Thermal regime of a highly regulated Italian river (Ticino River) and implications for aquatic communities

Thermal alteration is one of the adverse effects of flow regulation sharpened in recent years due to climate changes. In this work, we characterize the thermal regime of a highly regulated river located in Northern Italy, which is the emissary of Lake Maggiore. The thermal characteristics of the study reach are influenced by the presence of two dams and by climate warming. In the 15-year monitored period (1999-2013) a significant increase of the mean annual river temperature (i.e., +1.8°C) was indeed recorded. Minimum and maximum water temperatures were detected in correspondence of low-flow periods in February and August, respectively. During August the temperature reached a maximum of 27°C, that is a value below the upper thermal limit of tolerance for most of the aquatic taxa collected in the study area. Moreover, the local presence of seeps and the interaction with groundwater play an important ecological role by guaranteeing patches of cool water to the aquatic communities. Nevertheless, the sensitive early life stages of fish species that spawn in spring/summer may be affected by the high summer temperatures and the expected future climate warming. The wider range of temperatures preferred by alien fish species could result into a competitive disadvantage for autochthonous species. The current minimum flows established by local laws as environmental flows appeared to be able to guarantee an acceptable thermal alteration in morphologically heterogeneous reaches where river/groundwater interaction occurred, at least within the current climatic conditions.</p

Complexity index from a personalized wearable monitoring system for assessing remission in mental health

This study discusses a personalized wearable monitoring system, which provides information and communication technologies to patients with mental disorders and physicians managing such diseases. The system, hereinafter called the PSYCHE system, is mainly comprised of a comfortable t-shirt with embedded sensors, such as textile electrodes, to monitor electrocardiogram-heart rate variability (HRV) series, piezoresistive sensors for respiration activity, and triaxial accelerometers for activity recognition. Moreover, on the patient-side, the PSYCHE system uses a smartphone-based interactive platform for electronic mood agenda and clinical scale administration, whereas on the physician-side provides data visualization and support to clinical decision. The smartphone collects the physiological and behavioral data and sends the information out to a centralized server for further processing. In this study, we present experimental results gathered from ten bipolar patients, wearing the PSYCHE system, with severe symptoms who exhibited mood states among depression (DP), hypomania(HM), mixed state (MX), and euthymia (EU), i.e., the good affective balance. In analyzing more than 400 h of cardiovascular dynamics, we found that patients experiencing mood transitions from a pathological mood state (HM, DP, or MX - where depressive and hypomanic symptoms are simultaneously present) to EU can be characterized through a commonly used measure of entropy. In particular, the SampEn estimated on long-term HRV series increases according to the patients' clinical improvement. These results are in agreement with the current literature reporting on the complexity dynamics of physiological systems and provides a promising and viable support to clinical decision in order to improve the diagnosis and management of psychiatric disorders

Characterizing psychological dimensions in non-pathological subjects through autonomic nervous system dynamics

The objective assessment of psychological traits of healthy subjects and psychiatric patients has been growing interest in clinical and bioengineering research fields during the last decade. Several experimental evidences strongly suggest that a link between Autonomic Nervous System (ANS) dynamics and specific dimensions such as anxiety, social phobia, stress, and emotional regulation might exist. Nevertheless, an extensive investigation on a wide range of psycho-cognitive scales and ANS non-invasive markers gathered from standard and non-linear analysis still needs to be addressed. In this study, we analyzed the discerning and correlation capabilities of a comprehensive set of ANS features and psycho-cognitive scales in 29 non-pathological subjects monitored during resting conditions. In particular, the state of the art of standard and non-linear analysis was performed on Heart Rate Variability, InterBreath Interval series, and InterBeat Respiration series, which were considered as monovariate and multivariate measurements. Experimental results show that each ANS feature is linked to specific psychological traits. Moreover, non-linear analysis outperforms the psychological assessment with respect to standard analysis. Considering that the current clinical practice relies only on subjective scores from interviews and questionnaires, this study provides objective tools for the assessment of psychological dimensions

Real vs. immersive-virtual emotional experience: Analysis of psycho-physiological patterns in a free exploration of an art museum

[EN] Virtual reality is a powerful tool in human behaviour research. However, few studies compare its capacity to evoke the same emotional responses as in real scenarios. This study investigates psycho-physiological patterns evoked during the free exploration of an art museum and the museum virtualized through a 3D immersive virtual environment (IVE). An exploratory study involving 60 participants was performed, recording electroencephalographic and electrocardiographic signals using wearable devices. The real vs. virtual psychological comparison was performed using self-assessment emotional response tests, whereas the physiological comparison was performed through Support Vector Machine algorithms, endowed with an effective feature selection procedure for a set of state-of-the-art metrics quantifying cardiovascular and brain linear and nonlinear dynamics. We included an initial calibration phase, using standardized 2D and 360 degrees emotional stimuli, to increase the accuracy of the model. The self-assessments of the physical and virtual museum support the use of IVEs in emotion research. The 2-class (high/low) system accuracy was 71.52% and 77.08% along the arousal and valence dimension, respectively, in the physical museum, and 75.00% and 71.08% in the virtual museum. The previously presented 360 degrees stimuli contributed to increasing the accuracy in the virtual museum. Also, the real vs. virtual classifier accuracy was 95.27%, using only EEG mean phase coherency features, which demonstrates the high involvement of brain synchronization in emotional virtual reality processes. These findings provide an important contribution at a methodological level and to scientific knowledge, which will effectively guide future emotion elicitation and recognition systems using virtual reality.This work was supported by Ministerio de Economia y Competitividad de Espana (URL: http://www.mineco.gob.es/; Project TIN201345736-R and DPI2016-77396-R); Direccion General de Trafico, Ministerio Del Interior de Espana (URL: http://www.dgt.es/es/; Project SPIP2017-02220); and the Institut Valencia d'Art Modern (URL: https://www.ivam.es/).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Marín-Morales, J.; Higuera-Trujillo, JL.; Greco, A.; Guixeres, J.; Llinares Millán, MDC.; Gentili, C.; Scilingo, EP.... (2019). Real vs. immersive-virtual emotional experience: Analysis of psycho-physiological patterns in a free exploration of an art museum. PLoS ONE. 14(10):1-24. https://doi.org/10.1371/journal.pone.0223881S1241410Picard, R. W. (2003). Affective computing: challenges. International Journal of Human-Computer Studies, 59(1-2), 55-64. doi:10.1016/s1071-5819(03)00052-1Jerritta, S., Murugappan, M., Nagarajan, R., & Wan, K. (2011). Physiological signals based human emotion Recognition: a review. 2011 IEEE 7th International Colloquium on Signal Processing and its Applications. doi:10.1109/cspa.2011.5759912Harms, M. B., Martin, A., & Wallace, G. L. (2010). Facial Emotion Recognition in Autism Spectrum Disorders: A Review of Behavioral and Neuroimaging Studies. Neuropsychology Review, 20(3), 290-322. doi:10.1007/s11065-010-9138-6Lindal, P. J., & Hartig, T. (2013). Architectural variation, building height, and the restorative quality of urban residential streetscapes. Journal of Environmental Psychology, 33, 26-36. doi:10.1016/j.jenvp.2012.09.003Barrett, L. F. (2017). The theory of constructed emotion: an active inference account of interoception and categorization. Social Cognitive and Affective Neuroscience, 12(11), 1833-1833. doi:10.1093/scan/nsx060Russell, J. A., & Mehrabian, A. (1977). Evidence for a three-factor theory of emotions. Journal of Research in Personality, 11(3), 273-294. doi:10.1016/0092-6566(77)90037-xCalvo, R. A., & D’Mello, S. (2010). Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications. IEEE Transactions on Affective Computing, 1(1), 18-37. doi:10.1109/t-affc.2010.1Valenza, G., Greco, A., Gentili, C., Lanata, A., Sebastiani, L., Menicucci, D., … Scilingo, E. P. (2016). Combining electroencephalographic activity and instantaneous heart rate for assessing brain–heart dynamics during visual emotional elicitation in healthy subjects. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2067), 20150176. doi:10.1098/rsta.2015.0176Valenza, G., Lanata, A., & Scilingo, E. P. (2012). The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition. IEEE Transactions on Affective Computing, 3(2), 237-249. doi:10.1109/t-affc.2011.30Valenza, G., Nardelli, M., Lanata, A., Gentili, C., Bertschy, G., Paradiso, R., & Scilingo, E. P. (2014). Wearable Monitoring for Mood Recognition in Bipolar Disorder Based on History-Dependent Long-Term Heart Rate Variability Analysis. IEEE Journal of Biomedical and Health Informatics, 18(5), 1625-1635. doi:10.1109/jbhi.2013.2290382Marín-Morales, J., Higuera-Trujillo, J. L., Greco, A., Guixeres, J., Llinares, C., Scilingo, E. P., … Valenza, G. (2018). Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports, 8(1). doi:10.1038/s41598-018-32063-4Nakisa, B., Rastgoo, M. N., Tjondronegoro, D., & Chandran, V. (2018). Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors. Expert Systems with Applications, 93, 143-155. doi:10.1016/j.eswa.2017.09.062Baños, R. M., Botella, C., Alcañiz, M., Liaño, V., Guerrero, B., & Rey, B. (2004). Immersion and Emotion: Their Impact on the Sense of Presence. CyberPsychology & Behavior, 7(6), 734-741. doi:10.1089/cpb.2004.7.734Lange, E. (2001). The limits of realism: perceptions of virtual landscapes. Landscape and Urban Planning, 54(1-4), 163-182. doi:10.1016/s0169-2046(01)00134-7Baños, R. M., Liaño, V., Botella, C., Alcañiz, M., Guerrero, B., & Rey B. Changing induced moods via virtual reality. In: Springer, Berlin H, editor. International Conference on Persuasive Technology. 2006. pp. 7–15. doi: 10.1007/11755494_3Peperkorn, H. M., Alpers, G. W., & Mühlberger, A. (2013). Triggers of Fear: Perceptual Cues Versus Conceptual Information in Spider Phobia. Journal of Clinical Psychology, 70(7), 704-714. doi:10.1002/jclp.22057Meehan, M., Razzaque, S., Insko, B., Whitton, M., & Brooks, F. P. (2005). Review of Four Studies on the Use of Physiological Reaction as a Measure of Presence in StressfulVirtual Environments. Applied Psychophysiology and Biofeedback, 30(3), 239-258. doi:10.1007/s10484-005-6381-3Higuera-Trujillo, J. L., López-Tarruella Maldonado, J., & Llinares Millán, C. (2017). Psychological and physiological human responses to simulated and real environments: A comparison between Photographs, 360° Panoramas, and Virtual Reality. Applied Ergonomics, 65, 398-409. doi:10.1016/j.apergo.2017.05.006Bian, Y., Yang, C., Gao, F., Li, H., Zhou, S., Li, H., … Meng, X. (2016). A framework for physiological indicators of flow in VR games: construction and preliminary evaluation. Personal and Ubiquitous Computing, 20(5), 821-832. doi:10.1007/s00779-016-0953-5Baños, R. M., Etchemendy, E., Castilla, D., García-Palacios, A., Quero, S., & Botella, C. (2012). Positive mood induction procedures for virtual environments designed for elderly people. Interacting with Computers, 24(3), 131-138. doi:10.1016/j.intcom.2012.04.002Riva, G., Mantovani, F., Capideville, C. S., Preziosa, A., Morganti, F., Villani, D., … Alcañiz, M. (2007). Affective Interactions Using Virtual Reality: The Link between Presence and Emotions. CyberPsychology & Behavior, 10(1), 45-56. doi:10.1089/cpb.2006.9993Vecchiato, G., Jelic, A., Tieri, G., Maglione, A. G., De Matteis, F., & Babiloni, F. (2015). Neurophysiological correlates of embodiment and motivational factors during the perception of virtual architectural environments. Cognitive Processing, 16(S1), 425-429. doi:10.1007/s10339-015-0725-6Slater, M., & Wilbur, S. (1997). A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments. Presence: Teleoperators and Virtual Environments, 6(6), 603-616. doi:10.1162/pres.1997.6.6.603Bishop, I. ., & Rohrmann, B. (2003). Subjective responses to simulated and real environments: a comparison. Landscape and Urban Planning, 65(4), 261-277. doi:10.1016/s0169-2046(03)00070-7Kort, Y. A. W. de, IJsselsteijn, W. A., Kooijman, J., & Schuurmans, Y. (2003). Virtual Laboratories: Comparability of Real and Virtual Environments for Environmental Psychology. Presence: Teleoperators and Virtual Environments, 12(4), 360-373. doi:10.1162/105474603322391604Van der Ham, I. J. M., Faber, A. M. E., Venselaar, M., van Kreveld, M. J., & Löffler, M. (2015). Ecological validity of virtual environments to assess human navigation ability. Frontiers in Psychology, 6. doi:10.3389/fpsyg.2015.00637Eberhard, J. P. (2009). Applying Neuroscience to Architecture. Neuron, 62(6), 753-756. doi:10.1016/j.neuron.2009.06.001Nanda, U., Pati, D., Ghamari, H., & Bajema, R. (2013). Lessons from neuroscience: form follows function, emotions follow form. Intelligent Buildings International, 5(sup1), 61-78. doi:10.1080/17508975.2013.807767Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161-1178. doi:10.1037/h0077714Slater, M., Usoh, M., & Steed, A. (1994). Depth of Presence in Virtual Environments. Presence: Teleoperators and Virtual Environments, 3(2), 130-144. doi:10.1162/pres.1994.3.2.130Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9. Journal of General Internal Medicine, 16(9), 606-613. doi:10.1046/j.1525-1497.2001.016009606.xBradley, M. M., & Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49-59. doi:10.1016/0005-7916(94)90063-9Cousineau, D., & Chartier, S. (2010). Outliers detection and treatment: a review. International Journal of Psychological Research, 3(1), 58-67. doi:10.21500/20112084.844Tarvainen, M. P., Ranta-aho, P. O., & Karjalainen, P. A. (2002). An advanced detrending method with application to HRV analysis. IEEE Transactions on Biomedical Engineering, 49(2), 172-175. doi:10.1109/10.979357Tarvainen, M. P., Niskanen, J.-P., Lipponen, J. A., Ranta-aho, P. O., & Karjalainen, P. A. (2014). Kubios HRV – Heart rate variability analysis software. Computer Methods and Programs in Biomedicine, 113(1), 210-220. doi:10.1016/j.cmpb.2013.07.024Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C. M., & Suri, J. S. (2006). Heart rate variability: a review. Medical & Biological Engineering & Computing, 44(12), 1031-1051. doi:10.1007/s11517-006-0119-0Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049. doi:10.1152/ajpheart.2000.278.6.h2039Peng, C. ‐K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: An Interdisciplinary Journal of Nonlinear Science, 5(1), 82-87. doi:10.1063/1.166141Grassberger, P., & Procaccia, I. (1983). Characterization of Strange Attractors. Physical Review Letters, 50(5), 346-349. doi:10.1103/physrevlett.50.346Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21. doi:10.1016/j.jneumeth.2003.10.009Colomer Granero, A., Fuentes-Hurtado, F., Naranjo Ornedo, V., Guixeres Provinciale, J., Ausín, J. M., & Alcañiz Raya, M. (2016). A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents. Frontiers in Computational Neuroscience, 10. doi:10.3389/fncom.2016.00074Kober, S. E., Kurzmann, J., & Neuper, C. (2012). Cortical correlate of spatial presence in 2D and 3D interactive virtual reality: An EEG study. International Journal of Psychophysiology, 83(3), 365-374. doi:10.1016/j.ijpsycho.2011.12.003Hyvärinen, A., & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13(4-5), 411-430. doi:10.1016/s0893-6080(00)00026-5Mormann, F., Lehnertz, K., David, P., & E. Elger, C. (2000). Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D: Nonlinear Phenomena, 144(3-4), 358-369. doi:10.1016/s0167-2789(00)00087-7Schölkopf, B., Smola, A. J., Williamson, R. C., & Bartlett, P. L. (2000). New Support Vector Algorithms. Neural Computation, 12(5), 1207-1245. doi:10.1162/089976600300015565Yan, K., & Zhang, D. (2015). Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical, 212, 353-363. doi:10.1016/j.snb.2015.02.025Chang, C.-C., & Lin, C.-J. (2011). LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27. doi:10.1145/1961189.1961199Gorini, A., Capideville, C. S., De Leo, G., Mantovani, F., & Riva, G. (2011). The Role of Immersion and Narrative in Mediated Presence: The Virtual Hospital Experience. Cyberpsychology, Behavior, and Social Networking, 14(3), 99-105. doi:10.1089/cyber.2010.0100Glass, L. (2001). Synchronization and rhythmic processes in physiology. Nature, 410(6825), 277-284. doi:10.1038/35065745Stam, C. J. (2005). Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field. Clinical Neurophysiology, 116(10), 2266-2301. doi:10.1016/j.clinph.2005.06.011Zhao, Q., Zhang, L., & Cichocki, A. (2009). EEG-based asynchronous BCI control of a car in 3D virtual reality environments. Chinese Science Bulletin, 54(1), 78-87. doi:10.1007/s11434-008-0547-3Baumgartner, T., Valko, L., Esslen, M., & Jäncke, L. (2006). Neural Correlate of Spatial Presence in an Arousing and Noninteractive Virtual Reality: An EEG and Psychophysiology Study. CyberPsychology & Behavior, 9(1), 30-45. doi:10.1089/cpb.2006.9.30Koelstra, S., Muhl, C., Soleymani, M., Jong-Seok Lee, Yazdani, A., Ebrahimi, T., … Patras, I. (2012). DEAP: A Database for Emotion Analysis ;Using Physiological Signals. IEEE Transactions on Affective Computing, 3(1), 18-31. doi:10.1109/t-affc.2011.15Kim, J., & Andre, E. (2008). Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), 2067-2083. doi:10.1109/tpami.2008.26Yuan-Pin Lin, Chi-Hong Wang, Tzyy-Ping Jung, Tien-Lin Wu, Shyh-Kang Jeng, Jeng-Ren Duann, & Jyh-Horng Chen. (2010). EEG-Based Emotion Recognition in Music Listening. IEEE Transactions on Biomedical Engineering, 57(7), 1798-1806. doi:10.1109/tbme.2010.2048568Combrisson, E., & Jerbi, K. (2015). Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. Journal of Neuroscience Methods, 250, 126-136. doi:10.1016/j.jneumeth.2015.01.010De Borst, A. W., & de Gelder, B. (2015). Is it the real deal? Perception of virtual characters versus humans: an affective cognitive neuroscience perspective. Frontiers in Psychology, 6. doi:10.3389/fpsyg.2015.00576Mitchell, R. L. C., & Phillips, L. H. (2015). The overlapping relationship between emotion perception and theory of mind. Neuropsychologia, 70, 1-10. doi:10.1016/j.neuropsychologia.2015.02.018Powers, M. B., & Emmelkamp, P. M. G. (2008). Virtual reality exposure therapy for anxiety disorders: A meta-analysis. Journal of Anxiety Disorders, 22(3), 561-569. doi:10.1016/j.janxdis.2007.04.006Critchley, H. D. (2009). Psychophysiology of neural, cognitive and affective integration: fMRI and autonomic indicants. International Journal of Psychophysiology, 73(2), 88-94. doi:10.1016/j.ijpsycho.2009.01.012Niedenthal, P. M. (2007). Embodying Emotion. Science, 316(5827), 1002-1005. doi:10.1126/science.1136930Leer, A., Engelhard, I. M., & van den Hout, M. A. (2014). How eye movements in EMDR work: Changes in memory vividness and emotionality. Journal of Behavior Therapy and Experimental Psychiatry, 45(3), 396-401. doi:10.1016/j.jbtep.2014.04.004Gentili, C. (2017). Why do we keep failing in identifying reliable biological markers in depression? Journal of Evidence-Based Psychotherapies, 17(2), 59-84. doi:10.24193/jebp.2017.2.4Debener, S., Minow, F., Emkes, R., Gandras, K., & de Vos, M. (2012). How about taking a low-cost, small, and wireless EEG for a walk? Psychophysiology, 49(11), 1617-1621. doi:10.1111/j.1469-8986.2012.01471.

A protocol for a multicentre, parallel-group, pragmatic randomised controlled trial to evaluate the NEVERMIND system in preventing and treating depression in patients with severe somatic conditions

Background Depressive symptoms are common in individuals suffering from severe somatic conditions. There is a lack of interventions and evidence-based interventions aiming to reduce depressive symptoms in patients with severe somatic conditions. The aim of the NEVERMIND project is to address these issues and provide evidence by testing the NEVERMIND system, designed to reduce and prevent depressive symptoms in comparison to treatment as usual. Methods The NEVERMIND study is a parallel-groups, pragmatic randomised controlled trial to assess the effectiveness of the NEVERMIND system in reducing depressive symptoms among individuals with severe somatic conditions. The NEVERMIND system comprises a smart shirt and a user interface, in the form of a mobile application. The system is a real-time decision support system, aiming to predict the severity and onset of depressive symptoms by modelling the well-being condition of patients based on physiological data, body movement, and the recurrence of social interactions. The study includes 330 patients who have a diagnosis of myocardial infarction, breast cancer, prostate cancer, kidney failure, or lower limb amputation. Participants are randomised in blocks of ten to either the NEVERMIND intervention or treatment as usual as the control group. Clinical interviews and structured questionnaires are administered at baseline, at 12 weeks, and 24 weeks to assess whether the NEVERMIND system is superior to treatment as usual. The endpoint of primary interest is Beck Depression Inventory II (BDI-II) at 12 weeks defined as (i) the severity of depressive symptoms as measured by the BDI-II. Secondary outcomes include prevention of the onset of depressive symptoms, changes in quality of life, perceived stigma, and self-efficacy. Discussion There is a lack of evidence-based interventions aiming to reduce and prevent depressive symptoms in patients with severe somatic conditions. If the NEVERMIND system is effective, it will provide healthcare systems with a novel and innovative method to attend to depressive symptoms in patients with severe somatic conditions. Trial registration DRKS00013391. Registered 23 November 2017

The NEVERMIND e-health system in the treatment of depressive symptoms among patients with severe somatic conditions: A multicentre, pragmatic randomised controlled trial

Background This study assessed the effectiveness of the NEVERMIND e-health system, consisting of a smart shirt and a mobile application with lifestyle behavioural advice, mindfulness-based therapy, and cognitive behavioural therapy, in reducing depressive symptoms among patients diagnosed with severe somatic conditions. Our hypothesis was that the system would significantly decrease the level of depressive symptoms in the intervention group compared to the control group. Methods This pragmatic, randomised controlled trial included 425 patients diagnosed with myocardial infarction, breast cancer, prostate cancer, kidney failure, or lower limb amputation. Participants were recruited from hospitals in Turin and Pisa (Italy), and Lisbon (Portugal), and were randomly assigned to either the NEVERMIND intervention or to the control group. Clinical interviews and structured questionnaires were administered at baseline, 12 weeks, and 24 weeks. The primary outcome was depressive symptoms at 12 weeks measured by the Beck Depression Inventory II (BDI-II). Intention-to-treat analyses included 425 participants, while the per-protocol analyses included 333 participants. This trial is registered in the German Clinical Trials Register, DRKS00013391. Findings Patients were recruited between Dec 4, 2017, and Dec 31, 2019, with 213 assigned to the intervention and 212 to the control group. The sample had a mean age of 59·41 years (SD=10·70), with 44·24% women. Those who used the NEVERMIND system had statistically significant lower depressive symptoms at the 12-week follow-up (mean difference=-3·03, p<0·001; 95% CI -4·45 to -1·62) compared with controls, with a clinically relevant effect size (Cohen's d=0·39). Interpretation The results of this study show that the NEVERMIND system is superior to standard care in reducing and preventing depressive symptoms among patients with the studied somatic conditions. Funding The NEVERMIND project received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 689691