The craze behind fancy branded watches has declined drastically as we see more and more men and women sporting a smart watch or a Fitbit band most of the time even when they are going to sleep. As kids, watches always astonished us and most of us have the experience of sleeping for days with our watch. This made an impression on our hands which greatly invoked our mom’s anger the next morning. But now, day or night, it is seen that people walk, talk, sleep and eat with a smartwatch or a fitness app in their hand that performs various roles. Smartphone has been the go-to gadget for individuals replacing several devices such as our calculators and alarm clocks. We are in an era where ECG results can be sent directly to our family physician from our apple watch! Details on sleep quality, the various sleep stages that we go through and the period we sustain through each sleep stage are available in the click of a button. The question now is on the reliability of these apps and gadgets!
Giving Yourself the Much-needed Sleep Break
Sleep is imperative for optimized physical and mental health. The human body is like a well-oiled machine which performs well on receiving good sleep. Any disturbance to this routine can damage the body badly. The present-day stress-driven world makes it difficult for individuals to get some peaceful uninterrupted sleep: a late-night call asking you to solve some emergency glitch in your software or a crash in the server are unforeseen and require immediate intervention. If not for this, it might be due to various other reasons that we don’t sleep well. Smartphones and fitness apps have taken this problem of ours seriously coming up with fancy solutions. We see a number of apps and wristbands dedicated to improve sleep quality in individuals with their secret ways to monitor our sleep patterns showing us alarming numbers that could be corrected with much efforts. These are readily available and moderately priced to make it affordable for as many people as possible. The recent ones even include measuring heart rate to decipher sleep stages and analyse sleep-wake cycles using limb movement. The user is given a final number showing his/her total sleep time and the time spent in each sleep stage. Fitbit bands offer the consumer with a plethora of information that was impossible to achieve until now in a home setting.
Smartphones provide access to thousands of apps through online store purchases-Google and Apple enable the user to choose from more than a million apps which includes those for health and fitness (around 1,00,000) as well. Sleep- and sleep hygiene-related apps have been on the forerun to capture people’s hearts with their broad range of functionalities including smart alarm clocks, sleep aids, sound recording while sleeping and sleep analysis. There are even some trying to figure out heavy snoring and obstructive sleep apnea which can lead to stroke in due course. But there is a lingering doubt on the accuracy of these products as we are coming to conclusions regarding our health and precision is extremely important in such situations. We have various studies analysing the efficiency of sleep trackers with medical devices comparing sleep metrics that include total sleep time (TST), wake after sleep onset (WASO), sleep efficiency (SE) and different sleep stages (light sleep, deep sleep and rapid eye movement (REM) sleep). Results show that while some models overestimated sleep and some underestimated wakefulness recent models show accurate TST and SE but don’t perform equally with respect to sleep stages. None of the devices allow people to gain access to the exact mechanism used to measure sleep quality but it is observed that many of them rely on 3-axis accelerometers that convert information into an activity count to fetch desired numbers. Activity count measurements are input into algorithms that help in understanding whether the person is awake or asleep.
Accuracy of Fitbit Wristbands
The study included 28 participants who were selected based on various grounds. All of them were above 18 years of age and agreed to the installation of the Fitbit app on their mobile. They were shown how to synchronize the app with the Fitbit device. They were provided with various items (Fitbit Charge 2, medical device called Sleep Scope, electrodes, chargers and manuals) for data collection and were asked to fill a PSQI questionnaire to measure sleep quality. PSQI is an instrument used to assess sleep quality over the past month and a value ≥5 denotes poor sleep quality. The participants measured sleep using both devices for 3 nights in their homes. All of them wore the Fitbit device on the nondominant hand.
Sleep data was collected from both devices. Fitbit analysed sleep stage every 30 seconds with the help of a user’s movement and heart rate data. Data from the medical device was analysed by another company. Both the device’s data were synchronized to ensure that start time was aligned. 5 participants were eliminated from analysis as they failed to provide ‘stage level data’ leaving the research team with 23 participants. 8 of 23 participants had a PSQI greater than 5 showing unsatisfactory sleep quality. Statistical difference was found between men and women in terms of wake time and ratio of sleep stage 1, in terms of TST, transition probability from deep sleep to light sleep and the probability of staying in light sleep above 25 years. The following transitions rarely occurred: deep sleep to REM sleep and wake, light sleep to REM sleep, REM sleep to deep sleep, and REM sleep to light sleep. There was difference in measuring this between the two devices. The Fitbit band underestimated sleep stage transition dynamics, overestimated the probability of staying in a specific sleep stage while underestimating the transition from a specific stage to a different stage. Accuracy of Fitbit in measuring sleep transitions deteriorated as sleep became more dynamic. PSQI value below 5 indicated decreased errors in the probability of staying in deep sleep stage but was linked to increased errors in transitioning from waking to REM sleep.
Wake time greater than 30 minutes indicated greater errors in transitioning from light sleep to REM sleep but decreased errors in transition probability from light sleep to wake stage. Sleep efficiency (SE) rates above 90% was linked to increased measurement errors in transition probability from REM sleep to light sleep. Hence, Fitbit underestimates sleep stage transition dynamics compared to the medical device and measurement accuracy is impacted by sleep quality, sleep continuity and SE. We are clear now that such trackers cannot help in making health-related decisions as they don’t provide accurate results.
Study on Fitness Trackers’ Accuracy in Measuring Sleep Quality
The study included participants aged 19 years and above who were selected after imposing several exclusion criteria. Totally there were 79 of them who were split into two groups.
ActiGraph GT9X Link: This is a monitor that could be worn at the waist, wrist or ankle which uses the Sadeh and Cole-Kripke algorithm to analyse sleep data.
SenseWear Mini armband: This is a tracker that’s worn over the triceps of the user’s non-dominant arm. A triaxial accelerometer captures movement, skin response, skin temperature and rate at which heat is dissipated from the body. There is a minute-by-minute update for data analysis and codes ‘Sleep’ and ‘Lying Down’ were denoted with 1 and 0 to denote the presence or absence of an action.
Basic Peak: This is a wrist-worn activity monitor that provides the user with information on daily activity with activity trends over time. Each of the participant’s sleep trends were entered manually and saved.
Fitbit Charge HR: This is a wrist-worn activity monitor measuring the user’s movement to give information on step count, intensity, distance travelled, stairs climbed, calories burned and TST. Sleep data was manually stored for each user for comparisons later.
Jawbone UP3: This is also a wrist-worn activity monitor measuring movement using a triaxial accelerometer to get data on step count, intensity and resting heart rate. Sleep-related data was entered manually and saved.
Garmin Vivosmart: This is a wrist-worn activity monitor measuring movement using a triaxial accelerometer to fetch details on step count, sleep time, energy expenditure and distance covered. Sleep data was entered manually and saved.
Consensus Sleep Diary-Expanded: This is a sleep diary created by insomnia researchers which is standardized using focus groups with good sleepers, individuals with insomnia and individuals with sleep apnea. There were two sections-one to be filled right before going to bed and another that should be filled right after getting up. Sleep duration was noted down in minutes.
All the participants participated in a four-day study (three nights of data) in their own homes to create a free-living condition. They were split into group 1 and 2. Group 1 participants wore ActiGraph GT9X Link, SenseWear Mini Armband, Basis Peak, and Fitbit Charge HR. Group 2 participants wore the ActiGraph GT9X Link, the Garmin Vivosmart, and the Jawbone UP3. All of them wore the device all through the study except when they were showering or swimming. They were given a sleep diary and requested to fill the same throughout the study period.
The 79 participants were between 19 and 66 years of age but one was eliminated as he/she did not fill the sleep diary. Of them, 18 had one night of data and 60 had 3 nights of data. Mean absolute percentage error (MAPE) was calculated for TST and time in bed (TIB) ranged between 11.7% and 31.6% for TST and 11.1% and 30.9% for TIB. The Jawbone UP3 showed least value for TST (11.7%) and the SenseWear Mini Armband showed the lowest value for TIB (11.1%). With respect to TST, Fitbit Charger HR and Jawbone UP3 were the only monitors to fully fall within the 10% equivalence zone and for TIB, the SenseWear Mini Armband, Garmin Vivosmart, and Jawbone UP3 bands fell within the 10% equivalence zone.
This study was in line with other studies that proved that wearable trackers have high sensitivity (ability to correctly identify sleep) and low specificity (ability to correctly identify wake). Depending on the tracker there was low to moderate correlation between the diary and the tracker for TDT and TIB. It also confirmed the view that the activity tracker’s poor specificity was due to the poor correlation seen between the sleep diary and trackers for WASO. It was concluded that SenseWear, Fitbit Charge HR, Jawbone UP3, and Garmin Vivosmart can be treated as valid measures for TST and TIB in comparison to a sleep diary but these don’t provide authentic data for wake times during a night of sleep.
On analysis it is seen that there are only a few studies that probe into the effectiveness of fitness trackers and apps that detect sleep quality but these studies too were on specific population groups. Some studies were only for one night bringing out the ‘first night effect’ as the participants remained tensed and sleet badly through the first night of testing while technical failure was also prominent in a few studies.
Two fitness trackers Fitbit and Jawbone UP were compared with polysomnography and actigraphy in a couple of studies in kids and adults. All of the studies reported the sleep devices to overestimate sleeping time, sleep efficiency and latency to sleep onset and underestimate awake time after falling asleep. There are other studies which noted huge differences in results for those who had the most disturbed sleep.
Studies that probe into the accuracy of smartphone apps show inaccurate results for determining absolute sleep parameters such as total sleep time, sleep efficiency, wake time after sleep onset and sleep onset latency and also when trying to compare the different sleep stages compared to polysomnography. There are also age-related differences observed as adults are likelier to lie still when awake and activity monitors are prone to overestimate sleep time in adults. Sleep tracking devices are observed to show inaccurate data for individuals suffering from short sleep duration and disturbed sleep but these devices could help in differentiating poor sleep hygiene from insomnia disorder. The trackers provide reasonably correct measurements in healthy individuals for overall sleep time. Using this data we can predict whether insufficient sleep is the cause behind daytime sleepiness and fatigue.
It is seen that apps and bands don’t provide 100% accurate results and those without any sleep problems should not make changes to their routine to fulfil sleep targets set by their smartphone apps or fitness wearables. There are some devices that promise to wake up people when they are in the ‘light sleep’ stage but the results are highly based on chance. Individuals must decide upon their adequate sleeping hours based on their body rather than depending on a smart alarm to wake them at some specific stage of sleep. Closely monitoring sleep changes helps in improving sleep duration but there are no studies that reveal the dangers of excessive monitoring of sleep patterns.
Comparison of Wearable Trackers’ Ability to Estimate Sleep: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6025478/
Apps and Fitness Trackers that Measure Sleep: Are they Useful? https://mdedge-files-live.s3.us-east-2.amazonaws.com/files/s3fs-public/Document/May-2017/mansukhani_fitnesstrackers.pdf
Accuracy of Fitbit Wristband in Measuring Sleep Stage Transitions & the Effect of User-specific Factors: https://mhealth.jmir.org/2019/6/e13384/
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