Objective: We investigated the sleep parameters and clinical factors related to short sleep onset latency (SL) in cancer patients. Methods: We retrospectively reviewed the medical records of 235 cancer patients. Patient Health Questionnaire-9, State and Trait Anxiety Inventory (State subcategory), Insomnia Severity Index (ISI), Cancer-related Dysfunctional Beliefs about Sleep, and Fear of Progression scale scores and sleep related parameters including sleeping pill ingestion time, bedtime, sleep onset time, and wake-up time were collected. We also calculated the duration from sleeping pill ingestion to bedtime, sleep onset time, and wake-up time; duration from wake-up time to bedtime and sleep onset time; and time spent in bed over a 24 hours period. Results: Among patients not taking sleeping pills (n = 145), early wake-up time (adjusted odds ratio [OR]: 0.39, 95% confidence interval [CI] 0.19−0.78), early sleep onset time (OR: 0.50, 95% CI 0.27−0.93), and low ISI score (OR: 0.82, 95% CI 0.71−0.93) were identified as expecting variables for SL ≤ 30 minutes. Longer duration from wake-up time to bedtime (OR: 2.49, 95% CI 1.48−4.18) predicted SL ≤ 30 minutes. Among those taking sleeping pills (n = 90), early sleep onset time (OR: 0.54, 95% CI 0.39−0.76) and short duration from pill ingestion to sleep onset time (OR: 0.05, 95% CI 0.02−0.16) predicted SL ≤ 30 minutes. Conclusion Cancer patients who fell asleep quickly spent less time in bed during the day. Thus, before cancer patients with insomnia are prescribed sleeping pills, their sleep parameters should be examined to improve their SL.

Electromagnetic interference (EMI) can affect various medical devices. Herein, we report the case of EMI from wireless local area network (WLAN) on an electrocardiogram (ECG) monitoring system. A patient who had a prior myocardial infarction participated in the cardiac rehabilitation program in the sports medicine center of our hospital under the wireless ECG monitoring system. After WLAN was installed, wireless ECG monitoring system failed to show a proper ECG signal. ECG signal was distorted when WLAN was turned on, but it was normalized after turning off the WLAN.
Electrocardiography ; Humans ; Local Area Networks ; Magnets ; Myocardial Infarction ; Sports Medicine ; Wireless Technology

Objective: The aim of this study was to explore the factors that can influence the severity of insomnia in the general population. We also aimed to examine whether sleep effort mediates the association between dysfunctional beliefs about sleep or the discrepancy between desired time in bed and desired total sleep time (DBST) and insomnia severity in individuals. Methods: A total of 387 participants enrolled in this e-survey study. The symptoms were rated using the insomnia severity index (ISI), Patients Health Questionnaire-9 items, Dysfunctional Beliefs about Sleep-2 items, Glasgow Sleep Effort Scale, and Stress and Anxiety to Viral Epidemics-6 items. In addition, we defined a new sleep index named the DBST index. A linear regression analysis was performed to explore the factors predicting ISI scores, and mediation analysis was implemented to explore whether persistent preoccupation with sleep mediated the influence of dysfunctional beliefs about sleep and DBST on insomnia severity. Results: A linear regression analysis investigated depression (β=0.17, p<0.001), sleep effort (β=0.50, p<0.001), dysfunctional beliefs about sleep (β=0.13, p=0.001), and DBST (β=0.09, p=0.014) (adjusted R2=0.50, F=65.7, p<0.001). Additionally, we observed that persistent preoccupation with sleep partially mediated the influence of dysfunctional beliefs about sleep and DBST on insomnia severity. Conclusion Depression, preoccupation with sleep, dysfunctional beliefs about sleep, and DBST influenced the insomnia severity of the general population. We also observed that a persistent preoccupation with sleep partially mediated the influence of dysfunctional beliefs about sleep and the DBST index on insomnia severity.