Testing Utility of Commercially Available Sleep Trackers for Physician-Patient Communication

Study Overview
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Brief Summary
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Sleep related disorders are common in primary care practice. Sleep wear related data has not been utilized to improve sleep related communication between patients and providers. The study team is conducting a randomized study to improve physical-patient communication regarding sleep through a novel intervention based upon sleep wear and the Sleeplife® app.

Detailed Description
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Based on a National US survey in 2012, 69% adults track at least one health indicator using either a tracking device or some other means. The main health indicators tracked were diet, weight, and exercise. Although not as extensive as the above health indicators, certain studies also looked at sleep indicators through the trackers to support validity of their use. Based on the study team's literature review, none of the studies looked at an intervention designed to utilize data-trackers-based data to improve physician-patient communication regarding sleep. Commercially available and inexpensive exercise, fitness and sleep trackers are broadly available and consumer use is growing rapidly. Industry analysts estimate that over 30 million Americans have access to their sleep tracking data (e.g. Fitbit. Jawbone). Physicians seldom use patient-generated (i.e. subjective) sleep data (e.g. sleep diaries) and have been slow to integrate objective sleep data collected from commercial sleep trackers. Two commercial sleep trackers have been validated by independent testing. The National Sleep Foundation (NSF) has led recent efforts to establish normative data (i.e. appropriate ranges) for sleep duration and sleep quality. NSF, together with the Consumer Electronics Association (now Consumer Technology Association), has established a work-group involving over 40 sleep tracking technology companies which is working to standardize sleep tracking data collection and reporting. Finally, NSF has developed a tool (SleepLife) that translates data retrieved from all commercially available sleep trackers into a personal sleep tracking record. This product has been tested rigorously for two years and publicly released in January 2016. These developments present the timely opportunity to test a new paradigm for patient and physician communication using objective patient data (sleep). The study team will utilize a combination of observational and interventional study designs to achieve study objectives.

Official Title
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Testing Utility of Commercially Available Sleep Trackers for Physician-Patient Communication Around Sleep Experience, Habits, and Behaviors

Conditions
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Insomnia

Intervention / Treatment
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* Behavioral: SleepLife Application w/FitBit
* Behavioral: FitBit w/Minimal to No SleepLife App.


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: 18 and older Have insomnia as identified by electronic record and/or a validated questionnaire Prescription medication for insomnia with International Classification of Disease (ICD) codes: 327.*, 780.5*, 347.*; icd-10's G47* and medications: Ambien (zolpidem), Belsomra (suvorexant), Butisol (butabarbital), Doral (quazepam), Edluar (zolpidem), Estazolam, Flurazepam, Halcion (triazolam), Hetlioz (tasimelteon), Intermezzo (zolpidem), Lunesta (eszopiclone), Restoril (temazepam), Rozerem (ramelteon), Seconal (secobarbital), Silenor (doxepin), Sonata (zaleplon), and Zolpimist (zolpidem) 3. English speaking 4. Consentable in-person 5. Have access to a telephone with smart phone capabilities. (iOS/Android) Exclusion Criteria: Not English speaking Have ischemic or hemorrhagic cerebrovascular disease affecting collection of study outcomes (via ICD codes I6*, 43*) History of dementia (via ICD codes F0*, 290*) History of Bipolar/Schizophrenia/Depression (via ICD codes F2*, F31*, 296*, 295*) History of alcohol or substance abuse (via ICD codes F1*, 304*, 303*) Incarcerated/Long Term Care (LTC) Unable to complete study questionnaires due to hearing loss or blindness

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Health Services Research
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: This is a cluster randomized trial that is randomized at the clinic level.
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: SleepLife Application w/FitBit<br>Subject receives a FitBit. Subjects receive access to the SleepLife Application. Subjects receive training and assistance setting up use and access to the SleepLife Application. Subject physicians will receive subject sleep data. Subject and physicians have the option of messaging each other through the SleepLife application. | Behavioral: SleepLife Application w/FitBit<br>* Subjects receive a FitBit. Subjects receive access to the SleepLife Application. Subjects receive training and assistance setting up use and access to the SleepLife Application. Subjects' physicians will receive subject sleep data. Subjects and physicians have the option of messaging each other through the SleepLife application.<br>|
| Active Comparator: FitBit w/Minimal to No SleepLife App.<br>Subjects will receive a FitBit Subjects will be told about the SleepLife Application (but not be shown how to access it). Subjects will receive no training with regard to how to access SleepLife Application. Subjects' physicians will receive no subject sleep data. | Behavioral: FitBit w/Minimal to No SleepLife App.<br>* Subjects will receive a FitBit. Subjects will be told about the SleepLife Application (but not be shown how to access it). Subjects will receive no training with regard to how to access SleepLife Application. Subjects' physicians will receive no subject sleep data.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of physicians using a commercially available sleep tracker assessed by the Physician Satisfaction/Communication questionnaire who saw an improvement in physician-patient dialogue regarding sleep and related behaviors and habits | For patient-physician communications from the physicians' end, the team will collect all scores, ranging from 1 to 5, for all the Communication questions in the Physician Satisfaction/Communication questionnaire. The scores will be summed up as the total communication score from physician, and the total score will be treated as continuous response variable. Then a binary variable indicating whether the physician is in the intervention (=1) or control (=0) arm will be treated as the main explanatory variable, a continuous variable regarding the time where the measurements are recorded, and the set of general demographic variables (age, race, gender, etc) will be used as covariates. We will use linear regression model, and select relevant variables using Bayesian information criterion (BIC) in a step-wise manner. The SleepLife app will be pulling time-to-sleep (TST), amount of time in minutes to sleep, number of awakenings greater than 5 minutes, and sleep efficiency. | Six Months | 
| Number of patient-physician communicationdialog assessed by using a commercially available sleep tracker assessed by the Patient Satisfaction questionnaire. | For patient-physician communications from the patients' end, the team will collect all the scores, ranging from 1 to 5, for all the Communication questions in the Patient Satisfaction questionnaire. The scores will be summed up as the total communication score from the patients' end, and the total score will be treated as continuous response variable. Then a binary variable indicating whether the patient is in the intervention (=1) or control (=0) arm will be treated as the main explanatory variable, a continuous variable regarding the time where the measurements are recorded, and the set of general demographic variables (age, race, gender, etc) will be used as covariates. Considering the linear responses and the cluster design, the team will use generalized estimating equation (GEE) model with an identity link function, and the team will select relevant variables using QIC in a step-wise manner. | Six Months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of physician subjects with satisfaction with sleep counseling that improves when presented with objective patient sleep data. | For physicians' satisfactory score, the team will collect all the scores, ranging from 1 to 5, for all the GS questions in the Physician Satisfaction/Communication questionnaire. The scores will be summed up as the total physicians' satisfaction score, and the total score will be treated as continuous response variable. Then a binary variable indicating whether the physician is in the intervention (=1) or control (=0) arm will be treated as the main explanatory variable, a continuous variable regarding the time where the measurements are recorded, and the set of general demographic variables (age, race, gender, etc) will be used as covariates. The team will use linear regression model, and select relevant variables using BIC in a step-wise manner. | Six Months | 
| Number of patients who feel that their communication with their physician has improved as a result of the program as measured by the Patient Satisfaction survey. | For patients' satisfaction, the team will collect all scores, ranging from 1 to 5, for all the General Satisfaction questions in the Patient Satisfaction questionnaire. These scores will be summed up as the total patients' satisfaction score for the treatment and interaction with the physician, as a result of the program. Then a binary variable indicating whether the patient is in the intervention (=1) or control (=0) arm will be treated as the main explanatory variable, a continuous variable regarding the time where the measurements are recorded, and the set of general demographic variables (age, race, gender, etc) will be used as covariates. Considering the linear responses and the cluster design, the team will use GEE model with an identity link function, and we will select relevant variables using QIC in a step-wise manner. | Six Months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Early awakening