Revised minimal important difference values for the visual analogue scale and Foot Health Status Questionnaire when used for plantar heel pain.

backgroundThe visual analogue scale (VAS) and the Foot Health Status Questionnaire (FHSQ) are patient-reported outcome measures that are frequently used to evaluate the management of plantar heel pain. This study aimed to re-calculate (i.e. revise) the minimal important differences (MIDs) of the VAS and the FHSQ when used for plantar heel pain to enhance the validity and precision of previous estimates.

methodsThis study used an anchor-based method to calculate MIDs and incorporated best-practice analyses to ensure credibility of the estimates. Data from 369 participants previously recruited from the community into four randomised controlled trials that evaluated interventions for plantar heel pain were used. VAS and FHSQ data from these participants at baseline and follow-up were pooled to calculate the MIDs (95% confidence intervals). A 15-point global rating of change Likert scale was used at follow-up as the transition scale, which was anchored to baseline. For the VAS, MIDs for two distinct types of pain were calculated: average pain and first-step pain. For the FHSQ, MIDs for two domains were calculated: foot pain and foot function.

resultsThe revised MIDs for the 100 mm VAS were -8.5 mm (95% CI: -12.2 to -4.7) for average pain and -19.2 mm (95% CI: -24.7 to -13.8) for first-step pain, which represent improvements in pain. The MIDs for the FHSQ were 12.4 points (95% CI: 6.9 to 18.0) for foot pain and 6.4 points (95% CI: 0.9 to 11.9) for foot function, which represent improvements in foot health status.

conclusionsThe revised MIDs from this study for the VAS and the FHSQ when used for plantar heel pain have enhanced validity and precision compared to previous estimates. This is important for clinicians and researchers as it provides a better understanding of how much improvement is required by an individual before an important change is experienced. The revised MIDs will also assist researchers with prospective sample size calculations, so future clinical trials are appropriately powered from a statistical standpoint.