Nicholas C. Jacobson, PhD
Assistant Professor of Biomedical Data Science and Psychiatry, Director, Treatment Development & Evaluation Core, Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College
Nick Jacobson is an assistant professor in the departments of Biomedical Data Science and Psychiatry within the Center for Technology and Behavioral Health (CTBH) in the Geisel School of Medicine at Dartmouth College. He directs the Treatment Development and Evaluation Core within CTBH. He directs the AI and Mental Health: Innovation in Technology Guided Healthcare (AIM HIGH) Laboratory.
Dr. Jacobson researches the use of technology to enhance both the assessment and treatment of anxiety and depression. His work has focused on (1) enhancing precision assessment of anxiety and depression using intensive longitudinal data, (2) conducting multimethod assessment utilizing passive sensor data from smartphones and wearable devices, and (3) providing scalable, personalized technology-based treatments utilizing smartphones. He has a strong interest in creating personalized just-in-time adaptive interventions and the quantitative tools that make this work possible. To date, Dr. Jacobson’s smartphone applications which assess and treat anxiety and depression have been downloaded and installed by more than 50,000 people in over 100 countries. Dr. Jacobson is the principal investigator of an R01 Awarded from the National Institute of Mental Health studying the use of personalized deep learning models to predict rapid changes in major depressive disorder symptoms using passive sensor data from smartphones and wearable devices.
Dr. Jacobson enjoys singing karaoke, hiking, biking, and skiing. He is a vegan and is always on the lookout for a great bite.
Dr. Jacobson is actively recruiting members to join his team. You can learn more about opportunities here (http://nicholasjacobson.com/#join). Please contact him at Nicholas.C.Jacobson@dartmouth.edu if you’re interested.
Selected Publications
- Scodari BT, Chacko S, Matsumura R, Jacobson NC. Using machine learning to forecast symptom changes among subclinical depression patients receiving stepped care or usual care. J Affect Disord. 2023 Nov 1;340:213-220. doi: 10.1016/j.jad.2023.08.004.PMID: 37541599.
- Collins AC, Price G, Woodworth RJ, Jacobson NC. Predicting individual response to a web-based positive psychology intervention: A machine learning approach. Journal of Positive Psychology. 2023 Sep 3.
- Lekkas D, Gyorda JA, Price GD, Jacobson NC. Depression deconstructed: Wearables and passive digital phenotyping for analyzing individual symptoms. Behav Res Ther. 2023 Sep;168:104382. doi: 10.1016/j.brat.2023.104382. PMID: 37544229.
- Klein RJ, Gyorda JA, Lekkas D, Jacobson NC. Dysregulated emotion and trying substances in childhood: Insights from a large nationally representative cohort study. Subst Use Misuse. 2023;58(13):1625-1633. doi: 10.1080/10826084.2023.2223290. PMID: 37572018.
- Wang B, Nemesure MD, Park C, Price GD, Heinz MV, Jacobson NC. Leveraging deep learning models to understand the daily experience of anxiety in teenagers over the course of a year. J Affect Disord. 2023 May 15;329:293-299. doi: 10.1016/j.jad.2023.02.084. PMID: 36858267; PMCID: PMC10091447.
- Heinz MV, Bhattacharya S, Trudeau B, Quist R, Song SH, Lee CM, Jacobson NC. Testing domain knowledge and risk of bias of a large-scale general artificial intelligence model in mental health. Digit Health. 2023 Apr 17;9:20552076231170499. doi: 10.1177/20552076231170499. PMID: 37101589; PMCID: PMC10123874.
- Jacobson NC, Erickson T, Quach CM, Singh NB. Low emotional complexity as a transdiagnostic risk factor: Comparing idiographic markers of emotional complexity to emotional granularity as predictors of anxiety, depression, and personality pathology. Cogn Ther Res. 2023 Apr;47, 181–194.
- Gyorda JA, Lekkas D, Price G, Jacobson NC. Evaluating the impact of mask mandates and political party affiliation on mental health internet search behavior in the United States during the COVID-19 pandemic: Generalized additive mixed model framework. J Med Internet Res. 2023 Mar 3;25:e40308. doi: 10.2196/40308. PMID: 36735836; PMCID: PMC9994425.
- Nemesure MD, Park C, Morris RR, Chan WW, Fitzsimmons-Craft EE, Rackoff GN, Fowler LA, Taylor CB, Jacobson NC. Evaluating change in body image concerns following a single session digital intervention. Body Image. 2023 Mar;44:64-68. doi: 10.1016/j.bodyim.2022.11.007. PMID: 36495690; PMCID: PMC10134195.
- Jacobson NC, Funk B, Abdullah S. Editorial: Quantitative modeling of psychopathology using passively collected data. Front. Psychol. 2023 Jan 24; 13.
- Lekkas D, Gyorda JA, Jacobson NC. A machine learning investigation into the temporal dynamics of physical activity-mediated emotional regulation in adolescents with anorexia nervosa and healthy controls. Eur Eat Disord Rev. 2023 Jan;31(1):147-165. doi: 10.1002/erv.2949. PMID: 36005065.
- Gyorda JA, Nemesure MD, Price G, Jacobson NC. Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention. J Affect Disord. 2023 Jan 1;320:201-210. doi: 10.1016/j.jad.2022.09.112. PMID: 36167247.
- Yom-Tov E, Lekkas D, Heinz MV, Nguyen T, Barr PJ, Jacobson NC. Digitally filling the access gap in mental health care: An investigation of the association between rurality and online engagement with validated self-report screens across the United States. J Psychiatr Res. 2023 Jan;157:112-118. doi: 10.1016/j.jpsychires.2022.11.024. PMID: 36462251; PMCID: PMC9898139.
- Klein RJ, Nguyen ND, Gyorda JA, Jacobson NC. Adolescent emotion regulation and future psychopathology: A prospective transdiagnostic analysis. J Res Adolesc. 2022 Dec;32(4):1592-1611. doi: 10.1111/jora.12743. PMID: 35301763.
- Lekkas D, Gyorda JA, Moen EL, Jacobson NC. Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning. PLoS One. 2022 Nov 30;17(11):e0277516. doi: 10.1371/journal.pone.0277516. PMID: 36449466.
- Price GD, Heinz MV, Zhao D, Nemesure M, Ruan F, Jacobson NC. An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia. J Affect Disord. 2022 Nov 1;316:132-139. doi: 10.1016/j.jad.2022.08.013. PMID: 35964770.
- Jacobson NC, Feng B. Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life. Transl Psychiatry. 2022 Aug 17;12(1):336. doi: 10.1038/s41398-022-02038-1. PMID: 35977932; PMCID: PMC9385727.
- Price GD, Heinz MV, Nemesure MD, McFadden J, Jacobson NC. Predicting symptom response and engagement in a digital intervention among individuals with schizophrenia and related psychoses. Front Psychiatry. 2022 Aug 11;13:807116. doi: 10.3389/fpsyt.2022.807116. PMID: 36032242; PMCID: PMC9403124.
- Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson NC. Exploring the digital footprint of depression: A PRISMA systematic literature review of the empirical evidence. BMC Psychiatry. 2022 July; 22(421).
- Klein RJ, Gyorda JA, Jacobson NC. Anxiety, depression, and substance experimentation in childhood. PLoS One. 2022 May 24;17(5):e0265239. doi: 10.1371/journal.pone.0265239. PMID: 35609016.
- Heinz MV, Price GD, Ruan F, Klein RJ, Nemesure M, Lopez A, Jacobson NC. Association of selective serotonin reuptake inhibitor use with abnormal physical movement patterns as detected using a piezoelectric accelerometer and deep learning in a nationally representative sample of noninstitutionalized persons in the US. JAMA Netw Open. 2022 Apr 1;5(4):e225403. doi: 10.1001/jamanetworkopen.2022.5403. PMID: 35389502.
- Fitzsimmons-Craft EE, Chan WW, Smith AC, Firebaugh ML, Fowler LA, Topooco N, DePietro B, Wilfley DE, Taylor CB, Jacobson NC. Effectiveness of a chatbot for eating disorders prevention: A randomized clinical trial. Int J Eat Disord. 2022 Mar;55(3):343-353. doi: 10.1002/eat.23662. PMID: 35274362.
- Jacobson NC, Bhattacharya S. Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behav Res Ther. 2022 Feb;149:104013. doi: 10.1016/j.brat.2021.104013. PMID: 35030442; PMCID: PMC8858490.
- Lekkas D, Gyorda JA, Price GD, Wortzman Z, Jacobson NC. Using the COVID-19 pandemic to assess the influence of news affect on online mental health-related search behavior across the United States: Integrated sentiment analysis and the circumplex model of affect. J Med Internet Res. 2022 Jan 27;24(1):e32731. doi: 10.2196/32731. PMID: 34932494; PMCID: PMC8805454.
- Chan WW, Fitzsimmons-Craft EE, Smith AC, Firebaugh ML, Fowler LA, DePietro B, Topooco N, Wilfley DE, Taylor CB, Jacobson NC. The challenges in designing a prevention chatbot for eating disorders: Observational study. JMIR Form Res. 2022 Jan 19;6(1):e28003. doi: 10.2196/28003. PMID: 35044314.
- Lekkas D, Price G, McFadden J, Jacobson NC. The application of machine learning to online mindfulness intervention data: A primer and empirical example in compliance assessment. Mindfulness. 2021 Oct;12, 2519–2534.
- Jacobson NC, Lekkas D, Huang R, Thomas N. Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17-18 years. J Affect Disord. 2021 Mar 1;282:104-111. doi: 10.1016/j.jad.2020.12.086. PMID: 33401123; PMCID: PMC7889722.
- Nemesure MD, Heinz MV, Huang R, Jacobson NC. Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence. Sci Rep. 2021 Jan 21;11(1):1980. doi: 10.1038/s41598-021-81368-4. PMID: 33479383; PMCID: PMC7820000.