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.
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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Enrollment
200
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.
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.
Regenstrief Institute
Indianapolis, Indiana, United States
RECRUITINGNumber 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.
Time frame: 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.
Time frame: Six Months
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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.
Time frame: 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.
Time frame: Six Months