Work

Use of mobile health technology in the assessment of statin-associated muscle symptoms: A pilot feasibility study

Year: 2026
Type: article
Abstract: BACKGROUND: Statins are the cornerstone pharmacotherapy for lowering low-density lipoprotein cholesterol (LDL-C) and reducing risk of atherosclerotic cardiovascular disease (ASCVD). However, statin initiation and adherence are limited by statin-associated muscle symptoms (SAMS). Mobile health (mHealth) tools offer novel ways to assess real-time symptom data and facilitate shared decision-making. OBJECTIVE: To assess the feasibility and usability of an automated, text-message-based SAMS symptom-tracking platform to longitudinally track SAMS in patients who previously experienced muscle symptoms while on statin therapy. METHODS: We enrolled 19 patients and recorded baseline demographics, statin history, and technology use via an online survey. Quantitative SAMS scores were gathered via daily text messages, and qualitative symptom data were collected in weekly text messages. Upon study completion, participants reported study perceptions via an online exit survey. RESULTS: A total of 18 patients collected data, and 15 patients completed the 90-day study protocol. The response rate to text messages was 91.5% (92.2% for daily SAMS messages, 86.0% for weekly muscle symptom prompts). Overall reported statin adherence was 70%. Nine patients reported intolerable muscle symptoms at least once during the study, and intolerable symptom scores represented 3% of all reported symptom scores. Patients reported overall satisfaction with the study and found the SAMS scores helpful. CONCLUSIONS: Patients with previously reported SAMS successfully engaged with a text-message-based symptom-tracking platform with high interactivity and acceptability. This study demonstrates the feasibility and usability of an automated mHealth platform for longitudinally tracking SAMS in patients on statin therapy, and may provide a means for improving shared decision making to tailor statin therapy and improve adherence.
Language: English
Open Access status: hybrid
Estimated APC paid: $3,510
OpenAlex ID: W7123354794
FWCI: 8.253
Cites: 18
Cited by: 1
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