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

Insomnia Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03795129
• Other study ID #: Merck - 34
• Status: Recruiting
• Phase: N/A
• Start date: June 10, 2018
• Est. completion date: February 28, 2019

Study information

• Verified date: January 2019
• Source: Regenstrief Institute, Inc.
• Contact: Jarod R Baker, MS
• Phone: 317-274-9274
• Email: bakerjar[@]regenstrief.org
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

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.

Description:

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 200
• Est. completion date: February 28, 2019
• Est. primary completion date: December 31, 2018
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

1. 18 and older

2. Have insomnia as identified by electronic record and/or a validated questionnaire

3. 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:

1. Not English speaking

2. Have ischemic or hemorrhagic cerebrovascular disease affecting collection of study outcomes (via ICD codes I6*, 43*)

3. History of dementia (via ICD codes F0*, 290*)

4. History of Bipolar/Schizophrenia/Depression (via ICD codes F2*, F31*, 296*, 295*)

5. History of alcohol or substance abuse (via ICD codes F1*, 304*, 303*)

6. Incarcerated/Long Term Care (LTC)

7. Unable to complete study questionnaires due to hearing loss or blindness

Related Conditions & MeSH terms

• Insomnia

Intervention

Behavioral:
• SleepLife Application w/FitBit
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.
• FitBit w/Minimal to No SleepLife App.
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.

Locations

Country	Name	City	State
United States	Regenstrief Institute	Indianapolis	Indiana

Sponsors (3)

Lead Sponsor	Collaborator
Regenstrief Institute, Inc.	Merck Sharp & Dohme Corp., National Sleep Foundation

Country where clinical trial is conducted

United States, 

References & Publications (12)

Campbell C, Lockyer J, Laidlaw T, Macleod H. Assessment of a matched-pair instrument to examine doctor-patient communication skills in practising doctors. Med Educ. 2007 Feb;41(2):123-9. — View Citation

Charlson ME, Sax FL, MacKenzie CR, Fields SD, Braham RL, Douglas RG Jr. Resuscitation: how do we decide? A prospective study of physicians' preferences and the clinical course of hospitalized patients. JAMA. 1986 Mar 14;255(10):1316-22. — View Citation

D'Hoore W, Sicotte C, Tilquin C. Risk adjustment in outcome assessment: the Charlson comorbidity index. Methods Inf Med. 1993 Nov;32(5):382-7. — View Citation

de Zambotti M, Baker FC, Willoughby AR, Godino JG, Wing D, Patrick K, Colrain IM. Measures of sleep and cardiac functioning during sleep using a multi-sensory commercially-available wristband in adolescents. Physiol Behav. 2016 May 1;158:143-9. doi: 10.10 — View Citation

de Zambotti M, Claudatos S, Inkelis S, Colrain IM, Baker FC. Evaluation of a consumer fitness-tracking device to assess sleep in adults. Chronobiol Int. 2015;32(7):1024-8. doi: 10.3109/07420528.2015.1054395. — View Citation

Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act. 2015 Dec 18;12:159. doi: 10.1186/s12966-015-0314-1. Review. — View Citation

Fox S, & Duggan M. Tracking for health. Pew Research Center, Pew Internet and American Life Project. 2013.

Hays, R.D., Davies, A.R., & Ware, J.E. (1987). Scoring the medical outcomes study patient satisfaction questionnaire: PSQ-III. MOS Memorandum.

Herr KA, Garand L. Assessment and measurement of pain in older adults. Clin Geriatr Med. 2001 Aug;17(3):457-78, vi. — View Citation

KATZ S, FORD AB, MOSKOWITZ RW, JACKSON BA, JAFFE MW. STUDIES OF ILLNESS IN THE AGED. THE INDEX OF ADL: A STANDARDIZED MEASURE OF BIOLOGICAL AND PSYCHOSOCIAL FUNCTION. JAMA. 1963 Sep 21;185:914-9. — View Citation

Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969 Autumn;9(3):179-86. — View Citation

National Sleep Foundation. Sleep Health Index Questionnaire. (2017).

* Note: There are 12 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	To determine if data improves over time for measures related to total sleep time (TST) and satisfaction with sleep.	the team will collect all the scores, ranging from 0 to 100, for all the "Sleep Outcomes" questions in the sleep outcome questionnaire. These scores will be summed up as the total patients' sleep outcomes. 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, we will use GEE model with an identity link function, and we will select relevant variables using QIC in a step-wise manner.	Six Months
Primary	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
Primary	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	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
Secondary	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