"Predictive Models of Human Body Morphological Evolution to Improve Adherence and Motivation in Dietary-Nutritional Treatments for Overweight and Obesity" [Modelos Predictivos de la Evolución Morfológica Del Cuerpo Humano Para Mejorar la Adherencia y Motivación en Tratamientos Dietético-nutricionales Del Sobrepeso y la Obesidad].
Phase: NA
Conditions: Body Image, Executive Function (Cognition), Obesity &Amp;Amp;Amp; Overweight
Sponsor: Jorge Azorin Lopez

 
 
 
 
 
February 1st, 2024 
 
 
 
 
Predictive models of the morphological 
evolution of the human body to improve 
adherence and motivation in dietetic-
nutritional treatments of overweight and 
obesity. Tech4D-Predict 
 
 REFERENCE: PID2020-119144RB-I00 
 
 
 
NCT NUMBER: PENDING 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
RESEARCH PROJECT PROTOCOL 
 
1. Project Title 
 
“Predictive models of the morphological evolution of the human body to improve 
adherence and motivation in dietetic-nutritional treatments of overweight and 
obesity. Tech4D-Predict.” REFERENCE: PID2020-119144RB-I00  
 
2. Principal Investigator 
 
Jorge Azorín López.  
 
3. Summary 
 
Backgroud: This project is framed within the improvement of obesity treatment from 
a multidisciplinary perspective, leveraging the potential of rapidly developing areas 
such as 3D modeling and virtual reality to address open challenges, including the 
psychological aspects of treatment adherence, creating opportunities both in dietary-
nutritional intervention and in tackling new technological challenges. 
 
Currently, more than 50% of the Spanish population over 18 years old suffers from 
overweight or obesity, contributing to the development of multiple chronic diseases 
(hypertension, diabetes, cancer, etc.) and to the loss of cognitive and functional 
capacity. This situation results in a decreased quality of life and a significant increase 
in healthcare costs. Disease-related expenses range from 0.09% to 0.61% of the 
annual GDP at the European level. Lack of adherence to treatment has been 
considered one of the main causes of failure in intervention programs, turning obesity 
into a chronic condition. Improving adherence would enhance treatment outcomes 
and their long-term maintenance, directly impacting healthcare cost savings. Recent 
research shows that lack of adherence can be improved through the use of 
multimedia technologies to reinforce the cognitive experience. However, there are no 
studies that exploit the maturity of 4D modeling and virtual reality technologies for 
this purpose. These findings suggest that obesity treatment can be enhanced through 
the use of 4D body visualization systems, which improve the patient’s cognitive 
experience during weight loss, increasing adherence. Additionally, introducing a 
multidimensional component opens new avenues for studying more precise metrics 
to quantify treatment effects. 
 
Objective: The main objective of this research project is to develop a 4D visual 
representation model of the human body that allows the analysis of morphological 
changes resulting from obesity treatment. This 4D model will serve as the core of a 
body visualization system aimed at improving obesity treatment. The system will 
provide immersive virtual reality visualizations and personalized simulations of body 

 
 
 
evolution during treatment. Furthermore, the effect of these realistic visualizations on 
patient motivation and adherence to treatment will be studied. 
 
Method: The proposed methodology for obtaining the morphological change 
representation model presents a significant research challenge. The multidisciplinary 
intervention combines clinical, psychological, and technological assessments in 
patients with overweight or obesity. The body is captured in 3D using RGBD sensors to 
create complete body models, and 4D models reflecting morphological changes over 
time are generated using advanced deformable registration techniques. These models 
feed a predictive system that anticipates body evolution under different dietary-
nutritional scenarios, facilitating immersive virtual reality visualizations to improve 
motivation and adherence. 
 
A longitudinal study design is proposed, with two groups (experimental and control) 
and evaluations at baseline and after six months. Clinical, anthropometric, 
psychological, nutritional, and technological data will be collected using validated 
questionnaires and precise measurements. 
 
The analysis includes health indicators, cognitive functions, emotional well-being, and 
adherence, aiming to validate the system and its impact on motivation and clinical 
outcomes. Both classical and advanced statistical analyses will be performed, 
combining traditional methods (t-tests, ANOVA, regressions) with machine learning to 
predict body evolution, clinical parameters, and adherence. Sample losses will be 
controlled, and psychological variables will be evaluated as predictors of therapeutic 
success. Tools such as SPSS, R, Python, TensorFlow, PyTorch, Unity 3D, and Blender 
will be used. 
 
 
4. Antecedentes, planteamiento del problema, justificación de la investigación 
 
Overweight and obesity are defined as abnormal or excessive fat accumulation that is 
detrimental to health. The prevalence of overweight and obesity has increased 
worldwide, tripling over the past three decades in European Union countries [1]. In 
Spain, according to data published by the National Health Survey, the prevalence of 
these conditions in the population over 18 years old is 53.7% [2]. In developed 
countries, a shift in nutritional patterns has been observed, characterized by higher 
consumption of fats and refined foods. These dietary changes, which contribute to 
overweight and obesity, are associated with the development of chronic diseases such 
as hypertension, type II diabetes, cancer, and neurodegenerative disorders like 
Alzheimer’s disease or dementia [3], [4], resulting in a higher risk of premature death, 
reduced quality of life, and increased frequency of healthcare visits. 
The treatment and healthcare follow-up required for patients with overweight and 
obesity has a significant impact on healthcare costs. At the European level, it is 
estimated that expenses associated with the burden of obesity range from 0.09% to 
0.61% of total annual GDP, representing an absolute cost of approximately €10 billion 

 
 
 
per year [5], [6]. This trend is also observed in Spain, showing a stable pattern similar 
to that found in other developed European economies [7]. 
Currently, most intervention programs for overweight and obese patients have 
focused on dietary-nutritional recommendations and promotion of physical activity 
[8], [9]. While these programs have generally been effective for short-term weight 
loss, their long-term effectiveness in maintaining weight is limited. Adherence to 
treatment has been identified as a key factor influencing the duration of treatment 
and the outcomes of interventions in these patients [10]. Recent studies have shown 
that high body mass indices (BMI) in obese patients and low adherence to treatment 
correlate significantly with levels of depression, anxiety, and the presence of cognitive 
impairments, particularly memory deficits, attention alterations, and poor executive 
function [3], [11], [12]. These impairments manifest as poorer decision-making, more 
disinhibited behavior, and inflexible patterns in controlling recurrent thoughts about 
high-calorie foods [11]. Evidence also shows that long-term nutritional interventions 
aimed at reducing caloric intake and weight in obese patients produce significant 
improvements in brain function and reduced oxidative stress when combined with 
regular physical exercise and adequate adherence to treatment [4]. For instance, a 
recent study in overweight patients demonstrated that an intensive 6-month 
nutritional intervention improved participants’ general health, BMI, and cognitive 
function [3]. 
Given the evidence that lack of adherence is a major cause of intervention failure, 
improving adherence in patients with overweight and obesity would enhance 
treatment outcomes and their long-term maintenance, reducing healthcare costs. 
Some authors have suggested that nutritional interventions reinforced through 
technology-based follow-up achieve sustained beneficial effects [12]. A review of 15 
studies on interventions for overweight and obese patients found that 60–80% of 
participants achieved significant long-term weight reduction when interventions were 
reinforced using interactive multimedia devices [13]. 
These results highlight how lack of adherence can be improved through the 
incorporation of image-based techniques (2D) showing the patient’s progress during 
dietary treatment, which enhances the cognitive experience [14]. However, to our 
knowledge, no studies have yet exploited or quantified the potential of realistic 3D 
images and virtual reality techniques to reinforce adherence and follow-up over time 
in overweight and obese patients. 
In recent years, 3D models have begun to be applied in healthcare to analyze 
parameters related to body volume and shape in obese patients [15], [16]. These 
approaches complement traditional BMI-based treatments with new anthropometric 
indicators, such as the Body Volume Index (BVI), which is based on the 3D shape of 
the body [17]. However, most of these studies have been limited to static 
measurements, without considering temporal body evolution (4D) or changes 

 
 
 
resulting from dietary treatment. 
3D scanning, initially developed for the textile industry [18], has shown great potential 
in healthcare applications such as epidemiology, diagnosis, and monitoring [19]. For 
example, body shape measurements have been used to address the increasing 
prevalence of obesity [20]. Companies such as Naked.fit offer body visualization and 
tracking in the fitness domain. Other systems like Fit3D use a single camera but 
require complex and expensive equipment. Body Labs also models the body in 3D, but 
without incorporating the temporal dimension. Some solutions focus on avatars or 3D 
printing, although they usually involve bulky equipment. 
Regarding sensors, laser-based devices (LiDAR, ToF) offer high depth accuracy but no 
color. Stereo cameras require precise calibration and are expensive. RGBD cameras 
(such as Microsoft Kinect or Intel RealSense), which combine color and depth using 
structured light or ToF, are gaining popularity due to their accuracy, portability, and 
low cost, facilitating integration into consumer mobile devices. 
Virtual reality has also advanced significantly in recent years. Head-mounted displays 
(HMDs) allow immersive experiences, and solutions such as Google Cardboard have 
drastically reduced costs. Numerous studies highlight VR’s ability to enhance 
concentration and adherence in learning processes [21], which could be useful in 
treatments for overweight patients. 
The state-of-the-art review shows an urgent need for effective solutions against 
obesity, given the limited effectiveness of traditional treatments. 3D/4D technology is 
mature in other fields, and low-cost RGBD devices are becoming popular. Likewise, VR 
has proven useful in multiple applications. However, these technologies have yet to 
be integrated to address temporal morphological changes (4D), nor combined with VR 
in 
obesity 
treatments. 
This 
presents 
significant 
scientific 
challenges 
for 
multidisciplinary research 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
5. Objective(s) 
 
 
The general objective of Tech4D-Predict is to develop methods for the acquisition and 
visualization of 4D human body models, aimed at supporting dietary-nutritional 
interventions and enhancing long-term patient motivation and adherence to 
treatment. Furthermore, the study will evaluate the impact of this tool on cognitive 
function and psychological well-being. 
In pursuit of this overarching objective, the following specific aims are defined: 
• Specific Objective 1: Development of flexible methods for the acquisition of 
3D human body models. The goal is to equip the acquisition system with 
flexibility, robustness, and portability without compromising accuracy. 
Flexibility refers to the system’s ability to tolerate subject movements during the 
acquisition process, and even to leverage these movements to capture multiple views 
of the subject in order to complete the 3D model. The ultimate aim is to progress 
towards 3D self-portraits. Portability involves the ability to easily transport the 
system/device, using a compact set of RGB-D cameras integrated into a readily 
transportable device. Robustness refers to the stability of the acquisition system 
against potential movements that could affect camera calibration, as well as 
variations in acquisition conditions (e.g., lighting). 
• Specific 
Objective 
2: 
Development 
of 
methods 
for 
obtaining 
multidimensional predictive models of morphological and clinical body 
evolution during dietary-nutritional treatment. 
The aim is to move beyond 4D memory-based models, which visualize the 
morphological changes experienced by the body during treatment, towards 
multidimensional predictive models that anticipate potential morphological changes 
and clinical outcomes for specific patient profiles. These predictions will be based on 
prior experience, the 4D morphological models of patients from the current project, 
and their corresponding clinical data. 
• Specific Objective 3: Evaluation of the effectiveness of multidimensional 
predictive model visualization on patient motivation during treatment and 
its impact on long-term adherence. 
The primary aim of this objective is to assess the impact of the methods developed in 
Specific Objectives 1 and 2—related to 4D model acquisition and visualization, and 
the construction of predictive models of clinical and morphological evolution—within 
a real clinical setting through a patient intervention. 
Accordingly, this objective encompasses the implementation of a clinical trial with 
two intervention groups (experimental and control) designed to evaluate the effect 

 
 
 
of a personalized intervention combining the benefits of 4D body modeling and virtual 
reality techniques with a dietary-nutritional treatment based on the Mediterranean 
Diet. The intervention aims to enhance motivation for change, treatment adherence, 
and health parameters in overweight and obese patients. 
Furthermore, this study will allow systematic collection of clinical, anthropometric, 
nutritional, and psychological data, which will be integrated into the multidimensional 
predictive model to improve its precision and personalization capabilities. 
Specifically, the clinical trial includes the following specific aims: 
• 
To study how a 4D computer vision model combined with virtual reality 
technology can improve clinical assessment, the analysis of temporal 
morphological changes of the human body, and adherence to dietary-
nutritional treatment. 
• 
To evaluate whether adherence to an intensive multifactorial dietary-
nutritional treatment based on the Mediterranean Diet, supported by 
immersive 4D virtual reality technology, leads to improvements in cognitive 
functioning. 
• 
To analyze the impact of a nutritional intervention program based on the 
Mediterranean Diet and the use of virtual reality on body image perception. 
• 
To determine whether a combined intensive intervention—based on the 
Mediterranean Diet and 4D virtual reality technology—can improve patients’ 
overall health. 
 
6. Methodology 
 
 
The intervention follows a multidisciplinary approach that combines clinical, 
psychological, and technological assessment of overweight or obese patients, using 
innovative tools based on 3D body reconstruction and predictive modeling. 
Challenges to Address and Technical Phases 
1. 3D Model Acquisition 
3D images of the body are captured from multiple viewpoints using RGB-D sensors, 
due to their precision, low cost, and portability. The images are aligned using 
articulated rigid registration techniques, enabling the construction of a complete body 
model. The research team has developed advanced RGB-D camera alignment 
methods, optimizing accuracy under real-world capture conditions [22–24]. 
2. 4D and Predictive Model Generation 
Starting from 3D models obtained at different stages of the treatment, deformable 

 
 
 
registration techniques (such as CPD or TPS-RPM) are applied to quantify 
morphological deformations. These variations are parametrically represented using 
methods such as Relative Elemental Growth Rate or GRID, facilitating temporal 
analysis of body changes [27–31]. Additionally, a predictive model of body shape and 
texture is developed, conditioned on dietary-nutritional and psychological variables. 
This model allows anticipation of the patient’s body evolution under different 
intervention scenarios, moving from a retrospective view to a future projection that 
can directly influence motivation. 
3. 3D and 4D Model Visualization 
The models are processed to generate realistic visualizations compatible with low-cost 
virtual reality devices. Solid surfaces are reconstructed from point clouds using 
specific geometric modeling techniques [32, 33]. The team has prior experience in 
advanced visualization in sectors such as footwear manufacturing and automated 
visual inspection. Multidimensional model visualization not only provides a clearer 
understanding of physical changes but also aims to generate a positive emotional 
impact on the patient. This personalized virtual representation of body evolution 
serves as a motivational reinforcement to maintain long-term adherence. 
4. Intervention Effect Analysis 
The effect of the system on motivation, treatment adherence, cognitive function, and 
psychological well-being will be assessed. An intensive intervention based on the 
Mediterranean Diet combined with 4D VR models will be compared to conventional 
treatment. Collection of clinical, anthropometric, and psychological data will allow 
system validation and feed the predictive models. Patient health will be monitored 
through indicators such as weight, BMI, body fat percentage, adipose tissue 
distribution, cardiovascular risk indicators, and metabolic syndrome parameters. 
Additionally, questionnaires assessing emotional, motivational, and cognitive status, 
as well as body image perception and executive function—which is related to the 
ability to maintain long-term healthy behaviors—will be included. 
 
Technical Capacity and Team Experience 
The project is multidisciplinary, with multiple disciplines collaborating to achieve the 
common goal of improving motivation and long-term adherence to dietary-nutritional 
treatments. The research team has a strong track record in 3D image capture and 
processing, RGB-D sensor calibration and registration [22–27], as well as realistic 
modeling and visualization. The team also includes professionals in nutrition, 
psychology, and clinical assessment with experience in national and European 
projects focused on obesity, treatment adherence, and mental health. 
 

 
 
 
Ethical Considerations 
The study was approved by the Research Ethics Committee of the University of 
Alicante (code UA-2021-11-18, dated 18/11/2021). Prior to study participation and 
data collection, all participants provided written informed consent. Data will be 
handled in accordance with the GDPR and LOPDGDD regulations, with anonymization 
implemented through unique participant codes. 
 
The following sections describe the study design, selected population, eligibility 
criteria, sampling strategy, sample size calculation, definition of included variables, 
measurement instruments, monitoring plan, and analysis strategies, along with 
potential limitations. 
Study Design 
A prospective longitudinal design with two assessment points was implemented: 
baseline (pretest) and post-intervention (posttest) after six months of treatment. The 
study comprised two groups: control and experimental. The control group received 
nutritional and physical activity education based on recommendations from a 
nutritionist. The experimental group received a specific, individualized dietary-
nutritional intervention program combined with 4D virtual reality. Both groups were 
assessed for cognitive, clinical, and nutritional outcomes at baseline (pretest) and 
after six months of intervention (posttest). 
To ensure sample representativeness and the generalizability of results, the sample 
size was calculated using the G*Power 3.1 statistical software. A statistical power of 
80%, a medium effect size (0.5), and a significance level (α) of 0.05 were established. 
Under these parameters, at least 35 participants per group were required to carry out 
the intervention. 
 
Selected Population 
 
The sample consisted of a total of 133 Spanish volunteers, both men and women, 
aged between 22 and 67 years (M = 47.11 years; SD = 9.38). Participants were 
randomly assigned to one of the two groups (experimental or control). 
 
Inclusion and Exclusion Criteria 
 
Inclusion: Participants must have a Body Mass Index (BMI) greater than 24.9 (normal 
weight: 18.5 ≤ BMI < 25; pre-obesity or overweight: 25 ≤ BMI < 30; obesity: BMI ≥ 30), 
according to the WHO classification (2000); be right-handed; be able to read and write 
fluently; and have Spanish as their native language. 
 
Exclusion: Participants will be excluded if they are currently undergoing or have 

 
 
 
undergone dietary-nutritional treatment with a nutrition specialist within the past 
year; have endocrine-metabolic disorders (including thyroid, pituitary, adrenal gland 
issues, or metabolic syndrome); have a history of neurological disease (e.g., stroke, 
Parkinson’s disease) or traumatic brain injury (with loss of consciousness exceeding 30 
minutes); have a history of severe psychopathology according to DSM-IV-TR 
diagnostic criteria; or are currently receiving psychiatric treatment. 
 
 
Description of Variables and Data Collection Procedures 
1. Personal and Sociodemographic Data: 
 
• Age, sex, marital status, nationality, city of residence, residential area, 
geographic region, employment status, current living situation, educational 
level, monthly income, tobacco use, alcohol consumption, presence of any 
medical conditions, and history of COVID-19 infection. 
These data were collected through a questionnaire administered via Google Forms. 
 
2. Anthropometric Variables: 
 
• 
Anthropometric measurements: Weight, height, BMI, body fat percentage, 
visceral fat percentage, trunk fat percentage, right and left arm fat, right and 
left leg fat, waist, hip, and wrist circumferences, muscle mass percentage, 
muscle mass in kilograms, and total body water. 
Weight, fat percentage, muscle mass, and total body water were measured using the 
TANITA MC-780MA P digital scale (TANITA Corporation, Arlington Heights, IL, USA). 
Height was measured using a portable stadiometer SECA 213 (SECA, Hamburg, 
Germany). BMI was calculated as weight divided by height squared (kg/m²). Waist, 
hip, and wrist circumferences were measured with a flexible tape measure with 0.1 
cm precision. 
 
3. Clinical Variables and General Health Status: 
 
• 
Clinical Measures: Blood glucose (mg/dL), total cholesterol (mg/dL), 
triglycerides (mg/dL), and blood pressure (systolic and diastolic). 
Capillary concentrations of cholesterol, glucose, and triglycerides were assessed using 
Accutrend® Plus with two drops of blood (15–40 μL) collected from different fingers 
using a lancet device (Accu-Chek® Softclix® Pro, Roche Diagnostics GmbH, Mannheim, 
Germany). Blood pressure was measured using an M7 Intelli IT sphygmomanometer 
(OMRON, M7 Corp., Kyoto, Japan). 
 

 
 
 
4. Nutritional and Lifestyle Variables: 
 
Questionnaires: 
• Adherence to the Mediterranean Diet: Assessed using the PREDIMED 
questionnaire (Martínez-González et al., 2004). 
• Physical Activity: Measured with the International Physical Activity 
Questionnaire (IPAQ) (Craig et al., 2003), which includes 7 questions on 
frequency, duration, and intensity (vigorous and moderate) of physical activity 
over the past 7 days, as well as walking and sedentary time on a workday. 
• 24-Hour Dietary Recall: Patient food intake over the previous 24 hours, 
collected through three 24-hour recalls (two weekdays and one weekend or 
holiday). 
• Food Frequency Questionnaire: Assessed frequency of food consumption 
(Martín-Moreno et al., 1993), aimed at recording the average number of times 
a specified amount was consumed throughout the year. 
Data were collected through an adapted questionnaire administered via Google 
Forms. 
 
5. Psychological Variables 
 
• Executive Functions: This variable was assessed using the CogniFit General 
Cognitive Assessment Battery (CAB), a computerized neuropsychological test 
battery commonly used in research protocols for cognitive skills. The test 
duration ranges from 25 to 45 minutes and evaluates a wide range of cognitive 
domains, including cognitive flexibility, divided attention, planning, visual 
perception, auditory perception, spatial perception, contextual memory, 
short-term visual memory, non-verbal memory, short-term memory, working 
memory, short-term auditory memory, processing speed, naming, estimation, 
monitoring, reaction time, visual scanning, recognition, hand-eye coordination, 
focused attention, and inhibition. Scores range from 0 to 800 points, with 
higher scores indicating better cognitive performance. 
Questionnaires: 
• Premorbid Intelligence: Assessed using the Word Accentuation Test (TAP) (Del 
Ser et al., 1997), which estimates premorbid intelligence through the 
accentuation of 30 words. The number of correct answers corresponds to an 
estimated premorbid IQ based on WAIS-IV scoring. 
• Emotional Eating: Measured with the Emotional Eater Questionnaire (CCE) 
(Garaulet et al., 2012). Higher scores indicate greater influence of emotions on 

 
 
 
food choices and eating habits. 
• Prefrontal Symptoms: Assessed using the Prefrontal Symptoms Inventory (ISP-
20) (Ruiz et al., 2012); the short version (Pedrero-Pérez et al., 2015) is a self-
report questionnaire measuring cognitive, emotional, and behavioral 
alterations in daily activities. 
• Impulsivity: Measured with the Barratt Impulsiveness Scale, BIS-II (Barratt, 
1959), comprising 30 items divided into three subscales: cognitive impulsivity 
(8 items), motor impulsivity (10 items), and non-planning impulsivity (12 
items). 
• Health-Related Quality of Life: Assessed using the Short Form-12 Health Survey 
(SF-12) (Ware et al., 1996). 
• Depression: Measured using the Beck Depression Inventory (BDI-II) (Beck, 
Steer & Brown, 1996). 
• Life Satisfaction: Measured using the Satisfaction With Life Scale (SWLS) 
(Diener et al., 1985). 
All questionnaire data were collected via Google Forms. 
 
 
6. Technological and Body Image Variables 
 
• 
Body Image: Assessed using the Body Self-Relations Questionnaire (MBSRQ – 
reduced Spanish version) (Botella et al., 2009). The instrument contains 45 
items divided into two distinct parts: one evaluating general body image and 
another assessing satisfaction with specific body areas. 
• 
3D and 4D Images: The 4D model represents morphological changes over 
time. Using wide-spectrum, low-cost 3D acquisition technologies (RGB-D 
sensors), a 4D model was constructed as the core of a human body 
visualization system, enabling accurate and realistic visualization over time. 
Throughout the intervention, morphological 3D images of participants were 
obtained, allowing them to observe their evolution through immersive Virtual 
Reality (VR) (only the experimental group observed these changes). 
Morphological 4D images of participants in each session were captured using the 3D 
machine, composed of RGB-D devices for 3D image acquisition. 
All anthropometric and clinical measurements were collected at the ALINUA Nutrition 
and Food Clinic, certified as a healthcare center affiliated with the Faculty of Health 
Sciences at the University of Alicante. 
All questionnaires and the neuropsychological battery were administered at two time 
points: at the start of the program (initial session) and after six months of 

 
 
 
intervention. Anthropometric data and 4D images were also collected at both time 
points, as well as during individual follow-up sessions conducted to monitor progress 
and prevent relapses. 
 
Monitoring Plan: 
Potential adverse effects arising from the use of Virtual Reality (e.g., dizziness, 
discomfort, anxiety) and the dietary program (e.g., malnutrition, minor side effects) 
will be monitored. Adverse events will be recorded in a dedicated database and 
reported to the Ethics Committee if they are serious or unexpected. Participants will 
also have the right to withdraw from the study at any time without any consequences. 
 
Data Analysis: 
Data analysis is structured in two main phases, encompassing both the development 
of the predictive model and its clinical and psychological validation. A mixed strategy 
of advanced quantitative and computational analysis will be applied. 
 
Data Preparation and Preprocessing 
• 
Cleaning of clinical, anthropometric, psychological, and neuropsychological 
data. 
• 
Standardization and normalization of variables (e.g., assessment scales, 
physical measurements). 
• 
Coding of categorical variables (e.g., sex, experimental/control group). 
• 
Outlier control and analysis of missing data (intent-to-treat analysis will be 
applied if missing data exceed 30%). 
 
Classical Statistical Analysis 
• 
Between-group comparisons (control vs. experimental): 
o t-tests or ANOVA for continuous measures. 
o Chi-square tests for categorical variables. 
o ANCOVA to adjust for covariates (age, sex, baseline BMI). 
• 
Longitudinal analysis: 
o Repeated measures models (repeated measures ANOVA or mixed 
models). 

 
 
 
o Study of changes between assessment points (0 and 6 months). 
 
Multidimensional Predictive Modeling 
• 
Use of multivariate regression models and machine learning to predict: 
o Body evolution (based on SMPL/STAR models). 
o Clinical parameters and psychological well-being. 
o Levels of adherence and motivation. 
• 
Model training using data from the first period (M9) and validation in the 
second period. 
 
Analysis of Predictive Psychological Variables 
• 
Correlations between cognitive functions and adherence levels. 
• 
Logistic/multiple regression analyses to identify significant predictors of 
therapeutic success. 
• 
Evaluation of the mediating effect of psychological variables on physical and 
clinical changes. 
 
Tools and Software 
• 
Statistics: IBM SPSS Statistics for Windows v.21.0 (IBM Corp, Armonk, NY; 
2012), R (latest stable version), and Python 3.11.9 with SciPy and Statsmodels 
libraries. 
• 
Machine Learning and Predictive Modeling: Scikit-learn for classical models 
and preprocessing. TensorFlow and PyTorch for developing deep learning 
models and neural networks, including generative networks (GANs) for 3D 
modeling and multidimensional analysis. 
• 
Visualization and Clinical Analysis: Unity 3D for integration and immersive 
virtual reality visualization, Blender for 3D model manipulation and creation, 
and internally developed modules for project-specific VR systems. 
 
Potential Limitations 
• 
Possible selection bias (motivated volunteers). 
• 
Loss to follow-up in a 6-month intervention. 

 
 
 
• 
Limited 
generalizability 
to 
the 
Spanish 
population 
with 
specific 
sociodemographic characteristics. 
• 
Risk of novelty effect with VR (results may reflect the appeal of the technology 
rather than the actual intervention effect). 
 
 
 
7. Expected impact 
 
In the ICT field, the project will provide methods and algorithms to advance the 
analysis and prediction of morphological evolution in relation to the parametric 
predictive model. Additionally, the challenge of acquisition using the RGB-D multi-
camera column system and 3D self-portraits for home implementation introduces the 
development of new techniques based on sparse-view approaches. 
In the Health Sciences, the project aims to evaluate the impact of predictive models 
on patient motivation and adherence to treatment for obesity and overweight. 
Special emphasis will be placed on the positive effect of realistic predictive 
visualization of individual body shape on cognitive functioning and psychological well-
being. 
Scientific and technical impact will be disseminated through presentations at 
international conferences (ECCV, CVPR, IJCNN in 2022; IUNS-ICN, ICCV in 2023) and 
publications in high-impact international journals starting in 2023 (IJCV, TPAMI, 
Pattern Recognition, ASOC, CVIU, EJCN, IJBNP, and JN). Results will also be shared in 
open-access repositories such as RUA. Data and results will be managed under privacy 
regulations (LOPDGDD and ENS) and made publicly available on open servers for use 
by the scientific community, with participants’ consent. Developed methods will be 
shared on repositories such as Bitbucket. Results will also be published on the 
Tech4Diet website. 
Knowledge transfer plan: The acquisition systems (column and 3D self-portrait), as 
well as modeling, visualization, and predictive software, will be patented and 
registered. Initial operations will take place at the designated center, “Aula de Salud.” 
During this period, the feasibility of establishing a Technology-Based Company (EBT) 
will be assessed. 
Social and economic impact is expected to be high, starting at the Aula de Salud and 
later transferring the technology to healthcare institutions in the province of Alicante. 
Collaboration has been established with a newly created group at ISABIAL, led by the 
Head of Endocrinology at the Alicante University General Hospital (HGUA). 
 
 
 

 
 
 
8. References 
 
 
[1] World Health Organization, “World Health Organization. Estrategia mundial sobre 
régimen alimentario, actividad física y salud: marco para el seguimiento y evaluación 
de la aplicación.,” in World Health Organization, 2012. 
[2] P. S. e I. Ministerio de Sanidad, “Encuesta nacional de salud 2011-2012.,” 2012. 
[3] C.-J. Boraxbekk, A. Stomby, M. Ryberg, B. Lindahl, C. Larsson, L. Nyberg, and T. 
Olsson, “Diet-Induced Weight Loss Alters Functional Brain Responses during an 
Episodic Memory Task,” Obes. Facts, vol. 8, no. 4, pp. 261–272, Jul. 2015. 
[4] J. Drigny, V. Gremeaux, O. Dupuy, M. Gayda, L. Bherer, M. Juneau, and A. Nigam, 
“Effect of interval training on cognitive functioning and cerebral oxygenation in 
obese patients: A pilot study,” J. Rehabil. Med., vol. 46, no. 10, pp. 1050–1054, 2014. 
[5] T. Lehnert, D. Sonntag, A. Konnopka, et. al, “Economic costs of overweight and 
obesity,” Best Pract. Res. Clin. Endocrinol. Metab., vol. 27, no. 2, pp. 105–115, 2013. 
[6] D. Withrow and D. A. Alter, “The economic burden of obesity worldwide: a 
systematic review of the direct costs of obesity,” Obes. Rev., vol. 12, no. 2, pp. 131–
141, Feb. 2011. 
[7] A. Sicras-Mainar, J. Gil, at. al, “Prevalencia e impacto económico de la obesidad en 
adultos durante el periodo 2003-2010,” Med. Clin. (Barc)., vol. 139, no. 13, pp. 599–
600, Dec. 2012. 
[8] P. N. Vieira, M. N. Silva, J. Mata, S. R. Coutinho, et.al, “Correlates of health-related 
quality of life, psychological well-being, and eating self-regulation after successful 
weight loss maintenance,” J. Behav. Med., vol. 36, no. 6, pp. 601–610, Dec. 2013. 
[9] K. D. Hall and P. N. Jordan, “Modeling weight-loss maintenance to help prevent 
body weight regain,” Am. J. Clin. Nutr., vol. 88, no. 6, pp. 1495–1503, Dec. 2008. 
[10] E. Sabaté, Adherence to Long-Term Therapies: Evidence For Action. World Health 
Organization, 2003. 
[11] A. G. Boeka and K. L. Lokken, “Neuropsychological performance of a clinical 
sample of extremely obese individuals,” Arch. Clin. Neuropsychol., vol. 23, no. 4, pp. 
467–474, Jul. 2008. 
[12] M. G. Roseman, M. C. Riddell, and J. N. Haynes, “A Content Analysis of 
Kindergarten-12th Grade School-based Nutrition Interventions: Taking Advantage of 
Past Learning,” J. Nutr. Educ. Behav., vol. 43, no. 1, pp. 2–18, Jan. 2011. 
[13] W. N. Ajie and K. M. Chapman-Novakofski, “Impact of Computer-Mediated, 
Obesity-Related Nutrition Education Interventions for Adolescents: A Systematic 
Review,” J. Adolesc. Heal., vol. 54, no. 6, pp. 631–645, Jun. 2014. 
[14] I. Kuzmar, M. Rizo, and E. Cortés-Castell, “Adherence to an overweight and 
obesity treatment: how to motivate a patient?,” PeerJ, vol. 2, p. e495, Jul. 2014. 

 
 
 
[15] A. D. Stewart, S. Klein, J. Young, S. Simpson, A. J. Lee, K. Harrild, P. Crockett, and 
P. J. Benson, “Body image, shape, and volumetric assessments using 3D whole body 
laser scanning and 2D digital photography in females with a diagnosed eating 
disorder: Preliminary novel findings,” Br. J. Psychol., vol. 103, no. 2, pp. 183–202, 
May 2012. 
[16] A. Giachetti, C. Lovato, F. Piscitelli, C. Milanese, and C. Zancanaro, “Robust 
Automatic Measurement of 3D Scanned Models for the Human Body Fat 
Estimation,” IEEE J. Biomed. Heal. Informatics, vol. 19, no. 2, pp. 660–667, Mar. 
2015. 
[17]    A. A. Tahrani, K. Bolaert, S. Palin, A. Field, H. Redmayne, R. Barnes, L. Aytok, and 
A. Rahim, “Body volume index: time to replace body mass index?,” 2008. 
[18] P. R. Apeagyei, “Application of 3D body scanning technology to human 
measurement for clothing Fit,” Int. J. Digit. Content Technol. its Appl., vol. 4, no. 7, 
pp. 58–68, Oct. 2010. 
[19] P. Treleaven and J. Wells, “3D Body Scanning and Healthcare Applications,” 
Computer (Long. Beach. Calif)., vol. 40, no. 7, pp. 28–34, Jul. 2007. 
[20] J.-D. Lin, W.-K. Chiou, H.-F. Weng, et. al, “Comparison of three-dimensional 
anthropometric body surface scanning to waist–hip ratio and body mass index in 
correlation with metabolic risk factors,” J. Clin. Epidemiol., vol. 55, no. 8, pp. 757–
766, Aug. 2002. 
[21] M. Hussein and C. Nätterdal, “The benefits of virtual reality in education-A 
comparision study,” no. June, 2015. 
[22] V. Morell-Gimenez, M. Saval-Calvo, J. Azorin-Lopez, J. Garcia-Rodriguez, M. 
Cazorla, S. Orts-Escolano, and A. Fuster-Guillo, “A comparative study of registration 
methods for RGB-D video of static scenes,” Sensors (Switzerland), vol. 14, no. 5, pp. 
8547–8576, 2014. 
[23] M. Saval-Calvo, J. Azorin-Lopez, A. Fuster-Guillo, and H. Mora-Mora, “μ-MAR: 
Multiplane 3D Marker based Registration for depth-sensing cameras,” Expert Syst. 
Appl., vol. 42, no. 23, pp. 9353–9365, 2015. 
[24] M. Saval-Calvo, J. Azorín-López, and A. Fuster-Guilló, “Model-Based Multi-view 
Registration for RGB-D Sensors,” 12th Int. Work. Artif. Neural Networks, IWANN 
2013, pp. 496–503, 2013. 
[25] V. Villena-Martínez, A. Fuster-Guilló, J. Azorín-López, M. Saval-Calvo, J. Mora-
Pascual, J., Garcia-Rodriguez, and A. Garcia-Garcia, A. “A quantitative comparison of 
calibration methods for RGB-D sensors using different technologies”. Sensors, 17(2), 
243, 2017 
[26] M. Saval-Calvo, J. Azorin-Lopez, A. Fuster-Guillo, and J. Garcia-Rodriguez, “Three-
dimensional planar model estimation using multi-constraint knowledge based on k-
means and RANSAC,” Appl. Soft Comput., vol. 34, pp. 572–586, 2015. 

 
 
 
[27] M. Saval-Calvo, “Methodology based on registration techniques for representing 
subjects and their deformations acquired from general purpose 3D sensors,” 2015. 
[28] H. Chui and A. Rangarajan, “A new point matching algorithm for non-rigid 
registration,” Comput. Vis. Image Underst., vol. 89, no. 2–3, pp. 114–141, Feb. 2003. 
[29] A. Myronenko and X. Song, “Point set registration: coherent point drift.,” IEEE 
Trans. Pattern Anal. Mach. Intell., vol. 32, no. 12, pp. 2262–75, Dec. 2010. 
[30] A. Walter, W. K. Silk, and U. Schurr, “Environmental effects on spatial and 
temporal patterns of leaf and root growth.,” Annu. Rev. Plant Biol., vol. 60, pp. 279–
304, Jan. 2009. 
[31] U. Grenander, A. Srivastava, and S. Saini, “A pattern-theoretic characterization of 
biological growth.,” IEEE Trans. Med. Imaging, vol. 26, no. 5, pp. 648–59, May 2007. 
[32] L. Linsen, “Point cloud representation,” Univ. Karlsruhe, Ger. Tech. Report, Fac. 
Informatics, pp. 1–18, 2001. 
[33] F. Remondino, “From point cloud to surface: the modeling and visualization 
problem,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XXXIV, pp. 24–28, 
2003.