SyMptOMS – GEOTEC

Sensor and MObile based Mental Health Solutions

SyMptOMS is a research project that marries the technical expertise of the GeoSpatial Technologies Research Lab (GEOTEC) with the knowledge in experimental psychology of the Laboratory of Psychology and Technology (LABPSITEC) to develop innovate solutions for the diagnosis, treatment, and relapse prevention of mental health disorders. Hereby, the fundamental observation and starting point is the fact that, despite its enormous potential, the use of geospatial and mobile technologies for mental health is insufficiently researched and used in practice. In SyMptOMS, we therefore investigate how the current capabilities of modern mobile phones (e.g., GPS coordinates, built-in sensors, environment and context detection) and other wearable devices (e.g., smartwatch, sports bracelets, wearable and/or embedded sensors, etc.) may be used and exploited to modernise or develop new psychological assessment and interventions. In a first phase of the project, the focus is particularly on mobile phones applied for so-called ecological momentary assessment and interventions, whereby the patient’s location and behaviour are monitored in order to deliver in-situ and real-time interventions. In a later phase, the developed solutions will be extended to include contextual information through wearables and context-aware analysis. The project was initiated end 2017 with GEOTEC’s own funding. In September 2018, an FPU scholarship was granted on the topic, and in the course of 2019, external funds were awarded from the Ministry of Science, Innovation and Universities.

Publications

27 entries « ‹ 1 of 6 › »

Matey-Sanz, Miguel; González-Pérez, Alberto; Casteleyn, Sven; Granell-Canut, Carlos

Implementing and Evaluating the Timed Up and Go Test Automation Using Smartphones and Smartwatches Journal Article

In: IEEE Journal of Biomedical and Health Informatics, vol. 28, iss. 11, pp. 6594 - 6605, 2024, ISSN: 2168-2208.

Abstract | Links | BibTeX

@article{Matey2024b,

title = {Implementing and Evaluating the Timed Up and Go Test Automation Using Smartphones and Smartwatches},

author = {Miguel Matey-Sanz and Alberto González-Pérez and Sven Casteleyn and Carlos Granell-Canut},

doi = {https://doi.org/10.1109/JBHI.2024.3456169},

issn = {2168-2208},

year  = {2024},

date = {2024-09-09},

urldate = {2024-09-09},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {28},

issue = {11},

pages = {6594 - 6605},

abstract = {Physical performance tests aim to assess the physical abilities and mobility skills of individuals for various healthcare purposes. They are often driven by experts and usually performed at their practice, and therefore they are resource-intensive and time-demanding. For tests based on objective measurements (e.g., duration, repetitions), technology can be used to automate them, allowing the patients to perform the test themselves, more frequently and anywhere, while alleviating the expert from supervising the test. The well-known Timed Up and Go (TUG) test, typically used for mobility assessment, is an ideal candidate for automation, as inertial sensors (among others) can be deployed to detect the various movements constituting the test without expert supervision. To move from expert-led testing to self-administered testing, we present a mHealth system capable of automating the TUG test using a pocket-sized smartphone or a wrist smartwatch paired with a smartphone, where data from inertial sensors are used to detect the activities carried out by the patient while performing the test and compute their results in real time. All processing (i.e., data processing, machine learning-based activity inference, results calculation) takes place on the smartphone. The use of both devices to automate the TUG test was evaluated (w.r.t. accuracy, reliability and battery consumption) and mutually compared, and set off with a reference method, obtaining excellent Bland-Altman agreement results and Intraclass Correlation Coefficient reliability. Results also suggest that the smartwatch-based system performs better than the smartphone-based system.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Gómez-Cambronero, Águeda; Mann, Anna-Lisa; Mira, Adriana; Doherty, Gavin; Casteleyn, Sven

Smartphone-based serious games for mental health: a scoping review Journal Article

In: Multimedia Tools and Applications, vol. 83, pp. 84047–84094, 2024, ISSN: 1573-7721.

Abstract | Links | BibTeX

@article{GomezCambronero2024a,

title = {Smartphone-based serious games for mental health: a scoping review},

author = {Águeda Gómez-Cambronero and Anna-Lisa Mann and Adriana Mira and Gavin Doherty and Sven Casteleyn},

doi = {https://doi.org/10.1007/s11042-024-18971-w},

issn = {1573-7721},

year  = {2024},

date = {2024-04-01},

urldate = {2024-04-01},

journal = {Multimedia Tools and Applications},

volume = {83},

pages = {84047–84094},

abstract = {The use of smartphone-based Serious Games in mental health care is an emerging and promising research field. Combining the intrinsic characteristics of games (e.g., interactiveness, immersiveness, playfulness, user-tailoring and engaging nature) with the capabilities of smartphones (e.g., versatility, ubiquitous connectivity, built-in sensors and anywhere–anytime nature) yields great potential to deliver innovative psychological treatments, which are engaging, effective, fun and always available. This article presents a scoping review, based on the PRISMA (scoping review extension) guidelines, of the field of smartphone-based serious games for mental health care. The review combines an analysis of the technical characteristics, including game design, smartphone and game-specific features, with psychological dimensions, including type and purpose of use, underlying psychological frameworks and strategies. It also explores the integration of psychological features into Serious Games and summarizes the findings of evaluations performed. A systematic search identified 40 smartphone-based Serious Games for mental health care. The majority consist of standalone and self-administrable interventions, applying a myriad of psychological strategies to address a wide range of psychological symptoms and disorders. The findings explore the potential of Serious Games as treatments and for enhancing patient engagement; we conclude by proposing several avenues for future research in order to identify best practices and success factors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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

Gómez-Cambronero, Águeda

"Horizon: Resilience": A Smartphone-based Serious Game Intervention for Depressive Symptoms PhD Thesis

Universitat Jaume I. INIT, 2023.

Abstract | Links | BibTeX

@phdthesis{GomezCambronero2023b,

title = {"Horizon: Resilience": A Smartphone-based Serious Game Intervention for Depressive Symptoms},

author = {Águeda Gómez-Cambronero},

url = {http://hdl.handle.net/10803/689528},

doi = {http://dx.doi.org/10.6035/14101.2023.544418},

year  = {2023},

date = {2023-12-11},

school = {Universitat Jaume I. INIT},

abstract = {Depression is the most prevalent mental issue in our society, leading to disability and suicide deaths. The COVID-19 pandemic has intensified the need for depression treatment and prevention. While effective, evidence-based psychological treatments for depression exists, only a small percentage of those in need actually receive them. Technology, particularly smartphone-based interventions, can help maximize the reach of these treatments while ensuring their effectiveness, although it comes with challenges, such as high dropout rates. Despite the potential

of this therapy, this is a field that requires considerably more research to fully explore the benefits that smartphones have to offer. Specifically, serious games, designed with a purpose beyond entertainment, have emerged as a promising treatment tool, leveraging advance smartphone capabilities, aligning with psychological treatment principles, and enhancing user engagement.



This dissertation introduces “Horizon: Resilience”, a smartphone-based Serious Game for depressive symptoms. It is a city builder game with a decision making narrative, in which the player (patient) manages a town. The objective is to make the town progress, ensuring the steady inflow of resources and fostering the psychological resilience of its inhabitants. The game is based on the Cognitive Behavioral Therapy (CBT) framework and includes Positive Psychology (PP) techniques. These psychological techniques are woven into the game’s gameplay, feedback, economy system, quests, graphics, and story. Noteworthy is the integration of promoting Physical Activity, detected using the phone’s motion sensors, as part of gameplay. The game draws on the findings of a scoping review on smartphone-based serious games in mental health, and was informed by consultations with therapists as part of a user-centered design. Therapists and patients furthermore provided a qualitative and quantitative evaluation of a Minimum Viable Product (MVP) of the game. Their positive impressions indicate high acceptance and positive expectation regarding the use of the game as an

intervention. Lastly, a pilot randomized controlled trial protocol is outlined to assess its preliminary effectiveness-},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Depression is the most prevalent mental issue in our society, leading to disability and suicide deaths. The COVID-19 pandemic has intensified the need for depression treatment and prevention. While effective, evidence-based psychological treatments for depression exists, only a small percentage of those in need actually receive them. Technology, particularly smartphone-based interventions, can help maximize the reach of these treatments while ensuring their effectiveness, although it comes with challenges, such as high dropout rates. Despite the potential
of this therapy, this is a field that requires considerably more research to fully explore the benefits that smartphones have to offer. Specifically, serious games, designed with a purpose beyond entertainment, have emerged as a promising treatment tool, leveraging advance smartphone capabilities, aligning with psychological treatment principles, and enhancing user engagement.

This dissertation introduces “Horizon: Resilience”, a smartphone-based Serious Game for depressive symptoms. It is a city builder game with a decision making narrative, in which the player (patient) manages a town. The objective is to make the town progress, ensuring the steady inflow of resources and fostering the psychological resilience of its inhabitants. The game is based on the Cognitive Behavioral Therapy (CBT) framework and includes Positive Psychology (PP) techniques. These psychological techniques are woven into the game’s gameplay, feedback, economy system, quests, graphics, and story. Noteworthy is the integration of promoting Physical Activity, detected using the phone’s motion sensors, as part of gameplay. The game draws on the findings of a scoping review on smartphone-based serious games in mental health, and was informed by consultations with therapists as part of a user-centered design. Therapists and patients furthermore provided a qualitative and quantitative evaluation of a Minimum Viable Product (MVP) of the game. Their positive impressions indicate high acceptance and positive expectation regarding the use of the game as an
intervention. Lastly, a pilot randomized controlled trial protocol is outlined to assess its preliminary effectiveness-

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

@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 = {},

pubstate = {published},

tppubtype = {inproceedings}

}