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Measuring Depression and Anxiety With 4 Items? Adaptation of the PHQ-4 to Increase Its Sensitivity to Subclinical Variability

Makowski, D., Te, A. S., Neves, A., & Chen, S. A.

Collabra: Psychology, 2025

The PHQ-4 screens depression and anxiety in four items, and its coarse response scale makes it blunt below clinical thresholds. Adding one option — "once or twice", between "not at all" and "several days" — was used heavily by participants and, across a pilot (N=485) and a validation study (N=836), improved sensitivity to subclinical variation at essentially no cost.

Abstract

The PHQ-4 is an ultra-brief (4 items) screening questionnaire for depression and anxiety. In this brief report, we test the benefits of adding one additional response option (“Once or twice”, in between “Not at all” and “Several days”) to improve the scale’s sensitivity to milder alterations, and thus increase its usefulness in subclinical populations. A first pilot study (N=485) showed a prevalent usage of the new response option by participants. Its utility was confirmed in a follow-up validation study (N=836), where we that the refined version offers an improved sensitivity to subclinical variability in depression (indexed by the BDI-II) as compared to the original version. In conclusion, adding the “once or twice” response option is a low-cost way of increasing the PHQ-4’s sensitivity to subclinical variability, making it a tool of choice for general population research.

Keywords: Psychometrics · Mental Health

Cite

Makowski, D., Te, A. S., Neves, A., & Chen, S. A. (2025). Measuring Depression and Anxiety With 4 Items? Adaptation of the PHQ-4 to Increase Its Sensitivity to Subclinical Variability. Collabra: Psychology. https://doi.org/10.1525/collabra.143431

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Exploring the neuropsychology of reality and its distortions.

The Reality Bending Lab, led by Dominique Makowski at the University of Sussex in Brighton, UK, conducts world-leading research on reality perception, illusions, fake news, AI-beliefs, deception and its links with emotions, cognitive control and the Self. Using recording signals from the body (ECG, EDA…) and the brain (EEG), we analyse data using advanced modelling (Bayesian statistics, chaos theory, computational models) and develop open-source tools to improve neuropsychological science.

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