METHODS: A mediation model was tested to test the hypothesis that social comparison orientation and fear of missing out would mediate the link between smartphone addiction and phubbing behavior. Additionally, a moderated mediation model was leveraged to examine loneliness as a moderator within the hypothesized model. The data collected were analyzed using SPSS.
RESULTS AND DISCUSSION: The findings show a significant positive relationship between smartphone addiction and phubbing behavior. The findings confirm the hypothesized associations and reveal that smartphone addiction is positively linked to phubbing behavior. The link, on the other hand, is partially and sequentially mediated by the fear of missing out and social comparison orientation. As a result, both mediators might be regarded as proximal variables of phubbing behavior. Moreover, the associations between both smart addiction and phubbing behaviors as well as social comparison orientation and phubbing behaviors are moderated by loneliness. These two effects were stronger for university students with high loneliness than for those with low loneliness. This study addresses a major gap in the clinical psychology literature through the attempt to explore the relationship between smartphone addiction and increased phubbing behavior among university students.
METHODOLOGY AND FINDINGS: A total of 1880 of older adults were selected by multistage stratified sampling. Life satisfaction and social support were measured with the Philadelphia Geriatric Center Morale Scale and Medical Outcomes Study Social Support Survey. The result shows living with children as the commonest type of living arrangement for older adults in peninsular Malaysia. Compared to living alone, living only with a spouse especially and then co-residency with children were both associated with better life satisfaction (p
OBJECTIVE: This study analyses the effect of estimating EGP for ICU patients with very low SI (severe insulin resistance) and its impact on identified, model-based insulin sensitivity identification, modeling accuracy, and model-based glycemic clinical control.
METHODS: Using clinical data from 717 STAR patients in 3 independent cohorts (Hungary, New Zealand, and Malaysia), insulin sensitivity, time of insulin resistance, and EGP values are analyzed. A method is presented to estimate EGP in the presence of non-physiologically low SI. Performance is assessed via model accuracy.
RESULTS: Results show 22%-62% of patients experience 1+ episodes of severe insulin resistance, representing 0.87%-9.00% of hours. Episodes primarily occur in the first 24 h, matching clinical expectations. The Malaysian cohort is most affected. In this subset of hours, constant model-based EGP values can bias identified SI and increase blood glucose (BG) fitting error. Using the EGP estimation method presented in these constrained hours significantly reduced BG fitting errors.
CONCLUSIONS: Patients early in ICU stay may have significantly increased EGP. Increasing modeled EGP in model-based glycemic control can improve control accuracy in these hours. The results provide new insight into the frequency and level of significantly increased EGP in critical illness.
Patients and Methods: STAR proposes 1-3 hours treatment based on individual insulin sensitivity variation and history of blood glucose, insulin, and nutrition. A total of 136 patients recorded data from STAR pilot trial in Malaysia (2017-quarter of 2019*) were used in the study to identify the gap between chosen administered insulin and nutrition intervention as recommended by STAR, and the real intervention performed.
Results: The results show the percentage of insulin compliance increased from 2017 to first quarter of 2019* and fluctuated in feed administrations. Overall compliance amounted to 98.8% and 97.7% for administered insulin and feed, respectively. There was higher average of 17 blood glucose measurements per day than in other centres that have been using STAR, but longer intervals were selected when recommended. Control safety and performance were similar for all periods showing no obvious correlation to compliance.
Conclusion: The results indicate that STAR, an automated model-based protocol is positively accepted among the Malaysian ICU clinicians to automate glycemic control and the usage can be extended to other hospitals already. Performance could be improved with several propositions.