- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05090995
A PPG Sensor-Based Feedback Intervention for Heavy Drinking Young Adults
A Photoplethysmography Sensor-based Personalized Feedback Intervention for Heavy-drinking Young Adults Targeting Heart Rate Variability, Resting Heart Rate, and Sleep
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
Study Type
Enrollment (Actual)
Phase
- Not Applicable
Contacts and Locations
Study Locations
-
-
Connecticut
-
New Haven, Connecticut, United States, 06510
- Yale University
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- 18-25 years of age
- Report ≥ 4 heavy drinking occasions in the past 28 days
- Report Alcohol Use Disorders Identification Test- Consumption (AUDIT-C) scores indictive of risk of drinking harm
- English Speaking
- Have a personal smartphone
Exclusion Criteria:
- Sleep Disorder History
- Night/ Rotating work shift
- Travel two or more time zones in the month prior to the study or anticipated travel two or more times during study participation
- Clinically severe AUD in past 12 months
- Currently enrolled in alcohol or sleep treatment
- Current, severe psychiatric illness
- Current DSM-V substance use disorder
- Positive urine drug screen for a substance other than marijuana
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Treatment
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Single
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Active Comparator: Self-Monitoring and Feedback
The intervention consists of subjects wearing a PPG device for 6 weeks.
Subjects will monitor their own health and report their sleep behaviors daily during this time.
On weeks two, four, and six subjects will receive personalized health feedback based on the PPG device data and sleep diaries.
|
Self-management brief health intervention that involves passive daily health monitoring using a PPG sensor, active self-monitoring of health and behavior using daily diaries, and the provision of personalized health feedback and advice.
|
|
Placebo Comparator: Self-Monitoring
The intervention consists of subjects wearing a PPG device for 6 weeks.
Subjects will monitor their own health and report their sleep behaviors daily during this time.
|
Self-management brief health intervention that involves passive daily health monitoring using a PPG sensor, and active self-monitoring of health and behavior using daily diaries.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Total Drinks Consumed
Time Frame: up to Week 10
|
Total drinks consumed will be measured using the Time Line Followback Interview at baseline, Week 6, and Week 10.
This standardized interview asks subjects to self-report how many drinks they consume each day over the past 4 weeks at baseline and then since the last assessment point at Weeks 6 and 10.
Higher scores indicate a greater number of drinks consumed.
Total drinks will be summed over the past 4 weeks at intake, Week 6, and Week 10.
Totals were transformed using a square root transformation since these values were not normally distributed.
Mixed effects models were then conducted to evaluate the effect of condition on total drinks over time with condition, time, and their interaction in the model and sex and baseline total drinks as covariates.
|
up to Week 10
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Drinks Per Drinking Day
Time Frame: up to 10 weeks
|
Total drinks per drinking day will be measured using the Time Line Followback Interview at baseline, Week 6, and Week 10. Total drinks/drinking day will be summed over the past 4 weeks at intake, Week 6, and Week 10. Totals were transformed using a square root transformation since these values were not normally distributed. Mixed effects models were then conducted to evaluate the effect of condition on total drinks/drinking day over time with condition, time, and their interaction in the model and sex and baseline drinks per drinking day as covariates. This tools asks subjects to self-report how many drinks they consume during a one month period. The score of this measure will be determined by the amount of self-reported alcohol consumption that occurred each day. A heavy drinking day for a man would be ≥5 drinks per sitting and for a women it would be ≥4 drinks per sitting. |
up to 10 weeks
|
|
Percent Heavy Drinking Days
Time Frame: up to 10 weeks
|
Self-reported percent heavy drinking days will be measured using the Time Line Followback Interview at baseline, Week 6, and Week 10.
This standardized interview asks subjects to self-report heavy drinking occasions over the past 4 weeks at baseline and then since the last assessment point at Weeks 6 and 10, defined as ≥5 drinks per sitting and for a women it would be ≥4 drinks per sitting.
Higher scores indicate a greater percentage of heavy drinking days.
The percentage of heavy drinking days will be summed over the past 4 weeks at intake, Week 6, and Week 10.
Mixed effects models were then conducted to evaluate the effect of condition on percent heavy drinking days over time with condition, time, and their interaction in the model and sex and baseline percent heavy drinking days as covariates.
|
up to 10 weeks
|
|
Percent Abstinent Days
Time Frame: up to 10 weeks
|
Self-reported percent abstinent days will be measured using the Time Line Followback Interview at baseline, Week 6, and Week 10.
This standardized interview asks subjects to self-report how many days they did not consume any alcohol each day over the past 4 weeks at baseline and then since the last assessment point at Weeks 6 and 10.
Higher scores indicate a greater percentage of abstinent days.
The percentage of abstinent days will be summed over the past 4 weeks at intake, Week 6, and Week 10.
Mixed effects models were then conducted to evaluate the effect of condition on percent abstinent days over time with condition, time, and their interaction in the model and sex and baseline percent abstinent days as covariates.
|
up to 10 weeks
|
|
Alcohol-related Consequences
Time Frame: baseline, Week 6, and Week 10
|
Mean alcohol related consequences were measured using the Brief Young Adult Alcohol Consequences Questionnaire at baseline, Week 6, and Week 10.
Each consequence is scored 1 point and a total score reflects the total number of consequences.
Higher scores indicated more consequences.Total score range 0-24.
The three timepoints are summed then averaged.
Mixed effects models were then conducted to evaluate the effect of condition on consequences over time with condition, time, and their interaction in the model and sex and baseline consequences as covariates.
|
baseline, Week 6, and Week 10
|
|
Sleep Quality
Time Frame: baseline and Week 10
|
Mean sleep quality will be measured using the PROMIS - Sleep Disturbance Form 8 assessment.
The sleep disturbance assessment has 8 questions that yield a total score (summed scores).
This raw score is then converted to a standardized T score from 0-100 with a mean score of 50.
A score above the mean would indicate that the subject experiences worse sleep quality.
Mixed effects models were then conducted to evaluate the effect of condition on sleep quality over time with condition, time, and their interaction in the model and sex and baseline sleep quality as covariates.
|
baseline and Week 10
|
|
Sleep-related Impairment
Time Frame: baseline and Week 10
|
Mean sleep quality will be measured using the PROMIS - Sleep-Related Impairment Form 8 assessment.
The sleep impairment assessment has 8 questions that yield a total score (summed score).
This raw score is then converted to a standardized T score from 0-100 with a mean score of 50.
A score above the mean would indicate that the subject experiences more sleep-related impairment.
Mixed effects models were then conducted to evaluate the effect of condition on sleep-related impairment over time with condition, time, and their interaction in the model and sex and baseline sleep-related impairment as covariates.
|
baseline and Week 10
|
|
Sleep Duration
Time Frame: up to 6 weeks
|
Mean sleep duration will be measured daily for 6 weeks by the PPG device.
Sleep duration will then be averaged in 2-week intervals at Weeks 2, 4, and 6 and evaluated over time using mixed effects models with condition, time, and their interaction in the model and sex and an indicator variable of weekday vs. weekend as covariates.
Sleep duration was transformed using a square-root transformation.
|
up to 6 weeks
|
|
Heart Rate Variability (HRV)
Time Frame: up to 6 weeks
|
Heart rate variability (HRV) will be measured daily for 6 weeks by the PPG device.
HRV will then be averaged in 2-week intervals at Weeks 2, 4, and 6 and evaluated over time using mixed effects models with condition, time, and their interaction in the model and sex and an indicator variable of weekday vs. weekend as covariates.
Sleep duration was transformed using a log transformation.
|
up to 6 weeks
|
|
Lowest Resting Heart Rate (RHR)
Time Frame: up to 6 weeks
|
Lowest Resting Heart Rate (RHR) will be measured daily for 6 weeks by the PPG device.
The lowest value will then be averaged in 2-week intervals at Weeks 2, 4, and 6 and evaluated over time using mixed effects models with condition, time, and their interaction in the model and sex and an indicator variable of weekday vs. weekend as covariates.
Sleep duration was transformed using a log transformation.
RHR can vary anywhere between 40-100 beats per minute.
Lower RHR would indicate better cardiovascular health.
|
up to 6 weeks
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Lisa Fucito, PhD, Associate Professor of Psychiatry; Director, Tobacco Treatment Service, Psychiatry
Publications and helpful links
General Publications
- Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003 Aug;35(8):1381-95. doi: 10.1249/01.MSS.0000078924.61453.FB.
- Beauchaine TP, Thayer JF. Heart rate variability as a transdiagnostic biomarker of psychopathology. Int J Psychophysiol. 2015 Nov;98(2 Pt 2):338-350. doi: 10.1016/j.ijpsycho.2015.08.004. Epub 2015 Aug 11.
- Hernando D, Roca S, Sancho J, Alesanco A, Bailon R. Validation of the Apple Watch for Heart Rate Variability Measurements during Relax and Mental Stress in Healthy Subjects. Sensors (Basel). 2018 Aug 10;18(8):2619. doi: 10.3390/s18082619.
- Yu L, Buysse DJ, Germain A, Moul DE, Stover A, Dodds NE, Johnston KL, Pilkonis PA. Development of short forms from the PROMIS sleep disturbance and Sleep-Related Impairment item banks. Behav Sleep Med. 2011 Dec 28;10(1):6-24. doi: 10.1080/15402002.2012.636266.
- Pilkonis PA, Choi SW, Reise SP, Stover AM, Riley WT, Cella D; PROMIS Cooperative Group. Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS(R)): depression, anxiety, and anger. Assessment. 2011 Sep;18(3):263-83. doi: 10.1177/1073191111411667. Epub 2011 Jun 21.
- Liu Y, Wheaton AG, Chapman DP, Cunningham TJ, Lu H, Croft JB. Prevalence of Healthy Sleep Duration among Adults--United States, 2014. MMWR Morb Mortal Wkly Rep. 2016 Feb 19;65(6):137-41. doi: 10.15585/mmwr.mm6506a1.
- Thayer JF, Ahs F, Fredrikson M, Sollers JJ 3rd, Wager TD. A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health. Neurosci Biobehav Rev. 2012 Feb;36(2):747-56. doi: 10.1016/j.neubiorev.2011.11.009. Epub 2011 Dec 8.
- Stein PK, Pu Y. Heart rate variability, sleep and sleep disorders. Sleep Med Rev. 2012 Feb;16(1):47-66. doi: 10.1016/j.smrv.2011.02.005. Epub 2011 Jun 11.
- Kinnunen H, Rantanen A, Kentta T, Koskimaki H. Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG. Physiol Meas. 2020 May 7;41(4):04NT01. doi: 10.1088/1361-6579/ab840a.
- Leyro TM, Buckman JF, Bates ME. Theoretical implications and clinical support for heart rate variability biofeedback for substance use disorders. Curr Opin Psychol. 2019 Dec;30:92-97. doi: 10.1016/j.copsyc.2019.03.008. Epub 2019 Apr 2.
- Stepanski EJ, Wyatt JK. Use of sleep hygiene in the treatment of insomnia. Sleep Med Rev. 2003 Jun;7(3):215-25. doi: 10.1053/smrv.2001.0246.
- Kaner EF, Beyer FR, Muirhead C, Campbell F, Pienaar ED, Bertholet N, Daeppen JB, Saunders JB, Burnand B. Effectiveness of brief alcohol interventions in primary care populations. Cochrane Database Syst Rev. 2018 Feb 24;2(2):CD004148. doi: 10.1002/14651858.CD004148.pub4.
- Prinsloo GE, Rauch HG, Derman WE. A brief review and clinical application of heart rate variability biofeedback in sports, exercise, and rehabilitation medicine. Phys Sportsmed. 2014 May;42(2):88-99. doi: 10.3810/psm.2014.05.2061.
- Monk TH, Reynolds CF, Kupfer DJ, Buysse DJ, Coble PA, Hayes AJ, MacHen MA, Petrie SR, Ritenour AM. The Pittsburgh Sleep Diary. J Sleep Res. 1994 Jun;3(2):111-120.
- Buscemi J, Murphy JG, Martens MP, McDevitt-Murphy ME, Dennhardt AA, Skidmore JR. Help-seeking for alcohol-related problems in college students: correlates and preferred resources. Psychol Addict Behav. 2010 Dec;24(4):571-80. doi: 10.1037/a0021122.
- Falk D, Yi HY, Hiller-Sturmhofel S. An epidemiologic analysis of co-occurring alcohol and drug use and disorders: findings from the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC). Alcohol Res Health. 2008;31(2):100-10.
- Administration, S.A.a.M.H.S. Key substance use and mental health indicators in the United States: Results from the National Survey on Drug Use and Health, Center for Behavioral Health Statistics and Quality. 2019; Available from: https://www.samhsa.gov/data/.
- Hingson RW, Zha W, Weitzman ER. Magnitude of and trends in alcohol-related mortality and morbidity among U.S. college students ages 18-24, 1998-2005. J Stud Alcohol Drugs Suppl. 2009 Jul;(16):12-20. doi: 10.15288/jsads.2009.s16.12.
- NIAAA, Alcohol involvement over the life course, E. U. S. Department of Health and Human Services, Editor. 2000 Bethesda, MD. p. p. 28-53.
- NIAAA, Alcohol and Other Drugs. Alcohol Alert, 2008. 76.
- Cronce JM, Larimer ME. Individual-focused approaches to the prevention of college student drinking. Alcohol Res Health. 2011;34(2):210-21.
- Carey KB, Scott-Sheldon LA, Carey MP, DeMartini KS. Individual-level interventions to reduce college student drinking: a meta-analytic review. Addict Behav. 2007 Nov;32(11):2469-94. doi: 10.1016/j.addbeh.2007.05.004. Epub 2007 May 17.
- Carey KB, Scott-Sheldon LA, Elliott JC, Bolles JR, Carey MP. Computer-delivered interventions to reduce college student drinking: a meta-analysis. Addiction. 2009 Nov;104(11):1807-19. doi: 10.1111/j.1360-0443.2009.02691.x. Epub 2009 Sep 10.
- Black DR, Coster DC. Interest in a stepped approach model (SAM): identification of recruitment strategies for university alcohol programs. Health Educ Q. 1996 Feb;23(1):98-114. doi: 10.1177/109019819602300107.
- Wu LT, Pilowsky DJ, Schlenger WE, Hasin D. Alcohol use disorders and the use of treatment services among college-age young adults. Psychiatr Serv. 2007 Feb;58(2):192-200. doi: 10.1176/ps.2007.58.2.192.
- Caldeira KM, Kasperski SJ, Sharma E, Vincent KB, O'Grady KE, Wish ED, Arria AM. College students rarely seek help despite serious substance use problems. J Subst Abuse Treat. 2009 Dec;37(4):368-78. doi: 10.1016/j.jsat.2009.04.005. Epub 2009 Jun 23.
- Fortuna RJ, Robbins BW, Halterman JS. Ambulatory care among young adults in the United States. Ann Intern Med. 2009 Sep 15;151(6):379-85. doi: 10.7326/0003-4819-151-6-200909150-00002.
- O'hara, B. and K. Caswell, Health status, health insurance, and medical services utilization: 2010. Curr Pop Rep, 2012. 2012: p. 70-133.
- Cadigan JM, Lee CM, Larimer ME. Young Adult Mental Health: a Prospective Examination of Service Utilization, Perceived Unmet Service Needs, Attitudes, and Barriers to Service Use. Prev Sci. 2019 Apr;20(3):366-376. doi: 10.1007/s11121-018-0875-8.
- Orzech KM, Salafsky DB, Hamilton LA. The state of sleep among college students at a large public university. J Am Coll Health. 2011;59(7):612-9. doi: 10.1080/07448481.2010.520051.
- Weinstock J, Petry NM, Pescatello LS, Henderson CE. Sedentary college student drinkers can start exercising and reduce drinking after intervention. Psychol Addict Behav. 2016 Dec;30(8):791-801. doi: 10.1037/adb0000207. Epub 2016 Sep 26.
- Fucito LM, DeMartini KS, Hanrahan TH, Yaggi HK, Heffern C, Redeker NS. Using Sleep Interventions to Engage and Treat Heavy-Drinking College Students: A Randomized Pilot Study. Alcohol Clin Exp Res. 2017 Apr;41(4):798-809. doi: 10.1111/acer.13342. Epub 2017 Feb 16.
- Rideout V. Generation Rx.com. What are young people really doing online? Mark Health Serv. 2002 Spring;22(1):26-30.
- Rideout, V. and S. Fox, Digital health practices, social media use, and mental wellbeing among teens and young adults in the US. 2018.
- Wartella, E., et al., Teens, health and technology: A national survey. Media and communication, 2016. 4(3): p. 13-23.
- DeMartini KS, Fucito LM. Variations in sleep characteristics and sleep-related impairment in at-risk college drinkers: a latent profile analysis. Health Psychol. 2014 Oct;33(10):1164-73. doi: 10.1037/hea0000115. Epub 2014 Aug 18.
- Singleton RA Jr, Wolfson AR. Alcohol consumption, sleep, and academic performance among college students. J Stud Alcohol Drugs. 2009 May;70(3):355-63. doi: 10.15288/jsad.2009.70.355.
- Hasler BP, Martin CS, Wood DS, Rosario B, Clark DB. A longitudinal study of insomnia and other sleep complaints in adolescents with and without alcohol use disorders. Alcohol Clin Exp Res. 2014 Aug;38(8):2225-33. doi: 10.1111/acer.12474. Epub 2014 Jun 27.
- Hasler BP, Kirisci L, Clark DB. Restless Sleep and Variable Sleep Timing During Late Childhood Accelerate the Onset of Alcohol and Other Drug Involvement. J Stud Alcohol Drugs. 2016 Jul;77(4):649-55. doi: 10.15288/jsad.2016.77.649.
- Miller MB, DiBello AM, Lust SA, Carey MP, Carey KB. Adequate sleep moderates the prospective association between alcohol use and consequences. Addict Behav. 2016 Dec;63:23-8. doi: 10.1016/j.addbeh.2016.05.005. Epub 2016 May 7.
- Wong MM, Brower KJ, Fitzgerald HE, Zucker RA. Sleep problems in early childhood and early onset of alcohol and other drug use in adolescence. Alcohol Clin Exp Res. 2004 Apr;28(4):578-87. doi: 10.1097/01.alc.0000121651.75952.39.
- Wong MM, Brower KJ, Nigg JT, Zucker RA. Childhood sleep problems, response inhibition, and alcohol and drug outcomes in adolescence and young adulthood. Alcohol Clin Exp Res. 2010 Jun;34(6):1033-44. doi: 10.1111/j.1530-0277.2010.01178.x. Epub 2010 Apr 5.
- Wong MM, Brower KJ, Zucker RA. Childhood sleep problems, early onset of substance use and behavioral problems in adolescence. Sleep Med. 2009 Aug;10(7):787-96. doi: 10.1016/j.sleep.2008.06.015. Epub 2009 Jan 12. Erratum In: Sleep Med. 2010 Jan;11(1):110-1.
- Wong MM, Robertson GC, Dyson RB. Prospective relationship between poor sleep and substance-related problems in a national sample of adolescents. Alcohol Clin Exp Res. 2015 Feb;39(2):355-62. doi: 10.1111/acer.12618. Epub 2015 Jan 16.
- Fucito LM, DeMartini KS, Hanrahan TH, Whittemore R, Yaggi HK, Redeker NS. Perceptions of Heavy-Drinking College Students About a Sleep and Alcohol Health Intervention. Behav Sleep Med. 2015;13(5):395-411. doi: 10.1080/15402002.2014.919919. Epub 2014 Jun 12.
- Irwin MR. Why sleep is important for health: a psychoneuroimmunology perspective. Annu Rev Psychol. 2015 Jan 3;66:143-72. doi: 10.1146/annurev-psych-010213-115205. Epub 2014 Jul 21.
- Worley SL. The Extraordinary Importance of Sleep: The Detrimental Effects of Inadequate Sleep on Health and Public Safety Drive an Explosion of Sleep Research. P T. 2018 Dec;43(12):758-763.
- Choi YK, Demiris G, Lin SY, Iribarren SJ, Landis CA, Thompson HJ, McCurry SM, Heitkemper MM, Ward TM. Smartphone Applications to Support Sleep Self-Management: Review and Evaluation. J Clin Sleep Med. 2018 Oct 15;14(10):1783-1790. doi: 10.5664/jcsm.7396.
- Goldman, D., Investing in the growing sleep-health economy. Prevalence, 2016.
- Campos, M. Heart rate variability: A new way to track well-being. 2019 Available from: https://www.health.harvard.edu/blog/heart-rate-variability-new-way-track-well2017112212789.
- Buccelletti E, Gilardi E, Scaini E, Galiuto L, Persiani R, Biondi A, Basile F, Silveri NG. Heart rate variability and myocardial infarction: systematic literature review and metanalysis. Eur Rev Med Pharmacol Sci. 2009 Jul-Aug;13(4):299-307.
- Young HA, Benton D. Heart-rate variability: a biomarker to study the influence of nutrition on physiological and psychological health? Behav Pharmacol. 2018 Apr;29(2 and 3-Spec Issue):140-151. doi: 10.1097/FBP.0000000000000383.
- Kemp AH, Quintana DS. The relationship between mental and physical health: insights from the study of heart rate variability. Int J Psychophysiol. 2013 Sep;89(3):288-96. doi: 10.1016/j.ijpsycho.2013.06.018. Epub 2013 Jun 22.
- Ralevski E, Petrakis I, Altemus M. Heart rate variability in alcohol use: A review. Pharmacol Biochem Behav. 2019 Jan;176:83-92. doi: 10.1016/j.pbb.2018.12.003. Epub 2018 Dec 6.
- Vaschillo EG, Vaschillo B, Buckman JF, Heiss S, Singh G, Bates ME. Early signs of cardiovascular dysregulation in young adult binge drinkers. Psychophysiology. 2018 May;55(5):e13036. doi: 10.1111/psyp.13036. Epub 2017 Nov 29.
- Ahmed, W., Podcast No. 43: Alcohol's effect on sleep, recovery and performance, in Whoop Podcast. 2019.
- de Zambotti M, Rosas L, Colrain IM, Baker FC. The Sleep of the Ring: Comparison of the OURA Sleep Tracker Against Polysomnography. Behav Sleep Med. 2019 Mar-Apr;17(2):124-136. doi: 10.1080/15402002.2017.1300587. Epub 2017 Mar 21.
- Roberts DM, Schade MM, Mathew GM, Gartenberg D, Buxton OM. Detecting sleep using heart rate and motion data from multisensor consumer-grade wearables, relative to wrist actigraphy and polysomnography. Sleep. 2020 Jul 13;43(7):zsaa045. doi: 10.1093/sleep/zsaa045.
- Portnoy DB, Scott-Sheldon LA, Johnson BT, Carey MP. Computer-delivered interventions for health promotion and behavioral risk reduction: a meta-analysis of 75 randomized controlled trials, 1988-2007. Prev Med. 2008 Jul;47(1):3-16. doi: 10.1016/j.ypmed.2008.02.014. Epub 2008 Feb 20.
- Friedrich A, Schlarb AA. Let's talk about sleep: a systematic review of psychological interventions to improve sleep in college students. J Sleep Res. 2018 Feb;27(1):4-22. doi: 10.1111/jsr.12568. Epub 2017 Jun 15.
- Chung KF, Lee CT, Yeung WF, Chan MS, Chung EW, Lin WL. Sleep hygiene education as a treatment of insomnia: a systematic review and meta-analysis. Fam Pract. 2018 Jul 23;35(4):365-375. doi: 10.1093/fampra/cmx122.
- Sobell LC, Agrawal S, Sobell MB, Leo GI, Young LJ, Cunningham JA, Simco ER. Comparison of a quick drinking screen with the timeline followback for individuals with alcohol problems. J Stud Alcohol. 2003 Nov;64(6):858-61. doi: 10.15288/jsa.2003.64.858.
- Trockel M, Manber R, Chang V, Thurston A, Taylor CB. An e-mail delivered CBT for sleep-health program for college students: effects on sleep quality and depression symptoms. J Clin Sleep Med. 2011 Jun 15;7(3):276-81. doi: 10.5664/JCSM.1072. Erratum In: J Clin Sleep Med. 2011 Aug 15;7(4):420. Tailor, Craig Barr [corrected to Taylor, Craig Barr].
- Kloss JD, Nash CO, Horsey SE, Taylor DJ. The delivery of behavioral sleep medicine to college students. J Adolesc Health. 2011 Jun;48(6):553-61. doi: 10.1016/j.jadohealth.2010.09.023. Epub 2010 Dec 18.
- Castaneda D, Esparza A, Ghamari M, Soltanpur C, Nazeran H. A review on wearable photoplethysmography sensors and their potential future applications in health care. Int J Biosens Bioelectron. 2018;4(4):195-202. doi: 10.15406/ijbsbe.2018.04.00125. Epub 2018 Aug 6.
- Pernice R, Javorka M, Krohova J, Czippelova B, Turianikova Z, Busacca A, Faes L; Member, IEEE. Comparison of short-term heart rate variability indexes evaluated through electrocardiographic and continuous blood pressure monitoring. Med Biol Eng Comput. 2019 Jun;57(6):1247-1263. doi: 10.1007/s11517-019-01957-4. Epub 2019 Feb 7.
- Pernice R, Javorka M, Krohova J, Czippelova B, Turianikova Z, Busacca A, Faes L. Reliability of Short-Term Heart Rate Variability Indexes Assessed through Photoplethysmography. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5610-5513. doi: 10.1109/EMBC.2018.8513634.
- Walch O, Huang Y, Forger D, Goldstein C. Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device. Sleep. 2019 Dec 24;42(12):zsz180. doi: 10.1093/sleep/zsz180.
- Fonseca P, Weysen T, Goelema MS, Most EIS, Radha M, Lunsingh Scheurleer C, van den Heuvel L, Aarts RM. Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults. Sleep. 2017 Jul 1;40(7). doi: 10.1093/sleep/zsx097.
- de Zambotti M, Cellini N, Goldstone A, Colrain IM, Baker FC. Wearable Sleep Technology in Clinical and Research Settings. Med Sci Sports Exerc. 2019 Jul;51(7):1538-1557. doi: 10.1249/MSS.0000000000001947.
- Structured Clinical Interview For DSM-V-RV Axis I Disorders Research Version. 2014.
- Demartini KS, Carey KB. Correlates of AUDIT risk status for male and female college students. J Am Coll Health. 2009 Nov-Dec;58(3):233-9. doi: 10.1080/07448480903295342.
- Pilkonis PA, Choi SW, Salsman JM, Butt Z, Moore TL, Lawrence SM, Zill N, Cyranowski JM, Kelly MA, Knox SS, Cella D. Assessment of self-reported negative affect in the NIH Toolbox. Psychiatry Res. 2013 Mar 30;206(1):88-97. doi: 10.1016/j.psychres.2012.09.034. Epub 2012 Oct 22.
- Ortega FB, Sanchez-Lopez M, Solera-Martinez M, Fernandez-Sanchez A, Sjostrom M, Martinez-Vizcaino V. Self-reported and measured cardiorespiratory fitness similarly predict cardiovascular disease risk in young adults. Scand J Med Sci Sports. 2013 Dec;23(6):749-57. doi: 10.1111/j.1600-0838.2012.01454.x. Epub 2012 Mar 15.
- Anderson, M., Technology Device Ownership: 2015. Pew Research Center 2015.
- Peters EN, Leeman RF, Fucito LM, Toll BA, Corbin WR, O'Malley SS. Co-occurring marijuana use is associated with medication nonadherence and nonplanning impulsivity in young adult heavy drinkers. Addict Behav. 2012 Apr;37(4):420-6. doi: 10.1016/j.addbeh.2011.11.036. Epub 2011 Dec 3.
- Rounsaville, B.J., K.M. Carroll, and L.S. Onken, A stage model of behavioral therapies research: Getting started and moving on from stage I. Clinical Psychology: Science and Practice, 2001. 8(2): p. 133-142.
- Sobell MB, Sobell LC, Leo GI. Does enhanced social support improve outcomes for problem drinkers in guided self-change treatment? J Behav Ther Exp Psychiatry. 2000 Mar;31(1):41-54. doi: 10.1016/s0005-7916(00)00007-0.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- 2000030417
- 1R21AA028886-01A1 (U.S. NIH Grant/Contract)
- 21-002981 (Other Grant/Funding Number: PI Assigned ID)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
Clinical Trials on Heavy Drinking
-
Cambridge Health AllianceRecruiting
-
The National Center on Addiction and Substance...Not yet recruitingHeavy Drinking
-
Wayne State UniversityCompleted
-
Centre for Addiction and Mental HealthCompleted
-
RANDUniversity of Southern CaliforniaRecruiting
-
University of California, Los AngelesNational Institute on Alcohol Abuse and Alcoholism (NIAAA)CompletedHeavy Drinking
-
University of HoustonCompletedHeavy DrinkingUnited States
-
University of HoustonCompleted
-
Peking University Sixth HospitalNot yet recruiting
-
Central Institute of Mental Health, MannheimCompletedHeavy DrinkingGermany
Clinical Trials on Behavioral Self-Management and Feedback
-
The University of Texas Health Science Center,...Completed
-
University of Alabama, TuscaloosaUniversity of Alabama at BirminghamRecruiting
-
Visiting Nurse Service of New YorkWeill Medical College of Cornell University; Cornell University; Agency for Healthcare... and other collaboratorsCompletedArthritis - Post Surgical | Other Activity-limiting PainUnited States
-
Stony Brook UniversityCompletedChronic Fatigue Syndrome | Medically Unexplained Chronic FatigueUnited States
-
University of Massachusetts, WorcesterCompletedType 2 Diabetes | Glycemic ControlUnited States
-
University of FloridaRecruiting
-
Yale UniversityNational Institute of Nursing Research (NINR); Milton S. Hershey Medical CenterCompletedHeart Failure | Pain | Fatigue | Sleep Initiation and Maintenance Disorders | Anxiety | Depressive Symptoms | Congestive Heart Failure | Sleep Disorders | Chronic Insomnia | Heart Failure, Congestive | Cardiac Failure | Disorders of Initiating and Maintaining SleepUnited States
-
VA Office of Research and DevelopmentCompleted
-
Changzhi Medical CollegeNot yet recruitingPsychological Stress | Nursing Students