Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT06125600 |
| Other study ID # |
IRB-MTP_2023_02_202301328 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
August 1, 2023 |
| Est. completion date |
September 15, 2023 |
Study information
| Verified date |
November 2023 |
| Source |
University Hospital, Montpellier |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Objective: To determine clusters among weight-loss-seeking individuals for personalised
obesity management and find questionnaires to help identify those who could benefit from
psychological support.
Design: In a cross-sectional analysis using an online platform (Aviitam®), a cluster analysis
was carried out in overweight/obese adults. The following questionnaires were studied:
Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale (PSS), Epworth
Sleepiness Scale, Morin's Insomnia Scale, Intuitive Eating Scale-2 (IES-2), Binge Eating
Scale (BES), a Physical Activity questionnaire and EQ-5D Quality-of-Life questionnaire.
Setting: An online weight management platform (Aviitam®) used by adults with obesity across
France.
Participants: Adults with body mass index (BMI) >25 kg/m² participating in a weight
management pathway who completed validated questionnaires assessing psychological and
lifestyle factors.
Main Outcomes: Identification of clusters based on questionnaire responses, BMI, age and
gender.
Description:
Obesity, a rapidly escalating public health issue worldwide, affects over 650 million adults.
It leads to serious health consequences such as cardiovascular disease, hepatic steatosis,
cancer and Obstructive Sleep Apnea syndrome (OSA). Obesity also has several social
repercussions impacting quality of life. These multifaceted health issues emerge from an
intricate interplay of biological, environmental and psychological factors.
Managing obese individuals in a primary care setting is difficult and time-consuming. It
requires addressing related conditions such as perceived stress, sleep disorders as well as
mental health issues including anxiety and depression. All of these conditions can influence
a patient's ability to lose weight and maintain weight loss over time. Moreover, factors such
as eating habits, compulsive eating and physical activity levels also play critical roles.
In response to these challenges, the investigators have developed an online platform
(Avittam®), a digitally-enabled, person-centred online platform designed not only for obesity
but also for various chronic conditions including Type 2 diabetes, sleep disorders, childhood
obesity, bariatric surgery pathways and even rare diseases like tuberous sclerosis. The
online platform (Aviitam®) streamlines healthcare consultations by providing a library of
self-administered questionnaires covering an array of factors such as nutrition, physical
activity, sleep, mental health, quality of life and medication adherence (Supplementary data
eTable 1). Operational since 2014, the platform serves nearly 3000 healthcare professionals
and around 18,000 patients across France.
This study focused exclusively on those who had engaged with the online platform's obesity
management pathways. It aimed to identify distinct psychological and behavioural clusters
among individuals seeking weight loss, thereby informing a more personalised, psychologically
supportive treatment strategy.
METHODS Study design This study is an exploratory, observational and cross-sectional analysis
of data collected from patients using the online platform. The primary aim was to identify
patient clusters based on various self-reported and clinically validated variables. The study
was approved by the Institutional Review Board (IRB) of the University Hospital of
Montpellier, France (identification number: IRB-MTP_2023_02_202301328). All participants in
this study provided informed consent by accepting the online platform's General Terms and
Conditions of Use (GTCU), which explicitly state that their data may be used for research
purposes. The online platform has received approval from the French data protection
authority, the "Commission Nationale de l'Informatique et des Libertés" (CNIL), ensuring GDPR
compliance and the confidentiality and protection of participants' rights. To maintain
privacy, all data used in this study were anonymised. Patients or the public were not
involved in the design, or conduct, or reporting, or dissemination plans of the research.
Setting The study uses an online platform (my.aviitam.com) which enables patients to provide
personal information, respond to health questionnaires (validated or specific to the
platform) and receive educational content about their pathology. An automatic summary is
generated by the platform to facilitate patient care by the practitioner. The platform is
accessible throughout France and is free of charge for both healthcare providers and
patients. Furthermore, the online platform is a patient follow-up tool, with patients and
healthcare professionals having access to the platform for patient management.
Participants the investigators included adult participants (Age ≥ 18) with a Body Mass Index
(BMI) ≥ 25 kg/m² who agreed to the terms of use for their data to be included in the study.
Exclusion criteria were the non-completion of mandatory questionnaires and a delay of more
than 90 days between weight measurement and questionnaire completion.
Variables The primary outcomes of the study were the psychological health, physical health
and eating behaviours of individuals, assessed through scores on various questionnaires.
These include the Hospital Anxiety and Depression Scale (HADS), the Perceived Stress Scale
(PSS), the Epworth Sleepiness Scale, Morin's Insomnia Scale, the Intuitive Eating Scale-2
(IES-2), the Binge Eating Scale (BES) and a Physical Activity questionnaire. A secondary
outcome was Quality of Life, assessed using the EQ-5D questionnaire, which was added nine
months after the initial launch of the obesity pathway on the online platform.
The study considered age and self-reported BMI which was subsequently validated by healthcare
providers.
Predictors for the study were the scores on the mental, eating behaviour, physical health and
quality of life questionnaires.
The following potential confounders were accounted for in the analysis: age, sex and time
interval between weight measurement and questionnaire completion.
Effect modifiers were not explicitly investigated in this study. Inclusion criteria for
participation in the study included: (i) age, (ii) BMI≥25 kg/m², (iii) completing all study
questionnaires (except the EQ-5D) and (iv) agreeing to the use of their data for research
purposes by accepting the platform's terms of use.
Data Sources/Measurement All data were initially self-reported via the online platform and
later validated by healthcare providers during clinical consultations. Data were collected
over a period of 67 months.
Bias The data were initially self-reported but then validated by healthcare providers during
medical visits to mitigate self-reporting biases.
Study Size All users meeting the eligibility criteria and with valid data were included in
our analysis. the investigators did not pre-calculate the sample size due to the exploratory
nature of the study and the availability of a large patient population.
Quantitative Variables Age and BMI were treated as continuous variables. The questionnaires
provided scores that were treated as continuous variables for analysis.
Data Sources and Variables The variables included in our analysis were age, BMI and overall
scores from all questionnaires. The questionnaires were used to assess the participants'
mental and physical health status, eating behaviours, physical activity and quality of life.
To ensure that the data were complete, the investigators included only participants who had
filled in all questionnaires, in addition to their age, sex, weight and height. As a result,
there were no missing data.
Statistical Analysis The statistical analysis was planned in advance of the data collection,
with the aim of identifying phenotypes of obese patients using clustering methods. The
analysis primarily used non-parametric methods, such as the Kruskal-Wallis and Chi-squared
tests, for data interpretation.
The investigators categorised BMI according to the WHO classification: underweight, normal
weight, overweight and varying grades of obesity .
Initial focus was on patients who completed all of the necessary questionnaires except the
EQ-5D and who provided their weight, height and gender data. A subsequent sensitivity
analysis was planned on a subset of patients who also completed the EQ-5D.
An effect size analysis was also conducted to highlight the main differences between the
patient groups identified.
All statistical tests were deemed significant at a two-sided p-value of less than 0.05.
Adjustments for multiple testing were made using the Bonferroni's correction.
