use has been associated with obesity in older girls [34], few studies have examined the link between computer use and PA. Vandewater et al. found a weak negative relationship between computer use and moderate PA [37] but Burke et al. found no relationship [33].

We have found a negative relationship between computer use and vigorous PA on weekends in 1600 young children [27]. This supported the work reported by Salmon et al. who found a negative correlation in 900 primary school children, between PA and having electronic games in the home, although time spent on electronic games was not measured [39]. Other studies have shown no associations between electronic game use and PA (e.g. [40,33]). More often than not electronic games are not singled out and are instead recorded together with general computer use and/or TV viewing, thus possibly distorting any true relationship. Marshall et al's review found an overall weak (r = -.141) negative correlation between electronic games or computer use and PA [23].

Currently there is no experimental evidence of a cause-effect relationship between electronic game use and overall PA. However, we have recently completed a pilot study with 12 children where their PA was compared between 8 week periods of having access to traditional electronic games and having no access to electronic games. Children wore Actical (Mini Mitter; Bend, OR) accelerometers for the final week in each condition. We found accelerometer assessed energy expenditure was reduced by 15% when children had access to traditional electronic games (no electronic games 1.07 kcal.min-1, traditional electronic games 0.91 kcal.min-1). Whist the pilot study lacked the power to detect the clinically significant effects, the trends support the hypothesis that playing traditional electronic games reduces overall PA and energy expenditure. This project will evaluate the effect of traditional electronic games on PA in a home-based RCT.

Current evidence for effect of electronic game exposure on PA may be limited because i) all electronic games have been grouped together or ii) the effect may differ between children.

In addressing the first issue, recently several studies have shown that PA during some electronic game playing can be significant with acceleration counts, energy expenditures and heart rates equivalent to activities such as skipping, jogging and stair climbing [41-44]. These studies used new domestic 'active' electronic game technology including dance mat, web camera and wireless controllers. [Dance mats are 1m2 with 9 pressure sensitive areas which respond to stepping – for images see http://en.wikipedia.org/wiki/Dance_pad webcite. The games involve stepping on the correct square in time with music. EyeToy is a webcam which senses the arm, leg and trunk position of the user and shows the user's image 'within' the game – for images see http://en.wikipedia.org/wiki/Eyetoy webcite. The games require the user to touch or avoid virtual objects. Wii uses 3D position and acceleration sensitive remotes to control bats and other game objects – see http://en.wikipedia.org/wiki/Wii webcite.].

Therefore, just as there may be different effects of TV viewing, computer use and electronic game playing, the current weak evidence regarding the effect of electronic game playing on PA may be due to different effects depending on which type of electronic game is being played. However, the current evidence that some electronic games can be active is all laboratory based, and children may not use the active games sufficiently to impact on PA in the real world. In our recent pilot study we also compared the PA of children having 8 weeks access to active electronic games with 8 weeks access to traditional electronic games. We found accelerometer assessed energy expenditure to be 32% greater on non-school days when children had access to active electronic games (traditional e games 0.91 kcal.min-1, active e games 1.20 kcal.min-1; see Figure 1). Whilst the effect on school days was smaller (5%) the direction was consistent suggesting traditional and active game exposure may result in different overall PA outcomes. This project will evaluate the effect of active electronic games on PA in a home-based RCT.

thumbnailFigure 1. Mean (SE) accelerometer assessed weekly energy expenditure during three electronic game access conditions.

The second reason why current evidence may be limited is that epidemiological studies have often used only age, sex and socioeconomic status as covariates in their models of the relationship between electronic game playing and PA when other variables may be equally or more influential.

Psychosocial variables are known to influence PA levels in children (e.g. perceived athletic competence [45]) and our research has found an impact on use of technology (e.g. children's experience of flow). The most widely used model of technology use (Davis' Technology Acceptance Model [46]) predicts that positive perceptions of technology increases the use of technology and this has been shown to be the case with computer use but has not been investigated with electronic game use. Biological variables are also known to influence PA by children. Children with poorer motor competence engage less in PA than other children [47] and overweight children spend more time with SBM [32]. Our pilot study data suggests the impact of electronic game access varied considerably between children. Therefore a further reason why current evidence is weak may be that sufficient appropriate covariates have not been added to epidemiological models. This project will explore a range of psychosocial and biological variables to inform future epidemiological studies.

The proposed mechanism for electronic game exposure to reduce PA is that it displaces more vigorous PA [25]. However there is to date little objective evidence for this, as electronic game use per se has rarely been specifically measured. Cummings et al., in a cross sectional study of 1490 adolescents, found that time spent on video-games was not correlated with time in active leisure for either boys or girls, but did correlate with less reading time [40]. Kautiainen et al. demonstrated that whilst TV viewing and computer use were positively correlated with overweight and obesity in adolescents, time spent playing digital games (video, computer and console games) was not associated with overweight and obesity [34]. Kautiainen et al. suggested that playing digital games may well be less sedentary than has been implied or is indeed simply related to a different lifestyle than viewing TV or using the computer [34]. This is supported by Olds et al. who have documented that certain children can combine high levels of sports participation with high SBM exposure, terming such children "technoactives" [48]. Similarly, Mutanga et al. found some children with high SBM use also had more PA [49].

Rey-Lopez et al. in their review of sedentary behaviour and obesity, conclude that there is a need for methodologically stronger studies to investigate whether (and what type of) sedentary behaviour displaces PA, and how sedentary behaviour influences health outcomes [35]. Our pilot study results suggest that electronic game use displaces some sedentary activities (watching TV, reading books) and some active leisure. We also propose that displacement patterns will be different depending on the nature of the technology (sedentary/active). Whether or not electronic games have an impact on overall PA, their impact on other activities needs to be understood. This project will examine the activities displaced by children when they have access to electronic games.

In summary, there is clearly a need to provide better evidence on this priority health issue [40,25,50]. Specifically whether there is a cause-effect relationship between electronic games and PA needs to be tested experimentally, and the role of potential confounds related to the type of electronic game played and characteristics of the child need to be considered. Finally, the displacement of other leisure activities by electronic game playing needs to be understood.

Methods/Design

Design and Aims

This study will use a randomised and controlled trial to assess the impact of electronic game use on PA in children by:

1) comparing PA and energy expenditure where children have either no electronic games or access to traditional electronic games. We hypothesise that PA and energy expenditure will be reduced when children have access to traditional electronic games.

2) comparing PA and energy expenditure where children have access to either traditional electronic games, new active electronic games and no electronic games. We hypothesise that PA and energy expenditure will be greater with new active electronic games, but still less than no games.

3) examining responses of different children to explore whether the impact of access to electronic games is greater in some children. We hypothesise a stronger effect on children with poor coordination skills, high adiposity, poor social confidence, more positive attitudes to technology and less positive attitudes to PA.

4) examining displacement of sedentary and active leisure activities by electronic games. We hypothesise that all electronic games will displace active non-electronic leisure activities.

Participants

36 boys and 36 girls (10–12 years of age) will be progressively recruited by mass media, community newsletters and local school notices. This age group has been selected as they are able to provide detailed information in diary and questionnaires, have a high use of electronic games and are developing activity patterns pre-puberty which may track into adulthood. Recruitment will be staggered over three years and targeted to enable participation of equal numbers of males and females, and children representative of a spread of socio-economic status, electronic game experience and motor competence Volunteers will be screened to ensure they are willing to participate after being informed of the full study responsibilities and meet the inclusion and exclusion criteria. Inclusion criteria are: aged 10–12 years at start of study and able to use electronic games on most days. Children will be excluded if they have a diagnosed disorder likely to impact their study participation, movement or electronic game use (other than developmental coordination disorder), live in a shared care arrangement where the child spends a significant amount of time in different houses and is unable to maintain game access condition, or live remote to the University campus. In 2009, 12 boys and 12 girls will be tested, with equal numbers tested 2010, and 2011.

For power calculations, daily moderate/vigorous PAL was estimated at 115+30 mins with a minimum effect size of 15 mins considered important based on effects in prior studies by us and others [51]. If the variation in the PA level between repeated time points in each individual is normally distributed with standard deviation 30 mins, and the true effect of game condition is 15 mins, a study with 72 subjects will reject the null hypothesis that this response difference is zero with probability (power) 0.986. The Type I error probability associated with this test of this null hypothesis is 0.05. If the Type I error is lowered to 0.01 to account for 'repeated' contrasts between conditions, the power is 0.943 [52]. We [51] have had a compliance rate of 95% for DLW data collection and 92% for accelerometry, and both measures have been widely used in studies of 50 to 100 children [53,9]. We have allowed for 10% attrition in data.

Volunteers and their parents were provided a detailed written description of the study purpose, procedures, risks and benefits and given an opportunity to ask research staff for clarification prior to signing assent (children) and consent (parents) to participate. The study has ethical approval from the Human Research Ethics Committee of Curtin University of Technology (approval number HR131/2006).

Intervention and control conditions

There will be three levels of electronic game access. 'No electronic games' will involve all electronic games removed from the family home with a contract that electronic games will be avoided where possible at other locations. 'Traditional electronic games' will involve the provision of a Sony PlayStation 2 with a range of non-violent games requiring game pad input. 'Active electronic games' will involve the provision of a Sony PlayStation 2 with EyeToy and dance mat input devices and a range of non-violent games. A condition period of 8 weeks has been found sufficient to show physical and psychological changes. It also allows for children to accommodate to each condition and is not so long to adversely affect compliance in the 'no games' condition. From our pilot study and discussions with 10 year olds, the removal of all electronic games will be acceptable as a way of getting access to a range of new games and equipment for four months. This is why a within subjects design is required.

Outcome measures

Physical activity

Time spent in moderate to vigorous intensity PA, as well as total movement, will be assessed over 7 days using two Actical accelerometers worn on the wrist and the hip. The MiniMitter Actical is the most widely used and validated accelerometer in studies of children and adolescents [54,9]. Seven days of accelerometer measurement are recommended for the purposes of acceptable measurement of moderate to vigorous PA [9]. Total weekly PA as well as weekend PA and after school weekday PA will be assessed.

Energy expenditure

Total energy expenditure (TEE) will be measured using the DLW technique [55,56]. This is the gold standard method for assessing free living TEE and has been used extensively in children and adolescents [57]. Due to the nature of the study with children being assessed in the home at different points in the day, in non-fasted states, a measured RMR estimate is not deemed a suitable. BMR will therefore be predicted from the subjects sex, age, weight and height using Schofields's equations for children aged 10–17 yrs of age [58]. Predicted BMR has been shown to have good agreement with measured RMR by indirect calorimetry in children of all ages [59,60].

From these two energy expenditure measurements, the habitual physical activity level (PAL) is calculated as the ratio of TEE: BMR. The PAL ratio is a convenient way of adjusting energy expenditure for age, sex, weight and body composition and is a widely accepted measure of habitual PA [61,62]. The DLW technique involves collection of a daily urine sample for 10 days. These samples will be stored in Perth, and sent to Brisbane for subsequent analysis.

Motor coordination

Motor competence will be assessed using Movement Assessment Battery for Children-2 (MABC-2) [63]. The MABC-2 comprises 8 tasks, three measuring manual dexterity, 3 measuring aiming and catching and 2 measuring balance. Age norms based on a standardisation sample of 1,172 children are used to determine a standard total score (M = 10, SD = 3). Separate standard scores can be determined for each of the sub-tests. In addition to the total score, a set of qualitative observations allows the examiner to record the child's performance characteristics during the testing. Cut-offs for impairment scores are at or below the 5th percentile for definite motor difficulties, whilst scores above the 5th percentile but below the 15th percentile suggest borderline difficulties. Minimum value of the test-retest reliability of the original MABC is 0.75 and the inter-tester reliability is 0.70. The original MABC has been found to correlate well with other movement tests [64,65]. Assessment of MABC2 will be conducted by a second research officer blind to participant condition.

Adiposity

Percentage body fat will be determined from the measurement of the 18O dilution space, which is calculated as part of the DLW technique for measuring energy expenditure. Taking into account the fact that 18O overestimates total body water by 1% [66], and using published total body hydration constants from children of different ages [67], fat-free mass can be calculated from total body water. Fat mass is calculated as the difference between total body weight and fat-free mass and expressed as a percentage.

Waist circumference measurements will be taken and age and sex adjusted BMI z-scores will be calculated to enable comparison with state-wide surveys (WA, QLD).

Child Self-Perceptions

Harter's Self-Perception Profile for Children [68] will assess self-perceptions across domains of Scholastic Competence, Social Acceptance, Athletic Competence, Physical Appearance and Behavioural Conduct, and it also includes a subscale designed to evaluate global self-worth that assesses self-esteem independent from the competence domains. It has been validated in samples of children from a wide range of cultural backgrounds, including in Australian children and has high levels of internal consistency ranging from .74 to .92 [68].

Attitudes to technology

Attitudes to technology will be assessed using Webster et al's measure of 'flow' which we have previously shown to have acceptable reliability when used with children aged 10–12 years (Cronbach's alpha .82) [69]. In addition we will use Deane et.al's measures to assess the Technology Acceptance Model factors of 'ease of use' and 'usefulness of technology' which show acceptable to good internal reliability (.67 and .82) [70].

Attitudes to PA

Attitudes to PA will be assessed using the revised Physical Activity Enjoyment Scale [71]. Internal consistency, test-retest reliability and construct validity has been demonstrated [72-74].

Leisure activity

To provide descriptive information on the type of activities performed, participants will use a modified version of the previous-day PA recall (PDPAR) in the form of a diary for 7 days. In the PDPAR the predominant activity for 30 minute blocks during waking hours is recorded. Use of the PDPAR over several consecutive days, in the form of a diary has also been shown to be valid, against measures of accelerometry, and feasible [75]. From this diary, displacement of activity categories will be calculated.

Covariates

Season

Prior PA research has identified significant differences between summer and winter seasons and interactions with sex (more reduction in PA in winter in girls) [21]. The potential seasonal effect will be allowed for in the design by having a balanced ordering of game conditions and a staggered start to cover the school year.

Electronic game experience

Computer and electronic game and interface experience which could confound the effect of the game condition will be measured using a questionnaire based on our prior studies and a large USA study [29].

Sex

Equal numbers of boys and girls will participate. Boys and girls are known to differ in the nature of their electronic game exposure and in the other measures being taken. The games selected will be based on discussions with children to ensure desirability by both girls and boys, as per our successful pilot study.

Procedure

Following screening, participants will be randomly allocated to an order of conditions by selection of an opaque sealed envelope. A balance of orders across the year will be achieved by having 4 sets of the 6 possible order permutations in each year cohort of 24, repeated over three years. After informed consent/assent from parent and child, a research officer (RO) will visit the home and instruct parent and child in baseline assessments. This visit will include measurement of resting energy expenditure, explanation of the DLW method and initial dosing, explanation of the assessment and questionnaire. The RO will return after 10 days to collect baseline assessments and set up the electronic game condition. This will involve either removal of all electronic games or setting up electronic game equipment and instructing parent and child in its use. Follow-up phone calls will be made the next day and after 6 days to ensure game equipment is working correctly. Towards the end of the 6th week in a condition the RO will visit again to set up the DLW and PA assessments. After 8 weeks the RO will collect DLW samples and completed measures and set up the next condition. After all 3 conditions a debriefing interview with parent and child will be conducted to gain qualitative details on the trial. The family structure including number, age and sex of siblings will be recorded, and the behaviour of siblings during the trial will be assessed at debriefing interview. Assessment will be scheduled to avoid school and public holidays where possible. Individualised reports will be provided to participants.

Trial flow

Figure 2 provides an overview of the trial flow. Following recruitment, screening and consent, participants are randomised to an order of electronic game conditions. After baseline assessments participants are setup in their first condition and are assessed at the end of the 8 week condition. Participants are then set up in their second condition for 8 weeks and then their third condition for 8 weeks, again with assessment occurring at the end of each condition.

thumbnailFigure 2. Trial flowchart. This figure provides an overview of the participant flow.

Analysis

To examine hypotheses 1 (that PA and energy expenditure will be reduced when children have access to traditional electronic games) and 2 (that PA and energy expenditure will be greater with new active electronic games, but still less than no games) a 4 (baseline and 3 game conditions) × 2 (sexes) repeated measures ANCOVA analysis with pre-specified contrasts will be conducted for each PA and energy expenditure variable. Prior game experience will be used as a covariate. A critical alpha level of 0.01 will be used to balance type 1 and type 2 errors.

To assess hypothesis 3 (that there will be a stronger effect on children with poor coordination skills, high adiposity, poor social confidence, more positive attitudes to technology and less positive attitudes to PA), separate multiple linear regression analyses for both traditional sedentary electronic and new active electronic games will examine the importance of individual characteristics on activity levels (total minutes of overall moderate/vigorous PA). Sex and amount of moderate/vigorous PA at the end of the no game period will be included as baseline covariates for the assessment of the independent effect on PA of; motor competence, adiposity, perceived athletic competence and attitudes to technology. To assess if individual characteristics may impact differently upon the relationship between PA and traditional sedentary electronic versus PA and new active electronic games, beta coefficients and associated 95% confidence intervals, and proportions of total variance explained by these variables, will be compared between the two models.

To assess hypothesis 4 (that all electronic games will displace active non-electronic leisure activities) the impact of the three study conditions on time spent in four outside school activities (reading, watching TV, using computers, playing sports and non-organised physical activity) will be examined using a 3 (no electronic games, traditional electronic games, active electronic games) ×2 (sex) repeated measures MANOVA with weekly minutes in each activity as the dependent variables.

Discussion

Increasing PA is a major health priority internationally due to the associated health burden. It is widely believed that access to electronic games decreases PA in children. Whilst there is considerable research on the impact of TV viewing on PA and obesity, there is little research specific to electronic games. The available evidence suggests electronic games and TV have different associations with PA. Playing electronic games is an important discretionary leisure activity for many children, yet the impact of this on PA has not been tested experimentally. Further, the influence of different game technologies and child characteristics on the impact is not known. Finally, whilst it is assumed playing electronic games displaces more vigorous activities, this has not been demonstrated. As far as we know, this randomised and controlled trial will be the first to experimentally evaluate the impact of access to electronic games in the home environment on children's PA.

Implications

This trial will provide critical information to understand whether access to electronic games effects children's PA. Given the vital importance of adequate PA to a healthy start to life and establishing patterns which may track into adulthood, this project can inform interventions which could have a profound impact on the long term health of children.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

All authors have contributed substantially to this protocol. LMS conceived the study, contributed to the study design and drafted the manuscript. RAA, JPP, CMP, PSD and AJS contributed to the study design and revised the manuscript. All authors have read and approved the final manuscript.

Acknowledgements

This randomised and controlled trial is funded by the National Health and Medical Research Council (NHMRC) of Australia through project grant #533526. Professor Leon Straker and Doctor Anne Smith are supported by fellowships awarded from the NHMRC. No funding or other input to the study has been received from any electronic game design, manufacture or supply company. The authors would like to thank Deborah Metcalf, Jason Chua, Amity Campbell and Jemma Coleman for assistance in protocol development.

References

  1. US Department of Health and Human Services: Healthy People 2010: Conference edition. Washington, USA: Department of Health and Human Services; 2000. OpenURL

  2. Department of Health: At least five a week: Evidence on the impact of physical activity and its relationship to health. London, UK: Department of Health; 2004. OpenURL

  3. CDC: Physical activity and health: A report of the Surgeon General. Atlanta: U.S. Department of Health and Human Services; 1996. OpenURL

  4. Blair SN, Kohl HW 3rd, Barlow CE, Paffenbarger RS Jr, Gibbons LW, Macera CA: Physical fitness and all-cause mortality; a prospective study of healthy men and women.

    JAMA 1989, 17:2395-2401. Publisher Full Text OpenURL

  5. Pate RR, Pratt M, Blair SN, Haskell WL, Macera CA, Bouchard C, Buchner D, Ettinger W, Heath GW, King AC: Physical activity and public health. A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine.

    JAMA 1995, 273:402-407. PubMed Abstract | Publisher Full Text OpenURL

  6. Stone EJ, McKenzie TL, Welk GJ, Booth ML: Effects of physical activity interventions in youth.

    Am J Prev Med 1998, 15:298-315. PubMed Abstract | Publisher Full Text OpenURL

  7. Stephenson J, Bauman A, Armstrong T, Smith B, Bellew B: The costs of illness attributable to physical inactivity in Australia: A preliminary study. Australian Government. Canberra: Population Heath Division Report No: 2704; 2000. OpenURL

  8. AIHW: Australia's Health. Canberra, ACT: Australian Institute of Health and Welfare; 2006. OpenURL

  9. Trost SG, McIver KL, Pate RR: Conducting accelerometer-based activity assessments in field-based research.

    Med Sci Sports Exer 2005, 37:12. OpenURL

  10. Raitakari OT, Porkka KV, Taimela S, Telama R, Räsänen L, Viikari JS: Effects of persistent physical activity and inactivity on coronary risk factors in children and young adults: The Cardiovascular Risk in Young Finns Study.

    Am J Epidemiol 1994, 140:195-205. PubMed Abstract | Publisher Full Text OpenURL

  11. Bradney M, Pearce G, Naughton G, Sullivan C, Bass S, Beck T, Carlson J, Seeman E: Moderate exercise during growth in prepubertal boys: changes in bone mass, size, volumetric density, and bone strength; a controlled prospective study.

    J Bone Miner Res 1998, 12:1814-1821. Publisher Full Text OpenURL

  12. Boyd KR, Hrycaiko DW: The effect of a physical activity intervention package on the self-esteem of pre-adolescent and adolescent females.

    Adolescence 1997, 32:693-708. PubMed Abstract OpenURL

  13. Biddle SJH, Gorely T, Dtensel DJ: Health enhancing physical activity and sedentary behaviour in children and adolescents.

    J Sports Sci 2004, 22:679-701. PubMed Abstract | Publisher Full Text OpenURL

  14. American Academy of Paediatrics Committee on Public Education: Children, adolescents and television: a policy statement.

    Pediatrics 2001, 107:423. PubMed Abstract | Publisher Full Text OpenURL

  15. Cavill N, Biddle S, Sallis JF: Health enhancing physical activity for young people: Statement of the United Kingdom expert consensus conference.

    Pediatr Exerc Sci 2001, 13:12-25. OpenURL

  16. Corbin CB, Pangrazi RP: Physical activity for children. A statement of guidelines. Reston VA: National Association for Sport and Physical Education; 1998. OpenURL

  17. Sallis JF, Patrick K: Physical activity guidelines for adolescents: Consensus Statement.

    Pediatr Exerc Sci 1994, 6:302-314. OpenURL

  18. The Public Health Agency of Canada: Canada's Physical Activity Guide to Healthy Active Living. Ontario: The Public Health Agency of Canada; 2002. OpenURL

  19. Department of Health and Aging: Australia's physical activity recommendations for 5–12 year olds. Canberra: Commonwealth of Australia; 2004. OpenURL

  20. Abbott RA, Mcacdonald D, Mackinnon L: Healthy Kids QLD Survey 2006 – Summary Report. Brisbane, QLD Health; 2007. OpenURL

  21. Booth ML, Okely AD, Chey T, Bauman AE, Macaskill P: Epidemiology of physical activity participation among New South Wales school students.

    Aust N Z J Public Health 2002, 26:371-374. PubMed Abstract | Publisher Full Text OpenURL

  22. Hands B, Parker H, Glasson C, Brinkman S, Read H: Physical activity and nutrition levels in Western Australian children and adolescents: Report. Perth: Western Australian Government; 2004. OpenURL

  23. Marshall SJ, Biddle SJ, Gorely T, Cameron N, Murdey I: Relationships between media use, body fatness and physical activity in children and youth: a meta-analysis.

    Int J Obes 2004, 28:1238-1246. Publisher Full Text OpenURL

  24. Motl RW, McAuley E, Birnbaum AS, Lytle LA: Naturally occurring changes in time spent watching television are inversely related to frequency of physical activity during early adolescence.

    J Adolesc 2006, 29:19-32. PubMed Abstract | Publisher Full Text OpenURL

  25. Trost SG: Discussion paper for the development of recommendations of children's and youths participation in health promoting physical activity. Canberra: DHA; 2005. OpenURL

  26. ABS: Children's participation in cultural and leisure activities. Canberra: Australian Bureau of Statistics Report No: 4901.0; 2003. OpenURL

  27. Straker L, Pollock CM, Zubrick SR, Kurinczuk JJ: The association between information and communication technology exposure and physical activity, musculoskeletal and visual symptoms and socio-economic status in five year olds.

    Child Care Health Dev 2006, 32:343-351. PubMed Abstract | Publisher Full Text OpenURL

  28. Marshall SJ, Gorely T, Biddle SJ: A descriptive epidemiology of screen-based media use in youth: A review and critique.

    J Adolesc 2006, 29:333-349. PubMed Abstract | Publisher Full Text OpenURL

  29. Roberts DF, Rideout V, Foehr UG: Generation M: Media in the lives of 8–18 year-olds. Menlo Park CA: The Henry J. Kaiser Family Foundation; 2005. OpenURL

  30. Straker L, Pollock C: Optimising the interaction of children with information and communication technologies.

    Ergonomics 2005, 48:506-521. PubMed Abstract | Publisher Full Text OpenURL

  31. Straker L, Briggs A, Greig A: The effect of individually adjusted workstations on upper quadrant posture and muscle activity in school children.

    Work 2002, 18:239-248. PubMed Abstract | Publisher Full Text OpenURL

  32. Hesketh K, Wake M, Graham M, Waters E: Stability of television viewing and electronic game/computer use in a prospective cohort study of Australian children: relationship with body mass index.

    IJBNPA 2007, 4:60. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  33. Burke VV, Beilin LJ, Durkin K, Stritzke WG, Houghton S, Cameron CA: Television, computer use, physical activity, diet and fatness in Australian adolescents.

    Int J Pediatr Obes 2006, 1:248-255. PubMed Abstract | Publisher Full Text OpenURL

  34. Kautiainen S, Koivusilta L, Lintonen T, Virtanen SM, Rimpelä A: Use of information and communication technology and prevalence of overweight and obesity among adolescents.

    Int J Obes 2005, 29:925-933. Publisher Full Text OpenURL

  35. Rey-Lopez JP, Vicente-Rodríguez G, Biosca M, Moreno LA: Sedentary behaviour and obesity development in children and adolescents.

    Nutr Metab Cardiovasc Dis 2008, 18:242-251. PubMed Abstract | Publisher Full Text OpenURL

  36. Grund A, Krause H, Siewers M, Rieckert H, Müller MJ: Is TV viewing an index of physical activity and fitness in overweight and normal weight children?

    Public Health Nutr 2001, 4:1245-1251. PubMed Abstract | Publisher Full Text OpenURL

  37. Vandewater EA, Shim MS, Caplovitz AG: Linking obesity and activity level with children's television and video game use.

    J Adolesc 2004, 27:71-85. PubMed Abstract | Publisher Full Text OpenURL

  38. Robinson TN: Reducing children's television viewing to prevent obesity: A randomized controlled trial.

    JAMA 1999, 282:1561-1567. PubMed Abstract | Publisher Full Text OpenURL

  39. Salmon J, Timperio A, Telford A, Carver A, Crawford D: Association of family environment with children's television viewing and with low level of physical activity.

    Obes Res 2005, 13:1939-1951. PubMed Abstract | Publisher Full Text OpenURL

  40. Cummings HM, Vandewater EA: Relation of adolescent video game play to time spent in other activities.

    Arch Pediatr Adolesc Med 2007, 161:684-689. PubMed Abstract | Publisher Full Text OpenURL

  41. Graves L: Comparison of energy expenditure in adolescents when playing new generation and sedentary computer games: cross sectional study.

    BMJ 2007, 335:1282. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  42. Lanningham-Foster L, Jensen TB, Foster RC, Redmond AB, Walker BA, Heinz D, Levine JA: Energy expenditure of sedentary screen time compared with active screen time for children.

    Pediatrics 2006, 118:1831-1835. Publisher Full Text OpenURL

  43. Maddison R, Mhurchu CN, Jull A, Jiang Y, Prapavessis H, Rodgers A: Energy expended playing video console games: An opportunity to increase children's physical activity?

    Pediatr Exerc Sci 2007, 19:1-24. PubMed Abstract OpenURL

  44. Straker L, Abbott R: Effect of screen-based media on energy expenditure and heart rate in 9- to 12-year-old children.

    Pediatr Exerc Sci 2007, 19:459-471. PubMed Abstract OpenURL

  45. Welk GJ, Schaben JA: Psychosocial correlates of physical activity in children – A study of relationships when children have similar opportunities to be active.

    Meas Phys Exerc Exerc Sci 2004, 8:63-81. Publisher Full Text OpenURL

  46. Davis FD: Perceived usefulness, perceived ease of use and user acceptance of information technology.

    MIS Quarterly 1989, 13:319-40. Publisher Full Text OpenURL

  47. Hay JA, Hawes R, Faught B: Evaluation of a screening instrument for developmental coordination disorder.

    J Adolesc Health 2004, 34:308-313. PubMed Abstract | Publisher Full Text OpenURL

  48. Olds T: Children and sport in South Australia: A report prepared for Australian Sports Commission. Belconnen, ACT; 2004. OpenURL

  49. Mutunga M, Gallagher AM, Boreham C, Watkins DC, Murray LJ, Cran G, Reilly JJ: Socioeconomic differences in risk factors for obesity in adolescents in Northern Ireland.

    Int J Pediatr Obes 2006, 1:114-119. PubMed Abstract | Publisher Full Text OpenURL

  50. Wake M, Hesketh K, Waters E: Television, computer use and body mass index in Australian primary school children.

    J Paediatr Child Health 2003, 39:130-134. PubMed Abstract | Publisher Full Text OpenURL

  51. Abbott RA, Davies PS: Correcting physical activity energy expenditure for body size in children.

    Ann Hum Biol 2004, 31:690-694. PubMed Abstract | Publisher Full Text OpenURL

  52. Dupont WD, Plummer WD: PS power and sample size program available for free on the Internet.

    Controlled Clin Trials 1997, 18:274. Publisher Full Text OpenURL

  53. Ball EJ, O'Connor J, Abbott R, Steinbeck KS, Davies PS, Wishart C, Gaskin KJ, Baur LA: Total energy expenditure, body fatness, and physical activity in children aged 6–9 y.

    Am J Clin Nutr 2001, 74:524-528. PubMed Abstract | Publisher Full Text OpenURL

  54. Payau MR, Adolph AL, Vohra FA, Butte NF: Validation and calibration of physical activity monitors in children.

    Obes Res 2002, 10:150-157. PubMed Abstract | Publisher Full Text OpenURL

  55. Davies PS, Gregory J, White A: Physical activity and body fatness in pre-school children.

    Int J Obes 1995, 19:6-10. OpenURL

  56. Davies PSW, Coward WA, Gregory J, White A, Mills A: Total energy expenditure and energy intake in the pre-school child: a comparison.

    Br J Nutr 1994, 72:13-20. PubMed Abstract | Publisher Full Text OpenURL

  57. Abbott RA, Harkness M, Davies PSW: Physical activity in children and its relation to flow mediated dilation of the brachial artery.

    Atherosclerosis 2002, 160:233-239. PubMed Abstract | Publisher Full Text OpenURL

  58. Schofield WN: Predicting basal metabolic rate, new standards and reviews of previous work.

    Hum Nutr Clin Nutr 1985, 39:S5-S41. OpenURL

  59. Firouzbakhsh S, Mathis RK, Dorchester WL, Oseas RS, Groncy PK, Grant KE, Finklestein JZ: Measured resting energy expenditure in children.

    J Paediatr Gastoenterol Nutr 1993, 16:136-142. OpenURL

  60. Allen JR, McCauley JC, Waters DL, O'Connor J, Roberts DCK, Gaskin KJ: Resting energy expenditure in children with phenlyketonuria.

    Am J Clin Nutr 1995, 62:797-801. PubMed Abstract | Publisher Full Text OpenURL

  61. Black AE, Coward WA, Cole TJK, Prentice AM: Human energy expenditure in affluent societies: an analysis of 574 doubly labelled water measurements.

    Eur J Clin Nutr 1996, 50:72-92. PubMed Abstract OpenURL

  62. Coward WA: Contributions of the doubly labelled water method to studies of energy balance in the Third World.

    Am J Clin Nutr 1998, 68(S1):S962-S969. OpenURL

  63. Henderson SE, Sugden DA: Movement Assessment Battery for Children. Second edition. Sydney, Pearson Assessments; 2007. OpenURL

  64. Laszlo JI, Bairstow PJ: Control of skilled movement: processes underlying acquisition and performance. In Perceptual-motor behaviour: Developmental assessment and therapy. Edited by Laszlo JI, Bairstow PJ. London: Holt, Rinehart & Winston; 1985:11-124. OpenURL

  65. Smits-Engelsman BCM, Henderson SE, Michels CGJ: The assessment of children with Developmental Coordination Disorder in the Netherlands: Relationship between Movement Assessment Battery for Children and the Korperkoordinations Test für Kinder.

    Hum Movement Sci 1998, 17:699-709. Publisher Full Text OpenURL

  66. Schoeller DA: Energy expenditure from doubly labelled water: some fundamental considerations in humans.

    Am J Clin Nutr 1983, 38:999-1005. PubMed Abstract | Publisher Full Text OpenURL

  67. Fomon SJ, Haschke F, Ziegler EE, Nelson SE: Body composition of reference children from birth to age 10 years.

    Am J Clin Nutr 1982, 35:1169-1175. PubMed Abstract | Publisher Full Text OpenURL

  68. Harter S: Manual for the self-perception profile for children: Revision of the Perceived Competence Scale for Children. Denver: University of Denver; 1985. OpenURL

  69. Webster J, Trevino LK, Ryan L: The dimensionality and correlates of flow in human-computer interactions.

    Comput Human Behav 1993, 9:411-426. Publisher Full Text OpenURL

  70. Deane F, Podd J: Relationship between self-report and log data estimates of information system usage.

    Comput Human Behav 1998, 14:621-636. Publisher Full Text OpenURL

  71. Moore JB, Yin Z, Duda J, Gutin B, Barbeau P: Measuring enjoyment of physical activity in children: validation of the Physical Activity Enjoyment Scale.

    J Appl Sport Psychol 2009, 21(1):S116-S129. Publisher Full Text OpenURL

  72. Kendzierski D, DeCarlo KJ: Physical Activity Enjoyment Scale: Two validation studies.

    J Sport Exercise Psy 1991, 13(1):50-64. OpenURL

  73. Motl RW, Dishman RK, Saunders R, Dowda M, Felton G, Pate RR: Measuring enjoyment of physical activity in adolescent girls.

    Am J Prev Med 2001, 21(2):110-117. PubMed Abstract | Publisher Full Text OpenURL

  74. Dishman RK, Motl RW, Saunders R, Felton G, Ward DS, Dowda M, Pate RR: Enjoyment mediates effects of a school-based physical-activity intervention.

    Med Sci Sport Exer 2005, 37(3):478-487. Publisher Full Text OpenURL

  75. Anderson CB, Hagströmer M, Yngve A: Validation of the PDPAR as an adolescent diary: Effect of accelerometer cut points.

    Med Sci Sports Exerc 2005, 37:1224-1230. PubMed Abstract | Publisher Full Text OpenURL

Pre-publication history

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