Tracker-Based Personal Advice to Support the Baby’s Healthy Development in a Novel Parenting App: Data-Driven Innovation
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».