Evaluation of the MyFertiCoach Lifestyle App for Subfertile Couples: Single-Center Evaluation of Augmented Standard Care
Notice bibliographique
Résumé
BACKGROUND: Many couples undergoing fertility treatment face multiple lifestyle risk factors that lower their chances of achieving pregnancy. The MyFertiCoach (MFC) app was designed as an integrated lifestyle program featuring modules on healthy weight management, nutrition, exercise, quitting smoking, reducing alcohol and drug use, and managing stress. We hypothesized that supplementing standard care with the MFC app would improve lifestyle outcomes. OBJECTIVE: This study aims to assess the impact of the MFC app on changing multiple lifestyle habits in women seeking fertility treatment. The primary outcome is the change in the total risk score (TRS) at 3- and six-month follow-ups. The TRS is calculated for each individual as the sum of all risk scores per behavior (eg, vegetable/fruit/folic acid intake, smoking, and alcohol use) at 3 and 6 months. A higher TRS indicates unhealthier nutrition and lifestyle habits and a lower likelihood of achieving pregnancy. The secondary endpoints include changes in BMI, activity score, preconception dietary risk score, distress score (eg, perceived burden), smoking habits, alcohol intake, and program adherence. METHODS: This retrospective, observational, single-center evaluation included patients between January 1, 2022, and December 31, 2023. Subfertile female patients aged 18-43 years and their partners, who were referred to a gynecologist, were invited to participate in online lifestyle coaching via the MFC app. The gynecologist selected relevant lifestyle modules based on the results of integrated screening questionnaires. We used (hierarchical) linear mixed models (LMMs) to estimate changes in outcomes. For missing data patterns deemed missing not at random, joint modeling was applied. Statistical significance was set at P≤.05, with methods in place to maintain the same false-positive rate. RESULTS: A total of 1805 patients were invited to participate in the evaluation, with an average of 737 (40.83%) completing the screening questionnaire at baseline. For the TRS, 798 (44.21%) patients were included at baseline, of whom 517 (64.8%) involved their partner. On average, 282 of 744 (37.9%) patients submitted at least one follow-up questionnaire. Patients rated the app above average (n=137, median score of 7 on a 1-10 scale) on days 7 and 14. The TRS decreased by an average of 1.5 points (P<.001) at T3 and T6 compared with baseline, a clinically meaningful improvement. All secondary outcomes showed statistically significant positive changes for patients who used a relevant lifestyle module (P<.001). Most improvements were achieved by 3 months and remained significant at 6 months (P<.001), except for alcohol intake (P<.53). These findings were consistent across both LMMs and joint models. CONCLUSIONS: Our evaluation of a mobile health app integrated into standard care demonstrates immediate and clinically meaningful improvements in key lifestyle parameters among women seeking to become pregnant. Additional scientific research is needed to identify the causal pathways leading to sustained effectiveness. To maintain and enhance these outcomes, further tailoring of patient-specific programs is essential.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».