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CTRI Number  CTRI/2024/12/077720 [Registered on: 05/12/2024] Trial Registered Prospectively
Last Modified On: 13/02/2025
Post Graduate Thesis  No 
Type of Trial  Interventional 
Type of Study   Other (Specify) [Artificial Intelligence Machine Learning ]  
Study Design  Other 
Public Title of Study   Study for Developing an AI Mobile Tool to Measure and Analyze Fatigue in Healthy Individuals and Chronic Liver Disease Patients for Establishing a Mobile tool that can be utilized in the future for assessing Fatigue if succeeds in this study.  
Scientific Title of Study   Machine learning AI Facial and voice Analysis applicaTion to appraIse fatiGUe in chronic liver disease patiEnts (mAI FATIGUE) 
Trial Acronym  MAI FATIGUE 
Secondary IDs if Any  
Secondary ID  Identifier 
EPID-I23-0381 version 1.0 dated 16-Oct-2024  Protocol Number 
 
Details of Principal Investigator or overall Trial Coordinator (multi-center study)  
Name  Dr Kinjalkumar Shah 
Designation  Sr. Clinical Trial Manager, Global Clinical Operations 
Affiliation  Abbott EPD 
Address  Plot No. 113, Marol Industrial Area, Road No. 15; MIDC, Andheri (East) - 400 093, Mumbai, India

Mumbai
MAHARASHTRA
400093
India 
Phone  9136572469  
Fax    
Email  kinjalkumar.shah@abbott.com  
 
Details of Contact Person
Scientific Query  
Name  Gamar Akhundova Unadkat 
Designation  Global Medical Director 
Affiliation  Abbott EPD - Development SMM 
Address  Abbott Products Operations AG Hegenheimermattweg 127 4123 Alschwill Switzerland



4123
Other 
Phone  0041614870363  
Fax    
Email  gamar.akhundovaunadkat@abbott.com  
 
Details of Contact Person
Public Query  
Name  Gamar Akhundova Unadkat 
Designation  Global Medical Director 
Affiliation  Abbott EPD - Development SMM 
Address  Abbott Products Operations AG Hegenheimermattweg 127 4123 Alschwill Switzerland



4123
Other 
Phone  0041614870363  
Fax    
Email  gamar.akhundovaunadkat@abbott.com  
 
Source of Monetary or Material Support  
Abbott Healthcare Products B.V. C. J. van Houtenlaan 36, 1381 CP Weesp, The Netherlands  
 
Primary Sponsor  
Name  Abbott Healthcare Products B.V. 
Address  Abbott Healthcare Products B.V. C. J. van Houtenlaan 36, 1381 CP Weesp, The Netherlands 
Type of Sponsor  Pharmaceutical industry-Global 
 
Details of Secondary Sponsor  
Name  Address 
NIL  NIL 
 
Countries of Recruitment     India  
Sites of Study
Modification(s)  
No of Sites = 3  
Name of Principal Investigator  Name of Site  Site Address  Phone/Fax/Email 
Dr Himanshukumar Patel  Aman Hospital & Research center  Consultation room, ground floor, 15, Shashwat, Opp ESI Hospital, Gotri Road, Vadodara, Gujarat, 390021, India
Vadodara
GUJARAT 		 918141001672

dr.himanshuvpatel@gmail.com 
Dr Raddipalli Naga Sudha Ashok   Renova Neelima Hospitals  Department of Surgical Gastro, 1st Floor, A Block, Opp. Voltas Company, Sanathnagar, Hyderabad, Telangana 500018, India
Hyderabad
TELANGANA 		 919985004367

nagasudhaashokr@gmail.com 
Dr Mahesh Mahadik  Sangvi Multispecialty Hospital Pvt Ltd  Sangvi Multispecialty Hospital Pvt Ltd Ground floor, OPD No-2 Sr No 71/1/2/189, City Survey No 2387, Krushna Chawk, Krushna Nagar, New Sangvi, Pune-411027, Maharashtra, India
Pune
MAHARASHTRA 		 919657890464

drmaheshmahadik29@gmail.com 
 
Details of Ethics Committee
Modification(s)  
No of Ethics Committees= 3  
Name of Committee  Approval Status 
Institutional Ethics Committee  Approved 
Institutional Ethics Committee Sangvi Multispecialty Hospital  Approved 
Institutional Ethics Committee, Neelima Hospitals  Approved 
 
Regulatory Clearance Status from DCGI  
Status 
Not Applicable 
 
Health Condition / Problems Studied  
Health Type  Condition 
Patients  (1) ICD-10 Condition: K769||Liver disease, unspecified,  
 
Intervention / Comparator Agent  
Type  Name  Details 
Intervention  Nil  Nil 
Comparator Agent  Nil  Nil 
 
Inclusion Criteria  
Age From  18.00 Year(s)
Age To  65.00 Year(s)
Gender  Both 
Details  For Cohort A and Cohort B

1. Adult participants aged 18 to 65 years (both inclusive) who are willing to provide written informed consent, including consent for AV recording via a mobile application, and who agree to adhere to all study procedures.

Only For Cohort A

2. Adult subjects who do not have CLD, as confirmed by a clinician, and who report no fatigue, as assessed by the Patient Global Impression of Severity (PGI-S) with a response of “none.”.

Only for Cohort B:

3. Participants diagnosed with CLD who are receiving standard care for CLD and self-reported moderate to severe fatigue, as assessed by the Patient Global Impression of Severity (PGI-S) with responses of “moderate,” “severe,” or “very severe”.
 
 
ExclusionCriteria 
Details  1. Participants will be excluded if they do not have access to a mobile device or if their mobile device does not meet the specifications required to use the Blueskeye AI application, as outlined in the user manual. This will be verified by trained site staff.
2. Participants receiving treatment for fatigue from the last 3 weeks prior to screening.
3. Participants with a known history of Parkinson’s disease.
4. Participants with a history of alcohol or drug abuse within the last three months prior to screening or those currently abusing alcohol (greaterthan 7 drinks per week for females and graterthan 14 drinks per week for males) or drugs, as determined by self-report or medical records.
5. Women of childbearing potential will be excluded if they are currently pregnant, planning to become pregnant during the study, or breastfeeding. Participants must agree to use effective contraception methods during the study. At screening, urine pregnancy tests will be conducted for Women of childbearing potential to confirm the eligibility.
6. Participants who are unable to speak or read English.
7. Participants who have undergone surgery within the past three months or have planned surgery within the next month from the screening date.
8. Participants with any comorbidity or concurrent medical condition (other than CLD or its related comorbidities), including but not limited to depression, that, at the discretion of the Investigator, might prevent adherence to the trial procedures.
 
 
Method of Generating Random Sequence   Not Applicable 
Method of Concealment   Not Applicable 
Blinding/Masking   Not Applicable 
Primary Outcome  
Outcome  TimePoints 
The primary analysis will be the evaluation of the correlation between each of the parameters assessed for fatigue score by the AI interactive tool and each PRO tool, the overall study visits in which both PROs and AI tool were assessed. The correlation will be calculated for all these visits combined as well as over both groups. In addition, both groups will be analyzed separately. The Pearson correlation coefficient will be used to assess correlation. Further details will be defined in the Statistical Analysis Plan (SAP). Primary endpoint outcomes will be assessed based on the PPS population.
As a pre-processing step, we will normalize the raw fatigue scores from PROs to the scale 0-1 using the min-max normalization technique. The maximum value of each PRO is the highest value that can be scored (1), and the minimum value of each PRO is the lowest value that can be scored (0).
 		 Visit 1(day -2 to day 1) to Visit 10 (Day 22) 
 
Secondary Outcome  
Outcome  TimePoints 
Secondary endpoint outcomes will be evaluated based on the FAS population.
Out of N equal to metrics, those that will have the highest correlation with PRO fatigue scores, the RMSE ranges will be estimated from the linear correlation analysis of combined cohorts from the Primary Efficacy outcomes.
Depending on the Primary Efficacy outcome, a numerical scale will be generated to provide value for the next phase of the study related to fatigue scoring models. Supporting information aims to be collected from the combined cohort correlation analysis to formulate a linear model for mapping a subset of relevant metrics identified in the Primary Efficacy to a normalized fatigue score.
AV recordings collected without PROs will be used for temporal analysis of the changes in metrics over time. This analysis intends to understand the differences in temporal trends in the collected metrics between Cohort A and Cohort B.
 		 Visit 1(day -2 to day 1) to Visit 10 (Day 22) 
 
Target Sample Size   Total Sample Size="100"
Sample Size from India="100" 
Final Enrollment numbers achieved (Total)= "Applicable only for Completed/Terminated trials"
Final Enrollment numbers achieved (India)="Applicable only for Completed/Terminated trials" 
Phase of Trial   N/A 
Date of First Enrollment (India)   16/12/2024 
Date of Study Completion (India) Applicable only for Completed/Terminated trials 
Date of First Enrollment (Global)  Date Missing 
Date of Study Completion (Global) Applicable only for Completed/Terminated trials 
Estimated Duration of Trial   Years="1"
Months="0"
Days="0" 
Recruitment Status of Trial (Global)   Not Applicable 
Recruitment Status of Trial (India)  Open to Recruitment 
Publication Details   N/A 
Individual Participant Data (IPD) Sharing Statement

Will individual participant data (IPD) be shared publicly (including data dictionaries)?  

Response - NO
Brief Summary  

Despite the potential utility of machine learning models in objectively identifying and assessing fatigue severity, few studies evaluating the benefits of machine learning technology in assessing fatigue in patients with chronic diseases have been published. This study is expected to add to the body of knowledge the insights of using a novel technology that may improve the identification of fatigue in patients with CLD, leading to better symptom management.

A total of 100 participants who meet the eligibility criteria and provide voluntary informed consent will be enrolled in this study. The study population comprises two cohorts as below:

 

Cohort A: 50 adults without CLD and self-reporting not being fatigued, as assessed by the Patient Global Impression of Severity (PGI-S) questionnaire with a response of “None” at Screening (Visit 1).

Cohort B: 50 adults with CLD and self-reported moderate to severe fatigue, as assessed by the PGI-S questionnaire with a response of “moderate”, “severe”, or “very severe” at Screening (Visit 1).

 
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