Welcome back, readers!
This week has been paradoxically quiet yet incredibly busy. In terms of my job, several coworkers went out of town, meaning my hours nearly doubled. As a result, I spent most of my week working early mornings and late nights, leading me to a pretty exhausted state. Coincidentally, Dr. Krahn also had a busy week, and we unfortunately did not have an opportunity to meet. Thus, I took this week to catch up on recent literature regarding actigraphy and its validity as a tool for measuring sleep quality. Most of my sources came from articles, journals, and books within the medical field; a majority of the sources compared actigraphy to other current means of tracking sleep patters, such as sleep logs, sleep labs, and polysomnography (PSG).
With actigraphy being a relatively new piece of technology, the main research focus has been on finding the extent to which it can provide reliable and significant data that can advance sleep studies as a whole. This research has been mostly driven by comparative means; since PSG has been considered the "gold standard" of sleep studies, consistently giving accurate and significant data in multiple areas of sleep, analyzing data points from the two methods side by side has been the primary way to categorize the actigraphy points as accurate or not. This method takes on the assumption that PSG's data is for the most part, completely accurate, and thus is used to account for any underestimates or overestimates in the actigraphy data.
The research has pointed to actigraphy as a solid means of data-gathering. One of the most beneficial features of actigraphy is its accessibility. Collecting data through actigraphy is as simple as wearing a wristwatch, whereas PSG involves wires and monitors, therefore only available in hospitals and clinics. Additionally, PSG has been criticized for not simulating natural sleep, given the wires and controlled clinic environment. Actigraph-based devices can remove this restraint, as the subject can wear at his disposal and in his own sleeping environment. The data is collected in epochs, which are often spaced in 1-minute time-frames. Given the non-intrusive nature of actigraphs, data can be collected in a multitude of environments, uncontrolled and controlled, allowing for an enormous amount of data points to be studied. This allows for the researcher to take into account any possible day-time sleepiness, as well as any inconsistencies within a given time frame (being a week, month, or even year). However, most of the studies agree that, at least as of now, the most optimal use of actigraphy is alongside other methods. Currently, this is due to a technological shortcoming which often does not account for specific areas of sleep, which is to be expected given the relative simplicity of actigraphs compared to PSG. Specifically, the main criticism of actigraphy is the inability to account for interruptions in sleep in subjects who have a below average sleep efficiency rating.
The most optimal use of actigraphy on its own is currently in aiding with sleep apnea and restless leg syndrome (RSL), though research is still in its primary stages, as is the technology being studied.
Thank you for the time, and I will keep updating on my findings and how they may come in use when aiding Dr. Krahn.
Until next post!
Wednesday, February 24, 2016
Saturday, February 13, 2016
First Week at Mayo
Hey, All!
Some say the best way to learn is by doing, and that's exactly what I did this week. For four days, I wore an Actiwatch--the device we used to measure sleep quality--and had my own sleep log for three nights. The database was created by analyzing any movement from my non-dominant arm through a built in accelerometer on a minute by minute basis. The Actiwatch also had few other measures, such as a light detector (measured in lux), and skin temperature indicator, but for this research, we focused primarily on the movement index as the key component. By analyzing the data points through my night schedule, I could see how much I moved around each minute for three nights; however, moving during sleep is completely natural, thus posing the question of what movement is actually significant movement. To determine which points signified an actual awakening, I compared each specific movement to a certain awakening, which included 4 consecutive minutes with movement indexes ranging from 100-950. The experiment did not go flawlessly, as in my first night with the Actiwatch, I took it off in my sleep, as I was not used to wearing it, and on the fourth night, the battery of the device died, thus only giving us two nights worth of concrete data. I was not able to find a guaranteed minimum threshold that was an awakening yet, but in future weeks I should be able to, and then compare that data with the dog's movement data and see if there is any correlation.
To prepare for the data analysis, I've been reading up on the features of actigraphy compared to known methods of measuring sleep quality, such as an electroencephalogram (EEG). I will keep you all posted in case any movement towards finding the smallest significant data point is made!
Thank you!
Gustavo
Some say the best way to learn is by doing, and that's exactly what I did this week. For four days, I wore an Actiwatch--the device we used to measure sleep quality--and had my own sleep log for three nights. The database was created by analyzing any movement from my non-dominant arm through a built in accelerometer on a minute by minute basis. The Actiwatch also had few other measures, such as a light detector (measured in lux), and skin temperature indicator, but for this research, we focused primarily on the movement index as the key component. By analyzing the data points through my night schedule, I could see how much I moved around each minute for three nights; however, moving during sleep is completely natural, thus posing the question of what movement is actually significant movement. To determine which points signified an actual awakening, I compared each specific movement to a certain awakening, which included 4 consecutive minutes with movement indexes ranging from 100-950. The experiment did not go flawlessly, as in my first night with the Actiwatch, I took it off in my sleep, as I was not used to wearing it, and on the fourth night, the battery of the device died, thus only giving us two nights worth of concrete data. I was not able to find a guaranteed minimum threshold that was an awakening yet, but in future weeks I should be able to, and then compare that data with the dog's movement data and see if there is any correlation.
To prepare for the data analysis, I've been reading up on the features of actigraphy compared to known methods of measuring sleep quality, such as an electroencephalogram (EEG). I will keep you all posted in case any movement towards finding the smallest significant data point is made!
Thank you!
Gustavo
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