Testing an Evidence-Based Self-Help Program for Infertility-Related Distress: Protocol for a Randomized Controlled Trial (Preprint)
Notice bibliographique
Résumé
BACKGROUND Infertility—the inability to achieve pregnancy despite ≥12 months of focused attempts to conceive—is experienced by 1 in 6 couples. Women typically carry a disproportionate share of the psychological burden associated with infertility, experiencing poor quality of life, and 30%-40% experiencing depressive mood or anxiety. Unfortunately, currently available psychological interventions targeting infertility-related distress are associated with modest effects. OBJECTIVE Our team, in collaboration with patient advisors, has designed a self-help intervention for infertility-related distress involving 7 weekly 10-minute videos addressing the cognitive, behavioral, and interpersonal challenges associated with infertility, delivered through a mobile app. A feasibility study suggests that it is well accepted and highly effective in reducing symptoms of anxiety and depressed mood among distressed individuals dealing with infertility. This study represents the next step in this line of research: a fully powered randomized controlled trial comparing the intervention to a waitlist control group. METHODS We will recruit 170 individuals struggling to become pregnant in Canada or the United States to be randomized to our 7-week self-help program or a treatment-as-usual condition. The primary outcome will be fertility quality of life, while secondary outcomes will include depressive symptoms, anxious symptoms, and relationship quality, assessed before and after the program as well as biweekly for 16 weeks following completion of the program. Self-reported health care use and the presence of diagnosed mood and anxiety disorders, assessed through a structured psychiatric interview, will also be assessed immediately following the intervention and at the 16-week follow-up assessment. Treatment adherence and retention will also be recorded throughout the intervention. Multilevel modeling will compare the intervention arm to the treatment-as-usual condition in terms of all continuous outcomes across the 9 measurement points. Logistic regression will be used to assess the occurrence of mood and anxiety disorders in the 2 treatment arms at the posttreatment assessment as well as at the 16-week follow-up. Sensitivity analyses will examine potential treatment moderators: membership in the LGBTQIA+ (lesbian, gay, bisexual, transgender, queer, intersex, and asexual) communities, baseline fertility quality of life, cultural background, disability status, and pursuit of conception through medical intervention. RESULTS We expect our intervention to be more effective than treatment-as-usual in improving all mental health parameters assessed and decreasing health care use related to both mental and reproductive health. Effects are expected to be larger with decreasing baseline quality of life and equally effective regardless of membership in the LGBTQIA+ communities, cultural background, or disability status. CONCLUSIONS If our intervention is successful, this would suggest that it should be scaled up and made publicly available. The availability of this program would fill an important gap in light of the high rates of psychopathology among those experiencing infertility and considering the current lack of effective psychotherapy approaches for infertility. CLINICALTRIAL Clinicaltrials.gov NCT06006936; https://classic.clinicaltrials.gov/ct2/show/NCT06006936 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/52662
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 | 0,039 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,004 |
| Méta-épidémiologie (sens large) | 0,012 | 0,007 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,009 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,145 | 0,021 |
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 ».