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Demographic, clinical, and linguistic features associated with engagement in message-based interventions for serious
Justin Tauscher1, Anna Larsen1, Trevor Cohen1
1Department of Psychiatry and Behavioral Sciences, Behavioral Research in Technology and Engineering Center, University of Washington.
Objective:
This study examines associations between patient demographics, clinical status, and linguistic features of text messages with engagement in a message-based intervention for serious mental illness.
Methods:
Data from a randomized controlled trial of a message-based mental health intervention were analyzed. Engagement was operationalized as total texts sent per day and total number of disengaged days. Linguistic Inquiry and Word Count identified expressions of affect, social processes, thinking styles, health, and time orientation. Generalized estimating equations assessed associations between demographic, clinical, and Linguistic Inquiry and Word Count variables with engagement across three different time intervals.
Results:
Among 39 participants, most were male (n = 23, 59%), with diagnoses of schizophrenia (n = 16, 41%), schizoaffective disorder (n = 9, 23%), bipolar disorder (n = 9, 23%), and major depressive disorder (n = 5, 13%). Participants sent approximately two messages per day, with 48% of days disengaged. Race, education, and diagnosis were associated with engagement. Black participants and those with at least some college education sent more texts while individuals with schizophrenia had more disengaged days. Messages containing language about anxiety, friendship, cognitive processes, and common verbs were associated with engagement. Significant relationships between message content and future engagement were observed, particularly in the first 2 weeks, as well as in messages sent the day and week before a disengaged day.
Conclusions And Implications For Practice:
Demographic, clinical, and linguistic features are related to engagement in message-based interventions for serious mental illness. Identifying these characteristics can help tailor interventions, enhancing engagement, and reducing dropout rates in digital mental health interventions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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