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
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 Internet Interventions, 32 , pp. 100624, 2023, ISSN: 2214-7829. @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 = {}, 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. |
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 Journal of Biomedical Informatics, 141 , pp. 104359, 2023, ISSN: 1532-0464. @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 = {}, 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. |
Acedo-Sánchez, Albert; González-Pérez, Alberto; Granell-Canut, Carlos; Casteleyn, Sven Emotive facets of place meet urban analytics Journal Article Transactions in GIS, 26 (7), pp. 2954–2974, 2022, ISSN: 1361-1682. @article{Acedo2022a, title = {Emotive facets of place meet urban analytics}, author = {Albert Acedo-Sánchez and Alberto González-Pérez and Carlos Granell-Canut and Sven Casteleyn}, doi = {https://doi.org/10.1111/tgis.12990}, issn = {1361-1682}, year = {2022}, date = {2022-11-30}, journal = {Transactions in GIS}, volume = {26}, number = {7}, pages = {2954–2974}, abstract = {The lack of a well-established and unified place theory across disciplines is decelerating its formalization, evolution, and especially its pragmatic implications and applicability. In this article, we identify research gaps in the emotive facets of place scholarship. We found that it: (1) rarely joins physical, social, and individual variables in the same model; (2) omits the immediately perceived and sensory dimensions; (3) disregards the analysis of how individual–place emotive relationships vary across time; and (4) overlooks the difficulties of reducing multifaceted emotive facets of place into geographic features. Next, we examine these research gaps through the lens of technology-based advancements in urban analytics. Finally, we discuss the need to combine social-oriented research and (spatial) data-driven disciplines to enrich and expand the research area of emotive facets of place and connected disciplines.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The lack of a well-established and unified place theory across disciplines is decelerating its formalization, evolution, and especially its pragmatic implications and applicability. In this article, we identify research gaps in the emotive facets of place scholarship. We found that it: (1) rarely joins physical, social, and individual variables in the same model; (2) omits the immediately perceived and sensory dimensions; (3) disregards the analysis of how individual–place emotive relationships vary across time; and (4) overlooks the difficulties of reducing multifaceted emotive facets of place into geographic features. Next, we examine these research gaps through the lens of technology-based advancements in urban analytics. Finally, we discuss the need to combine social-oriented research and (spatial) data-driven disciplines to enrich and expand the research area of emotive facets of place and connected disciplines. |
Matey-Sanz, Miguel; González-Pérez, Alberto; Casteleyn, Sven; Granell-Canut, Carlos Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices Inproceedings Artificial Intelligence in Medicine. AIME 2022, pp. 144-154, Springer, Cham, 2022, ISBN: 978-3031093418. @inproceedings{Matey2022a, title = {Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices}, author = {Miguel Matey-Sanz and Alberto González-Pérez and Sven Casteleyn and Carlos Granell-Canut}, doi = {https://doi.org/10.1007/978-3-031-09342-5_14}, isbn = {978-3031093418}, year = {2022}, date = {2022-07-09}, booktitle = {Artificial Intelligence in Medicine. AIME 2022}, volume = {13263}, pages = {144-154}, publisher = {Springer, Cham}, series = {Lectures Notes in Artificial Intelligence}, abstract = {Precision medicine pursues the ambitious goal of providing personalized interventions targeted at individual patients. Within this vision, digital health and mental health, where fine-grained monitoring of patients form the basis for so-called ecological momentary assessments and interventions, play a central role as complementary technology-based and data-driven instruments to traditional psychological treatments. Mobile devices are hereby key enablers: consumer smartphones and wearables are ubiquitously present and used in daily life, while they come with the necessary embedded physiological, inertial and movement sensors to potentially recognise user’s activities and behaviors. In this article, we explore whether real-time detection of fine-grained activities - relevant in the context of wellbeing - is feasible, applying machine learning techniques and based on sensor data collected from a consumer smartwatch device. We present the system architecture, whereby data collection is performed in the wearable device, real-time data processing and inference is delegated to the paired smartphone, and model training is performed offline. Finally, we demonstrate its use by instrumenting the well-known Timed Up and Go (TUG) test, typically used to assess the risk of fall in elderly people. Experiments show that consumer smartwatches can be used to automate the assessment of TUG tests and obtain satisfactory results, comparable with the classical manually performed version of the test.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Precision medicine pursues the ambitious goal of providing personalized interventions targeted at individual patients. Within this vision, digital health and mental health, where fine-grained monitoring of patients form the basis for so-called ecological momentary assessments and interventions, play a central role as complementary technology-based and data-driven instruments to traditional psychological treatments. Mobile devices are hereby key enablers: consumer smartphones and wearables are ubiquitously present and used in daily life, while they come with the necessary embedded physiological, inertial and movement sensors to potentially recognise user’s activities and behaviors. In this article, we explore whether real-time detection of fine-grained activities - relevant in the context of wellbeing - is feasible, applying machine learning techniques and based on sensor data collected from a consumer smartwatch device. We present the system architecture, whereby data collection is performed in the wearable device, real-time data processing and inference is delegated to the paired smartphone, and model training is performed offline. Finally, we demonstrate its use by instrumenting the well-known Timed Up and Go (TUG) test, typically used to assess the risk of fall in elderly people. Experiments show that consumer smartwatches can be used to automate the assessment of TUG tests and obtain satisfactory results, comparable with the classical manually performed version of the test. |
Díaz-Sanahuja, Laura; Miralles, Ignacio; Granell-Canut, Carlos; Mira, Adriana; González-Pérez, Alberto; Casteleyn, Sven; García-Palacios, Azucena; Bretón-López, Juana Client’s Experiences Using a Location-Based Technology ICT System during Gambling Treatments’ Crucial Components: A Qualitative Study Journal Article International Journal of Environmental Research and Public Health, 19 (7), pp. 3769, 2022, ISSN: 1660-4601. @article{diazsanchez2022a, title = {Client’s Experiences Using a Location-Based Technology ICT System during Gambling Treatments’ Crucial Components: A Qualitative Study}, author = {Laura Díaz-Sanahuja and Ignacio Miralles and Carlos Granell-Canut and Adriana Mira and Alberto González-Pérez and Sven Casteleyn and Azucena García-Palacios and Juana Bretón-López }, doi = {https://doi.org/10.3390/ijerph19073769}, issn = {1660-4601}, year = {2022}, date = {2022-03-22}, journal = {International Journal of Environmental Research and Public Health}, volume = {19}, number = {7}, pages = {3769}, abstract = {Cognitive Behavioral Therapy is the treatment of choice for Gambling Disorder (GD), with stimulus control (SC) and exposure with response prevention (ERP) being its two core components. Despite their efficacy, SC and ERP are not easy to deliver, so it is important to explore new ways to enhance patient compliance regarding SC and ERP. The aim of this study is to describe and assess the opinion of two patients diagnosed with problem gambling and GD that used the Symptoms app, a location-based ICT system, during SC and ERP. A consensual qualitative research study was conducted. We used a semi-structured interview, developed ad-hoc based on the Expectation and Satisfaction Scale and System Usability Scale. A total of 20 categories were identified within six domains: usefulness, improvements, recommendation to other people, safety, usability, and opinion regarding the use of the app after completing the intervention. The patients considered the app to be useful during the SC and ERP components and emphasized that feeling observed and supported at any given time helped them avoid lapses. This work can offer a starting point that opens up new research paths regarding psychological interventions for gambling disorder, such as assessing whether location-based ICT tools enhance commitment rates.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Cognitive Behavioral Therapy is the treatment of choice for Gambling Disorder (GD), with stimulus control (SC) and exposure with response prevention (ERP) being its two core components. Despite their efficacy, SC and ERP are not easy to deliver, so it is important to explore new ways to enhance patient compliance regarding SC and ERP. The aim of this study is to describe and assess the opinion of two patients diagnosed with problem gambling and GD that used the Symptoms app, a location-based ICT system, during SC and ERP. A consensual qualitative research study was conducted. We used a semi-structured interview, developed ad-hoc based on the Expectation and Satisfaction Scale and System Usability Scale. A total of 20 categories were identified within six domains: usefulness, improvements, recommendation to other people, safety, usability, and opinion regarding the use of the app after completing the intervention. The patients considered the app to be useful during the SC and ERP components and emphasized that feeling observed and supported at any given time helped them avoid lapses. This work can offer a starting point that opens up new research paths regarding psychological interventions for gambling disorder, such as assessing whether location-based ICT tools enhance commitment rates. |
IP: Sven Casteleyn ( sven.casteleyn@uji.es ) & Carlos Granell ( carlos.granell@uji.es )
Technical contact: Sven Casteleyn (sven.casteleyn@uji.es)
Website: http://www.symptoms.uji.es
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