An mHealth App to Support Fertility Patients Navigating the World of Infertility (Infotility): Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: The experience of infertility and its treatment engenders considerable stress and is often described as an emotional rollercoaster. A mobile health (mHealth) app may be a novel solution to address the psychoeducational and psychosocial support needs of fertility patients because of its potential to reduce stress and increase patient empowerment. There are a few fertility-related apps that provide information and support to both men and women undergoing fertility treatment; however, none have documented their development and evaluation process. OBJECTIVE: This study aims to describe the development and evaluation process of a bilingual mHealth app, Infotility, designed to meet the psychoeducational and psychosocial support needs of men and women undergoing fertility treatment. METHODS: To develop the Infotility app, we adhered to the Medical Research Council guidelines for the development and evaluation of complex interventions. First, we conducted literature reviews and needs assessment surveys of fertility patients and health care providers who informed the content and design of the app. Second, we tested the intervention with a small group of end users who provided feedback on the design and appropriateness of the app's content. Third, we evaluated the uptake and usability of the app using a pre-post study design. Finally, we updated the app's content based on participants' feedback and searched for partners to disseminate the app to the broader public. RESULTS: This study is the first to describe the development and evaluation process of an mHealth app for men and women undergoing fertility treatment. The app met its goal in providing fertility patients with a clinician-approved, portable resource for reliable information about medical and psychosocial aspects of infertility and its treatments and a confidential peer support forum monitored by trained peer supporters. Participants rated the engagement, functionality, information, and esthetics of the app positively, with an overall app quality mean score of 3.75 (SD 0.53) and a star rating of 3.43 (SD 0.75), with a total possible score and star rating of 5.00. CONCLUSIONS: By documenting the systematic development and evaluation of the mHealth app for men and women undergoing fertility treatment, this paper can facilitate the replication of the study intervention and the development of similar mHealth apps.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".