The CP-10: An Ultra-Brief Ecological Momentary Assessment Instrument for Dynamic Coherence Measurement

Author: Nathan Veil (Applied Coherence Institute)
Date: May 12, 2026
Classification: Ecological Momentary Assessment / Dynamic Measurement / Digital Phenotyping
Document Type: Instrument Development / EMA Protocol (Proposed)

Status Notice

Status	This paper describes a proposed ultra‑brief EMA instrument for dynamic coherence measurement. No empirical validation has been conducted. All items, protocols, and psychometric targets are proposed for future validation studies.

Abstract

The Coherence Metrics Framework has been operationalized through the CP-100 (deep assessment) and CP-25 (brief screening). Both instruments, however, are designed for static trait measurement. This paper introduces the CP-10, an ultra‑brief (10‑item, 30‑60 second) ecological momentary assessment (EMA) instrument for dynamic state measurement of coherence. The CP-10 samples one item per coherence domain (physiological, cognitive, behavioral, relational, environmental) × two dimensions (state affect, regulatory capacity). It is designed for high‑frequency repeated administration (4‑6× daily) over extended periods (7‑14 days). The paper presents: (1) theoretical rationale for dynamic coherence measurement, (2) item selection methodology, (3) proposed administration protocols, (4) integration with passive sensing (HRV, screen time, accelerometry), (5) EMA study design recommendations, (6) proposed psychometric targets (within‑person reliability, responsiveness to change, state‑trait decomposition), and (7) analysis frameworks (multilevel modeling, dynamic structural equation modeling). The CP-10 is offered as a research instrument for future validation.

Keywords: ecological momentary assessment, EMA, dynamic coherence, state measurement, within‑person variability, digital phenotyping

1. Introduction

Static trait measures capture baseline stability. They cannot capture:

Phenomenon	Why It Matters
Within‑day fluctuations	Coherence varies across morning, afternoon, evening
Stressor response	How quickly does coherence drop after an extraction event?
Recovery rate	How quickly does coherence return to baseline?
Intervention timing	When are coherence practices most effective?
Dynamic regulation	Is coherence stable, oscillating, or deteriorating?

The CP-10 is designed to fill this gap. It is an ultra‑brief (10‑item, 30‑60 second) ecological momentary assessment (EMA) instrument for high‑frequency repeated measurement of dynamic coherence.

Status Note: This is a proposed instrument. No empirical validation has been conducted.

2. Theoretical Rationale

2.1 Why EMA for Coherence?

Static Trait (CP-25)	Dynamic State (CP-10)
“Generally, I can focus”	“Right now, I can focus”
Monthly measurement	4‑6× daily for 7‑14 days
Between‑person differences	Within‑person fluctuations
Baseline stability	Stressor response, recovery
Trait coherence	State coherence

Both are needed. Trait coherence predicts long‑term resilience. State coherence captures real‑time regulatory dynamics.

2.2 CP-10 Design Principles

Principle	Implementation
Ultra‑brief	10 items, 30‑60 seconds completion time
Domain coverage	One item per coherence domain (physiological, cognitive, behavioral, relational, environmental)
Dual dimension	State affect (“right now I feel…”) + regulatory capacity (“right now I can…”)
EMA‑optimized	Simple language, consistent response scale, mobile‑first
Low burden	Minimizes compliance decay over 7‑14 days

3. CP-10 Item Set (Proposed)

3.1 Response Scale

All items use a 5‑point Likert scale:

Value	Label
1	Not at all
2	Slightly
3	Moderately
4	Very much
5	Extremely

3.2 Core Items

Physiological Coherence (P)

Item	Dimension	Source
P1: “Right now, my body feels calm.”	State affect	CP-25 P‑S1
P2: “Right now, I have steady energy.”	Regulatory capacity	CP-25 P‑T3

Cognitive Coherence (C)

Item	Dimension	Source
C1: “Right now, I can focus without distraction.”	Regulatory capacity	CP-25 C‑T1
C2: “Right now, my mind feels clear.”	State affect	CP-25 C‑S5 (new)

Behavioral Coherence (B)

Item	Dimension	Source
B1: “Right now, I am doing what I intend to do.”	Regulatory capacity	CP-25 B‑T1 (adapted)
B2: “Right now, I feel aligned with my values.”	State affect	CP-25 B‑T3 (adapted)

Relational Coherence (R)

Item	Dimension	Source
R1: “Right now, I feel safe in my current interactions.”	State affect	CP-25 R‑S1
R2: “Right now, I can connect with others if I want to.”	Regulatory capacity	CP-25 R‑T5 (new)

Environmental Coherence (E)

Item	Dimension	Source
E1: “Right now, my environment feels predictable.”	State affect	CP-25 E‑T1
E2: “Right now, I can find what I need.”	Regulatory capacity	CP-25 E‑S5

3.3 Ultra‑Brief Version (CP-5, for extreme burden contexts)

Domain	Single Item (Regulatory capacity focus)
P	“Right now, my body feels calm.”
C	“Right now, I can focus.”
B	“Right now, I am doing what I intend to do.”
R	“Right now, I feel safe with others.”
E	“Right now, my environment feels predictable.”

The CP-5 is proposed for studies where 10 items remain burdensome (e.g., 8+ daily prompts over 14 days).

4. Proposed Administration Protocols

4.1 EMA Schedule

Parameter	Recommendation	Rationale
Prompts per day	4‑6	Sampling across morning, afternoon, evening
Prompt window	10 AM – 8 PM	Avoids sleep interference
Minimum interval	2 hours	Precludes back‑to‑back prompts
Response window	15‑30 minutes	Balances compliance and recall
Duration	7‑14 days	Sufficient for stability estimation
Random vs. fixed	Semi‑random (random within blocks)	Avoids anticipation effects

4.2 Sample Prompt Schedule (4× day)

Prompt	Time Window
1	10:00‑11:00 AM
2	1:00‑2:00 PM
3	4:00‑5:00 PM
4	7:00‑8:00 PM

4.3 Mobile Interface Requirements

Requirement	Implementation
Notification	Push notification with sound (user configurable)
Response	Tap‑based rating (1‑5, emoji‑anchored optional)
Completion time	Display timer (optional)
Missed prompts	Reminder after 10 minutes (1x only)
Compliance tracking	Automatic logging of response times, missed prompts
Offline mode	Store responses locally, upload when connectivity restored

5. Integration with Passive Sensing

Passive Sensor	Metric	Hypothesized Relationship with CP-10
HRV (wearable)	RMSSD, HF power	Positive with P items
Screen time (phone)	Minutes, unlocks	Negative with C items
Accelerometry	Movement, step count	Moderate with P items
Location (GPS)	Time at home, work	Contextual variable
Bluetooth	Proximity to known contacts	Positive with R items
Light sensor	Ambient light	Contextual variable (circadian)

Example passive‑EMA integration model:

Level	Variable	Source
Within‑person (Level 1)	CP-10 state scores	EMA self‑report
Within‑person (Level 1)	HRV, screen time	Passive sensing
Between‑person (Level 2)	Trait coherence	CP-25 baseline
Day‑level (Level 2)	Sleep duration, stressor events	Daily diary

6. Proposed Psychometric Targets

6.1 Within‑Person Reliability

Statistic	Target	Method
Within‑person reliability (Rc)	> 0.70	Multilevel reliability (Bolger et al., 2012)
Number of prompts needed for reliable person mean	10‑15	Generalizability theory
Within‑subject SD	0.30‑0.50 (1‑5 scale)	Descriptive

6.2 Between‑Person Reliability (Trait)

Statistic	Target	Method
ICC (average of 14 days)	> 0.80	Multilevel modeling
Separation of persons	> 2.0	Reliability of person mean

6.3 Responsiveness to Change

Parameter	Target	Interpretation
Minimal detectable change (MDC)	0.3‑0.5 points	Smallest change detectable beyond measurement error
Responsiveness to intervention	Cohen’s d > 0.50	CP-10 should detect intervention effects
Stressor response slope	Negative 0.1‑0.3 points per hour	CP-10 should detect recovery curves

6.4 State‑Trait Decomposition

Variance Component	Target	Interpretation
Between‑person (trait)	40‑60%	Stable individual differences
Within‑person (state)	30‑50%	Fluctuation, responsiveness
Measurement error	10‑20%	Acceptable for EMA

7. Proposed EMA Study Designs

7.1 Naturalistic Fluctuation Study

Parameter	Specification
Design	Observational, no intervention
Duration	14 days
Prompts	4‑6× daily
Sample	N = 100
Passive sensing	HRV, screen time, accelerometry
Daily diary	Sleep, stressors, coherence practices

Hypothesis: CP-10 state coherence will show within‑person variability associated with sleep, stressors, and practices.

7.2 Stressor Response Protocol

Parameter	Specification
Design	Controlled stressor (e.g., Trier Social Stress Test)
Measurement	Pre‑stressor, immediate post‑stressor, 30min, 60min, 120min
Sample	N = 50
Comparison	CP-10 vs. HRV vs. self‑reported stress

Hypothesis: CP-10 will drop post‑stressor and recover faster for high‑trait coherence individuals.

7.3 Intervention EMA Study

Parameter	Specification
Design	Pre‑post intervention with EMA during last week of each phase
Duration	Baseline (7d EMA) → 8‑week intervention → Post (7d EMA)
Sample	N = 100 (50 intervention, 50 waitlist control)
Intervention	Coherence intervention protocol (Paper 2)

Hypothesis: CP-10 state coherence will increase post‑intervention; within‑person variability may decrease (stabilization).

7.4 Practice Micro‑Randomized Trial

Parameter	Specification
Design	Micro‑randomized trial (MRT)
Duration	14 days
Randomization	Daily random assignment to coherence practice (yes/no)
Proximal outcome	CP-10 score 30‑60 minutes post‑practice
Sample	N = 50

Hypothesis: Coherence practices cause immediate increases in CP-10 state coherence.

8. Analysis Frameworks

8.1 Multilevel Modeling (MLM)

Model	Equation
Null model	CP10_it = β0_i + ε_it
Within‑person	CP10_it = β0_i + β1(Time_it) + β2(Stressor_it) + ε_it
Between‑person	β0_i = γ00 + γ01(Trait_i) + ζ0_i

8.2 Dynamic Structural Equation Modeling (DSEM)

Feature	Application
Autoregressive (AR1)	Coherence inertia (how much does past predict future?)
Cross‑lagged	Do practices predict subsequent coherence?
Time‑varying effects	Does practice efficacy change over study duration?
Multilevel DSEM	Separate within‑person and between‑person dynamics

8.3 Change Point Detection

Method	Application
Bayesian change point	Detect when coherence trajectory shifts (e.g., after intervention begins)
CUSUM	Identify periods of destabilization (pre‑collapse warning)

8.4 Dynamic Time Warping

Application	Use
Person‑specific patterns	Cluster individuals by coherence trajectory shape
Practice optimization	Identify practice timing most effective for each individual

9. Relationship to CP-100, CP-25, and CP-O

Instrument	Function	Time Scale	Administration
CP-100	Deep trait assessment	Month‑year	Baseline (20 min)
CP-25	Brief trait screening	Week‑month	Occasional (5 min)
CP-O	Observer trait	Week‑month	Per observer (5‑10 min)
CP-10	Dynamic state assessment	Moment‑day	EMA (30‑60 sec, 4‑6× daily)
CP-5	Ultra‑brief state	Moment	EMA (15‑20 sec, high frequency)

10. Planned Validation Studies

Study	Description	Sample	Status
1	EMA feasibility (compliance, burden)	N = 50	Planned
2	Within‑person reliability (14 days)	N = 100	Planned
3	Stressor response (laboratory)	N = 50	Planned
4	State‑trait decomposition (EMA + CP-25)	N = 100	Planned
5	Intervention EMA (pre‑post)	N = 100	Planned
6	Micro‑randomized trial (practice optimization)	N = 50	Planned

11. Limitations

Limitation	Mitigation
No empirical validation yet	Proposed instrument; validation studies required
EMA compliance burden	Ultra‑brief (30‑60 sec); incentives; user‑friendly design
Response bias	Anchored scales; random prompt timing
State measurement error	Multiple prompts per day; aggregation improves reliability
Generalizability	Multi‑sample validation needed
Retrospective recall	Minimized by close‑to‑event measurement

12. Conclusion

The CP-10 is a proposed ultra‑brief (10‑item, 30‑60 second) EMA instrument for dynamic state measurement of coherence across five domains. It enables:

Within‑person variability analysis
Stressor response and recovery measurement
Intervention response tracking
Practice micro‑randomized trials
Integration with passive sensing (HRV, screen time, accelerometry)

The CP-10 does not replace static trait measures (CP-25, CP-100). It complements them, capturing the temporal dynamics of coherence that static measures cannot.

“Coherence is not a photograph. It is a film. The CP-10 captures the frames.”

13. References

(Full references as in prior papers, plus EMA literature)

Bolger, N., & Laurenceau, J. P. (2013). Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. Guilford Press.

Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1‑32.

Trull, T. J., & Ebner‑Priemer, U. W. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151‑176.

Wichers, M. (2014). The dynamic nature of depression: A new micro‑level perspective of mental disorder that meets current challenges. Psychological Medicine, 44(7), 1349‑1360.