<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03795129</url>
  </required_header>
  <id_info>
    <org_study_id>Merck - 34</org_study_id>
    <nct_id>NCT03795129</nct_id>
  </id_info>
  <brief_title>Testing Utility of Commercially Available Sleep Trackers for Physician-Patient Communication</brief_title>
  <official_title>Testing Utility of Commercially Available Sleep Trackers for Physician-Patient Communication Around Sleep Experience, Habits, and Behaviors</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Regenstrief Institute, Inc.</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Merck Sharp &amp; Dohme LLC</agency>
      <agency_class>Industry</agency_class>
    </collaborator>
    <collaborator>
      <agency>National Sleep Foundation</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Regenstrief Institute, Inc.</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Sleep related disorders are common in primary care practice. Sleep wear related data has not&#xD;
      been utilized to improve sleep related communication between patients and providers. The&#xD;
      study team is conducting a randomized study to improve physical-patient communication&#xD;
      regarding sleep through a novel intervention based upon sleep wear and the Sleeplife® app.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Based on a National US survey in 2012, 69% adults track at least one health indicator using&#xD;
      either a tracking device or some other means. The main health indicators tracked were diet,&#xD;
      weight, and exercise. Although not as extensive as the above health indicators, certain&#xD;
      studies also looked at sleep indicators through the trackers to support validity of their&#xD;
      use. Based on the study team's literature review, none of the studies looked at an&#xD;
      intervention designed to utilize data-trackers-based data to improve physician-patient&#xD;
      communication regarding sleep.&#xD;
&#xD;
      Commercially available and inexpensive exercise, fitness and sleep trackers are broadly&#xD;
      available and consumer use is growing rapidly. Industry analysts estimate that over 30&#xD;
      million Americans have access to their sleep tracking data (e.g. Fitbit. Jawbone). Physicians&#xD;
      seldom use patient-generated (i.e. subjective) sleep data (e.g. sleep diaries) and have been&#xD;
      slow to integrate objective sleep data collected from commercial sleep trackers. Two&#xD;
      commercial sleep trackers have been validated by independent testing. The National Sleep&#xD;
      Foundation (NSF) has led recent efforts to establish normative data (i.e. appropriate ranges)&#xD;
      for sleep duration and sleep quality. NSF, together with the Consumer Electronics Association&#xD;
      (now Consumer Technology Association), has established a work-group involving over 40 sleep&#xD;
      tracking technology companies which is working to standardize sleep tracking data collection&#xD;
      and reporting. Finally, NSF has developed a tool (&quot;SleepLife&quot;) that translates data retrieved&#xD;
      from all commercially available sleep trackers into a personal sleep tracking record. This&#xD;
      product has been tested rigorously for two years and publicly released in January 2016. These&#xD;
      developments present the timely opportunity to test a new paradigm for patient and physician&#xD;
      communication using objective patient data (sleep).&#xD;
&#xD;
      The study team will utilize a combination of observational and interventional study designs&#xD;
      to achieve study objectives.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">June 10, 2018</start_date>
  <completion_date type="Anticipated">February 28, 2019</completion_date>
  <primary_completion_date type="Anticipated">December 31, 2018</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <intervention_model_description>This is a cluster randomized trial that is randomized at the clinic level.</intervention_model_description>
    <primary_purpose>Health Services Research</primary_purpose>
    <masking>None (Open Label)</masking>
    <masking_description>Masking will not occur.</masking_description>
  </study_design_info>
  <primary_outcome>
    <measure>Number of physicians using a commercially available sleep tracker assessed by the &quot;Physician Satisfaction/Communication&quot; questionnaire who saw an improvement in physician-patient dialogue regarding sleep and related behaviors and habits</measure>
    <time_frame>Six Months</time_frame>
    <description>For patient-physician communications from the physicians' end, the team will collect all scores, ranging from 1 to 5, for all the &quot;Communication&quot; questions in the &quot;Physician Satisfaction/Communication&quot; 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.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Number of patient-physician communicationdialog assessed by using a commercially available sleep tracker assessed by the &quot;Patient Satisfaction&quot; questionnaire.</measure>
    <time_frame>Six Months</time_frame>
    <description>For patient-physician communications from the patients' end, the team will collect all the scores, ranging from 1 to 5, for all the &quot;Communication&quot; questions in the &quot;Patient Satisfaction&quot; 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.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Number of physician subjects with satisfaction with sleep counseling that improves when presented with objective patient sleep data.</measure>
    <time_frame>Six Months</time_frame>
    <description>For physicians' satisfactory score, the team will collect all the scores, ranging from 1 to 5, for all the &quot;GS&quot; questions in the &quot;Physician Satisfaction/Communication&quot; 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.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Number of patients who feel that their communication with their physician has improved as a result of the program as measured by the &quot;Patient Satisfaction&quot; survey.</measure>
    <time_frame>Six Months</time_frame>
    <description>For patients' satisfaction, the team will collect all scores, ranging from 1 to 5, for all the &quot;General Satisfaction&quot; questions in the &quot;Patient Satisfaction&quot; 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.</description>
  </secondary_outcome>
  <other_outcome>
    <measure>To determine if data improves over time for measures related to total sleep time (TST) and satisfaction with sleep.</measure>
    <time_frame>Six Months</time_frame>
    <description>the team will collect all the scores, ranging from 0 to 100, for all the &quot;Sleep Outcomes&quot; 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.</description>
  </other_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Anticipated">200</enrollment>
  <condition>Insomnia</condition>
  <arm_group>
    <arm_group_label>SleepLife Application w/FitBit</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>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.&#xD;
Subject physicians will receive subject sleep data. Subject and physicians have the option of messaging each other through the SleepLife application.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>FitBit w/Minimal to No SleepLife App.</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Subjects will receive a FitBit Subjects will be told about the SleepLife Application (but not be shown how to access it).&#xD;
Subjects will receive no training with regard to how to access SleepLife Application.&#xD;
Subjects' physicians will receive no subject sleep data.</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>SleepLife Application w/FitBit</intervention_name>
    <description>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.&#xD;
Subjects' physicians will receive subject sleep data. Subjects and physicians have the option of messaging each other through the SleepLife application.</description>
    <arm_group_label>SleepLife Application w/FitBit</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>FitBit w/Minimal to No SleepLife App.</intervention_name>
    <description>Subjects will receive a FitBit. Subjects will be told about the SleepLife Application (but not be shown how to access it).&#xD;
Subjects will receive no training with regard to how to access SleepLife Application.&#xD;
Subjects' physicians will receive no subject sleep data.</description>
    <arm_group_label>FitBit w/Minimal to No SleepLife App.</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. 18 and older&#xD;
&#xD;
          2. Have insomnia as identified by electronic record and/or a validated questionnaire&#xD;
&#xD;
          3. Prescription medication for insomnia with International Classification of Disease&#xD;
             (ICD) codes: 327.*, 780.5*, 347.*; icd-10's G47* and medications: Ambien (zolpidem),&#xD;
             Belsomra (suvorexant), Butisol (butabarbital), Doral (quazepam), Edluar (zolpidem),&#xD;
             Estazolam, Flurazepam, Halcion (triazolam), Hetlioz (tasimelteon), Intermezzo&#xD;
             (zolpidem), Lunesta (eszopiclone), Restoril (temazepam), Rozerem (ramelteon), Seconal&#xD;
             (secobarbital), Silenor (doxepin), Sonata (zaleplon), and Zolpimist (zolpidem)&#xD;
&#xD;
        3. English speaking 4. Consentable in-person 5. Have access to a telephone with smart phone&#xD;
        capabilities. (iOS/Android)&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Not English speaking&#xD;
&#xD;
          2. Have ischemic or hemorrhagic cerebrovascular disease affecting collection of study&#xD;
             outcomes (via ICD codes I6*, 43*)&#xD;
&#xD;
          3. History of dementia (via ICD codes F0*, 290*)&#xD;
&#xD;
          4. History of Bipolar/Schizophrenia/Depression (via ICD codes F2*, F31*, 296*, 295*)&#xD;
&#xD;
          5. History of alcohol or substance abuse (via ICD codes F1*, 304*, 303*)&#xD;
&#xD;
          6. Incarcerated/Long Term Care (LTC)&#xD;
&#xD;
          7. Unable to complete study questionnaires due to hearing loss or blindness&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Babar Khan, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Regenstrief Institute, Inc.</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Jarod R Baker, MS</last_name>
    <phone>317-274-9274</phone>
    <email>bakerjar@regenstrief.org</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Bridget A Fultz</last_name>
    <phone>317-274-9088</phone>
    <email>bafultz@iupui.edu</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Regenstrief Institute</name>
      <address>
        <city>Indianapolis</city>
        <state>Indiana</state>
        <zip>46202</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Lori Rawlings</last_name>
      <phone>317-274-9052</phone>
      <email>rawlingl@regenstrief.org</email>
    </contact>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <results_reference>
    <citation>Fox S, &amp; Duggan M. Tracking for health. Pew Research Center, Pew Internet and American Life Project. 2013.</citation>
  </results_reference>
  <results_reference>
    <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.</citation>
    <PMID>26684758</PMID>
  </results_reference>
  <results_reference>
    <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: 10.1016/j.physbeh.2016.03.006. Epub 2016 Mar 9.</citation>
    <PMID>26969518</PMID>
  </results_reference>
  <results_reference>
    <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.</citation>
    <PMID>26158542</PMID>
  </results_reference>
  <results_reference>
    <citation>Knutson KL, Phelan J, Paskow MJ, Roach A, Whiton K, Langer G, Hillygus DS, Mokrzycki M, Broughton WA, Chokroverty S, Lichstein KL, Weaver TE, Hirshkowitz M. The National Sleep Foundation's Sleep Health Index. Sleep Health. 2017 Aug;3(4):234-240. doi: 10.1016/j.sleh.2017.05.011. Epub 2017 Jun 20.</citation>
    <PMID>28709508</PMID>
  </results_reference>
  <results_reference>
    <citation>Hays, R.D., Davies, A.R., &amp; Ware, J.E. (1987). Scoring the medical outcomes study patient satisfaction questionnaire: PSQ-III. MOS Memorandum.</citation>
  </results_reference>
  <results_reference>
    <citation>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. doi: 10.1111/j.1365-2929.2006.02657.x.</citation>
    <PMID>17269944</PMID>
  </results_reference>
  <results_reference>
    <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. doi: 10.1001/jama.255.10.1316.</citation>
    <PMID>3944949</PMID>
  </results_reference>
  <results_reference>
    <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. doi: 10.1001/jama.1963.03060120024016. No abstract available.</citation>
    <PMID>14044222</PMID>
  </results_reference>
  <results_reference>
    <citation>Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969 Autumn;9(3):179-86. No abstract available.</citation>
    <PMID>5349366</PMID>
  </results_reference>
  <results_reference>
    <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.</citation>
    <PMID>8295545</PMID>
  </results_reference>
  <results_reference>
    <citation>Herr KA, Garand L. Assessment and measurement of pain in older adults. Clin Geriatr Med. 2001 Aug;17(3):457-78, vi. doi: 10.1016/s0749-0690(05)70080-x.</citation>
    <PMID>11459715</PMID>
  </results_reference>
  <verification_date>January 2019</verification_date>
  <study_first_submitted>May 3, 2018</study_first_submitted>
  <study_first_submitted_qc>January 3, 2019</study_first_submitted_qc>
  <study_first_posted type="Actual">January 7, 2019</study_first_posted>
  <last_update_submitted>January 3, 2019</last_update_submitted>
  <last_update_submitted_qc>January 3, 2019</last_update_submitted_qc>
  <last_update_posted type="Actual">January 7, 2019</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Early awakening</keyword>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
    <ipd_description>The Regenstrief research team will use Protected Health Information (PHI) to conduct this study. Data that does not identify the subject will be shared with Merck Sharp &amp; Dohme Corp. and the National Sleep Foundation. At this time, that is only the study visit day.</ipd_description>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03795129
org study id: Merck - 34
nct id: NCT03795129
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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.
allocation: Randomized
intervention model: Parallel Assignment
intervention model description: This is a cluster randomized trial that is randomized at the clinic level.
primary purpose: Health Services Research
masking: None (Open Label)
masking description: Masking will not occur.
measure: 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
time frame: Six Months
description: 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.
measure: Number of patient-physician communicationdialog assessed by using a commercially available sleep tracker assessed by the "Patient Satisfaction" questionnaire.
time frame: Six Months
description: 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.
measure: Number of physician subjects with satisfaction with sleep counseling that improves when presented with objective patient sleep data.
time frame: Six Months
description: 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.
measure: 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.
time frame: Six Months
description: 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.
measure: To determine if data improves over time for measures related to total sleep time (TST) and satisfaction with sleep.
time frame: Six Months
description: 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.
arm group label: SleepLife Application w/FitBit
arm group type: Experimental
description: 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.
arm group label: FitBit w/Minimal to No SleepLife App.
arm group type: Active Comparator
description: 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.
intervention type: Behavioral
intervention name: SleepLife Application w/FitBit
description: 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.
arm group label: SleepLife Application w/FitBit
intervention type: Behavioral
intervention name: FitBit w/Minimal to No SleepLife App.
description: 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.
arm group label: FitBit w/Minimal to No SleepLife App.
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Babar Khan, MD
role: Principal Investigator
affiliation: Regenstrief Institute, Inc.
last name: Jarod R Baker, MS
phone: 317-274-9274
email: bakerjar@regenstrief.org
last name: Bridget A Fultz
phone: 317-274-9088
email: bafultz@iupui.edu
facility: 
status: Recruiting
contact: 
country: United States
citation: Fox S, & Duggan M. Tracking for health. Pew Research Center, Pew Internet and American Life Project. 2013.
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.
PMID: 26684758
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: 10.1016/j.physbeh.2016.03.006. Epub 2016 Mar 9.
PMID: 26969518
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.
PMID: 26158542
citation: Knutson KL, Phelan J, Paskow MJ, Roach A, Whiton K, Langer G, Hillygus DS, Mokrzycki M, Broughton WA, Chokroverty S, Lichstein KL, Weaver TE, Hirshkowitz M. The National Sleep Foundation's Sleep Health Index. Sleep Health. 2017 Aug;3(4):234-240. doi: 10.1016/j.sleh.2017.05.011. Epub 2017 Jun 20.
PMID: 28709508
citation: Hays, R.D., Davies, A.R., & Ware, J.E. (1987). Scoring the medical outcomes study patient satisfaction questionnaire: PSQ-III. MOS Memorandum.
citation: 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. doi: 10.1111/j.1365-2929.2006.02657.x.
PMID: 17269944
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. doi: 10.1001/jama.255.10.1316.
PMID: 3944949
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. doi: 10.1001/jama.1963.03060120024016. No abstract available.
PMID: 14044222
citation: Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969 Autumn;9(3):179-86. No abstract available.
PMID: 5349366
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.
PMID: 8295545
citation: Herr KA, Garand L. Assessment and measurement of pain in older adults. Clin Geriatr Med. 2001 Aug;17(3):457-78, vi. doi: 10.1016/s0749-0690(05)70080-x.
PMID: 11459715
responsible party type: Sponsor
sharing ipd: No
ipd description: The Regenstrief research team will use Protected Health Information (PHI) to conduct this study. Data that does not identify the subject will be shared with Merck Sharp & Dohme Corp. and the National Sleep Foundation. At this time, that is only the study visit day.