A Mobile Health Intervention for Fetal Alcohol Spectrum Disorders (Families Moving Forward Connect): Development and Qualitative Evaluation of Design and Functionalities
Bibliographic record
Abstract
BACKGROUND: Fetal alcohol spectrum disorders (FASD) affect approximately 2% to 5% of the US population. However, most families are unable to access FASD-informed interventions. Barriers to care include the lack of a knowledgeable and skilled workforce and family-level barriers such as limited financial resources, inability to access childcare, and stigma. As a result, families often try peer-to-peer and self-help support strategies. However, they often take these strategies from disparate sources, which have quite variable intervention quality and empirical support. OBJECTIVE: This study aimed to initiate systematic development and evaluation of a mobile health intervention (app) for caregivers raising children with FASD. Focus groups were conducted to elicit participant perspectives on app design and functionalities to inform further app development. METHODS: The app, called FMF Connect, was derived from the scientifically validated Families Moving Forward (FMF) Program, a clinician-delivered behavioral consultation intervention. FMF Connect was intended for caregiver self-delivery and included five main components: (1) Learning Modules, (2) Family Forum, (3) Library, (4) Notebook, and (5) Dashboard. Focus group methods were used to solicit perspectives from diverse families during the early stages of app development. Questions were asked about interface design, relevance of components and content, and perceived barriers and facilitators of use. A total of 25 caregivers participated in 7 focus groups across 5 US cities. Data were analyzed thematically. RESULTS: Focus group participants were generally enthusiastic about the app interface design and components. Four global positive impression themes emerged, including (1) ease of access, (2) how the app guides and organizes information, (3) connection to other users and information, and (4) ability to share some content with others. Themes arose not only in discussions relating to positive app features but also when participants were asked about motivators for app use. Participants related how these positive global themes could address some system-level barriers, such as limited access to services, feeling isolated, and increased advocacy needs related to the societal lack of FASD knowledge. Participants identified many positive features about individual app components and functionalities. They also communicated potential barriers to use and raised important concerns and considerations relating to several app components. These included recognizability of the app based on the logo, and the balance of following the planned intervention sequence versus obtaining immediate answers. Also mentioned were privacy and dynamics within the Family Forum. CONCLUSIONS: FMF Connect is a promising novel intervention with potential to reach many families in need and reduce significant barriers to care, resulting in a broader public health impact. Study findings will guide further app development both in terms of content and technological advances to optimize intervention effects. FMF Connect app development provides useful directions for other apps aimed at changing parenting practices.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".