Sleep Medication in Older Adults: Identifying the Need for Support by a Community Pharmacist

Masse, Morgane; Henry, Héloïse; Cuvelier, Elodie; Pinçon, Claire; Pavy, Margot; Beeuwsaert, Audrey; Barthélémy, Christine; Cuny, Damien; Gautier, Sophie; Kambia, Nicolas; Lefebvre, Jean‐Marc; Mascaut, Daniel; Mitoumba, Fabrice; Puisieux, François; Standaert-Vitse, Annie; Wierre, Patrick; Beuscart, Jean-Baptiste; Roche, Jean; Décaudin, Bertrand

doi:10.3390/healthcare10010147

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…In addition, the study did not investigate specific problems associated with different types of sleep disorders [41] or the effects of underlying diseases (including medication) on sleep duration [42]. We hope to continue our investigations from an epidemiological standpoint using other available national statistics data to focus on other factors relating to sleep habits in the future.…”

Section: Discussionmentioning

confidence: 99%

Subjective Symptoms Linked to Sleep Duration: An Analysis from Japanese National Statistics

Kato,

Komatsuzaki,

Ono

et al. 2023

Medicines

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Background: There is a high prevalence of sleep disorders in Japan, and they are a factor in a decreased quality of life. The main objective of this study was to clarify the background factors of sleep disorders that affect sleep duration, such as subjective symptoms and working hours. Methods: We performed a cross-sectional study on the Japanese national statistics data. Answers from a household questionnaire were used to analyze risk factors for decreases in sleep duration. The subjects were a total of 3972 men and women aged 40–59 years, the age group that forms the core of the working population. For the analysis, a univariate analysis (contingency table) between sleep duration (two groups: sleep duration ≥ 6 h and <6 h) and 42 subjective symptoms was carried out. A multivariate analysis (binomial logistic regression) was conducted using sleep duration and subjective health assessment as objective variables, and odds ratios (ORs) adjusted for sex, working hours, and other factors were obtained. Results: The univariate analysis by subjective symptom showed significant ORs for eight symptoms, including poor sleep quality (OR: 2.24), constipation (OR: 2.24), and dizziness (OR: 1.77). In the multivariate analysis, the model with sleep duration as the objective variable showed significantly adjusted ORs for four variables, including constipation (1.72) and poor sleep quality (1.66). The model with subjective health assessment as the objective variable showed significantly adjusted ORs for eight variables, including dizziness (4.18), while poor sleep quality (1.45) was not significant. Conclusions: The present results suggest the presence of subjective symptoms that may be inferred to be related to decreases in sleep duration.

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Section: Discussionmentioning

confidence: 99%

Subjective Symptoms Linked to Sleep Duration: An Analysis from Japanese National Statistics

Kato,

Komatsuzaki,

Ono

et al. 2023

Medicines

View full text Add to dashboard Cite

show abstract

“…In addition, the study did not examine the health problems of people who sleep for long durations [ 42 ] or the effects of sleep medication use [ 43 ]. These problems are of particular concern amongst the elderly, and we would like to carry out further studies in the future using other available data to focus on additional factors relating to sleep habits.…”

Section: Discussionmentioning

confidence: 99%

How Sleep Quality Relates to Bodily and Oral Symptoms: An Analysis from Japanese National Statistics

Yokoi

Komatsuzaki

2022

Healthcare

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Background: Sleep is one of the most important health-related factors. This cross-sectional study focused on sleep quality relates to systemic symptoms, including dental symptoms. Methods: Resource data were compiled from 7995 men and women aged 30 to 69 years, which is the core of the Japanese working population. The subjects were divided into four groups based on their answers to two questions, one on sleep time and one on sleep sufficiency, and groups were compared with other items in the questionnaire by means of a contingency table analysis (χ2 test). Results: Relationships were found between the sleep groups and basic attributes, the presence of subjective symptoms, and the presence of hospital visits. The items with significant relationships included 14 symptoms, such as lower back pain (p < 0.01) and four diseases, including high blood pressure (p < 0.01). A multinomial logistic regression was conducted with the sleep groups as objective variables. In the poor sleep group, significant odds ratios were found for four items, including hours of work (odds ratio: 2.53) and feeling listless (2.01). Conclusions: The results allowed multiple symptoms and diseases related to sleep quality to be identified, and different trends in the response rates of the groups were found. These results suggest that the useful classification of sleep quality groups according to health problems contributes to understanding the effects of different symptoms.

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“…Additionally, poor quality of sleep has been associated with decreased productivity at work and at school, increased stress, and decreased quality of life [16][17][18][19]. To combat the debilitating consequences of sleep disorders, a plethora of pharmacologic treatments have been introduced to the market and prescribed by physicians [20][21][22][23][24][25][26]. While medications have shown efficacy in decreasing sleep latency, significant side effects have been associated with these medications [27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Use of machine learning to identify risk factors for insomnia

Huang

2023

PLoS ONE

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Importance Sleep is critical to a person’s physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders. Objective The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. Design, setting, and participants A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Methods A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia. Results Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%). Conclusion Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

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Sleep Medication in Older Adults: Identifying the Need for Support by a Community Pharmacist

Cited by 5 publications

References 35 publications

Subjective Symptoms Linked to Sleep Duration: An Analysis from Japanese National Statistics

Subjective Symptoms Linked to Sleep Duration: An Analysis from Japanese National Statistics

How Sleep Quality Relates to Bodily and Oral Symptoms: An Analysis from Japanese National Statistics

Use of machine learning to identify risk factors for insomnia

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