ReviewBehaviour research and therapy2023
Analyzing text message linguistic features: Do people with depression communicate differently with their close and non-close contacts?
Review in Behaviour research and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06676059 (SMART-r), which is not on this map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
SMART-r: Substance Monitoring and Active Relapse Tracking Repository
Who cites it
14 citing papers in PubMed.
- Depressive Symptoms Are Associated with Linguistic Features of Negative but Not Positive Autobiographical Narratives.Affective science · 2026Article
- Cognitive modeling of real-world behavior for understanding mental health.Trends in cognitive sciences · 2026Review
- In their own words: case studies of adolescent smartphone language preceding suicide-related hospitalizations.NPP - digital psychiatry and neuroscience · 2026Article
- Interpretable depression assessment using a large language model.PLOS digital health · 2026Article
- Using a fine-tuned large language model for symptom-based depression evaluation.NPJ digital medicine · 2025Article
- A randomised cross over trial examining the linguistic markers of depression and anxiety in symptomatic adults.Npj mental health research · 2025Article
- Mental Health and Mobile Communication Profiles of Crowdsourced Participants.IEEE journal of biomedical and health informatics · 2024Article
- Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Identifying factors impacting missingness within smartphone-based research: Implications for intensive longitudinal studies of adolescent suicidal thoughts and behaviors.Journal of psychopathology and clinical science · 2024Article
- Language sentiment predicts changes in depressive symptoms.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.Npj mental health research · 2024Article
- Postgraduate psychological stress detection from social media using BERT-Fused model.PloS one · 2024Article
- Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure.Frontiers in psychology · 2024Article
- Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety.Internet interventions · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundRelatively little is known about how communication changes as a function of depression severity and interpersonal closeness. We examined the linguistic features of outgoing text messages among individuals with depression and their close- and non-close contacts.
methods419 participants were included in this 16-week-long observational study. Participants regularly completed the PHQ-8 and rated subjective closeness to their contacts. Text messages were processed to count frequencies of word usage in the LIWC 2015 libraries. A linear mixed modeling approach was used to estimate linguistic feature scores of outgoing text messages.
resultsRegardless of closeness, people with higher PHQ-8 scores tended to use more differentiation words. When texting with close contacts, individuals with higher PHQ-8 scores used more first-person singular, filler, sexual, anger, and negative emotion words. When texting with non-close contacts these participants used more conjunctions, tentative, and sadness-related words and fewer first-person plural words.
conclusionWord classes used in text messages, when combined with symptom severity and subjective social closeness data, may be indicative of underlying interpersonal processes. These data may hold promise as potential treatment targets to address interpersonal drivers of depression.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.