@article{SUKEI2023100657,

title = {Automatic patient functionality assessment from multimodal data using deep learning techniques \textendash Development and feasibility evaluation},

author = {Emese S\"{u}kei and Santiago Leon-Martinez and Pablo M Olmos and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {https://www.sciencedirect.com/science/article/pii/S221478292300057X},

doi = {https://doi.org/10.1016/j.invent.2023.100657},

issn = {2214-7829},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Internet Interventions},

volume = {33},

pages = {100657},

abstract = {Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations. This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients. Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.},

keywords = {Attention models, Digital phenotyping, Ecological momentary assessment, In-situ patient monitoring, Time-series modelling, Transfer learning},

pubstate = {published},

tppubtype = {article}

}

@article{PORRASSEGOVIA2022145,

title = {Real-world feasibility and acceptability of real-time suicide risk monitoring via smartphones: A 6-month follow-up cohort},

author = {Alejandro Porras-Segovia and Isaac D\'{i}az-Oliv\'{a}n and Maria Luisa Barrig\'{o}n and Manon Moreno and Antonio Art\'{e}s-Rodr\'{i}guez and Mercedes M Perez-Rodriguez and Enrique Baca-Garc\'{i}a},

url = {https://www.sciencedirect.com/science/article/pii/S0022395622001078},

doi = {https://doi.org/10.1016/j.jpsychires.2022.02.026},

issn = {0022-3956},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Journal of Psychiatric Research},

volume = {149},

pages = {145-154},

abstract = {Active and passive Ecological Momentary Assessment of suicide risk is crucial for suicide prevention. We aimed to assess the feasibility and acceptability of active and passive smartphone-based EMA in real-world conditions in patients at high risk for suicide. We followed 393 patients at high risk for suicide for six months using two mobile health applications: the MEmind (active) and the eB2 (passive). Retention with active EMA was 79.3% after 1 month and 22.6% after 6 months. Retention with passive EMA was 87.8% after 1 month and 46.6% after 6 months. Satisfaction with the MEmind app, uninstalling the eB2 app and diagnosis of eating disorders were independently associated with stopping active EMA. Satisfaction with the eB2 app and uninstalling the MEmind app were independently associated with stopping passive EMA. Smartphone-based active and passive EMA are feasible and may increase accessibility to mental healthcare.},

keywords = {Ecological momentary assessment, eHealth, Mhealth, Suicide, Suicide attempt, Suicide ideation},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes20b,

title = {Ecological Momentary Assessment for Monitoring Risk of Suicide Behavior},

author = {Patricia Carretero and Juan Jos\'{e} Campa\~{n}a-Montes and Antonio Art\'{e}s-Rodr\'{i}guez},

doi = {https://doi.org/10.1007/7854_2020_170},

year  = {2020},

date = {2020-08-15},

journal = {Current Topics in Behavioral Neurosciences},

keywords = {Big data, Digital footprint, Digital phenotype, e-health, Ecological momentary assessment, Machine learning, Mobile health, Suicidal risk, Wearable devices},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes20c,

title = {Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study},

author = {Alejandro Porras-Segovia and Rosa Mar\'{i}a Molina-Madue\~{n}o and Sofian Berrouiguet and Jorge L\'{o}pez-Castrom\'{a}n and Maria Luisa Barrig\'{o}n and Mar\'{i}a Sandra P\'{e}rez-Rodr\'{i}guez and Jos\'{e} Heliodoro Marco and Isaac D\'{i}az-Oliv\'{a}n and Santiago de Le\'{o}n and Philippe Courtet and Antonio Art\'{e}s-Rodr\'{i}guez and Enrique Baca-Garc\'{i}a},

doi = {https://doi.org/10.1016/j.jad.2020.05.067},

year  = {2020},

date = {2020-05-26},

urldate = {2020-05-26},

journal = {Journal of Affective Disorders},

volume = {274},

pages = {733-741},

keywords = {Ecological momentary assessment, Wearable devices},

pubstate = {published},

tppubtype = {article}

}

@article{AArtes20,

title = {Study protocol of a randomised clinical trial testing whether metacognitive training can improve insight and clinical outcomes in schizophrenia},

author = {Javier-David Lopez-Morinigo and Ver\'{o}nica Gonz\'{a}lez Ruiz-Ruano and Adela S\'{a}nchez Escribano Mart\'{i}nez and Mar\'{i}a Luisa Barrig\'{o}n and L. Mata-Iturralde and L. Mu\~{n}oz-Lorenzo and S. S\'{a}nchez-Alonso and Antonio Art\'{e}s-Rodr\'{i}guez and Anthony S David and Enrique Baca-Garc\'{i}a },

doi = {https://doi.org/10.1186/s12888-020-2431-x},

year  = {2020},

date = {2020-01-29},

urldate = {2020-01-29},

journal = {BMC Psychiatry},

volume = {20},

number = {30},

keywords = {Ecological momentary assessment, Insight, Metacognitive training, Schizophrenia spectrum disorders},

pubstate = {published},

tppubtype = {article}

}