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. 2025 Mar 5;40(2):daae195. doi: 10.1093/heapro/daae195

Child’s-eye views of smartphone-based gaming content: objective insights from Aotearoa New Zealand

Marcus Gurtner 1,✉, Ryan Gage 2, Moira Smith 3, James Stanley 4, Louise Signal 5
PMCID: PMC11879639  PMID: 40037910

Abstract

Children’s engagement with smartphone-based (online) gaming content is rapidly increasing. There appears to be no existing objective evidence of children’s exposure to this content captured in real time. Evidence on preteens’ smartphone-based gaming is especially scarce. This study aimed to objectively explore the nature and extent of preteens’ exposure to smartphone-based gaming content. Sixty-six children aged 11–13 years from 16 schools in the Wellington region of New Zealand used Zoom video teleconferencing software to record real-time, screen-shared internet use for 4 consecutive days. On average, children recorded 164 minutes each over the 4-day study period. Recordings were coded for gaming content by activity, using game applications and watching gaming content using social media. Game application characteristics were also recorded. Of every online hour recorded, 28.6% showed gaming content—using game applications (18%) and watching (10.7%). Male and low-deprivation children recorded more gaming content as part of their screen-shared internet use than female and high-deprivation children. Game application time comprised gameplay (56.6%), non-gameplay (43.4%), and included advertising 16.4% of the time. Most games were ‘Advergames’ [games including advertising (85.7%)], were free-to-play (98.4%), and included in-game purchases (87.3%). One-quarter (25.5%) included ‘Random Items’ (e.g. loot boxes) as part of these purchases, and 28.6% allowed users to interact with other users. While ‘playing’ using smartphone game applications, children are exposed to highly commercialized contexts that include manipulative design features, adult themes and advertising. Children, who are most vulnerable to online harms, must be protected in accordance with the United Nations Convention on the Rights of the Child.

Keywords: children, online, participatory, observational study, rights, phone, gaming

Contribution to Health Promotion.

  • Children are more likely to use digital technologies, play video games and spend more time online than adults.

  • Children mainly use smartphone game applications to ‘play’, yet there is a lack of child-centred, participatory research investigating their real-time engagement with them.

  • This study found that 28.6% of children’s online smartphone time involves exposure to gaming content.

  • This study shows that considerable periods of children’s game application time include commercialized contexts, manipulative design features, adult themes and advertising.

  • This research emphasizes the need for healthy public policy to ensure game applications are safe and the rights of children are protected.

INTRODUCTION

Modern children’s play has become increasingly virtual given that digital technologies enable children to ‘play’ within virtual environments (Stoilova et al., 2021; Alanko, 2023) Smartphones are the most commonly used, largest and fastest-growing video-gaming platform worldwide (Newzoo, 2021), and the primary device children use to access the largely unregulated internet-based world (Stoilova et al., 2021; Gurtner et al., 2022). In highly industrialized countries, smartphone use and gaming, are considerable. For example, 8–17 year-olds in the USA spend an average of 4.5 hours per day using smartphones (with some spending up to 16 hours) (Common Sense Media, 2023), and are estimated to spend 1–2 hours per day gaming (Alanko, 2023). How much of this gaming is smartphone-based remains unclear, but is likely substantial. Health-related gaming literature highlights many positive and negative aspects of gaming (Alanko, 2023). Playing online games can help children build and maintain relationships with peers (Alanko, 2023), and proved useful in mitigating the effects of isolation and loneliness during the recent (an infectious disease caused by the SARS-CoV-2 virus) COVID-19 pandemic (Li et al., 2022). Even brief periods of play alleviate stress and anxiety in children (Alanko, 2023). The relationship between gaming and physical inactivity is an area of concern (Alanko, 2023). Physical inactivity has been described as a global pandemic, is widely acknowledged as a leading risk factor for non-communicable diseases such as obesity, and is detrimental to mental health (Guthold et al., 2018). Whether violent games increase aggression is another key research issue, although recent evidence has largely refuted this claim (Drummond et al., 2020). Stoilova et al. highlight that perceived internet-based risks do not inevitably translate to offline harm, and that the potential for online risks to transform into offline harm is modulated via an interplay of individual and device-specific characteristics (Stoilova et al., 2021). Irrespective, it is evident that digital technologies influence the broader ‘social, commercial, and environmental determinants of health’ (Kickbusch et al., 2021).

Smartphone game applications are easily accessible to children and are typically free to download and play [referred to as ‘F2P’ (free-to-play) games]. F2P games are based on a ‘Freemium’ business model, ‘whereby basic services are provided free of charge while more advanced features must be paid for’ (OxfordLanguages., 2024b). Game developers routinely embed ‘dark patterns’ (Zagal et al., 2013) within game application contexts. Dark patterns are design techniques aimed to ‘steer, deceive, coerce, or manipulate consumers into making choices that often are not in their best interests’ (OECD, 2022).

F2P smartphone game applications including advertising are known as ‘Advergames’ and combine ‘elements of entertainment, advertising, and gaming’ in order to ‘generate positive emotions among consumers and stimulate increased interest in products and services’ (Başev, 2024). Traditional media once operated within defined blocks of space (hoardings) and time (time slots). Today, these parameters are transcended, given round-the-clock access to consumers via smartphones (Lee and Cho, 2020). The growth in digital advertising correlates with increased digital media consumption, owing to increasing internet and mobile phone usage (Truong et al., 2010; UNICEF, 2017) Marketers capitalize on smartphone ownership and leverage them as advertising channels (UNICEF, 2017). Digital advertising is a key component driving the ever-increasing commercialization of childhood (Linn, 2010).

For the generations ‘Alpha’ (born 2010 and onwards) and ‘Z’ (born 1995–2010), online gaming content is omnipresent (Newzoo., 2022a). Globally, the average weekly gaming time for each cohort is estimated at 6 hr 49 min and 6 hr 10 min, respectively (Newzoo., 2022a). Internationally, 94% of Gen Alpha are considered ‘game enthusiasts’, consumers who ‘engage with gaming through playing, viewing, owning, and/or social behavior’ (Newzoo, 2023). Despite preteens being most engaged with smartphone-based gaming content globally, there is a dearth of objective evidence on their exposure to this content, and how this may impact their health and wellbeing. Kickbusch and Holly argue that ‘digital environments, like other environments, should be safe, stimulating, satisfying, and enjoyable’ (Kickbusch and Holly, 2023). Despite smartphone-based game applications being digital environments preteens frequent, objective evidence on their nature is critically lacking. Given these considerations, this study sought to objectively explore the nature and extent of preteens’ exposure to smartphone-based (online) gaming content.

METHODS

Method for Kids Online Aotearoa

This paper is an analysis of data from Kids Online Aotearoa (KOA), a cross-sectional observational study of children’s real-time experiences of the internet-based world (Gurtner et al., 2022). Year 8 students (aged 11–13 years; n = 156) from 16 schools in New Zealand’s (NZ) Wellington region were recruited using a stratified sampling design to provide equal explanatory power by deprivation and ethnicity [Māori, Pacific and non-Māori non-Pacific (NMNP)]. Data collection involved participants using Zoom video teleconferencing software (Zoom Video Communications Inc, 2022) to record screen-shared internet-based content for 4 consecutive days (Thursday to Sunday). Participants were encouraged to record as much as possible, even when using devices for short periods. Children were given three secure Zoom accounts operated by the University of Otago, enabling them to record on up to three screen devices (smartphones, laptops or Chromebooks and personal computers (PCs)). Children were asked to go about their online lives normally. Data are only accessible to investigators and the project has strict data access protocols. Following data collection, children were asked if they recorded anything they did not wish the researchers to see, and if so, that content was deleted without being viewed; this was rarely requested. Demographic data were collected from caregivers, including NZiDep, ‘a small set of indicators of an individual’s deprivation that is appropriate for all ethnic groups and can be combined into a single and simple index of individual socioeconomic deprivation’ (Salmond et al., 2006). Ethical approval was obtained from the University of Otago Human Ethics Committee (Health; 20/006). Further information on KOA methods can be found elsewhere (Gurtner et al., 2022).

Method for KOA smartphone-based gaming

Coding

Data displaying smartphone-based gaming content were coded using Behavioral Observation Research Interaction Software (BORIS—version 7.11.1) (Friard and Gamba, 2016) A BORIS coding ethogram, ‘a catalogue or table of all the different kinds of behavior or activity observed’ (OxfordLanguages, 2021), and coding protocol were devised during trial explorations of participants’ recordings (Supplementary File 1). An inter-rater reliability test was conducted to ensure the ethogram and protocol were interpretable and fit-for-purpose. Two researchers achieved ≥90% agreement against a test set of five recordings. Each recording included numerous examples of each event of interest requiring coding.

Inactive and active time

The KOA recordings did not always include screen-shared activity. Periods of inactivity were coded to exclude portions of recordings that did not display participants actively using smartphones, or were related to the KOA study methodology, such as children’s short messaging service communication with researchers; and all Zoom user-interface screens. Inactive data were omitted from further analysis.

Active time was defined as portions of recordings displaying participants navigating phone operating systems and using applications. Active time was evident, based on a continuous evolution of the displayed on-screen scene (scrolling through social media, or typing a message, etc.), and corroborated by visual indications of participants’ touch interaction with smartphones. When no evolution of an on-screen scene was observed for more than 30 seconds, that is, 30 seconds had elapsed since an obvious, precedent change in scene, active codes were stopped. Inactive codes were applied from the first frame following the 30-second mark, and then stopped as soon as participants re-engaged with their phones.

Gaming content

Traditionally, gaming has been defined as ‘the action or practice of playing video games’ (OxfordLanguages., 2024c). In this study, we use a broader definition of ‘gaming content’, which includes all instances of clearly displayed textual/visual representations of, or references to gaming. Coded gaming content included participants’ use of smartphone game applications (gameplay/non-gameplay/game application advertising), and their use of social media (TikTok, Instagram, Facebook, Snapchat, YouTube) to watch gaming content.

Using game applications

Each instance of participants’ game application use was coded for duration, from the first frame showing them using the application, to an end frame displaying them closing or exiting the application.

Gameplay

Gameplay has yet to be definitively defined in the literature (Djaouti et al., 2008). We defined gameplay as the time participants spent within game application levels/worlds/maps, within which the ‘play’ component of the gaming application occurred.

Non-gameplay

Non-gameplay time was time participants spent using game applications that did not involve gameplay. It included navigating game application menu screens, settings menus, character customization screens, and commercially intended/shop-like screens.

Game application advertising

Every advertisement, ‘a notice or announcement in a public medium promoting a product, service, or event or publicizing a job vacancy’ (OxfordLanguages., 2024a), appearing within a game application, was coded for the duration. These included banner advertisements, rectangular banners that covered a portion of the game application screen (predominantly during gameplay), and pop-up advertisements, those which interrupted gameplay to cover the entire screen.

Watching gaming content on social media

Watching gaming content on social media included all clearly displayed textual/visual representations of, or references to gaming, appearing on-screen while participants used social media applications or websites.

Game application characteristics

To ascertain additional information about the game applications participants used, we searched the Google Play portal (Android Apps on Google Play). The International Age Rating Coalition (IARC) label associated with each application was recorded in Microsoft Word and paired with game application codes annotated within BORIS. Game developers obtain IARC ratings by answering a questionnaire. Ratings are displayed as labels on web-based storefronts, such as Google Play (International Age Rating Coalition, 2024). IARC labels detail whether game applications are F2P; require purchasing; include in-game advertising and/or in-game purchases; whether in-game purchases include ‘Random Items’ (e.g. loot boxes); whether players can interact; and include words describing game themes, such as ‘Moderate violence’, ‘Mild Swearing’, ‘Sex’ and ‘Parental Guidance Recommended’.

Statistical analysis

Statistical analyses were performed in Stata/18 (StataCorp, 2024). Durations of gaming content exposure were collated by the child for the 4-day observation period. The average time spent gaming or being exposed to gaming content was analysed using negative binomial regression, with active recording hours as the exposure offset. This time was reported as a percentage of each hour that children spent online. Children with no recorded hours were excluded from the analysis. Results are reported by sex, ethnicity and NZiDep. To further explore the nature of children’s gaming, a sub-analysis was conducted to identify the proportion of gaming time related to gameplay; non-gameplay; and game application advertising. These analyses used gaming time as the exposure offset and excluded children who did not play games. Estimates of gaming time and exposures to specific gaming content are reported with 95% confidence intervals (95% CIs). We used an unadjusted negative binomial regression model to assess differences in gaming content by sex, ethnicity and NZiDep.

RESULTS

Sample characteristics

In total, 66 participants’ data were included (Supplementary File 2). The mean participant age was 12.9 years (SD = 0.8). The study sample included 37 females (56%), 27 males (41%) and 2 participants identifying as non-binary (3%). About two-thirds of participants were from low-deprivation backgrounds [n = 45 (68%)—NZiDep scores of 1 or 2] (Salmond et al., 2006) and of NMNP ethnicity [n = 47 (71%)]. Thirteen participants were Māori (20%). Six participants were Pacific (9%).

Active time

On average, participants recorded 164 minutes of active time over the study period (Supplementary File 3). Male participants recorded less active time than female participants (mean difference over the study period = −70 minutes, 95% CI = −159 to 20). Māori and Pacific participants recorded less active time than NMNP participants (mean differences = −57 minutes (95% CI = −171 to 58) and −43 minutes (95% CI = −202 to 115), respectively. High NZiDep participants recorded less active time than low NZiDep participants (mean difference = −24 minutes, 95% CI = −128 to 79).

Gaming content

On average, over one-quarter (28.6%—see Table 1) of participants’ active recording time showed gaming content (using game applications = 18%, watching gaming content = 10.7%). On average, male participants’ recordings evidenced more than double the amount of gaming content than female participants [unadjusted incidence rate ratio (IRR) = 2.21, 95% CI = 1.21–4.06]. This was primarily due to male participants, on average, watching significantly more gaming content than female participants (unadjusted IRR = 5.81 minutes, 95% CI = 2.65–12.72). There was no clear difference between male and female participants’ use of game applications (unadjusted IRR = 1.38 minutes, 95% CI = 0.52–3.66). On average, high NZiDep participants recorded significantly less gaming content than low NZiDep participants (unadjusted IRR = 0.47 minutes, 95% CI = 0.24–0.93). High NZiDep participants’ recordings showed significantly less game application usage than those recorded by low NZiDep participants (unadjusted IRR = 0.28, 95% CI = 0.10–0.78), but they recorded similar amounts of watching (unadjusted IRR = 0.84, 95% CI = 0.31–2.27). No significant differences were observed by ethnicity.

Table 1:

Percentage of online time spent gaming with 95% CIs and IRRs by sex, ethnicity and NZiDep: results from negative binomial regression

Characteristic Group Gaming content (using game applications and watching gaming content on social media) Using game applications Watching gaming content on social media
Percentage (95% CI) IRR (95% CI) Percentage (95% CI) IRR (95% CI) Percentage (95% CI) IRR (95% CI)
All children All children 28.6 (21.0, 39.1) 18.0 (11.2, 29.0) 10.7 (7.0, 16.6)
Sex Females 19.2 (12.1, 30.3) 1 15.6 (8.4, 29.1) 1 3.6 (2.1, 6.3) 1
Males 42.5 (29.4, 61.5) 2.21 (1.21, 4.06) 21.6 (10.2, 45.7) 1.38 (0.52, 3.66) 21.3 (12.3, 36.9) 5.81 (2.65, 12.72)
Ethnicity NMNP 31.5 (21.3, 46.6) 1 20.8 (11.9, 36.2) 1 10.9 (6.1, 19.3) 1
Māori 22.1 (13.7, 35.5) 0.70 (0.32, 1.51) 9.6 (4.0, 22.9) 0.46 (0.14, 1.50) 12.4 (6.6, 23.2) 1.14 (0.38, 3.42)
Pacific 21.5 (7.9, 58.6) 0.68 (0.23, 2.02) 14.5 (1.3, 165.0) 0.73 (0.14, 3.79) 6.3 (2.0, 20.4) 0.55 (0.12, 2.65)
NZiDep (binary) Low (1–2) 31.5 (21.8, 45.6) 1 20.8 (12.1, 35.9) 1 10.7 (6.0, 19.2) 1
High (3–5) 15.1 (9.1, 24.9) 0.47 (0.24, 0.93) 5.8 (2.5, 13.8) 0.28 (0.10, 0.78) 9.2 (5.0, 17.1) 0.84 (0.31, 2.27)
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Boldface denotes statistical significance.

Sub-analysis of participants who used game applications

Forty-one (64.1%) participants used game applications, and on average, 18% of their active recording time displayed game application usage (see Table 1). On average, roughly half of participants’ game application time showed gameplay (56.6%) and non-gameplay (43.4%) (see Table 2). On average, Māori participants’ game application use showed significantly less gameplay than NMNP participants’ game application use (unadjusted IRR = 0.30 minutes, 95% CI = 0.12–0.79). There were no clear differences in gameplay by sex. On average, 16.4% of participants’ game application time included advertising. On average, female participants recorded more than double the amount of game application advertising than male participants (19.8% versus 8.9%), but this difference was not significant. There were no clear differences for game application advertising by NZiDep or ethnicity.

Table 2:

Percentage of game application time comprising gameplay and advertising, with 95% CIs and IRRs by sex, ethnicity and NZiDep: results from negative binomial regression

Characteristic Group % of total game application time coded as gameplay (95% CI) % of total game application time that included advertising (95% CI)
Percentage (95% CI) IRR (95% CI) Percentage (95% CI) IRR (95% CI)
All children All children 56.6 (47.5, 67.4) 16.4 (8.8, 30.7)
Sex Female 56.9 (44.3, 73.1) 1 19.8 (8.2, 47.5) 1
Male 59.7 (50.4, 70.7) 1.49 (0.68, 3.30) 8.9 (3.6, 22.0) 0.40 (0.11, 1.51)
Ethnicity NMNP 62.2 (53.4, 72.4) 1 15.7 (7.8, 31.7) 1
Māori 40.8 (20.6, 80.8) 0.30 (0.12, 0.79) 23.7 (7.5, 74.9) 0.96 (0.21, 4.31)
NZiDep (binary) Low (1–2) 53.4 (43.0, 66.3) 1 14.6 (6.9, 30.7) 1
High (3–5) 69.3 (50.3, 95.4) 0.38 (0.15, 0.96) 27.6 (8.2, 92.9) 0.88 (0.19, 4.17)
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Boldface denotes statistical significance.

Game application characteristics

Overall, 41 (64.1%) participants used 63 discrete game applications, 62 (98.4%) of which were free to download and install (F2P) from the Google Play web portal. Only ‘Minecraft’ required purchasing ($12.99 NZD). A variety of game types were played, including racing, turn-based strategy, shooter, fantasy, player-versus-player and simulation games. ‘Roblox’ and ‘Subway Surfers’ were especially popular. Most games were IARC rated for 3+ (n = 37); the others were 4+ (n = 1); 7+ (n = 11); 12+ (n = 11); and 16+ (n = 3). Sixty (95.2%) game applications’ IARC ratings were age appropriate for these participants. Words describing game themes as part of IARC labels included: ‘mild violence’; ‘moderate violence’; ‘strong violence’; ‘sex’; ‘sexual innuendo’; ‘nudity’; ‘fear’; ‘mild swearing’; and ‘parental guidance recommended’. Of the 55 games (87.3%) offering in-application purchases, 14 (25.5%) included ‘Random Items’ as part of those purchases, of which 7 (50%) were IARC rated for 3+. One game rated 3+ shared users’ locations while being used. Eighteen (28.6%) games allowed participants to interact with other users (Users Interact). Fifty-four (85.7%) game applications were ‘Advergames’.  Figure 1 shows examples of participants’ screen-shared gaming content.

Fig. 1:

Fig. 1:

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Examples of participants’ screen-shared gaming content. (A) Gameplay: a ‘Subway Surfers’ game application level. (B) Gameplay: the ‘BitLife-Life Simulator’ game application, and banner advertisement for ‘Jack Daniel’s No.7’ brand Tennessee Whiskey. (C) Gaming content: an advertisement for ‘Final Fantasy XIV Online’ on TikTok. (D) Gameplay: an ‘Agent Hunt – Hitman Shooter’ game application level, and banner advertisement for ‘Monopoly Go!’. (E) Game application advertising: pop-up advertisement for ‘888 Casino’, an online gambling platform. (F) Non-gameplay: a countdown screen after gameplay within the ‘Paper.io 2’ game application, including a banner advertisement for ‘Air Asia’, and an embedded dark pattern button (‘EXTRA LIFE!’)—offering a bonus should a user watch an advertisement (green/white, play triangle icon). (G) Gameplay: a ‘Bloons Monkey City’ game application world. Embedded dark pattern buttons/icons are visible. (H) Non-gameplay: a commercial screen within the ‘Sumikkogurashi Farm’ game application. A variety of microtransactions and embedded dark patterning are visible.

DISCUSSION

In this objective study exploring the nature and extent of preteens’ smartphone-based gaming, participants spent over a quarter of their online time (28.6%) exposed to gaming content—using game applications (18%) or watching gaming content using social media (10.7%). On average, participants recorded 164 minutes of active smartphone use over the study period. There were no clear differences in active recording time by sex, ethnicity or deprivation.

Despite no clear difference in game application time by sex, male participants watched significantly more gaming content than female participants. This is interesting given that other research reports that from age 14, males play video games (including gaming consoles) up to five times as much as females (Leonhardt and Overå, 2021). It has been suggested that gaming is not as socially accepted among females (Leonhardt and Overå, 2021), whereas males are more likely to be ‘Core’ gamers, defined as ‘those who agree that gaming is an important part of their life’ (Newzoo, 2017). Our current research suggests this may not be the case for preteens’ smartphone-based gaming.

Higher-deprivation participants recorded significantly less game application time than lower-deprivation participants. Evidence shows that digital capabilities (including finding, downloading and using applications) are influenced by numerous factors, including access to technology, education, employment status, income, disability and self-confidence, ‘cutting across age and impacting people’s level of digital exclusion’ (Allmann, 2022). High-deprivation participants were children who are more likely to experience some degree of digital exclusion, and more likely to use cheaper or older smartphones. These smartphones can experience compatibility/functionality issues while operating applications, and as in this study, those for gaming, social media and Zoom.

Nearly two-thirds of our participants used game applications, spending 18% of their recorded active time doing so, yet just 56.6% of this time was coded as gameplay. While there was no strong evidence for differences in the proportion of gameplay by sex or deprivation, Māori participants recorded smaller proportions of gameplay time while using game applications than NMNP participants, indicating higher proportions of non-gameplay (e.g. menu navigation). Given that Māori participants used gaming applications at similar rates to Pacific and NMNP participants, this finding may indicate differences in the types of game applications used. Overall, nearly half of participants’ game application time involved non-gameplay. To the best of our knowledge, this is the first study internationally to explore non-gameplay durations within smartphone game applications using objective data.

Almost one-fifth of participants’ game application time included advertising. This is unsurprising given that most applications used were ‘Advergames’. But alarming nonetheless, considering that preteens have reported that game application advertising negatively impacts their health and wellbeing (Martínez, 2017). While there were no clear differences for game application advertising by sex, ethnicity or deprivation, female participants recorded more advertising, on average, than male participants. Again, this may reflect differences in the types of game applications they used. Previous research shows that women are more likely to identify as ‘casual’ gamers than men (Newzoo, 2024), spend less money on games (Newzoo, 2017), and are predominantly ‘Time Filler’ gamers, those who play games [typically on smartphones (Newzoo., 2022b)] to pass the time (Newzoo, 2019). It is possible that female participants were more likely to play ‘Advergames’, as opposed to F2P game applications that did not include advertising, but are tailored toward ‘core’ gamers (who are predominantly male), are more heavily monetized, and typically require ongoing time-investment.

Participants used a variety of game applications. Almost all were age appropriate; three were age inappropriate, IARC rated for 16+ (4.8%). While being age appropriate based on IARC age ratings, many of these game applications included label descriptors such as ‘strong violence’, ‘sex’, ‘fear’ and ‘parental guidance recommended’. Most game applications included dark patterned in-game purchases. Of these, one in four also included ‘Random Items’, half of which were IARC rated for 3+. These findings indicate that the game applications our participants used exposed them to a range of harms. While gaming is often characterized as a social activity (Alanko, 2023), less than one-third of the game applications permitted interaction with other users. This may suggest that smartphone gaming is less prosocial than gaming on other platforms and could even have a protective effect against inappropriate social interactions.

STRENGTHS/LIMITATIONS

To the best of our knowledge, this study is the first globally to use objective screen-share data to explore the nature and extent of children’s smartphone-based gaming content. Previous research estimates that 11–17 year-olds spend an average of 4.5 hours per day using smartphones (Common Sense Media, 2023). Using this as a proxy, we coded about 15% of the estimated daily time this demographic is likely to spend using smartphones. Given that smartphone usage increases as this group ages, the 12 year-olds in this study may have recorded more smartphone use. While a small study from Aotearoa (NZ), this research reports on a key demographic for which there is a dearth of objective evidence globally. Given the largely homogenized nature of the online world, our findings are likely to be of interest to other nations.

Implications

In this study, over one-quarter of children’s screen-recorded online smartphone time included gaming content. Although children frequently use smartphone game applications to ‘play’, almost half of our participants’ game application time did not involve gameplay. This study shows that these children navigate highly commercialized and manipulative non-gameplay contexts within smartphone game applications. Moreover, participants were exposed to advertising for almost one-fifth of their game application usage time. This finding is in stark contrast to the call by the United Nations to prohibit, by law, the practice of advertising to children within ‘virtual and augmented reality environments to promote products, applications and services’ (OHCHR, 2021). While many game applications were seemingly age appropriate, most of these included content that could be considered harmful, such as adult themes and in-game purchases (some including ‘Random Items’). This emphasizes the need for ‘further scrutiny of the relationship between the commercial and digital determinants of health’ (Kickbusch and Holly, 2023). Additionally, this research highlights the importance of ‘enfranchising young people to understand and tackle the digital determinants of health’ (Kickbusch and Holly, 2023).

Further research

The need for participatory, child-centred research methods is urgent (Kickbusch et al., 2021; Stoilova et al., 2021). The perspectives of children on issues relating to the online world are critically important, given their exposure to digital technologies is higher than adults, and they spend the most time online (Kickbusch et al., 2021). They are therefore, most likely to encounter the potential harms ‘that may derive from them and uniquely equipped to shape positive health futures through codesign of digital health solutions and participatory research and decision making’ (Kickbusch et al., 2021). Further investigations could consider employing bespoke screen-sharing software to maximize retrieval of children’s smartphone use and address inequities in the collection of data. Photo or video elicitation, using data such as that captured in our study, could be used to further encourage children’s engagement and evoke reflection (Thomas et al., 2024). Investigations exploring children’s views of smartphone-based game application advertising and manipulative design features (dark patterns) are warranted. Further research exploring the nature of the gaming content children watch while using social media applications is also necessary.

CONCLUSION

Children encounter large amounts of smartphone-based gaming content while online. They frequently use game applications to ‘play’, but substantial periods of this play involve highly commercialized contexts, manipulative design features, adult themes and advertising. Healthy public policy is urgently required globally, to ensure that game applications are supportive environments for all, but particularly for children, who are most vulnerable to harm (Kickbusch and Holly, 2023), and who deserve to be protected in accordance with the United Nations Convention on the Rights of the Child (OHCHR, 1989).

Supplementary Material

daae195_suppl_Supplementary_Files_1
daae195_suppl_supplementary_files_1.docx (2.7MB, docx)
daae195_suppl_Supplementary_Files_2
daae195_suppl_supplementary_files_2.docx (17.4KB, docx)
daae195_suppl_Supplementary_Files_3
daae195_suppl_supplementary_files_3.docx (16.6KB, docx)

ACKNOWLEDGEMENTS

We thank the children, parents, caregivers and schools who participated in and supported this research. Thank you to Prof. Louise Signal, Prof. James Stanley, Dr Moira Smith and Mr Ryan Gage, for your guidance and contribution to this manuscript. This article was supported by a University of Otago Research Grant (20406).

Contributor Information

Marcus Gurtner, Health Promotion & Policy Research Unit, Department of Public Health, University of Otago – Wellington, 43 Hanson St, Newtown, 6242 Wellington, New Zealand.

Ryan Gage, Health Promotion & Policy Research Unit, Department of Public Health, University of Otago – Wellington, 43 Hanson St, Newtown, 6242 Wellington, New Zealand.

Moira Smith, Health Promotion & Policy Research Unit, Department of Public Health, University of Otago – Wellington, 43 Hanson St, Newtown, 6242 Wellington, New Zealand.

James Stanley, Health Promotion & Policy Research Unit, Department of Public Health, University of Otago – Wellington, 43 Hanson St, Newtown, 6242 Wellington, New Zealand.

Louise Signal, Health Promotion & Policy Research Unit, Department of Public Health, University of Otago – Wellington, 43 Hanson St, Newtown, 6242 Wellington, New Zealand.

AUTHORS’ CONTRIBUTIONS

M.G., R.G., M.S. and L.S. conceived the idea and developed the study protocol. M.G., M.S. and L.S. collected the data. M.G. developed the coding framework, watched and coded the data. R.G., J.S. and M.G. analysed the data. M.G. prepared the manuscript draft. R.G., M.S., J.S. and L.S. provided supervision. All authors contributed to the manuscript and approved the final version.

DATA AVAILABILITY

The data underlying this article will be shared on reasonable request to the corresponding author. The data are not publicly available due to ethical and privacy restrictions.

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Associated Data

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Supplementary Materials

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daae195_suppl_supplementary_files_2.docx (17.4KB, docx)
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Data Availability Statement

The data underlying this article will be shared on reasonable request to the corresponding author. The data are not publicly available due to ethical and privacy restrictions.

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