Bigger Than PainBigger Than Pain

The Bigger Than Pain Systems Model of Pain Recovery

The Bigger Than Pain Systems Model is a dynamic model of chronic pain recovery developed by Sports Rehabilitator David Mc Gettigan (BSc.Hons. Sports Rehabilitation). Rather than measuring pain as a static number, it measures the direction a nervous system is travelling — toward recovery, or toward entrenchment — by weighing the brain's safety signals against its danger signals over time. It formalises established pain neuroscience into a single recovery-trajectory equation.

Why direction matters more than today's pain score

A pain score tells you where you are. It tells you nothing about where you're heading. Two people both rating their pain a “7 out of 10” can be on opposite trajectories — one whose system is slowly settling, one whose system is becoming more sensitised by the week. In persistent pain, the trajectory is what actually matters, because it predicts whether next month is better or worse. The Bigger Than Pain Systems Model exists to make that trajectory visible.

The core principle: pain tracks danger; recovery tracks safety

Modern pain science reframed pain as a protective output of the brain, not a readout of tissue damage. As Moseley put it, “pain increases with evidence of danger to body tissue, and decreases with evidence of safety” (Moseley 2007; Melzack 2001). Recovery, then, isn't about chasing a lower number today. It's about consistently tipping the balance toward safety, so the nervous system gradually re-learns that it no longer needs to protect so hard.

The model

dR/dt = β · ( 𝒮afety − 𝒟anger ) · R

In plain terms: the direction of your recovery depends on whether your safety signals outweigh your danger signals — multiplied by how much reserve your system has, and how readily it's currently learning.

  • dR/dt — the recovery trajectory. The rate and direction your system is changing. Positive = healing, capacity expanding; negative = becoming more entrenched; zero = the plateau, managing symptoms but the baseline isn't shifting.
  • R — systemic resilience. Your nervous system's current reserve, acting as a multiplier. High reserve, you respond faster; near-empty (burnout), even good inputs work slowly at first (McEwen 1998).
  • β — the neuroplastic rate. How readily your brain updates its internal maps. The goal isn't less plasticity — it's biasing its direction toward safety rather than threat (Flor 2003; Kuner 2017).

The Danger Load (𝒟) — the inputs we turn down

The sum of everything telling the brain that protection is still needed. Each one is modifiable — that's the point. 𝒟 = S(n) + P(e) + A(v) + E(s)

S(n)

Sensitisation

The noise floor — the nervous system firing at lower thresholds, so ordinary input can register as pain, even without ongoing tissue damage. (Woolf 2011)

P(e)

Prediction error

The brain anticipating and simulating pain before movement even happens — a learned “phantom threat.” (Tabor 2017; Büchel 2014)

A(v)

Affective vigilance

The volume knob — fear, hyper-focus and worry about the sensation, which amplify it and drive avoidance. (Vlaeyen 2000)

E(s)

Environmental load

The context — poor sleep, stress, systemic inflammation and social isolation. (Finan 2013; Eisenberger 2012; McEwen 1998)

The Safety Coherence (𝒮) — the inputs we build up

The sum of everything proving to the brain that the system is secure. Recovery happens when these grow faster than the danger load. 𝒮 = I(d) + M(c) + C(x)

I(d)

Internal inhibition

Your own pharmacy — the body’s built-in ability to dampen threat signals through descending inhibition and endogenous opioids; trainable. (Yarnitsky 2010)

M(c)

Movement confidence

The evidence — successful, non-threatening movement that disconfirms the brain’s pain prediction and overwrites the old “danger map.” (Vlaeyen 2001)

C(x)

Contextual safety

The anchor — external safety cues like a trusted clinician, social connection and stability, which recruit the brain’s own pain-control systems. (Colloca 2009)

What the evidence says

The model's central premise — that shifting the balance from danger to safety changes the trajectory of chronic pain — is exactly what the strongest recent trial in the field demonstrated. In a randomised controlled trial in JAMA Psychiatry, a treatment built on reappraising pain's danger value left 66% of chronic-back-pain patients pain-free or nearly pain-free, with effects “rarely observed in chronic pain trials” — and the benefit held at five-year follow-up (Ashar 2022).

The Bigger Than Pain Systems Model doesn't replace that science — it synthesises it. Central sensitisation, predictive processing, fear-avoidance, endogenous inhibition, graded exposure and context effects are usually taught as separate ideas. The model's contribution is to put them on one balance sheet, and to track the direction that balance produces over time.

From model to practice — and how the app applies it

The Bigger Than Pain Systems Model is the science behind the platform. Inside the app, it's put to work — helping you reduce the danger load, build safety coherence, and bias your nervous system's plasticity toward recovery, adapting to where you are day to day rather than just logging a pain score. The model is the why; the app is the how. It's a self-management approach, designed to work alongside your existing care, not instead of it.

What this is — and what it isn't

This is a conceptual model, offered with appropriate humility: it is consistent with the predictive-processing account of pain and grounded in the research below, but it is a framework for understanding recovery — not a diagnostic tool, not a measurement of you, and not medical advice. Persistent pain is individual, and some pain needs medical assessment first. Always see your GP about new, severe or changing symptoms.

References

  1. [1] Moseley GL (2007). Reconceptualising pain according to modern pain science. Physical Therapy Reviews. doi:10.1179/108331907X223010
  2. [2] Melzack R (2001). Pain and the neuromatrix in the brain. Journal of Dental Education. PMID:11780656
  3. [3] Ashar YK, et al. (2022). Effect of Pain Reprocessing Therapy vs Placebo and Usual Care for Patients With Chronic Back Pain: A Randomized Clinical Trial. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2021.2669
  4. [4] Woolf CJ (2011). Central sensitization: implications for the diagnosis and treatment of pain. Pain. doi:10.1016/j.pain.2010.09.030
  5. [5] Tabor A, Thacker MA, Moseley GL, Körding KP (2017). Pain: A Statistical Account. PLoS Computational Biology. doi:10.1371/journal.pcbi.1005142
  6. [6] Büchel C, Geuter S, Sprenger C, Eippert F (2014). Placebo analgesia: a predictive coding perspective. Neuron. doi:10.1016/j.neuron.2014.02.042
  7. [7] Vlaeyen JWS, Linton SJ (2000). Fear-avoidance and its consequences in chronic musculoskeletal pain: a state of the art. Pain. doi:10.1016/S0304-3959(99)00242-0
  8. [8] Finan PH, Goodin BR, Smith MT (2013). The association of sleep and pain: an update and a path forward. The Journal of Pain. doi:10.1016/j.jpain.2013.08.007
  9. [9] Eisenberger NI (2012). The neural bases of social pain. Psychosomatic Medicine. doi:10.1097/PSY.0b013e3182464dd1
  10. [10] McEwen BS (1998). Stress, adaptation, and disease: allostasis and allostatic load. Annals of the New York Academy of Sciences. doi:10.1111/j.1749-6632.1998.tb09546.x
  11. [11] Yarnitsky D (2010). Conditioned pain modulation (the diffuse noxious inhibitory control-like effect). Current Opinion in Anaesthesiology. doi:10.1097/ACO.0b013e32833c348b
  12. [12] Vlaeyen JWS, de Jong J, Geilen M, Heuts PHTG, van Breukelen G (2001). Graded exposure in vivo in the treatment of pain-related fear. Behaviour Research and Therapy. doi:10.1016/S0005-7967(99)00174-6
  13. [13] Colloca L, Benedetti F (2009). Placebo analgesia induced by social observational learning. Pain. doi:10.1016/j.pain.2009.01.033
  14. [14] Flor H (2003). Cortical reorganisation and chronic pain: implications for rehabilitation. Journal of Rehabilitation Medicine. doi:10.1080/16501960310010179
  15. [15] Kuner R, Flor H (2017). Structural plasticity and reorganisation in chronic pain. Nature Reviews Neuroscience. doi:10.1038/nrn.2016.162

— David Mc Gettigan (BSc.Hons. Sports Rehabilitation)

See what may be driving your pain

Your Clarity Report is a free, personalised starting point.

Start your Clarity Report