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Linardon J, Chen K, Gajjar S, Eadara A, Wang S, Flathers M, Burns J, Torous J. Smartphone digital phenotyping in mental health disorders: A review of raw sensors utilized, machine learning processing pipelines, and derived behavioral features. Psychiatry Res 2025; 348:116483. [PMID: 40187059 DOI: 10.1016/j.psychres.2025.116483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2024] [Revised: 03/18/2025] [Accepted: 04/01/2025] [Indexed: 04/07/2025]
Abstract
With increased access to digital technology, there has been a surge in the use of and interest in digital phenotyping as a tool to calculate various features from raw smart device data. However, the increased usage of digital phenotyping has created confusion. The vast number of sensors that can be utilized to collect passive data, and diverse methods utilized to convert that sensor data into features has introduced conflicting results and conclusions into the literature. Consequently, there is an identified need for standardizing how digital phenotyping data is measured and collected. This review evaluates the different sensors and methods utilized in digital phenotyping research across 112 papers, with the goal of finding the most common platforms, sensors, and methods for each behavioral measure. This should help guide future digital phenotyping research, and resolve some existing confusion in the field. Information on each study's data sensor variables were tracked and consolidated into a double-coded Codebook. Variables assessed included but were not limited to data sensors, features extracted from data sensors, statistical methods used, phone type, patient access to phones, and characteristics of patient population. This review found that most studies used Android devices (n = 67) or both Android and iPhone (n = 38) with an average duration of 14.3 weeks. The GPS sensor was also found to be the most frequently used sensor. This review underscores the need for standardization in methodological reporting, sensor utilization, and feature extraction across mental health studies.
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Affiliation(s)
- Jake Linardon
- SEED Lifespan Strategic Research Centre, School of Psychology, Faculty of Health, Deakin University, Geelong, Australia
| | - Kelly Chen
- Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02115, Boston, MA, USA
| | - Shruti Gajjar
- Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02115, Boston, MA, USA
| | - Amrik Eadara
- Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02115, Boston, MA, USA
| | - Shiwei Wang
- Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02115, Boston, MA, USA
| | - Matthew Flathers
- Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02115, Boston, MA, USA
| | - James Burns
- Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02115, Boston, MA, USA
| | - John Torous
- Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02115, Boston, MA, USA.
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Jolly A, Pandey V, Sahni M, Leon-Castro E, Perez-Arellano LA. Modern Smart Gadgets and Wearables for Diagnosis and Management of Stress, Wellness, and Anxiety: A Comprehensive Review. Healthcare (Basel) 2025; 13:411. [PMID: 39997286 PMCID: PMC11855179 DOI: 10.3390/healthcare13040411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2024] [Revised: 01/03/2025] [Accepted: 02/07/2025] [Indexed: 02/26/2025] Open
Abstract
The increasing development of gadgets to evaluate stress, wellness, and anxiety has garnered significant attention in recent years. These technological advancements aim to expedite the identification and subsequent treatment of these prevalent conditions. This study endeavors to critically examine the latest smart gadgets and portable techniques utilized for diagnosing depression, stress, and emotional trauma while also exploring the underlying biochemical processes associated with their identification. Integrating various detectors within smartphones and smart bands enables continuous monitoring and recording of user activities. Given their widespread use, smartphones, smartwatches, and smart wristbands have become indispensable in our daily lives, prompting the exploration of their potential in stress detection and prevention. When individuals experience stress, their nervous system responds by releasing stress hormones, which can be easily identified and quantified by smartphones and smart bands. The study in this paper focused on the examination of anxiety and stress and consistently employed "heart rate variability" (HRV) characteristics for diagnostic purposes, with superior outcomes observed when HRV was combined with "electroencephalogram" (EEG) analysis. Recent research indicates that electrodermal activity (EDA) demonstrates remarkable precision in identifying anxiety. Comparisons with HRV, EDA, and breathing rate reveal that the mean heart rate employed by several commercial wearable products is less accurate in identifying anxiety and stress. This comprehensive review article provides an evidence-based evaluation of intelligent gadgets and wearable sensors, highlighting their potential to accurately assess stress, wellness, and anxiety. It also identifies areas for further research and development.
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Affiliation(s)
- Aman Jolly
- Department of Computer Science and Engineering, Delhi Technological University, Delhi 110042, India;
| | - Vikas Pandey
- Electrical Engineering Department, Babu Banarasi Das University, Lucknow 226028, India;
| | - Manoj Sahni
- Department of Mathematics, Pandit Deendayal Energy University, Gandhinagar 382007, India
| | - Ernesto Leon-Castro
- Faculty of Economics and Administrative Sciences, Universidad Católica de la Santísima Concepción, Concepción 4070129, Chile;
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Jean T, Guay Hottin R, Orban P. Forecasting mental states in schizophrenia using digital phenotyping data. PLOS DIGITAL HEALTH 2025; 4:e0000734. [PMID: 39919138 PMCID: PMC11805420 DOI: 10.1371/journal.pdig.0000734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/22/2024] [Indexed: 02/09/2025]
Abstract
The promise of machine learning successfully exploiting digital phenotyping data to forecast mental states in psychiatric populations could greatly improve clinical practice. Previous research focused on binary classification and continuous regression, disregarding the often ordinal nature of prediction targets derived from clinical rating scales. In addition, mental health ratings typically show important class imbalance or skewness that need to be accounted for when evaluating predictive performance. Besides it remains unclear which machine learning algorithm is best suited for forecast tasks, the eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) algorithms being 2 popular choices in digital phenotyping studies. The CrossCheck dataset includes 6,364 mental state surveys using 4-point ordinal rating scales and 23,551 days of smartphone sensor data contributed by patients with schizophrenia. We trained 120 machine learning models to forecast 10 mental states (e.g., Calm, Depressed, Seeing things) from passive sensor data on 2 predictive tasks (ordinal regression, binary classification) with 2 learning algorithms (XGBoost, LSTM) over 3 forecast horizons (same day, next day, next week). A majority of ordinal regression and binary classification models performed significantly above baseline, with macro-averaged mean absolute error values between 1.19 and 0.77, and balanced accuracy between 58% and 73%, which corresponds to similar levels of performance when these metrics are scaled. Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. In conclusion, when using performance metrics that properly account for class imbalance, ordinal forecast models demonstrated comparable performance to the prevalent binary classification approach without losing valuable clinical information from self-reports, thus providing richer and easier to interpret predictions.
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Affiliation(s)
- Thierry Jean
- Research Center of the Montreal Mental Health University Institute, Montreal, Canada
- Department of Psychiatry and Addictology, University of Montreal, Montreal, Canada
| | - Rose Guay Hottin
- Research Center of the Montreal Mental Health University Institute, Montreal, Canada
| | - Pierre Orban
- Research Center of the Montreal Mental Health University Institute, Montreal, Canada
- Department of Psychiatry and Addictology, University of Montreal, Montreal, Canada
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Ringwald WR, Nielsen SR, Mostajabi J, Vize CE, van den Berg T, Manuck SB, Marsland AL, Wright AG. Characterizing Stress Processes by Linking Big Five Personality States, Traits, and Day-to-Day Stressors. JOURNAL OF RESEARCH IN PERSONALITY 2024; 110:104487. [PMID: 38708104 PMCID: PMC11067701 DOI: 10.1016/j.jrp.2024.104487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
The accumulation of day-to-day stressors can impact mental and physical health. How people respond to stressful events is a key mechanism responsible for the effects of stress, and individual differences in stress responses can either perpetuate or prevent negative consequences. Most research on daily stress processes has focused on affective responses to stressors, but stress responses can involve more than just affect (e.g., behavior, cognitions). Additionally, most research has studied the role of neuroticism in shaping those responses, but many other individual differences are associated with stress. In this study, we more broadly characterized daily stress processes by expanding the nomological networks of stress responses to include Big Five personality states. We also linked those stress responses to all Big Five traits, as well as individual differences in stress variety, severity, and controllability. We studied a sample of participants (N = 1,090) who reported on stressful events, their appraisal of events in terms of severity and controllability, and their Big Five personality states daily for 8-10 days (N = 8,870 observations). Multi-level structural equation models were used to separate how characteristics of the perceived stressful situation and characteristics of the person play into daily stress processes. Results showed that (1) all Big Five personality states shift in response to perceived stress, (2) all Big Five personality traits relate to average levels of perceived stress variety, severity, and controllability, (3) individual differences in personality and average perceived stress variety and perceived severity relate to the strength of personality state responses to daily stress, albeit in a more limited fashion. Our results point to new pathways by which stressors affect people in everyday life and begin to clarify processes that may explain individual differences in risk or resilience to the harmful effects of stress.
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Choi A, Ooi A, Lottridge D. Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review. JMIR Mhealth Uhealth 2024; 12:e40689. [PMID: 38780995 PMCID: PMC11157179 DOI: 10.2196/40689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 10/03/2022] [Accepted: 09/27/2023] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Unaddressed early-stage mental health issues, including stress, anxiety, and mild depression, can become a burden for individuals in the long term. Digital phenotyping involves capturing continuous behavioral data via digital smartphone devices to monitor human behavior and can potentially identify milder symptoms before they become serious. OBJECTIVE This systematic literature review aimed to answer the following questions: (1) what is the evidence of the effectiveness of digital phenotyping using smartphones in identifying behavioral patterns related to stress, anxiety, and mild depression? and (2) in particular, which smartphone sensors are found to be effective, and what are the associated challenges? METHODS We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) process to identify 36 papers (reporting on 40 studies) to assess the key smartphone sensors related to stress, anxiety, and mild depression. We excluded studies conducted with nonadult participants (eg, teenagers and children) and clinical populations, as well as personality measurement and phobia studies. As we focused on the effectiveness of digital phenotyping using smartphones, results related to wearable devices were excluded. RESULTS We categorized the studies into 3 major groups based on the recruited participants: studies with students enrolled in universities, studies with adults who were unaffiliated to any particular organization, and studies with employees employed in an organization. The study length varied from 10 days to 3 years. A range of passive sensors were used in the studies, including GPS, Bluetooth, accelerometer, microphone, illuminance, gyroscope, and Wi-Fi. These were used to assess locations visited; mobility; speech patterns; phone use, such as screen checking; time spent in bed; physical activity; sleep; and aspects of social interactions, such as the number of interactions and response time. Of the 40 included studies, 31 (78%) used machine learning models for prediction; most others (n=8, 20%) used descriptive statistics. Students and adults who experienced stress, anxiety, or depression visited fewer locations, were more sedentary, had irregular sleep, and accrued increased phone use. In contrast to students and adults, less mobility was seen as positive for employees because less mobility in workplaces was associated with higher performance. Overall, travel, physical activity, sleep, social interaction, and phone use were related to stress, anxiety, and mild depression. CONCLUSIONS This study focused on understanding whether smartphone sensors can be effectively used to detect behavioral patterns associated with stress, anxiety, and mild depression in nonclinical participants. The reviewed studies provided evidence that smartphone sensors are effective in identifying behavioral patterns associated with stress, anxiety, and mild depression.
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Affiliation(s)
- Adrien Choi
- School of Computer Science, Faculty of Science, University of Auckland, Auckland, New Zealand
| | - Aysel Ooi
- School of Computer Science, Faculty of Science, University of Auckland, Auckland, New Zealand
| | - Danielle Lottridge
- School of Computer Science, Faculty of Science, University of Auckland, Auckland, New Zealand
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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; 12:336. [PMID: 35977932 PMCID: PMC9385727 DOI: 10.1038/s41398-022-02038-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Generalized anxiety disorder (GAD) is a highly prevalent condition. Monitoring GAD symptoms requires substantial time, effort, and cost. The development of digital phenotypes of GAD may enable new scalable, timely, and inexpensive assessments of GAD symptoms. METHOD The current study used passive movement data collected within a large national cohort (N = 264) to assess GAD symptom severity. RESULTS Using one week of movement data, machine learning models accurately predicted GAD symptoms across a continuum (r = 0.511) and accurately detected those individuals with elevated GAD symptoms (AUC = 0.892, 70.0% Sensitivity, 95.5% Specificity, Brier Score = 0.092). Those with a risk score at the 90th percentile or above had 21 times the odds of having elevated GAD symptoms compared to those with lower risk scores. The risk score was most strongly associated with irritability, worry controllability, and restlessness (individual rs > 0.5). The risk scores for GAD were also discriminant of major depressive disorder symptom severity (r = 0.190). LIMITATIONS The current study examined the detection of GAD symptom severity rather than the prediction of GAD symptom severity across time. Furthermore, the instant sample of data did not include nighttime actigraphy, as participants were not asked to wear the actigraphs at night. CONCLUSIONS These results suggest that artificial intelligence can effectively utilize wearable movement data collected in daily life to accurately infer risk of GAD symptoms.
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Affiliation(s)
- Nicholas C Jacobson
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, PA, USA.
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, PA, USA.
- Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Lebanon, PA, USA.
| | - Brandon Feng
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, PA, USA
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Vidal Bustamante CM, Coombs G, Rahimi-Eichi H, Mair P, Onnela JP, Baker JT, Buckner RL. Fluctuations in behavior and affect in college students measured using deep phenotyping. Sci Rep 2022; 12:1932. [PMID: 35121741 PMCID: PMC8816914 DOI: 10.1038/s41598-022-05331-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/05/2022] [Indexed: 12/31/2022] Open
Abstract
College students commonly experience psychological distress when faced with intensified academic demands and changes in the social environment. Examining the nature and dynamics of students’ affective and behavioral experiences can help us better characterize the correlates of psychological distress. Here, we leveraged wearables and smartphones to study 49 first-year college students continuously throughout the academic year. Affect and sleep, academic, and social behavior showed substantial changes from school semesters to school breaks and from weekdays to weekends. Three student clusters were identified with behavioral and affective dissociations and varying levels of distress throughout the year. While academics were a common stressor for all, the cluster with highest distress stood out by frequent report of social stress. Moreover, the frequency of reporting social, but not academic, stress predicted subsequent clinical symptoms. Two years later, during the COVID-19 pandemic, the first-year cluster with highest distress again stood out by frequent social stress and elevated clinical symptoms. Focus on sustained interpersonal stress, relative to academic stress, might be especially helpful to identify students at heightened risk for psychopathology.
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Affiliation(s)
- Constanza M Vidal Bustamante
- Department of Psychology, Harvard University, Northwest Science Building 280.05, 52 Oxford Street, Cambridge, MA, 02138, USA. .,Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA.
| | - Garth Coombs
- Department of Psychology, Harvard University, Northwest Science Building 280.05, 52 Oxford Street, Cambridge, MA, 02138, USA.,Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA
| | - Habiballah Rahimi-Eichi
- Department of Psychology, Harvard University, Northwest Science Building 280.05, 52 Oxford Street, Cambridge, MA, 02138, USA.,Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, 02114, USA.,Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, 02478, USA
| | - Patrick Mair
- Department of Psychology, Harvard University, Northwest Science Building 280.05, 52 Oxford Street, Cambridge, MA, 02138, USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA
| | - Justin T Baker
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02114, USA.,Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, 02478, USA
| | - Randy L Buckner
- Department of Psychology, Harvard University, Northwest Science Building 280.05, 52 Oxford Street, Cambridge, MA, 02138, USA.,Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, 02114, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Charlestown, MA, 02129, USA
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Castro R, Ribeiro-Alves M, Oliveira C, Romero CP, Perazzo H, Simjanoski M, Kapciznki F, Balanzá-Martínez V, De Boni RB. What Are We Measuring When We Evaluate Digital Interventions for Improving Lifestyle? A Scoping Meta-Review. Front Public Health 2022; 9:735624. [PMID: 35047469 PMCID: PMC8761632 DOI: 10.3389/fpubh.2021.735624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/29/2021] [Indexed: 01/07/2023] Open
Abstract
Background: Lifestyle Medicine (LM) aims to address six main behavioral domains: diet/nutrition, substance use (SU), physical activity (PA), social relationships, stress management, and sleep. Digital Health Interventions (DHIs) have been used to improve these domains. However, there is no consensus on how to measure lifestyle and its intermediate outcomes aside from measuring each behavior separately. We aimed to describe (1) the most frequent lifestyle domains addressed by DHIs, (2) the most frequent outcomes used to measure lifestyle changes, and (3) the most frequent DHI delivery methods. Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) Extension for Scoping Reviews. A literature search was conducted using MEDLINE, Cochrane Library, EMBASE, and Web of Science for publications since 2010. We included systematic reviews and meta-analyses of clinical trials using DHI to promote health, behavioral, or lifestyle change. Results: Overall, 954 records were identified, and 72 systematic reviews were included. Of those, 35 conducted meta-analyses, 58 addressed diet/nutrition, and 60 focused on PA. Only one systematic review evaluated all six lifestyle domains simultaneously; 1 systematic review evaluated five lifestyle domains; 5 systematic reviews evaluated 4 lifestyle domains; 14 systematic reviews evaluated 3 lifestyle domains; and the remaining 52 systematic reviews evaluated only one or two domains. The most frequently evaluated domains were diet/nutrition and PA. The most frequent DHI delivery methods were smartphone apps and websites. Discussion: The concept of lifestyle is still unclear and fragmented, making it hard to evaluate the complex interconnections of unhealthy behaviors, and their impact on health. Clarifying this concept, refining its operationalization, and defining the reporting guidelines should be considered as the current research priorities. DHIs have the potential to improve lifestyle at primary, secondary, and tertiary levels of prevention-but most of them are targeting clinical populations. Although important advances have been made to evaluate DHIs, some of their characteristics, such as the rate at which they become obsolete, will require innovative research designs to evaluate long-term outcomes in health.
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Affiliation(s)
- Rodolfo Castro
- Escola Nacional de Saúde Pública Sergio Arouca, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
- Instituto de Saúde Coletiva, Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marcelo Ribeiro-Alves
- Instituto Nacional de Infectologia Evandro Chagas, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - Cátia Oliveira
- Centro de Desenvolvimento Tecnológico em Saúde, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - Carmen Phang Romero
- Centro de Desenvolvimento Tecnológico em Saúde, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - Hugo Perazzo
- Instituto Nacional de Infectologia Evandro Chagas, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - Mario Simjanoski
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Flavio Kapciznki
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
- Bipolar Disorder Program, Laboratory of Molecular Psychiatry, Instituto Nacional de Ciência e Tecnologia Translacional em Medicina, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Vicent Balanzá-Martínez
- Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, CIBERSAM, Valencia, Spain
| | - Raquel B. De Boni
- Institute of Scientific and Technological Communication and Information in Health, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
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Xiang Y, Li S, Zhang P. An exploration in remote blood pressure management: Application of daily routine pattern based on mobile data in health management. FUNDAMENTAL RESEARCH 2022; 2:154-165. [PMID: 38933904 PMCID: PMC11197610 DOI: 10.1016/j.fmre.2021.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 09/02/2021] [Accepted: 11/12/2021] [Indexed: 11/24/2022] Open
Abstract
Blood pressure (BP) is an important indicator of an individual's health status and is closely related to daily behaviors. Thus, a continuous daily measurement of BP is critical for hypertension control. To assist continuous measurement, BP prediction based on non-physiological data (ubiquitous mobile phone data) was studied in the research. An algorithm was proposed that predicts BP based on patients' daily routine, which includes activities such as sleep, work, and commuting. The aim of the research is to provide insight into the application of mobile data in telemonitoring and the continuous unobtrusive daily measurement of BP. A half-year data set from October 2017 of 320 individuals, including telecom data and BP measurement data, was analyzed. Two hierarchical Bayesian topic models were used to extract individuals' location-driven daily routine patterns (topics) and calculate probabilities among these topics from their day-level mobile trajectories. Based on the topic probability distribution and patients' contextual data, their BP were predicted using different models. The prediction model comparison shows that the long short-term memory (LSTM) method exceeds others when the data has a high dependency. Otherwise, the Random Forest regression model outperforms the LSTM method. Also, the experimental results validate the effectiveness of the topics in BP prediction.
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Affiliation(s)
- Yidan Xiang
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shaochun Li
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Pengzhu Zhang
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China
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Madrid-García A, León-Mateos L, Pato E, Jover JA, Fernández-Gutiérrez B, Abasolo L, Menasalvas E, Rodríguez-Rodríguez L. Predictors of health-related quality of life in musculoskeletal disease patients: a longitudinal analysis. Ther Adv Musculoskelet Dis 2021; 13:1759720X211034063. [PMID: 34367344 PMCID: PMC8317252 DOI: 10.1177/1759720x211034063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 06/28/2021] [Indexed: 11/16/2022] Open
Abstract
Introduction Rheumatic and musculoskeletal diseases (RMDs) have a significant impact on patients' health-related quality of life (HRQoL) exacerbating disability, reducing independence and work capacity, among others. Predictors' identification affecting HRQoL could help to place efforts that minimize the deleterious impact of these conditions on patients' wellbeing. This study evaluates the influence of demographic and clinical predictors on the HRQoL of a cohort of RMD patients, measured using the Rosser classification index (RCI). Methods We included patients attending the Hospital Clínico San Carlos (HCSC) rheumatology outpatient clinic from 1 April 2007 to 30 November 2017. The primary outcome was the HRQoL assessed in each of the patient's visits using the RCI. Demographic and clinical variables extracted from a departmental electronic health record (EHR) were used as predictors: RMD diagnoses, treatments, comorbidities, and averaged HRQoL values from previous periods (for this last variable, values were imputed if no information was available). Association between predictors and HRQoL was analyzed using penalized generalized estimating equations (PGEEs). To account for imputation bias, the PGEE model was repeated excluding averaged HRQoL predictors, and common predictors were considered. Discussion A total of 18,187 outpatients with 95,960 visits were included. From 410 initial predictors, 19 were independently associated with patients' HRQoL in both PGEE models. Chronic kidney disease (CKD), an episode of prescription of third level analgesics, monoarthritis, and fibromyalgia diagnoses were associated with worse HRQoL. Conversely, the prescription in the previous visit of acid-lowering medication, colchicine, and third level analgesics was associated with better HRQoL. Conclusion We have identified several diagnoses, treatments, and comorbidities independently associated with HRQoL in a cohort of outpatients attending a rheumatology clinic.
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Affiliation(s)
- Alfredo Madrid-García
- Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, IdISSC, Madrid, Spain
| | - Leticia León-Mateos
- Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, IdISSC, Madrid, Spain
| | - Esperanza Pato
- Rheumatology Service, Hospital Clínico San Carlos, IdISSC, Madrid, Spain
| | - Juan A Jover
- Rheumatology Service, Hospital Clínico San Carlos, IdISSC, Madrid, Spain
| | | | - Lydia Abasolo
- Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, IdISSC, Madrid, Spain
| | - Ernestina Menasalvas
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Parque Científico y Tecnológico de la UPM, Madrid, Spain
| | - Luis Rodríguez-Rodríguez
- Grupo de Patología Musculoesquelética, Hospital Clínico San Carlos, c\ Prof. Martin Lagos s/n, Madrid 28040, Spain
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11
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Chong KPL, Woo BKP. Emerging wearable technology applications in gastroenterology: A review of the literature. World J Gastroenterol 2021; 27:1149-1160. [PMID: 33828391 PMCID: PMC8006095 DOI: 10.3748/wjg.v27.i12.1149] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/12/2021] [Accepted: 03/11/2021] [Indexed: 02/06/2023] Open
Abstract
The field of gastroenterology has recently seen a surge in wearable technology to monitor physical activity, sleep quality, pain, and even gut activity. The past decade has seen the emergence of wearable devices including Fitbit, Apple Watch, AbStats, and ingestible sensors. In this review, we discuss current and future devices designed to measure sweat biomarkers, steps taken, sleep efficiency, gastric electrical activity, stomach pH, and intestinal contents. We also summarize several clinical studies to better understand wearable devices so that we may assess their potential benefit in improving healthcare while also weighing the challenges that must be addressed.
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Affiliation(s)
- Kimberly PL Chong
- College of Osteopathic Medicine, Western University of Health Sciences, Pomona, CA 91766, United States
| | - Benjamin KP Woo
- Department of Psychiatry and Biobehavioral Sciences, Olive View - University of California Los Angeles Medical Center, Sylmar, CA 91342, United States
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12
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Keshmiri S. Conditional Entropy: A Potential Digital Marker for Stress. ENTROPY (BASEL, SWITZERLAND) 2021; 23:286. [PMID: 33652891 PMCID: PMC7996836 DOI: 10.3390/e23030286] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/20/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022]
Abstract
Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.
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Affiliation(s)
- Soheil Keshmiri
- Advanced Telecommunications Research Institute International (ATR), Kyoto 619-0237, Japan
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13
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Melcher J, Hays R, Torous J. Digital phenotyping for mental health of college students: a clinical review. EVIDENCE-BASED MENTAL HEALTH 2020; 23:161-166. [PMID: 32998937 PMCID: PMC10231503 DOI: 10.1136/ebmental-2020-300180] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/02/2020] [Accepted: 09/03/2020] [Indexed: 12/20/2022]
Abstract
Experiencing continued growth in demand for mental health services among students, colleges are seeking digital solutions to increase access to care as classes shift to remote virtual learning during the COVID-19 pandemic. Using smartphones to capture real-time symptoms and behaviours related to mental illnesses, digital phenotyping offers a practical tool to help colleges remotely monitor and assess mental health and provide more customised and responsive care. This narrative review of 25 digital phenotyping studies with college students explored how this method has been deployed, studied and has impacted mental health outcomes. We found the average duration of studies to be 42 days and the average enrolled to be 81 participants. The most common sensor-based streams collected included location, accelerometer and social information and these were used to inform behaviours such as sleep, exercise and social interactions. 52% of the studies included also collected smartphone survey in some form and these were used to assess mood, anxiety and stress among many other outcomes. The collective focus on data that construct features related to sleep, activity and social interactions indicate that this field is already appropriately attentive to the primary drivers of mental health problems among college students. While the heterogeneity of the methods of these studies presents no reliable target for mobile devices to offer automated help-the feasibility across studies suggests the potential to use these data today towards personalising care. As more unified digital phenotyping research evolves and scales to larger sample sizes, student mental health centres may consider integrating these data into their clinical practice for college students.
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Affiliation(s)
- Jennifer Melcher
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Ryan Hays
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
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14
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Huckins JF, daSilva AW, Wang W, Hedlund E, Rogers C, Nepal SK, Wu J, Obuchi M, Murphy EI, Meyer ML, Wagner DD, Holtzheimer PE, Campbell AT. Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study. J Med Internet Res 2020; 22:e20185. [PMID: 32519963 PMCID: PMC7301687 DOI: 10.2196/20185] [Citation(s) in RCA: 369] [Impact Index Per Article: 73.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 06/06/2020] [Accepted: 06/09/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The vast majority of people worldwide have been impacted by coronavirus disease (COVID-19). In addition to the millions of individuals who have been infected with the disease, billions of individuals have been asked or required by local and national governments to change their behavioral patterns. Previous research on epidemics or traumatic events suggests that this can lead to profound behavioral and mental health changes; however, researchers are rarely able to track these changes with frequent, near-real-time sampling or compare their findings to previous years of data for the same individuals. OBJECTIVE By combining mobile phone sensing and self-reported mental health data among college students who have been participating in a longitudinal study for the past 2 years, we sought to answer two overarching questions. First, have the behaviors and mental health of the participants changed in response to the COVID-19 pandemic compared to previous time periods? Second, are these behavior and mental health changes associated with the relative news coverage of COVID-19 in the US media? METHODS Behaviors such as the number of locations visited, distance traveled, duration of phone usage, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments of the Patient Health Questionnaire-4. The participants were 217 undergraduate students, with 178 (82.0%) students providing data during the Winter 2020 term. Differences in behaviors and self-reported mental health collected during the Winter 2020 term compared to previous terms in the same cohort were modeled using mixed linear models. RESULTS During the first academic term impacted by COVID-19 (Winter 2020), individuals were more sedentary and reported increased anxiety and depression symptoms (P<.001) relative to previous academic terms and subsequent academic breaks. Interactions between the Winter 2020 term and the week of the academic term (linear and quadratic) were significant. In a mixed linear model, phone usage, number of locations visited, and week of the term were strongly associated with increased amount of COVID-19-related news. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, and phone usage), both anxiety (P<.001) and depression (P=.03) were significantly associated with COVID-19-related news. CONCLUSIONS Compared with prior academic terms, individuals in the Winter 2020 term were more sedentary, anxious, and depressed. A wide variety of behaviors, including increased phone usage, decreased physical activity, and fewer locations visited, were associated with fluctuations in COVID-19 news reporting. While this large-scale shift in mental health and behavior is unsurprising, its characterization is particularly important to help guide the development of methods to reduce the impact of future catastrophic events on the mental health of the population.
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Affiliation(s)
- Jeremy F Huckins
- Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, United States
| | - Alex W daSilva
- Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, United States
| | - Weichen Wang
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Elin Hedlund
- Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, United States
| | - Courtney Rogers
- Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, United States
| | - Subigya K Nepal
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Jialing Wu
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Mikio Obuchi
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
| | - Eilis I Murphy
- Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, United States
| | - Meghan L Meyer
- Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, United States
| | - Dylan D Wagner
- Department of Psychology, Ohio State University, Columbus, OH, United States
| | - Paul E Holtzheimer
- National Center for PTSD, White River Junction, VT, United States.,Department of Psychiatry, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States
| | - Andrew T Campbell
- Department of Computer Science, Dartmouth College, Hanover, NH, United States
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15
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Pryss R, John D, Schlee W, Schlotz W, Schobel J, Kraft R, Spiliopoulou M, Langguth B, Reichert M, O'Rourke T, Peters H, Pieh C, Lahmann C, Probst T. Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessment Pilot Study. JMIR Mhealth Uhealth 2019; 7:e13978. [PMID: 31670692 PMCID: PMC6913730 DOI: 10.2196/13978] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 06/22/2019] [Accepted: 08/19/2019] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND The mobile phone app, TrackYourStress (TYS), is a new crowdsensing mobile health platform for ecological momentary assessments of perceived stress levels. OBJECTIVE In this pilot study, we aimed to investigate the time trend of stress levels while using TYS for the entire population being studied and whether the individuals' perceived stress reactivity moderates stress level changes while using TYS. METHODS Using TYS, stress levels were measured repeatedly with the 4-item version of the Perceived Stress Scale (PSS-4), and perceived stress reactivity was measured once with the Perceived Stress Reactivity Scale (PSRS). A total of 78 nonclinical participants, who provided 1 PSRS assessment and at least 4 repeated PSS-4 measurements, were included in this pilot study. Linear multilevel models were used to analyze the time trend of stress levels and interactions with perceived stress reactivity. RESULTS Across the whole sample, stress levels did not change while using TYS (P=.83). Except for one subscale of the PSRS, interindividual differences in perceived stress reactivity did not influence the trajectories of stress levels. However, participants with higher scores on the PSRS subscale reactivity to failure showed a stronger increase of stress levels while using TYS than participants with lower scores (P=.04). CONCLUSIONS TYS tracks the stress levels in daily life, and most of the results showed that stress levels do not change while using TYS. Controlled trials are necessary to evaluate whether it is specifically TYS or any other influence that worsens the stress levels of participants with higher reactivity to failure.
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Affiliation(s)
- Rüdiger Pryss
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
| | - Dennis John
- Lutheran University of Applied Sciences, Nuremberg, Germany
| | - Winfried Schlee
- Department of Psychiatry and Psychotherapy, University of Regensburg at Bezirksklinikum, Regensburg, Germany
| | - Wolff Schlotz
- Max Planck Institute for Empirical Aesthetics, Frankfurt, Germany
| | - Johannes Schobel
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
| | - Robin Kraft
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
| | - Myra Spiliopoulou
- Faculty of Computer Science, Otto-von-Guericke-University, Magdeburg, Germany
| | - Berthold Langguth
- Department of Psychiatry and Psychotherapy, University of Regensburg at Bezirksklinikum, Regensburg, Germany
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
| | - Teresa O'Rourke
- Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria
| | - Henning Peters
- Department of Psychiatry and Psychotherapy, LMU Munich, Munich, Germany
| | - Christoph Pieh
- Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria
| | - Claas Lahmann
- Faculty of Medicine, Department of Psychosomatic Medicine and Psychotherapy, Medical Center-University of Freiburg, Freiburg, Germany
| | - Thomas Probst
- Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria
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16
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Goodday SM, Friend S. Unlocking stress and forecasting its consequences with digital technology. NPJ Digit Med 2019; 2:75. [PMID: 31372508 PMCID: PMC6668457 DOI: 10.1038/s41746-019-0151-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 07/08/2019] [Indexed: 12/20/2022] Open
Abstract
Chronic stress is a major underlying origin of the top leading causes of death, globally. Yet, the mechanistic explanation of the association between stress and disease is poorly understood. This stems from the inability to adequately measure stress in its naturally occurring state and the extreme heterogeneity by inter and intraindividual characteristics. The growth and availability of digital technologies involving wearable devices and mobile phone apps afford the opportunity to dramatically improve measurement of the biological stress response in real time. In parallel, the advancement and capabilities of artificial intelligence (AI) and machine learning could discern heterogeneous, multidimensional information from individual signs of stress, and possibly inform how these signs forecast the downstream consequences of stress in the form of end-organ damage. The marriage of these tools could dramatically enhance the field of stress research contributing to impactful and empowering interventions for individuals bridging knowledge to practice, and intervention to real-world use. Here we discuss this potential, anticipated challenges, and emerging opportunities.
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Affiliation(s)
- Sarah M Goodday
- 4YouandMe, Seattle, WA USA.,2Department of Psychiatry, University of Oxford, Oxford, UK
| | - Stephen Friend
- 4YouandMe, Seattle, WA USA.,2Department of Psychiatry, University of Oxford, Oxford, UK
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17
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Acikmese Y, Alptekin SE. Prediction of stress levels with LSTM and passive mobile sensors. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.procs.2019.09.221] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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