We thank the participants for their time.
Funding: This study was supported by the following grants from the National Institutes of Health: P01 AG03991; P01 AG026276; P50 AG05681; K76 AG054863; UL1 TR000448; KL2 TR000450. The Ellison Medical Foundation and a Physician Scientist Training Award from the American Sleep Medicine Foundation also supported this study. The funding sources had no role in the study design, data collection, management, analysis, interpretation of the data, or manuscript preparation.

This protocol was approved by the Washington University Human Research Protection Office.
Instructions were written specific to commercially available devices and their related software for data collection (see Table of Materials).
1. Set up of the sleep monitoring devices prior to the participant visit
<ol>
	<li>Connect a fully charged actigraphy device to the docking station and open the actigraphy software. Initialize actigraphy device to record for the desired timeframe14. Use the following parameters.
	<ol>
		<li>Select the 30 second epoch length from the drop-down menu for epoch duration. This allows for comparison to 30 second epochs on the single-channel EEG and HST recordings.</li>
		<li>Select activity and light logging mode from the drop-down menu for logging mode. The light logging mode allows measurement of light exposure at night.</li>
		<li>Collect actigraphy data for the duration of HST and single-channel EEG device recordings. Measure actigraphy for approximately one week for this protocol.</li>
	</ol>
	</li>
	<li>Connect a charged, single-channel EEG device to computer. Open device software and select data management and edit patient data prior to the start of the recording15.</li>
	<li>For home sleep testing, insert batteries and data card into the HST device. Connect HST device to computer and power the device on.
	<ol>
		<li>Open device software and select Device Configuration from the menu. Choose the appropriate study montage10.</li>
		<li>Select Auto Start. Enter times for auto start and stop beginning 2 hours prior to and ending 2 hours after the participant&#39;s habitual bedtime to ensure the entire sleep period is recorded.</li>
		<li>Choose the option to automatically record for 2 consecutive nights. Click on the good study indicator tab and choose 6 hours.</li>
		<li>Use a Positive Airway Pressure (PAP) adapter kit in place of the thermistor and pressure transducer to monitor airflow if the participant wears PAP.</li>
	</ol>
	</li>
</ol>
2. Participant visit for instruction on wearing the sleep monitoring devices
<ol>
	<li>Instruct participant to wear the actigraphy device on the non-dominant wrist for the duration of the study with the exception of bathing or swimming.
	<ol>
		<li>Ask the participant to press the event button at the bedtime and wake time to set a lights-out and lights-on marker on the actogram.</li>
		<li>Complete the sleep log every morning upon awakening. Instruct the participant to document the following: 
		Bedtime and wake-up time; 
		How long it took to fall asleep; 
		Number of nighttime awakenings; 
		Occurrence of unusual events that may impact sleep.</li>
		<li>Place the device on the participant&#39;s wrist during the visit and provide detailed written instructions to the participant.</li>
	</ol>
	</li>
	<li>Instruct the participant regarding the Home Sleep Test Device (HST) and review the education manual. 
	NOTE: Following the educational session, participants were given the opportunity to independently apply the device using the manual and then receive verbal feedback from a study team member.
	<ol>
		<li>Position the blue belt with the holster around the chest directly under armpits with the buckle almost centered.
		<ol>
			<li>Center holster on the chest and tighten the belt until it is snug enough so that the holster will stay in the center of chest when moving during the night. Point the arrow on the belt buckle facing down.</li>
			<li>Place the black lanyard strap around the neck and tighten until it is comfortably snug. Place the other blue belt around the waist and tighten the belt until it is snug enough that it does not slide when moving during the night. Point the arrow on the belt buckle facing down.</li>
			<li>Snap the white box into the holster on the chest belt. Ensure that there are 2 clicks as the box goes into the holster. Orient the label of the HST device outward.</li>
		</ol>
		</li>
		<li>Open the device by sliding the button at the top of the device. Refrain from pressing the power button since the device is set to auto start and stop. Look at the device window. 
		NOTE: All the sensors will stop flashing when they are either plugged into the device or placed on the body.</li>
		<li>Instruct participant that the line connected to the mouth (purple rectangle) will flash the entire time if used with PAP.</li>
		<li>Plug the chest (thoracic) wires into the ports on the bottom of the chest belt buckle. Repeat this step and plug abdominal wires into the ports on the bottom of the abdominal belt buckle.</li>
		<li>If PAP is worn, skip steps the following sub-steps.
		<ol>
			<li>Place the prongs of the white wire (thermistor) in the nose and wrap the wire around ears. Pull the plastic piece up toward the neck to secure.</li>
			<li>Position the second set of prongs (clear nasal cannula) in nostrils and wrap tubing around ears. Ensure that the two prongs from the cannula fit just inside the nose and position the longer prong in front of the mouth, trim to fit area by mouth opening.</li>
			<li>Slide up the plastic piece toward the neck to secure. Use a small piece of tape to affix both sets of tubing to each cheek.</li>
		</ol>
		</li>
		<li>Run the long red and white leg wires through each pant leg along the side to avoid them getting tangled during the night. Use sticky patches to attach a red and white wire to each leg.</li>
		<li>Place a hand on the shin and flex one foot up and down to locate the anterior tibialis muscle on the outside (lateral front) of the lower leg and place patches directly on the muscle at least 2 finger widths apart from one another.</li>
		<li>Apply tape over each sticky patch sufficient to cover patch, wire, and skin to secure. With knee bent (to give plenty of slack in the wires), tape the wire above each knee to prevent the wires from getting tangled.</li>
		<li>Use an oximeter to measure oxygen levels throughout the night.
		<ol>
			<li>Pinch the top of the oximeter to open the clip. Match up the picture of a fingernail on the outside of the clip to the fingernail of middle or index finger and place the oximeter on index or middle finger.</li>
			<li>Wrap tape around the outside of the oximeter clip to secure. Place a piece of tape over the oximeter wire to attach it to hand just above the wrist. Tuck the extra oximeter cable in the chest belt to avoid getting tangled in the wire.</li>
		</ol>
		</li>
		<li>Look at the man on the display screen and confirm that no signals are flashing. Once all sensors are on properly and lines are solid (unless using PAP), close the device.</li>
		<li>Instruct the participant that if the yellow light on the outside of the device starts flashing during the night, the device has lost a signal. If this happens, open up the device to check the display window to determine which signal has been lost. Once identified, reattach the sensor(s) that has come off.</li>
		<li>Press the circular event button on the outside of the device at lights out and again at lights on. Open the device and check the good study indicator circle. 
		NOTE: Participants are asked to call the study team in the morning if the good study indicator does not display a complete study. The device is set to record a second night for this reason. If the participant is willing to wear the device a second night, then discuss troubleshooting measures over the phone and encourage the participant to call again at night with any questions or concerns.</li>
		<li>After waking up in the morning for the last time, make an entry in the sleep log.</li>
	</ol>
	</li>
	<li>If participant wears PAP, attach a PAP titration kit to the device to measure PAP flow. Do this by twisting it into the connector designated for the nasal cannula. Ask the participant to attach the PAP mask to one side of the titration kit adaptor and the hose to the other side of the adapter. Provide a picture to the participant and demonstrate this during the office visit. Inform the participant that all masks and hoses are universal and will fit the provided kit. 
	NOTE: If the hose does not fit in the adapter, ask participants to feel the end of the hose for a hard-plastic piece. If the mask adapter is still in the hose, the PAP kit adapter will not fit until it is removed.</li>
	<li>Instruct participant regarding single-channel EEG device11 and review the education manual.
	<ol>
		<li>Place the device over the participant&#39;s head to demonstrate proper placement. Instruct the participant on how to adjust the headgear for proper fit and where to affix the electrodes on the forehead for accurate placement.</li>
		<li>Provide charging instructions and a demonstration in the office.</li>
		<li>Provide extra electrodes to participant in case they are needed.</li>
		<li>Ask participant wearing PAP to bring his current mask to the visit as a mask fitting may be required.
		<ol>
			<li>Check PAP masks for a forehead component. If present, provide a loaner mask for the duration of the study to ensure proper fit of the single-channel EEG device.</li>
			<li>Educate the participant about the application of the mask and headgear. Allow participant to apply the mask and adjust the headgear as needed. Instruct participant to apply the device before the mask at night. This will allow removal of mask without dislodging the device. 
			NOTE: Hard-of-hearing participants are encouraged to call the helpline when attaching the single-channel EEG so that the research coordinator can confirm the single-channel EEG is properly initialized.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. Data Download and Processing
<ol>
	<li>Place the actigraphy device on the docking station to connect to computer for download.
	<ol>
		<li>Open software and retrieve data14. End the recording and put watch to sleep until next use.</li>
		<li>Launch actogram to view recording. Use a previously described protocol for reviewing and scoring actigraphy device and sleep log data16.
		<ol>
			<li>Remove all automatically scored intervals and manually score sleep interval using sleep log bedtime and wake time17.</li>
			<li>If the reported times are misaligned with actigraphy by more than 30 min and the event marker button was pressed within 30 min of activity changes, score the sleep interval using the event markers.</li>
		</ol>
		</li>
		<li>Set the following data processing parameters prior to generating reports.
		<ol>
			<li>Set Wake Threshold to low with a value of 20.</li>
			<li>Set Immobile Minutes for sleep onset and sleep end to 10.</li>
			<li>Generate a clinician&#39;s report and convert data to analysis software (e.g., Excel) for further analysis.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Connect the single-channel EEG device to computer, open portal and select data management to upload study15.
	<ol>
		<li>Convert the study to a European Data Format (EDF) files in the portal. Select Record, click on Actions and Reports and choose Export EDF File. Download EDF file to computer. If needed, use a split EDF tool to timestamp and split multiple studies on a saved file.</li>
		<li>Set sleep tags using the sleep log bedtimes and wake times.</li>
		<li>Score EDF study using AASM scoring criteria5 and generate a report using any sleep system software.</li>
	</ol>
	</li>
	<li>Connect the HST device to computer, open software, click on File and Import to upload study to database.
	<ol>
		<li>Visually inspect the data after download to ensure that there are at least 4 hours of scorable data.</li>
		<li>Score study in the HST database using AASM scoring criteria18. To generate a report, click on Report and choose Generate PSG Report.</li>
	</ol>
	</li>
</ol>
4. Quality assurance procedures
NOTE: All recordings are reviewed by a registered sleep technologist and a board-certified sleep physician. Specific markers of quality assurance are reviewed as follows:
<ol>
	<li>Review the actograms to ensure the lights out and lights on times are set accurately to the sleep log times or the event markers per protocol.</li>
	<li>Review the single-channel EEG recording while referencing the sleep log and actigraphy. Recordings are rejected for the following reasons: there is &#62;10% artifact present in the single-channel EEG recording and movement on actogram does not align with artifact; or the EEG recording started 30 minutes late or ended 30 minutes early in comparison to the actogram.</li>
	<li>Reject HST recordings if there is &#60;4 hours of scorable data. A board-certified sleep physician reviews each study epoch by epoch to ensure accuracy of scoring.</li>
</ol>

<ol><li>Ju, Y. -E., Lucey, B. P., Holtzman, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Sleep+and+Alzheimer+disease+pathology-a+bidirectional+relationship%5BTitle%5D)+AND+%22Nature+Reviews+Neurology%22%5BJournal%5D)">Sleep and Alzheimer disease pathology-a bidirectional relationship.</a> Nature Reviews Neurology. 10 (2), 115-119 (2014).</li>
<li>Musiek, E. S., Holtzman, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mechanisms+linking+circadian+clocks%2C+sleep%2C+and+neurodegeneration%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">Mechanisms linking circadian clocks, sleep, and neurodegeneration.</a> Science. 354 (6315), 1004-1008 (2016).</li>
<li>Kushida, C. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Practice+parameters+for+the+indications+for+polysomnography+and+related+procedures%3A+an+update+for+2005%5BTitle%5D)+AND+%22Sleep%22%5BJournal%5D)">Practice parameters for the indications for polysomnography and related procedures: an update for 2005.</a> Sleep. 28 (4), 499-521 (2005).</li>
<li>Drogos, L. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Evidence+of+association+between+sleep+quality+and+APOE.%CE%B54+in+healthy+older+adults%3A+a+pilot+study%5BTitle%5D)+AND+%22Neurology%22%5BJournal%5D)">Evidence of association between sleep quality and APOE.ε4 in healthy older adults: a pilot study.</a> Neurology. 87 (17), 1836-1842 (2016).</li>
<li>Lucey, B. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comparison+of+a+single-channel+EEG+sleep+study+to+polysomnography%5BTitle%5D)+AND+%22Journal+Sleep+Research%22%5BJournal%5D)">Comparison of a single-channel EEG sleep study to polysomnography.</a> Journal Sleep Research. 25 (6), 625-635 (2016).</li>
<li>Bertera, E. M., Tran, B. Q., Wuertz, E. M., Bonner, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+study+of+the+receptivity+to+telecare+technology+in+a+community-based+elderly+minority+population%5BTitle%5D)+AND+%22Journal+of+Telemedicine+and+Telecare%22%5BJournal%5D)">A study of the receptivity to telecare technology in a community-based elderly minority population.</a> Journal of Telemedicine and Telecare. 13 (7), 327-332 (2007).</li>
<li>Ramon-Jeronimo, M. A., Peral-Peral, B., Arenas-Gaitan, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Elderly+Persons+and+Internet+Use%5BTitle%5D)+AND+%22Social+Science+Computer+Review%22%5BJournal%5D)">Elderly Persons and Internet Use.</a> Social Science Computer Review. 31 (4), 389-403 (2013).</li>
<li>Cohen-Mansfield, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Electronic+memory+aids+for+community-dwelling+elderly+persons%3A+attitudes%2C+preferences%2C+and+potential+utilization%5BTitle%5D)+AND+%22Journal+of+Applied+Gerontology%22%5BJournal%5D)">Electronic memory aids for community-dwelling elderly persons: attitudes, preferences, and potential utilization.</a> Journal of Applied Gerontology. 24 (1), 3-20 (2005).</li>
<li>Lin, C. I. C., Tang, W. -h, Kuo, F. -Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((%22Mommy+wants+to+learn+the+computer%22%3A+how+middle-aged+and+elderly+women+in+Taiwan+learn+ICT+through+social+support%5BTitle%5D)+AND+%22Adult+Education+Quarterly%22%5BJournal%5D)">"Mommy wants to learn the computer": how middle-aged and elderly women in Taiwan learn ICT through social support.</a> Adult Education Quarterly. 62 (1), 73-90 (2012).</li>
<li><a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Alice+PDX+Provider+Manual%22">Alice PDX Provider Manual</a>. , Koninklijke Philips Electronics N.V. (2013).</li>
<li><a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Sleep+Profiler+Patient+Instructions%22">Sleep Profiler Patient Instructions</a>. , Advanced Brain Monitoring. (2012).</li>
<li>Altaf, Q. -aA., Ali, A., Piya, M. K., Raymond, N. T., Tahrani, A. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+relationship+between+obstructive+sleep+apnea+and+intra-epidermal+nerve+fiber+density%2C+PARP+activation+and+foot+ulceration+in+patients+with+type+2+diabetes%5BTitle%5D)+AND+%22Journal+of+Diabetes+Complications%22%5BJournal%5D)">The relationship between obstructive sleep apnea and intra-epidermal nerve fiber density, PARP activation and foot ulceration in patients with type 2 diabetes.</a> Journal of Diabetes Complications. 30 (7), 1315-1320 (2016).</li>
<li>Nilius, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+randomized+controlled+trial+to+validate+the+Alice+PDX+ambulatory+device%5BTitle%5D)+AND+%22Nature+and+Science+of+Sleep%22%5BJournal%5D)">A randomized controlled trial to validate the Alice PDX ambulatory device.</a> Nature and Science of Sleep. 9, 171-180 (2017).</li>
<li><a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Actiwatch+technicial+guide%22">Actiwatch technicial guide</a>. , Koninklijke Philips N.V. (2015).</li>
<li><a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Sleep+profiler+portal+technical+manual%22">Sleep profiler portal technical manual</a>. , Advanced Brain Monitoring. (2013).</li>
<li>Ju, Y. -E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Sleep+quality+and+preclinical+Alzheimer%27s+disease%5BTitle%5D)+AND+%22JAMA+Neurolgy%22%5BJournal%5D)">Sleep quality and preclinical Alzheimer's disease.</a> JAMA Neurolgy. 70 (5), 587-593 (2013).</li>
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<li>Berry, R. B., et al. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Version 2.4. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aBerry%2C+RB+%22American+Academy+of+Sleep+Medicine%22">American Academy of Sleep Medicine</a>. , (2017).</li>
<li>Agnew, H., Webb, W. B., Williams, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+first+night+effect%3A+an+EEG+study+of+sleep%5BTitle%5D)+AND+%22Psychophysiology%22%5BJournal%5D)">The first night effect: an EEG study of sleep.</a> Psychophysiology. 2 (3), 263-266 (1966).</li>
<li>Barnes, C. M., Drake, C. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Prioritizing+sleep+health%3A+public+health+policy+recommendations%5BTitle%5D)+AND+%22Perspectives+on+Psychological+Science%22%5BJournal%5D)">Prioritizing sleep health: public health policy recommendations.</a> Perspectives on Psychological Science. 10 (6), 733-737 (2015).</li>
</ol>

CDT, JSM, CMS, IRB, JB, and BPL: Authors have nothing to disclose.
DMH co-founded and is on the scientific advisory board of C2N Diagnostics. DMH consults for Genentech, AbbVie, Proclara, and Denali. Washington University receives research grants to the lab of DMH from C2N Diagnostics, AbbVie, and Denali.
Neither Dr. Morris nor his family owns stock or has equity interest (outside of mutual funds or other externally directed accounts) in any pharmaceutical or biotechnology company. Dr. Morris is currently participating in clinical trials of antidementia drugs from Eli Lilly and Company and Biogen. He receives research support from Eli Lilly/Avid Radiopharmaceuticals and is funded by NIH grants # P50AG005681; P01AG003991; P01AG026276 and UF01AG032438.

This protocol is the novel application of instructional manuals in conjunction with participant education for in-home ambulatory sleep monitoring. Based on the results, the implementation of the protocol with instruction manuals improves the feasibility of in-home sleep testing in older adults. In-lab polysomnography remains the gold standard for sleep monitoring but may be limited by cost as well as disrupted sleep due to the new environment (i.e., the &#34;first night&#34; effect19). This protoc...

The relationship between sleep and Alzheimer&#39;s disease (AD) is a growing area of research with sleep disturbances hypothesized to have a role in both AD pathogenesis and as a biomarker for AD pathology1,2. In order to study the relationship between sleep and AD biomarkers, cognitively normal or very mildly impaired participants aged &#8805;65 years old are recruited from a longitudinal study of aging at the Knight Alzheimer&#39;s Disease Research Center (ADRC) at Washington University School of Medicine. Although this study was focused on AD research, the methods presented here have broad applicability to home sleep testing in older adults. Attended in-lab polysomnography is the gold standard for sleep monitoring3, but such monitoring can be cost-prohibitive and inconvenient for participants. An alternative is home sleep testing. The accuracy of home sleep studies may be improved by having trained sleep technicians assist participants inside their homes with device placement, but this can also be intrusive and time-consuming4. Therefore, this protocol was developed to instruct the participants how to set up sleep monitoring devices at home and still collect reliable data.
Participants were asked to wear a home sleep test (HST) for measuring sleep-disordered breathing (e.g., obstructive sleep apnea) and periodic leg movements. Following HST recording, participants wore a single-channel EEG device for 6 nights to monitor brain waves for EEG-based sleep staging. Previous findings indicate that the single-channel EEG device has a high level of agreement with polysomnography for multiple sleep parameters5. Throughout the single-channel EEG and HST monitoring period, participants completed a sleep log and were asked to wear actigraphy on their nondominant wrist for the entire duration of the sleep study to track activity. Studies were defined as acceptable if there were at least 2 nights recorded by the single-channel EEG device with &#60;10% artifact and at least 1 night recorded on the HST with &#8805;4 hours of scorable data. Initially, the failure rate due to poor data quality was ~40&#8722;50%. Repeat monitoring for participants with poor data quality was deemed too burdensome, therefore, this protocol was developed.
Previous work found that many elderly persons have difficulty adjusting to technological interventions6,7,8,9. This impacts numerous fields from geriatrics to education and is particularly relevant to medical studies in which older adults must use or interact with unfamiliar technologies. In order to reduce in-home study failure rates, education manuals were created that provided pictures and step-by-step directions to set up the HST and single-channel EEG devices. The education manuals were derived from the device user manuals10,11. Additionally, a 24-hour helpline was provided to participants, so they could reach a member of the study team at any time with any questions or concerns.
To analyze the impact of this protocol, a retrospective review was conducted on the success and failure rates for acceptable data quality from the at-home sleep monitoring before and after implementation of the education manuals. The data sources were successful recordings and participant calls to research study staff with questions. Participants were asked to come into the sleep center to learn about the sleep monitoring equipment. During the 2-hour visit, a study team member educated each participant about the equipment using the manuals, which provided step-by-step instructions for device usage. After reviewing the manuals in the office and being guided through the application and use of the devices, participants were given the opportunity to independently practice applying the home sleep monitoring devices using the manuals as a guide. Feedback was provided to participants during the visit and they were given the opportunity to ask questions in addition to reapplying the equipment as needed. Participants then took the equipment home, put the ambulatory equipment on themselves at night, and were encouraged to call a study team member at any time, day or night, for assistance troubleshooting any questions or problems.
All single-channel EEG studies were scored manually by registered sleep technologists who were validated, gold-standard scorers using modified American Academy of Sleep Medicine (AASM) scoring criteria5. HST recordings included airflow measured by nasal pressure transducer and thermistor, respiratory effort measured by thoracic and abdominal respiratory inductance plethysmograph belts, body position, pulse oximetry, and leg electromyogram (EMG) using the optional ExG yoke. Lights off and lights on were determined by the time that each participant pressed the event button on the HST device and/or entry in a sleep log. A registered sleep technologist manually scored the HST studies and then a board-certified sleep physician performed an epoch-by-epoch review of each study.
Following the introduction of this protocol, the failure rate was reduced to 19% and reliable data was obtained. The protocol represents a novel, low-cost, and effective way to increase the success rate of at-home sleep studies. While the HST device has been used in some studies, it is primarily used as a diagnostic tool and not for scientific studies12,13. This protocol also provides a method that allows participants to easily use a Positive Airway Pressure (PAP) machine while they are wearing either the HST or single-channel EEG device. The use of the HST and single-channel EEG devices with education manuals is a particularly useful research tool that could be more widely utilized using the method shown in this protocol.

<table><tbody><tr><td>Actigraph</td><td>Phillips Respironics</td><td>1048090</td><td>Actiwatch 2</td></tr><tr><td>Actiware site license</td><td>Phillips Respironics</td><td>1114828</td><td>Actigraphy software</td></tr><tr><td>Actigraphy docking station</td><td>Phillips Respironics</td><td>1048092</td><td>Actiwatch charger</td></tr><tr><td>Home Sleep Test (HST)</td><td>Phillips Respironics</td><td>1043941</td><td>Alice PDX device</td></tr><tr><td>Sleepware G3 software</td><td>Phillips Respironics</td><td>1082462</td><td>Alice PDX software</td></tr><tr><td>CPAP titration kit</td><td>Pro-tech</td><td>P1391</td><td>Connects PAP to HST</td></tr><tr><td>&amp;nbsp;3-foot SpO2 Extension Cable (Nonin)&amp;nbsp;</td><td>Phillips Respironics</td><td>927-3</td><td>Oximeter cable for HST</td></tr><tr><td>SpO2 Sensor (Nonin)</td><td>Phillips Respironics</td><td>936</td><td>Oximeter finger clip for HST</td></tr><tr><td>SD Card Reader</td><td>Phillips Respironics</td><td>1047300</td><td>card reader for HST</td></tr><tr><td>EXG yoke</td><td>Phillips Respironics</td><td>1040808</td><td>HST cable for leg leads</td></tr><tr><td>Dual Snap Leg Lead Wires</td><td>MVAP</td><td>TLC0048</td><td>leg leads for HST</td></tr><tr><td>Small Foam Electrodes</td><td>MVAP</td><td>5000ZT</td><td>electrodes for leg leads</td></tr><tr><td>Thermistor Airflow Sensor</td><td>Pro-tech</td><td>P1388</td><td>HST thermoistor</td></tr><tr><td>Oral/Nasal Air flow Pressure Cannula</td><td>Saltar Labs</td><td>5760-7</td><td>Pressure Transducer</td></tr><tr><td>Zrip DuraBelt kit</td><td>Phillips Respironics</td><td>P1837</td><td>HST Thoracic and Abdominal belt kit</td></tr><tr><td>Single-channel EEG</td><td>Advanced Brain Monitoring</td><td>SP40-1001</td><td>Sleep Profiler device</td></tr><tr><td>SP Sensor EEG kit</td><td>Advanced Brain Monitoring</td><td>SP40-4225.1</td><td>Sleep Profiler electrodes</td></tr><tr><td>CPAP masks</td><td>Resmed</td><td>62925, 63504, 63506, 63507, 63445, 63446, 63447</td><td></td></tr></tbody></table>

multi-modal home sleep monitoring in older adults

The gold standard for sleep monitoring is attended in-lab polysomnography; however, this method may be cost-prohibitive and inconvenient for patients and research participants. Home sleep testing has gained momentum in the field of sleep medicine due to its convenience and lower cost, as well as being more naturalistic. The accuracy and quality of home sleep testing, however, may be variable because studies are not monitored by sleep technologists. There has been some success in improving the accuracy of home sleep studies by having trained sleep technicians assist participants inside their homes with putting on the devices, but this can be intrusive and time-consuming for those involved. In this protocol, participants undergo at-home sleep monitoring with multiple devices: 1) a single-channel EEG device; 2) a home sleep test for sleep-disordered breathing and periodic limb movements; 3) actigraphy; and 4) sleep logs. A major challenge of this study is obtaining high-quality sleep monitoring data on the first attempt in order to minimize participant burden. This protocol describes the implementation of educational manuals with step-by-step instructions and photos. The goal is to improve the quality of home sleep testing.

Single-channel EEG 
At the start of the study, an acceptable overnight recording with the single-channel EEG device was defined as 1) aligning with the sleep period defined by sleep log and/or actigraphy device, and 2) &#60;10% of the recording unscorable due to movement, myogenic, electrode, or other artifacts. Each participant needed at least 2 nights meeting these criteria. Prior to the implementation of the single-channel EEG manual, 14 participants wore the sing...

Watch this Scientific Journal Video about Multi-Modal Home Sleep Monitoring in Older Adults at JoVE.com

Multi-Modal Home Sleep Monitoring in Older Adults

Here, we present a protocol to improve the quality of data from home sleep testing by providing a method to enhance instructions through a structured participant visit. This protocol includes the implementation of a step-by-step educational manual with photos to ensure proper placement of equipment.

The gold standard for sleep monitoring is attended in-lab polysomnography; however, this method may be cost-prohibitive and inconvenient for patients and ...

multi-modal-home-sleep-monitoring-in-older-adults

Research

JoVE Journal

Medicine

7.6K Views.  Washington University School of Medicine. Ons protocol heeft zowel de betrouwbaarheid van het verwerven van hoge kwaliteit slaap monitoring gegevens in het huis als het gebruiksgemak voor oudere volwassenen. De implementatie van een educatieve flip handleiding heeft gezorgd voor consistentie tijdens persoonlijke training. Naast het verstrekken van stapsgewijze instructies voor deelnemers om te verwijzen thuis.Dit protocol verbeterde de betrouwbaarheid van thuis slaap testen die kunnen worden gebruikt om een aantal slaapstoorni...

Video: Multi-Modal Home Sleep Monitoring in Older Adults

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

Behavior

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

Traditionally, sleep is monitored by an electroencephalogram (EEG). EEG studies in rodents require surgical implantation of the electrodes followed by a long recovery period. To perform an EEG recording, the animal is connected to a receiver, creating an unnatural tether to the head-mount. EEG monitoring is time consuming, carries risk to the animal, and is not a completely natural setting for the measurement of sleep. Alternative methods to detect sleep, particularly in a high-throughput fashion, would greatly advance the field of sleep research. Here, we describe a validated method for detecting sleep via activity-based home-cage monitoring. Previous studies have shown that sleep assessed via this method has a high degree of agreement with sleep defined by traditional EEG-based measures. Whereas this method is validated for total sleep time, it is important to note that sleep bout duration should be assessed by an EEG which has better temporal resolution. The EEG can also differentiate rapid eye movement (REM) and non-REM sleep, giving more detail about the exact nature of sleep. Nevertheless, activity-based sleep determination can be used to analyze multiple days of undisturbed sleep and to assess sleep as a response to an acute event (like stress). Here, we show the power of this system to detect the response of mice to daily intraperitoneal injections.

We describe a high-throughput method of measuring sleep by means of activity-based home-cage monitoring. This method offers advantages over traditional EEG-based methods. It is well validated for the determination of total sleep duration and can be a powerful tool to monitor sleep in rodent models of human disease.

Traditionally, sleep is monitored by an electroencephalogram (EEG). EEG studies in rodents require surgical implantation of the electrodes followed by a ...

Method to Obtain Pattern of Breathing in Senescent Mice through Unrestrained Barometric Plethysmography

Unrestrained barometric plethysmography (UBP) is a method for quantifying the pattern of breathing in mice, where breathing frequency, tidal volume, and minute ventilation are routinely reported. Moreover, information can be collected regarding the neural output of breathing, including the existence of central apneas and augmented breaths. An important consideration for UBP is obtaining a breathing segment with a minimal impact of anxious or active behaviors, to elucidate the response to breathing challenges. Here, we present a protocol that allows for short, quiet baselines to be obtained in aged mice, comparable to waiting for longer bouts of quiet breathing. The use of shorter time segments is valuable, as some strains of mice may be increasingly excitable or anxious, and longer periods of quiet breathing may not be achieved within a reasonable timeframe. We placed 22 month-old mice in a UBP chamber and compared four 15 s quiet breathing segments between minutes 60&#8211;120 to a longer 10 min quiet breathing period that took&#160;2&#8211;3 h to acquire. We also obtained counts of central apneas and augmented breaths prior to the quiet breathing segments, following a 30 min familiarization period. We show that 10 min of quiet breathing is comparable to using a much shorter 15 s duration. Additionally, the time leading up to these 15 s quiet breathing segments can be used to gather data regarding apneas of central origin. This protocol allows investigators to collect pattern-of-breathing data in a set amount of time and makes quiet baseline measures feasible for mice that may exhibit increased amounts of excitable behavior. The UBP methodology itself provides a useful and noninvasive way to collect pattern-of-breathing data and allows for mice to be tested over several time points.

Unrestrained barometric plethysmography is used to quantify the pattern of breathing in awake mice. We show that 15 s segments under a standardized protocol display similar values to an extended duration of quiet breathing. This methodology also allows for the quantification of apnea and augmented breaths during the first hour in the chamber.

Unrestrained barometric plethysmography (UBP) is a method for quantifying the pattern of breathing in mice, where breathing frequency, tidal volume, and ...

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom- measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field&#8217;s most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person&#8217;s stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision.&#160;

This protocol offers a digitization of portions of traditional clinical tasks commonly used to measure cognition and motor control in Parkinson&#8217;s disease. Clinical tasks are digitized while biophysical rhythms are co-registered from different functional levels of the nervous systems, ranging from voluntary, spontaneous, automatic to autonomic.

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard ...

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Sleep loss and circadian misalignment contribute to a meaningful proportion of operational accidents and incidents. Countermeasures and work scheduling designs aimed at mitigating fatigue are typically evaluated in controlled laboratory environments, but the effectiveness of translating such strategies to operational environments can be challenging to assess. This manuscript summarizes an approach for collecting sleep, circadian, fatigue, and performance data in a complex operational environment. We studied 44 airline pilots over 34 days while they flew a fixed schedule, which included a baseline data collection with 5 days of mid-morning flights, four early flights, four high-workload mid-day flights, and four late flights that landed after midnight. Each work block was separated by 3&#8211;4 days of rest. To assess sleep, participants wore a wrist-worn research-validated activity monitor continuously and completed daily sleep diaries. To assess the circadian phase, pilots were asked to collect all urine produced in four or eight hourly bins during the 24 h after each duty block for the assessment of 6-sulfatoxymelatonin (aMT6s), which is a biomarker of the circadian rhythm. To assess subjective fatigue and objective performance, participants were provided with a touch-screen device used to complete the Samn-Perelli Fatigue Scale and Psychomotor Vigilance Task (PVT) during and after each flight, and at waketime, mid-day, and bedtime. Using these methods, it was found that sleep duration was reduced during early starts and late finishes relative to baseline. Circadian phase shifted according to duty schedule, but there was a wide range in the aMT6s peak between individuals on each schedule. PVT performance was worse on the early, high-workload, and late schedules relative to baseline. Overall, the combination of these methods was practical and effective for assessing the influence of sleep loss and circadian phase on fatigue and performance in a complex operational environment.

Sleep loss and circadian misalignment contribute to numerous operational accidents and incidents. The effectiveness of countermeasures and work scheduling designs aimed at mitigating fatigue can be challenging to evaluate in operational environments. This manuscript summarizes an approach for collecting sleep, circadian, fatigue, and performance data in complex operational environments.

Sleep loss and circadian misalignment contribute to a meaningful proportion of operational accidents and incidents. Countermeasures and work scheduling ...

Advancing Sleep Disorder Treatments: A Drosophila-Based Screening Approach

High-Throughput Small Molecule Drug Screening For Age-Related Sleep Disorders Using Drosophila melanogaster

Sleep, an essential component of health and overall well-being, often presents challenges for older individuals who frequently experience sleep disorders characterized by shortened sleep duration and fragmented patterns. These sleep disruptions also correlate with an increased risk of various illnesses in the elderly, including diabetes, cardiovascular diseases, and psychological disorders. Unfortunately, existing drugs for sleep disorders are associated with significant side effects such as cognitive impairment and addiction. Consequently, the development of new, safer, and more effective sleep disorder medications is urgently needed. However, the high cost and lengthy experimental duration of current drug screening methods remain limiting factors.
This protocol describes a cost-effective and high-throughput screening method that utilizes Drosophila melanogaster, a species with a highly conserved sleep regulation mechanism compared to mammals, making it an ideal model for studying sleep disorders in the elderly. By administering various small compounds to aged flies, we can assess their effects on sleep disorders. The sleep behaviors of these flies are recorded using an infrared monitoring device and analyzed with the open-source data package Sleep and Circadian Analysis MATLAB Program 2020 (SCAMP2020). This protocol offers a low-cost, reproducible, and efficient screening approach for sleep regulation. Fruit flies, due to their short life cycle, low husbandry cost, and ease of handling, serve as excellent subjects for this method. As an illustration, Reserpine, one of the tested drugs, demonstrated the ability to promote sleep duration in elderly flies, highlighting the effectiveness of this protocol.

Author Spotlight: Overcoming Challenges in Drosophila Sleep Measurement Using DAM System 

Presented is a protocol for high-throughput drug screening to improve sleep by monitoring the sleep behavior of fruit flies in an elderly Drosophila model.

Sleep, an essential component of health and overall well-being, often presents challenges for older individuals who frequently experience sleep disorders ...

Home-Based Monitor for Gait and Activity Analysis

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical evaluators. These measures are slightly subjective and are performed during a visit to a clinic or hospital and constitute therefore a point assessment. Point assessments can be influenced by daily patient condition or factors such as fatigue, motivation, and intercurrent illness. To enable home-based monitoring of gait and activity, a wearable magneto-inertial sensor (WMIS) has been developed. This device is a movement monitor composed of two very light watch-like sensors and a docking station. Each sensor contains a tri-axial accelerometer, gyroscope, magnetometer, and a barometer that record linear acceleration, angular velocity, the magnetic field of the movement in all directions, and barometric altitude, respectively. The sensors can be worn on the wrist, ankle, or wheelchair to record the subject&#8217;s movements during the day. The docking station enables data uploading and recharging of sensor batteries during the night. Data are analyzed using proprietary algorithms to compute parameters representative of the type and intensity of the performed movement. This WMIS can record a set of digital biomarkers, including cumulative variables, such as total number of meters walked, and descriptive gait variables, such as the percentage of the most rapid or longest stride that represents the top performance of patient over a predefined period of time.

This innovative device uses magneto-inertial sensors to permit gait and activity analysis in uncontrolled environments. Currently in the qualification process as an outcome measure in the European Medical Agency, one of the applications will be to serve as a clinical endpoint in clinical trials in neuromuscular diseases.

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical ...

Bioengineering

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Three vigilance states dominate mammalian life: wakefulness, non-rapid eye movement (non-REM) sleep, and REM sleep. As more neural correlates of behavior are identified in freely moving animals, this three-fold subdivision becomes too simplistic. During wakefulness, ensembles of global and local cortical activities, together with peripheral parameters such as pupillary diameter and sympathovagal balance, define various degrees of arousal. It remains unclear the extent to which sleep also forms a continuum of brain states-within which the degree of resilience to sensory stimuli and arousability, and perhaps other sleep functions, vary gradually-and how peripheral physiological states co-vary. Research advancing the methods to monitor multiple parameters during sleep, as well as attributing to constellations of these functional attributes, is central to refining our understanding of sleep as a multifunctional process during which many beneficial effects must be executed. Identifying novel parameters characterizing sleep states will open opportunities for novel diagnostic avenues in sleep disorders.
We present a procedure to describe dynamic variations of mouse non-REM sleep states via the combined monitoring and analysis of electroencephalogram (EEG)/electrocorticogram (ECoG), electromyogram (EMG), and electrocardiogram (ECG) signals using standard polysomnographic recording techniques. Using this approach, we found that mouse non-REM sleep is organized into cycles of coordinated neural and cardiac oscillations that generate successive 25-s intervals of high and low fragility to external stimuli. Therefore, central and autonomic nervous systems are coordinated to form behaviorally distinct sleep states during consolidated non-REM sleep. We present surgical manipulations for polysomnographic (i.e., EEG/EMG combined with ECG) monitoring to track these cycles in the freely sleeping mouse, the analysis to quantify their dynamics, and the acoustic stimulation protocols to assess their role in the likelihood of waking up. Our approach has already been extended to human sleep and promises to unravel common organizing principles of non-REM sleep states in mammals.

Here, we present experimental and analytical procedures to describe the temporal dynamics of the neural and cardiac variables of non-REM sleep in mice, which modulate sleep responsiveness to acoustic stimuli.

Three vigilance states dominate mammalian life: wakefulness, non-rapid eye movement (non-REM) sleep, and REM sleep. As more neural correlates of behavior ...

Neuroscience

Measuring Neural Mechanisms Underlying Sleep-Dependent Memory Consolidation During Naps in Early Childhood

Sleep is critical for daily functioning. One important function of sleep is the consolidation of memories, a process that makes them stronger and less vulnerable to interference. The neural mechanisms underlying the benefit of sleep for memory can be investigated using polysomnography (PSG). PSG is a combination of physiological recordings including signals from the brain (EEG), eyes (EOG), and muscles (EMG) that are used to classify sleep stages. In this protocol, we describe how PSG can be used in conjunction with behavioral memory assessments, actigraphy, and parent-report to examine sleep-dependent memory consolidation. The focus of this protocol is on early childhood, a period of significance as children transition from biphasic sleep (consisting of a nap and overnight sleep) to monophasic sleep (overnight sleep only). The effects of sleep on memory performance are measured using a visuospatial memory assessment across periods of sleep and wakeful-rest. A combination of actigraphy and parent report is used to assess sleep rhythms (i.e., characterizing children as habitual or non-habitual nappers). Finally, PSG is used to characterize sleep stages and qualities of those stages (such as frequencies and the presence of spindles) during naps. The advantage of using PSG is that it is the only tool currently available to assess sleep quality and sleep architecture, pointing to the relevant brain state that supports memory consolidation. The main limitations of PSG are the length of time it takes to prepare the recording montage and that recordings are typically taken over one sleep bought. These limitations can be overcome by engaging young participants in distracting tasks during application and combining PSG with actigraphy and self/parent-report measures to characterize sleep cycles. Together, this unique combination of methods allows for investigations into how naps support learning in preschool children.

This protocol describes methods used to examine neural mechanisms underlying sleep-dependent memory consolidation during naps in early childhood. It includes procedures for examining the effect of sleep on behavioral memory performance, as well as the application and recording of both polysomnography and actigraphy.

Sleep is critical for daily functioning. One important function of sleep is the consolidation of memories, a process that makes them stronger and less ...

Real-Life EEG and ECG Synchronization in Infant-Caregiver Interactions

Home-Based EEG Hyperscanning for Infant-Caregiver Social Interactions

Prior hyperscanning studies that record the brain activities of caregivers and children concurrently have primarily been conducted within the confines of the laboratory, thus limiting the generalizability of results to real-life settings. Here, a comprehensive protocol for capturing synchronized electroencephalography (EEG), electrocardiography (ECG), and behavioral recordings from infant-caregiver dyads during various interactive tasks at home is proposed. This protocol demonstrates how to synchronize the different data streams and report EEG data retention rates and quality checks. Additionally, critical issues and possible solutions with respect to the experimental setup, tasks, and data collection in home settings are discussed. The protocol is not limited to infant-caregiver dyads but can be applied to various dyadic constellations. Overall, we demonstrate the flexibility of EEG hyperscanning setups, which allow experiments to be conducted outside of the laboratory to capture participants' brain activities in more ecologically valid environmental settings. Yet, movement and other types of artifacts still constrain the experimental tasks that can be performed in the home setting.

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

This protocol describes how synchronized electroencephalography, electrocardiography, and behavioral recordings were captured from infant-caregiver dyads in a home setting.

Prior hyperscanning studies that record the brain activities of caregivers and children concurrently have primarily been conducted within the confines of ...

Multi-Modal Home Sleep Monitoring in Older Adults

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