METHODS: A systematic literature search was conducted using CINAHL, EMBASE, Ovid MEDLINE, PsycINFO and SPORTDiscus databases to retrieve articles published from 1st January 2000 to 31st December 2017. Randomised controlled trials (RCTs) and quasi-experimental studies comparing different strategies in managing overweight and obesity among schoolchildren (6 to 12 years of age) were included. The main outcomes of interest were reductions in weight related variables included anthropometry and body composition measurements. All variables were analysed using random effects meta-analyses.
RESULTS: Fourteen studies were reviewed, 13 were RCTs and one was a quasi-experimental study. The risk of bias for randomisation was low risk for all of RCTs except for one, which was unclear. The risk of bias for randomisation was high for the quasi-experimental study. Most interventions incorporated lifestyle changes and behavioural strategies such as coping and problem solving skills with family involvement. The meta-analyses did not show significant effects of the intervention in reducing weight related outcomes when compared with controls.
CONCLUSION: Meta-analyses of the selected studies did not show significant effects of the interventions on weight related outcomes among overweight and obese schoolchildren when compared with controls. The role of interdisciplinary team approaches with family involvement using behaviour and lifestyle strategies to curb obesity among schoolchildren is important.
DESIGN: Cross-sectional study.
SETTING: Three public primary care clinics in a district in Selangor, Malaysia.
PARTICIPANTS: Registered patients aged 55 years and above.
MEASUREMENTS: A face-to-face interview was conducted using a validated questionnaire of Medical Outcome Study 36-item short form health survey (SF-36). The outcome measure was the health related quality of life (HRQoL) and other factors measured were socio demography, physical activity, social support (Duke-UNC Functional Social Support Questionnaire), and presence of non-communicable diseases.
RESULTS: A total of 347 participants had non-communicable diseases which included hypertension (41.8%), type 2 diabetes (33.7%), asthma (4.8%), hyperlipidaemia (1.7%), coronary heart disease (1.2%), and osteoarthritis (0.2%). Age ≥ 65 years old (OR =2.23; 95%CI=1.42, 3.50), single (OR=1.75; 95%CI=1.06,2.90), presence of co-morbid condition (OR=1.66; 95%CI=1.06, 2.61), and poorer social support (OR=2.11; 95%CI=1.27, 3.51; p=0.002) were significant predictors of poorer physical component of HRQoL . In predicting lower mental health component of HRQoL, the significant predictors were women (OR=2.28; 95%CI=1.44, 3.62), Indian ethnicity (OR=1.86; 95%CI=1.08, 3.21) and poorer social support (OR=2.71; 95%CI=1.63, 4.51). No interactions existed between these predictors.
CONCLUSION: Older people with non-communicable diseases were susceptible to lower health related quality of life. Increasing age, single, presence of co-morbid conditions, and poorer social support were predictors of lower physical health component of HRQoL. While the older women, Indian ethnicity and poorer social support reported lower mental health component of HRQoL.
METHOD: Pub Med, Web of Science, Science Direct, Ovid Medline, EBSCO, ProQuest, Google Scholar, and the Scientific Information Database (SID) were searched for English and Persian language studies published between 2009 and 2017. The primary outcome of this review was to assess the effects of behavioral interventions on glycosylated hemoglobin. Changes in the blood pressure, Lipid profiles, BMI, Self-efficacy, knowledge, attitude, practice, Self-care behaviors, social support, anxiety, and depression were the secondary outcomes.
RESULTS: Comprehensive search procedures resulted in twenty-three experimental studies with 2208 participants. Eleven studies were included in the Meta-analysis. Compared with the control group, behavioral interventions significantly lower glycosylated hemoglobin -0.61% (95% CI -0.80, -0.41). To explore the effects of the study intervention (regarding what aspects of the intervention are most effective), we then conducted a stratified analysis for HbA1c. Larger effects were found in interventions with a longer duration and higher intensity, delivered in the group format, interventions offered to individuals with higher baseline HbA1c, and interventions delivered by a multidisciplinary team. Moreover, behavioral interventions were effective in improving blood glucose, lipid profiles, knowledge, attitude, practice, self-efficacy, quality of life, and self-care.
CONCLUSION: In line with other behavioral studies, our study shows that behavioral interventions improve self-management in Iranian adults with type 2 diabetes.
METHODS: A qualitative case study was employed for this research. Semi-structured, in-depth interviews and focus group discussions were conducted on WeChat. Participants were purposively sampled through snowball sampling in Hainan and Dalian, China. A total of 28 older adults aged 60-75 and six adult children were interviewed until data saturation was achieved, followed by a thematic analysis.
RESULTS: The expectations of smart nursing homes include: 1) quality of care supported by governments and societies; 2) smart technology applications; 3) the presence of a skilled healthcare professional team; 4) access to and scope of basic medical services; and 5) integration of medical services. The acceptability of smart nursing homes included factors such as stakeholders' perceived efficaciousness, usability, and collateral damages of using smart technologies, and the coping process of adoption was influenced by factors such as age, economic status, health status, education, and openness to smart technologies among older adults.
CONCLUSIONS: Chinese older adults and their family members have a positive perception of the smart nursing home model. The qualitative evidence regarding their expectations and acceptability of smart nursing homes contributes valuable insights for a wide range of stakeholders involved in the planning and implementation of smart nursing homes.
METHODS: This was an exploratory sequential mixed methods study, where the qualitative case study was conducted in Hainan and Dalian, while the survey was conducted in Xi'an, Nanjing, Shenyang, and Xiamen. The validation of EASNH-Q also included exploratory and confirmatory factor analyses. Multinomial logistic regression analysis was used to estimate the determinants of expectations and acceptability of SNHs.
RESULTS: The newly developed EASNH-Q uses a Likert Scale ranging from 1 (strongly disagree) to 5 (strongly agree), and underwent validation and refinement from 49 items to the final 24 items. The content validity indices for relevance, comprehensibility, and comprehensiveness were all above 0.95. The expectations and acceptability of SNHs exhibited a strong correlation (r = 0.85, p