2024
Matey-Sanz, Miguel
Human Activity Recognition with Consumer Devices and Real-Life Perspectives PhD Thesis
2024.
Abstract | Links | BibTeX | Tags: activity recognition, machine learning, smartphone app, smartwatch
@phdthesis{Matey2024c,
title = {Human Activity Recognition with Consumer Devices and Real-Life Perspectives},
author = {Miguel Matey-Sanz},
doi = {http://dx.doi.org/10.6035/14101.2024.663821},
year = {2024},
date = {2024-10-30},
abstract = {During the last decade, research on human activity recognition has grown due to its applications in diverse fields such as video surveillance, exercise monitoring or health monitoring systems. In the latter case, researchers are putting their efforts into using human activity recognition in monitoring elderly people, for example, for fall prevention and detection applications. Existing research usually has drawbacks regarding their requirements regarding sensing devices (e.g., cost, quantity, location). Therefore, research needs to keep these drawbacks in mind to have a real impact on society. This thesis addresses the abovementioned issue by focusing on the feasibility of the use of consumer devices such as smartphones and smartwatches, and cheap devices like microcontrollers, for human activity recognition and its application in real-life problems.},
keywords = {activity recognition, machine learning, smartphone app, smartwatch},
pubstate = {published},
tppubtype = {phdthesis}
}
During the last decade, research on human activity recognition has grown due to its applications in diverse fields such as video surveillance, exercise monitoring or health monitoring systems. In the latter case, researchers are putting their efforts into using human activity recognition in monitoring elderly people, for example, for fall prevention and detection applications. Existing research usually has drawbacks regarding their requirements regarding sensing devices (e.g., cost, quantity, location). Therefore, research needs to keep these drawbacks in mind to have a real impact on society. This thesis addresses the abovementioned issue by focusing on the feasibility of the use of consumer devices such as smartphones and smartwatches, and cheap devices like microcontrollers, for human activity recognition and its application in real-life problems.
González-Pérez, Alberto; Díaz-Sanahuja, Laura; Matey-Sanz, Miguel; Osma, Jorge; Granell-Canut, Carlos; Bretón-López, Juana; Casteleyn, Sven
Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app Journal Article
In: Digital Health, vol. 10, pp. 1-17, 2024, ISBN: 2055-2076.
Abstract | Links | BibTeX | Tags: exposure therapy, mental health, mHealth, smartphone app
@article{Gonzalez-Perez2024a,
title = {Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app},
author = {Alberto González-Pérez and Laura Díaz-Sanahuja and Miguel Matey-Sanz and Jorge Osma and Carlos Granell-Canut and Juana Bretón-López and Sven Casteleyn},
doi = {https://doi.org/10.1177/20552076241283942},
isbn = {2055-2076},
year = {2024},
date = {2024-10-28},
urldate = {2024-10-28},
journal = {Digital Health},
volume = {10},
pages = {1-17},
abstract = {Objective
While exposure therapy (ET) has the potential to help people tolerate intense situation-specific emotions and change avoidance behaviours, no smartphone solution exists to guide the process of in-vivo ET. A geolocation-based smartphone software component was designed and developed to instrumentalize patient guidance in in-vivo ET and its psychological validity was assessed by a group of independent psychology experts.
Methods
A team of computer scientists and psychologists developed the ET Component for in-vivo ET using geolocation-based technology, following the process-centred design methodology. The ET Component was integrated into the SyMptOMS-ET Android application, which was developed following the co-design methodology. Next, nine independent psychology experts tested and evaluated the ET Component and the SyMptOMS-ET app in the field, following the think-aloud methodology. Participants also completed the Mobile Application Rating Scale (MARS) instrument to quantitatively evaluate the solutions.
Results
We present the SyMptOMS-ET app’s main features and the ET Component exposure workflow. Next, we discuss the feedback obtained and the results of the MARS instrument. Participants who tested the app were satisfied with the ET Component during exposure scenarios (score of mu4.32 out of 5 [mu 0.28] on MARS quality aspects), agreed on the soundness of the theoretical foundations of the solutions developed (score of mu4.57 [mu0.48] on MARS treatment support aspects), and provided minor think-a-loud comments to improve them.
Conclusions
The results of the expert evaluation demonstrate the psychological validity of the ET Component and the SyMptOMS-ET app. However, further studies are needed to discern the acceptability and efficacy of the mHealth tool in the target population.},
keywords = {exposure therapy, mental health, mHealth, smartphone app},
pubstate = {published},
tppubtype = {article}
}
Objective
While exposure therapy (ET) has the potential to help people tolerate intense situation-specific emotions and change avoidance behaviours, no smartphone solution exists to guide the process of in-vivo ET. A geolocation-based smartphone software component was designed and developed to instrumentalize patient guidance in in-vivo ET and its psychological validity was assessed by a group of independent psychology experts.
Methods
A team of computer scientists and psychologists developed the ET Component for in-vivo ET using geolocation-based technology, following the process-centred design methodology. The ET Component was integrated into the SyMptOMS-ET Android application, which was developed following the co-design methodology. Next, nine independent psychology experts tested and evaluated the ET Component and the SyMptOMS-ET app in the field, following the think-aloud methodology. Participants also completed the Mobile Application Rating Scale (MARS) instrument to quantitatively evaluate the solutions.
Results
We present the SyMptOMS-ET app’s main features and the ET Component exposure workflow. Next, we discuss the feedback obtained and the results of the MARS instrument. Participants who tested the app were satisfied with the ET Component during exposure scenarios (score of mu4.32 out of 5 [mu 0.28] on MARS quality aspects), agreed on the soundness of the theoretical foundations of the solutions developed (score of mu4.57 [mu0.48] on MARS treatment support aspects), and provided minor think-a-loud comments to improve them.
Conclusions
The results of the expert evaluation demonstrate the psychological validity of the ET Component and the SyMptOMS-ET app. However, further studies are needed to discern the acceptability and efficacy of the mHealth tool in the target population.
While exposure therapy (ET) has the potential to help people tolerate intense situation-specific emotions and change avoidance behaviours, no smartphone solution exists to guide the process of in-vivo ET. A geolocation-based smartphone software component was designed and developed to instrumentalize patient guidance in in-vivo ET and its psychological validity was assessed by a group of independent psychology experts.
Methods
A team of computer scientists and psychologists developed the ET Component for in-vivo ET using geolocation-based technology, following the process-centred design methodology. The ET Component was integrated into the SyMptOMS-ET Android application, which was developed following the co-design methodology. Next, nine independent psychology experts tested and evaluated the ET Component and the SyMptOMS-ET app in the field, following the think-aloud methodology. Participants also completed the Mobile Application Rating Scale (MARS) instrument to quantitatively evaluate the solutions.
Results
We present the SyMptOMS-ET app’s main features and the ET Component exposure workflow. Next, we discuss the feedback obtained and the results of the MARS instrument. Participants who tested the app were satisfied with the ET Component during exposure scenarios (score of mu4.32 out of 5 [mu 0.28] on MARS quality aspects), agreed on the soundness of the theoretical foundations of the solutions developed (score of mu4.57 [mu0.48] on MARS treatment support aspects), and provided minor think-a-loud comments to improve them.
Conclusions
The results of the expert evaluation demonstrate the psychological validity of the ET Component and the SyMptOMS-ET app. However, further studies are needed to discern the acceptability and efficacy of the mHealth tool in the target population.
2023
Matey-Sanz, Miguel; Casteleyn, Sven; Granell-Canut, Carlos
Dataset of inertial measurements of smartphones and smartwatches for human activity recognition Journal Article
In: Data in Brief, vol. 51, pp. 109809, 2023, ISSN: 2352-3409.
Abstract | Links | BibTeX | Tags: activity recognition, dataset, machine learning, smartphone app, smartwatch, symptoms
@article{Matey2023c,
title = {Dataset of inertial measurements of smartphones and smartwatches for human activity recognition},
author = {Miguel Matey-Sanz and Sven Casteleyn and Carlos Granell-Canut},
doi = {https://doi.org/10.1016/j.dib.2023.109809},
issn = {2352-3409},
year = {2023},
date = {2023-12-15},
journal = {Data in Brief},
volume = {51},
pages = {109809},
abstract = {This article describes a dataset for human activity recognition with inertial measurements, i.e., accelerometer and gyroscope, from a smartphone and a smartwatch placed in the left pocket and on the left wrist, respectively. Twenty-three heterogeneous subjects (μ = 44.3, σ = 14.3, 56% male) participated in the data collection, which consisted of performing five activities (seated, standing up, walking, turning, and sitting down) arranged in a specific sequence (corresponding with the TUG test). Subjects performed the sequence of activities multiple times while the devices collected inertial data at 100 Hz and were video-recorded by a researcher for data labelling purposes. The goal of this dataset is to provide smartphone- and smartwatch-based inertial data for human activity recognition collected from a heterogeneous (i.e., age-diverse, gender-balanced) set of subjects. Along with the dataset, the repository includes demographic information (age, gender), information about each sequence of activities (smartphone's orientation in the pocket, direction of turns), and a Python package with utility functions (data loading, visualization, etc). The dataset can be reused for different purposes in the field of human activity recognition, from cross-subject evaluation to comparison of recognition performance using data from smartphones and smartwatches.},
keywords = {activity recognition, dataset, machine learning, smartphone app, smartwatch, symptoms},
pubstate = {published},
tppubtype = {article}
}
This article describes a dataset for human activity recognition with inertial measurements, i.e., accelerometer and gyroscope, from a smartphone and a smartwatch placed in the left pocket and on the left wrist, respectively. Twenty-three heterogeneous subjects (μ = 44.3, σ = 14.3, 56% male) participated in the data collection, which consisted of performing five activities (seated, standing up, walking, turning, and sitting down) arranged in a specific sequence (corresponding with the TUG test). Subjects performed the sequence of activities multiple times while the devices collected inertial data at 100 Hz and were video-recorded by a researcher for data labelling purposes. The goal of this dataset is to provide smartphone- and smartwatch-based inertial data for human activity recognition collected from a heterogeneous (i.e., age-diverse, gender-balanced) set of subjects. Along with the dataset, the repository includes demographic information (age, gender), information about each sequence of activities (smartphone's orientation in the pocket, direction of turns), and a Python package with utility functions (data loading, visualization, etc). The dataset can be reused for different purposes in the field of human activity recognition, from cross-subject evaluation to comparison of recognition performance using data from smartphones and smartwatches.
Matey-Sanz, Miguel; Torres-Sospedra, Joaquín; González-Pérez, Alberto; Casteleyn, Sven; Granell-Canut, Carlos
Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition Proceedings Article
In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 391–405, Springer, Cham, 2023, ISBN: 978-3-031-49018-7.
Abstract | Links | BibTeX | Tags: activity recognition, machine learning, smartphone app, smartwatch, symptoms
@inproceedings{Matey2023b,
title = {Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition},
author = {Miguel Matey-Sanz and Joaquín Torres-Sospedra and Alberto González-Pérez and Sven Casteleyn and Carlos Granell-Canut},
doi = {https://doi.org/10.1007/978-3-031-49018-7_28},
isbn = {978-3-031-49018-7},
year = {2023},
date = {2023-12-01},
booktitle = {Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications},
volume = {14469},
pages = {391–405},
publisher = {Springer, Cham},
series = {Lecture Notes in Computer Science},
abstract = {The ubiquity of consumer devices with sensing and computational capabilities, such as smartphones and smartwatches, has increased interest in their use in human activity recognition for healthcare monitoring applications, among others. When developing such a system, researchers rely on input data to train recognition models. In the absence of openly available datasets that meet the model requirements, researchers face a hard and time-consuming process to decide which sensing device to use or how much data needs to be collected. In this paper, we explore the effect of the amount of training data on the performance (i.e., classification accuracy and activity-wise F1-scores) of a CNN model by performing an incremental cross-subject evaluation using data collected from a consumer smartphone and smartwatch. Systematically studying the incremental inclusion of subject data from a set of 22 training subjects, the results show that the model’s performance initially improves significantly with each addition, yet this improvement slows down the larger the number of included subjects. We compare the performance of models based on smartphone and smartwatch data. The latter option is significantly better with smaller sizes of training data, while the former outperforms with larger amounts of training data. In addition, gait-related activities show significantly better results with smartphone-collected data, while non-gait-related activities, such as standing up or sitting down, were better recognized with smartwatch-collected data.},
keywords = {activity recognition, machine learning, smartphone app, smartwatch, symptoms},
pubstate = {published},
tppubtype = {inproceedings}
}
The ubiquity of consumer devices with sensing and computational capabilities, such as smartphones and smartwatches, has increased interest in their use in human activity recognition for healthcare monitoring applications, among others. When developing such a system, researchers rely on input data to train recognition models. In the absence of openly available datasets that meet the model requirements, researchers face a hard and time-consuming process to decide which sensing device to use or how much data needs to be collected. In this paper, we explore the effect of the amount of training data on the performance (i.e., classification accuracy and activity-wise F1-scores) of a CNN model by performing an incremental cross-subject evaluation using data collected from a consumer smartphone and smartwatch. Systematically studying the incremental inclusion of subject data from a set of 22 training subjects, the results show that the model’s performance initially improves significantly with each addition, yet this improvement slows down the larger the number of included subjects. We compare the performance of models based on smartphone and smartwatch data. The latter option is significantly better with smaller sizes of training data, while the former outperforms with larger amounts of training data. In addition, gait-related activities show significantly better results with smartphone-collected data, while non-gait-related activities, such as standing up or sitting down, were better recognized with smartwatch-collected data.
Gómez-Cambronero, Águeda; Casteleyn, Sven; Bretón-López, Juana; García-Palacios, Azucena; Mira, Adriana
A smartphone-based serious game for depressive symptoms: Protocol for a pilot randomized controlled trial Journal Article
In: Internet Interventions, vol. 32, pp. 100624, 2023, ISSN: 2214-7829.
Abstract | Links | BibTeX | Tags: depression, serious games, smartphone app, symptoms
@article{GomezCambronero2023a,
title = {A smartphone-based serious game for depressive symptoms: Protocol for a pilot randomized controlled trial},
author = {Águeda Gómez-Cambronero and Sven Casteleyn and Juana Bretón-López and Azucena García-Palacios and Adriana Mira},
doi = {https://doi.org/10.1016/j.invent.2023.100624},
issn = {2214-7829},
year = {2023},
date = {2023-04-28},
journal = {Internet Interventions},
volume = {32},
pages = {100624},
abstract = {Background
Depression is the most prevalent mental disorder, with detrimental effects on the patient's well-being, high disability, and a huge associated societal and economic cost. There are evidence-based treatments, but it is difficult to reach all people in need. Internet-based interventions, and more recently smartphone-based interventions, were explored to overcome barriers to access. Evidence shows them to be effective alternatives to traditional treatments. This paper presents the protocol of a pilot study whose primary aim is to investigate the efficacy of a smartphone-based serious game intervention for patients with mild to moderate depressive symptoms.
Methods
This randomized controlled pilot trial protocol foresees two arms design: 1/ smartphone- based serious game intervention (based on Cognitive Behavior Therapy with particular emphasis on Behavioral Activation and Physical Activity), 2/ waiting list control group. The study is expected to recruit 40 participants (18+), which will be randomly assigned to one of the experimental conditions. The duration of the intervention is two months. The primary outcome measure will be depressive symptomatology. Secondary outcomes will include other variables such as physical activity, resilience, anxiety, depression impairment, and positive and negative affect. Treatment expectation, satisfaction, usability, and game playability will also be measured. The data will be analyzed based on the intention-to-treat and per protocol analyses.
Discussion
The study aims to establish initial evidence for the efficacy of a smartphone-based serious game intervention, to serve as input for a larger-scale randomized control trial. The intervention exploits advanced smartphone capabilities, such as the use of a serious game as delivery mode, with the potential benefit of engagement and treatment adherence, and motion sensors to monitor and stimulate physical activity. As a secondary objective, the study aims to gather initial evidence on the user's expectations, satisfaction, usability and playability of the serious game as a treatment.},
keywords = {depression, serious games, smartphone app, symptoms},
pubstate = {published},
tppubtype = {article}
}
Background
Depression is the most prevalent mental disorder, with detrimental effects on the patient's well-being, high disability, and a huge associated societal and economic cost. There are evidence-based treatments, but it is difficult to reach all people in need. Internet-based interventions, and more recently smartphone-based interventions, were explored to overcome barriers to access. Evidence shows them to be effective alternatives to traditional treatments. This paper presents the protocol of a pilot study whose primary aim is to investigate the efficacy of a smartphone-based serious game intervention for patients with mild to moderate depressive symptoms.
Methods
This randomized controlled pilot trial protocol foresees two arms design: 1/ smartphone- based serious game intervention (based on Cognitive Behavior Therapy with particular emphasis on Behavioral Activation and Physical Activity), 2/ waiting list control group. The study is expected to recruit 40 participants (18+), which will be randomly assigned to one of the experimental conditions. The duration of the intervention is two months. The primary outcome measure will be depressive symptomatology. Secondary outcomes will include other variables such as physical activity, resilience, anxiety, depression impairment, and positive and negative affect. Treatment expectation, satisfaction, usability, and game playability will also be measured. The data will be analyzed based on the intention-to-treat and per protocol analyses.
Discussion
The study aims to establish initial evidence for the efficacy of a smartphone-based serious game intervention, to serve as input for a larger-scale randomized control trial. The intervention exploits advanced smartphone capabilities, such as the use of a serious game as delivery mode, with the potential benefit of engagement and treatment adherence, and motion sensors to monitor and stimulate physical activity. As a secondary objective, the study aims to gather initial evidence on the user's expectations, satisfaction, usability and playability of the serious game as a treatment.
Depression is the most prevalent mental disorder, with detrimental effects on the patient's well-being, high disability, and a huge associated societal and economic cost. There are evidence-based treatments, but it is difficult to reach all people in need. Internet-based interventions, and more recently smartphone-based interventions, were explored to overcome barriers to access. Evidence shows them to be effective alternatives to traditional treatments. This paper presents the protocol of a pilot study whose primary aim is to investigate the efficacy of a smartphone-based serious game intervention for patients with mild to moderate depressive symptoms.
Methods
This randomized controlled pilot trial protocol foresees two arms design: 1/ smartphone- based serious game intervention (based on Cognitive Behavior Therapy with particular emphasis on Behavioral Activation and Physical Activity), 2/ waiting list control group. The study is expected to recruit 40 participants (18+), which will be randomly assigned to one of the experimental conditions. The duration of the intervention is two months. The primary outcome measure will be depressive symptomatology. Secondary outcomes will include other variables such as physical activity, resilience, anxiety, depression impairment, and positive and negative affect. Treatment expectation, satisfaction, usability, and game playability will also be measured. The data will be analyzed based on the intention-to-treat and per protocol analyses.
Discussion
The study aims to establish initial evidence for the efficacy of a smartphone-based serious game intervention, to serve as input for a larger-scale randomized control trial. The intervention exploits advanced smartphone capabilities, such as the use of a serious game as delivery mode, with the potential benefit of engagement and treatment adherence, and motion sensors to monitor and stimulate physical activity. As a secondary objective, the study aims to gather initial evidence on the user's expectations, satisfaction, usability and playability of the serious game as a treatment.
González-Pérez, Alberto; Matey-Sanz, Miguel; Granell-Canut, Carlos; Díaz-Sanahuja, Laura; Bretón-López, Juana; Casteleyn, Sven
AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health Journal Article
In: Journal of Biomedical Informatics, vol. 141, pp. 104359, 2023, ISSN: 1532-0464.
Abstract | Links | BibTeX | Tags: context-aware computing, digital phenotyping, location-based services, mHealth, smartphone app, symptoms
@article{Gonzalez-Perez2023a,
title = {AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health},
author = {Alberto González-Pérez and Miguel Matey-Sanz and Carlos Granell-Canut and Laura Díaz-Sanahuja and Juana Bretón-López and Sven Casteleyn},
doi = {10.1016/j.jbi.2023.104359},
issn = {1532-0464},
year = {2023},
date = {2023-04-20},
journal = {Journal of Biomedical Informatics},
volume = {141},
pages = {104359},
abstract = {In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework’s design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.},
keywords = {context-aware computing, digital phenotyping, location-based services, mHealth, smartphone app, symptoms},
pubstate = {published},
tppubtype = {article}
}
In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework’s design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.
2022
Osma, Jorge; Martínez-García, Laura; Prado-Abril, Javier; Perís-Baquero, Óscar; González-Pérez, Alberto
In: Internet Interventions, vol. 30, pp. 100577, 2022, ISSN: 2214-7829.
Abstract | Links | BibTeX | Tags: emotional disorders, mental health, smartphone app, thematic content analysis
@article{Osma2022a,
title = {Developing a smartphone App based on the Unified Protocol for the transdiagnostic treatment of emotional disorders: A qualitative analysis of users and professionals' perspectives},
author = {Jorge Osma and Laura Martínez-García and Javier Prado-Abril and Óscar Perís-Baquero and Alberto González-Pérez},
doi = {https://doi.org/10.1016/j.invent.2022.100577},
issn = {2214-7829},
year = {2022},
date = {2022-12-01},
journal = {Internet Interventions},
volume = {30},
pages = {100577},
abstract = {Emotional Disorders have become the most prevalent mental disorders in the world. In relation to their high prevalence, mental health care from public health services faces major challenges. Consequently, finding solutions to deliver cost-effective evidence-based treatments has become a main goal of today's clinical psychology. Smartphone apps for mental health have emerged as a potential tool to deal with it. However, despite their effectiveness and advantages, several studies suggest the need to involve patients and professionals in the design of these apps from the first stage of the development process. Thus, this study aimed to identify, from both a group of users and professionals, the needs, opinions, expectations and design aspects of a future smartphone app based in the Unified Protocol (UP), that will allow to develop the subsequent technical work of the app engineers. Two focus groups were conducted, one with 7 professionals and the other with 9 users, both groups familiar with the UP. A thematic content analysis based in grounded theory was performed in order to define emergent categories of analysis derived from the interview data. The results revealed 8 common topics in both focus groups and 5 specific key topics were identified in the professionals' focus group. Of the total proposals, 93 % of the professionals' and 78 % of the users' are implemented in the preliminary version of the app.},
keywords = {emotional disorders, mental health, smartphone app, thematic content analysis},
pubstate = {published},
tppubtype = {article}
}
Emotional Disorders have become the most prevalent mental disorders in the world. In relation to their high prevalence, mental health care from public health services faces major challenges. Consequently, finding solutions to deliver cost-effective evidence-based treatments has become a main goal of today's clinical psychology. Smartphone apps for mental health have emerged as a potential tool to deal with it. However, despite their effectiveness and advantages, several studies suggest the need to involve patients and professionals in the design of these apps from the first stage of the development process. Thus, this study aimed to identify, from both a group of users and professionals, the needs, opinions, expectations and design aspects of a future smartphone app based in the Unified Protocol (UP), that will allow to develop the subsequent technical work of the app engineers. Two focus groups were conducted, one with 7 professionals and the other with 9 users, both groups familiar with the UP. A thematic content analysis based in grounded theory was performed in order to define emergent categories of analysis derived from the interview data. The results revealed 8 common topics in both focus groups and 5 specific key topics were identified in the professionals' focus group. Of the total proposals, 93 % of the professionals' and 78 % of the users' are implemented in the preliminary version of the app.