Currently accepted at: JMIR Formative Research
Date Submitted: Apr 30, 2026
Date Accepted: Oct 15, 2026
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/99699
The final accepted version (not copyedited yet) is in this tab.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
REMA (Relational Ecological Momentary Assessment): A Deterministic Framework for Real-Time Dyadic Co-Regulation in Digital Mental Health
ABSTRACT
Background:
Digital mental-health tools typically rely on individual-level metrics and adaptive intervention logic, while the dyadic dynamics through which distress, support, escalation, and repair unfold remain under-specified.
Objective:
This paper introduces Relational Ecological Momentary Assessment (REMA), a bounded, transparent, and auditable framework for modeling moment-level dyadic co-regulation in digital mental health.
Methods:
REMA maps three low-burden inputs (Primary User Emotional Zone [Z], Supporter Contact Availability [Cp], and Supporter Emotional Surplus [Cs]) to 48 relational states. Each state is linked to a dominant Micro-Intervention Unit (MIU) category through explicit ethical constraints and a fixed rule hierarchy that prioritizes safety, access, and sustainability over relational elaboration.
Results:
As a methodological framework paper, this article specifies REMA's decision architecture rather than reporting empirical outcomes. It presents the tri-code input model, the full 48-state mapping, bounded MIU categories, conservative fallback rules for incomplete dyadic input, and a staged validation roadmap covering feasibility, construct validation, and pilot testing.
Conclusions:
REMA offers a testable architecture for dyadic digital mental-health support. Its contribution is methodological: to make relational decision rules explicit enough to implement, inspect, criticize, and empirically evaluate without relying on opaque prediction or unconstrained personalization.
Citation
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Copyright
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