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Work 38 (2011) 401–412 DOI 10.3233/WOR-2011-1167 IOS Press
ITKids Part I: Children’s occupations and use of information and communication technologies Marina Ciccarellia,∗ , Leon Strakerb , Svend Erik Mathiassenc and Clare Pollockd a
School of Occupational Therapy & Social Work, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia b School of Physiotherapy, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia c Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of G¨avle, SE-801 76 G¨avle, Sweden d School of Psychology and Speech Pathology, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia Received 15 September 2010 Accepted 27 February 2011
Abstract. Objective: School children use information and communication technology (ICT) on a regular basis for a variety of purposes. The purpose of this study was to document how school children spend their time and the different types of ICT they use. Methods: Nine Australian primary school children were observed in their school and away-from-school environments during one school day to record their ICT usage, comparing self-report exposures with direct observations. Self-reported discomfort scores were obtained throughout the day. Results: Paper-based ICT (Old ICT) was mostly used for productive occupations at school, while electronic-based ICT (New ICT) was mostly used during leisure in away-from-school locations. Tasks involving no ICT (Non-ICT) accounted for the largest proportion of time in both locations during self-care, leisure and instrumental occupations. End-of-day self-reported time performing different occupations was consistent with data from independent observations. Self reported time using Old ICT and New ICT was marginally over-estimated, and time spent using Non-ICT was marginally under-estimated. Conclusion: The children in this study used a variety of ICT in the performance of daily occupations in their natural environments. New ICT use was primarily for leisure, but time spent was less than reported in other studies. Discomfort reports among the participants were low. Participants’ self-reports of occupations performed and ICT use was reliable and could be useful as an exposure assessment metric. Keywords: ICT, tasks, variation, direct observation, self-report
1. Introduction The use of information and communication technologies (ICT) by children around the world has in-
∗ Address for correspondence: Marina Ciccarelli, School of Occupational Therapy & Social Work, Curtin Health Innovation Research Institute, Curtin University, GPO Box U 1987, Perth, WA 6845, Australia. Tel.: +61 8 9266 3692; Fax: +61 8 9266 3636; E-mail:
[email protected].
creased rapidly in the last decade. Referred to as the ‘Net Generation’ [1], children in the 21st century use computers and other electronic devices for playing games, completing school and homework assignments and communicating with others [2,3]. Of the 2.7 million children in Australia aged 5–14 years, 92% use a computer on a regular basis at school, at home or in the community [4], and 79% use the Internet [5]. Although many children have access to computers and the Internet at school [6,7], households with children under the
1051-9815/11/$27.50 2011 – IOS Press and the authors. All rights reserved
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age of 18 years are far more likely to have a computer and access to the Internet, than childless homes [8– 10]. Research indicates no difference between boys and girls in their overall computer and Internet use; however, boys are more likely to play non-educational games [3,11] and girls more likely to use e-mail and play educational games [3]. The frequency of use increases with the age of the child [7,12]. Educational and social benefits of computer literacy and communication within the global cyberspace environment are proposed [13]; however, there have been concerns that the nature of electronic ICT tasks poses a health risk to this generation of children [3,14–16]. Physical activity among male and female primary school aged children is significantly less during nonschool days than school days [17] and there is an agerelated decline in physical activity levels, particularly among upper high school students [18]. Whether children’s physical activity patterns are related to their use of ICT is not clear. Objective measures of physical activity, such as heart rate monitors and accelerometers, do not give qualitative information about tasks being performed concurrently and there is little detailed research about the typical daily tasks of school children. Computer-related musculoskeletal discomfort of the head, neck, shoulders and wrists has been reported by children in several studies [3,19,20]. Risk factors for musculoskeletal disorders (MSDs) cited in these studies are similar to those commonly associated with the development of MSDs among adult computer users and include exposure to awkward postures [20–22], repetitive actions [23], long durations on task and a lack of variation [24–27]. Lack of variation in job tasks has been identified as a concern for the development of MSDs in adult workers due to maintenance of postures and muscle loading, leading to muscular strain, fatigue and myalgia [28]. An increase in the time spent continuously engaged in computer-based tasks has been associated with an increase in musculoskeletal discomfort reports in children and adolescents [2,20,29]. At present, there is inadequate research regarding children’s daily task patterns. Population studies can provide general estimates of the number of children accessing computers and the total time they spend using computer-based ICT; however, there are few detailed studies of children that describe when, where, and for which purposes ICT is used [2,20]. It is therefore difficult to draw conclusions about the impact of ICT on children from the ergonomics research on adult users, and to suggest guidelines for appropriate ICT use that are specifically suited to the behavioral patterns of children.
The use of self-report is commonplace in the assessment of exposures in vocational contexts; however, debate continues about the reliability of self-report as an exposure assessment method [30]. Preliminary evidence suggests that primary school children can provide reliable and valid health self reports when using age appropriate instruments [31,32], and thus self-reported information about children’s daily occupations, tasks and ICT use may contribute to a better understanding of the diversity of children’s ICT exposure. The purpose of this study was to document the occupations, tasks and types of ICT used by a small sample of children in school and away-from-school locations during one typical school day. Children’s selfreported exposures were compared to observational data to determine whether primary school children can provide reliable information regarding their patterns of ICT use, and to describe any experiences of discomfort that children report throughout the course of the day.
2. Method 2.1. Study design This was a descriptive study conducted in the school, and away-from- school, environments of Australian school children. To overcome some of the problems associated with self-report methods, real-time direct observation and documentation of children’s daily tasks were used. These observations were conducted at the same time as the direct technical measurement of posture and muscle activity (via inclinometry of the head, upper torso and arm postures and surface electromyography) (See ITKids Part II: Variation of postures and muscle activity in children using different information and communications technologies in this edition). 2.2. Participants Nine Australian elementary school children (five boys and four girls) were recruited via invitations placed in school newsletters. Participants were righthand dominant and of similar age, weight, and height (mean (SD) age 9.1 (0.3) years, height 135.1 (4.3) cm, weight 28.4 (1.9) kg. Children were accepted into the study if they reported using electronic-based media, i.e., computer, television, telephone, within school and/or away-from-school environments for at least 30 minutes per day, and agreed to be monitored and observed at and away-from school over a 12 hour peri-
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od. They were excluded if they reported a congenital or acquired musculoskeletal disorder. The Human Research Ethics Committee at Curtin University approved this study. The children and their parents/guardians provided written informed assent/consent. 2.3. Data collection 2.3.1. Direct observation of exposure Children were observed in real time during one typical school day. It was intended that the observation would be conducted over a 12 hour period (9am-9pm) to include school and away-from-school tasks and in various geographical locations. However, because most of the participants were in bed prior to 9pm, observation was often concluded by 8pm. Direct observation data were entered into an electronic task log in a personal data assistant (PDA), using time-stamped software with a minute-to-minute resolution (PocketCreationsTM , OT International, Perth, Australia) by a trained observer. The observer (the first author) positioned herself several metres from the child so as to clearly see the tasks being performed. There was minimal interaction to minimize the impact of the researcher’s presence on the tasks performed or postures adopted by the participants. 2.3.1.1. Classifying occupations In the task log, the daily occupations that school children typically engaged in were listed in an observation template. These occupations included productive (school), self-care, leisure, and instrumental activities of daily living, as defined by the American Occupational Therapy Association [33]. Each occupation included a variety of tasks. Productive tasks included schoolwork at school or homework done after school. Self-care tasks included taking care of one’s own body, e.g., toileting, bathing, dressing, eating and sleeping) leisure tasks included playing a sport for fun, reading for pleasure, watching movies or TV or playing with friends. Instrumental activities of daily living referred to complex daily tasks completed to sustain and manage their lives and included household chores, travelling to and from school, and packing school bags. 2.3.1.2. Classifying categories of ICT The following definitions were developed to discriminate between children’s use of different ICT. Old ICT included paper-based methods for completion of information and communication tasks such as reading a book, and writing or drawing with a pen or pencil. New ICT described electronic-assisted interfaces including
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computers, hand held gaming devices, television, telephones and calculators. The same task, e.g., reading could be done using Old ICT (turning pages of a book) or with New ICT (scrolling down text on a computer screen). Combined ICT involved the use of both New and Old ICT to complete the task. For example, composing a written document using a desktop computer with keyboard, mouse and display while reading from hard copy text, such as a book or handwritten notes. Non-ICT included tasks that did not involve any Old or New ICT media. These tasks included physical sports, board games or eating a meal. 2.3.1.3. Classifying locations Participants were observed in their natural environments at school and away-from-school locations. School locations included the classroom, library and other educational facilities, the playground and onsite sporting facilities. Away-from-school locations included the children’s home, homes of friends and relatives, public buildings and facilities, public roadways/walkways and open space such as parks and gardens. 2.3.1.4. Documenting observations For each observed task performed, detailed information was entered recorded into the electronic activity log, including: (i) occupation (productive, self-care, leisure, instrumental) and the type of task; (ii) type of ICT being used (Old, New, Non-ICT), and the device and control; (iii) geographical location in which the person was functioning (school or away-from-school); (iv) gross posture (sitting, standing, walking); and, (v) use of upper extremity support from the external environment or the person’s own body. Reports of any discomfort experienced were also collected during natural and scheduled breaks in tasks over the recording period. A front and rear aspect body map with a scale of 0–10 (with 0 = no discomfort and 10 = worst possible discomfort) was used to assist participants identify location and intensity [34]. Observed tasks were electronically logged into a time-stamped data file. When the participant changed task, location or type of ICT being used, a new file was created. Time stamped photographs were taken to illustrate specific tasks performed and to capture information about the general environments in which tasks were performed. Notes were also recorded indicating other aspects of the task that may be significant, including the initiator of a task, e.g., participant, teacher or parent. The activity log was stored electronically in an
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HP Jornada 565TM PDA (Hewlett Packard, Palo Alto, USA) and at the completion of the observation period data were uploaded to a desktop computer for storage and later analysis. 2.3.2. Self-reports of exposure At the end of the observation period participants were instructed to complete a diary of the tasks they performed during the observation period. A paper-pen version of a two-page task diary divided into 30 minute increments was completed by each participant to record the main task performed during each time period, and the ICT type and interface used to complete the task. A list of possible ICT options was provided and multiple selections of ICT type and interface were allowed for each task. Completing the task diary was excluded from the task observation analysis. To determine if wearing the direct monitoring equipment or the presence of the observer influenced the tasks performed, participants were asked to complete a brief questionnaire about additional tasks they might have typically performed, but had not done so on the day of recording; and why these tasks were not performed. They were also asked if the tasks were performed for longer or shorter durations than usual, if any tasks were atypical, and if so the reason for this variation in routine. To determine if the exposures to occupations/tasks and types of ICT on the day of observation were representative of the school week, participants were asked to complete an additional task diary over the next four consecutive school days, using the same format as the task diary completed on the day of observation. Together with the diary from the day of observation, this provided diary data for a total of five consecutive school days. 2.4. Data analyses The task observation log for each participant, containing time-stamped codes for the categories of occupation, type of ICT used, and geographic location, was analyzed using a custom-designed program in LabVIEWTM (National Instruments, Austin, Texas). The output of the analysis included descriptive statistics about the category of interest, including the number of times the category was participated in, and the sum, mean and median of the time periods in which the category occurred were calculated both in absolute terms (minutes), and as a proportion of the total observation period.
Data from the task diaries were used to determine the self-reported amount of time spent engaged in the different occupations and using the different types of ICT on different days. To assess how representative the day of recording was, Friedman analyses of variance were performed on the time engaged in different occupations and with different ICT as reported in the task diary on the day of recording and each of the subsequent four school days. A critical alpha probability level of 0.05 was used. 3. Results Task observation data were obtained for a mean (SD) total of 577 (42) minutes per participant, which was less than the intended duration of 720 minutes per child. This was due to the shorter recordings of participants who had bedtimes earlier than 9pm, and exclusion of procedural tasks related to the study that were not part of the participants’ daily routines. Sixty-one percent of the mean (SD) total recording time was spent at school (352 (50) minutes) and 39% (225 (30) minutes) away-from-school. 3.1. Occupations performed 3.1.1. Total time engaged in different occupations The group mean (SD) of the children’s total time spent engaged in productive occupations was 324 (66) minutes, compared to leisure (168 (98) minutes), self-care (49 (35) minutes) and instrumental occupations (36 (30) minutes). 3.1.2. Proportion of time engaged in different occupations at and away-from-school The children spent 82% (290 (80) minutes) of their time at school performing productive tasks. Fifteen percent (34 (23) minutes) of the time in away-from-school locations was spent performing productive tasks, and these tasks were related to the completion of homework. Leisure tasks were performed for 10% (36 (16) minutes) of time at school, and for 59% (133 (46) minutes) of time away-from-school. Instrumental and selfcare tasks accounted for less than 30 minutes at either location. 3.1.3. Common tasks within different occupations A summary of the observed tasks performed within each of the four occupations is listed in Table 1. The productive and self-care tasks were common to nearly all children, but there was greater variation in the leisure and instrumental tasks performed.
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Table 1 Observed tasks performed by the children on the day of recording Occupation
Performed at school only
Performed awayfrom-school only
Performed at school and away-from-school Compose a document Read a book Search Internet
Productive
Class discussion Physical education Calculations School assembly Play an instrument Singing Dancing/drama Watch TV
Self-care
Change clothing
Eat a meal/snack Drink Toilet/wash hands
Leisure
Talk with family Electronic gaming Imaginary play Jump rope Puzzles
Talk with friends Ball games Search Internet Watch TV Email/messaging
Make a snack Travel in a car/bus Load dishwasher Doctor’s appointment
Pack/unpack bag
Instrumental
Tidy desk
3.2. ICT use 3.2.1. Total time spent using different ICT For the mean (SD) total observation period (577(42) minutes), Non-ICT tasks were performed most frequently (36 (53) minutes), followed by Old ICT (154 (70) minutes) and New ICT tasks (87 (49) minutes). Combined ICT was not used by any participants during the observation period. This was because participants used computers mostly for playing interactive games, completing web-based tasks and communicating via email, but not composing electronic documents from hard copy text or handwritten notes. 3.2.2. Proportion of time using various types of ICT at and away-from-school Thirty percent of time at school was spent using Old ICT (118 (54) minutes) compared to 16% in awayfrom-school locations (36 (31) minutes). Old ICT tasks included reading, writing, drawing and coloring. At school, Old ICT was used both inside and outside of the classroom and when seated on the floor, at a desk and when standing. New ICT use was observed for approximately 2% of the total time at school (6 (4) minutes) and 36% of the time away-from-school (81 (25) minutes). Email messaging, composing documents and searching the Internet were performed in both locations; electronic gaming occurred only in away-from-school locations.
Computer-based New ICT was used for 23% of the observed time (51 (31) minutes) and non-computerbased New ICT, such as television and video gaming, for 13% (29 (17) minutes) of observed time in awayfrom-school locations. Non-ICT tasks accounted for the highest proportion of the observation period in both school and away-fromschool locations. At school, Non-ICT tasks included physical education, free play during recess and lunch, school assembly, small group and whole class discussion, some types of art, drama and dance instruction. In away-from-school locations, Non-ICT tasks included organized sports, free play, travel in the community, household chores, and self-care tasks. 3.3. Use of ICT in different occupations 3.3.1. Total time using different ICT in different occupations Using observation data, the greatest total time for any occupation using a particular type of ICT was in the performance of productive tasks using Non-ICT (188 (89) minutes). It also represented the main ICT type used in each of the other three occupations. Computerbased New ICT (5 (7) minutes) and non-computerbased (4 (5) minutes) was used only briefly during productive tasks; and computer-based New ICT was mostly used during leisure tasks.
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M. Ciccarelli et al. / ITKids Part I: Children’s occupations and use of ICT Table 2 Mean [SD] hours engaged in different occupations and different ICT use as self-reported in task diary Days recorded in task diary 3 4
1a
2
Total time
10.8 [0.7]
11.4 [0.4]
11.5 [0.4]
11.4 [0.6]
11.4 [4.1]
Mean of days 2–5 11.5 [0.3]
Occupation Productive Self-care Leisure Instrumental
5.8 [1.1] 1.0 [0.5] *3.3 [0.8] 0.7 [0.6]
5.2 [0.8] 1.5 [0.5] 3.8 [0.4] 0.9 [0.5]
5.5 [0.8] 1.4 [0.4] 3.8 [0.8] 0.8 [0.7]
5.4 [0.9] 1.3 [0.5] 4.2 [1.0] 0.5 [0.4]
5.3 [0.6] 1.3 [0.6] 4.4 [0.3] 0.4 [0.3]
5.3 [0.7] 1.4 [0.4] 4.1 [0.4] 0.7 [0.5]
3.3 [0.9] 2.2 [0.9] 5.3 [1.2]
3.7 [1.2] 2.4 [1.3] 5.7 [1.5]
4.0 [1.1] 2.3 [1.0] 5.2 [1.7]
3.6 [1.3] 2.6 [1.2] 5.1 [1.8]
3.4 [1.0] 2.1 [0.8] 5.9 [1.4]
3.7 [0.9] 2.4 [0.6] 5.5 [1.3]
ICT type Old New Non a Day
5
of observation; *Difference in time spent (p < 0.05) compared to mean of days 2–5.
Fig. 1. Group mean [SD] of individual total time spent using different ICT during different occupations at school.
3.3.2. Time spent using different ICT in occupations at and away-from-school The observed mean total time engaged in different occupations using different ICT is presented in Fig. 1 (at school) and Fig. 2 (away-from-school). The data indicate that the main variation in ICT usage between locations was during leisure tasks. At school, participants predominately performed leisure tasks with Non-ICT, playing with balls and running around; whereas awayfrom-school leisure tasks involved more computerbasedNew ICT. Similarly, productive tasks at school more often used Old ICT and Non-ICT, compared to computer-based New ICT when away-from-school. The large standard deviations for productive tasks using Old and Non-ICT at school, and leisure tasks in away-from-school locations indicates that participants
differed considerably in the time they spent on activities using these types of ICT. The large standard deviation in productive tasks in away-from-school locations was due to some participants having assigned homework on the day of observation, and others not. 3.4. Self reported five-day diary data The mean (SD) times in which participants reported performing their daily occupations, and using different types of ICT across five school days, are presented in Table 2. There was a systematic difference in leisure occupations between the day of observation and days 2–5. Self-reported time spent performingleisure occupations on the day of observation (3.3 hours) was less than the
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Fig. 2. Group mean [SD] of individual total time spent using different ICT during different occupations away-from-school.
mean leisure time of Days 2–5 (p = 0.021), especially on day 5 (4.4 hours; p = 0.003). The increase in leisure time on non-observed days is reflected in an increase in total reported time on Days 2–5. There were no differences in self-reported use of different ICT types between the day of observation and days 2–5. A variance component analysis was performed on the data from only the non-observed periods (days 2– 5) to quantify variability between ‘typical days’. Table 3 shows dispersion between participants was least for productive tasks. Variability between days was analyzed both in terms of the dispersion of grouped averages for the four specific days, and in terms of each individual participant’s time spent performing tasks at different days. Leisure tasks showed about twice the variability of the other occupations, both in the grouped analysis and for individual participants. With regard to different ICT types used across days 2–5, Table 3 shows that dispersion between participants was least for New ICT tasks. Non-ICT tasks had the greatest variability for grouped averages between days, and for individual participant’s time spent performing tasks on different days. 3.5. Comparison of exposure using direct observation and self-report methods Percentage of time spent in each occupation as recorded from the task observation and the self-report diary on Day 1 is presented in Table 4. The total time for which occupations were self-reported on Day 1 averaged 10.8 hours (648 minutes). The difference in mean
Table 3 Variability between and within subjects of hours reported performing different occupations and using different ICT types
Occupation Productive Self-care Leisure Instrumental ICT type Old New Non
Between subjects
Variability Grouped between days
Individual between days
0.21 0.37 0.42 0.40
0.24 0.17 0.42 0.20
0.47 0.30 0.70 0.35
0.88 0.53 1.23
0.24 0.22 0.35
0.81 1.04 1.06
total time (71 minutes) between the task observation data (577 minutes) and the self-reported diary data can be attributed to time spent changing batteries on direct monitoring equipment and checking EMG impedance during the observation period. These procedural tasks were not included in the task observations recorded by the observer; however, some of the children reported this time as instrumental or productive tasks. While the total time accounted for is different, the estimate of the percentage of time engaged in the four occupations is very similar for time estimates derived from task observation and from diary data as shown in Table 4, indicating that the participants provided reliable self-reports of their daily occupations. Table 4 also compares the percentage of time spent using different types of ICT as recorded from the task observation and from the self-report diary on Day 1. The participants over-estimated the time spent using
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M. Ciccarelli et al. / ITKids Part I: Children’s occupations and use of ICT Table 4 Percentage of total time (mean minutes) engaged in occupations and different ICT use on Day 1 from Task Observation and Task Diary Total time (minutes) Occupation Productive Self-care Leisure Instrumental ICT type Old New Non
Task observation 100% (577)
Task diary 100% (648)
56% (325) 8% (49) 29% (167) 6% (36)
54% (348) 9% (60) 30% (198) 6% (42)
27% (154) 15% (88) 58% (335)
31% (198) 20% (132) 49% (318)
both Old ICT and New ICT by 44 minutes, i.e., a 4% and 5% difference, respectively, and under-estimated the time spent performing Non-ICT tasks by only 17 minutes (approximately a 9% difference). 3.6. Reported deviation from typical tasks and task patterns Six of the nine participants reported in the end of day questionnaire that the tasks performed and durations of tasks on the day of recording were representative of their typical tasks on that day of the week. Three participants reported they would have normally participated in more vigorous physical activity for leisure; however, they were concerned about damaging the direct monitoring equipment. One participant reported they would have normally spent more time using a desktop computer for leisure. 3.7. Reported discomfort Three of the nine participants (one female and two males) reported discomfort during the observation period. Location of discomfort included the head, neck, shoulder and scapulae. The highest discomfort reported was 3/10. One male participant reported a headache (2/10) at the beginning of the observation period and then no discomfort for the rest of the day. One female participant reported discomfort in the neck (3/10) and right shoulder (2/10) at morning recess, immediately following a 40 minute music class in which she was mostly sitting cross-legged on the floor while playing a musical instrument (Non-ICT). Her right and left scapulae were elevated and protracted, and neck was flexed to read the sheet music placed on the floor. This participant reported no discomfort for the remainder of the observation and recording period.
The second male participant reported no discomfort until the late afternoon when he reported discomfort in the right and left rhomboid muscles (3/10). This was reported when he stood to stretch after playing a hand-held video game at home for 1 hour 40 minutes. Three hours later (after eating a meal, doing a jigsaw puzzle and completing the end of day questionnaire), the participant continued to report discomfort (2/10) in the same location. Of the three participants reporting discomfort, only one commenced the school day with a discomfort intensity level of 1/10 or above. The maximum discomfort intensity rating was 3/10. Two participants who had reported discomfort earlier in the day, gave a 0/10 discomfort rating at the end of the day.
4. Discussion This is likely the first study to provide a rich description of school children’s occupations and tasks, and the different ICT used to perform those tasks, derived from detailed observations during a full school day. These data are important as children’s participation in different occupations and tasks, and the extent to which these different occupations involve different ICT, influence the level of MSD risk for children associated with lack of variation in tasks, postures and/or muscle loading. This study confirmed that the 9–10 year old participants were able to provide reliable end-of-day reports of their exposure to different occupations and ICT, based on a comparison with data from real time direct observations. This has significance for future studies aiming to collect data of the activities and time spent using different ICT among a large cohort of children and across several days, using less labor intense data collection methods. Self-reports of computer exposure among adults are reportedly biased and less precise than objective assessment metrics, such as observation and direct monitoring [35]. Thus, self- reports have consistently led to longer durations of exposure at detailed task levels, including durations of keyboard and mouse use [35–38]. Self-reports of total computer use time appear to be sufficiently highly correlated with objective metrics that they could be used in epidemiological studies of exposure as a proxy for direct measurement data [35]. One important reason for possible differences between self-reported and objectively monitored computer use may be that these different instruments measure different constructs of the concept of ‘computer work’ [39].
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The consistency in self-reported occupational exposures across the five diary days suggests that participants had consistent routines each day, for both school and away-from-school activities. The participants used different ICT types within most of their daily occupations. If using different types of ICT demands different postures and muscle loading, then these data suggest that participants were exposed to a range of postures and muscle loadings during the day of observation. This hypothesis is tested in the Part II companion paper. Recent population data indicate that school aged children have similar access to computer-based New ICT including the Internet at school and in away-fromschool locations [4]. The data in the current study were collected in 2005, and the duration of New ICT use among participants at school was very low (mean (SD) 6 (4) minutes) in comparison to away-from-school use (81 (25) minutes). Although access to computers at school is increasingly widespread, the actual time spent using the computers at school may be relatively low among primary school aged children. This is consistent with the findings of a survey of 12–18 year olds [40], that identified school computer use was brief and infrequent. The extent of the application of New ICT within learning environments in schools also varies between schools and classrooms, and depends on factors other than just the provision of computers; including the perceived competency and skills of individual teachers to integrate new technologies into the classroom [41], and having sufficient time for students to access the computers [42]. The low use of New ICT among participants in the current study is similar to previous findings; that increased availability of computers in learning environments does not necessarily imply increased use [41]. New ICT used away-from-school was in the family home for all children, and was mostly for leisure activities including watching television, electronic communication with friends and playing electronic games. Video gaming equipment was observed in the homes of all participants, but was not accessed on the day of observation by all participants. Mean New ICT use was observed and reported in the task diaries to be approximately 2.2 hours/day, and accounted for the least time compared to Old and Non-ICT tasks. Mean computerbased New ICT use was < 1 hour/day for most of the participants, which is less than survey study findings from the United Kingdom [6], but similar to findings from the USA [43] and Australia [4]. The family home was the most important location for computer use among participants in the current study;
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however, seven of the nine children used home desktop computers placed on adult-sized desks. If this is typical of other family homes, it is an important issue to note when identifying environmental factors that influence children’s healthy use of computers. The participants’ daily occupations were under the control of both the participants themselves and others. Most tasks at school were directed by a teacher. Regardless of which school the children attended, the hours of school attendance and scheduling of recess and lunch were the same, and all participants were in classes that ranged from 30–45 minutes in duration. Within each school, there were various scheduled learning tasks that occurred at the same time on the same day of the week. The timing and duration of most self-care, and leisure and instrumental tasks at school were also mostly teacher-directed, i.e., based on when the school bell rang, although the children had more control over how they performed these tasks. For example, during recess, some children chose to sit and talk with friends while eating a snack, whereas others spent the entire recess period playing ball games. In away-from-school locations parental control was evident for homework time (productive), when the evening meal began, the participants’ bed or bath times (self-care), and instrumental tasks such as unpacking school bags, household chores, e.g., setting the table, loading dishwasher; or attendance at medical appointments. However, participants chose and initiated their leisure tasks (which accounted for the highest proportion of time in away-from-school locations), including playing games, computer and video game use and television watching. Choice of postures during leisure tasks were also at the discretion of the participants. The high proportion of time spent in child-directed leisure tasks and the extent to which they appeared to control the choice of leisure task and the associated ICT used meant that some participants engaged in a very narrow selection of leisure tasks, performed with little task variation. This again highlights the influence of the family home and parental control (or lack thereof) on participants’ healthy use of New ICT. Participants mainly used Non-ICT when away from school. Data from task diaries indicated that six of the nine participants either played a musical instrument or trained for organized sports after school. This is consistent with data from an Australian Bureau of Statistics report on children’s participation in cultural and leisure activities that shows the percentage of school-aged children (5–14 years) who played a musical instrument increased from 13% in 2003 to 20% in 2009 [12]. In
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addition, 63% of children aged 5–14 years participated in organized sports outside of school hours [12]. School and away-from-school locations provided the participants with opportunities for participation in different occupations involving different ICT types. These data suggest that participants experienced a variety of occupations, tasks and ICT across a typical school day. Engaging in different occupations and activities may be important for reducing the risks of MSD in children; however if the combination of activities does not result in a sufficient variation of posture and muscle loading, this risk reduction may not be realized. This could happen if the combined activities do not differ much in mean exposure and/or if the exposure variation within each activity is small. Also, if an activity involves repetitive actions such as key strokes or mouse clicks, there may be low variation in posture and muscle load, in the sense that movements are stereotyped and occur repeatedly. Changing activities with little diversity, i.e., small differences in exposure [44] may not result in any particular change in posture and muscle load, which, in turn, may lead to discomfort and fatigue. Participants reported little discomfort during the observation period, which suggests that there may have been sufficient variation of exposures to minimize such discomfort.
5. Limitations Due to the small number (n = 9) and restricted age range of participants in this study, the results must be interpreted with caution. The typical task patterns and ICT use of younger and older children may differ. It is possible that younger children engage in more leisure tasks and Non-ICT activities. With increased age, greater use of computer-based New ICT for productive (learning) tasks and leisure tasks, e.g., electronic gaming and social communication, can be expected. Likewise, older children may participate in fewer leisure based Non-ICT tasks concurrent with a decline in physical activity levels with age [18]. Only data on the occupations/tasks and ICT use during week days were collected. It is expected that task and ICT exposures will be different on weekends. Leisure time on weekends is expected to be greater, with more control by the children regarding how they play, and subsequently greater New ICT used for leisure on weekends [3]. Performing direct observation of the children’ tasks and ICT used for only one day was due to the labor intensive method of data collection. The self-reported
task diary indicated no major difference in the mean time spent performing productive, self-care and instrumental occupations and using different ICT on the day of observation compared to the subsequent four school days, suggesting the observations made were representative of the participants’ typical non-leisure occupations and ICT use. The presence of the observer may have influenced the type and duration of tasks performed and ICT used by the participants. As mentioned, the observer positioned herself at a short distance from each child to minimize the impact of direct observation, but there was evidence that less time was spent engaged in leisure activities on the day of observation than on the subsequent school days. Comparisons of the total time engaged in the four occupations from direct observation and the task diary indicated a similar picture of time spent in these occupations from both data sources, further suggests that diary data may accurately represent daily activity for these 9–10 year old participants.
6. Conclusions Participants engaged in productive, self-care, leisure and instrumental occupations in school and away-fromschool locations on the day of observation. Selfreported diary data indicated that similar amounts of time were spent engaged in these occupations on subsequent school days. Different types of ICT were used to differing extents in different occupations. Old and Non-ICT was used for productive learning tasks at school and away-from-school. Non-ICT was also mainly used during leisure, self-care and instrumental occupations in both school and away-from-school locations. Computer-based New ICT was mostly used for leisure away-from-school, but for periods less than reported in other studies. These 9–10 year old children did not use Combined ICT in any location. The 9–10 year old participants in the study provided end-of-day self-reports of the occupations they performed and ICT used that were consistent with data obtained from direct observations; suggesting self-reports of task exposures may be a viable assessment metric to use with children in future studies. Understanding how children spend their time during a typical day and the types of ICT they use for what types of activities may help to identify children’s risk of developing MSDs. From our data, it appears that these children engaged in a variety of occupations and used different types of ICT, which suggest that they may not have been exposed to
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sustained postures or continuous muscle loading to any great extent. Low discomfort reports by the children support this. Measurement of the postures and muscle activity during a day, and in particular their patterns of variation between and within activities, can provide additional information about potential risks for MSDs, and the companion paper in this edition (ITKids Part II: Variation of postures and muscle activity in children using different Information and Communication Technologies) reports such data for the participants in the present study.
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