Patient Preference and Adherence (Jul 2022)
Self-Efficacy, Exercise Anticipation and Physical Activity in Elderly: Using Bayesian Networks to Elucidate Complex Relationships
Abstract
Xiaoying Chen,1,* Shuang Yang,2,* Huiwen Zhao,3 Rui Li,1 Wen Luo,3 Xiuli Zhang3 1The 2nd Ward of Knee Trauma Department, Tianjin Hospital, Tianjin, People’s Republic of China; 2Traumatic Orthopedics Department, The 2nd Ward of Hip Joint Surgery, Tianjin Hospital, Tianjin, People’s Republic of China; 3The 2nd Ward of Joint Surgery, Tianjin Hospital, Tianjin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wen Luo; Xiuli Zhang, The 2nd Ward of Joint Surgery, Tianjin Hospital, No. 406 Jiefangnan Road, Tianjin, 300211, People’s Republic of China, Tel +86 22-13116190054, Email [email protected]; [email protected]: To explore the correlation of exercise anticipation, self-efficacy and lower limb function in the elderly, and identify active predictors of exercise. The time up and go (TUG) has been used to access basic mobility skills, as well as strength, balance and agility, which is used in a range of population.Methods: A cross-sectional survey approach was employed in this study, assessing the functional relationship of the level of exercise anticipation, modified gait efficacy scale (mGES), self-efficacy for exercise scale (SEE), perceived efficacy of patient–physician interactions (PEPPI-10), behavioral regulation in exercise questionnaire (BREQ), and the time up and go (TUG) and International Physical Activity Questionnaire (IPAQ). Consequently, we constructed the Bayesian network model utilizing Genie 2.3, in order to effectively determine clear negative and positive correlations.Results: This investigation incorporated a total of 285 patients. The results of Spearman’s correlation analysis indicated that the TUG effectively correlated with age (r = 0.158, P < 0.01), drinking (r=− 0.362, P < 0.01), mGES (r=− 0.254, P < 0.01), PEPPI (r=− 0.329, P < 0.01), SEE (r =− 0.408, P < 0.01), BREQ (r = 0.676, P < 0.01), EA (r =− 0.688, P < 0.01) and IPAQ (r =− 0.742, P < 0.01). TUG can be used as the direct influencing factor of IPA, and five nodes in the model can be considered the primary indirect influencing factors of TUG, such as drinking, EA, age, sex and mGES in Bayesian network. The sensitivity analysis of the model confirmed that TUG (0.059), drinking (0.087), EA (0.335), age (0.080), sex (0.164), mGES (0.028) and hypertension (0.030) can become the sensitivity evaluation indicators of IPAQ in the elderly community population, in which the area under the ROC curve (AUC) was 59.6% (2207/3705), indicating a suitable prediction performance.Conclusion: Exercise anticipation and life behavior habit can effectively predict physical activity capability in the elderly. These findings can help clinicians establish effective intervention to improve the physical activity regularly of the elderly.Keywords: self-efficacy, physical activity, exercise anticipation, elderly
