Methods: The study was correlational with a questionnaire survey design. A purposive sample with a total of 301 valid cases was drawn from 2 teaching hospitals in central Taiwan. Structured questionnaires used for data collection included the Chinese Illness Perception Questionnaire-Revised (including identity, cause and illness representations), the Medication Adherence Inventory (MAI), the Self-Management Adherence Inventory (SMAI), the Treatment Satisfaction Questionnaire for Medication (TSQM), the Hypertension Quality of Life Questionnaire (HQOLQ), and Profile of Mood States (POMS). The IBM SPSS statistics (version 19.0 for PC) were used to explore descriptive statistics. The software of LISREL version 8.54 was used to examine the hypothesized structure model.
Results: The results of structural equation models showed that the modified model was better fit to the data than the theoretical model proposed by the SRM, because after adding illness identity into the original self- regulation model as an antecedent, the model fit indices were improved significantly. Illness identity was significantly and directly associated with negative illness representations, control, cause, HQOLQ and POMS. Negative illness representations were negatively related to TSQM and positively associated with SMAI. Control was negatively associated with SMAI and positively associated with MAI. Cause was negatively associated with MAI. Both SMAI and MAI were negatively and significantly related to both HQOLQ and POMS.
Conclusion: The findings suggested that illness identity may affect patients’ quality of life and mood states directly and indirectly through illness perceptions, adherence to prescribed medication and self- management. Yet, negative illness representations, control and cause may affect behavior outcomes indirectly through different pathways. In sum, the study findings provide evidence explaining possible mechanisms associated with adherence behaviors and behavior outcomes of patients with hypertension. Further study may validate the causal relationships using an experimental study design.
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