Access to Device and Interface Types
Parents were asked which technology devices their child had access to in their home (Table 4) and which devices their child could use independently (Table 5). No specific time frame was indicated in the question regarding access to technology (the question was more about what devices are available in the home at present), but the question regarding time spent on technology was asked for any given day. For all age groups, the most reported devices which were available were tablets (iPad™ and other brands), and personal computers/laptops. The most popular games console was Nintendo Wii, closely followed by Nintendo DS™. Parents reported preschool children having access to median 4 devices (including gaming consoles) (interquartile range (IQR) = [2, 5], maximum = 10), children (up to 12 years) having access to median 5 devices (IQR = [3, 6], maximum = 10), teenagers (up to 17 years) having access to median 3 devices (IQR = [2, 5], maximum = 9), young adults (up to 25 years) having access to median 4 devices (IQR = [3.25, 5], maximum = 9), and adults (26 years and over) having access to median 3 devices (IQR = [1, 4], maximum = 8). The reported use of augmented and alternative communication devices (AAC) was very low in the sample—with only 5 parents reporting access in the home.
Differences in the mean number of devices accessed by children were explored between age groups, learning disability (with vs. without), language ability (verbally fluent vs. delayed/learning) and reading level (fluent vs. learning vs. non-reader). An analysis of variance test (ANOVA) revealed that there was a statistically significant difference between the number of devices accessed by age groups; F(4, 383) = 4.167, p = .002). Post-hoc analysis using Tukey Honest Significant Difference (Tukey HSD) tests showed that significant differences were present between mean number of devices accessed between children and adults (p < .01), and children and pre-schoolers (p < .02), with all other comparisons showing non-statistically significant differences. There was no significant difference between mean number of devices accessed by individuals with a learning disability (mean = 4.39) and individuals without a learning disability (mean = 3.97; t(339.78 = − 1.89, p = .059). Verbally fluent individuals accessed more devices (mean = 4.55) than individuals with less verbal language production (mean = 3.31), and a statistically significant difference was reported; t(219.42) = − 5.59, p < .001. Finally, an ANOVA revealed a difference between individuals who were fluent readers (mean = 4.72), individualised who were learning to read (mean = 3.91) and individuals who could not read (mean = 2.75; F(2, 347) = 30.51, p < .001). A post-hoc Tukey HSD test showed that fluent readers had access to significantly more debices than those who were learning/could not read (p < .001), and those learning to read had access to significantly more devices than those who could not read (p < .001).
Parents were asked about which technology interfaces their child could use independently (Table 5). For nearly all interfaces, except large touchscreens, a higher percentage of pre-schoolers, children, and teenagers were able to use it independently, compared with adults. In each group, touch screen interfaces, followed by mouse and keyboard, were reported as more frequently independently used by individuals. The median number of interfaces that preschool children could use independently was 5 (out of 8 listed in the survey) (IQR = [2, 7]; maximum = 8), for children the median was 5.5 (IQR = [4, 7], maximum = 8), and for teenagers the median was 5 (IQR = [3, 7]; maximum = 8). The median number of interfaces that young adults could independently use was 5 (IQR = [3.25, 7]; maximum = 8), and for adults (aged 26 and older), the median number of interfaces used independently was 2 (IQR = [1.5, 5.75], maximum = 8).
Group differences in the mean number of interfaces that children could use independently were examined, contrasting based on age group (preschool vs. children vs. teenagers vs. young adults vs, adults), learning disability (with vs. without), language ability (verbal vs. learning/delayed), and reading level (fluent vs. learning vs. non-reader). An ANOVA revealed that there were significant differences between age groups on number of interfaces used independently, F(4, 383) = 5.05, p < .001. Tukey HSD comparisons revealed significant differences between preschool children (mean = 4.52) and adults (mean = 3.09; p = .01), and between children (mean = 5.06) and adults (p < .001). No other comparisons were statistically significant. Individuals with a learning disability (mean = 5.4) independently used more devices than individuals without a learning disability (mean = 4.82; t(339.53) = − 2.32, p = .02). Individuals with phrase speech and above (mean = 5.97) independently used more devices than individuals who were learning to speak (mean = 3.14; t(189.95) = 122.3, p < .001). Tukey HSD comparisons confirmed significant differences between all three types of readers, showing that fluent readers could use more interfaces than those who were learning to read (p < .001), and those learning to read could use more interfaces than those who could not read (p < .001).
Frequently Used Software and Functions
The ‘function’ of a technology in this context refers to the purpose for which parents report it is used for by their child. A closed-ended set of options were presented in the survey, allowing parents to choose all that applied to their child. Options included reading, playing games, listening to music or browsing the web, etc, as well as an open-ended “other” option. For both children and adults, frequency counts by device-type showed that the most common uses of technology were playing games, watching YouTube and listening to music (see Fig. 2). The least popular uses of technology were shopping, administration, and AAC. There did not appear to be notable differences in the patterns of technology functions by age group.
Autism-Specific Technology Use
The top 10 apps most frequently reported by survey respondents in the UK and Spain are presented in Fig. 3. By far, the most popular apps across all devices were YouTube and video/mobile games, plus popular characters or “top-grossing” apps like Angry Birds™, Pou™ (Spain only) and the Toca Boca series. Across all participants and apps mentioned, only one autism-specific app made it into the top ten, and that was reported in Spain only—ZAC Browser™ (https://zacbrowser.com/). Other autism-specific apps were sparingly referenced within the data, and parents who did report use of autism-specific technology were more likely to write “apps for autism” (sic), rather than name specific applications. The pattern of specific apps and types of apps used by autistic people between groups, as well as across countries, appear similar and some of the same popular applications appear across multiple devices (e.g. YouTube™ and Minecraft™).
Time Spent in Technology-Mediated Activities, Breakdown by Device, and Predictors
In Table 6 we can see that tablets (especially Apple products) are used for longer durations than most other technologies: more than an hour per day on average. For all age groups, the most used devices were iPads (mean across groups = 81.19 min), other tablet brands (mean = 54.89 min), and PCs (mean = 70.61). Gaming devices were reportedly more popular, and used for longer by children, teenagers, and young adults than in other groups.
For each participant, the median length of time they were reported to spend using technology per day (across different devices) was calculated. A regression examined the influence of individual age, presence of learning disability, language ability, reading ability, and the number of devices the individual can access on the total time that the individual reportedly spent using technology (see Table 7 for results). The significant predictors of time spent using technology were the individual’s reading level and the number of devices they could access in the home: in both cases higher levels indicated longer time periods. The individual’s age, presence of learning disability, and language level did not predict time spent using technology.
Parent Attitudes in Relation to Technology Use and Demographics
Parents were asked whether they were worried about the time their child spent using technology (scored on a 5-point scale from strongly disagree to strongly agree), and this was compared to total reported time spent using technology (cumulative across devices). An ANOVA revealed a significant relationship between parents’ concern, and the actual time their child spent using technology (F(4, 307) = 6.31, p < .001). Parents who were more concerned about how much time their child spent on technology reported that their child spent longer using technology than parents who were less concerned (see Fig. 4).
The survey contained ten questions about parents’ thoughts about their child’s technology use (see Fig. 5). Of these, three items (“I worry about how much time my child spends using technology”, “I have had problems with my child being obsessed with technology”, and “Technology prevents my child from interacting with other people”) were summed into a scale capturing attitude to technology, with a Cronbach’s alpha of 0.76, indicating scale reliability. The median attitude score for the whole sample was 9 (IQR = [7, 11], range = 3–15), where 3 = most negative/worried attitude and 15 = most positive/relaxed attitude. An ANOVA reported that attitude score did not differ between age groups of children (F(4, 304) = 2.269, p = .06).
A regression analysis explored whether parent factors (parents’ age, and age left education) and child factors (age, presence of learning disability, reading and language level, the number of devices they accessed in the home) were related to the reported time that individuals spent using technology (see Table 8). The only significant predictor of parent attitude to technology was the individual’s reading level: better reading was associated with more time spent using technology.