Publications by authors named "Nicholas C Jacobson"

51 Publications

The Challenges in Designing a Prevention Chatbot for Eating Disorders: Observational Study.

JMIR Form Res 2022 Jan 19;6(1):e28003. Epub 2022 Jan 19.

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.

Background: Chatbots have the potential to provide cost-effective mental health prevention programs at scale and increase interactivity, ease of use, and accessibility of intervention programs.

Objective: The development of chatbot prevention for eating disorders (EDs) is still in its infancy. Our aim is to present examples of and solutions to challenges in designing and refining a rule-based prevention chatbot program for EDs, targeted at adult women at risk for developing an ED.

Methods: Participants were 2409 individuals who at least began to use an EDs prevention chatbot in response to social media advertising. Over 6 months, the research team reviewed up to 52,129 comments from these users to identify inappropriate responses that negatively impacted users' experience and technical glitches. Problems identified by reviewers were then presented to the entire research team, who then generated possible solutions and implemented new responses.

Results: The most common problem with the chatbot was a general limitation in understanding and responding appropriately to unanticipated user responses. We developed several workarounds to limit these problems while retaining some interactivity.

Conclusions: Rule-based chatbots have the potential to reach large populations at low cost but are limited in understanding and responding appropriately to unanticipated user responses. They can be most effective in providing information and simple conversations. Workarounds can reduce conversation errors.
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http://dx.doi.org/10.2196/28003DOI Listing
January 2022

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 2021 Dec 11;149:104013. Epub 2021 Dec 11.

Dartmouth College, 46 Centerra Parkway; Suite 300, Office # 333S, Lebanon, NH, 03766, USA.

Smartphones are capable of passively capturing persons' social interactions, movement patterns, physiological activation, and physical environment. Nevertheless, little research has examined whether momentary anxiety symptoms can be accurately assessed using these methodologies. In this research, we utilize smartphone sensors and personalized deep learning models to predict future anxiety symptoms among a sample reporting clinical anxiety disorder symptoms. Participants (N = 32) with generalized anxiety disorder and/or social anxiety disorder (based on self-report) installed a smartphone application and completed ecological momentary assessment symptoms assessing their anxiety and avoidance symptoms hourly for the course of one week (T = 2007 assessments). During the same period, the smartphone app collected information about physiological activation (heart rate and heart rate variability), exposure to light, social contact, and GPS location. GPS locations were coded to reveal the type of location and the weather information. Personalized deep learning models using the smartphone sensor data were capable of predicting the majority of total variation in anxiety symptoms (R = 0.748) and predicting a large proportion of within-person variation at the hour-by-hour level (mean R = 0.385). These results suggest that personalized deep learning models using smartphone sensor data are capable of accurately predicting future anxiety disorder symptom changes.
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http://dx.doi.org/10.1016/j.brat.2021.104013DOI Listing
December 2021

The Language of the Times: Using the COVID-19 Pandemic to Assess the Influence of News Affect on Online Mental Health-Related Search Behavior across the United States.

J Med Internet Res 2021 Nov 30. Epub 2021 Nov 30.

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra ParkwaySuite 300, Office #313S, Lebanon, US.

Background: The digital era has ushered in an unprecedented volume of readily accessible information, including news coverage of current events. Research has shown that the sentiment of news articles can evoke emotional responses from readers on a daily basis with specific evidence for increased anxiety and depression in response to coverage of the recent coronavirus (COVID-19) pandemic. Given the primacy and relevance of such information exposure, its daily impact on the mental health of the general population within this modality warrants further nuanced investigation.

Objective: Using the COVID-19 pandemic as a subject-specific example, this work aimed to profile and examine associations between the dynamics of semantic affect in online local news headlines and same-day online mental health term search behavior over time across the United States.

Methods: Using COVID-related news headlines from a database of online news stories in conjunction with mental health-related online search data from Google Trends, this paper first explored the statistical and qualitative affective properties of state-specific COVID news coverage across the United States from January 23, 2020 to October 22, 2020. The resultant operationalizations and findings from the joint application of dictionary-based sentiment analysis and the circumplex theory of affect informed the construction of subsequent hypothesis-driven mixed-effects models. Daily state-specific counts of mental health search queries were regressed on circumplex-derived features of semantic affect, time, and state (as a random effect) to model the associations between the dynamics of news affect and search behavior throughout the pandemic. Search terms were also grouped into depression symptoms, anxiety symptoms, and non-specific depression and anxiety symptoms to model the broad impact of news coverage on mental health.

Results: Exploratory efforts revealed patterns in day-to-day news headline affect variation across the first nine months of the pandemic. In addition, circumplex mapping of the most frequently used words in state-specific headlines uncovered time-agnostic similarities and differences across the United States, including the ubiquitous use of negatively valenced and strongly arousing language. Subsequent mixed-effects modeling implicated increased consistency in affective tone (SpinVA β=-0.207; p<.001) as predictive of increased depression-related search term activity, with emotional language patterns indicative of affective uncontrollability (FluxA β=0.221; p<.001) contributing generally to an increase in online mental health search term frequency.

Conclusions: The present study demonstrated promise in applying the circumplex model of affect to written content and provided a practical example for how circumplex theory can be integrated with sentiment analysis techniques to interrogate mental health-related associations. The findings from pandemic-specific news headlines highlighted arousal, flux, and spin as potentially significant affect-based foci for further study. Future efforts may also benefit from more expansive sentiment analysis approaches to more broadly test the practical application and theoretical capabilities of the circumplex model of affect on text-based data.

Clinicaltrial:
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http://dx.doi.org/10.2196/32731DOI Listing
November 2021

A randomized controlled feasibility trial of internet-delivered guided self-help for generalized anxiety disorder (GAD) among university students in India.

Psychotherapy (Chic) 2021 Dec;58(4):591-601

Department of Psychiatry, Stanford University.

Online guided self-help may be an effective and scalable intervention for symptoms of generalized anxiety disorder (GAD) among university students in India. Based on an online screen for GAD administered at 4 Indian universities, 222 students classified as having clinical (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria) or subthreshold (Generalized Anxiety Disorder Questionnaire, Fourth Edition, score ≥ 5.7) GAD were randomly assigned to receive either 3 months of guided self-help cognitive-behavioral therapy (n = 117) or a waitlist control condition (n = 105). Guided self-help participants recorded high program usage on average across all participants enrolled (M = 9.99 hr on the platform; SD = 20.87). Intent-to-treat analyses indicated that participants in the guided self-help condition experienced significantly greater reductions than participants in the waitlist condition on GAD symptom severity (d = -.40), worry (d = -.43), and depressive symptoms (d = -.53). No usage variables predicted symptom change in the guided self-help condition. Participants on average reported that the program was moderately helpful, and a majority (82.1%) said they would recommend the program to a friend. Guided self-help appears to be a feasible and efficacious intervention for university students in India who meet clinical or subthreshold GAD criteria. The trial is registered with ClinicalTrials.gov (NCT02410265). (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/pst0000383DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8744990PMC
December 2021

Using Smartphone App Use and Lagged-Ensemble Machine Learning for the Prediction of Work Fatigue and Boredom.

Comput Human Behav 2022 Feb 24;127. Epub 2021 Sep 24.

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College.

Intro: As smartphone usage becomes increasingly prevalent in the workplace, the physical and psychological implications of this behavior warrant consideration. Recent research has investigated associations between workplace smartphone use and fatigue and boredom, yet findings are not conclusive.

Methods: To build off recent efforts, we applied an ensemble machine learning model on a previously published dataset of = 83 graduate students in the Netherlands to predict work boredom and fatigue from passively collected smartphone app use information. Using time-based feature engineering and lagged variations of the data to train, validate, and test idiographic models, we evaluated the efficacy of a lagged-ensemble predictive paradigm on sparse temporal data. Moreover, we probed the relative importance of both derived app use variables and lags within this predictive framework.

Results: The ability to predict fatigue and boredom trajectories from app use information was heterogeneous and highly person-specific. Idiographic modeling reflected moderate to high correlative capacity ( > 0.4) in 47% of participants for fatigue and 24% for boredom, with better overall performance in the fatigue prediction task. App use relating to duration, communication, and patterns of use frequency were among the most important features driving predictions across lags, with longer lags contributing more heavily to final ensemble predictions compared with shorter ones.

Conclusion: A lag- specific ensemble predictive paradigm is a promising approach to leveraging high-dimensional app use behavioral data for the prediction of work fatigue and boredom. Future research will benefit from evaluating associations on densely collected data across longer time scales.
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http://dx.doi.org/10.1016/j.chb.2021.107029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589273PMC
February 2022

Integration of discrete and global structures of affect across three large samples: Specific emotions within-persons and global affect between-persons.

Emotion 2021 Sep 30. Epub 2021 Sep 30.

Department of Psychology.

Researchers have held a long-standing debate regarding the validity of discrete emotions versus global affect. The current article tries to integrate these perspectives by explicitly examining the structures of state emotions and trait affect across time. Across three samples (Sample 1: = 176 U.S. undergraduates in a 50 day daily diary study-total observations = 7,504; Sample 2: = 2,104 in a 30 day daily diary study within a community sample in Germany-total observations = 28,090; Sample 3: = 245, ecological momentary assessment study within the United States from an outpatient psychiatry clinic completing five measurements per day for 21 days-total observations = 29,950), participants completed the Positive and Negative Affect Schedule. An exploratory multilevel factor analysis in Sample 1 allowed for the simultaneous estimation of state factors (i.e., within-person factor analysis) and trait factors (i.e., between-persons factor analysis). Confirmatory multilevel factor models examined the generalizability of the multilevel factor solutions to Samples 2 and 3. Across all samples, the results suggested strong support for a two-factor solution for trait affect and a seven-factor solution for state emotion. Taken together, these results suggest that positive affect and negative affect can be used to describe differences across people, but at least seven differentiated emotions are experienced within persons across time. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/emo0001022DOI Listing
September 2021

Integration of discrete and global structures of affect across three large samples: Specific emotions within-persons and global affect between-persons.

Emotion 2021 Sep 30. Epub 2021 Sep 30.

Department of Psychology.

Researchers have held a long-standing debate regarding the validity of discrete emotions versus global affect. The current article tries to integrate these perspectives by explicitly examining the structures of state emotions and trait affect across time. Across three samples (Sample 1: = 176 U.S. undergraduates in a 50 day daily diary study-total observations = 7,504; Sample 2: = 2,104 in a 30 day daily diary study within a community sample in Germany-total observations = 28,090; Sample 3: = 245, ecological momentary assessment study within the United States from an outpatient psychiatry clinic completing five measurements per day for 21 days-total observations = 29,950), participants completed the Positive and Negative Affect Schedule. An exploratory multilevel factor analysis in Sample 1 allowed for the simultaneous estimation of state factors (i.e., within-person factor analysis) and trait factors (i.e., between-persons factor analysis). Confirmatory multilevel factor models examined the generalizability of the multilevel factor solutions to Samples 2 and 3. Across all samples, the results suggested strong support for a two-factor solution for trait affect and a seven-factor solution for state emotion. Taken together, these results suggest that positive affect and negative affect can be used to describe differences across people, but at least seven differentiated emotions are experienced within persons across time. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/emo0001022DOI Listing
September 2021

Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review.

J Med Internet Res 2021 09 28;23(9):e29412. Epub 2021 Sep 28.

Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.

Background: The number of smartphone apps that focus on the prevention, diagnosis, and treatment of depression is increasing. A promising approach to increase the effectiveness of the apps while reducing the individual's burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. JITAIs are designed to improve the effectiveness of the intervention and reduce the burden on the person using the intervention by providing the right type of support at the right time. The right type of support and the right time are determined by measuring the state of vulnerability and the state of receptivity, respectively.

Objective: The aim of this study is to systematically assess the use of JITAI mechanisms in popular apps for individuals with depression.

Methods: We systematically searched for apps addressing depression in the Apple App Store and Google Play Store, as well as in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. The relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, 2 authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, Google Scholar, IEEE Xplore, Web of Science, ACM Portal, and Science Direct), publications cited on the app's website, information on the app's website, and the app itself. All types of measurements (eg, open questions, closed questions, and device analytics) found in the apps were recorded and reviewed.

Results: None of the 28 reviewed apps used JITAI mechanisms to tailor content to situations, states, or individuals. Of the 28 apps, 3 (11%) did not use any measurements, 20 (71%) exclusively used self-reports that were insufficient to leverage the full potential of the JITAIs, and the 5 (18%) apps using self-reports and passive measurements used them as progress or task indicators only. Although 34% (23/68) of the reviewed publications investigated the effectiveness of the apps and 21% (14/68) investigated their efficacy, no publication mentioned or evaluated JITAI mechanisms.

Conclusions: Promising JITAI mechanisms have not yet been translated into mainstream depression apps. Although the wide range of passive measurements available from smartphones were rarely used, self-reported outcomes were used by 71% (20/28) of the apps. However, in both cases, the measured outcomes were not used to tailor content and timing along a state of vulnerability or receptivity. Owing to this lack of tailoring to individual, state, or situation, we argue that the apps cannot be considered JITAIs. The lack of publications investigating whether JITAI mechanisms lead to an increase in the effectiveness or efficacy of the apps highlights the need for further research, especially in real-world apps.
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http://dx.doi.org/10.2196/29412DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512178PMC
September 2021

Initial evaluation of domain-specific episodic future thinking on delay discounting and cannabis use.

Exp Clin Psychopharmacol 2021 Jun 7. Epub 2021 Jun 7.

Geisel School of Medicine, Dartmouth College, Center for Technology and Behavioral Health.

Episodic Future Thinking (EFT), mental simulation of personally relevant and positive future events, may modulate delay discounting (DD) in cannabis users. Whether EFT impacts cannabis use, whether DD mediates this effect, and whether EFT can be enhanced by prompting future events across specific life domains is unknown. Active, adult cannabis users (n = 90) recruited from Amazon mTurk and Qualtrics Panels were administered an Episodic Specificity Induction (ESI) to enhance quality of imagined events before being randomized to EFT, domain-specific-EFT (DS-EFT), or Episodic Recent Thinking (ERT). All participants created four, positive life events; DS-EFT participants imagined social, leisure, health, and financial events. Event-quality ratings were assessed (e.g., enjoyment). DD was assessed at baseline (Day 1), post-intervention (Days 2-4), and follow-up (Days 9-12). Cannabis use was assessed at baseline and follow-up. Differences in change in days and grams of cannabis use between conditions and mediation of changes in use by DD were examined. No differences in DD were observed between conditions. DS-EFT, but not EFT, showed significantly greater reductions in grams (d = .54) and days of cannabis use (d = .50) than ERT. DS-EFT and EFT demonstrated significantly greater event-quality ratings than ERT (ds > .55). EFT-based interventions showed potential for reducing cannabis use. Unexpectedly, effects on DD did not mediate this effect. Further testing with larger samples of cannabis users is needed to better understand EFT's mechanisms of action and determine optimal implementation strategies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/pha0000501DOI Listing
June 2021

Initial evaluation of domain-specific episodic future thinking on delay discounting and cannabis use.

Exp Clin Psychopharmacol 2021 Jun 7. Epub 2021 Jun 7.

Geisel School of Medicine, Dartmouth College, Center for Technology and Behavioral Health.

Episodic Future Thinking (EFT), mental simulation of personally relevant and positive future events, may modulate delay discounting (DD) in cannabis users. Whether EFT impacts cannabis use, whether DD mediates this effect, and whether EFT can be enhanced by prompting future events across specific life domains is unknown. Active, adult cannabis users (n = 90) recruited from Amazon mTurk and Qualtrics Panels were administered an Episodic Specificity Induction (ESI) to enhance quality of imagined events before being randomized to EFT, domain-specific-EFT (DS-EFT), or Episodic Recent Thinking (ERT). All participants created four, positive life events; DS-EFT participants imagined social, leisure, health, and financial events. Event-quality ratings were assessed (e.g., enjoyment). DD was assessed at baseline (Day 1), post-intervention (Days 2-4), and follow-up (Days 9-12). Cannabis use was assessed at baseline and follow-up. Differences in change in days and grams of cannabis use between conditions and mediation of changes in use by DD were examined. No differences in DD were observed between conditions. DS-EFT, but not EFT, showed significantly greater reductions in grams (d = .54) and days of cannabis use (d = .50) than ERT. DS-EFT and EFT demonstrated significantly greater event-quality ratings than ERT (ds > .55). EFT-based interventions showed potential for reducing cannabis use. Unexpectedly, effects on DD did not mediate this effect. Further testing with larger samples of cannabis users is needed to better understand EFT's mechanisms of action and determine optimal implementation strategies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/pha0000501DOI Listing
June 2021

Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma.

Sci Rep 2021 05 13;11(1):10303. Epub 2021 May 13.

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, Suite 300, Lebanon, NH, 03766, USA.

Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is difficult. Fortunately, advances in mobile technology, passive sensing, and analytics offer promising avenues for research and development. The present study examined the ability to utilize Global Positioning System (GPS) data, derived passively from a smartphone across seven days, to detect PTSD diagnostic status among a cohort (N = 185) of high-risk, previously traumatized women. Using daily time spent away and maximum distance traveled from home as a basis for model feature engineering, the results suggested that diagnostic group status can be predicted out-of-fold with high performance (AUC = 0.816, balanced sensitivity = 0.743, balanced specificity = 0.8, balanced accuracy = 0.771). Results further implicate the potential utility of GPS information as a digital biomarker of the PTSD behavioral repertoire. Future PTSD research will benefit from application of GPS data within larger, more diverse populations.
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http://dx.doi.org/10.1038/s41598-021-89768-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8119967PMC
May 2021

Emotion network density is a potential clinical marker for anxiety and depression: Comparison of ecological momentary assessment and daily diary.

Br J Clin Psychol 2022 Jan 7;61 Suppl 1:31-50. Epub 2021 May 7.

Dartmouth College, Hanover, New Hampshire, USA.

Objectives: Using two intensive longitudinal data sets with different timescales (90 minutes, daily), we examined emotion network density, a metric of emotional inflexibility, as a predictor of clinical-level anxiety and depression.

Design: Mobile-based intensive longitudinal assessments.

Methods: 119 participants (61 anxious and depressed, 58 healthy controls) completed ecological momentary assessment (EMA) to rate a variety of negative (NE) and positive emotions (PE) 9 times per day for 8 days using a mobile phone application. 169 participants (97 anxious and depressed and 72 healthy controls) completed an online daily diary on their NE and PE for 50 days. Multilevel vector autoregressive models were run to compute NE and PE network densities in each data set.

Results: In the EMA data set, both NE and PE network densities significantly predicted participants' diagnostic status above and beyond demographics and the mean and standard deviation of NE and PE. Greater NE network density and lower PE network density were associated with anxiety and depression diagnoses. In the daily diary data set, NE and PE network densities did not significantly predict the diagnostic status.

Conclusions: Greater inflexibility of NE and lower inflexibility of PE, indexed by emotion network density, are potential clinical markers of anxiety and depressive disorders when assessed at intra-daily levels as opposed to daily levels. Considering emotion network density, as well as the mean level and variability of emotions in daily life, may contribute to diagnostic prediction of anxiety and depressive disorders.

Practitioner Points: Emotion network density, or the degree to which prior emotions predict and influence current emotions, indicates an inflexible or change-resistant emotion system. Emotional inflexibility or change resistance over a few hours, but not daily, may characterize anxiety and depressive disorders. Inflexible negative emotion systems are associated with anxiety and depressive disorders, whereas inflexible positive emotion systems may indicate psychological health. Considering emotional inflexibility within days may provide additional information beyond demographics and mean level and variability of emotions in daily life for detecting anxiety and depressive disorders.
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http://dx.doi.org/10.1111/bjc.12295DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8572316PMC
January 2022

Mental Health and Behavior of College Students During the COVID-19 Pandemic: Longitudinal Mobile Smartphone and Ecological Momentary Assessment Study, Part II.

J Med Internet Res 2021 06 4;23(6):e28892. Epub 2021 Jun 4.

Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States.

Background: Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected with the disease, while billions of people have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale behavioral and mental health changes; however, few studies have been able to track these changes with frequent, near real-time sampling or compare these changes to previous years of data for the same individuals.

Objective: By combining mobile phone sensing and self-reported mental health data in a cohort of college-aged students enrolled in a longitudinal study, we seek to understand the behavioral and mental health impacts associated with the COVID-19 pandemic, measured by interest across the United States in the search terms coronavirus and COVID fatigue.

Methods: Behaviors such as the number of locations visited, distance traveled, duration of phone use, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife mobile smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments, including the Patient Health Questionnaire-4. The participants were 217 undergraduate students. Differences in behaviors and self-reported mental health collected during the Spring 2020 term, as compared to previous terms in the same cohort, were modeled using mixed linear models.

Results: Linear mixed models demonstrated differences in phone use, sleep, sedentary time and number of locations visited associated with the COVID-19 pandemic. In further models, these behaviors were strongly associated with increased interest in COVID fatigue. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, phone use, sedentary time), both anxiety and depression (P<.001) were significantly associated with interest in COVID fatigue. Notably, these behavioral and mental health changes are consistent with those observed around the initial implementation of COVID-19 lockdowns in the spring of 2020.

Conclusions: In the initial lockdown phase of the COVID-19 pandemic, people spent more time on their phones, were more sedentary, visited fewer locations, and exhibited increased symptoms of anxiety and depression. As the pandemic persisted through the spring, people continued to exhibit very similar changes in both mental health and behaviors. Although these large-scale shifts in mental health and behaviors are unsurprising, understanding them is critical in disrupting the negative consequences to mental health during the ongoing pandemic.
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http://dx.doi.org/10.2196/28892DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183598PMC
June 2021

Harnessing mobile technology to reduce mental health disorders in college populations: A randomized controlled trial study protocol.

Contemp Clin Trials 2021 04 11;103:106320. Epub 2021 Feb 11.

Department of Psychiatry, Washington University School of Medicine, Mailstop 8134-29-2100, 660 S. Euclid Ave., St. Louis, MO 63110, USA. Electronic address:

About a third of college students struggle with anxiety, depression, or an eating disorder, and only 20-40% of college students with mental disorders receive treatment. Inadequacies in mental health care delivery result in prolonged illness, disease progression, poorer prognosis, and greater likelihood of relapse, highlighting the need for a new approach to detect mental health problems and engage college students in services. We have developed a transdiagnostic, low-cost mobile mental health targeted prevention and intervention platform that uses population-level screening to engage college students in tailored services that address common mental health problems. We will test the impact of this mobile mental health platform for service delivery in a large-scale trial across 20+ colleges. Students who screen positive or at high-risk for clinical anxiety, depression, or an eating disorder and who are not currently engaged in mental health services (N = 7884) will be randomly assigned to: 1) intervention via the mobile mental health platform; or 2) referral to usual care (i.e., campus health or counseling center). We will test whether the mobile mental health platform, compared to referral, is associated with improved uptake, reduced clinical cases, disorder-specific symptoms, and improved quality of life and functioning. We will also test mediators, predictors, and moderators of improved mental health outcomes, as well as stakeholder-relevant outcomes, including cost-effectiveness and academic performance. This population-level approach to service engagement has the potential to improve mental health outcomes for the millions of students enrolled in U.S. colleges and universities.
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http://dx.doi.org/10.1016/j.cct.2021.106320DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8089064PMC
April 2021

Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of Patient Voice.

J Med Internet Res 2021 02 19;23(2):e20545. Epub 2021 Feb 19.

The Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth College, Lebanon, NH, United States.

COVID-19 cases are exponentially increasing worldwide; however, its clinical phenotype remains unclear. Natural language processing (NLP) and machine learning approaches may yield key methods to rapidly identify individuals at a high risk of COVID-19 and to understand key symptoms upon clinical manifestation and presentation. Data on such symptoms may not be accurately synthesized into patient records owing to the pressing need to treat patients in overburdened health care settings. In this scenario, clinicians may focus on documenting widely reported symptoms that indicate a confirmed diagnosis of COVID-19, albeit at the expense of infrequently reported symptoms. While NLP solutions can play a key role in generating clinical phenotypes of COVID-19, they are limited by the resulting limitations in data from electronic health records (EHRs). A comprehensive record of clinic visits is required-audio recordings may be the answer. A recording of clinic visits represents a more comprehensive record of patient-reported symptoms. If done at scale, a combination of data from the EHR and recordings of clinic visits can be used to power NLP and machine learning models, thus rapidly generating a clinical phenotype of COVID-19. We propose the generation of a pipeline extending from audio or video recordings of clinic visits to establish a model that factors in clinical symptoms and predict COVID-19 incidence. With vast amounts of available data, we believe that a prediction model can be rapidly developed to promote the accurate screening of individuals at a high risk of COVID-19 and to identify patient characteristics that predict a greater risk of a more severe infection. If clinical encounters are recorded and our NLP model is adequately refined, benchtop virologic findings would be better informed. While clinic visit recordings are not the panacea for this pandemic, they are a low-cost option with many potential benefits, which have recently begun to be explored.
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http://dx.doi.org/10.2196/20545DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899201PMC
February 2021

Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence.

Sci Rep 2021 01 21;11(1):1980. Epub 2021 Jan 21.

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, Lebanon, NH, 03766, USA.

Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could foster the detection of GAD and MDD in primary care settings. We approached the problem of predicting MDD and GAD using a novel machine learning pipeline to re-analyze data from an observational study. The pipeline constitutes an ensemble of algorithmically distinct machine learning methods, including deep learning. A sample of 4,184 undergraduate students completed the study, undergoing a general health screening and completing a psychiatric assessment for MDD and GAD. After explicitly excluding all psychiatric information, 59 biomedical and demographic features from the general health survey in addition to a set of engineered features were used for model training. We assessed the model's performance on a held-out test set and found an AUC of 0.73 (sensitivity: 0.66, specificity: 0.7) and 0.67 (sensitivity: 0.55, specificity: 0.7) for GAD, and MDD, respectively. Additionally, we used advanced techniques (SHAP values) to illuminate which features had the greatest impact on prediction for each disease. The top predictive features for MDD were being satisfied with living conditions and having public health insurance. The top predictive features for GAD were vaccinations being up to date and marijuana use. Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. By identifying important predictors of GAD and MDD, these results may be used in future research to aid in the early detection of MDD and GAD.
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http://dx.doi.org/10.1038/s41598-021-81368-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7820000PMC
January 2021

The temporal dynamics of sleep disturbance and psychopathology in psychosis: a digital sampling study.

Psychol Med 2021 Jan 12:1-10. Epub 2021 Jan 12.

Department of Psychosis Studies, Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, UK.

Background: Sleep disruption is a common precursor to deterioration and relapse in people living with psychotic disorders. Understanding the temporal relationship between sleep and psychopathology is important for identifying and developing interventions which target key variables that contribute to relapse.

Methods: We used a purpose-built digital platform to sample self-reported sleep and psychopathology variables over 1 year, in 36 individuals with schizophrenia. Once-daily measures of sleep duration and sleep quality, and fluctuations in psychopathology (positive and negative affect, cognition and psychotic symptoms) were captured. We examined the temporal relationship between these variables using the Differential Time-Varying Effect (DTVEM) hybrid exploratory-confirmatory model.

Results: Poorer sleep quality and shorter sleep duration maximally predicted deterioration in psychosis symptoms over the subsequent 1-8 and 1-12 days, respectively. These relationships were also mediated by negative affect and cognitive symptoms. Psychopathology variables also predicted sleep quality, but not sleep duration, and the effect sizes were smaller and of shorter lag duration.

Conclusions: Reduced sleep duration and poorer sleep quality anticipate the exacerbation of psychotic symptoms by approximately 1-2 weeks, and negative affect and cognitive symptoms mediate this relationship. We also observed a reciprocal relationship that was of shorter duration and smaller magnitude. Sleep disturbance may play a causal role in symptom exacerbation and relapse, and represents an important and tractable target for intervention. It warrants greater attention as an early warning sign of deterioration, and low-burden, user-friendly digital tools may play a role in its early detection.
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http://dx.doi.org/10.1017/S0033291720004857DOI Listing
January 2021

Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17-18 years.

J Affect Disord 2021 03 27;282:104-111. Epub 2020 Dec 27.

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, United States.

Background: Recent studies have demonstrated that passive smartphone and wearable sensor data collected throughout daily life can predict anxiety symptoms cross-sectionally. However, to date, no research has demonstrated the capacity for these digital biomarkers to predict long-term prognosis.

Methods: We utilized deep learning models based on wearable sensor technology to predict long-term (17-18-year) deterioration in generalized anxiety disorder and panic disorder symptoms from actigraphy data on daytime movement and nighttime sleeping patterns. As part of Midlife in the United States (MIDUS), a national longitudinal study of health and well-being, subjects (N = 265) (i) completed a phone-based interview that assessed generalized anxiety disorder and panic disorder symptoms at enrollment, (ii) participated in a one-week actigraphy study 9-14 years later, and (iii) completed a long-term follow-up, phone-based interview to quantify generalized anxiety disorder and panic disorder symptoms 17-18 years from initial enrollment. A deep auto-encoder paired with a multi-layered ensemble deep learning model was leveraged to predict whether participants experienced increased anxiety disorder symptoms across this 17-18 year period.

Results: Out-of-sample cross-validated results suggested that wearable movement data could significantly predict which individuals would experience symptom deterioration (AUC = 0.696, CI [0.598, 0.793], 84.6% sensitivity, 52.7% specificity, balanced accuracy = 68.7%).

Conclusions: Passive wearable actigraphy data could be utilized to predict long-term deterioration of anxiety disorder symptoms. Future studies should examine whether these methods could be implemented to prevent deterioration of anxiety disorder symptoms.
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http://dx.doi.org/10.1016/j.jad.2020.12.086DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7889722PMC
March 2021

Using Artificial Intelligence to Predict Change in Depression and Anxiety Symptoms in a Digital Intervention: Evidence from a Transdiagnostic Randomized Controlled Trial.

Psychiatry Res 2021 01 29;295:113618. Epub 2020 Nov 29.

Dartmouth College.

While digital psychiatric interventions reduce treatment barriers, not all persons benefit from this type of treatment. Research is needed to preemptively identify who is likely to benefit from these digital treatments in order to redirect those people to a higher level of care. The current manuscript used an ensemble of machine learning methods to predict changes in major depressive and generalized anxiety disorder symptoms from pre to 9-month follow-up in a randomized controlled trial of a transdiagnostic digital intervention based on participants' (N=632) pre-treatment data. The results suggested that baseline characteristics could accurately predict changes in depressive symptoms in both treatment groups (r=0.482, 95% CI[0.394, 0.561]; r=0.477, 95% CI[0.385, 0.560]) and anxiety symptoms in both treatment groups (r=0.569, 95% CI[0.491, 0.638]; r=0.548, 95% CI[0.464, 0.622]). These results suggest that machine learning models are capable of preemptively predicting a person's responsiveness to digital treatments, which would enable personalized decision-making about which persons should be directed towards standalone digital interventions or towards blended stepped-care.
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http://dx.doi.org/10.1016/j.psychres.2020.113618DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839310PMC
January 2021

Using Artificial Intelligence to Predict Change in Depression and Anxiety Symptoms in a Digital Intervention: Evidence from a Transdiagnostic Randomized Controlled Trial.

Psychiatry Res 2021 01 29;295:113618. Epub 2020 Nov 29.

Dartmouth College.

While digital psychiatric interventions reduce treatment barriers, not all persons benefit from this type of treatment. Research is needed to preemptively identify who is likely to benefit from these digital treatments in order to redirect those people to a higher level of care. The current manuscript used an ensemble of machine learning methods to predict changes in major depressive and generalized anxiety disorder symptoms from pre to 9-month follow-up in a randomized controlled trial of a transdiagnostic digital intervention based on participants' (N=632) pre-treatment data. The results suggested that baseline characteristics could accurately predict changes in depressive symptoms in both treatment groups (r=0.482, 95% CI[0.394, 0.561]; r=0.477, 95% CI[0.385, 0.560]) and anxiety symptoms in both treatment groups (r=0.569, 95% CI[0.491, 0.638]; r=0.548, 95% CI[0.464, 0.622]). These results suggest that machine learning models are capable of preemptively predicting a person's responsiveness to digital treatments, which would enable personalized decision-making about which persons should be directed towards standalone digital interventions or towards blended stepped-care.
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http://dx.doi.org/10.1016/j.psychres.2020.113618DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839310PMC
January 2021

Artificial Intelligence Chatbot for Depression: Descriptive Study of Usage.

JMIR Form Res 2020 Nov 13;4(11):e17065. Epub 2020 Nov 13.

Palo Alto University, Palo Alto, CA, United States.

Background: Chatbots could be a scalable solution that provides an interactive means of engaging users in behavioral health interventions driven by artificial intelligence. Although some chatbots have shown promising early efficacy results, there is limited information about how people use these chatbots. Understanding the usage patterns of chatbots for depression represents a crucial step toward improving chatbot design and providing information about the strengths and limitations of the chatbots.

Objective: This study aims to understand how users engage and are redirected through a chatbot for depression (Tess) to provide design recommendations.

Methods: Interactions of 354 users with the Tess depression modules were analyzed to understand chatbot usage across and within modules. Descriptive statistics were used to analyze participant flow through each depression module, including characters per message, completion rate, and time spent per module. Slide plots were also used to analyze the flow across and within modules.

Results: Users sent a total of 6220 messages, with a total of 86,298 characters, and, on average, they engaged with Tess depression modules for 46 days. There was large heterogeneity in user engagement across different modules, which appeared to be affected by the length, complexity, content, and style of questions within the modules and the routing between modules.

Conclusions: Overall, participants engaged with Tess; however, there was a heterogeneous usage pattern because of varying module designs. Major implications for future chatbot design and evaluation are discussed in the paper.
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http://dx.doi.org/10.2196/17065DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7695525PMC
November 2020

Sifting through the weeds: Relationships between cannabis use frequency measures and delay discounting.

Addict Behav 2021 01 24;112:106573. Epub 2020 Jul 24.

Geisel School of Medicine, Dartmouth College, Center for Technology and Behavioral Health, 46 Centerra Parkway, Suite 315, Lebanon, NH, USA.

Background: Delay Discounting (DD) relates to more frequent cannabis use, but results are variable, potentially because of variations in whether integrated or single-item measures are used, and whether the timeframe of measures is narrow or broad. Explicating the relationship between DD and cannabis use may result from comparing use indices that vary on these characteristics.

Methods: This online study of current cannabis users (n = 1,800) assessed DD and three cannabis use frequency items: number of days of use in the past month, times used per day, and weekly-monthly use. A fourth index derived with Latent Class Analysis (LCA) integrated days per month and times per day to try to better characterize frequency patterns. Effect sizes reflecting relations between cannabis use frequency indices and DD were compared.

Results: Three frequency classes emerged from the LCA (Low-Moderate-High). DD was significantly associated with times per day (r = 0.11, d = 0.21), days of use (r = 0.09, d = 0.18), and the LCA index (r = 0.06, d = 0.13), but not weekly-monthly use (r = 0.04, d = 0.09). Times per day was more strongly related to DD than LCA classes (p < 0.01) and weekly-monthly use (p < 0.05), but not days of use (p = 0.66). Days of use exhibited a stronger relationship with DD than weekly-monthly use (p < 0.001), but not LCA classes (p = 0.06).

Conclusions: Cannabis use frequency measures with narrower timeframes may demonstrate stronger positive relationships to DD. The LCA index did not improve the relationship between frequency and DD, potentially because of shared variance between use days and times per day. Specific characteristics of cannabis use frequency may be particularly indicative of excessive DD.
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http://dx.doi.org/10.1016/j.addbeh.2020.106573DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572823PMC
January 2021

A randomized controlled trial of a smartphone-based application for the treatment of anxiety.

Psychother Res 2021 04 14;31(4):443-454. Epub 2020 Jul 14.

Department of Psychiatry, Stanford University, Stanford, CA, USA.

Generalized anxiety disorder (GAD) is prevalent among college students. Smartphone-based interventions may be a low-cost treatment method. College students with self-reported GAD were randomized to receive smartphone-based guided self-help ( = 50), or no treatment ( = 50). Post-treatment and six-month follow-up outcomes included the Depression Anxiety Stress Scales-Short Form Stress Subscale (DASS Stress), the Penn State Worry Questionnaire (PSWQ-11), and the State-Trait Anxiety Inventory-Trait (STAI-T), as well as diagnostic status assessed by the GAD-Questionnaire, 4th edition. From pre- to post-treatment, participants who received guided self-help (vs. no treatment) experienced significantly greater reductions on the DASS Stress ( = -0.408) and a greater probability of remission from GAD ( = -0.445). There was no significant between-group difference in change on the PSWQ-11 ( = -0.208) or STAI-T ( = -0.114). From post to six-month follow-up there was no significant loss of gains on DASS Stress scores ( = -0.141) and of those who had remitted, 78.6% remained remitted. Yet rates of remitted participants no longer differed significantly between conditions at follow-up (= -0.229). Smartphone-based interventions may be efficacious in treating some aspects of GAD. Methods for improving symptom reduction and long-term outcome are discussed.
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http://dx.doi.org/10.1080/10503307.2020.1790688DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7855205PMC
April 2021

Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones.

Sensors (Basel) 2020 Jun 24;20(12). Epub 2020 Jun 24.

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA.

Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression ( = 31). Using a combination of nomothetic and idiographically-weighted machine learning models, the results suggest that depressed mood can be accurately predicted from hour to hour with an average correlation between out of sample predicted depressed mood levels and observed depressed mood of 0.587, CI [0.552, 0.621]. This suggests that passively collected smartphone data can accurately predict future depressed mood among a sample reporting clinical levels of depression. If replicated in other samples, this modeling framework may allow just-in-time adaptive interventions to treat depression as it changes in the context of daily life.
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http://dx.doi.org/10.3390/s20123572DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349045PMC
June 2020

Social criticism moderates the relationship between anxiety and depression 10 years later.

J Affect Disord 2020 09 21;274:15-22. Epub 2020 May 21.

The Pennsylvania State University, United States.

Background: Research has consistently documented anxiety and depression as bidirectional risk factors for one another. However, little research investigates the sequential comorbidity of anxiety and depression over lengthy durations, and the influence of contextual variables on this relationship have not been fully empirically investigated.

Method: The current study examined perceived social criticism as a moderator of the relationship between a history of anxiety and a past 12-month depressive episode at least 10 years later (and vice versa) utilizing the National Comorbidity Survey Baseline (N = 8,098) and Re-interview data (N = 5,001). History of anxiety and depressive diagnoses were assigned at Wave 1, past year diagnosis at Wave 2, and perceived social criticism was assessed at Wave 1.

Results: Structural equation modeling indicated that when controlling for a Wave 1 latent depression factor, a positive relationship between Wave 1 latent anxiety and a Wave 2 latent depression emerged for those endorsing higher perceived social criticism from friends and relatives, respectively. Unexpectedly, when controlling for Wave 1 latent anxiety, a negative relationship between Wave 1 latent depression and Wave 2 latent anxiety emerged for those endorsing higher perceived social criticism from friends, but no relationship when moderated by perceived social criticism from relatives.

Limitations: Perceived social criticism was self-reported, which may introduce self-perception bias.

Conclusions: Results identified perceived social criticism as an important moderator in the sequential comorbidity of anxiety and depression over a long period of time.
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http://dx.doi.org/10.1016/j.jad.2020.05.030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7365767PMC
September 2020

Flattening the Mental Health Curve: COVID-19 Stay-at-Home Orders Are Associated With Alterations in Mental Health Search Behavior in the United States.

JMIR Ment Health 2020 Jun 1;7(6):e19347. Epub 2020 Jun 1.

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.

Background: The coronavirus disease (COVID-19) has led to dramatic changes worldwide in people's everyday lives. To combat the pandemic, many governments have implemented social distancing, quarantine, and stay-at-home orders. There is limited research on the impact of such extreme measures on mental health.

Objective: The goal of this study was to examine whether stay-at-home orders produced differential changes in mental health symptoms using internet search queries on a national scale.

Methods: In the United States, individual states vary in their adoption of measures to reduce the spread of COVID-19; as of March 23, 2020, 11 of the 50 states had issued stay-at-home orders. The staggered rollout of stay-at-home measures across the United States allows us to investigate whether these measures impact mental health by exploring variations in mental health search queries across the states. This paper examines the changes in mental health search queries on Google between March 16-23, 2020, across each state and Washington, DC. Specifically, this paper examines differential changes in mental health searches based on patterns of search activity following issuance of stay-at-home orders in these states compared to all other states. The participants were all the people who searched mental health terms in Google between March 16-23. Between March 16-23, 11 states underwent stay-at-home orders to prevent the transmission of COVID-19. Outcomes included search terms measuring anxiety, depression, obsessive-compulsive, negative thoughts, irritability, fatigue, anhedonia, concentration, insomnia, and suicidal ideation.

Results: Analyzing over 10 million search queries using generalized additive mixed models, the results suggested that the implementation of stay-at-home orders are associated with a significant flattening of the curve for searches for suicidal ideation, anxiety, negative thoughts, and sleep disturbances, with the most prominent flattening associated with suicidal ideation and anxiety.

Conclusions: These results suggest that, despite decreased social contact, mental health search queries increased rapidly prior to the issuance of stay-at-home orders, and these changes dissipated following the announcement and enactment of these orders. Although more research is needed to examine sustained effects, these results suggest mental health symptoms were associated with an immediate leveling off following the issuance of stay-at-home orders.
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http://dx.doi.org/10.2196/19347DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265799PMC
June 2020

Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors.

J Med Internet Res 2020 05 29;22(5):e16875. Epub 2020 May 29.

Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.

Background: Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier.

Objective: This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset.

Methods: In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants' social anxiety symptom severity.

Results: The results suggested that these passive sensor data could be utilized to accurately predict participants' social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect.

Conclusions: These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.
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http://dx.doi.org/10.2196/16875DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293055PMC
May 2020

Ethical dilemmas posed by mobile health and machine learning in psychiatry research.

Bull World Health Organ 2020 Apr 25;98(4):270-276. Epub 2020 Feb 25.

Department of Psychology, Harvard University, Cambridge, USA.

The application of digital technology to psychiatry research is rapidly leading to new discoveries and capabilities in the field of mobile health. However, the increase in opportunities to passively collect vast amounts of detailed information on study participants coupled with advances in statistical techniques that enable machine learning models to process such information has raised novel ethical dilemmas regarding researchers' duties to: (i) monitor adverse events and intervene accordingly; (ii) obtain fully informed, voluntary consent; (iii) protect the privacy of participants; and (iv) increase the transparency of powerful, machine learning models to ensure they can be applied ethically and fairly in psychiatric care. This review highlights emerging ethical challenges and unresolved ethical questions in mobile health research and provides recommendations on how mobile health researchers can address these issues in practice. Ultimately, the hope is that this review will facilitate continued discussion on how to achieve best practice in mobile health research within psychiatry.
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http://dx.doi.org/10.2471/BLT.19.237107DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7133483PMC
April 2020

Cognitive-Behavioral Therapy in the Digital Age: Presidential Address.

Behav Ther 2020 01 8;51(1):1-14. Epub 2019 Aug 8.

Massachusetts General Hospital/Harvard Medical School.

Our field has come a long way in establishing cognitive-behavioral therapy as the empirically supported treatment of choice for a wide range of mental and behavioral health problems. Nevertheless, most individuals with mental disorders do not receive any care at all, and those who do often have difficulty accessing care that is consistently high in quality. Addressing these issues is complex and costly and thus progress has been slow. We are entering an exciting stage in which emerging technologies might offer novel solutions to the treatment gap. This paper discusses a number of technology-enabled solutions to our field's challenges, including Internet-based and smartphone-based cognitive-behavioral therapy. Nevertheless, we must remain attentive to potential pitfalls of these emerging technologies. The paper incorporates suggestions for how the field may approach these potential pitfalls and provides a vision for how we might develop powerful, scalable, precisely timed, personalized interventions to enhance global mental health.
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http://dx.doi.org/10.1016/j.beth.2019.08.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7155747PMC
January 2020

Using Digital Phenotyping to Accurately Detect Depression Severity.

J Nerv Ment Dis 2019 10;207(10):893-896

Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.

Development of digital biomarkers holds promise for enabling scalable, time-sensitive, and cost-effective strategies to monitor symptom severity among those with major depressive disorder (MDD). The current study examined the use of passive movement and light data from wearable devices to assess depression severity in 15 patients with MDD. Using over 1 week of movement data, we were able to significantly assess depression severity with high precision for self-reported (r = 0.855; 95% confidence interval [CI], 0.610-0.950; p = 4.95 × 10) and clinician-rated (r = 0.604; 95% CI, 0.133-0.894; p = 0.017) symptom severity. Pending replication, the present data suggest that the use of passive wearable sensors to inform healthcare decisions holds considerable promise.
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http://dx.doi.org/10.1097/NMD.0000000000001042DOI Listing
October 2019
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