The purpose of this study was to describe heart failure patients' abilities to manage their disease. A descriptive correlational design was used in this study. A convenient sample of 120 adult patients with heart failure was surveyed using Self-Management of Heart Failure tool; the New York Heart Association (NYHA) Functional Classification was used to measure functional status. The findings of this study showed that 60% of patients' age ranged from 50 to < 60 years. Men accounted for 66.7% of the patient population; 33.3% were women. Most participants were married. The study showed that recognizing a change in signs and symptoms was positively correlated with both implementing and evaluating treatment with statistical significance. In addition, the results showed the statistical significant differences between levels of patients' education and both implementing and evaluating treatment. Finally, statistically significant differences were found between functional status of patients and their ability to recognize change as well as evaluate treatment. Findings of this study highlight the need for using the Self-Management of Heart Failure tool in practice to direct the medical and nursing staff towards the specific problem area for each patient.
IntroductionStudies have shown that caregivers of children with (ADHD) are at a higher risk of mood disorders such as depression. The presence of mood disorders among the caregivers of children with ADHD has negative repercussion in terms of prognostic indicator, utilization of the health care service and the resultant quality of life.ObjectivesTo solicit the performance of indices of depression among caregivers of children with ADHD and to explore the relationship between severity of mood score, subtypes of ADHD and socio-demographic factors.MethodsA cross-sectional study conducted in a tertiary hospital in Oman dispensing child and adolescent mental health services. Arabic-version of PHQ-9 was used screen for the presence of depression among the caregivers of children diagnosed with ADHD based on DSM 5. The severity and subtypes of ADHD were quantified using Vanderbilt ADHD Parent/Teacher Rating Scale. Socio-demographic background and clinical data were gathered from medical records or attending caregivers.ResultsThe study included 100 caregivers of children with ADHD. Most of the primary caregivers were mothers (92%). Using the cutoff score of 12 on the PHQ-9, rates of depression for the mother was 14%. Some socio-demographic factors were strongly associated with severity of depressive symptom.ConclusionThis study suggests that depressive symptoms as elicited by PHQ-9 are common among caregivers of children with ADHD. The rate of depressive symptoms is higher compared to the general population in Oman. This study lays groundwork for contemplating mechanisms to mitigate depressive symptoms among caregivers of children with ADHD.Disclosure of interestThe authors have not supplied their declaration of competing interest.
A heterogeneous network (HetNet) is a network comprised of many different wireless network nodes with varying capabilities and features deployed within the coverage area of cellular service. Low power nodes such as pico-cells are deployed within the coverage area of a large macro-cell to cover areas with high user density or areas not well-covered by the macro eNB. In this paper, we focus on a model comprised of macro eNBs and pico eNBs serving both human-to-human (H2H) and machine-to-machine (M2M) devices that have different quality of service (QoS) requirements. We propose a new Q-learning based scheme for cell association and network load balancing for both types of devices. The scheme is comprised of two independent algorithms: an algorithm applied at the M2M devices that uses Q-learning to associate the device with the eNB that best meets its QoS requirements and a second algorithm applied at pico eNBs that uses Q-learning to tune the parameters of the cell range expansion to balance the load between the macro-cell and pico-cells. To evaluate the proposed scheme performance, we compare the H2H and M2M blocking probability and the M2M uplink transmission power with the traditional method and a scheme that uses Q-learning at the UE devices to assist the load balancing. The results indicate that the proposed scheme reduces the blocking probability by about 10% for both M2M and H2H devices and also reduces the uplink transmission power for M2M devices by 50% even under high load conditions.
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