eHealth Tools That Assess and Track Health and Well-being in Children and Young People: Systematic Review
Bibliographic record
Abstract
BACKGROUND: eHealth tools that assess and track health outcomes in children or young people are an emerging type of technology that has the potential to reform health service delivery and facilitate integrated, interdisciplinary care. OBJECTIVE: The aim of this review is to summarize eHealth tools that have assessed and tracked health in children or young people to provide greater clarity around the populations and settings in which they have been used, characteristics of digital devices (eg, health domains, respondents, presence of tracking, and connection to care), primary outcomes, and risks and challenges of implementation. METHODS: A search was conducted in PsycINFO, PubMed or MEDLINE, and Embase in April 2020. Studies were included if they evaluated a digital device whose primary purpose was to assess and track health, focused on children or young people (birth to the age of 24 years), reported original research, and were published in peer-reviewed journals in English. RESULTS: A total of 39 papers were included in this review. The sample sizes ranged from 7 to 149,329 participants (median 163, mean 5155). More studies were conducted in urban (18/39, 46%) regions than in rural (3/39, 8%) regions or a combination of urban and rural areas (8/39, 21%). Devices were implemented in three main settings: outpatient health clinics (12/39, 31%), hospitals (14/39, 36%), community outreach (10/39, 26%), or a combination of these settings (3/39, 8%). Mental and general health were the most common health domains assessed, with a single study assessing multiple health domains. 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. It was more common for information to be collected from a single informant (ie, the child or young person, trained health worker, clinician, and parent or caregiver) than from multiple informants. The health of children or young people was assessed as a primary or secondary outcome in 36% (14/39) of studies; however, only 3% (1/39) of studies assessed whether using the digital tool improved the health of users. Most papers reported early phase research (formative or process evaluations), with fewer outcome evaluations and only 3 randomized controlled trials. Identified challenges or risks were related to accessibility, clinical utility and safety, uptake, data quality, user interface or design aspects of the device, language proficiency or literacy, sociocultural barriers, and privacy or confidentiality concerns; ways to address these barriers were not thoroughly explored. CONCLUSIONS: eHealth tools that assess and track health in children or young people have the potential to enhance health service delivery; however, a strong evidence base validating the clinical utility, efficacy, and safety of tools is lacking, and more thorough investigation is needed to address the risks and challenges of using these emerging technologies in clinical care. At present, there is greater potential for the tools to facilitate multi-informant, multidomain assessments and longitudinally track health over time and room for further implementation in rural or remote regions and community settings around the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".