Mobile Momentary Assessment and Biobehavioral Feedback for Adolescents with Type 1 Diabetes: Feasibility and Engagement Patterns
Open accessAbstract
BACKGROUND: Integration of momentary contextual and psychosocial factors within self-management feedback may provide more specific, engaging, and personalized targets for problem solving. METHODS: Forty-four youth ages 13-19 with type 1 diabetes (T1D) were provided a Bluetooth meter and completed the 30-day protocol. Participants were randomized to "app + meter" or "meter-only" groups. App + meter participants completed mealtime and bedtime assessment each day. Assessments focused on psychosocial and contextual information relevant for self-management. Graphical feedback integrated self-monitored blood glucose (SMBG), insulin, and Bluetooth-transmitted blood glucose data with the psychosocial and contextual data. App + meter participants completed an interview to identify data patterns. RESULTS: The median number of momentary assessments per participant was 80.0 (range 32-120) with 2.60 per day. By 2 weeks participants had an average of 40.77 (SD 12.23) assessments. Dose-response analyses indicated that the number of app assessments submitted were significantly related to higher mean daily SMBG (r = -0.44, P < 0.05) and to lower% missed mealtime SMBG (r = -0.47, P < 0.01). Number of feedback viewing sessions was also significantly related to a lower% missed mealtime SMBG (r = -0.44, P < 0.05). Controlling for baseline variables, mixed-effects analyses did not indicate group × time differences in mean daily SMBG. Engagement analyses resulted in three trajectory groups distinguished by assessment frequencies and rates of decline. Engagement group membership was significantly related to gender, mean daily SMBG, and HbA1c values. CONCLUSIONS: Momentary assessment combined with device data provided a feasible means to provide novel personalized biobehavioral feedback for adolescents with T1D. A 2-week protocol provided sufficient data for self-management problem identification. In addition to feedback, more intensive intervention may need to be integrated for those patients with the lowest self-management at baseline.
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- Authors
- Mulvaney, S. A., Vaala, S., Hood, K. K., Lybarger, C., Carroll, R., Williams, L., Schmidt, D. C., Johnson, K., Dietrich, M. S., & Laffel, L.
- Affiliations
- Vanderbilt University (US)
- Stanford University (US)
- Vanderbilt University Medical Center (US)
- Joslin Diabetes Center (US)
- Corresponding author
- Mulvaney, Shelagh A.
- ORCID
- Venue
- Diabetes Technology & Therapeutics
- Publisher
- SAGE Publications
- Published
- 2018-07
- Volume
- 20
- Issue
- 7
- Pages
- 465-474
- ISSN
- 1520-9156 (ISSN-L)
- 1557-8593
- PMC
- PMC6025702
- OpenAlex
- W2807235612
- Cited by
- 29
- References
- 63
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 2 · 2023: 2 · 2021: 5 · 2020: 6 · 2019: 2 · 2018: 1
- FWCI
- 2.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Diabetes Technology & Therapeutics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- American Diabetes Association
- National Institutes of Health (UL1TR000445, 1dp3dk097706-01, TR000445)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK092986, P30DK036836)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- type 1 diabetes, adolescents, momentary assessment, mobile health apps, self-management, self-monitoring of blood glucose, engagement patterns, personalized feedback, HbA1c
- MeSH terms
- Ecological Momentary Assessment, Adolescent, Diabetes Mellitus, Type 1, Feasibility Studies, Female, Humans, Hypoglycemic Agents, Insulin, Male, Patient Compliance, Blood Glucose Self-Monitoring, Feedback, Psychological, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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