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Objective:To analyze the influence of E-Coaching self-management model on health behavior change in perimenopausal women.Methods:In this randomized controlled trial, 230 perimenopausal women who participated in health management prograam in the Health Management Center of Hangzhou Wuyunshan Hospital from January 2020 to October 2021 were selected as study objects by convenience sampling method. The subjects were divided into the experimental group and the control group with random number table (115 cases in each group). The experimental group was managed by health coaches with E-Coaching self-management model, and the control group was routinely managed by health managers. The intervention lasted for 6 months. Finally, 29 cases were lost to follow-up due to the failure of the subjects to comply with protocol requirements or voluntary withdrawal. So, a total of 201 subjects were included in the analysis (107 cases in the experimental group and 94 cases in the control group). χ2 test and t test were used to analyze the differences in modified Kupperman symptom score, perimenopausal knowledge and belief, regular exercise and dietary healthy behavior stage between the two groups. And the influence of E-Coaching self-management model on health behavior change in perimenopausal women was analyzed too. Results:After the intervention, the total score of modified Kupperman scale and the scores of insomnia, anxiety and fatigue in the experimental group were all lower than those in the control group [(7.36±2.91) vs (10.01±2.78) points, (0.49±1.13) vs (1.27±1.20) points, (0.80±0.99) vs (1.68±1.39) points, (0.67±0.55) vs (0.93±0.64) points]( t=6.553, 4.785, 5.219, 3.013, all P<0.05); and the total score of knowledge and belief questionnaire and the score of knowledge or belief dimension in the experimental group were significantly higher than those in control group [(25.15±1.55) vs (21.05±1.64) points, (9.61±0.56) vs (9.03±0.68) points, (15.54±1.53) vs (12.02±1.28) points] ( t=-18.238, -6.570, -17.801, all P<0.05). After the intervention, the proportions of the experimental group in the precontemplation and contemplation stage of exercise and diet were both significantly lower than those before intervention ( χ2=116.616, 139.964, both P<0.001), and were lower than those in the control group (the proportion of precontemplation stage of exercise was 7.5% vs 38.3%, and the contemplation stage of exercise was 26.2% vs 34.0%, χ2=38.330; the proportion of precontemplation stage of diet was 3.7% vs 23.4%, and the contemplation stage of diet was 18.7% vs 29.8%, χ2=25.399; all P<0.001). After the intervention, the proportion of the subjects in the preparation stage and action stage the experimental group were significantly higher than those before intervention ( χ2=116.616, 139.964, both P<0.001), and were higher than those in the control group (the proportion in preparation stage of exercise 18.7% vs 8.5%, and the action stage of exercise 47.7% vs 19.1%, χ2=38.330; the proportion in preparation stage of diet 20.6% vs 14.9%, and the action stage of diet 57.0% vs 31.9%, χ2=25.399; all P<0.001). Conclusion:E-Coaching self-management model can improve women′s perimenopausal symptoms in certain degrees, it improves their understanding of perimenopausal knowledge, enhances self-management beliefs and promotes healthy behavior changes.
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Objective:To investigate the relationship between mild cognitive impairment (MCI) and body mass index (BMI) in older adult patients with type 2 diabetes mellitus (T2DM).Methods:A total of 327 patients with T2DM who received treatment in Wuyunshan Sanatorium from January 2016 to May 2019 were included in the T2DM group. Patients in theT2DM group were subdivided into an MCI group ( n = 73) and a non-MCI group ( n = 254) according to whether they had MCI. An additional 100 older adult volunteers who concurrently received physical examination were included in the control group. Sex, age, years of education, monthly family income, body mass index, living habits (drinking, smoking) and laboratory indexes were compared among the groups. The influential factors of MCI in patients with T2DM were analyzed by logistic regression model. The predictive value of BMI for MCI in older adult patients with T2DM was evaluated by receiver operating characteristic (ROC) curve. Results:Age, monthly family income, the proportion of patients with a history of diabetes mellitus, BMI, fasting blood glucose, glycosylated hemoglobin (HbA1C), low-density lipoprotein cholesterol (LDL-C) in the T2DM group were (73.10 ± 9.56) years old, 8 926 yuan RMB, 189/327, (24.18 ± 2.64) kg/m 2, (6.96 ± 0.88) mmol/L, (7.10 ± 0.84)%, (7.32 ± 0.84) mmol/L, respectively, which were higher than those in the control group [(68.28 ± 8.21) years old, 6 715 yuan RMB, 13/100, (22.30 ± 1.74) kg/m 2, (4.51 ± 0.72) mmol/L, (5.62 ± 0.68)%, (7.04 ± 0.67) mmol/L, t = 4.554, χ2 = 18.601, 61.654, t = 6.668, t = 25.360, 16.077, 3.049, all P < 0.05]. In the MCI group, the proportion of patients having a monthly family income < 5 000 yuan RMB, the proportion of patients having a history of diabetes mellitus, BMI, HbA1c value were 29/73, 60/73, (24.92 ± 2.43) kg/m 2, (7.54 ± 0.88)%, respectively , while they were 70/254, 129/254, (23.77 ± 2.59) kg/m 2, (6.92 ± 0.81)%, respectively in the non-MCI group. There were significant differences in these indexes between MCI and non-MCI groups ( χ2 = 6.144, 22.927, t = 3.389, 5.652, all P < 0.05). Multivariate logistic regression analysis showed that BMI and HbA1c were the influential factors of MCI complicated by T2DM in older adult patients ( OR = 0.274, 0.192, both P < 0.05). Monthly family income and family history of diabetes mellitus were not closely related to the development of MCI in older adult patients with T2DM ( OR = - 0.154, 0.093, both P > 0.05). The ROC curve revealed that when BMI value was 24.49 kg/m 2, Youden index was the largest (0.510), the corresponding sensitivity was 83.86%, and the specificity was 67.12%. The area under the ROC curve was 0.766 [95% CI (0.699 - 0.832)]. Conclusion:BMI is an influential factor of MCI development in older adult patients with T2DM, and may be one of the important indicators for early prediction of MCI.
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ABSTRACT Sarcopenia, a concept reflecting the loss of skeletal muscle mass, was reported to be associated with the prognosis of several tumors. However, the prognostic value of sarcopenia in patients with renal cancer remains unclear. We carried out this metaanalysis and systematic review to evaluate the prognostic value of sarcopenia in patients with renal cell carcinomas. We comprehensively searched PubMed, Embase, and Cochrane Library from inception to December 2018. Hazard ratio (HR) and 95% confidence interval (CI) were pooled together. A total of 5 studies consisting of 771 patients were enrolled in this quantitative analysis, 347 (45.0%) of which had sarcopenia. Patients with sarcopenia had a worse OS compared with those without sarcopenia (HR=1.76; 95%CI, 1.35-2.31; P <0.001). In the subgroup of patients with localized and advanced/metastatic diseases, sarcopenia was also associated with poor OS (HR=1.48, P=0.039; HR=2.14, P <0.001; respectively). With a limited sample size, we did not observe difference of PFS between two groups (HR=1.56, 95% CI, 0.69-3.50, P=0.282). In the present meta-analysis, we observed that patients with sarcopenia had a worse OS compared with those without sarcopenia in RCC. Larger, preferably prospective studies, are needed to confirm and update our findings.
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Humans , Carcinoma, Renal Cell/complications , Sarcopenia/complications , Kidney Neoplasms/complications , Prognosis , Prospective StudiesABSTRACT
ObjectiveTo analyze the influencing factors and explore the countermeasures of patients with complication of lower respiratory tract infection after tracheotomy in intensive care unit (ICU).Methods The clinical data of 382 patients with tracheotomy admitted to ICU of Hangzhou Third People's Hospital from March 2015 to March 2016 were retrospectively analyzed, including 153 patients with complicated lower respiratory tract infection as the infected group, and 229 cases without the infection as the no-infected group. The gender, age, emphysema, respiratory failure, time of admission to ICU, the kinds of antimicrobial agents used, time length of applying antimicrobial agents, aerosol inhalation, airway opening time, invasive operation, surgical opportunity and so on were analyzed in the two groups by univariate analysis. In order to screen out the independent risk factors for patients with complication of lower respiratory tract infection after tracheotomy in ICU, the multiple logistic regression analysis was used on the statistically significant risk factors found by using univariate analysis.Results There were statistically significant differences in age, emphysema, primary disease, respiratory failure, time of admission to ICU, the kinds of antimicrobial agents used,time length of using antimicrobial agents, aerosol inhalation, airway opening time, invasive operation and the time of mechanical ventilation between infected group and non-infected group (allP < 0.05). The single factor analysis showed that age [odds ratio (OR) = 5.868, 95% confidence interval (95%CI) = 2.790-10.342,P = 0.000), cerebral hemorrhage (OR = 3.920, 95%CI = 2.250-6.540,P = 0.034), cerebral infarction (OR = 1.048, 95%CI = 1.005-1.092,P = 0.027), emphysema (OR = 5.995, 95%CI = 2.851-8.374,P = 0.001), respiratory failure (OR = 5.022, 95%CI = 2.107-10.244, P = 0.009), time of admission to ICU (OR = 4.968,95%CI = 2.461-8.236,P = 0.003), airway opening time (OR = 4.149, 95%CI = 1.298-9.027,P = 0.019), the kinds of antimicrobial agents used (OR = 4.364, 95%CI = 1.166-9.339,P =0.029), time length of using antimicrobial agents (OR = 3.944, 95%CI = 1.546-7.622,P = 0.027), aerosol inhalation (OR = 2.052, 95%CI = 1.150-5.042,P = 0.014), invasive operation (OR = 3.467, 95%CI = 2.869-8.956,P = 0.000), surgical opportunity (OR = 0.366, 95%CI = 0.175-0.763,P = 0.037), the time of mechanical ventilation (OR = 0.981, 95%CI = 0.966-0.996,P = 0.041)were risk factors for patients with lower respiratory tract infection after tracheotomy in ICU. The multivariate logistic regression analysis showed that the risk factor sequence of influencing degree from high to low on occurrence of lower respiratory tract infection in patients after tracheotomy in ICU was as follows: time of admission to ICU (OR = 5.697, 95%CI = 2.891-8.739,P = 0.001), respiratory failure (OR = 5.543, 95%CI = 2.347-9.882, P = 0.012), emphysema (OR = 5.388, 95%CI = 2.671-7.963,P = 0.002), invasive operation (OR = 4.987, 95%CI =3.644-9.876,P = 0.014), time of using antimicrobial agents (OR = 4.823, 95%CI = 1.369-8.542,P = 4.823), the kinds of antimicrobial agents used (OR = 4.514, 95%CI = 1.369-8.542,P = 0.022), age (OR = 4.395, 95%CI = 2.194-8.786, P = 0.013), airway opening time (OR = 3.287, 95%CI = 2.542-9.677,P = 0.036) and aerosol inhalation (OR = 2.141, 95%CI = 1.242-5.211,P = 0.045).Conclusions The time of admission to ICU, invasive operation, emphysema and so on are the main risk factors of patients with complication of lower respiratory tract infection after tracheotomy in ICU, thus, corresponding measures should be directed to the risk factors and formulated to strengthen the prevention in order to control the occurrence of lower respiratory tract infections after tracheotomy in ICU.