Strengths-Based Behavioral Intervention for Parents of Adolescents With Type 1 Diabetes Using an mHealth App (Type 1 Doing Well): Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Supportive parent involvement for adolescents' type 1 diabetes (T1D) self-management promotes optimal diabetes outcomes. However, family conflict is common and can interfere with collaborative family teamwork. Few interventions have used explicitly strengths-based approaches to help reinforce desired management behaviors and promote positive family interactions around diabetes care. OBJECTIVE: The aim of this protocol was to describe the development of a new, strengths-based behavioral intervention for parents of adolescents with T1D delivered via a mobile-friendly Web app called Type 1 Doing Well. METHODS: Ten adolescent-parent dyads and 5 diabetes care providers participated in a series of qualitative interviews to inform the design of the app. The 3- to 4-month pilot intervention will involve 82 parents receiving daily prompts to use the app, in which they will mark the diabetes-related strength behaviors (ie, positive attitudes or behaviors related to living with or managing T1D) their teen engaged in that day. Parents will also receive training on how to observe diabetes strengths and how to offer teen-friendly praise via the app. Each week, the app will generate a summary of the teen's most frequent strengths from the previous week based on parent reports, and parents will be encouraged to praise their teen either in person or from a library of reinforcing text messages (short message service, SMS). RESULTS: The major outcomes of this pilot study will include intervention feasibility and satisfaction data. Clinical and behavioral outcomes will include glycemic control, regimen adherence, family relationships and conflict, diabetes burden, and health-related quality of life. CONCLUSIONS: This strengths-based, mobile health (mHealth) intervention aims to help parents increase their awareness of and efforts to support their adolescents' engagement in positive diabetes-related behaviors. If efficacious, this intervention has the potential to reduce the risk of family conflict, enhance collaborative family teamwork, and ultimately improve diabetes outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT02877680; https://clinicaltrials.gov/ct2/show/NCT02877680 (Archived by WebCite at http://www.webcitation.org/6xTAMN5k2).
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.008 |
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".