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| Name | Class |
|---|---|
| Hospital Clinic of Barcelona | OTHER |
| European Institute of Innovation and Technology | UNKNOWN |
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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 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 remote collected data.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Control | n=30 healthy participants will be recruited using social networks and leaflets of information distributed by the research team at the Hospital ClinÃc 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. |
| |
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Monitoring with a device | Behavioral | Participants will be monitored by an App. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Change in Self-reported Anxiety symptoms assessed with the State-Trait Anxiety Inventory (STAI) | 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. | One month, questionnaire taken 4 times (1/week) |
| Sleep pattern | Monitoring of circadian rhythm using a motion sensor Fitbit and cardiac activity sensor | One month |
| REM sleep time | monitoring of REM sleep patterns using a motion sensor Fitbit and cardiac activity sensor | One month |
| Heart Rate | monitoring of heart rate using Fitbit sensor | One month |
| Step count | Monitoring of daily step count using motion sensor FitBit | One month |
| Distance travelled | Monitoring of distance travelled using GPS phone | One month |
| Mobile device usage | Monitoring how often the patient's mobile device is used, determined by tracking the presence of a signal from their device | One month |
| Sociability (number of incoming an outgoing calls and text messages) |
| Measure | Description | Time Frame |
|---|---|---|
| Usability of the mobile application | 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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Inclusion Criteria:
Exclusion Criteria:
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There will be a sample n = 60, with 30 healthy control subjects and 30 afflicted experimental subjects.
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| Name | Affiliation | Role |
|---|---|---|
| Antoni Gual, MD | Director of Addictions Unit. Hospital ClÃnic de Barcelona | Principal Investigator |
| Elsa Caballeria | Clinic Foundation for Biomedical Research | Study Chair |
| Hugo Lopez-Pelayo, MD | Addictions Unit, Hospital ClÃnic de Barcelona | Study Chair |
| Nuria Pastor Hernandez, MSc | humanITcare, FollowHealth SL | Study Director |
| Unai Sanchez Luque, MSc | humanITcare, FollowHealth SL | Study Director |
| Elizabeth Katayoun Khalilian | humanITcare, University of Texas at Austin | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospitcal ClÃnic de Barcelona | Barcelona | 08036 | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 29356216 | Background | 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. | |
| 21896369 |
| Label | URL |
|---|---|
| World Health Organization. Global Status Report on Alcohol and Health (2018). | View source |
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Individual participant data sets that underlie results in the final study report will be available for sharing.
IPD will be available by the estimated date of September 2019, when study results are expected to be published
Participants can access their own individual data records, only hospital researchers can access the aggregated data sets.
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| ID | Term |
|---|---|
| D001008 | Anxiety Disorders |
| D000437 | Alcoholism |
| D003866 | Depressive Disorder |
| D003863 | Depression |
| ID | Term |
|---|---|
| D001523 | Mental Disorders |
| D019973 | Alcohol-Related Disorders |
| D019966 | Substance-Related Disorders |
| D064419 | Chemically-Induced Disorders |
Not provided
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The number of incoming and outgoing calls and text messages will be monitored using the UShine app algorithm |
| One month |
| Change in Self-reported Depression symptoms assessed with the Beck's Depression Inventory (BDI-II) | 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. | One month, questionnaire taken 4 times (1/week) |
| Change in Self-reported Alcohol Abuse Symptoms assesed with the Alcohol Use Disorders Identification Test (AUDIT) | 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. | One month, questionnaire taken 4 times (1/week) |
| One month |
| Satisfaction with the application | 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. | One month |
| Background |
| 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. |
| 28236288 | Background | 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. |
| 24671165 | Background | 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. |
| 21799550 | Background | 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. |
| 27630736 | Background | 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. |
| 26747695 | Background | 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. |
| 28973224 | Background | 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. |
| 28682534 | Background | 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. |
| 26487813 | Background | 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. |
| 26180009 | Background | 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. |
| 28798010 | Background | 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. |
| 29884610 | Background | 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. |
| 26543914 | Background | 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. |
| 26818126 | Background | 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. |
| 27491723 | Background | 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. |
| Background | 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. |
| World Health Organization. Mental depression and other common disorders: Global health estimates (2017). | View source |
| D019964 | Mood Disorders |
| D001526 | Behavioral Symptoms |
| D001519 | Behavior |