Table 1.

Recommendations for the next generation of pain research of psychological factors.

1. Use and report precise definitions of your constructs.57 Ontologies may help to manage and structure constructs, definitions, and their relationships.51

2. Critically appraise whether the (self-report) instrument actually measures the intended construct and is not conceptually overlapping with the outcome. This can be achieved using content validity methods.14,35

3. Investigate whether participants understand the self-report items as intended by the researcher. This can be achieved by cognitive interviewing techniques.4

4. Select an appropriate timescale that allows to detect the effect of the cause.28 The effect of some causes may only emerge after a large time scale. Other causes have immediate and short-lived effects, in which more time-intensive designs (Ecological Momentary Assessment methods and measurement burst designs) should be preferred.73

5. Take into account alternative explanations (eg, confounders) in designing a study and analyzing data.26 Graphic visualizations such as knowledge graphs and causal directed acyclic graphs have proven to be useful tools.20,77

6. Reflect on whether and how other (background) causes might interact.45,68 If insufficient knowledge about the causal relationships and synergies between risk factors exists within the literature or experts (top-down), they may also be identified from the data (bottom–up) with causal discovery methods.23

7. Be transparent and explicit about your causal thinking. Prediction and causation are 2 different objectives in data science. When the aim is to prevent or intervene on pain problems, the interest is in causation.20,77