The study protocol was prospectively published on Open Scientific Framework [25]. This systematic review adhered to the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) [26] (Checklist 1).

This systematic review was exempted from ethics or institutional review board approval, as it did not involve human participants, and ethics approval was not required to review published or publicly reported literature.

Searches were conducted in the following electronic databases: CINAHL, Embase, MEDLINE, ProQuest, PsycInfo, Scopus, SPORTDiscus, and Web of Science (Core Collection). These databases were selected to ensure coverage across multiple research disciplines relevant to the review topic, that is, health and medical sciences (MEDLINE and Embase), psychology and behavioral sciences (PsycInfo), nursing and allied health (CINAHL), as well as multidisciplinary sources (Scopus, Web of Science, and ProQuest). The search targeted papers published between January 1, 2010, and September 1, 2024, to capture contemporary technology and was conducted using a combination of search terms relating to adults’ perspectives, technology use, target population (ie, parents, health professionals, and children), and method (ie, questionnaire). A full list of search strategies by databases is provided in Multimedia Appendix 1. Additionally, reference lists of included papers were searched for additional relevant studies.

All retrieved studies were exported into EndNote (version 20; Clarivate Analytics). Two reviewers (CLR and IPHA) independently screened titles and abstracts to identify potentially relevant papers using Research Screener, a software system that uses artificial intelligence to iteratively learn from screening decisions, reducing the need to review irrelevant papers [27]. Following title and abstract screening, full text screening of papers was completed independently by the same 2 reviewers. Any researcher disagreement on the eligibility of a particular study was resolved through discussion, with a third reviewer (JZ) to resolve discrepancies.

Studies were included if they explored parents’ and health professionals’ perspectives on young children’s (aged birth to 5 years) technology use, including screen- and nonscreen-based devices, using questionnaires (eg, self-report survey instruments and diaries). Studies were included if they reported the use of a questionnaire relevant to the review aim, regardless of whether full details on the questionnaire and its development were reported. Only original peer-reviewed papers, published in English, were considered.

Review studies, expert opinions, editorial letters, and studies that focused on children with chronic health conditions (eg, cancer, diabetes, and epilepsy) were excluded.

The methodological quality of the included studies was assessed using the Quality Assessment Checklist for Survey Studies in Psychology (Q-SSP) [28]. The Q-SSP was selected, as it was specifically developed to evaluate the quality of survey-based studies, which aligned with the focus of this review. The Q-SSP was developed through a multistage process, which included reviewing existing quality assessment tools, establishing expert consensus on selected items, and refining the instrument based on validity assessments. The Q-SSP is a 20-item checklist that evaluates survey quality across 4 domains: introduction (4 items), participants (3 items), data (10 items), and ethics (3 items). Each item has the following response categories: yes (met criteria), no (did not meet criteria), not stated clearly, or not applicable. For each study, an overall quality score and a quality score for each domain were calculated as the percentage of “yes” out of the total applicable items. Depending on the number of applicable items, studies were required to achieve a set threshold score to be rated as “acceptable” in quality, while studies with scores less than this threshold were deemed to have “questionable” quality. Specifically, studies were classified as having “acceptable” methodological quality if they achieved an overall score ≥73% in accordance with the threshold defined in the Q-SSP [28]. The following were used as thresholds for “acceptable” quality for each domain as recommended: ≥75% for introduction, ≥67% for participants, ≥70% for data, and ≥100% for ethics. Of note, item 19 (“Were participants debriefed at the end of data collection?” ethics domain) was considered as not applicable, as debriefing could be argued as being impractical and that the data collection was harmless, given that all included studies were based on questionnaires. Study quality was independently assessed by 2 reviewers (CLR and IPHA), and any discrepancies were resolved by discussion without the requirement of a third reviewer.

The following data were extracted from the included studies: study characteristics, questionnaire characteristics, questionnaire items, and perspectives from study participants. Study characteristics included author, year of publication, country where the study was completed, country setting (ie, country income classification), study aim and design, sample sizes, sample characteristics, and recruitment strategy. Sample characteristics included age, gender identity, and relationship to the child. Information on the following questionnaire details was extracted: questionnaire details (ie, title, number of items, and administration format), questionnaire development, number of items and scoring details, and questionnaire psychometric properties.

Reported psychometric properties of the questionnaires were descriptively synthesized. When available, this included evidence of validity (ie, face validity, content validity, construct validity, and criterion validity) and reliability (ie, internal consistency and test-retest reliability). Questionnaire items related to adults’ perspectives on children’s technology use and the reported perspectives were extracted from each study and entered into NVivo (version 14; QSR International) [29] to facilitate coding and identification of common themes related to the focus of the questionnaires and reported perspectives. All data were extracted independently by 2 reviewers (CLR and IPHA) and cross-checked for any discrepancies, which were resolved without requiring a third reviewer.

A 2-level coding framework was developed to systematically assess (1) technology type, that is, technologies the questionnaire referred to, and (2) questionnaire content focus, that is, the focus of the items within the questionnaire regarding adults’ perspectives on children’s technology use. Following this framework, each questionnaire was first coded based on the technology type it referred to using the following codes (level 1): applications, computer, digital media or digital device, gaming, mobile touch screen devices, no specific hardware, screen time, and television. Next, a deductive thematic analysis [30] was used to investigate the content focus of each item and the reported perspectives, which were classified into 4 key dimensions based on the child-technology interaction model [14] to facilitate reader understanding:

Figure 1 shows the PRISMA flowchart of search results and study exclusions. A total of 7143 records were identified based on the initial database search, of which 85 studies met the inclusion criteria for this review.

Results of the quality assessment are presented in Table S1 in Multimedia Appendix 2 [31-115]. The average overall quality score was 61% (SD 15%; range 32%‐100%), with 62 studies scoring below the threshold for acceptable quality (ie, 73%), indicating questionable quality. Generally, studies had higher quality scores for introduction, as 84 studies scored above the threshold for acceptable quality (overall mean 98%, SD 8%; range 50%‐100%). In total, 47 and 17 studies, respectively, had acceptable quality related to information about study participants (mean 49%, SD 26%; range 0%‐100%) and data (mean 49%, SD 19%; range 10%‐100%). A total of 49 studies provided acceptable quality information related to ethics (mean 60%, SD 48%; range 0%‐100%).

The characteristics of included studies are provided in Table 1. Studies were published between 2012 and 2024, with most studies published in 2022 (11/85, 13%) and 2023 (22/85, 26%). Only 3 studies aimed to explicitly develop and validate questionnaires for measuring parents’ perspectives on children’s use of technology [33,39,112]. Among the remaining studies, most studies aimed to investigate adults’ perspectives of children’s use of technology (48/85, 56%) and/or determinants of children’s technology use (19/85, 22%).

Among the 85 included studies, most were cross-sectional (74/85, 87%), 70 were based on questionnaire responses only, and 15 used a mixed methods approach of both questionnaires and interviews. The studies were conducted in a wide range of countries, but primarily in the United States (11/85, 13%), the United Kingdom (5/85, 6%), and Australia (5/85, 6%). The average number of study participants was 495 (SD 562; range 10‐3413), of which, 82 studies only consisted of parents. In 3 studies, grandparents were also included [47,57,97]. No studies included health professionals. Accordingly, only parental perspectives are reported on in this paper. Among the 59 studies that reported participants’ gender, most were women (mean 80%, SD 14%; range 47%‐100%). Participants’ age was reported in 46 studies and mostly ranged between 30 and 40 years.

Multimedia Appendix 3 [31-146] provides the characteristics of questionnaires used among the 85 included studies. In total, 19 studies reported the title of the questionnaire, and 76 reported the item type. Among the studies describing questionnaire item type, 52 reported using questionnaires with closed-ended items, and 24 used questionnaires with a combination of closed- and open-ended items. A total of 63 studies provided information on administration format, of which most (n=42) used an online questionnaire. Information on how the questionnaire was developed was provided by 74 studies, most commonly (n=45) reported as being developed specifically for the study.

In total, 30 studies reported the total number of items in the questionnaire (mean 34; range 6‐86), and 67 studies reported the number of items specifically related to parents’ perspective of children’s use of digital technologies (mean 11; range 1‐33). Details on item scoring was reported in 61 studies, with a Likert-scale to measure level of agreement most commonly used (n=49).

Psychometric properties of questionnaires were reported by 52 studies. Among these, 14 studies conducted pilot tests to ensure face validity [33,34,47,52,55,71-73,76-78,93,100,109]. In total, 12 studies evaluated content validity by consulting experts within the field [39,44,52,53,64,65,72,73,76,86,108,110], of which, 3 studies reported content validity indexes, which ranged between 0.83 and 1.00 [44,76,108].

A total of 10 studies reported measures of construct validity based on results from a type of factor analysis [39,53,57,64,86,91,93,98,99,112]. Confirmatory factor analysis (CFA) was used in 4 studies [53,57,99,112], though the reported metrics varied. Chen and Tu [53] reported a root-mean-square error of approximation (RMSEA) of 0.06, a standardized root-mean-square residual (SRMR) of 0.05, a composite reliability of 0.88, and average variance extracted of 0.66. Dong et al [57] also performed a CFA and found that the model explained 43% of total variance, with eigenvalues ranging from 13.18 to 2.92, but no additional performance metrics were reported. Van Petegem et al [99] reported an RMSEA of 0.06 and SRMR of 0.02, while Vaiopoulou et al [112] found an RMSEA of 0.027 and SRMR of 0.07. Exploratory factor analysis was applied in 5 studies [39,64,86,93,112]. Balaban Dağal and Bayındır [39] used principal component analysis, reporting factor loadings of 0.54‐0.87. González-Sanmamed et al [64] found loadings between 0.52 and 0.90, while Luo et al [86] identified a 2D structure with an internal reliability of 0.95 (specific metric not reported). Nikken [93] identified a 5D structure, with item loadings >0.45, and Vaiopoulou et al [112] used principal component analysis alongside CFA, reporting factor loadings of 0.54‐0.89. In total, 2 studies conducted a factor analysis, but did not specify which type [91,98]. Nabi and Krcmar [91] identified a 2D structure, while Ophir et al [98] reported a 2D structure explaining 69%‐70% of variance, with factor loadings ranging from 0.69 to 0.81.

Cronbach α was predominantly reported (n=26 studies) as a measure of internal consistency reliability, ranging from 0.24 to 0.95 (mean 0.80), and one study used McDonald ω as a measure of internal consistency [46], which ranged between 0.62 and 0.69. A total of 7 studies reported a measure related to assessing reliability, but did not specify which metric this was based on [77,81,86,91,100,101,109]. One study reported a test-retest reliability based on intraclass correlation (ranging between 0.64 and 0.98) [87].

Taken together, these findings show that internal consistency reliability was the most frequently reported psychometric property (n=35), whereas fewer studies reported evidence of validity, including face validity (n=14), content validity (n=12), and construct validity [10]. Few studies (n=15) reported more than a single aspect of validity and/or reliability.

A total of 67 studies reported either all or some examples of the questionnaire items used to measure parents’ perspectives on children’s technology use (Table S1 in Multimedia Appendix 4 [31-115]). When considering which type of digital technology each questionnaire covered (ie, results of the level 1 coding), most questionnaires focused on digital media or digital devices (n=22), where technology was broadly referred to without a particular hardware, software, or content type. In total, 12 studies focused on screen time, which either covered children’s exposure to any screens or to specific device types (eg, television and smartphones) [37,40,44,49,50,67,76,78,87,98,106]. Among specific device types, 9 studies focused on children’s exposure to mobile touch screen devices (eg, smartphones and tablets) [32-34,55,56,70,79,97,113], and 8 studies focused on television viewing [38,42,43,62,71,94,95,111]. A total of 7 studies did not specify a particular device (eg, referring to general internet use) [35,48,53,77,102,104,105]. Gaming and computer use were referred to in 4 [36,39,75,99] and 3 [73,89,92] studies, respectively. Finally, 2 studies focused on applications [60,112].

Table 2 provides an overview of the content focus of the questionnaire items according to the 4 overall dimensions of the child-technology interaction model. When considering the overall dimensions covered, most of the 437 reported items were related to the child dimension (n=187). Within this dimension, the most common content focus of the items was parents’ perspectives on the potential impact of technology on children’s physical health (n=48), including sleep issues and lack of physical activity, followed by the potential influence of technology on children’s emotions and behaviors (n=47) and cognition (n=12). A total of 56 items could be considered as having a content focus under the other people dimension, of which, most items aimed to measure parental regulation of children’s technology use (n=32). A total of 176 items were related to the task dimension, of which, most focused on parents’ perspectives on the potential impact of technology use on how children learn (n=82) and how technology can be used for social aspects (n=40). Only 14 items could be considered as related to the technology dimension, of which, safety was the most common theme (n=16).

A total of 75 studies reported the study participants’ perspectives on children’s use of technology. Table S1 in Multimedia Appendix 5 [31-115] provides a full summary of study findings related to perspectives toward children’s technology use based on questionnaire responses. Multimedia Appendix 6 shows the distribution of reported perspectives within each of the 4 dimensions of the child-technology interaction model (ie, child, other people, tasks, and technology) according to the classification codes represented in Table 2. Please note that perspectives were coded based on reported findings in Table S1 in Multimedia Appendix 5; thus, each study could contribute to multiple themes.

Across the 4 dimensions of the child-technology interaction model, parents expressed diverse perspectives on children’s use of technology.

When considering the child dimension, 23 studies reported that parents were concerned about potential negative impacts of technology on their children’s physical health. Commonly raised concerns were related to lack of physical activity, sleep issues, and poor eyesight. In total, 4 studies reported that parents believed that technology could have beneficial impacts on their children’s physical health, such as improvement in movement skills [45,72,82,103]. A total of 13 studies reported that parents were concerned about technology leading to addiction [31,37,47,52,59,64,65,73,83,86,97,105,109]. Parents’ perspectives on the impact of technology on children’s emotions and behaviors were addressed in 11 studies [40,68,72-74,82,93,102,105,113,115]. While most of these studies (n=9) reported that parents had concerns about technologies leading to behavioral issues [40,68,73,74,93,102,105,113,115], some also reported that parents believe technology could have positive impacts on children’s emotions and behaviors, for example, in supporting emotional regulation [72,82,93,105].

When considering the task dimension of the child-technology interaction model, a total of 41 studies reported perspectives related to the use of and/or potential influence of technology on children’s learning. Among these studies, all participants reported positive perspectives, particularly in relation to the role of technology for enhancing children’s academic performance. Mixed perspectives were found for the role of technology on children’s social development. In total, 9 studies reported that the participants had concerns about technology negatively impacting children’s social skills and development [47,56,70,73,81,93,105,108], while 4 studies reported that the participants believed technology had a positive impact, particularly in relation to social interaction [68,72,82,105]. A total of 9 studies reported that parents acknowledged technology as a positive source of entertainment and relaxation for children [32,38,47,50,56,62,92,105,110].

For the other people dimension, parents were concerned about technology interfering with family time [37,68,73,89]. Finally, for the technology dimension, a commonly held concern among the study participants was related to children’s safety when using technology, particularly in relation to children being exposed to inappropriate content [47,51,52,59,60,64,70,80].

This systematic review aimed to investigate the psychometric properties of questionnaires that have been used to measure perspectives of parents and health professionals on young children’s technology use and to synthesize these perspectives. Overall, the majority of the included studies were of questionable methodological quality (n=62, 73%), particularly in terms of describing the development and validation of the questionnaires used for measuring perspectives. Of the 85 included studies, 52 studies reported psychometric properties of the questionnaire used, but the psychometric evaluation was often limited and incomplete, often focusing on a single metric for validity. When assessing the questionnaires themselves, most items focused on asking participants about how technology could aid children’s learning or how it could influence their physical health, emotions and behaviors, and social skills. As none of the studies recruited health professionals, we could only provide a synthesis of parental perspectives on young children’s technology use. This synthesis indicated that parents generally had positive perspectives on the use of technology for academic and educational purposes, but concerns were expressed regarding the potential negative influences of technology on children’s health and well-being.

High-quality methods for capturing the perspectives of parents and health professionals on children’s technology use are essential for obtaining accurate, unbiased, and reliable data. The development of a valid and reliable questionnaire is an extensive process, in which several steps are taken to ensure face, content, and construct validity as well as internal reliability [147,148]. Overall, while many studies included in this review reported some acceptable validity and reliability metrics, gaps remained in the consistent use of metrics, with only 15 studies evaluating more than 1 measure of validity and/or reliability [39,52,53,57,64,72,73,76,77,86,93,99,100,108,109]. Moreover, only 3 studies specifically aimed to develop and validate a questionnaire for measuring parents’ perspectives on children’s technology use [33,39,112]. Among these, the study by Balaban Dağal and Bayındır [39] conducted the most detailed validity and reliability analysis, assessing content, construct, and internal validity as well as test-retest reliability. In contrast, the study by Al-Balushi and Al-Shihi [33] only evaluated face and content validity, and the study by Vaiopoulou et al [112] only evaluated construct and internal validity. Overall, these findings suggest that there is a need for further development of high-quality questionnaires within this research field.

When assessing the focus of questionnaire items used to measure perspectives, there was a lack of clarity and heterogeneity in the definition of technology adopted by studies. Most studies used the term “digital media” or “digital devices,” which covered a range of hardware, software, and content. The lack of specificity could have influenced the responses from the study participants. For example, for the same item, some respondents may have thought of digital media as being social media, while others may have considered it to be television programs or learning apps. This could lead to inconsistent or even contradictory results and limit the accuracy and comparability of study findings. When considering the questionnaire items’ focus, most items aimed to measure perspectives related to the potential impact of technology on how children learn, their physical health, emotions and behaviors, as well as social skills and interactions. It is worth noticing that the phrasing of the items themselves differed substantially both within and between studies, with some being negatively phrased (eg, “[computers] have a negative impact on children’s socialisation” used in Ilgar and Karakurt [73]) and others being positively phrased (eg, “[digital media] help children communicate and interact with others” used in Ihmeideh and Alkhawaldeh [72]). This choice of item phrasing may stem from an implicit position of technological determinism, in which technology is believed to be socially causative [149]. It may be that the assumptions underlying the item development (eg, that technology use is harmful) and phrasing of the items directly influenced how participants interpreted the question and thereby how they chose to reply [150]. Specifically, negatively phrased items might have primed the respondents to focus on potential harms of technology use, whereas positively phrased items might have encouraged considerations of potential benefits. The framing of questions may also be influenced by the discipline context of the research team. As a result, the overall conclusions drawn about parental perspectives on children’s technology use may be biased.

Despite the lack of construct clarity and large variability in phrasing of items, some general trends were found when synthesizing the parental perspectives reported among the included studies. In general, positive perspectives were reported in relation to how technologies can be used for enhancing children’s learning, particularly in terms of academic skills and digital literacy. In contrast, concerns were often raised about technology’s impact on children’s physical health and emotions and behaviors. Mixed perspectives were found in relation to children’s social skills. Such positive and negative perspectives on young children’s use of technology have also been reported for early childhood educators [151]. Our results also concur with the findings of previous qualitative studies of parents [18-20]. The reviews by Choy et al [19], Chong et al [18], and Visier-Alfonso et al [20] reported parental concerns about technology negatively impacting their children’s physical (eg, eyesight and sleep patterns), attention issues, and social development. However, Choy et al [19] also reported that parents believed that digital devices could positively contribute to children learning new skills. Taken together, the findings from previous qualitative studies and our results suggest that parents have mixed perspectives on the role of technology in their children’s lives. These perceptions are likely influenced by a range of factors, including sociodemographic, cultural influences, parental age, age of their children, and parents’ own experiences with technology [16]. Moreover, parental mixed perspectives may also be related to a lack of clear, evidence-based guidance on healthy technology use [17], although existing evidence does support parents’ perspectives that technology use has potential risks and benefits [5].

Based on our evaluation of questionnaires used for capturing the perspectives of parents on children’s technology use, it is apparent that more work is required for developing high-quality questionnaires within this research field. The lack of validated questionnaires to capture the perspectives of health professionals also highlights a need. We encourage future research to prioritize the development of accurate, unbiased, and reliable questionnaires and propose that several steps are followed to support this. First, we would advise researchers to clearly define the research question, target population, their literacy level, and constructs aimed to be measured. Some of the key considerations include the type of technology, the age of children, the purpose of technology use, and the setting in which technologies are used, as these are likely to influence adults’ perspectives. For example, gaming consoles could be more appropriate to ask parents of older children about who are commonly more exposed to this technology compared to younger children [3]. We would also encourage researchers to carefully consider which aspects of technology to measure, including not only the amount of time using technology (ie, quantity) but also the content and context in which it is used (ie, quality). Second, when developing the items and item response categories, we suggest using clear and concise language with balanced statements regarding potential benefits and risks associated with children’s technology use to ensure that the respondents are not led toward a particular answer. Finally, we highly recommend that researchers thoroughly evaluate the validity and reliability of their questionnaire by using well-established approaches for measuring face, content, construct, and internal validity as well as assessing stability over time. Until further validated questionnaires are developed, we recommend the use of existing questionnaires with substantive validity evidence, such as the Balaban Dağal and Bayındır [39] questionnaire, if it matches the study’s research question.

Our synthesis of perspectives revealed that parents have mixed views on the role of technology in their children’s lives. While the potential benefits of technology, particularly for enhancing academic skills and social interactions, were acknowledged, concerns were often expressed about potential negative impacts on children’s physical and mental health and overall development. These findings suggest that parents are cognizant of the complex role of technology in young children’s lives, and this aligns with prior reports that parents are struggling to navigate conflicting narratives surrounding children’s technology use [17]. These findings highlight several priorities for future research. Specifically, parents may be exposed to a broad range and, at times, controversial guidance from educators, health professionals, and media sources, making it challenging to make decisions regarding healthy technology use. We encourage future research to investigate the basis for parent perspectives, including what influences their perspectives. Moreover, prior research has reported sources parents use to gain information about health issues [16], but whether parents have the ability to independently develop a clear understanding of the evidence regarding the likely impacts of technology use on child outcomes is not known. Thus, this presents an important area for further investigation. Future research should also investigate the impact of social desirability on parental perspectives. Further, more work is required to understand whether perspectives are evidence-based or reflect parental views on ideal childhoods and other values. Addressing these research gaps would enable educators and health professionals to better support parents through the challenges of raising children in a digital world by providing a better understanding of how parents’ perspectives are developed and how these relate to their decision-making regarding their children’s use of technology.

Strengths of this systematic review included the developed search strategy applied in a wide range of databases as well as the cross-checking of study screening, quality assessment, and data extraction independently by 2 authors (CLR and IPHA). The development of a comprehensive, theory-based, 2-level coding framework for classifying the focus of the questionnaires and the items used was another strength. This framework, based on the child-technology interaction model [14], enabled a structured and systematic approach for assessing the diverse ways in which studies measured parental perspectives. Although we acknowledge that some codes could be classified under multiple dimensions of the child-technology interaction model, the application of this structured framework enhances the reproducibility of our findings and provides an early foundation for future research seeking to improve the measurement of perspectives on children’s technology use.

Only reports published in English were considered for inclusion, and potentially relevant studies might have been missed due to unclear titles or abstracts and artificial intelligence–supported screening. Moreover, we aimed to report how perspectives on children’s technology use may depend on the population group (eg, parents and health professionals) and the age of the child. However, these assessments were not feasible, as the only participants in the included studies were parents, and the age of the children was rarely specified.

This systematic review found that parents’ perspectives on children’s technology use have been assessed using questionnaires in several studies. However, the reported psychometric evaluation of the questionnaires was limited and incomplete, with most studies focusing on a single metric. Further, varied definitions of technologies make it difficult to establish to what extent the existing literature is examining variations in technologies relative to parents’ perspectives. Study participants consisted of parents only, highlighting a clear evidence gap in understanding health professionals’ perspectives on children’s technology use. The reported parental perspectives were mixed. While positive views were found, considering the use of technology for enhancing children’s learning and skill development, concerns were commonly raised about potential negative impacts on children’s physical health and emotions and behaviors. As parents’ perspectives may influence their children’s use of technology and potentially associated development, there is a need for better-quality tools to measure adults’ perspectives, understand how perspectives are developed, and how they influence practice. We encourage such work to focus on improving questionnaire design based on a clear theoretical understanding of technologies to prevent confusion in defining terms and item phrasing.