Text-based messaging to support rheumatoid arthritis care: an analysis of frequency and content of text-messages
Bibliographic record
Abstract
Text-based messaging support can improve rheumatoid arthritis (RA) care delivery by connecting patients with healthcare providers (HCPs) in an efficient and convenient manner. However, the nature and appropriateness of patient messaging in this new care model are unknown. The aim of this study was to (1) analyze the frequency and nature of text messages patients sent to their HCPs, and (2) to identify patient characteristics associated with higher texting frequency in a pilot of a text-based messaging (using the WelTel platform) added to usual rheumatology care. Seventy patients with RA participated in a 6-month pilot. Automated “How are you?” texts were sent monthly, and patients were encouraged to respond according to their current situation. Qualitative content analysis was conducted to thematically categorize and quantify common words and phrases. Regression analysis was conducted to determine if a relationship existed between the number of text messages and age, sex, care complexity (using a validated instrument), number of medications, and burden of comorbidities. A total of 1404 text messages were sent by patients, with 257 messages requiring a response. Three main themes for texting topics emerged: RA symptom reporting, medication management, and COVID-19 questions. Patients with higher care complexity had a higher frequency of texting (p = 0.025); however, no association was observed with other patient characteristics. Patients with higher complexity texted HCPs more frequently. Messages were highly aligned with patient care needs. Future directions should include assessing the impact of text messaging-enhanced care on patient outcomes and overall healthcare utilization. Not applicable. • We identified three main texting topics that HCPs received from patients with RA: RA symptom reporting, medication management, and COVID-19 related questions. • Using self-reported survey data from 70 patients with RA, those with higher care complexity texted their HCPs more frequently than patients with low care complexity. • These findings suggest that text messaging-enhanced RA care can support patient care needs between rheumatology in-person assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".