A Novel Smartphone App for Self-Monitoring of Neonatal Jaundice Among Postpartum Mothers: Qualitative Research Study
Bibliographic record
Abstract
Background: Neonatal jaundice (NNJ) or hyperbilirubinemia is a ubiquitous condition in newborn infants. Currently, the transcutaneous bilirubinometer is used to screen for NNJ in health care facilities, where neonates need to be physically present (ie, a centralized model of care for NNJ screening). Mobile health (mHealth) apps present a low-cost, home-based, and noninvasive system that could facilitate self-monitoring of NNJ and could allow mothers the convenience of screening for NNJ remotely. However, end users' acceptability of such mHealth apps is of fundamental importance before the incorporation of such apps into clinical practice. Objective: The study aimed to explore the perception of postpartum mothers toward self-monitoring of NNJ using a novel mHealth app. Methods: Mothers attending video consultations for early postpartum care at 2 Singapore primary care clinics watched an instructional video for a hyperbilirubinemia-screening mHealth app (HSMA). An independent researcher used a semistructured topic guide to conduct in-depth interviews with 25 mothers, assessing their views on HSMAs. All interviews were audio recorded, transcribed verbatim, and checked for accuracy before data analysis. Two researchers independently analyzed the transcripts via thematic analysis. Data were managed using NVivo qualitative data management software. Results: The identified themes were grouped under perceived usability and utility. Mothers valued the convenience and utility of HSMAs for remote monitoring of NNJ. They appreciated the objectivity the app readings provided compared to visual inspection. However, they perceived that the app's applicability would be restricted to severe jaundice, were concerned about its accuracy and restriction to the English language, and lacked confidence in using it. Nevertheless, they were willing to use it once its accuracy was proven and when they received adequate guidance from health care professionals. They also suggested including an action plan for the measured readings and clinical signs within the app. Mothers proposed pairing teleconsultations with HSMAs to boost their confidence and enhance adoption. Conclusions: Mothers were receptive to using HSMAs but had concerns. Multiple languages, proof of accuracy, and resources to guide users should be incorporated into the app in the next phase to increase its successful adoption. Complementing such apps with a teleconsultation service presents a plausible and pragmatic NNJ care delivery model in general practice.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".