Evaluation of Satisfaction With a Secure, Connected Mobile App for Women in Assisted Reproductive Technology Programs: Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Telemedicine has emerged rapidly as a novel and secure tool to deliver medical information and prescriptions. A secure, connected health care app (WiStim) has been developed in order to facilitate dialogue between patients and the medical team during an ovarian stimulation cycle for medically assisted reproduction (MAR). OBJECTIVE: This study aimed to evaluate the patients' and midwives' levels of satisfaction with the connected mobile app. METHODS: We conducted a prospective, observational, single-center study at Lille University Hospital, France. From May 1 to July 31, 2021, all women undergoing ovarian stimulation started to receive their treatment advice through the mobile app. A total of 184 women were included and they filled out the 30-item Usefulness Satisfaction and Ease-of-Use (USE) questionnaire, which examines the users' opinions in 4 dimensions: usefulness, ease of use, ease of learning, and satisfaction. The women also answered a series of closed and open questions. The 5 midwives in our assisted reproductive technology center filled out the French version of the 10-item System Usability Scale (SUS) when the app was implemented and then after 3 and 6 months of use. We also performed semistructured interviews with the midwives. RESULTS: Overall, 183 women using the app completed the questionnaire. None refused to use the app, and 1 withdrew from the study. The mean scores for the four USE dimensions were all significantly greater than 4, that is, the middle of the response scale. The women liked the app's ease of use, the access to tutorial videos, and the reminders about appointments and treatments. In particular, the women liked to be able to (re)read the information; this reassured them, might have reduced the number of missed appointments and treatments, and made them more independent during the day, especially when they were working. Some of the women regretted the loss of direct contact with the midwife. The mean SUS score was 76 (SD 13.54) at the start of the study, 75 (SD 17.16) after 3 months, and 84 (11.21) after 6 months. According to the adjective rating scale, these scores corresponded to good usability for the app. After the requisite training and a familiarization period, the midwives reported that using the app saved them 2 hours a day. The mobile app enabled better transmission of information and thus probably helped to decrease treatment errors. CONCLUSIONS: The WiStim connected mobile app is one of the first reliable, secure apps in the field of MAR. The app reassured the patients during the ovarian stimulation. Women and the medical team considered that the app was easy and intuitive to use. Given the growth in demand for MAR programs and the medical team's workload, the time savings provided by the app constitute a nonnegligible advantage.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".