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Updated: Aug 6, 2026
Real-World M3-BREATHE: Toward Multimodal Mobile Monitoring of Behaviour, Respiration, and Exposures for Treatment and Health Evaluation
Published on: June 5, 2026
Rapid Development and Testing of Behavioral Text Message Reminders for Antidepressant Adherence via Online Panels:
Tia R Tropea1, Steven C Marcus2, Amy Bucher3
1Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market Street, 3rd Fl, Philadelphia, PA, 19104, United States, 1 (215) 898-0457.
Background:
SMS text message reminders have been used to promote many health behaviors, such as improving diet and physical activity, managing chronic health conditions, reminding patients about medical appointments, and supporting medication adherence across a range of health conditions. Despite their promise, developing effective reminders tailored to specific patient populations is resource-intensive. AI may facilitate item development, and online research panels may provide an efficient way to test message content with target users prior to implementing large-scale trials.
Objective:
This study aimed to (1) develop a library of antidepressant adherence-promoting SMS text messages that are perceived as helpful, (2) test whether an online panel approach can be used to evaluate them, and (3) identify message characteristics perceived as most helpful by patients with depression taking antidepressant medication.
Methods:
In total, 83 SMS text message reminders were developed based on barriers to adherence and behavior change technique pairings, with approximately half authored by the study team, and half generated by AI. Using an online panel, we recruited 181 American adults with depression currently prescribed an antidepressant medication. Each participant rated a subset of messages on how much they thought each would help them remember to take their medication. Associations between message characteristics and ratings were estimated using generalized linear models in Stata. Survey weights were used in analyses to align the sample with national antidepressant user demographics.
Results:
The online panel was able to rapidly recruit a sample of participants, who provided 7520 item ratings in total. AI-generated messages were rated as significantly more helpful than those authored by humans (adjusted mean difference 0.24 on a 5-point scale, 95% CI 0.12-0.36; P<.001). Messages addressing delayed symptom benefit were preferred over other adherence barriers, and behavior change techniques emphasizing self-monitoring (P<.001), habit formation (P<.001), and natural consequences (P<.001) received significantly higher ratings than those using external influence or support. No difference was observed between motivational and informational message content.
Conclusions:
Online panels offer a rapid, scalable approach to evaluating SMS text message reminders for patients currently taking antidepressants. When provided with specific instructions and human-led examples, AI can efficiently generate message content perceived to be helpful in promoting medication adherence. Given that AI-generated content received higher ratings than human-authored messages, future work may consider using this tool to support rapid intervention development. In addition, identifying common barriers to adherence and applying behavior change techniques to address those barriers can inform targeted message development and support adherence. Taken together, these findings demonstrate the utility of combining low-cost methods such as online panel research with AI to accelerate the design and preliminary evaluation of digital health interventions.