Figure 3 - uploaded by Paul P Doghramji
Content may be subject to copyright.
Source publication
Starting page 18, in depth look at insomnia in primary care
Similar publications
Citations
... These sleep disorders carry significant personal and societal consequences. Despite the high prevalence of sleep disorders in the general population, an exceedingly small percentage of patients in primary care (Ͻ1%) are actually diagnosed with sleep disorders (Doghramji, 2001). Health care providers may miss sleep-related complaints for a number of reasons, including a lack of recognition and shortage of time to address patient complaints (Roth et al., 2002). ...
... Insomnia is one of the most common sleep disorders yet still has significant gaps in diagnosis and evaluation (Doghramji, 2001). Appropriate treatment of insomnia symptoms depends on recognition of comorbid and contributing disorders. ...
Objective:
A complaint of insomnia may have many causes. A brief tool examining contributing factors may be useful for nonsleep specialists. This study describes the development of the Insomnia Symptoms Assessment (ISA) for examining insomnia complaints.
Method:
ISA questions were designed to identify symptoms that may represent 1 of 8 possible factors contributing to insomnia symptoms, including delayed sleep phase syndrome (DSPS), shift work sleep disorder (SWSD), obstructive sleep apnea (OSA), mental health, chronic pain, restless leg syndrome (RLS), poor sleep hygiene, and psychophysiological insomnia (PI). The ISA was completed by 346 new patients. Patients met with a sleep specialist who determined primary and secondary diagnoses.
Results:
Mean age was 45 (18-85) years and 51% were male. Exploratory factor analysis (n = 217) and confirmatory factor analysis (n = 129) supported 5 factors with good internal consistency (Cronbach's alpha), including RLS (.72), OSA (.60), SWSD (.67), DSPS (.64), and PI (.80). Thirty percent had 1 sleep diagnosis with a mean of 2.2 diagnoses per patient. No diagnosis was entered for 1.2% of patients. The receiver operating characteristics were examined and the area under the curves calculated as an indication of convergent validity for the primary diagnosis (N = 346) were .97 for SWSD, .78 for OSA, .67 for DSPS, .54 for PI, and .80 for RLS.
Conclusion:
The ISA demonstrated good internal consistency and corresponds well to expert diagnoses. Next steps include setting sensitivity/specificity cutoffs to suggest initial treatment recommendations for use in other settings. (PsycINFO Database Record
Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual’s sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals. This has led to the development of technologies for more accurate detection of various sleep disorders, including insomnia. This paper explores the algorithms and techniques for automatic insomnia detection.
Methods: We followed the recommendations given in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) during our process of content discovery. Our review encompasses research papers published between 2015 and 2023, with a specific emphasis on automating the identification of insomnia. From a se- lection of well-regarded journals, we included more than 30 publications dedicated to insomnia detection. In our analysis, we assessed the performance of various meth- ods for detecting insomnia, considering different datasets and physiological signals. A common thread across all the papers we reviewed was the utilization of artificial intel- ligence (AI) models, trained and tested using annotated physiological signals. Upon closer examination, we identified the utilization of 15 distinct algorithms for this de- tection task.
Results: Result: The major goal of this research is to conduct a thorough study to categorize, compare, and assess the key traits of automated systems for identifying insomnia. Our analysis offers complete and in-depth information. The essential com- ponents under investigation in the automated technique include the data input source, objective, machine learning (ML) and deep learning (DL) network, training framework, and references to databases. We classified pertinent research studies based on ML and DL model perspectives, considering factors like learning structure and input data types.
Conclusion: Based on our review of the studies featured in this paper, we have identi- fied a notable research gap in the current methods for identifying insomnia and oppor- tunities for future advancements in the automation of insomnia detection. While the current techniques have shown promising results, there is still room for improvement in terms of accuracy and reliability. Future developments in technology and machine learning algorithms could help address these limitations and enable more effective and efficient identification of insomnia.
Sleep disturbances are common among older adults. Many factors can contribute to sleep difficulties in the older adult population, including age-related changes in sleep patterns, changes in circadian rhythms, increased medical co-morbidities and medication use, and the high prevalence of certain sleep disorders found among the elderly. In this article, we review the normal changes in sleep-related processes that are experienced with increased age, as well as pathological processes that impede healthy sleep in older adults. Drawing on a growing literature, we also discuss common sleep disorders among the older adult population, and their etiology, diagnosis, and treatment.
