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The Effect of Integrated Intervention Based on Protection Motivation Theory and Implementation Intention to Promote Physical Activity and Physiological Indicators of Patients with Type 2 Diabetes.

Mohammad Ali MorowatisharifabadMohammad AsadpourMohammad Ali ZakeriMahdi Abdolkarimi
Published in: BioMed research international (2021)
Despite benefits of physical activity, the level of physical activity is not desirable in patients with type 2 diabetes. The aim of this study is the using of integration of intervention based on the theory of protection motivation and implementation intention in order to improve the level of activity in patients with diabetes. This field trial study has been performed on 125 patients with type 2 diabetes. Samples have been randomly selected, and they are divided into two intervention and control groups. In the intervention group, training sessions were conducted based on the protection motivation theory and implementation intention. Physical activity levels, VO2 max, and hemoglobin A1C were measured before and three months after the intervention in the two groups. Data were analyzed by using SPSS 18, and independent t-test, paired t-test, and equivalent nonparametric tests were used for analyzing abnormal data. The results of this study showed that the level of physical activity was higher in the intervention group (p = 0.02). Also, the amount of hemoglobin A1c in the intervention group has been decreased significantly three months later (p < 0.001). In this study, VO2 max and blood lipids were not significantly different in the two groups. However, there was higher VO2 max compared to before the intervention in the intervention group. The present study showed that combining motivational interventions and implementing intention intervention can be effective in promoting the physical activity of patients with type 2 diabetes.
Keyphrases
  • physical activity
  • randomized controlled trial
  • body mass index
  • healthcare
  • primary care
  • clinical trial
  • sleep quality
  • machine learning
  • electronic health record
  • deep learning
  • double blind