- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT03991650
Monitoring Telemedicine Platform in Patients With Anxiety Symptoms and Alcohol Use Disorder (REMOTE)
REMOTE: Monitoring Telemedicine Platform in Patients With Anxiety Symptoms and Alcohol Use Disorder: Smartphone and Wearable Sensors
Study Overview
Status
Intervention / Treatment
Detailed Description
The objective of this study is to analyze the physiological patterns of two groups of patients, one control and one with anxiety disorder and alcoholic abuse disorder using sensor data from mobile devices and wearables. This data, collected by sensors, will be compared to the data presented by three clinical questionnaires: State-trait Anxiety Inventory (STAI), the Alcohol Use Disorders Identification Test (AUDIT), and the Beck's Depression Inventory (BDI-II) in order to determine the feasibility of using remote collected data in routine clinical practice. Furthermore, patient satisfaction with the app will be evaluated after the data collection phase.
This is a unicentric, case-controlled, longitudinal, evaluational, national study with two groups, one control of healthy individuals without symptoms of anxiety or depression, and another experimental group in which participants fulfill the given selection criteria for the trial. There are a total of 60 participants split evenly between the two groups.
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Barcelona, Spain, 08036
- Hospitcal Clínic de Barcelona
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- 18-65 years of age.
- Alcohol use disorder (DSM 5) as main substance.
- Anxiety (STAI > percentile 33) and, if depressive symptoms, not clinically relevant as to accomplish DSM5 diagnostic criteria for mood disorders (major depressive disorder, bipolar disorder…).
- Having a mobile phone compatible with Android.
- Sign informed consent.
Exclusion Criteria:
- Mood disorder diagnoses (DSM5).
- Cognitive deficits that prevent the participation.
- Active intake of other substances (except for nicotine).
Study Plan
How is the study designed?
Design Details
- Observational Models: Case-Control
- Time Perspectives: Prospective
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
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Control
n=30 healthy participants will be recruited using social networks and leaflets of information distributed by the research team at the Hospital Cliníc de Barcelona. The participants will be monitored over the course of one month using the humanITcare app "U-Shine," which will track participant's sociability, device usage, and location frequency using mobile sensors. Participants' data will also be monitored with a FitBit device to track sleep schedules, heart rate, and step count. During the weekly follow-up, they will have to complete the three clinical questionnaires taken at the initial visit. |
Participants will be monitored by an App.
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Experimental
The recruitment process will be carried out in patients of external consultations and the day hospital of the Addictions Unit at the Hospital Clinic of Barcelona. n=30 patients with Anxiety and Alcohol Use Disorder. The participants will be monitored over the course of one month using the humanITcare app "U-Shine," which will track participant's sociability, device usage, and location frequency using mobile sensors. Participants will also be monitored using a FitBit device to track sleep schedules, heart rate, and step count. During the weekly follow-up, they will have to complete the three clinical questionnaires taken at the initial visit. |
Participants will be monitored by an App.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Change in Self-reported Anxiety symptoms assessed with the State-Trait Anxiety Inventory (STAI)
Time Frame: One month, questionnaire taken 4 times (1/week)
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Taken through the application "Ushine".
Participants receive STAI scores ranging from 0-60, 0 being lowest amount of symptoms, and 60 being the greatest amount of symptoms, which are then transformed into percentiles according to age and sex.
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One month, questionnaire taken 4 times (1/week)
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Sleep pattern
Time Frame: One month
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Monitoring of circadian rhythm using a motion sensor Fitbit and cardiac activity sensor
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One month
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REM sleep time
Time Frame: One month
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monitoring of REM sleep patterns using a motion sensor Fitbit and cardiac activity sensor
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One month
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Heart Rate
Time Frame: One month
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monitoring of heart rate using Fitbit sensor
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One month
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Step count
Time Frame: One month
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Monitoring of daily step count using motion sensor FitBit
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One month
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Distance travelled
Time Frame: One month
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Monitoring of distance travelled using GPS phone
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One month
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Mobile device usage
Time Frame: One month
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Monitoring how often the patient's mobile device is used, determined by tracking the presence of a signal from their device
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One month
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Sociability (number of incoming an outgoing calls and text messages)
Time Frame: One month
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The number of incoming and outgoing calls and text messages will be monitored using the UShine app algorithm
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One month
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Change in Self-reported Depression symptoms assessed with the Beck's Depression Inventory (BDI-II)
Time Frame: One month, questionnaire taken 4 times (1/week)
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Taken through the application "Ushine".
Participants receive BDI-II scores ranging from 1-63, with a score of 0-13 indicating minimal depression, 14-19 indicating mild depression, 20-28 indicating moderate depression, and 29-63 indicating severe depression.
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One month, questionnaire taken 4 times (1/week)
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Change in Self-reported Alcohol Abuse Symptoms assesed with the Alcohol Use Disorders Identification Test (AUDIT)
Time Frame: One month, questionnaire taken 4 times (1/week)
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Taken through the application "Ushine".
Participants receive AUDIT scores ranging from 0-40.
A score of 8 or more is associated with harmful or hazardous drinking, a score of 13 or more in women, and 15 or more in men, is likely to indicate alcohol dependence.
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One month, questionnaire taken 4 times (1/week)
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Usability of the mobile application
Time Frame: One month
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Ease of usability of the mobile applications by participants will be scaled using the System Usability Scale (SUS).
Final scores range from 0-100.
A score of 51 or under means the system has serious faults in usability, a score of 68 means the system usability is about average, and a score of 80.3 or higher means the system usability is considered appreciable.
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One month
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Satisfaction with the application
Time Frame: One month
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Participants' satisfaction with the Ushine application will be scaled using the Post-Study Usability Questionnaire (PSSUQ).
Scores range from 0-100, 0 being least satisfactory and 100 being the most satisfactory.
The PSSUQ has 3 sub-scores representing system usefulness, information quality, and interface quality, also scaled from 0-100.
In all score sets, 0 is considered the least satisfactory and 100 is considered the most satisfactory.
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One month
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Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Antoni Gual, MD, Director of Addictions Unit. Hospital Clínic de Barcelona
- Study Chair: Elsa Caballeria, Clinic Foundation for Biomedical Research
- Study Chair: Hugo Lopez-Pelayo, MD, Addictions Unit, Hospital Clínic de Barcelona
- Study Director: Nuria Pastor Hernandez, MSc, humanITcare, FollowHealth SL
- Study Director: Unai Sanchez Luque, MSc, humanITcare, FollowHealth SL
- Study Chair: Elizabeth Katayoun Khalilian, humanITcare, University of Texas at Austin
Publications and helpful links
General Publications
- Wittchen HU, Jacobi F, Rehm J, Gustavsson A, Svensson M, Jonsson B, Olesen J, Allgulander C, Alonso J, Faravelli C, Fratiglioni L, Jennum P, Lieb R, Maercker A, van Os J, Preisig M, Salvador-Carulla L, Simon R, Steinhausen HC. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol. 2011 Sep;21(9):655-79. doi: 10.1016/j.euroneuro.2011.07.018.
- Bandelow B, Michaelis S. Epidemiology of anxiety disorders in the 21st century. Dialogues Clin Neurosci. 2015 Sep;17(3):327-35. doi: 10.31887/DCNS.2015.17.3/bbandelow.
- Gustafson DH, McTavish FM, Chih MY, Atwood AK, Johnson RA, Boyle MG, Levy MS, Driscoll H, Chisholm SM, Dillenburg L, Isham A, Shah D. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiatry. 2014 May;71(5):566-72. doi: 10.1001/jamapsychiatry.2013.4642.
- Insel TR. Digital Phenotyping: Technology for a New Science of Behavior. JAMA. 2017 Oct 3;318(13):1215-1216. doi: 10.1001/jama.2017.11295. No abstract available.
- Alonso J, Liu Z, Evans-Lacko S, Sadikova E, Sampson N, Chatterji S, Abdulmalik J, Aguilar-Gaxiola S, Al-Hamzawi A, Andrade LH, Bruffaerts R, Cardoso G, Cia A, Florescu S, de Girolamo G, Gureje O, Haro JM, He Y, de Jonge P, Karam EG, Kawakami N, Kovess-Masfety V, Lee S, Levinson D, Medina-Mora ME, Navarro-Mateu F, Pennell BE, Piazza M, Posada-Villa J, Ten Have M, Zarkov Z, Kessler RC, Thornicroft G; WHO World Mental Health Survey Collaborators. Treatment gap for anxiety disorders is global: Results of the World Mental Health Surveys in 21 countries. Depress Anxiety. 2018 Mar;35(3):195-208. doi: 10.1002/da.22711. Epub 2018 Jan 22.
- Barrio P, Ortega L, Lopez H, Gual A. Self-management and Shared Decision-Making in Alcohol Dependence via a Mobile App: a Pilot Study. Int J Behav Med. 2017 Oct;24(5):722-727. doi: 10.1007/s12529-017-9643-6.
- Reddy MS. Depression: the disorder and the burden. Indian J Psychol Med. 2010 Jan;32(1):1-2. doi: 10.4103/0253-7176.70510. No abstract available.
- Menger V, Spruit M, Hagoort K, Scheepers F. Transitioning to a Data Driven Mental Health Practice: Collaborative Expert Sessions for Knowledge and Hypothesis Finding. Comput Math Methods Med. 2016;2016:9089321. doi: 10.1155/2016/9089321. Epub 2016 Aug 17.
- Hsin H, Torous J, Roberts L. An Adjuvant Role for Mobile Health in Psychiatry. JAMA Psychiatry. 2016 Feb;73(2):103-4. doi: 10.1001/jamapsychiatry.2015.2839. No abstract available.
- Bernert RA, Hom MA, Iwata NG, Joiner TE. Objectively Assessed Sleep Variability as an Acute Warning Sign of Suicidal Ideation in a Longitudinal Evaluation of Young Adults at High Suicide Risk. J Clin Psychiatry. 2017 Jun;78(6):e678-e687. doi: 10.4088/JCP.16m11193.
- Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, Mohr DC. Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study. J Med Internet Res. 2015 Jul 15;17(7):e175. doi: 10.2196/jmir.4273.
- Saeb S, Lattie EG, Kording KP, Mohr DC. Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR Mhealth Uhealth. 2017 Aug 10;5(8):e112. doi: 10.2196/mhealth.7297.
- Sano A, Taylor S, McHill AW, Phillips AJ, Barger LK, Klerman E, Picard R. Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study. J Med Internet Res. 2018 Jun 8;20(6):e210. doi: 10.2196/jmir.9410.
- Torous J, Staples P, Shanahan M, Lin C, Peck P, Keshavan M, Onnela JP. Utilizing a Personal Smartphone Custom App to Assess the Patient Health Questionnaire-9 (PHQ-9) Depressive Symptoms in Patients With Major Depressive Disorder. JMIR Ment Health. 2015 Mar 24;2(1):e8. doi: 10.2196/mental.3889. eCollection 2015 Jan-Mar.
- Onnela JP, Rauch SL. Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health. Neuropsychopharmacology. 2016 Jun;41(7):1691-6. doi: 10.1038/npp.2016.7. Epub 2016 Jan 28. No abstract available.
- Trautmann S, Rehm J, Wittchen HU. The economic costs of mental disorders: Do our societies react appropriately to the burden of mental disorders? EMBO Rep. 2016 Sep;17(9):1245-9. doi: 10.15252/embr.201642951. Epub 2016 Aug 4.
- Ghandeharioun, A., Fedor, S., Sangermano, L., Ionescu, D., Alpert, J., Dale, C., ... & Picard, R. (2017, October). Objective assessment of depressive symptoms with machine learning and wearable sensors data. In Affective Computing and Intelligent Interaction (ACII), 2017 Seventh International Conference on (pp. 325-332). IEEE.
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 (Estimate)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- humanITcare: Remote
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
IPD Sharing Time Frame
IPD Sharing Access Criteria
IPD Sharing Supporting Information Type
- Study Protocol
- Statistical Analysis Plan (SAP)
- Informed Consent Form (ICF)
- Clinical Study Report (CSR)
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.
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