Supporting Women Undergoing IVF Treatment With Timely Patient Information Through an App: Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Since the introduction of assisted reproductive technologies in 1978, over 2 million in vitro fertilization (IVF) babies have been born worldwide. Patients play a vital role in the success of this treatment. They are required to take fertility medication (hormone injections) to activate the ovaries to produce a sufficient number of oocytes. Later, they need to take medication to increase the chance of the embryo surviving inside the uterus. Patients are educated during an intake consultation at the start of the treatment to minimize the emotional burden and reduce noncompliance. The consultation lasts about 30 to 45 minutes and covers all essential subjects. Even though ample time and energy is spent on patient education, patients still feel anxious, unknowledgeable, and unsupported. As such, electronic health utilizing a smartphone or tablet app can offer additional support, as it allows health care professionals to provide their patients with the correct information at the right time by using push notifications. OBJECTIVE: This randomized controlled trial aimed to evaluate the capacity of an app to support IVF patients throughout the different phases of their treatment and assess its effectiveness. The study's primary outcome was to determine the patients' level of satisfaction with the information provided. The secondary outcomes included their level of knowledge, ability to administer the medication, overall experienced quality of the treatment, health care consumption, and app usage. METHODS: This study was performed at a specialized fertility clinic of the nonacademic teaching hospital Elisabeth-TweeSteden Ziekenhuis in Tilburg, the Netherlands. Patients who were scheduled for IVF or intracytoplasmic sperm injection treatments between April 2018 and August 2019 were invited to participate in a physician-blinded, randomized controlled trial. RESULTS: In total, 54 patients participated (intervention group: n=29). Patients in the intervention group demonstrated a higher level of satisfaction on a 0 to 10 scale (mean 8.43, SD 1.03 vs mean 7.70, SD 0.66; P=.004). In addition, they were more knowledgeable about the different elements of the treatment on a 7 to 35 scale (mean 27.29, SD 2.94 vs mean 23.05, SD 2.76; P<.001). However, the difference disappeared over time. There were no differences between the two patient groups on the other outcomes. In total, 25 patients in the intervention group used the app 1425 times, an average of 57 times per patient. CONCLUSIONS: Our study demonstrates that, in comparison with standard patient education, using an app to provide patients with timely information increases their level of satisfaction. Furthermore, using the app leads to a higher level of knowledge about the steps and procedures of IVF treatment. Finally, the app's usage statistics demonstrate patients' informational needs and their willingness to use an electronic health application as part of their treatment. TRIAL REGISTRATION: Netherlands Trial Register (NTR) 6959; https://www.trialregister.nl/trial/6959.
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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 | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 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 ».