@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 = {gambling, geolocation, mental health, symptoms},

pubstate = {published},

tppubtype = {article}

}

@article{Shubina2020,

title = {Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era},

author = {Victoriia Shubina and Sylvia Holcer and Michael Gould and Elena Simona Lohan },

doi = {https://doi.org/10.3390/data5040087 },

year  = {2020},

date = {2020-01-31},

journal = {Data},

volume = {5},

number = {4},

pages = {87},

keywords = {A-wear, geolocation, geoprivacy, mobi, Monitoring},

pubstate = {published},

tppubtype = {article}

}

@article{Santa2019,

title = {A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments},

author = {Fernando Santa and Roberto Henriques and Joaquín Torres-Sospedra and Edzer Pebesma},

doi = {10.3390/su11030595},

year  = {2019},

date = {2019-03-13},

journal = {Sustainability  },

volume = {11},

number = {3},

pages = {595},

abstract = {An in-depth descriptive approach to the dynamics of the urban population is fundamental as a first step towards promoting effective planning and designing processes in cities. Understanding the behavioral aspects of human activities can contribute to their effective management and control. We present a framework, based on statistical methods, for studying the spatio-temporal distribution of geolocated tweets as a proxy for where and when people carry out their activities. We have evaluated our proposal by analyzing the distribution of collected geolocated tweets over a two-week period in the summer of 2017 in Lisbon, London, and Manhattan. Our proposal considers a negative binomial regression analysis for the time series of counts of tweets as a first step. We further estimate a functional principal component analysis of second-order summary statistics of the hourly spatial point patterns formed by the locations of the tweets. Finally, we find groups of hours with a similar spatial arrangement of places where humans develop their activities through hierarchical clustering over the principal scores. Social media events are found to show strong temporal trends such as seasonal variation due to the hour of the day and the day of the week in addition to autoregressive schemas. We have also identified spatio-temporal patterns of clustering, i.e., groups of hours of the day that present a similar spatial distribution of human activities.},

note = {IF:},

keywords = {GEO-C, Geo-social media, geolocation},

pubstate = {published},

tppubtype = {article}

}

@article{Rodríguez-Pupo2017,

title = {On Metrics for Location-aware Games},

author = {Luis Enrique Rodríguez-Pupo and Sven Casteleyn and Carlos Granell-Canut},

doi = {10.3390/ijgi6100299 },

issn = {2220-9964},

year  = {2017},

date = {2017-10-01},

journal = {ISPRS International Journal of Geo-Information},

volume = {6},

number = {10},

pages = {299},

abstract = {Metrics are important and well-known tools to measure users’ behavior in games, and gameplay in general. Particularities of location-aware games—a class of games where the player’s location plays a central role-demand specific support in metrics to adequately address the spatio-temporal features such games exhibit. In this article, we analyse and discuss how existing game analytics platforms address the spatio-temporal features of location-aware games. Our analysis reveals that little support is available. Next, based on the analysis, we propose a classification of spatial metrics, embedded in existing literature, and discuss three types of spatial metrics-point-, trajectory- and area-based metrics-, and elaborate examples and difficulties. Finally, we discuss how spatial metrics may be deployed to improve gameplay in location-aware games},

keywords = {geogames, geolocation, RyC-Casteleyn, RyC-Granell, Software metrics},

pubstate = {published},

tppubtype = {article}

}