9 research outputs found
On the reactivity of sleep monitoring with diaries
The declining costs of wearable sensors have made self-monitoring of sleep related behavior easier for personal use but also for sleep studies. Several monitor devices come with apps that make use of diary entries to provide people with an overview of their sleeping habits and give remotely advice. However, it could be that filling in a sleep diary impacts people's perception of their sleep or the very behavior that is being measured. A small-scale field study about the effects of sleep monitoring (keeping a sleep diary) on a cognitive and a behavioral level is discussed. The method was designed to be as open as possible in order to focus on the effects of sleep monitoring where participants are not given a goal, motivation or feedback. Some behavioral modifications were observed, for example, differences in total sleep time and bedtimes were found (compared to a non-monitoring week and a monitoring week). Nevertheless, what the causes are of these changes remains unclear, as it turned out that the two actigraph devices used in this study differed greatly. In addition, some participants became more aware of their sleeping routine, but changing a sleeping habit was found challenging because of other priorities. It is important to know what the effects may be of sleep monitoring as the outcomes may already have an effect on the participant behavior which could cause researchers to work with data that do not represent a real life situation. In addition, the self-monitoring may serve as an intervention for facilitating healthier sleeping habits.</p
Does being monitored during sleep affect people on a cognitive and a behavioral level?
Nowadays it is possible to monitor behavior or physiological features with specially-made devices that make self-monitoring an accessible and simple activity. Unknown is the effect these wearable devices may have on people's lives and this also applies to the area of sleep monitoring devices. The aim of this preliminary study is to address the extent to which sleep monitoring devices affect people on a cognitive and behavioral level. Four participants aged from 34 to 60, filled out a sleep diary for three consecutive weeks and wore in the latter two weeks a sleep monitoring device. Adjustments on a cognitive and behavioral level were observed, but this was probably due to participating in this study and completing the sleep diary as was indicated by the participants. Since the market for self-monitoring devices is rapidly developing and more accessible for lay people, it is important to investigate the reactive outcomes of these devices as they may have consequences for people who have a high adherence to self-control. Moreover, the knowledge about self-monitoring will improve which will lead to better interventions carried out by, for example, sleep coaches
On the reactivity of sleep monitoring with diaries
The declining costs of wearable sensors have made self-monitoring of sleep related behavior easier for personal use but also for sleep studies. Several monitor devices come with apps that make use of diary entries to provide people with an overview of their sleeping habits and give remotely advice. However, it could be that filling in a sleep diary impacts people's perception of their sleep or the very behavior that is being measured. A small-scale field study about the effects of sleep monitoring (keeping a sleep diary) on a cognitive and a behavioral level is discussed. The method was designed to be as open as possible in order to focus on the effects of sleep monitoring where participants are not given a goal, motivation or feedback. Some behavioral modifications were observed, for example, differences in total sleep time and bedtimes were found (compared to a non-monitoring week and a monitoring week). Nevertheless, what the causes are of these changes remains unclear, as it turned out that the two actigraph devices used in this study differed greatly. In addition, some participants became more aware of their sleeping routine, but changing a sleeping habit was found challenging because of other priorities. It is important to know what the effects may be of sleep monitoring as the outcomes may already have an effect on the participant behavior which could cause researchers to work with data that do not represent a real life situation. In addition, the self-monitoring may serve as an intervention for facilitating healthier sleeping habits
Determinants of perceived sleep quality in normal sleepers including night-to-night variability
Determinants of perceived sleep quality in normal sleepers including night-to-night variability
Determinants of perceived sleep quality in normal sleepers
Objective: This study aimed to establish the determinants of perceived sleep quality over a longer period of time, taking into account the separate contributions of actigraphy-based sleep measures and self-reported sleep indices. Methods: Fifty participants (52±6.6years; 27 females) completed two consecutive weeks of home monitoring, during which they kept a sleepâwake diary while their sleep was monitored using a wrist-worn actigraph. The diary included questions on perceived sleep quality, sleepâwake information, and additional factors such as well-being and stress. The data were analyzed using multilevel models to compare a model that included only actigraphy-based sleep measures (model Acti) to a model that included only self-reported sleep measures to explain perceived sleep quality (model Self). In addition, a model based on the self-reported sleep measures and extended with nonsleep-related factors was analyzed to find the most significant determinants of perceived sleep quality (model Extended). Results: Self-reported sleep measures (model Self) explained 61% of the total variance, while actigraphy-based sleep measures (model Acti) only accounted for 41% of the perceived sleep quality. The main predictors in the self-reported model were number of awakenings during the night, sleep onset latency, and wake time after sleep onset. In the extended model, the number of awakenings during the night and total sleep time of the previous night were the strongest determinants of perceived sleep quality, with 64% of the variance explained. Conclusion: In our cohort, perceived sleep quality was mainly determined by self-reported sleep measures and less by actigraphy-based sleep indices. These data further stress the importance of taking multiple nights into account when trying to understand perceived sleep quality