Tracker-Based Personal Advice to Support the Baby’s Healthy Development in a Novel Parenting App: Data-Driven Innovation
Bibliographic record
Abstract
BACKGROUND: The current generation of millennial parents prefers digital communications and makes use of apps on a daily basis to find information about child-rearing topics. Given this, an increasing amount of parenting apps have become available. These apps also allow parents to track their baby's development with increasing completeness and precision. The large amounts of data collected in this process provide ample opportunity for data-driven innovation (DDI). Subsequently, apps are increasingly personalized by offering information that is based on the data tracked in the app. In line with this, Philips Avent has developed the uGrow app, a medical-grade app dedicated to new parents for tracking their baby's development. Through so-called insights, the uGrow app seeks to provide a data-driven solution by offering parents personal advice that is sourced from user-tracked behavioral and contextual data. OBJECTIVE: The aim of this study was twofold. First, it aimed to give a description of the development process of the insights for the uGrow app. Second, it aimed to present results from a study about parents' experiences with the insights. METHODS: The development process comprised 3 phases: a formative phase, development phase, and summative phase. In the formative phase, 3 substudies were executed in series to understand and identify parents' and health care professionals' (HCPs) needs for insights, using qualitative and quantitative methods. After the formative phase, insights were created during the development phase. Subsequently, in the summative phase, these insights were validated against parents' experience using a quantitative approach. RESULTS: As part of the formative phase, parents indicated having a need for smart information based on a data analysis of the data they track in an app. HCPs supported the general concept of insights for the uGrow app, although specific types of insights were considered irrelevant or even risky. After implementing a preliminary set of insights in a prototype version of the uGrow app and testing it with parents, the majority of parents (87%) reported being satisfied with the insights. From these outcomes, a total of 89 insights were implemented in a final version of the uGrow app. In the summative phase, the majority of parents reported experiencing these insights as reassuring and useful (94%), as adding enjoyment (85%), and as motivating for continuing tracking for a longer period of time (77%). CONCLUSIONS: Parents experienced the insights in the uGrow app as useful and reassuring and as adding enjoyment to their use of the uGrow app and tracking their baby's development. The insights development process we followed showed how the quality of insights can be guaranteed by ensuring that insights are relevant, appropriate, and evidence based. In this way, insights are an example of meaningful DDI.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".