The term eHealth has been variously defined in the literature. According to a systematic review, 51 unique definitions have been used for the term, without a clear consensus on a single definition, and the definitions differ in how inclusively they are conceptualized [28,29]. We have chosen to use a definition based on the conceptualization of eHealth offered by Vegesna et al [30] because of its relevance and consistency with the overarching aims of this review; digital technologies are thus defined as noninvasive digital devices that have been used to assess and track the health of a patient or consumer. We used the World Health Organization’s definition of childhood and youth as the period spanning birth to 24 years, whereby children are aged 0 to 9 years, and young people are aged 10 to 24 years [31].

The search was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [32]. PsycINFO, PubMed or MEDLINE, and Embase were searched via OVID by 3 members of the research team (ES, HY, and AR) on April 27, 2020. The following terms were used ((child*) OR (adolescen*) OR (young person) OR (infan*)) AND ((wellbeing) OR (health)) AND (((digital tool) OR (digital AND tool)) OR (eHealth) OR ((mobile application) OR (mobile AND application))). A wildcard (*) was placed at the end of each applicable search term to ensure that all relevant terms were captured. All Medical Subject Heading terms were explored to broaden the search for relevant studies. Date limits were not set on any of the database searches. The reference lists of relevant reviews and identified empirical studies were searched to identify further studies, as per the ancestry method.

Studies were included if they met the following criteria:

Studies were excluded if they had the following characteristics:

Figure 1 displays the process of study selection. The search retrieved 3688 papers, and an additional 16 papers were identified by searching the reference lists of relevant papers and reviews. Of these papers, 95.59% (3541/3704) remained after duplicates were removed. The titles and abstracts of all papers were screened by 2 reviewers (ES and HY). Of the 3541 papers, 84 (2.37%) full-text papers met the inclusion criteria and were obtained. A conservative approach was taken to ensure that relevant papers were not missed, and full-text papers were reviewed if the reviewers could not determine with certainty whether the inclusion criteria were met. The manuscripts of these 84 papers were reviewed by 2 independent raters (ES and HY), and discrepancies were resolved via discussion. Of the 84 papers, 45 (54%) papers were excluded, leaving 39 (46%) papers that were included in the review.

The following information was extracted from each paper:

Descriptive analyses were used to summarize variables of interest, including health domain, location, language, type of data, intended user, presence of certain features (ie, tracking over time and connection to care), and type of evaluation. Frequency data and percentages were used to examine and compare studies on key outcome measures. This approach to analysis was taken because of considerable variability in study objectives and designs and as most studies reported simple quantitative, descriptive statistics or qualitative findings.

To evaluate the methodological quality of the studies, 2 checklists were used. The Downs and Black checklist [36] was completed for quantitative studies, which measures the quality of both randomized and nonrandomized studies evaluating novel health interventions. The National Institute for Health and Care Excellence Quality Appraisal Checklist was completed for studies reporting qualitative findings [37]. Studies reporting both qualitative and quantitative data were appraised using both checklists. A full description of the checklists and scoring criteria is included in Multimedia Appendix 1 [5-9,16-20,38-66].

Table 1 summarizes the characteristics of the 39 studies included in this review. All (38/39, 97%) but a single (1/39, 3%) study was published in the past decade (2010-2020), and over one-third of the studies (15/39, 39%) were published in the past year (2019-2020; Figure 2). Most studies were conducted in a single country (35/39, 90%), most commonly America (7/39, 18%) or Australia (6/39, 15%). English was the sole language of communication in 49% (19/39) of studies; 13% (5/39) of studies evaluated tools that used English and at least one other language, and 21% (8/39) used languages other than English; the remaining 18% (7/39) of studies did not report enough information to determine which language was used in the tool. Regarding locality, studies were conducted in urban (18/39, 46%), rural (3/39, 8%), or a mixture of urban and rural settings (8/39, 21%); 26% (10/39) studies did not report enough information to determine locality. Digital devices were implemented across 3 main settings: outpatient health clinics (12/39, 31%), hospitals (ie, inpatient units and emergency departments; 14/39, 36%), and community outreach (ie, community spaces that were not formal health clinics; 10/39, 26%) or a combination of these settings (3/39, 8%).

Table 2 summarizes the characteristics of the studied digital devices.

Mental and general health were the most common eHealth domains assessed, with each evaluated in 26% (10/39) of studies. Other health domains assessed included child development (5/39, 13%), infectious diseases (5/39, 13%), oral health (3/39, 8%), neurological illnesses (2/39, 5%), ear nose and throat (1/39, 3%), emergency medicine (1/39, 3%), physical health (1/39, 3%), vision (1/39, 3%), and pediatric surgery (1/39, 3%). A single study assessed multiple health domains (mental and general health) [38]. Given that mental health was more commonly assessed than other health domains, we examined these studies further to determine their aim or purpose and the type of information collected. Of the 23% (9/39) of studies that solely assessed mental health, 33% (3/9) focused on suicide prevention [8,39,40], 22% (2/9) focused on early intervention and prevention of mental illness [7,17], and 44% (4/9) focused on multidimensional assessment or management of mental health symptoms [41-44]. Of the 23% (9/39) of studies that solely assessed general health, 22% (2/9) focused on symptom detection and monitoring [9,45], 22% (2/9) provided a platform for patients to view and monitor their health information [46,47], 33% (3/9) focused on digitalized tracking of clinical decision-making [19,48,49], and 22% (2/9) were primarily for assessment [50,51].

All devices measured the health of a child or young person; however, devices differed in the person who entered the health information (ie, the respondent: child or young person, parent or caregiver, clinician, and trained health worker). Under half of the devices collected information from multiple respondents (16/39, 41%); other tools collected information solely from a child or young person (8/39, 21%), clinician (6/39, 15%), trained health worker (6/39, 15%), or parent or caregiver (3/39, 8%). Approximately 15% (6/39) of studies collected data in multiple forms (ie, questionnaire or survey, physiological data, or images); otherwise, data were collected solely in the form of questionnaires or surveys (26/39, 67%), physiological data (5/39, 13%), or images (2/39, 5%). Most eHealth tools (31/39, 80%) were configured to collect data on a mobile phone, of which some (9/39, 23%) were also configured to collect data on another device (ie, desktop or tablet).

Just under half of the devices tracked children’s health over time (16/39, 41%), and two-thirds (25/39, 64%) connected children or young people to clinical care, whereas the remainder did not.

Table 3 summarizes the sample size, type of evaluation, study type and design, and primary outcomes, and a more detailed description of the main findings for each study is presented in Multimedia Appendix 1 (see Table S1). The sample sizes ranged from 7 to 149,329 participants (median 163, mean 5155). Most studies were formative (20/39, 51%) or process (11/39, 28%) evaluations, with fewer outcome evaluation studies (8/39, 21%). Just over one-third of the studies (14/39, 36%) assessed the health of children or young people as either a primary or secondary outcome; however, only a single (1/14, 7%) study assessed whether using the digital tool improved the health of children or young people [43]. This study examined whether the use of Mobiletype, an eHealth tool that allowed general practitioners and young people to monitor symptoms of mood, stress, and daily activities in general practice, was associated with improved mental health outcomes compared with treatment as usual. The authors found that use of the device was associated with a significant improvement in emotional self-awareness but found no changes in symptoms of depression, anxiety, or stress; post hoc analyses showed enhanced mental health care at the initial assessment among general practitioners using the tool compared with those who did not.

Table 3 summarizes the challenges and risks of using the tools identified in each study. Of the studies examined, most (30/39, 77%) identified at least one challenge or risk, which was related to accessibility (11/39, 28%), clinical utility (9/39, 23%) or clinical safety (5/39, 13%) of the tool, uptake by users (6/39, 15%), data quality (6/39, 15%), user interface or design aspects of the device (user experience; 5/39, 13%); language proficiency or literacy barriers (4/39, 10%), sociocultural barriers (1/39, 3%), and privacy concerns (1/39, 3%). More specifically, accessibility problems were related to poor internet connection, inability to recharge devices because of power cuts, slow or inefficient upload of information, lack of access to a device, and low technological literacy of end users. Clinical utility and clinical safety concerns were related to the validity of data among people with different health conditions, lack of appropriate training of staff, input from a health care professional rather than entirely self-report data to ensure safe and accurate interpretation of results, whether the tool added value over and above standard clinical care, and the safety of tools that had not yet been validated to detect clinical symptoms. Uptake of tools was a frequently cited barrier; however, there was often no further investigation or explanation as to why uptake was lower than expected. Data quality concerns were centered on inaccurate or incomplete data entry (because of human or computer error) and poor-quality images. User experience or design barriers referred to the eHealth tool lacking the necessary features to make it functional and usable for end users. Language proficiency or literacy barriers were centered on users lacking the comprehension and literacy levels to understand and take action from the presented information; this was a concern reported when end users were children, non–English speaking, or from lower socioeconomic backgrounds. Sociocultural barriers were related to the appropriateness of questions and the risk of causing offense or harm.

Studies were financially supported by the public sector (ie, government, universities, research institutes, and professional associations) and commercial or NFP organizations (28/39, 72%). Receiving funding from ≥1 sector was the most common (16/39, 41%), followed by funding solely from public sources (6/39, 15%) and NFPs (5/39, 13%). No study was funded solely by the commercial sector; however, commercial funding contributed to nearly one-third of studies with combined funding sources (5/16, 31%). The remainder of the studies did not receive external financial support or did not report it in the paper (11/39, 28%).

The methodological quality of the Downs and Black checklist was rated for 95% (35/39) of studies that included quantitative data: 64% (25/39) of studies had a low chance of bias, 36% (14/39) of studies had a moderate chance of bias, and no studies had a high chance of bias (see Table S2, Multimedia Appendix 1). The National Institute for Health and Care Excellence Quality Appraisal Checklist was completed for 44% (17/39) of studies that included qualitative data: 59% (10/17) of studies received a maximum score of 2 for quality, and 41% (7/17) of studies received a partial score of 1; no studies received a score of 0 (see Table S3, Multimedia Appendix 1).

The aim of this systematic review was to summarize eHealth tools designed to assess and track health outcomes in children and young people to clarify the current scope, nature, and efficacy of this emerging type of technology in health care. Our findings revealed exponential growth in the development and evaluation of these tools over the past 10 years; however, the results showed that the research is still in its infancy, with most studies assessing feasibility, acceptability, usability, or uptake of a device rather than the efficacy of tools in relation to health outcomes. Overall, the current tools showed potential to enhance the assessment and tracking of children or young people in health services around the world. Further research is needed to evaluate the efficacy of tools for improving health outcomes and clinical care delivery, as well as to identify and address the risks and challenges of implementing these tools as part of standard clinical care.

There are numerous potential advantages of using eHealth solutions for children and young people, including the ability to conduct multidomain and multi-informant assessments, undertake continuous monitoring, and assist with timely connection to personalized clinical care [1,2,13]. Encouragingly, over half of the tools facilitated a connection between the child or young person and a health care provider; however, less than half tracked children’s health data over time or collected information from multiple informants (ie, child or young person, parent or caregiver, and health care professional). These findings demonstrate an untapped potential of eHealth solutions in facilitating multi-informant assessments and longitudinally tracking health over time among children or young people, which is key to achieving comprehensive, multidisciplinary care [2]. In addition, data were most commonly collected in the form of surveys or questionnaires, illustrating a lag in uptake and integration of newer technologies (eg, biosensors or wearables to collect physiological data). Such technology has the potential to enhance symptom detection and clinical decision-making [13,14,19,48,49] and may be an important area for future research to explore.

Another potential advantage of eHealth is its ability to overcome geographical, financial, and social barriers that hinder the provision of health services in specific populations and locations [10,67]. A small number of studies evaluated devices that were implemented across multiple countries [45,52-55], highlighting the ability of digital technologies to provide health care with greater reach. However, fewer tools were implemented in rural areas compared with urban areas, and there was less implementation in community outreach settings compared with hospitals and health clinics. Although using eHealth tools in health clinics and hospitals is a step forward from traditional paper-based methods in terms of data management and integrated care, there is greater potential for the tools to engage hard-to-reach populations in regional and community settings [9,55]. The higher percentage of devices used in health clinics and hospitals may be as community settings do not always incorporate systematic health tracking into their procedures or reflect a lack of availability of skilled health professionals to collect and enter health information in community settings. A number of studies overcame this issue by using trained health workers (ie, staff who received specific training in the health condition and digital device but were not specialists in the field); these trained health workers were able to collect information in the community, with studies finding that this did not compromise the validity or reliability of data or clinical care [6,7,48,50,56,57]. Another solution was to collect information solely from the consumer (ie, child or young person or their parent or guardian), which is particularly common in studies examining mental health [8,43,44]. Together, these results demonstrate room for broader implementation in rural or remote regions and community settings around the world. Nevertheless, an important issue to recognize is that rural and vulnerable populations are currently experiencing the largest digital divide [68-70]. Inequalities in access exist because of variations in location, age, education, and income level. For instance, the cost of internet access is higher in rural or remote regions than in urban areas, which is compounded by the fact that some rural residents have less disposable income than their metropolitan counterparts. Thus, to truly overcome geographical, financial, and social barriers and reach these populations, researchers must consider the broader socioeconomic context from which these access issues stem.

The eHealth tools studied focused on various domains of health, including infectious diseases, child development, and neurological conditions; however, the most widely assessed domains were general and mental health. The focus on mental health tools may reflect a growing need and demand for mental health care among children and young people, increasing evidence supporting eHealth in the field of mental health, and increased funding for mental health tools [71,72]. Only one of the eHealth tools assessed multiple health domains [38], despite this often being important for gaining a holistic picture of a child or young person’s health concerns. The development of eHealth tools that assess health multidimensionally is likely to be important in future eHealth tools, perhaps assessing not only current symptoms but also broader social or environmental factors related to the etiology and trajectory of illness and barriers to or facilitators of accessing care [10,13].

Various challenges and risks were identified in relation to the implementation and use of eHealth tools. These barriers were related to the accessibility and functionality of devices, including poor internet connection [6,9,15,18,44,48,52,55,58] and user interface or design aspects of the tool [16,18,41,46,59]. Clinical utility was another barrier identified in papers, mainly relating to the tool lacking features that provided added value to standard care [8,19,45,51,59-61]. User uptake was a challenge, with multiple studies reporting lower than expected uptake and reluctance to use the digital tool; this was an issue reported among diverse user groups, including clinicians, parents or carers, and children or young people [39,46,48,51,59,60,62]. These findings are in line with the Eysenbach [73] law of attrition, which is based on the observation that high participant dropout rates are common in eHealth research focusing on novel digital health tools; although researchers may dismiss or underreport this information, the observation meaningfully reflects the real-world uptake of digital tools currently. Some of the reasons for low uptake included implementation barriers (eg, competing time, modified professional roles, and organizational restructuring) [46,48,62], privacy concerns [6], socioeconomic factors (eg, cost of data) [42], and language proficiency or literacy issues [6,44,51]. Incorporating participatory design (co-design) and user testing methodologies into future protocols may help to understand and address these barriers [10,22]. Data quality was another barrier that was reported, which was related to human error in data entry [51,55] or incomplete data input [9]. A study overcame the issue of human error by minimizing free-text input and using predefined options [51]. Although this is not feasible for all tools, such as when obtaining qualitative health information, it provides a solution for quantitative health data. Sociocultural issues were mentioned in just 1 study; Den Boer [18] reported that parents or carers in some communities found questions about smoking in children aged 6 to 11 years insulting and inappropriate. The study researchers justified the inclusion of the questions by saying that they were important and relevant for certain communities or user groups. This raises the issue of whether universal questionnaires can be used in eHealth tools or whether customized question sets need to be developed for the target group. The findings of Singh [51] supported a configurable or individualized approach, with the researchers concluding that individualization was critical to the clinical utility and safety of eHealth tools. Despite studies identifying challenges and risks, there was minimal discussion on how to address the identified issues; moreover, just under a quarter of studies did not report any potential risks or challenges of implementing the device [17,38,43,47,49,54,63-65].

Our review of funding sources, which showed that financial support came from a mixture of public, commercial, and NFP bodies, is unsurprising, as the development and implementation of digital tools often involve the collaboration of professionals from multiple disciplines who belong to different bodies [25,74]. Interestingly, funding from commercial bodies was uncommon. This may reflect the fact that the tools reviewed were in the preliminary stages of research (development and implementation) rather than at a more advanced stage of commercialization, the latter of which we would expect to attract more investment from commercial organizations [74].

The findings of this review demonstrate a clear need for further research into the efficacy and validity of eHealth tools that assess and track health outcomes in children and young people. Future evaluation studies should focus on changes in the health outcomes of users, as well as the clinical care pathways. Further investigation of the risks and challenges of implementing devices is also important, particularly relating to sociocultural factors, language proficiency or literacy, and privacy concerns, as these were seldom mentioned but are likely to affect the clinical utility, safety, and uptake of tools [10,23]. Overall, these findings are consistent with results from a prior systematic review of eHealth solutions in adults, which found a gap between the postulated and empirically demonstrated benefits of eHealth technologies, a lack of robust research trials into validity and efficacy, and inadequate investigation of risks or challenges of using these technologies in health care [75]. This review has uncovered several features of eHealth tools that may facilitate comprehensive assessments and integrated care in future technologies:

Although this review provides important insights into a novel field of eHealth, the conclusions that can be drawn about the efficacy and validity of eHealth solutions are limited as most studies were formative and process evaluations that assessed feasibility, acceptability, usability, or uptake of a device. Outcome evaluation studies were rare, with just 3 randomized controlled trials conducted to date. These early phase research studies are necessary precursors to more rigorous validity and efficacy studies; however, they need to be followed by more thorough evaluation studies to determine whether the tools are effective in improving health outcomes and clinical care. We limited our search to studies published in English, which may have biased our results. Furthermore, although the strength of this review is that it presents the state of eHealth tools for supporting health in children or young people, it inevitably fails to consider the immense variation that lies within each health domain. Our search strategy was not without limitations. We did not include all relevant terms (eg, internet and technology) as the inclusion of these broader terms returned >15,000 articles, which was not considered realistic for screening. Nevertheless, we believe that the search strategy balanced scientific rigor and feasibility and was sufficiently rigorous to pick up relevant articles. Finally, this paper was not preregistered with PROSPERO; however, the search strategy remained the same over time.

eHealth tools that assess and track health outcomes in children or young people and connect individuals with personalized care options have enormous potential in health services around the world. Many of the existing tools are in the early stages of pilot and feasibility testing; however, the literature is promising in the potential to use these tools in future clinical care. Further research is needed to evaluate the validity and efficacy of these eHealth tools and investigate the potential risks and challenges of implementation as part of standard clinical care. With future research and development efforts in place, these tools have the potential to facilitate collaborative decision-making, improved communication, transmission of remote health data, and real-time assessment and tracking and take a positive step forward in digitalizing health practices.