ArticleJMIR formative research2020
Investigation of a Mobile Health Texting Tool for Embedding Patient-Reported Data Into Diabetes Management (i-Matter): Development and Usability Study.
Article in JMIR formative research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
i-Matter: Investigating an mHealth Texting Tool for Embedding Patient-reported Data Into Diabetes Management
Development of a Mobile Health Intervention to Improve Blood Pressure Management in Pregnancy (Moms@Home)
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Electronic Implementation of Patient-Reported Outcome Measures in Primary Health Care: Mixed Methods Systematic Review.Journal of medical Internet research · 2025Pooled it
- Systematic review of dynamically tailored eHealth interventions targeting physical activity and healthy diet in chronic disease.NPJ digital medicine · 2025Article
- Patient-Reported Outcome Measures in Adults with Type 2 Diabetes-With a Focus on Older Populations: An AI-Assisted Rapid Review of Use and Implementation in Clinical and Organizational Practice.Healthcare (Basel, Switzerland) · 2025Review
- Key Elements and Theoretical Foundations for the Design and Delivery of Text Messages to Boost Medication Adherence in Patients With Diabetes, Hypertension, and Hyperlipidemia: Scoping Review.Journal of medical Internet research · 2025Article
- Validation of the Longitudinal Effect of an Integrated Nursing Intervention Based on the Integrated Theory of Health Behavior Change (ITHBC) on Multidimensional Health Indicators in Older Radiotherapy Patients.Clinical interventions in aging · 2025Article
- Technology-Based Interventions to Promote Diabetes Self-Management Behaviors for Persons Newly Diagnosed with Type 2 Diabetes: A Scoping Review.Current diabetes reports · 2024Article
- Opening the Black Box of an mHealth Patient-Reported Outcome Tool for Diabetes Self-Management: Interview Study Among Patients With Type 2 Diabetes.JMIR formative research · 2023Article
- Analyzing User Engagement Within a Patient-Reported Outcomes Texting Tool for Diabetes Management: Engagement Phenotype Study.JMIR diabetes · 2022Article
- From Digital Health to Digital Well-being: Systematic Scoping Review.Journal of medical Internet research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundPatient-reported outcomes (PROs) are increasingly being used in the management of type 2 diabetes (T2D) to integrate data from patients' perspective into clinical care. To date, the majority of PRO tools have lacked patient and provider involvement in their development, thus failing to meet the unique needs of end users, and lack the technical infrastructure to be integrated into the clinic workflow.
objectiveThis study aims to apply a systematic, user-centered design approach to develop i-Matter (investigating a mobile health [mHealth] texting tool for embedding patient-reported data into diabetes management), a theory-driven, mobile PRO system for patients with T2D and their primary care providers.
methodsi-Matter combines text messaging with dynamic data visualizations that can be integrated into electronic health records (EHRs) and personalized patient reports. To build i-Matter, we conducted semistructured group and individual interviews with patients with T2D and providers, a design thinking workshop to refine initial ideas and design the prototype, and user testing sessions of prototypes using a rapid-cycle design (ie, design-test-modify-retest).
resultsUsing an iterative user-centered process resulted in the identification of 6 PRO messages that were relevant to patients and providers: medication adherence, dietary behaviors, physical activity, sleep quality, quality of life, and healthy living goals. In user testing, patients recommended improvements to the wording and timing of the PRO text messages to increase clarity and response rates. Patients also recommended including motivational text messages to help sustain engagement with the program. The personalized report was regarded as a key tool for diabetes self-management by patients and providers because it aided in the identification of longitudinal patterns in the PRO data, which increased patient awareness of their need to adopt healthier behaviors. Patients recommended adding individualized tips to the journal on how they can improve their behaviors. Providers preferred having a separate tab built into the EHR that included the personalized report and highlighted key trends in patients' PRO data over the past 3 months.
conclusionsPRO tools that capture patients' well-being and the behavioral aspects of T2D management are important to patients and providers. A clinical trial will test the efficacy of i-Matter in 282 patients with uncontrolled T2D.
trial registrationClinicalTrials.gov NCT03652389; https://clinicaltrials.gov/ct2/show/NCT03652389.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.