Page 97 - THE PERCEPT STUDY Illness Perceptions in Physiotherapy Edwin de Raaij
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For our research question ‘Is there a difference in added predictive value of poor recovery between the 4DSQ and the Brief IPQ-DLV?’, two regression models were built to examine the predictive value of baseline ‘poor recovery’ at 3 months.
In our first model, we entered age, gender and the baseline scores for generic prognostic factors (PI, limitations in PF, number of pain sites and duration of pain) and added the baseline score of the 4DSQ.
In our second model, we replaced the 4DSQ with the Brief-IPQ-DLV. To test the discrimination of the each model, a ROC-curve with Area Under the Curve (AUC) was applied. To compare the two AUCs, we used the empirical (non-parametric) method with NCSS 2020 software.
Results
A total of 251 (Nmax) participants was included in this study (Table1). We found missing data to be Missing Completely at Random (Little’s MCAR test p > 0.05). Numbers of missing items are reported in Table 2 in the ‘n’ column. A total of 237 participants was present at follow- up. The baseline characteristics of the fourteen participants lost to follow-up are described in Table 1 last column.
We found poor clinical recovery in 79 out of 204 participants (39%) for PI, 109 out of 200 (54.5%) for PF, and 59 out of 199 (30%) for GPE. Distribution of the generic prognostic factors according at baseline IPs for good or poor recovery, see Table 3.
Univariate logistic regression of Illness Perceptions with poor clinical recovery
Table 4 shows the results of the univariate logistic regression of baseline IPs with poor clinical recovery.
For the hierarchical model, the following IP dimensions were statistically significant and were therefore selected for entering in Block 2: for the clinical outcome PI, Timeline, Treatment Control, Identity, Concern, Coherence and Emotional Response; for PF, Consequences, Time- line, Identity, Concern and Emotional Response; for GPE, Consequences, Timeline, Treatment Control, Identity, Concern and Emotional Response.
In Block 2 of the model, we added all the univariate significantly associated IPs (Table 4) with the backward stepwise method. We report only the final models.
A multi-center longitudinal study
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