Smartphone Addiction Proneness Is Associated With Subjective-Objective Sleep Discrepancy in Patients With Insomnia Disorder

Self-reported and accelerometry measures of sleep components in adolescents living in Pacific Island countries and territories: Exploring the role of sociocultural background

OBJECTIVES

The objective of this study is to assess the concordance and its association with sociocultural background of a four-question survey with accelerometry in a multiethnic adolescent population, regarding sleep components. Based on questions from the Pittsburgh Sleep Quality Index and adapted to a school context, the questionnaire focussed on estimating sleep onset time, wake-up time and sleep duration on both weekdays and weekends. This subjective survey was compared with accelerometry data while also considering the influence of sociocultural factors (sex, place of living, ethnic community and socio-economic status).

METHODS

Adolescents aged 10.5-16 years (n = 182) in New Caledonia completed the survey and wore an accelerometer for seven consecutive days. Accelerometry was used to determine sleep onset and wake-up time using validated algorithms. Based on response comparison, Bland-Altman plots provided agreement between subjective answers and objective measures. We categorized participants' answers to the survey into underestimated, aligned and overestimated categories based on time discrepancies with accelerometry data. Multinomial regressions highlighted the sociocultural factors associated with discrepancies.

RESULTS

Concordance between the accelerometer and self-reported assessments was low particularly during weekends (18%, 26% and 19% aligned for onset sleep time, wake-up time and sleep duration respectively) compared with weekdays (36%, 53% and 31% aligned, respectively). This means that the overall concordance was less than 30%. When considering the sociocultural factors, only place of living was associated with discrepancies in onset sleep time and wake-up time primarily on weekdays. Rural adolescents were more likely to overestimate both onset sleep time (B = -1.97, p < 0.001) and wake-up time (B = -1.69, p = 0.003).

CONCLUSIONS

The study found low concordance between self-assessment and accelerometry outputs for sleep components. This was particularly low for weekend days and for participants living in rural areas. While the adapted four-item questionnaire was useful and easy to complete, caution should be taken when making conclusions about sleep habits based solely on this measurement.

Reconsidering sleep perception in insomnia: from misperception to mismeasurement

So-called 'sleep misperception' refers to a phenomenon in which individuals have the impression of sleeping little or not at all despite normal objective measures of sleep. It is unknown whether this subjective-objective mismatch truly reflects an abnormal perception of sleep, or whether it results from the inability of standard sleep recording techniques to capture 'wake-like' brain activity patterns that could account for feeling awake during sleep. Here, we systematically reviewed studies reporting sleep macro- and microstructural, metabolic, and mental correlates of sleep (mis)perception. Our findings suggest that most individuals tend to accurately estimate their sleep duration measured with polysomnography (PSG). In good sleepers, feeling awake during sleep is the rule at sleep onset, remains frequent in the first non-rapid eye movement sleep cycle and almost never occurs in rapid eye movement (REM) sleep. In contrast, there are patients with insomnia who consistently underestimate their sleep duration, regardless of how long they sleep. Unlike good sleepers, they continue to feel awake after the first sleep cycle and importantly, during REM sleep. Their mental activity during sleep is also more thought-like. Initial studies based on standard PSG parameters largely failed to show consistent differences in sleep macrostructure between these patients and controls. However, recent studies assessing sleep with more refined techniques have revealed that these patients show metabolic and microstructural electroencephalography changes that likely reflect a shift towards greater cortical activation during sleep and correlate with feeling awake. We discuss the significance of these correlates and conclude with open questions and possible ways to address them.

Clinical phenotypes of obstructive sleep apnea: a cluster analysis based on sleep perception and sleep quality

PURPOSE

To determine obstructive sleep apnea (OSA) phenotypes using cluster analysis including variables of sleep perception and sleep quality and to further explore factors correlated with poor sleep quality in different clusters.

METHODS

This retrospective study included patients with OSA undergoing polysomnography (PSG) between December 2020 and April 2022. Two-step cluster analysis was performed to detect distinct clusters using sleep perception variables including discrepancy in total sleep time (TST), sleep onset latency (SOL), and wakefulness after sleep onset (WASO); objective TST, SOL, and WASO; and sleep quality. One-way analysis of variance or chi-squared tests were used to compare clinical and PSG characteristics between clusters. Binary logistic regression analyses were used to explore factors correlated with poor sleep quality.

RESULTS

A total of 1118 patients were included (81.6% men) with mean age ± SD 43.3 ± 13.1 years, Epworth sleepiness score, 5.7 ± 4.4, and insomnia severity index 3.0 ± 2.4. Five distinct OSA clusters were identified: cluster 1 (n = 254), underestimated TST; cluster 2 (n = 158), overestimated TST; cluster 3 (n = 169), overestimated SOL; cluster 4 (n = 155), normal sleep discrepancy and poor sleep quality; and cluster 5 (n = 382), normal sleep discrepancy and good sleep quality. Patients in cluster 2 were older, more commonly had hypertension, and had the lowest apnea-hypopnea index and oxygen desaturation index. Age and sleep efficiency were correlated with poor sleep quality in clusters 1, 2, and 5, and also AHI in cluster 2.

CONCLUSION

Subgroups of patients with OSA have different patterns of sleep perception and quality that may help us to further understand the characteristics of sleep perception in OSA and provide clues for personalized treatment.