Text-Based Messaging to Support Rheumatoid Arthritis Care: An Analysis of the Frequency and Content of Text Messages
Bibliographic record
Abstract
Objectives Rheumatoid arthritis (RA) requires regular follow-up appointments with rheumatologists to monitor disease activity, however, this may be difficult to achieve due to physician and patient schedules and a limited rheumatology workforce.[1] Virtual care has been used to improve health care delivery by connecting patients with healthcare providers (HCPs).[2] In this study, it was used to enhance RA care by allowing patients to connect with their rheumatology team in between appointments on a secure two-way text-messaging platform called WelTel. The objectives of this study were to 1) analyze the frequency and content of text messages sent by patients to their HCPs, and 2) to determine patient characteristics that were associated with higher texting frequency. Methods Seventy participants diagnosed with RA participated in a 6-month pilot using the WelTel platform. Automated “How are you?” texts were sent monthly, and participants were encouraged to respond according to their current situation. Participants could also initiate messages if they had questions/concerns between appointments. Text messages were monitored and answered primarily by the clinical pharmacist for the rheumatology clinic. Qualitative content analysis was conducted to thematically categorize and quantify common words and phrases. Once categories were quantified and themes were established, regression analysis was conducted to determine if a relationship existed between the number of text messages and age, sex, care complexity level, number of medications, and burden of comorbidities. Care complexity was measured using the Intermed Self-Assessment, a validated patient-reported instrument that assesses biopsychosocial complexity. Results A total of 1404 text messages were sent by patients with 257 messages requiring a response from the participating pharmacist. Three main content themes were identified: RA symptoms, medication questions, and COVID-19 concerns. There was a significant association between text messaging frequency and patient care complexity levels (p = 0.025), however, no association was identified between text messaging frequency and age, sex, number of medications, or burden of comorbidities. Conclusion The present study piloted the novel use of text messaging using the WelTel virtual care platform for providing additional RA care in between rheumatologist visits. Our analysis identified the common concerns that patients raise with their care team via messaging. The content of text messages received was highly relevant and directly related to patient care needs. Patient care complexity was associated with significantly more text messages to discuss health concerns, highlighting a population who may benefit in particular from the intervention. [1.] Smolen J. Ann Rheum Dis 2010;69:631-7. [2.] Gajarawala S. J Nurse Pract 2021;17:218-2.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".