The Use of Smart Devices in Capturing Digital Biomarkers in Eating Disorders

Univa Health

Status: Enrolling

Conditions: Eating Disorders

Treatments: Device: UH100

Study type: Observational

Funder types: Industry

Identifiers

NCT06544226

ST001

Details and patient eligibility

About

This study aims to explore how smart devices can be used to monitor the health of individuals with eating disorders. Eating disorders are serious mental health conditions that impact both mental and physical health. Effective monitoring is crucial for developing treatment plans and ensuring the safety of individuals both in hospitals and at home. Currently, healthcare professionals use manual methods to measure important health indicators like heart rate, blood pressure, and BMI. These methods can be time-consuming and may not always accurately reflect a patient's health due to the possibility of patients concealing the severity of their condition. Furthermore, monitoring at home is challenging due to the lack of professional equipment and training for caregivers. With advancements in digital technology, smartphones and smartwatches now have the potential to collect and analyse health data in real-time. These devices can capture data on heart rate, blood pressure, respiratory rate, and other vital signs through non-invasive methods like analysing facial and fingertip blood volume, namely the photoplethysmography technology. Additionally, video recordings from smartphone cameras can be used to assess physical and mental health by analysing facial expressions, voice patterns, and physical movements. By utilising these digital tools, combined with validated questionnaires and tasks to assess participants' psychological status and the severity of disorders, this study expects to create a more efficient and accessible way for individuals with eating disorders to monitor their health at home. The study will collect data from participants both in hospital settings and during outpatient care to ensure the reliability and effectiveness of these digital methods across participants with different levels of severity. This comprehensive approach aims to improve early detection of health issues, optimise treatment plans, and ultimately enhance the quality of life for individuals with eating disorders.

Full description

Eating disorders are complex mental health conditions characterised by abnormal eating habits and distressing thoughts about body weight and shape, significantly impacting both mental and physical health. According to the International Classification of Diseases, 11th Revision, feeding and eating disorders include several subtypes such as anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED). These subtypes often involve patterns of restrictive or excessive food intake, leading to severe health issues and impairments in daily functioning.

Given the rising incidence and profound impacts of eating disorders in recent years, including high mortality rates and significant economic burdens, there is an urgent need for innovative management strategies and more efficient triage and monitoring systems for early intervention and home-based care.

For the care of individuals with eating disorders, comprehensive and continuous assessment of physical and psychiatric conditions is essential. As recommended by the UK's National Institute for Health and Care Excellence (NICE) and the Royal College of Psychiatrists, key clinical markers monitored, include weight loss, BMI, heart rate, blood pressure, temperature, hydration status, and muscular function. Monitoring these markers is crucial for early detection of medical complications, such as electrolyte imbalances, cardiac arrhythmias, and orthostatic hypotension, which pose serious health risks. Additionally, these markers help evaluate the disorder's severity, guide treatment adjustments, and ensure patient safety during recovery.

However, traditional methods used to assess these biomarkers are burdensome and time-consuming. With the advancement of technology, novel smart devices can efficiently detect traditional biomarkers such as heart rate and blood pressure, while also exploring potential novel measures of the disease. Therefore, this study aims to validate and explore the potential of these technologies to provide monitoring comparable to traditional methods and to integrate collected data to generate sophisticated insights into the health status of individuals with eating disorders.

According to previous research, the analysis of facial information, including static features and dynamic movements, combined with advanced algorithms and machine learning, can estimate body weight, BMI, parotid gland size, and skin condition. When voice pattern analysis is integrated with facial dynamics during the video diary entry phase and the image response task, where participants share their thoughts on specific images, it is expected to further assess physical and psychological states, particularly when discussing sensitive topics or images, such as high-calorie foods. These estimations can be used to interpret the health status of individuals with eating disorders. Additionally, photoplethysmography (PPG) using smart devices can detect subtle changes in the colour spectrum induced by blood volume dynamics in facial and fingertip areas, allowing for the estimation of heart rate (HR), blood pressure (BP), respiratory rate (RR), blood oxygen level, blood glucose, and body temperature. This technology, which has been validated in healthy subjects, shows significant potential for application in patients with eating disorders, who are prone to cardiovascular and respiratory issues due to physiological stress and nutritional imbalances. This approach provides essential insights into their physical health status, particularly given the significant impairments in muscle strength often observed in individuals with eating disorders.

In addition to physical health, this study will also examine psychological traits that may improve the accuracy of identifying eating disorder status. This will include questionnaire-based assessments and a computerised task to measure psychological processes. Specifically, the 7-item Generalised Anxiety Disorder Questionnaire (GAD-7) and the 9-item Patient Health Questionnaire (PHQ-9) will assess anxiety and depression severity, respectively, while the Eating Disorder Examination Questionnaire (EDE-Q) will evaluate eating disorder traits, including eating restraint, eating concern, shape concern, and weight concern. Adolescent versions of these questionnaires will be used for younger participants. Moreover, since impulsivity and cognitive control are often altered in individuals with eating disorders, this study will assess these cognitive functions using an adapted Stop Signal Task (SST) that incorporates sensitive cues, such as high-calorie food cues (food-specific SST, FSST). This task will aid in monitoring cognitive control related to the progression of eating disorders and potentially improve the accuracy of health status assessments in these individuals.

This study aims to validate the aforementioned biomarkers and models captured by the smart device and to explore changes in these biomarkers and psychological status across different stages and severities of eating disorders. Data will be collected over 16 weeks from both hospitalised patients and outpatients. Most data, including vital and physical biomarkers, facial information, and self-reported anxiety and depression measures, will be collected weekly, either once or twice a week, with adjustments for those with less frequent visits. Whereas the EDE-Q, the FSST task, and the patient acceptance questionnaire, which assesses patients' acceptance of the data collection procedures, will be conducted three times during the study, in weeks 1, 8, and 16. By conducting this study, the investigators expect to enhance the usability and acceptance of non-invasive monitoring tools, providing valuable insights into the health status of individuals with eating disorders.

Enrollment: 130 estimated patients

Sex: All

Ages: 10+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• All participants must be aged 10 years or older.
• Diagnosed with an eating disorder by a clinician as per the World Health Organization's ICD-10 (F50.0 through F50.9) or ICD-11 (6B80 - 6B85; 6B8Y, 6B8Z) classification
• Must have a minimum once weekly in-person clinic physical assessment as part of their current treatment plan at the start of the study participation
• Fluent in English
• Capable of reading and understanding the information sheets and consent forms to provide written informed consent.
• For participants aged between 10 and 16 years, parental consent is required first before offering the opportunity to the child. A parent or legal guardian must also be able to read and understand the information sheets and consent forms to provide written informed consent on behalf of the child under 16 years of age.

Exclusion criteria

• Active substance use such as drug or alcohol misuse

  • For alcohol consumption of more than 21 units of alcohol per week (1 unit is equivalent to half a pint of beer (285ml), 25ml of spirits, or one glass of wine)
  • Diagnostic coding for current mental and behavioural disorders due to substances (ICD10: F10 through F19; ICD11: QE10 through QE1Z and 6C40 through 6C4H)

• A diagnosis of a neurological disorder, including but not limited to cerebrovascular diseases, either currently or in the past or where the eating disorder for which the participant is being treated is considered aetiologically-secondary to a neurological disorder (e.g. pica secondary to a brain injury).

• A diagnosis of schizophrenia or related psychotic disorder.

• Pregnancy.

• A diagnosis of developmental learning disorder (ICD10 F80.0 through F81.9: ICD11: 6A03) or intellectual disorders (ICD10: F70.0 through F79.9; ICD11 6A00).

Trial design

130 participants in 1 patient group

Group of eating disorder patients aged above 10

Description:

This is a cohort study in which all participants are diagnosed with eating disorders and undertake the same set of assessments and tasks, although the frequency of these assessments and tasks is subject to their current care plan.

Treatment:

Device: UH100