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| ID | Type | Description | Link |
|---|---|---|---|
| 1R21AA028886-01A1 | U.S. NIH Grant/Contract | View source | |
| 21-002981 | Other Grant/Funding Number | PI Assigned ID |
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| Name | Class |
|---|---|
| National Institute on Alcohol Abuse and Alcoholism (NIAAA) | NIH |
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Heavy alcohol use among young adults is a significant public health problem. Advances in technology may offer an innovative solution. This project will conduct the first controlled test of a feedback intervention for reducing drinking and improving health in young adults by targeting heart rate variability, resting heart rate, and sleep via biosensors and electronic diary methods.
Proposed is a study to conduct the first controlled test of a feedback intervention targeting heart rate variability, resting heart rate, and sleep for heavy-drinking young adults (N=60; ages 18-25) and will leverage the capabilities of a consumer-marketed PPG sensor/mobile app. This study will evaluate the feasibility, acceptability, and preliminary efficacy of this intervention for promoting improvements in drinking, sleep, and health.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Self-Monitoring and Feedback | Active Comparator | 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-Monitoring | Placebo Comparator | 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Behavioral Self-Management and Feedback | Behavioral | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Total Drinks Consumed | 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Drinks Per Drinking Day | 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. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Lisa Fucito, PhD | Associate Professor of Psychiatry; Director, Tobacco Treatment Service, Psychiatry | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Yale University | New Haven | Connecticut | 06510 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 23584812 | Background | 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. | |
| Background | 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/. | ||
| 19538908 |
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| ID | Title | Description |
|---|---|---|
| FG000 | 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. Behavioral Self-Management and Feedback: 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. |
| FG001 | 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. Behavioral Self-Management: 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. |
| Title | Milestones | Reasons Not Completed | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
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| ID | Title | Description |
|---|---|---|
| BG000 | 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. Behavioral Self-Management and Feedback: 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. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Total Drinks Consumed | 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. | All randomized participants who started either treatment condition. | Posted | Least Squares Mean | Standard Error | drinks | up to Week 10 |
|
10 weeks
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | 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. Behavioral Self-Management and Feedback: 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. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Lisa Fucito, PhD | Yale University School of Medicine | 203-200-1470 | Lisa.fucito@yale.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Nov 11, 2022 | May 21, 2024 | Prot_SAP_001.pdf |
| ICF | No | No | Yes | Informed Consent Form | Jan 27, 2022 | Feb 13, 2024 | ICF_000.pdf |
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Eligible subjects will be randomized into either the Assessment or Feedback group. Subjects in the assessment group will not receive personalized health feedback or advice on their health behaviors. They will wear a consumer- level PPG device daily and complete electronic diaries about bedtime behavior and sleep from the previous night for six weeks. Subjects in the feedback group will also wear a consumer-level PPG device and complete diaries each morning for six weeks. Subjects in this group will monitor their health behaviors and receive personalized feedback and advice. Subjects will also be able to receive daily information regarding their health behaviors from the PPG device application. At weeks two, four, and six subjects will will receive written reports that summarize their alcohol usage and the links made between their drinking and health outcomes from the PPG application results. Subjects will also receive advice on how to improve health behaviors.
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| Behavioral Self-Management | Behavioral | 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. |
|
| up to 10 weeks |
| Percent Heavy Drinking Days | 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 | 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 | 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 | 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 | 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 | 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) | 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) | 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 |
| Background |
| 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. |
| Background | NIAAA, Alcohol involvement over the life course, E. U. S. Department of Health and Human Services, Editor. 2000 Bethesda, MD. p. p. 28-53. |
| Background | NIAAA, Alcohol and Other Drugs. Alcohol Alert, 2008. 76. |
| 22330220 | Background | Cronce JM, Larimer ME. Individual-focused approaches to the prevention of college student drinking. Alcohol Res Health. 2011;34(2):210-21. |
| 17590277 | Background | 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. |
| 19744139 | Background | 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. |
| 21198220 | Background | 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. |
| 8822404 | Background | 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. |
| 17287375 | Background | 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. |
| 19553064 | Background | 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. |
| 19755363 | Background | 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. |
| Background | O'hara, B. and K. Caswell, Health status, health insurance, and medical services utilization: 2010. Curr Pop Rep, 2012. 2012: p. 70-133. |
| 29476653 | Background | 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. |
| 29411197 | Background | 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. |
| 21823956 | Background | 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. |
| 27669095 | Background | 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. |
| 28118486 | Background | 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. |
| 11881541 | Background | Rideout V. Generation Rx.com. What are young people really doing online? Mark Health Serv. 2002 Spring;22(1):26-30. |
| Background | Rideout, V. and S. Fox, Digital health practices, social media use, and mental wellbeing among teens and young adults in the US. 2018. |
| Background | Wartella, E., et al., Teens, health and technology: A national survey. Media and communication, 2016. 4(3): p. 13-23. |
| 25133844 | Background | 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. |
| 19371486 | Background | 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. |
| 24976511 | Background | 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. |
| 27340970 | Background | 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. |
| 27395437 | Background | 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. |
| 15100609 | Background | 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. |
| 20374209 | Background | 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. |
| 19138880 | Background | 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. |
| 25598438 | Background | 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. |
| 24924956 | Background | 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. |
| 26890214 | Background | 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. |
| 25061767 | Background | 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. |
| 30559589 | Background | 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. |
| 30353814 | Background | 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. |
| Background | Goldman, D., Investing in the growing sleep-health economy. Prevalence, 2016. |
| Background | 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. |
| 26272488 | Background | 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. |
| 22178086 | Background | 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. |
| 19694345 | Background | 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. |
| 29543648 | Background | 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. |
| 23797149 | Background | 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. |
| 30529588 | Background | 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. |
| 29193139 | Background | 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. |
| 21658979 | Background | 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. |
| 31055246 | Background | 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. |
| Background | Ahmed, W., Podcast No. 43: Alcohol's effect on sleep, recovery and performance, in Whoop Podcast. 2019. |
| 28323455 | Background | 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. |
| 32215550 | Background | 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. |
| 18403003 | Background | 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. |
| 12927121 | Background | 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. |
| 28618185 | Background | 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. |
| 29194467 | Background | 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. |
| 24875976 | Background | 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. |
| 14743950 | Background | 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. |
| 21697139 | Background | 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. |
| 22250775 | Background | 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. |
| 21677898 | Background | 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. |
| 21575813 | Background | 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. |
| 30906922 | Background | 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. |
| 32217820 | Background | 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. |
| 30730027 | Background | 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. |
| 30103376 | Background | 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. |
| 30441608 | Background | 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. |
| 31579900 | Background | 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. |
| 28838130 | Background | 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. |
| 30789439 | Background | 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. |
| Background | Structured Clinical Interview For DSM-V-RV Axis I Disorders Research Version. 2014. |
| 19959437 | Background | 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. |
| 10983746 | Background | 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. |
| 10607115 | Background | 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. |
| 23083918 | Background | 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. |
| 22417235 | Background | 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. |
| 12900694 | Background | 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. |
| Background | Anderson, M., Technology Device Ownership: 2015. Pew Research Center 2015. |
| 22189052 | Background | 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. |
| Background | 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. |
| BG001 | 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. Behavioral Self-Management: 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. |
| BG002 | Total | Total of all reporting groups |
| years |
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| Sex: Female, Male | Count of Participants | Participants |
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| Ethnicity (NIH/OMB) | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
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| Region of Enrollment | Number | participants |
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| Gender | Count of Participants | Participants |
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The intervention consists of subjects wearing a PPG device for 6 weeks. Subjects will monitor their own health and report their sleep and related behaviors daily during this time using mobile daily diaries. On weeks two, four, and six subjects will receive personalized health feedback based on the PPG device data and sleep diaries. Behavioral Self-Management and Feedback: 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. |
| OG001 | Self-Monitoring | The intervention consists of subjects wearing a PPG device for 6 weeks. Subjects will monitor their own health and report their sleep and related behaviors daily during this time using mobile daily diaries. Behavioral Self-Management: 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. |
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| Secondary | Drinks Per Drinking Day | 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. | All randomized participants who started either condition. | Posted | Least Squares Mean | Standard Error | drinks per drinking day | up to 10 weeks |
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| Secondary | Percent Heavy Drinking Days | 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. | All randomized participants who started either treatment condition. | Posted | Least Squares Mean | Standard Error | percentage of heavy drinking days | up to 10 weeks |
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| Secondary | Percent Abstinent Days | 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. | All randomized participants who started either treatment condition. | Posted | Least Squares Mean | Standard Error | percentage of abstinent days | up to 10 weeks |
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| Secondary | Alcohol-related Consequences | 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. | All randomized participants who started either treatment condition. | Posted | Least Squares Mean | Standard Error | score on scale | baseline, Week 6, and Week 10 |
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| Secondary | Sleep Quality | 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. | All randomized participants who started either condition. | Posted | Least Squares Mean | Standard Error | T-score | baseline and Week 10 |
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| Secondary | Sleep-related Impairment | 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. | All randomized participants who started either condition. | Posted | Least Squares Mean | Standard Error | T-score | baseline and Week 10 |
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| Secondary | Sleep Duration | 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. | All randomized participants who started either condition. | Posted | Least Squares Mean | Standard Error | square root in milliseconds | up to 6 weeks |
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| Secondary | Heart Rate Variability (HRV) | 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. | All randomized participants who started either treatment condition. | Posted | Least Squares Mean | Standard Error | milliseconds | up to 6 weeks |
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| Secondary | Lowest Resting Heart Rate (RHR) | 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. | All randomized participants who started either treatment condition. | Posted | Least Squares Mean | Standard Error | beats per minute | up to 6 weeks |
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| 0 |
| 30 |
| 0 |
| 30 |
| 0 |
| 30 |
| EG001 | 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. Behavioral Self-Management: 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. | 0 | 30 | 0 | 30 | 0 | 30 |
Not provided
Not provided
Not provided
| <.0001 |
| Other |
| 0.04 |
| Other |
| Weeks 5-6 |
|
| 0.02 |
| Other |
| Main effects of type of day of the week (weekday vs. weekend) | Mixed Models Analysis | 0.0007 | Other |
| Mixed Models Analysis | 0.02 | Other |
| Weeks 5-6 |
|
| Weeks 5-6 |
|
| <.0001 |
| Other |
| Main effects of type of day of the week (weekday vs. weekend) | Mixed Models Analysis | <0.0001 | Other |
| Mixed Models Analysis | 0.03 | Other |