Supporting Women Undergoing IVF Treatment With Timely Patient Information Through an App: Randomized Controlled Trial
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".