Using Intervention Mapping to co-develop and pilot Orchid: a new digital tool for reproductive life planning. (Preprint)
Notice bibliographique
Résumé
Abstract Background Most people make no health or lifestyle changes before pregnancy, missing a key opportunity to improve outcomes. Consequently, nearly half of UK pregnancies are unplanned, disproportionately affecting underserved groups and widening health inequalities. Digital health interventions (DHIs) offer promise but require systematic, theory-driven development to ensure effectiveness and real-world applicability. Objective This study aimed to use Intervention Mapping to codevelop and pilot Orchid, a novel DHI designed to support people of reproductive age to understand their pregnancy preferences and develop a reproductive life plan (RLP). Methods We used Intervention Mapping steps 1‐4 to guide the systematic, theory-informed codevelopment of Orchid. A multidisciplinary planning group and codevelopment group of 21 members of the public contributed throughout. At step 1, previous research and a scoping review of existing RLPs informed the program goals and logic model of the problem. During step 2, we identified performance objectives and behavioral determinants to specify practical strategies for each target behavior. At step 3, we applied the Capability, Opportunity, Motivation–Behavior (COM-B) model and relevant behavior change techniques to guide intervention design. Finally, at step 4, Orchid was co-designed as a website and mobile app providing users with a pregnancy preference group and prediction of pregnancy, a dynamic RLP, tailored evidence-based information, and optional goal-setting features to support behavior change. Orchid was piloted between January and May 2025 to explore its feasibility and acceptability in health care settings. Interviews with users, nonusers, and health care professionals were conducted and quantitative data from users were collected. Results These findings indicate that implementation was feasible, and health care professionals found it acceptable to recommend Orchid to patients, though noted barriers including time constraints and competing priorities. Overall, 153 people signed up to Orchid; 68% (72/106) of eligible users received a pregnancy preference group and 27% (32/119) of eligible users completed a full RLP. Users were positive about Orchid, appreciating its content and design, noting that Orchid contained a wealth of information about reproductive health presented in an easy-to-understand manner. They valued the autonomy, convenience, and privacy afforded by the digital format, and found it acceptable to be recommended Orchid within a health care setting. Orchid uptake was lower than anticipated, and use was limited; this was partly expected given the short pilot period, but feedback also suggested targeted recruitment and navigation improvements could enhance uptake and engagement. Conclusions Orchid is the first co-designed DHI to support reproductive health across the life course. Its systematic development, theoretical foundation, strong user involvement, and positive pilot testing position it as a promising, scalable innovation to support reproductive health, deliver credible information in accessible formats, and promote preventative, community-based care across the National Health Service.
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 | 0,018 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,004 |
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 ».