Lab

Clinimetría (IBIMA F-14) (PAIDI CTS-631)

About the lab

The group is formed by different types of professionals within health and biomedicine: physiotherapy, nursing, occupational therapy, medicine, podiatry, bioengineering...

Featured projects (1)

Project
iGame is a 4-year pan European project funded by the EU H2020 Marie Skłodowska-Curie Actions under Research and Innovation Staff Exchange programme. The project investigates evidence-based gamification techniques for eHealth and mHealth and develops a multi-dimensional intervention support architecture to improve the efficacy of gamified eHealth products (web-based health tools and health apps). The iGame project aims at developing advanced game production techniques with ready-to-use toolkits to accelerate the innovation process for eHealth and mHealth apps. Outputs of the project will help the digital industry (games, IoT and ICT) to innovate new products and services with science and technology underpinnings. More details: https://h2020-igame.eu/

Featured research (12)

Real-time multilingual phrase detection from/during online video presentations—to support instant remote diagnostics—requires near real-time visual (textual) object detection and preprocessing for further analysis. Connecting remote specialists and sharing specific ideas is most effective using the native language. The main objective of this paper is to analyze and propose—through DEtection TRansformer (DETR) models, architectures, hyperparameters—recommendation, and specific procedures with simplified methods to achieve reasonable accuracy to support real-time textual object detection for further analysis. The development of real-time video conference translation based on artificial intelligence supported solutions has a relevant impact in the health sector, especially on clinical practice via better video consultation (VC) or remote diagnosis. The importance of this development was augmented by the COVID-19 pandemic. The challenge of this topic is connected to the variety of languages and dialects that the involved specialists speak and that usually needs human translator proxies which can be substituted by AI-enabled technological pipelines. The sensitivity of visual textual element localization is directly connected to complexity, quality, and the variety of collected training data sets. In this research, we investigated the DETR model with several variations. The research highlights the differences of the most prominent real-time object detectors: YOLO4, DETR, and Detectron2, and brings AI-based novelty to collaborative solutions combined with OCR. The performance of the procedures was evaluated through two research phases: a 248/512 (Phase1/Phase2) record train data set, with a 55/110 set of validated data instances for 7/10 application categories and 3/3 object categories, using the same object categories for annotation. The achieved score breaks the expected values in terms of visual text detection scope, giving high detection accuracy of textual data, the mean average precision ranging from 0.4 to 0.65.
Shoulder kinematics is a measure of interest in the clinical setting for diagnosis, evaluating treatment, and quantifying possible changes. The aim was to compare shoulder scaption kinematics between symptomatic and asymptomatic subjects by inertial sensors. Methods: Scaption kinematics of 27 subjects with shoulder symptomatology and 16 asymptomatic subjects were evaluated using four inertial sensors placed on the humerus, scapula, forearm, and sternum. Mobility, velocity, and acceleration were obtained from each sensor and the vector norm was calculated from the three spatial axis (x,y,Z). Shoulder function was measured by Upper Limb Functional Index and Disabilities of the Arm, Shoulder, and Hand questionnaires. One way ANOVA was calculated to test differences between the two groups. Effect size was calculated by Cohen's d with 95% coefficient Intervals. Pearson's correlation analysis was performed between the vector norms humerus and scapula kinematics against DASH and ULFI results in symptomatic subjects. Results: The asymptomatic group showed higher kinematic values, especially in the humerus and forearm. Symptomatic subjects showed significantly lower values of mobility for scapular protraction-retraction (Cohen's d 2.654 (1.819-3.489) and anteriorisation-posteriorisation (Cohen's d 1.195 (0.527-1.863). Values were also lower in symptomatic subjects for velocity in all scapular planes of motion. Negative correlation showed that subjects with higher scores in ULFI or DASH had lower kinematics values. Conclusion: Asymptomatic subjects tend to present greater kinematics in terms of mobility, velocity, and linear acceleration of the upper limb, and lower humerus and scapula kinematics in symptomatic subjects is associated with lower levels of function.
There are a large number of mobile applications that allow the monitoring of health status. The quality of the applications is only evaluated by users and not by standard criteria. This study aimed to examine depression-related applications in major mobile application stores and to analyze them using the rating scale tool Mobile Application Rating Scale (MARS). A search of digital applications for the control of symptoms and behavioral changes in depression was carried out in the two reference mobile operating systems, Apple (App Store) and Android (Play Store), by means of two reviewers with a blind methodology between September and October 2019 in stores from Spain and the United Kingdom. Eighteen applications from the Android Play Store and twelve from the App Store were included in this study. The quality of the applications was evaluated using the MARS scale from 1 (inadequate) to 5 (excellent). The average score of the applications based on the MARS was 3.67 ±0.53. The sections with the highest scores were “Functionality” (4.51) and “Esthetics” (3.98) and the lowest “Application Subjective quality” (2.86) and “Information” (3.08). Mobile Health applications for the treatment of depression have great potential to influence the health status of users; however, applications come to the digital market without health control.
Background: Breast cancer survivors may have side effects from treatment, such as impaired upper limb function after surgery, which may be affected by a range of factors. Objective: To analyze the association between upper limb function and strength, fear avoidance, and central sensitization symptoms among breast cancer survivors, and to explore how these variables are associated with upper limb function. Design: Validation cohort. Setting: Institutional practice at a public hospital. Patients: One hundred seventy-four breast cancer survivors who had been undergone surgery for a primary tumor. Interventions: Not applicable. Main outcome measure: Upper limb function was measured by the Upper Limb Functional Index (ULFI-Sp). Independent outcomes were: handgrip strength, which was measured using a Jamar dynamometer on the dominant side; fear avoidance, measured using the Fear-Avoidance Components Scale (FACS-Sp); and central sensitization symptoms, which were measured using the Central Sensitisation Inventory (CSI-Sp). A linear regression model explaining the ULFI-Sp results was constructed with the variables. Results: The regression model was significant (F = 46.826; p < .0001), and explained 45% of the variance of the ULFI values. All variables showed strong associations with upper limb function. Conclusions: Greater upper limb function is associated with higher grip strength, lower fear-avoidance behavior and fewer central sensitization symptoms among breast cancer survivors. These variables explained 45% of the upper limb function in the regression model, and concur with earlier research showing that factors such as central sensitization symptoms and kinesiophobia negatively affect upper limb function in such patients. Clinicians should therefore take into account strength, fear avoidance, and central sensitization symptoms when considering interventions aimed at improving upper limb function among breast cancer survivors.

Lab head

Antonio Cuesta-Vargas
Department
  • Departamento de Fisioterapia. Instituto de Investigacion Biomedica de Malaga (IBIMA)
About Antonio Cuesta-Vargas
  • Antonio graduated from the University of Malaga in 1994, having completed the BSc (Physio) Honours in Science Degree. In 1996 he gained a MSc in Sports Physiotherapy, in 1999 a MSc in Manual Therapy and was awarded a PhD in Clinical Exercise Physiology in Faculty of Medicine at the University of Malaga in 2007.

Members (26)

Thessa I M Hilgenkamp
  • University of Nevada, Las Vegas
Manuel González Sánchez
  • University of Malaga
Randy Neblett
  • Productive Rehabilitation Institute of Dallas for Ergonomics
Cristina Roldan
  • University of Malaga
Andre Farasyn
  • Vrije Universiteit Brussel
M.teresa Labajos Manzanares
  • University of Malaga
Adrian Escriche Escuder
  • University of Malaga
Maria Ruiz-Muñoz
  • University of Malaga
Cristina Roldán Jiménez
Cristina Roldán Jiménez
  • Not confirmed yet
Cristina Roldán-Jiménez
Cristina Roldán-Jiménez
  • Not confirmed yet
Juan P. Leiva-Santos
Juan P. Leiva-Santos
  • Not confirmed yet
Antonio Ignacio
Antonio Ignacio
  • Not confirmed yet
David Pérez
David Pérez
  • Not confirmed yet
María José
María José
  • Not confirmed yet