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Self-determination theory (SDT) has been widely applied to understand human motivation and behavior. According to SDT, intrinsic motivation is driven by internal rewards, such as enjoyment and personal satisfaction [a] . In contrast, extrinsic motivation relies on external rewards or pressures, such as money or social approval [b] . Research has also shown that autonomy, competence, and relatedness are key psychological needs that foster intrinsic motivation and well-being [c] . Finally, interventions that promote autonomy-supportive environments, such as in education or workplaces, have been found to enhance motivation and performance [d] . These findings highlight the importance of understanding the underlying mechanisms of motivation in various contexts.

Coloque a citação às seguintes referências no local indicado com [a] 

Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Press.

Coloque a citação às seguintes referências no local indicado com [b] 

Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01

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Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology, 49(3), 182–185. https://doi.org/10.1037/a0012801

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Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01

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Cognitive behavioral therapy (CBT) has emerged as one of the most effective psychological interventions for a wide range of mental health conditions. For example, CBT has been shown to significantly reduce symptoms of depression and anxiety in both clinical and non-clinical populations [a] . Additionally, mindfulness-based interventions, such as mindfulness-based stress reduction (MBSR), have demonstrated efficacy in improving emotional regulation and reducing stress levels [b] . In the context of trauma, eye movement desensitization and reprocessing (EMDR) has been widely used to alleviate symptoms of post-traumatic stress disorder [c] . Furthermore, positive psychology interventions, such as gratitude journaling and strengths-based approaches, have been linked to increased well-being and life satisfaction [d] . Digital mental health tools, including smartphone apps and online therapy platforms, have also gained popularity for their accessibility and effectiveness in delivering evidence-based interventions [e] . Finally, research on the role of neuroplasticity in psychotherapy has highlighted the potential for psychological interventions to induce lasting changes in brain structure and function [f] . These advancements underscore the diverse and evolving nature of psychological interventions.

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Hofmann, S. G., Asnaani, A., Vonk, I. J. J., Sawyer, A. T., & Fang, A. (2012). The efficacy of cognitive behavioral therapy: A review of meta-analyses. Cognitive Therapy and Research, 36(5), 427–440. https://doi.org/10.1007/s10608-012-9476-1

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Kabat-Zinn, J. (2003). Mindfulness-based interventions in context: Past, present, and future. Clinical Psychology: Science and Practice, 10(2), 144–156. https://doi.org/10.1093/clipsy.bpg016

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Shapiro, F. (2014). The role of eye movement desensitization and reprocessing (EMDR) therapy in medicine: Addressing the psychological and physical symptoms stemming from adverse life experiences. Permanente Journal, 18(1), 71–77. https://doi.org/10.7812/TPP/13-098

Coloque a citação às seguintes referências no local indicado com [d] 

Seligman, M. E. P., Steen, T. A., Park, N., & Peterson, C. (2005). Positive psychology progress: Empirical validation of interventions. American Psychologist, 60(5), 410–421. https://doi.org/10.1037/0003-066X.60.5.410

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Andersson, G., & Cuijpers, P. (2009). Internet-based and other computerized psychological treatments for adult depression: A meta-analysis. Cognitive Behaviour Therapy, 38(4), 196–205. https://doi.org/10.1080/16506070903318960

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Kandel, E. R. (1998). A new intellectual framework for psychiatry. American Journal of Psychiatry, 155(4), 457–469. https://doi.org/10.1176/ajp.155.4.457

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The role of social support in mental health has been extensively studied, with research highlighting its protective effects against stress and psychological distress. For example, perceived social support has been shown to buffer the negative effects of stress on mental health outcomes [a] . Additionally, the quality of social relationships, such as close friendships and family ties, has been linked to greater resilience and well-being [b] . In contrast, social isolation and loneliness have been associated with increased risk for depression and anxiety [c] . Finally, interventions aimed at enhancing social connectedness, such as group therapy and community programs, have demonstrated positive effects on mental health [d] . These findings underscore the importance of social factors in promoting psychological well-being.

Coloque a citação às seguintes referências no local indicado com [a] 

Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2), 310–357. https://doi.org/10.1037/0033-2909.98.2.310

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Cohen, S. (2004). Social relationships and health. American Psychologist, 59(8), 676–684. https://doi.org/10.1037/0003-066X.59.8.676

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Cacioppo, J. T., & Hawkley, L. C. (2009). Perceived social isolation and cognition. Trends in Cognitive Sciences, 13(10), 447–454. https://doi.org/10.1016/j.tics.2009.06.005

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Cohen, S., & Wills, T. A. (1985). Social relationships. American Psychologist, 59(8), 676–684. https://doi.org/10.1037/0003-066X.59.8.676

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The rapid growth of digital health technologies has transformed healthcare delivery across multiple domains. For example, telemedicine platforms have expanded access to care, particularly in rural and underserved areas [a] . Mobile health (mHealth) applications have been widely adopted for self-management of chronic conditions, such as hypertension and diabetes [b] . Additionally, machine learning algorithms have been applied to predict hospital readmissions by analyzing large datasets of patient records [c] . In the field of mental health, digital interventions, such as cognitive behavioral therapy (CBT) apps, have shown efficacy in reducing symptoms of depression and anxiety [d] . Wearable devices have also gained popularity for monitoring physical activity and sleep quality, providing actionable insights for improving overall health [e] . Finally, blockchain technology has been explored for enhancing the security and interoperability of electronic health records [f] . These innovations underscore the potential of digital health to address longstanding challenges in healthcare.

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Bashshur, R. L., Shannon, G. W., Krupinski, E. A., Grigsby, J., Kvedar, J. C., Weinstein, R. S., Sanders, J. H., Rheuban, K. S., Nesbitt, T. S., Alverson, D. C., Merrell, R. C., Linkous, J. D., Ferguson, A. S., Waters, R. J., Stachura, M. E., Ellis, D. G., Antoniotti, N. M., Johnston, B., Doarn, C. R., … Yellowlees, P. (2016). The empirical foundations of telemedicine interventions for chronic disease management. Telemedicine and e-Health, 22(5), 342–375. https://doi.org/10.1089/tmj.2015.0206

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Arnett, D. K., Blumenthal, R. S., Albert, M. A., Buroker, A. B., Goldberger, Z. D., Hahn, E. J., Himmelfarb, C. D., Khera, A., Lloyd-Jones, D., McEvoy, J. W., Michos, E. D., Miedema, M. D., Muñoz, D., Smith, S. C., Virani, S. S., Williams, K. A., Yeboah, J., & Ziaeian, B. (2019). 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation, 140(11), e596–e646. https://doi.org/10.1161/CIR.0000000000000678

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Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., & Litsch, K. (2018). Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1(1), 18. https://doi.org/10.1038/s41746-018-0029-1

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Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., & Sarris, J. (2017). The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry, 16(3), 287–298. https://doi.org/10.1002/wps.20472

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Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12(1), 159. https://doi.org/10.1186/s12966-015-0314-1

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Kuo, T.-T., Kim, H.-E., & Ohno-Machado, L. (2017). Blockchain distributed ledger technologies for biomedical and health care applications. Journal of the American Medical Informatics Association, 24(6), 1211–1220. https://doi.org/10.1093/jamia/ocx068

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Recent advancements in wearable technology have enabled continuous monitoring of physiological and behavioral metrics, such as heart rate variability (HRV; [a] , sleep patterns [b] , and physical activity levels [c] . These metrics have been shown to provide valuable insights into an individual’s overall health and well-being, particularly in the context of chronic disease management and mental health monitoring. For example, HRV has been linked to stress and anxiety levels, while sleep patterns are often disrupted in individuals with depression. Physical activity levels, on the other hand, are strongly associated with cardiovascular health and metabolic outcomes.

Coloque a citação às seguintes referências no local indicado com [a] 

Shaffer, F., & Ginsberg, J. P. (2017). An Overview of Heart Rate Variability Metrics and Norms. Frontiers in Public Health, 5, 258. https://doi.org/10.3389/fpubh.2017.00258

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Bei, B., Wiley, J. F., Trinder, J., & Manber, R. (2016). Beyond the Mean: A Systematic Review on the Correlates of Daily Intraindividual Variability of Sleep/Wake Patterns. Sleep Medicine Reviews, 28, 108–124. https://doi.org/10.1016/j.smrv.2015.06.003

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Troiano, R. P., Berrigan, D., Dodd, K. W., Mâsse, L. C., Tilert, T., & McDowell, M. (2008). Physical Activity in the United States Measured by Accelerometer. Medicine & Science in Sports & Exercise, 40(1), 181–188. https://doi.org/10.1249/mss.0b013e31815a51b3

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Major depressive disorder (MDD), the leading cause of disability worldwide [a] , is characterized by major emotional difficulties [b] that can manifest inmaladaptive patterns of emotion regulation, such as difficulties downregulating emotional arousal. Even though there are effective treatments for MDD, studies suggest that these only work for about 50% of patients [c] .

1. Coloque a citação às seguintes referências no local indicado com [a] 

Friedrich, M. J. (2017). Depression is the leading cause of disability around the world. JAMA, 317(15), Article 1517. https://doi.org/10.1001/jama .2017.3826

2. Coloque a citação às seguintes referências no local indicado com [b] 

Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2010). Emotion- regulation strategies across psychopathology: A meta-analytic review. Clinical Psychology Review, 30(2), 217–237. https://doi.org/10.1016/j.cpr .2009.11.004

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Cuijpers, P., Karyotaki, E., Weitz, E., Andersson, G., Hollon, S. D., & van Straten, A. (2014). The effects of psychotherapies for major depression in adults on remission, recovery and improvement: A meta-analysis. Journal of Affective Disorders, 159,118–126. https://doi.org/10.1016/j.jad.2014.02.026

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The integration of artificial intelligence (AI) in healthcare has revolutionized the way diseases are diagnosed and treated. For instance, AI algorithms have demonstrated remarkable accuracy in detecting early-stage cancers from medical imaging data [a] . Similarly, natural language processing (NLP) techniques have been employed to analyze electronic health records (EHRs) for predicting patient outcomes and identifying at-risk populations [b] . Furthermore, AI-driven wearable devices have enabled real-time monitoring of chronic conditions, such as diabetes, by analyzing continuous glucose levels and physical activity data [c] . These advancements highlight the potential of AI to improve healthcare delivery and patient outcomes.

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Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056

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Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., & Litsch, K. (2018). Scalable and accurate deep learning with electronic health records. npj Digital Medicine, 1(1), 18. https://doi.org/10.1038/s41746-018-0029-1

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Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., Cobelli, C., Dassau, E., DeVries, J. H., Donaghue, K. C., Dovc, K., Doyle, F. J., Garg, S., Grunberger, G., Heller, S., … Phillip, M. (2019). Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the international consensus on time in range. Diabetes Care, 42(8), 1593–1603. https://doi.org/10.2337/dci19-0028

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Successful goal pursuit requires balancing persistent striving, on the one hand, and flexible goal adjustment, on the other hand [a] . This basic principle applies to all goal pursuits, including the pursuit of emotion goals. Emotion regulation is the process by which we pursue emotion goals, which are desired or undesired emotions [b] .

Gollwitzer, P. M., Parks-Stamm, E. J., Jaudas, A., & Sheeran, P. (2008). Flexible tenacity in goal pursuit. In J. Shah & W. Gardner (Eds.), Handbook ofmotivation science (pp. 325–341). Guilford Press.

Brandstatter, V., & Bernecker, K. (2022). Persistence and disengagement in personal goal pursuit. Annual Review of Psychology, 73(1), 271–299. https://doi.org/10.1146/annurev-psych-020821-110710

2. Coloque a citação às seguintes referências no local indicado com [b] 

Tamir, M. (2016). Why do people regulate their emotions? A taxonomy of motives in emotion regulation. Personality and Social Psychology Review, 20(3), 199–222. https://doi.org/10.1177/1088868315586325

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Major Depressive Disorder is one of world’s most prevalent disorders, affecting estimably 5% of the adult population [a] . Although several treatments are proposed for addressing depression [b] , the recurrence and relapse rates [c] , point to the lack of comprehension about the development and maintenance mechanisms of the disorder [d] . Current cognitive models of depression posit that the interplay between emotion regulation strategies and cognitive impairments might be integral to these maintenance mechanisms [e] .

World Health Organization. (2023). Depressive Disorder. https://www.who.int/news-room/fact-sheets/detail/depression

American Psychological Association. (2019). Depression Treatments for Adults.

Richards, D. (2011). Prevalence and clinical course of depression: A review. Clinical Psychology Review, 31(7), 1117–1125. https://doi.org/10.1016/j.cpr.2011.07.004

Lemmens, L. H. J. M., Müller, V. N. L. S., Arntz, A., & Huibers, M. J. H. (2016). Mechanisms of change in psychotherapy for depression: An empirical update and evaluation of research aimed at identifying psychological mediators. Clinical Psychology Review, 50, 95–107. https://doi.org/10.1016/j.cpr.2016.09.004

Disner, S. G., Beevers, C. G., Haigh, E. A. P., & Beck, A. T. (2011). Neural mechanisms of the cognitive model of depression. Nature Reviews Neuroscience, 12(8), 467–477. https://doi.org/10.1038/nrn3027

Beck, A. T., & Bredemeier, K. (2016). A unified model of depression: Integrating clinical, cognitive, biological, and evolutionary perspectives. Clinical Psychological Science, 4(4), 596–619. https://doi.org/10.1177/2167702616628523

LeMoult, J., & Gotlib, I. H. (2019). Depression: A cognitive perspective. Clinical Psychology Review, 69(June), 51–66. https://doi.org/10.1016/j.cpr.2018.06.008

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Within student and young adults cohorts, passive detection of activity changes using accelerometry, GPS, and phone utilization data has shown promise for identifying individuals at risk for self-reported depression or anxiety [a] as well as potential upstream determinants of future mental ill health, such as self-reported stress [b] . Although mood disorders are most commonly clinically diagnosed between the ages of 25 and 30 [c] , it is increasingly recognized that psychological distress may predate this by many years, either as subclinical symptoms or because of delayed help seeking relating to stigma and poor expectations of clinical support.

1. Coloque a citação às seguintes referências no local indicado com [a] 

Palmius, N., Tsanas, A., Saunders, K. E. A., Bilderbeck, A. C., Geddes, J. R., Goodwin, G. M., & De Vos, M. (2017). Detecting Bipolar Depression From Geographic Location Data. IEEE transactions on bio-medical engineering, 64(8), 1761–1771. https://doi.org/10.1109/TBME.2016.2611862

Place, S., Blanch-Hartigan, D., Rubin, C., Gorrostieta, C., Mead, C., Kane, J., Marx, B. P., Feast, J., Deckersbach, T., Pentland, A. S., Nierenberg, A., & Azarbayejani, A. (2017). Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders. Journal of medical Internet research, 19(3), e75. https://doi.org/10.2196/jmir.6678

Faherty, L. J., Hantsoo, L., Appleby, D., Sammel, M. D., Bennett, I. M., & Wiebe, D. J. (2017). Movement patterns in women at risk for perinatal depression: use of a mood-monitoring mobile application in pregnancy. Journal of the American Medical Informatics Association : JAMIA, 24(4), 746–753. https://doi.org/10.1093/jamia/ocx005

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Gjoreski, M., Gjoreski, H., Luštrek, M., & Gams, M. (2015). Automatic Detection of Perceived Stress in Campus Students Using Smartphones. 2015 International Conference on Intelligent Environments, 132-135. https://doi.org/10.1109/IE.2015.27

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Weissman, M. M., Bland, R. C., Canino, G. J., Faravelli, C., Greenwald, S., Hwu, H. G., Joyce, P. R., Karam, E. G., Lee, C. K., Lellouch, J., Lépine, J. P., Newman, S. C., Rubio-Stipec, M., Wells, J. E., Wickramaratne, P. J., Wittchen, H., & Yeh, E. K. (1996). Cross-national epidemiology of major depression and bipolar disorder. JAMA, 276(4), 293–299.