The disclosed concept pertains to methods and systems for effectively administering sleep aids, and, in particular, to methods and systems for optimizing sleep through the administration of exogenous melatonin.
In 2016, 27 percent of people in a new Consumer Reports survey of 4,023 U.S. adults said they had trouble falling asleep or staying asleep most nights, and 68 percent—or an estimated 164 million Americans—struggled with sleep at least once a week. Of interest, approximately 5.2% of the 2002 NHIS Alternative Health/Complementary and Alternative Medicine supplement survey respondents reported using melatonin and 27.5% of those users reported insomnia as a reason for taking the supplement, regardless of proven efficacy. As individuals in the United States and beyond look to combat sleep issues, melatonin supplements have garnered a strong over the counter market with a global value of 1 billion USD in 2020.
Indeed, exogenous melatonin has become one of the most frequently requested non-prescription sleep aids due to its regulator role in the internal timing of biological rhythms, including promotion/regulation of sleep. Melatonin is marketed to help promote total sleep time, aid with fatigue from jet lag, or balance circadian rhythms from jet lag and rotating shift work. Evidence suggests melatonin may reduce the time it takes for people with delayed sleep phase syndrome to fall asleep as well as to help reset the body's sleep-wake cycle.
Melatonin is secreted during the hours of darkness and is low during daylight hours. As such, exogenous melatonin use is affected by light exposure in addition to other user factors and behavior including age, stress, physical activity, diet, and influence of other hormones. Melatonin in a range of doses (0.5-6 mg) in different formulations (fast and slow release) given at different times before bedtime (0.5-2 h) has been shown in some studies to improve some subjective and objective sleep parameters, as measured by actigraphy or polysomnography, but conflicting data exist. In literature reviews of melatonin effectiveness, general findings of studies with exclusively healthy volunteers make weak recommendations in favor of melatonin use for initiating sleep or sleep efficiency and daytime sleepiness or somnolence. Inconsistent study results can be attributed, in part, to individual-to-individual variation. Despite the large global market and the millions of US adults who report using melatonin, the question of appropriate dose and formulation of melatonin for the adjustment of circadian rhythms and sleep problems has not been resolved. Accordingly, there is room for improvement in methods and systems for determining personally relevant dosing schedules of melatonin.
Accordingly, it is an object of the present invention to provide, in an exemplary embodiment, a melatonin optimization system for optimizing the effectiveness of exogenous melatonin in achieving a desired sleep outcome for a user, the system including: a user interface configured to accept information input to the user interface regarding health conditions, self-reported behavior, and a desired sleep outcome of the user; a sleep architecture detection module configured to perform monitoring of a sleep architecture of the user and to detect a hormone sensitivity of the user through the monitoring; a behavior detection module configured to detect and collect information about behavior of the user in order to define a detected behavior of the user; an initial dose algorithm module configured to define an initial advised dose of melatonin for the user; an effectiveness evaluation module configured to determine an outcome difference between the desired sleep outcome of the user and a measured sleep outcome of the user; and a recommendation engine configured to define an intervention for the user to reduce the outcome difference. The initial dose algorithm module is configured to define the initial advised dose of melatonin based on the information input to the user interface, and the recommendation engine is configured to define the intervention based on the outcome difference, the monitoring of the sleep architecture, the detected behavior of the user, and the initial advised dose of melatonin.
These and other objects, features, and characteristics of the present invention, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention.
As used herein, the singular form of “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
As used herein, the statement that two or more parts or components are “coupled” shall mean that the parts are joined or operate together either directly or indirectly, i.e., through one or more intermediate parts or components, so long as a link occurs.
As used herein, the term “number” shall mean one or an integer greater than one (i.e., a plurality).
As used herein, the term “controller” shall mean a number of programmable analog and/or digital devices (including an associated memory part or portion) that can store, retrieve, execute and process data (e.g., software routines and/or information used by such routines), including, without limitation, a field programmable gate array (FPGA), a complex programmable logic device (CPLD), a programmable system on a chip (PSOC), an application specific integrated circuit (ASIC), a microprocessor, a microcontroller, a programmable logic controller, or any other suitable processing device or apparatus. The memory portion can be any one or more of a variety of types of internal and/or external storage media such as, without limitation, RAM, ROM, EPROM(s), EEPROM(s), FLASH, and the like that provide a storage register, i.e., a non-transitory machine readable medium, for data and program code storage such as in the fashion of an internal storage area of a computer, and can be volatile memory or nonvolatile memory.
As used herein, the term “intervention” shall refer to a dosage of exogenous melatonin and/or a set of behaviors recommended for a person seeking to change a number of characteristics of his or her sleep.
As used herein, the term “machine learning model” shall mean a software system that develops and builds a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so, including, without limitation, a computer software system that develops that has been trained to recognize patterns from a set of training data, and subsequently develops algorithms to recognize patterns from the training data set in other data sets.
Directional phrases used herein, such as, for example and without limitation, top, bottom, left, right, upper, lower, front, back, and derivatives thereof, relate to the orientation of the elements shown in the drawings and are not limiting upon the claims unless expressly recited therein.
The disclosed concept, as described in greater detail herein in connection with various particular exemplary embodiments, provides methods and systems for effectively administering exogenous melatonin so as to meaningfully accelerate the onset of sleep. It should be noted that exogenous melatonin is administered to effect a timing function of sleep rather than a hypnotic effect; that is, exogenous melatonin is administered to influence when a person falls asleep but may not affect the total amount of time that a person sleeps. Research literature shows that responses to melatonin administered exogenously (i.e. ingested) are greatest when at times when endogenous levels (i.e. natural bodily production levels) are not normally present, that is, during the day. Conversely, the effect of taking melatonin during the time when it is already being produced by the body (i.e. during the night) is minimal. When taken in the late night/morning, melatonin causes phase delays (shifts to a later time) in the body's biological night as defined by the body's circadian rhythm, and when ingested in the afternoon or evening, exogenous melatonin causes phase advances (shifts to an earlier time) in the body's biological night as defined by the body's circadian rhythm.
The conditions during which melatonin is administered appear to be very important and may dictate the effectiveness of any given dose, particularly with respect to acute changes in core body temperature (CBT) and sleepiness. Accordingly, the disclosed concept provides a system for optimizing the effectiveness of melatonin treatment by detecting hormone sensitivity through sleep architecture monitoring and personalizing dosing in relation to users' behaviors, needs and health conditions. As used herein, the terms “dose”, “dosage”, and “dosing” encompass both the quantity of melatonin to be taken and the timing of ingestion of the melatonin relative to a desired sleep event or time of day.
Referring to
A sleep architecture detection module 13 includes a number of sensors and/or trackers and a number of algorithms for detecting the user's sleep architecture. Any type of sensor, tracker, or other device/method for collecting sleep architecture can be used without departing from the disclosed concept. Non-limiting examples of devices that can be used to detect sleep architecture include wrist worn devices, mattresses with sleep trackers, and user-entered sleep diaries. An effectiveness evaluation module 14 compares the user's desired sleep outcome (as indicated by the user's input to user interface 11) to the actual sleep outcome (as detected by the sleep architecture detection module 13), and a recommendation engine 16 (described in more detail below) then directs the user to either change the dosage/timing of the melatonin intake or execute a different intervention for changing a behavioral aspect based on the findings of the effectiveness evaluation module 14.
A behavior detection module 15 collects and processes sensor data and the user's self-reported information (input to the user interface 11) to detect information about the user's behavior, including but not limited to the user's exposure to light, physical activity, food intake, and stress level. In an exemplary embodiment of the disclosed concept, the behavior detection module 15 includes, at a minimum: a wearable sensor for light exposure detection, a stress detector, an activity tracker, and an input from the user interface 11 for self-reporting of food/alcohol/caffeine intake. The wearable sensor for light exposure detection can, for example and without limitation, detect and collect information about the duration, intensity and timing of both sun and artificial light to which a user is exposed throughout the day and up to the user's bed time. The stress detector can, for example and without limitation, be a wearable device that detects user physiological data such as heart rate variability and/or skin conductance. The assessment of stress level can be performed either by an on-device algorithm or by a third party via an application programming interface (API) call from the stress detector. The activity tracker can, for example and without limitation, be a wearable device that detects the number of minutes that the user is engaged in an aerobic activity, with aerobic activity being characterized by the user's heart rate level reaching between 55% and 85% of the user's maximum heart rate, the maximum heart rate (maxHR) being calculated using Equation (1) below:
maxHR=207−0.7*(age of user) (1)
The user interface for self-reporting of food/alcohol/caffeine intake enables a user to report the amount and timing of his or her food, alcohol, and/or caffeine intake throughout the day.
The recommendation engine 16 comprises a melatonin dose adjustment module 17 and a behavioral changes module 18 that evaluates the melatonin dose effectiveness and analyzes the detected user behavior along with the user's stated preferences to recommend melatonin dosing adjustments and/or behavioral changes for the user. More specifically, the dose adjustment module 17 determines the difference between the user's desired sleep onset latency and measured sleep onset latency (or any other chosen sleep metric), and recommends an intervention based on the effectiveness of any previous melatonin intervention, the current day's activities, and the prior night's sleep. If the recommended intervention is melatonin dosing and the previous melatonin dose already reached a predetermined maximum allowed level, then only behavioral changes will be recommended. The behavioral changes module 18 computes the difference between recommended behavior for the user and the user's measured behavior. If the behavior difference exceeds a pre-defined threshold, then an intervention is recommended.
Behavioral change recommendations are provided only for the actual behaviors that are being monitored. For example and without limitation, if the particular implementation of a system 10 being used does not include a light sensor, then the behavioral changes module 18 will not provide light exposure recommendations. A non-limiting list of behavioral change recommendations that can be provided by the behavioral changes module 18 includes: directing the user to engage in an outdoor activity such as walking for at least 30 minutes a day, directing the user to turn off artificial lights at least two hours before the desired time of sleep onset, directing the user to engage in breathing exercises to assist in reducing the user's stress level, and directing the user to avoid caffeine intake after a specified time of day.
Referring once more to the effectiveness module 14 of systems 10, 100, in addition to comparing the user's desired sleep outcome to the actual sleep outcome, the effectiveness module 14 can additionally predict non-intervention sleep onset (i.e. naturally occurring sleep onset) using a number of statistical or empirical techniques, and use the predicted non-intervention sleep onset as part of the evaluation of the effectiveness of any current intervention. The predicted non-intervention sleep onset can then be used by the recommendation engine 16 to determine a recommended intervention for the upcoming night (or other period of sleep). Referring to
Still referring to
y=0.56*100.12x (2)
where x is the predicted alertness for a specified time of day (i.e. found by locating the S+C curve point corresponding to the time of day) and y is the sleep onset latency. It will be appreciated that the constants 0.56 and 0.12 in Equation (2) result from using known parameter estimation methods, wherein these constants were found by defining a best-fit relationship between observed alertness level and sleep onset latency of several research study test subjects. More specifically, for a lowest level of predicted alertness where x=1, it was found that the sleep latency y 0.5 minutes.
The effectiveness module 14 can include a SOL prediction module 140 incorporating a machine learning model 145 (the SOL prediction module 140 and machine learning model 145 both being described in more detail with respect to
Referring to
error(X)=Σi=0K(measuredSOL(i)−predictedSOL(i)(X))2 (3)
wherein X represents all needed parameters (all such parameters being the parameters chosen as input to the multi-modal input SOL prediction module 140), and K is the number of days in the training phase. It will be appreciated that a number of known techniques for solving the minimization of an error function exist, for example and without limitation the gradient descent technique, and that any known optimization algorithm or other technique for minimizing the error function defined in Equation (3) can be used without departing from the scope of the disclosed concept.
Still referring to
In
At step 205, the machine learning model 145 analyzes the baseline period data, the testing period data, and the behavior detection module 15 data (if applicable) for each day of the data collection period and compares the data to the user's detected sleep architecture collected every day of the data collection period by the sleep architecture detection module 13 in order to determine patterns indicative of SOL. As previously stated with respect to steps 201 and 202 of process 200, if a personalized SOL prediction is not sought, user baseline data and testing data may not be provided to the machine learning model 145 at step 205, and research subject baseline data and testing data are instead provided so that the machine learning model 145 can compare the research subjects' baseline data and testing data to the research subjects' sleep architecture data in order to determine patterns indicative of SOL.
Once the machine learning model 145 has been trained using method 200 to recognize patterns and associations between sleep architecture data and baseline period data, testing period data, and behavior data (if applicable), the machine learning model 145 is capable of making a reliable SOL prediction based on newly provided data about the user's sleep architecture from the previous night (or other sleeping period), the most recent melatonin dose taken by the user, and the user's most recent behavior (if applicable). It will be appreciated that several techniques are available for building a machine learning model such as the machine learning model 145 used in the SOL prediction module 140, including but not limited to decision tree regressors, generalized linear models, and other regression modeling task solutions, and that any technique for building a machine learning model may be used to build machine learning model 145 without departing from the scope of the disclosed concept.
In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word “comprising” or “including” does not exclude the presence of elements or steps other than those listed in a claim. In a device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The word “a” or “an” preceding an element does not exclude the presence of a plurality of such elements. In any device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain elements are recited in mutually different dependent claims does not indicate that these elements cannot be used in combination.
Although the invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.
This application claims the benefit of U.S. Provisional Application No. 63/054,197, filed on 20 Jul. 2020. This application is hereby incorporated by reference herein.
Number | Date | Country | |
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63054197 | Jul 2020 | US |