Experiences of Using a Digital Text Messaging Intervention to Support Oral Medication Adherence for People Living With Type 2 Diabetes: Qualitative Process Evaluation
Bibliographic record
Abstract
BACKGROUND: Poor adherence to oral medications for type 2 diabetes can increase the risk of health complications. Digital interventions can affect people's experiences of self-managing a chronic condition, and SMS text messages may provide an effective delivery method for an intervention. The Support Through Mobile Messaging and Digital Health Technology for Diabetes (SuMMiT-D) intervention uses evidence-based SMS text messages to support people with type 2 diabetes with regular and consistent use of diabetes medication. OBJECTIVE: This process analysis, conducted alongside a randomized controlled trial of SuMMiT-D, aimed to explore (1) the contextual factors that may interact with the SuMMiT-D intervention and (2) the self-reported mechanisms through which change in behavior or attitude might occur. METHODS: A nested qualitative process study was conducted within primary care in England. A total of 43 trial participants diagnosed with type 2 diabetes were assigned to receive the SuMMiT-D intervention, were undergoing oral glucose-lowering treatment, blood pressure-lowering treatment or lipid-lowering treatment, either alone or in combination, and had access to a mobile phone, took part in semistructured telephone interviews. Data were analyzed using inductive thematic analysis. RESULTS: In total, 2 overarching themes were developed exploring relevant contextual factors and potential mechanisms of change. The system exerted a range of holistic benefits and supported the cognitions, beliefs, and behaviors necessary for longitudinal self-management. The perceived value of the messages was fluid and linked to contextual need. Appraisal of the system was influenced by existing routines, lifestyle disruption, people's understanding of type 2 diabetes, relationships with other people, and subjective attitudes toward living with type 2 diabetes in contemporary society. CONCLUSIONS: This work demonstrates the value of engaging people longitudinally in thinking about their general health, the importance of interrogating context, and the holistic benefit of health messaging. Many people perceived wide-ranging and unexpected benefits from using the intervention over time, challenging assumptions about who might be expected to appraise the system more positively and who should be offered access to it.
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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 | 0.042 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".