Behnke, M. et al. CEPAV Dataset – Competitive Esports: Physiological, Affective, and Video Dataset. Open Sci. Framework https://doi.org/10.17605/OSF.IO/KGDSX (2024).
Sharpe, B. T. et al Reappraisal and mindset interventions on pressurised esport performance. Appl. Psychol. 1–22 (2024).
Sharpe, B. T., Obine, E. A., Birch, P. D., Pocock, C. & Moore, L. J. Performance breakdown under pressure among esports competitors. Sport Exerc. Perform. Psychol. 13, 89–109 (2024).
Behnke, M., Gross, J. J. & Kaczmarek, L. D. The role of emotions in esports performance. Emotion 22, 1059–1070 (2022).
Behnke, M., Kosakowski, M. & Kaczmarek, L. D. Social challenge and threat predict performance and cardiovascular responses during competitive video gaming. Psychol. Sport Exerc. 46, 101584 (2020).
Behnke, M. et al. Applying a synergistic mindsets intervention to an esports context. R. Soc. Open Sci. 11, 240691 (2024).
Yeager, D. S. et al. A synergistic mindsets intervention protects adolescents from stress. Nature 607, 512–520 (2022).
Huang, W., Liu, G. & Wen, W. MAPD: A Multi-subject Affective Physiological Database. In Computational Intelligence and Design (2014).
Kutt, K. et al. BIRAFFE2: A multimodal dataset for emotion-based personalization in rich affective game environments. Sci. Data 9, 274 (2022).
Miranda-Correa, J. A., Abadi, M. K., Sebe, N. & Patras, I. Amigos: A dataset for affect personality and mood research on individuals and groups. IEEE Trans. Affective Comput. 12, 479–493 (2018).
Subramanian, R. et al. ASCERTAIN: Emotion and personality recognition using commercial sensors. IEEE Trans. Affective Comput. 9, 147–160 (2016).
Abadi, M. K. et al. DECAF: MEG-based multimodal database for decoding affective physiological responses. IEEE Trans. Affective Comput. 6, 209–222 (2015).
Kang, S. et al. K-EmoPhone: A mobile and wearable dataset with in-situ emotion, stress, and attention labels. Sci. Data 10, 351 (2023).
Ranganathan, H., Chakraborty, S. & Panchanathan, S. Multimodal emotion recognition using deep learning architectures. In Winter Conf. Appl. Comput. Vision (2016).
Shui, X. et al. A dataset of daily ambulatory psychological and physiological recording for emotion research. Sci. Data 8, 161 (2021).
Smerdov, A. et al. Collection and validation of psychophysiological data from professional and amateur players: A multimodal esports dataset. arXiv preprint arXiv:2011.00958 (2020).
Song, T. et al. MPED: A multi-modal physiological emotion database for discrete emotion recognition. IEEE Access 7, 12177–12191 (2019).
Behnke, M., Buchwald, M., Bykowski, A., Kupiński, S. & Kaczmarek, L. D. Psychophysiology of positive and negative emotions dataset of 1157 cases and 8 biosignals. Sci. Data 9, 10 (2022).
Melhart, D., Liapis, A. & Yannakakis, G. N. The arousal video game annotation (AGAIN) dataset. IEEE Trans. Affective Comput. 13, 2171–2184 (2022).
Siqueira, E. S. et al. An automated approach to estimate player experience in game events from psychophysiological data. Multimedia Tools Appl. 82, 19189–19220 (2023).
Yang, W., Rifqi, M., Marsala, C. & Pinna, A. Physiological-based emotion detection and recognition in a video game context. In Int. Joint Conf. Neural Networks (IJCNN) 1–8 (2018).
Behnke, M. et al. Supplementary materials for applying a synergistic mindsets intervention to an esports context. Open Sci. Framework https://osf.io/62yge (2024).
O’Brien, S. T. et al. SEMA3: A free smartphone platform for daily life surveys. Behav. Res. Methods 1–16 (2024).
Behnke, M. et al. CEPAV Dataset, Processed Data Component. Open Sci. Framework https://doi.org/10.17605/OSF.IO/NBYV4 (2024).
Bailey, H. Open Broadcasting Software. Retrieved from https://obsproject.com/ (2018).
Sherwood, A. et al. Methodological guidelines for impedance cardiography. Psychophysiology 27, 1–23 (1990).
van Lien, R., Neijts, M., Willemsen, G. & de Geus, E. J. Ambulatory measurement of the ECG T‐wave amplitude. Psychophysiology 52, 225–237 (2015).
McKinney, W. Data structures for statistical computing in Python. Proc. 9th Python Sci. Conf. 56–61 (2010).
Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Behnke, M. et al. CEPAV Dataset, Code Component. Open Science Framework https://doi.org/10.17605/OSF.IO/GFZ3M (2024).
Behnke, M. et al. CEPAV Dataset, Videos Component. Open Science Framework https://doi.org/10.17605/OSF.IO/QKD5B (2024).
Behnke, M. et al. CEPAV Dataset, Raw_Physio Component. Open Science Framework https://doi.org/10.17605/OSF.IO/HKDUY (2024).
Levenson, R. W. The autonomic nervous system and emotion. Emotion Rev. 6, 100–112 (2014).
Thong, J. T. L., Sim, K. S. & Phang, J. C. H. Single‐image signal‐to‐noise ratio estimation. Scanning 23, 328–336 (2001).
Sijbers, J., Scheunders, P., Bonnet, N., Van Dyck, D. & Raman, E. Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure. Magn. Reson. Imaging 14, 1157–1163 (1996).
Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).
Leys, C., Delacre, M., Mora, Y. L., Lakens, D. & Ley, C. How to classify, detect, and manage univariate and multivariate outliers, with emphasis on pre-registration. Int. Rev. Soc. Psychol. 32, (2019).
Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: A meta-analysis of individual data for one million adults in 61 prospective studies. The Lancet 360, 1903–1913 (2002).
Epel, E. S. et al. More than a feeling: A unified view of stress measurement for population science. Front. Neuroendocrinol. 49, 146–169 (2018).
Hughes, B. M., Lü, W. & Howard, S. Cardiovascular stress-response adaptation: Conceptual basis, empirical findings, and implications for disease processes. Int. J. Psychophysiol. 131, 4–12 (2018).
Van Rossum, G. & Drake, F. L. Jr Python tutorial (Vol. 620). Centrum voor Wiskunde en Informatica. Available at: https://scicomp.ethz.ch/public/manual/Python/3.9.9/tutorial.pdf (1995).
Kluyver, T. et al. Jupyter Notebooks—a publishing format for reproducible computational workflows. Positioning and Power in Academic Publishing: Players, Agents and Agendas – Proc. 20th Int. Conf. Electron. Publ. (eds. Loizides, F. & Schmidt, B.) (IOS Press, 2016).
Fredrickson, B. L. Positive emotions broaden and build. Adv. Exp. Soc. Psychol. 47, 1–53 (2013).
Medland, H., De France, K., Hollenstein, T., Mussoff, D. & Koval, P. Regulating emotion systems in everyday life: Reliability and validity of the RESS-EMA scale. Eur. J. Psychol. Assess. 36, 437 (2020).
Dweck, C. S. Mindset: The New Psychology of Success. Random House (2006).
Kanafa-Chmielewska, D. & Bartosz, B. Poczucie kontroli w sferze osobistej, interpersonalnej i socjopolitycznej, nastawienie na stałość lub zmienność a oceny na studiach. Pol. J. Appl. Psychol. 16, (2018).
Crum, A. J., Salovey, P. & Achor, S. Rethinking stress: the role of mindsets in determining the stress response. J. Pers. Soc. Psychol. 104, 716 (2013).
Mierzejewska-Floreani, D., Banaszkiewicz, M. & Gruszczyńska, E. Psychometric properties of the Stress Mindset Measure (SMM) in the Polish population. PLoS One 17, e0264853 (2022).
Robins, R. W., Hendin, H. M. & Trzesniewski, K. H. Measuring global self-esteem: Construct validation of a single-item measure and the Rosenberg Self-Esteem Scale. Pers. Soc. Psychol. Bull. 27, 151–161 (2001).
Shields, S. A., Mallory, M. E. & Simon, A. The body awareness questionnaire: reliability and validity. J. Pers. Assess. 53, 802–815 (1989).
Brytek-Matera, A. & Kozieł, A. The body self-awareness among women practicing fitness: a preliminary study. Pol. Psychol. Bull. 46, 104–111 (2015).
Kjell, O. N. & Diener, E. Abbreviated three-item versions of the Satisfaction with Life Scale and the Harmony in Life Scale yield as strong psychometric properties as the original scales. J. Pers. Assess. 103, 183–194 (2021).
Diener, E., Emmons, R. A., Larsen, R. J. & Griffin, S. The Satisfaction with Life Scale. J. Pers. Assess. 49, 71–75 (1985).
Jankowski, K. S. Is the shift in chronotype associated with an alteration in well-being? Biol. Rhythm Res. 46, 237–248 (2015).
Diener, E. et al. New measures of well-being: Flourishing and positive and negative feelings. Soc. Indic. Res. 39, 247–266 (2009).
Kaczmarek, Ł. D. Pozytywne interwencje psychologiczne. Zachowania intencjonalne a dobrostan. Zysk i Ska Wydawnictwo (2016).
Ware, J. E. Jr. & Sherbourne, C. D. The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Med. Care 30, 473–483 (1992).
Kroenke, K., Spitzer, R. L. & Williams, J. B. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med. Care 41, 1284–1292 (2003).
Kroenke, K., Spitzer, R. L., Williams, J. B., Monahan, P. O. & Löwe, B. Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection. Ann. Intern. Med. 146, 317–325 (2007).
Pontes, H. M. et al. Measurement and conceptualisation of Gaming Disorder according to the World Health Organization framework: The development of the Gaming Disorder Test. Int. J. Ment. Health Addict. 19, 508–528 (2021).
Preece, D. A. et al. The Perth Alexithymia Questionnaire-Short form (PAQ-S): A 6-item measure of alexithymia. J. Affect. Disord. 325, 493–501 (2023).
Becerra, R., Preece, D. A. & Gross, J. J. Assessing beliefs about emotions: Development and validation of the Emotion Beliefs Questionnaire. PLoS One 15, e0231395 (2020).
Uusberg, A. et al. Appraisal shifts during reappraisal. Emotion 23, 1985–2001 (2023).
Moore, L. J., Vine, S. J., Wilson, M. R. & Freeman, P. The effect of challenge and threat states on performance: An examination of potential mechanisms. Psychophysiology 49, 1417–1425 (2012).
Moore, L. J., Wilson, M. R., Vine, S. J., Coussens, A. H. & Freeman, P. Champ or chump?: Challenge and threat states during pressurized competition. J. Sport Exerc. Psychol. 35, 551–562 (2013).
Moore, L. J., Vine, S. J., Wilson, M. R. & Freeman, P. Reappraising threat: How to optimize performance under pressure. J. Sport Exerc. Psychol. 37, 339–343 (2015).
Tomaka, J., Blascovich, J., Kelsey, R. M. & Leitten, C. L. Subjective, physiological, and behavioral effects of threat and challenge appraisal. J. Pers. Soc. Psychol. 65, 248–260 (1993).
Koelstra, S. et al. Deap: A database for emotion analysis; using physiological signals. IEEE Trans. Affective Comput. 3, 18–31 (2011).
Soleymani, M., Lichtenauer, J., Pun, T. & Pantic, M. A multimodal database for affect recognition and implicit tagging. IEEE Trans. Affective Comput. 3, 42–55 (2011).
Ringeval, F., Sonderegger, A., Sauer, J. & Lalanne, D. Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions. In Face Gesture Recognit. (2013).
Eysenck, H. J. & Eysenck, S. B. G. Manual of the Eysenck Personality Questionnaire (Junior & Adult). Hodder and Stoughton Educational (1975).
Koldijk, S., Sappelli, M., Verberne, S., Neerincx, M. A. & Kraaij, W. The swell knowledge work dataset for stress and user modeling research. In Multimodal Interaction (2014).
Zhang, Z. et al. Multimodal spontaneous emotion corpus for human behavior analysis. In IEEE Conf. Comput. Vision Pattern Recognit. 3438–3446 (2016).
Zhang, L. et al. “BioVid Emo DB”: A multimodal database for emotion analyses validated by subjective ratings. In IEEE Symp. Series Comput. Intelligence (SSCI) 1–6 (2016).
Perugini, M. & Di Blas, L. Analyzing personality-related adjectives from an etic-emic perspective: The Big Five Marker Scale (BFMS) and the Italian AB5C taxonomy. In Big Five Assessment (Hogrefe & Huber, 2002).
Hsu, Y. L., Wang, J. S., Chiang, W. C. & Hung, C. H. Automatic ECG-based emotion recognition in music listening. IEEE Trans. Affective Comput. 11, 85–99 (2017).
Katsigiannis, S. & Ramzan, N. DREAMER: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J. Biomed. Health Inform. 22, 98–107 (2017).
Quiroz, J. C., Geangu, E. & Yong, M. H. Emotion recognition using smart watch sensor data: Mixed-design study. JMIR Ment. Health 5, e10153 (2018).
Schmidt, P., Reiss, A., Duerichen, R., Marberger, C. & Van Laerhoven, K. Introducing wesad: A multimodal dataset for wearable stress and affect detection. In Multimodal Interaction (2018).
Markova, V., Ganchev, T. & Kalinkov, K. Clas: A database for cognitive load, affect, and stress recognition. In Biomed. Innov. Appl. (2019).
Sharma, K. et al. A dataset of continuous affect annotations and physiological signals for emotion analysis. Sci. Data 6, 196 (2019).
Park, C. Y. et al. K-EmoCon: A multimodal sensor dataset for continuous emotion recognition in naturalistic conversations. Sci. Data 7, 293 (2020).
Benet-Martínez, V. & John, O. P. Los Cinco Grandes across cultures and ethnic groups: Multitrait-multimethod analyses of the Big Five in Spanish and English. J. Pers. Soc. Psychol. 75, 729–750 (1998).
John, O. P., Donahue, E. M. & Kentle, R. L. The Big Five Inventory – Versions 4a and 54. (University of California, Berkeley, Institute of Personality and Social Research, 1991).
Wang, Z. et al. Reliability and validity of the Chinese version of Beck Depression Inventory-II among depression patients. Chin. Ment. Health J. 25, 476–480 (2011).
Rosenberg, M. Society and the Adolescent Self-Image. Princeton University Press (1965).
Christy, A. G., Schlegel, R. J. & Cimpian, A. Why do people believe in a “true self”? The role of essentialist reasoning about personal identity and the self. J. Pers. Soc. Psychol. 117, 386–416 (2019).
Singelis, T. M. The measurement of independent and interdependent self-construals. Pers. Soc. Psychol. Bull. 20, 580–591 (1994).
Steger, M. F., Frazier, P., Oishi, S. & Kaler, M. The meaning in life questionnaire. J. Couns. Psychol. 53, 80–93 (2015).
Gosling, S. D., Rentfrow, P. J. & Swann, W. B. Jr. A very brief measure of the Big-Five personality domains. J. Res. Pers. 37, 504–528 (2003).
Li, J. Psychometric properties of Ten-Item Personality Inventory in China. Chin. J. Health Psychol. 21, 1688–1692 (2013).
Gao, Z., Cui, X., Wan, W., Zheng, W. & Gu, Z. ECSMP: A dataset on emotion cognition, sleep, and multi-model physiological signals. Data Brief 39, 107660 (2021).
Gross, J. J. & John, O. P. Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. J. Pers. Soc. Psychol. 85, 348–362 (2003).
William, W. K. Zung self-rating depression scale. Encycl. Qual. Life Well Being Res 7317 (2014).
Beili, Z. Introduction to the POMS scale and the short-form Chinese norm. J. Tianjin Sports Inst. 35–37 (1995).
Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R. & Kupfer, D. J. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Res. 28, 193–213 (1989).
Sabour, R. M. et al. Ubfc-phys: A multimodal database for psychophysiological studies of social stress. IEEE Trans. Affective Comput. (2021).
Raheel, A., Majid, M. & Anwar, S. M. DEAR-MULSEMEDIA: Dataset for emotion analysis and recognition in response to multiple sensorial media. Inf. Fusion 65, 37–49 (2021).
Costa, P. & McCrae, R. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five Factor Inventory (NEO-FFI). Professional manual. Psychological Assessment Resources (1992).
IJsselsteijn, W. A., de Kort, Y. A. W. & Poels, K. The Game Experience Questionnaire. Technische Universiteit Eindhoven (2013).
Saganowski, S. et al. Emognition dataset: Emotion recognition with self-reports, facial expressions, and physiology using wearables. Sci. Data 9, 158 (2022).
Kroenke, K., Spitzer, R. L. & Williams, J. B. W. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613 (2001).
Goldberg, D. P. & Hillier, V. F. A scaled version of the general health questionnaire. Psychol. Med. 9, 139–145 (1979).
Ab. Aziz, N. A. K. T. et al. Asian affective and emotional state (A2ES) dataset of ECG and PPG for affective computing research. Algorithms 16, 130 (2023).
Cohn, M. A., Fredrickson, B. L., Brown, S. L., Mikels, J. A. & Conway, A. M. Happiness unpacked: Positive emotions increase life satisfaction by building resilience. Emotion 9, 361–368 (2009).
Fredrickson, B. L. et al. Positive emotion correlates of meditation practice: A comparison of mindfulness meditation and loving-kindness meditation. Mindfulness 8, 1623–1633 (2017).
Wang, K. et al. A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic. Nat. Hum. Behav. 5, 1089–1110 (2021).
De France, K. & Hollenstein, T. Assessing emotion regulation repertoires: The regulation of emotion systems survey. Pers. Individ. Differ. 119, 204–215 (2017).
De France, K. & Hollenstein, T. Emotion regulation and relations to well-being across the lifespan. Dev. Psychol. 55, 1768–1778 (2019).
Wylie, M. S. et al. Momentary emotion regulation strategy use and success: Testing the influences of emotion intensity and habitual strategy use. Emotion 22, 83–95 (2022).
Crum, A. J., Akinola, M., Martin, A. & Fath, S. The role of stress mindset in shaping cognitive, emotional, and physiological responses to challenging and threatening stress. Anxiety Stress Coping 30, 379–395 (2017).
Klussman, K., Lindeman, M. I. H., Nichols, A. L. & Langer, J. Fostering stress resilience among business students: The role of stress mindset and self-connection. Psychol. Rep. 124, 1462–1480 (2021).
Haimovitz, K. & Dweck, C. S. What predicts children’s fixed and growth intelligence mind-sets? Not their parents’ views of intelligence but their parents’ views of failure. Psychol. Sci. 27, 859–869 (2016).
Marengo, D., Montag, C., Sindermann, C., Elhai, J. D. & Settanni, M. Examining the links between active Facebook use, received likes, self-esteem and happiness: A study using objective social media data. Telemat. Inform. 58, 101523 (2021).
Panzeri, A. et al. Factors impacting resilience as a result of exposure to COVID-19: The ecological resilience model. PLoS One 16, e0256041 (2021).
Rodgers, R. F. et al. A biopsychosocial model of social media use and body image concerns, disordered eating, and muscle-building behaviors among adolescent girls and boys. J. Youth Adolesc. 49, 399–409 (2020).
Crucianelli, L., Enmalm, A. & Ehrsson, H. H. Interoception as independent cardiac, thermosensory, nociceptive, and affective touch perceptual submodalities. Biol. Psychol. 172, 108355 (2022).
Zamariola, G. et al. Relationship between interoceptive accuracy, interoceptive sensibility, and alexithymia. Pers. Individ. Differ. 125, 14–20 (2018).
Lawes, M., Hetschko, C., Schöb, R., Stephan, G. & Eid, M. The impact of unemployment on cognitive, affective, and eudaimonic well-being facets: Investigating immediate effects and short-term adaptation. J. Pers. Soc. Psychol. 124, 659–681 (2023).
Majka, E. A., Guenther, M. F. & Raimondi, S. L. Science bootcamp goes virtual: A compressed, interdisciplinary online CURE promotes psychosocial gains in STEM transfer students. J. Microbiol. Biol. Educ. 22, 10–1128 (2021).
Nilsson, A. H., Hellryd, E. & Kjell, O. Doing well-being: Self-reported activities are related to subjective well-being. PLoS One 17, e0270503 (2022).
Lambert, L., Passmore, H. A. & Joshanloo, M. A positive psychology intervention program in a culturally-diverse university: Boosting happiness and reducing fear. J. Happiness Stud. 20, 1141–1162 (2019).
Kluck, J. P., Stoyanova, F. & Krämer, N. C. Putting the social back into physical distancing: The role of digital connections in a pandemic crisis. Int. J. Psychol. 56, 594–606 (2021).
Roksa, J. & Kinsley, P. The role of family support in facilitating academic success of low-income students. Res. High. Educ. 60, 415–436 (2019).
Carstensen, L. L., Shavit, Y. Z. & Barnes, J. T. Age advantages in emotional experience persist even under threat from the COVID-19 pandemic. Psychol. Sci. 31, 1374–1385 (2020).
Fingerman, K. L. et al. Living alone during COVID-19: Social contact and emotional well-being among older adults. J. Gerontol. B 76, e116–e121 (2021).
Sibley, C. G. et al. Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes toward government, and well-being. Am. Psychol. 75, 618–630 (2020).
MAPI Research Institute. Patient Health Questionnaire (PHQ). Available at: https://eprovide.mapi-trust.org/instruments/patient-health-questionnaire (accessed July 2023).
Daly, M., Sutin, A. R. & Robinson, E. Depression reported by US adults in 2017–2018 and March and April 2020. J. Affect. Disord. 278, 131–135 (2021).
Gualano, M. R., Lo Moro, G., Voglino, G., Bert, F. & Siliquini, R. Effects of Covid-19 lockdown on mental health and sleep disturbances in Italy. Int. J. Environ. Res. Public Health 17, 4779 (2020).
Matar Boumosleh, J. & Jaalouk, D. Depression, anxiety, and smartphone addiction in university students—A cross-sectional study. PLoS One 12, e0182239 (2017).
MAPI Research Institute. Generalized Anxiety Disorder – 7 (GAD-7). Available at: https://eprovide.mapi-trust.org/instruments/generalized-anxiety-disorder-7 (accessed July 2023).
Staples, L. G. et al. Psychometric properties and clinical utility of brief measures of depression, anxiety, and general distress: The PHQ-2, GAD-2, and K-6. Gen. Hosp. Psychiatry 56, 13–18 (2019).
Cudo, A., Montag, C. & Pontes, H. M. Psychometric assessment and gender invariance of the Polish version of the Gaming Disorder Test. Int. J. Ment. Health Addict. 1–24 (2022).
Becerra, R., Gainey, K., Murray, K. & Preece, D. A. Intolerance of uncertainty and anxiety: The role of beliefs about emotions. J. Affect. Disord. 324, 349–353 (2023).
Monsoon, A. D., Preece, D. A. & Becerra, R. Control and acceptance beliefs about emotions: Associations with psychological distress and the mediating role of emotion regulation flexibility. Aust. Psychol. 57, 236–248 (2022).
Preece, D. A. et al. Emotion generation and emotion regulation: The role of emotion beliefs. J. Affect. Disord. Rep. 9, 100351 (2022).
Schleider, J. L. et al. Acceptability and utility of an open-access, online single-session intervention platform for adolescent mental health. JMIR Ment. Health 7, e20513 (2020).
Dobias, M. L., Schleider, J. L., Jans, L. & Fox, K. R. An online, single-session intervention for adolescent self-injurious thoughts and behaviors: Results from a randomized trial. Behav. Res. Ther. 147, 103983 (2021).
Schleider, J. L., Mullarkey, M. C. & Weisz, J. R. Virtual reality and web-based growth mindset interventions for adolescent depression: Protocol for a three-arm randomized trial. JMIR Res. Protoc. 8, e13368 (2019).
Abbey, J. D. & Meloy, M. G. Attention bydesign: Using attention checks to detect inattentive respondentsand improve data quality. J. Oper. Manag. 53, 63–70 (2017).
Meade, A. W. & Craig, S. B. Identifying carelessresponses in survey data. Psychol. Methods 17, 437–455 (2012).