Behavioral diagnosis
Also known as: Behavioural diagnosis
Pinpointing exactly which behavior, by whom, and which barriers drive a problem before designing any solution.
What it means
Behavioral diagnosis is the disciplined first phase of intervention design: define the specific target behavior in observable terms, identify the precise audience that must perform it, and analyze the determinants — capabilities, opportunities, motivations, and frictions — that currently block or enable it. It insists on understanding the problem before reaching for a tool, countering the common reflex of applying a favorite nudge to a vaguely specified issue. Frameworks like COM-B and the Theoretical Domains Framework supply the diagnostic categories, while observation and data supply the evidence. Done poorly, it slides into assumption; done well, it converts 'people should recycle more' into 'these residents don't separate food waste because bins are inconvenient and the norm is invisible.' It matters because the wrong diagnosis guarantees the wrong cure, however elegant the intervention.
Where the idea comes from
The word 'diagnosis' is borrowed on purpose from clinical medicine: examine before you prescribe. Lawrence Green and Marshall Kreuter built this logic into health promotion in 1980, when the first edition of their planning model (later PRECEDE-PROCEED) carried the subtitle 'A Diagnostic Approach' and made planners work backward from an outcome through behavioral and educational diagnosis before choosing a program. Decades later Susan Michie and colleagues formalised the same instinct for behavioural science with the Behaviour Change Wheel, whose hub, the COM-B model, is described explicitly as the basis for a 'behavioural diagnosis.' Its companion Theoretical Domains Framework unpacks those broad categories into fourteen determinant domains. The through-line across both traditions is a refusal to let a favoured solution precede a structured reading of the problem.
How a diagnosis is done
A diagnosis moves from a vague concern to a testable account in a few steps. First, specify the behaviour in observable terms, who performs which action, in what context, and when, so 'improve safety' becomes 'ward nurses clean their hands before touching a patient.' Second, where several behaviours could help, choose the few with the most impact and the best odds of shifting. Third, interrogate the determinants: use COM-B or the TDF as a checklist, and fill it with observation, interviews and routine data rather than assumption. The output is a determinant statement, which capability, opportunity or motivation is missing, that points at the intervention functions likely to work. Triangulation matters, because what people say blocks them and what actually blocks them often differ.
What the evidence shows
The diagnostic frameworks are now the default vocabulary of applied behavioural science: COM-B, the TDF and the Behaviour Change Wheel appear in thousands of studies and public-health toolkits. That adoption mainly reflects their usefulness as an organising language, not proof that diagnosis raises success rates. Reviews of applied work find the frameworks are often used to classify barriers after the fact, applied inconsistently, and seldom used to predict which determinant matters most. COM-B is a descriptive model, not a falsifiable theory, and critics note that almost any factor can be filed into one of its boxes, which makes it hard to be wrong. Trials comparing carefully diagnosed interventions with undiagnosed ones are scarce, so the stronger claim is procedural: diagnosis disciplines design, even where its own effect size is unmeasured.
Where it breaks down
The failure modes are mostly about false confidence. Tidy categories can lend a guess the appearance of a finding: observations get fitted into COM-B boxes, and the diagnosis merely confirms the practitioner's prior. Because the analysis is only as good as its inputs, one built on self-reported barriers can miss the real friction, since people rationalise and stated reasons rarely match observed behaviour. Diagnosing at the level of the individual can also mask structural causes: when the true problem is price, staffing or policy, no amount of capability-and-motivation mapping will name it. And the discipline can tip into analysis paralysis. The safeguard is to treat a diagnosis as a hypothesis to test against behaviour, not a verdict, and to revisit it once the intervention meets reality.
Examples
Before designing a hand-hygiene drive, a hospital diagnoses that nurses know and care but lack accessible sanitizer at the point of care — an opportunity gap, not a motivation gap.
A council assumes residents skip food-waste recycling out of apathy; watching them shows the caddy sits outside in the rain — an opportunity problem, not a motivation one.
Before a campaign urging staff to save more, a firm finds people are willing but cannot locate the form, so the target behavior becomes 'log in and click once', not 'care about retirement'.
A revenue agency assumes late filers are evasive; interviews show most lose track of deadlines and find the form confusing, so the target becomes a reminder and fewer fields, not tougher penalties.
A software team blames low feature adoption on user apathy; session recordings show newcomers never find the setup wizard, reframing the problem as a discoverability barrier rather than a motivation one.
First described in Applied behavioral science practice.
Key references
- Sari, T. B., Ningsih, A. P., Makkau, B. A., & Sudirham. (2024). Capabilities, opportunities and motivations (COM) model for understanding changes in behavior: a critical examination. Journal of Public Health, 46(2), e336-e337. doi.org/10.1093/pubmed/fdad252
- Atkins, L., Francis, J., Islam, R., et al. (2017). A guide to using the Theoretical Domains Framework of behaviour change to investigate implementation problems. Implementation Science, 12, 77. doi.org/10.1186/s13012-017-0605-9
- Cane, J., O'Connor, D., & Michie, S. (2012). Validation of the theoretical domains framework for use in behaviour change and implementation research. Implementation Science, 7, 37. doi.org/10.1186/1748-5908-7-37
- Michie, S., van Stralen, M. M., & West, R. (2011). The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implementation Science, 6, 42. doi.org/10.1186/1748-5908-6-42
- Green, L. W., Kreuter, M. W., Deeds, S. G., & Partridge, K. B. (1980). Health Education Planning: A Diagnostic Approach. Palo Alto, CA: Mayfield. libraryopac.searo.who.int/bib/172
- Green, L. W., & Kreuter, M. W. (2005). Health Program Planning: An Educational and Ecological Approach (4th ed.). New York: McGraw-Hill. books.google.com/books/about/Health_Program_Planning_An_Educational_a.html?id=_VRqAAAAMAAJ