# Optimizing just-in-time adaptive interventions for interpersonal distress: mechanisms, prediction, and the challenge of engagement

**Authors:** Agata Jaremba, Sarah O’Reilly, Liam Mason, Tobias Nolte, Madiha Shaikh, Ciarán O’Driscoll

PMC · DOI: 10.1038/s41598-026-39518-z · Scientific Reports · 2026-02-11

## TL;DR

This study explores how to best deliver mental health support at the right moment by analyzing factors that influence engagement and distress prediction in people with common mental health disorders.

## Contribution

The paper introduces a novel approach to understanding and optimizing just-in-time adaptive interventions for interpersonal distress using dynamic symptom networks and engagement predictors.

## Key findings

- Dynamic symptom networks revealed stable communities like interpersonal threat and social connection linked through mood.
- High stress and perceived criticism predicted non-engagement despite high need for support.
- A dynamic prediction model achieved fair performance in forecasting distress with AUC of 0.66.

## Abstract

Common mental health disorders (CMD) feature fluctuating emotional and interpersonal symptoms inadequately addressed by traditional weekly therapies. Ecological momentary interventions offer potential for timely support, yet their mechanisms and optimal delivery contexts remain unclear. This secondary analysis of a randomized trial (N = 77) compared mindfulness and mentalization micro-interventions triggered by personalized symptom thresholds. We examined dynamic symptom networks, proximal effectiveness, engagement predictors, and distress forecasting in adults with CMD. Dynamic networks revealed stable communities (interpersonal threat, social connection, affective states) with mood as a key bridge. No significant proximal intervention effects were observed. Non-engagement was significantly predicted by high stress (OR = 1.21), elevated mood (OR = 1.22), and perceived criticism (OR = 1.22). Conversely, cumulative symptom triggers (OR = 0.69) and social contact (OR = 0.83) facilitated engagement. The dynamic prediction model achieved fair performance (AUC = 0.66) for next-beep distress. Beyond autoregressive effects, perceived criticism (OR: 1.12) and paradoxically perceived support predicted future distress (OR = 1.14), while warmth was protective (OR = 0.87). Micro-interventions operate through stable networks and may yield cumulative rather than immediate benefits. High stress and criticism impede intervention use despite high need highlighting the necessity for context-sensitive, low-friction adaptive designs to align clinical need with receptivity.

The online version contains supplementary material available at 10.1038/s41598-026-39518-z.

## Full-text entities

- **Genes:** MUC1 (mucin 1, cell surface associated) [NCBI Gene 4582] {aka ADMCKD, ADMCKD1, ADTKD2, CA 15-3, CD227, Ca15-3}, PCSK1 (proprotein convertase subtilisin/kexin type 1) [NCBI Gene 5122] {aka BMIQ12, NEC1, PC1, PC1/3, PC3, SPC3}
- **Diseases:** Stress (MESH:D000079225), symptom (MESH:D012816), Criticism (MESH:D016638), Generalized Anxiety Disorder (MESH:C000726808), Expressed (MESH:D001039), anxiety (MESH:D001007), CMD (OMIM:603663), Mood (MESH:D019964), Depression (MESH:D003866), fatigue (MESH:D005221), executive dysfunction (MESH:D006331), Distress (MESH:D012128), CMDs (MESH:C567129)
- **Chemicals:** EMI (-), cortisol (MESH:D006854)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12972048/full.md

## References

8 references — full list in the complete paper: https://tomesphere.com/paper/PMC12972048/full.md

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Source: https://tomesphere.com/paper/PMC12972048