Sleep Quality

the sleep quality metric provides insights into the overall quality and restorative nature of users' sleep it considers factors such as sleep duration and sleep wake patterns to assess how well the user is recovering and preparing for the day ahead by understanding sleep quality, users can make informed decisions to improve their rest and overall well being it allows users to quantify the quality of their sleep and corresponding ability to face greater challenges during the day furthermore, s leep quality can be used to grow your active users by offering a daily powerful morning check in within your platform how is it calculated? the sleep quality metric is calculated using a combination of different data sources biometric data heart rate (hr), heart rate variability (hrv), and stress levels user reported information qualitative user input and contextual data about sleep habits facial keypoints drooping/hanging eyelids, paler skin, redder eyes, more puffy/swollen eyes, dark circles under the eyes, more wrinkles/fine lines and more drooping corners of the mouth by integrating these data points, intelliprove can provide a comprehensive assessment of sleep quality and deliver an accurate insight into the restorative effectiveness of the user’s sleep when is the first reading available? at least two face scans need to be performed before 10 am within one week in general, a first reading is obtained after the first week accuracy to validate the accuracy of the sleep quality calculation through the intelliprove algorithm, a benchmarking study was performed between 'the groningen sleep quality score' and the intelliprove sleep quality metric in 87% of the cases a poor night's sleep, with negative impact on energy level, is correctly identified how can it be used? this metric helps users understand their sleep quality and emotional well being each morning, guiding them on whether they are ready for peak performance or if they should prioritize rest and recovery sleep quality can also support users in improving their sleep patterns and optimizing sleep therapy by tracking progress and analyzing the effectiveness of sleep interventions, users can refine both the duration and quality of their sleep routines a lower sleep quality score can serve as a starting point to guide users toward sleep enhancing content, like articles, videos, exercises, or therapy options interpreting results sleep quality is returned as an integer on a scale of 0 to 100 and can be requested as a widgets docid\ oorapckooi6 89xrafduv or via the rest api docid\ biupvixvhltbukklr2ati definitions name unit range programmatic name health profile sleep quality percentage \[%] 0 100 sleep quality energy & sleep values value meaning zone example user text <20 low red stress or bad sleep could be sneaking up on you focus on hydration, rest and a good night’s sleep 20 – 39 low medium yellow your sleep quality scores are leaning towards fatigue try to bring them up with a good night’s rest 40 – 59 medium green your sleep quality score indicates that you have recovered quite well plan for some moderate physical exercises or mental activities, but don’t overdo it 60 – 79 medium high cyan you’re well rested! your biomarkers are showing great recovery and adaptability to stress 80 – 100 high blue you’re a sleep expert! you’ve fueled your body to sharpen focus, mood and resilience throughout the day well done! the table above shows value the possible values for the health insight meaning what does the value mean zone we distinguish three zones, useful – for example when creating a widget to clarify the feel towars the user green = optimal to normal yellow = normal to average red = outside the typical range; may require attention example user text an example of what could be communicated to the user in case this value is measured scientific papers sundelin t, lekander m, kecklund g, van someren ej, olsson a, axelsson j 2013 cues of fatigue effects of sleep deprivation on facial appearance sleep 36(9) 1355–1360 van de water at, holmes a, hurley da 2011 objective measurements of sleep for non laboratory settings as alternatives to polysomnography—a systematic review j sleep res 20(1 pt 2) 183–200 peng x, luo j, glenn c, chi lk, zhan j 2018 sleep deprived fatigue pattern analysis using large scale selfies from social media arxiv preprint arxiv 1802 08310