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Jahanshad N, Lenzini P, Bijsterbosch J. Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01938-8. [PMID: 39117903 DOI: 10.1038/s41386-024-01938-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/05/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024]
Abstract
Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.
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Affiliation(s)
- Neda Jahanshad
- Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, 90292, USA.
| | - Petra Lenzini
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA
| | - Janine Bijsterbosch
- Department of Radiology, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
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Alexander JD, Linkersdörfer J, Toda-Thorne K, Sullivan RM, Cummins KM, Tomko RL, Allen NB, Bagot KS, Baker FC, Fuemmeler BF, Hoffman EA, Kiss O, Mason MJ, Nguyen-Louie TT, Tapert SF, Smith CJ, Squeglia LM, Wade NE. Passively sensing smartphone use in teens with rates of use by sex and across operating systems. Sci Rep 2024; 14:17982. [PMID: 39097657 PMCID: PMC11297944 DOI: 10.1038/s41598-024-68467-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 07/24/2024] [Indexed: 08/05/2024] Open
Abstract
Youth screen media activity is a growing concern, though few studies include objective usage data. Through the longitudinal, U.S.-based Adolescent Brain Cognitive Development (ABCD) Study, youth (mage = 14; n = 1415) self-reported their typical smartphone use and passively recorded three weeks of smartphone use via the ABCD-specific Effortless Assessment Research System (EARS) application. Here we describe and validate passively-sensed smartphone keyboard and app use measures, provide code to harmonize measures across operating systems, and describe trends in adolescent smartphone use. Keyboard and app-use measures were reliable and positively correlated with one another (r = 0.33) and with self-reported use (rs = 0.21-0.35). Participants recorded a mean of 5 h of daily smartphone use, which is two more hours than they self-reported. Further, females logged more smartphone use than males. Smartphone use was recorded at all hours, peaking on average from 8 to 10 PM and lowest from 3 to 5 AM. Social media and texting apps comprised nearly half of all use. Data are openly available to approved investigators ( https://nda.nih.gov/abcd/ ). Information herein can inform use of the ABCD dataset to longitudinally study health and neurodevelopmental correlates of adolescent smartphone use.
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Affiliation(s)
| | - Janosch Linkersdörfer
- Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, MC 0405, La Jolla, CA, 92093, USA
| | | | | | | | | | | | - Kara S Bagot
- University of California, Los Angeles, Los Angeles, USA
| | | | | | | | | | | | - Tam T Nguyen-Louie
- Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, MC 0405, La Jolla, CA, 92093, USA
| | - Susan F Tapert
- Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, MC 0405, La Jolla, CA, 92093, USA
| | - Calen J Smith
- Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, MC 0405, La Jolla, CA, 92093, USA
| | | | - Natasha E Wade
- Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, MC 0405, La Jolla, CA, 92093, USA.
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Scholich T, Raj S, Lee J, Newman MW. Augmenting clinicians' analytical workflow through task-based integration of data visualizations and algorithmic insights: a user-centered design study. J Am Med Inform Assoc 2024:ocae183. [PMID: 39003519 DOI: 10.1093/jamia/ocae183] [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: 02/02/2024] [Revised: 06/07/2024] [Accepted: 07/03/2024] [Indexed: 07/15/2024] Open
Abstract
OBJECTIVES To understand healthcare providers' experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians' use of patientgenerated data from Type 1 diabetes devices. MATERIALS AND METHODS This qualitative study was conducted in three phases. In Phase 1, 11 clinicians reviewed data using commercial diabetes platforms in a think-aloud data walkthrough activity followed by semistructured interviews. In Phase 2, GlucoGuide was developed. In Phase 3, the same clinicians reviewed data using GlucoGuide in a think-aloud activity followed by semistructured interviews. Inductive thematic analysis was used to analyze transcripts of Phase 1 and Phase 3 think-aloud activity and interview. RESULTS 3 high level tasks, 8 sub-tasks, and 4 challenges were identified in Phase 1. In Phase 2, 3 requirements for GlucoGuide were identified. Phase 3 results suggested that clinicians found GlucoGuide easier to use and experienced a lower cognitive burden as compared to the commercial diabetes data reports that were used in Phase 1. Additionally, GlucoGuide addressed the challenges experienced in Phase 1. DISCUSSION The study suggests that the knowledge of analytical tasks and task-specific visualization strategies in implementing features of data interfaces can result in tools that lower the perceived burden of engaging with data. Additionally, supporting clinicians in contextualizing algorithmic insights by visual analysis of relevant data can positively influence clinicians' willingness to leverage algorithmic support. CONCLUSION Task-aligned tools that combine multiple data-driven approaches, such as visualization strategies and algorithmic insights, can improve clinicians' experience in reviewing device data.
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Affiliation(s)
- Till Scholich
- School of Information, University of Michigan, Ann Arbor, MI 48109, United States
| | - Shriti Raj
- Department of Medicine, Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, United States
- Institute for Human-Centered AI, Stanford University, Stanford, CA 94305, United States
| | - Joyce Lee
- Susan B. Meister Child Health Evaluation and Research Center (CHEAR), University of Michigan, Ann Arbor, MI 48109, United States
- Division of Pediatric Endocrinology, University of Michigan, Ann Arbor, MI 48109, United States
| | - Mark W Newman
- School of Information, University of Michigan, Ann Arbor, MI 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States
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Piccin J, Viduani A, Buchweitz C, Pereira RB, Zimerman A, Amando GR, Cosenza V, Ferreira LZ, McMahon NA, Melo RF, Richter D, Reckziegel FD, Rohrsetzer F, Souza L, Tonon AC, Costa-Valle MT, Zajkowska Z, Araújo RM, Hauser TU, van Heerden A, Hidalgo MP, Kohrt BA, Mondelli V, Swartz JR, Fisher HL, Kieling C. Prospective Follow-Up of Adolescents With and at Risk for Depression: Protocol and Methods of the Identifying Depression Early in Adolescence Risk Stratified Cohort Longitudinal Assessments. JAACAP OPEN 2024; 2:145-159. [PMID: 38863682 PMCID: PMC11163476 DOI: 10.1016/j.jaacop.2023.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/07/2023] [Indexed: 06/13/2024]
Abstract
Objective To present the protocol and methods for the prospective longitudinal assessments-including clinical and digital phenotyping approaches-of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study, which comprises Brazilian adolescents stratified at baseline by risk of developing depression or presence of depression. Method Of 7,720 screened adolescents aged 14 to 16 years, we recruited 150 participants (75 boys, 75 girls) based on a composite risk score: 50 with low risk for developing depression (LR), 50 with high risk for developing depression (HR), and 50 with an active untreated major depressive episode (MDD). Three annual follow-up assessments were conducted, involving clinical measures (parent- and adolescent-reported questionnaires and psychiatrist assessments), active and passive data sensing via smartphones, and neurobiological measures (neuroimaging and biological material samples). Retention rates were 96% (Wave 1), 94% (Wave 2), and 88% (Wave 3), with no significant differences by sex or group (p > .05). Participants highlighted their familiarity with the research team and assessment process as a motivator for sustained engagement. Discussion This protocol relied on novel aspects, such as the use of a WhatsApp bot, which is particularly pertinent for low- to-middle-income countries, and the collection of information from diverse sources in a longitudinal design, encompassing clinical data, self-reports, parental reports, Global Positioning System (GPS) data, and ecological momentary assessments. The study engaged adolescents over an extensive period and demonstrated the feasibility of conducting a prospective follow-up study with a risk-enriched cohort of adolescents in a middle-income country, integrating mobile technology with traditional methodologies to enhance longitudinal data collection.
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Affiliation(s)
- Jader Piccin
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Anna Viduani
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Claudia Buchweitz
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Rivka B. Pereira
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Aline Zimerman
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Guilherme R. Amando
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Laboratório de Cronobiologia e Sono, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Victor Cosenza
- Universidade Federal de Pelotas (UFPEL), Pelotas, Brazil
| | | | - Natália A.G. McMahon
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | | | - Danyella Richter
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Frederico D.S. Reckziegel
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Fernanda Rohrsetzer
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Laila Souza
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - André C. Tonon
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Laboratório de Cronobiologia e Sono, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Marina Tuerlinckx Costa-Valle
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
| | - Zuzanna Zajkowska
- King’s College London, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom
| | | | - Tobias U. Hauser
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom and with Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Alastair van Heerden
- Human and Social Development, Human Sciences Research Council, Pietermaritzburg, South Africa and Medical Research Council/Wits Developmental Pathways for Health Research Unit, University of the Witwatersrand, Johannesburg, South Africa
| | - Maria Paz Hidalgo
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Laboratório de Cronobiologia e Sono, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | | | - Valeria Mondelli
- King’s College London, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom
- National Institute for Health and Care Research Maudsley Mental Health Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | | | - Helen L. Fisher
- King’s College London, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom
- ESRC Centre for Society and Mental Health, King’s College London, London, United Kingdom
| | - Christian Kieling
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Prodia - Child & Adolescent Depression Program, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
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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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Jayousi S, Barchielli C, Alaimo M, Caputo S, Paffetti M, Zoppi P, Mucchi L. ICT in Nursing and Patient Healthcare Management: Scoping Review and Case Studies. SENSORS (BASEL, SWITZERLAND) 2024; 24:3129. [PMID: 38793983 PMCID: PMC11125011 DOI: 10.3390/s24103129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/21/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024]
Abstract
Over the past few decades, Information and Communication Technologies (ICT) have revolutionized the fields of nursing and patient healthcare management. This scoping review and the accompanying case studies shed light on the extensive scope and impact of ICT in these critical healthcare domains. The scoping review explores the wide array of ICT tools employed in nursing care and patient healthcare management. These tools encompass electronic health records systems, mobile applications, telemedicine solutions, remote monitoring systems, and more. This article underscores how these technologies have enhanced the efficiency, accuracy, and accessibility of clinical information, contributing to improved patient care. ICT revolution has revitalized nursing care and patient management, improving the quality of care and patient satisfaction. This review and the accompanying case studies emphasize the ongoing potential of ICT in the healthcare sector and call for further research to maximize its benefits.
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Affiliation(s)
- Sara Jayousi
- ICT Applications Lab, PIN—Polo Universitario “Città di Prato”, 59100 Prato, Italy
| | - Chiara Barchielli
- Management and Health Laboratory, Institute of Management, Sant’Anna School of Advanced Studies of Pisa, 56127 Pisa, Italy
| | - Marco Alaimo
- Department of Nursing and Midwifery, Local Health Unit Toscana Centro, 50134 Florence, Italy; (M.A.); (M.P.); (P.Z.)
| | - Stefano Caputo
- Department of Information Engineering, University of Florence, 50121 Florence, Italy; (S.C.); (L.M.)
| | - Marzia Paffetti
- Department of Nursing and Midwifery, Local Health Unit Toscana Centro, 50134 Florence, Italy; (M.A.); (M.P.); (P.Z.)
| | - Paolo Zoppi
- Department of Nursing and Midwifery, Local Health Unit Toscana Centro, 50134 Florence, Italy; (M.A.); (M.P.); (P.Z.)
| | - Lorenzo Mucchi
- Department of Information Engineering, University of Florence, 50121 Florence, Italy; (S.C.); (L.M.)
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Zhang X, Lewis S, Chen X, Zhou J, Wang X, Bucci S. Acceptability and experience of a smartphone symptom monitoring app for people with psychosis in China (YouXin): a qualitative study. BMC Psychiatry 2024; 24:268. [PMID: 38594713 PMCID: PMC11003104 DOI: 10.1186/s12888-024-05687-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 03/14/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Access to high-quality mental healthcare remains challenging for people with psychosis globally, including China. Smartphone-based symptom monitoring has the potential to support scalable mental healthcare. However, no such tool, until now, has been developed and evaluated for people with psychosis in China. This study investigated the acceptability and the experience of using a symptom self-monitoring smartphone app (YouXin) specifically developed for people with psychosis in China. METHODS Semi-structured interviews were conducted with 10 participants with psychosis to explore the acceptability of YouXin. Participants were recruited from the non-randomised feasibility study that tested the validity, feasibility, acceptability and safety of the YouXin app. Data analysis was guided by the theoretical framework of acceptability. RESULTS Most participants felt the app was acceptable and easy to use, and no unbearable burdens or opportunity costs were reported. Participants found completing the self-monitoring app rewarding and experienced a sense of achievement. Privacy and data security were not major concerns for participants, largely due to trust in their treating hospital around data protection. Participants found the app easy to use and attributed this to the training provided at the beginning of the study. A few participants said they had built some form of relationship with the app and would miss the app when the study finished. CONCLUSIONS The YouXin app is acceptable for symptom self-monitoring in people with experience of psychosis in China. Participants gained greater insights about their symptoms by using the YouXin app. As we only collected retrospective acceptability in this study, future studies are warranted to assess hypothetical acceptability before the commencement of study to provide a more comprehensive understanding of implementation.
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Affiliation(s)
- Xiaolong Zhang
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Shôn Lewis
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Xu Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jiaojiao Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xingyu Wang
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK.
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Zhu T, Kuang L, Piao C, Zeng J, Li K, Georgiou P. Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:236-246. [PMID: 38163299 DOI: 10.1109/tbcas.2023.3348844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Leveraging continuous glucose monitoring (CGM) systems, real-time blood glucose (BG) forecasting is essential for proactive interventions, playing a crucial role in enhancing the management of type 1 diabetes (T1D) and type 2 diabetes (T2D). However, developing a model generalized to a population and subsequently embedding it within a microchip of a wearable device presents significant technical challenges. Furthermore, the domain of BG prediction in T2D remains under-explored in the literature. In light of this, we propose a population-specific BG prediction model, leveraging the capabilities of the temporal fusion Transformer (TFT) to adjust predictions based on personal demographic data. Then the trained model is embedded within a system-on-chip, integral to our low-power and low-cost customized wearable device. This device seamlessly communicates with CGM systems through Bluetooth and provides timely BG predictions using edge computing. When evaluated on two publicly available clinical datasets with a total of 124 participants with T1D or T2D, the embedded TFT model consistently demonstrated superior performance, achieving the lowest prediction errors when compared with a range of machine learning baseline methods. Executing the TFT model on our wearable device requires minimal memory and power consumption, enabling continuous decision support for more than 51 days on a single Li-Poly battery charge. These findings demonstrate the significant potential of the proposed TFT model and wearable device in enhancing the quality of life for people with diabetes and effectively addressing real-world challenges.
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Monachelli R, Davis SW, Barnard A, Longmire M, Docherty JP, Oakley-Girvan I. Designing mHealth Apps to Incorporate Evidence-Based Techniques for Prolonging User Engagement. Interact J Med Res 2024; 13:e51974. [PMID: 38416858 PMCID: PMC11005439 DOI: 10.2196/51974] [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: 08/18/2023] [Revised: 11/14/2023] [Accepted: 02/27/2024] [Indexed: 03/01/2024] Open
Abstract
Maintaining user engagement with mobile health (mHealth) apps can be a challenge. Previously, we developed a conceptual model to optimize patient engagement in mHealth apps by incorporating multiple evidence-based methods, including increasing health literacy, enhancing technical competence, and improving feelings about participation in clinical trials. This viewpoint aims to report on a series of exploratory mini-experiments demonstrating the feasibility of testing our previously published engagement conceptual model. We collected data from 6 participants using an app that showed a series of educational videos and obtained additional data via questionnaires to illustrate and pilot the approach. The videos addressed 3 elements shown to relate to engagement in health care app use: increasing health literacy, enhancing technical competence, and improving positive feelings about participation in clinical trials. We measured changes in participants' knowledge and feelings, collected feedback on the videos and content, made revisions based on this feedback, and conducted participant reassessments. The findings support the feasibility of an iterative approach to creating and refining engagement enhancements in mHealth apps. Systematically identifying the key evidence-based elements intended to be included in an app's design and then systematically testing the implantation of each element separately until a satisfactory level of positive impact is achieved is feasible and should be incorporated into standard app design. While mHealth apps have shown promise, participants are more likely to drop out than to be retained. This viewpoint highlights the potential for mHealth researchers to test and refine mHealth apps using approaches to better engage users.
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Affiliation(s)
| | | | | | | | - John P Docherty
- Weill Cornell Medical College, White Plains, NY, United States
| | - Ingrid Oakley-Girvan
- Medable Inc, Palo Alto, CA, United States
- The Public Health Institute, Oakland, CA, United States
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O'Driscoll C, Singh A, Chichua I, Clodic J, Desai A, Nikolova D, Yap AJ, Zhou I, Pilling S. An Ecological Mobile Momentary Intervention to Support Dynamic Goal Pursuit: Feasibility and Acceptability Study. JMIR Form Res 2024; 8:e49857. [PMID: 38506904 PMCID: PMC10993123 DOI: 10.2196/49857] [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: 06/12/2023] [Revised: 02/02/2024] [Accepted: 02/22/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Individuals can experience difficulties pursuing their goals amid multiple competing priorities in their environment. Effective goal dynamics require flexible and generalizable pursuit skills. Supporting successful goal pursuit requires a perpetually adapting intervention responsive to internal states. OBJECTIVE The purpose of this study was to (1) develop a flexible intervention that can adapt to an individual's changing short to medium-term goals and be applied to their daily life and (2) examine the feasibility and acceptability of the just-in-time adaptive intervention for goal pursuit. METHODS This study involved 3 iterations to test and systematically enhance all aspects of the intervention. During the pilot phase, 73 participants engaged in an ecological momentary assessment (EMA) over 1 month. After week 1, they attended an intervention training session and received just-in-time intervention prompts during the following 3 weeks. The training employed the Capability, Opportunity, Motivation, and Behavior (COM-B) framework for goal setting, along with mental contrasting with implementation intentions (MCII). Subsequent prompts, triggered by variability in goal pursuit, guided the participants to engage in MCII in relation to their current goal. We evaluated feasibility and acceptability, efficacy, and individual change processes by combining intensive (single-case experimental design) and extensive methods. RESULTS The results suggest that the digital intervention was feasible and acceptable to participants. Compliance with the intervention was high (n=63, 86%). The participants endorsed high acceptability ratings relating to both the study procedures and the intervention. All participants (N=73, 100%) demonstrated significant improvements in goal pursuit with an average difference of 0.495 units in the outcome (P<.001). The results of the dynamic network modeling suggest that self-monitoring behavior (EMA) and implementing the MCII strategy may aid in goal reprioritization, where goal pursuit itself is a driver of further goal pursuit. CONCLUSIONS This pilot study demonstrated the feasibility and acceptability of a just-in-time adaptive intervention among a nonclinical adult sample. This intervention used self-monitoring of behavior, the COM-B framework, and MCII strategies to improve dynamic goal pursuit. It was delivered via an Ecological Momentary Intervention (EMI) procedure. Future research should consider the utility of this approach as an additional intervention element within psychological interventions to improve goal pursuit. Sustaining goal pursuit throughout interventions is central to their effectiveness and warrants further evaluation.
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Affiliation(s)
- Ciarán O'Driscoll
- CORE Data Lab, Centre for Outcomes Research and Effectiveness, University College London, London, United Kingdom
| | - Aneesha Singh
- UCL Interaction Centre, University College London, London, United Kingdom
| | - Iya Chichua
- CORE Data Lab, Centre for Outcomes Research and Effectiveness, University College London, London, United Kingdom
| | - Joachim Clodic
- CORE Data Lab, Centre for Outcomes Research and Effectiveness, University College London, London, United Kingdom
| | - Anjali Desai
- CORE Data Lab, Centre for Outcomes Research and Effectiveness, University College London, London, United Kingdom
| | - Dara Nikolova
- CORE Data Lab, Centre for Outcomes Research and Effectiveness, University College London, London, United Kingdom
| | - Alex Jie Yap
- CORE Data Lab, Centre for Outcomes Research and Effectiveness, University College London, London, United Kingdom
| | - Irene Zhou
- CORE Data Lab, Centre for Outcomes Research and Effectiveness, University College London, London, United Kingdom
| | - Stephen Pilling
- CORE Data Lab, Centre for Outcomes Research and Effectiveness, University College London, London, United Kingdom
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11
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Sun Y, Kargarandehkordi A, Slade C, Jaiswal A, Busch G, Guerrero A, Phillips KT, Washington P. Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study. JMIR Res Protoc 2024; 13:e46493. [PMID: 38324375 PMCID: PMC10882478 DOI: 10.2196/46493] [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: 08/25/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI)-powered digital therapies that detect methamphetamine cravings via consumer devices have the potential to reduce health care disparities by providing remote and accessible care solutions to communities with limited care solutions, such as Native Hawaiian, Filipino, and Pacific Islander communities. However, Native Hawaiian, Filipino, and Pacific Islander communities are understudied with respect to digital therapeutics and AI health sensing despite using technology at the same rates as other racial groups. OBJECTIVE In this study, we aimed to understand the feasibility of continuous remote digital monitoring and ecological momentary assessments in Native Hawaiian, Filipino, and Pacific Islander communities in Hawaii by curating a novel data set of longitudinal Fitbit (Fitbit Inc) biosignals with the corresponding craving and substance use labels. We also aimed to develop personalized AI models that predict methamphetamine craving events in real time using wearable sensor data. METHODS We will develop personalized AI and machine learning models for methamphetamine use and craving prediction in 40 individuals from Native Hawaiian, Filipino, and Pacific Islander communities by curating a novel data set of real-time Fitbit biosensor readings and the corresponding participant annotations (ie, raw self-reported substance use data) of their methamphetamine use and cravings. In the process of collecting this data set, we will gain insights into cultural and other human factors that can challenge the proper acquisition of precise annotations. With the resulting data set, we will use self-supervised learning AI approaches, which are a new family of machine learning methods that allows a neural network to be trained without labels by being optimized to make predictions about the data. The inputs to the proposed AI models are Fitbit biosensor readings, and the outputs are predictions of methamphetamine use or craving. This paradigm is gaining increased attention in AI for health care. RESULTS To date, more than 40 individuals have expressed interest in participating in the study, and we have successfully recruited our first 5 participants with minimal logistical challenges and proper compliance. Several logistical challenges that the research team has encountered so far and the related implications are discussed. CONCLUSIONS We expect to develop models that significantly outperform traditional supervised methods by finetuning according to the data of a participant. Such methods will enable AI solutions that work with the limited data available from Native Hawaiian, Filipino, and Pacific Islander populations and that are inherently unbiased owing to their personalized nature. Such models can support future AI-powered digital therapeutics for substance abuse. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/46493.
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Affiliation(s)
- Yinan Sun
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Ali Kargarandehkordi
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Christopher Slade
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Aditi Jaiswal
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Gerald Busch
- Department of Psychiatry, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Anthony Guerrero
- Department of Psychiatry, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Kristina T Phillips
- Center for Integrated Health Care Research, Kaiser Permanente Hawaii, Honolulu, HI, United States
| | - Peter Washington
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
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12
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Gleeson JF, McGuckian TB, Fernandez DK, Fraser MI, Pepe A, Taskis R, Alvarez-Jimenez M, Farhall JF, Gumley A. Systematic review of early warning signs of relapse and behavioural antecedents of symptom worsening in people living with schizophrenia spectrum disorders. Clin Psychol Rev 2024; 107:102357. [PMID: 38065010 DOI: 10.1016/j.cpr.2023.102357] [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: 03/08/2023] [Revised: 11/03/2023] [Accepted: 11/14/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND Identification of the early warning signs (EWS) of relapse is key to relapse prevention in schizophrenia spectrum disorders, however, limitations to their precision have been reported. Substantial methodological innovations have recently been applied to the prediction of psychotic relapse and to individual psychotic symptoms. However, there has been no systematic review that has integrated findings across these two related outcomes and no systematic review of EWS of relapse for a decade. METHOD We conducted a systematic review of EWS of psychotic relapse and the behavioural antecedents of worsening psychotic symptoms. Traditional EWS and ecological momentary assessment/intervention studies were included. We completed meta-analyses of the pooled sensitivity and specificity of EWS in predicting relapse, and for the prediction of relapse from individual symptoms. RESULTS Seventy two studies were identified including 6903 participants. Sleep, mood, and suspiciousness, emerged as predictors of worsening symptoms. Pooled sensitivity and specificity of EWS in predicting psychotic relapse was 71% and 64% (AUC value = 0.72). There was a large pooled-effect size for the model predicting relapse from individual symptom which did not reach statistical significance (d = 0.81, 95%CIs = -0.01, 1.63). CONCLUSIONS Important methodological advancements in the prediction of psychotic relapse in schizophrenia spectrum disorders are evident with improvements in the precision of prediction. Further efforts are required to translate these advances into effective clinical innovations.
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Affiliation(s)
- J F Gleeson
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia.
| | - T B McGuckian
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia
| | - D K Fernandez
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia
| | - M I Fraser
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia
| | - A Pepe
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia
| | - R Taskis
- Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia
| | - M Alvarez-Jimenez
- Orygen, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - J F Farhall
- Department of Psychology and Counselling, La Trobe University, Melbourne, VIC, Australia
| | - A Gumley
- Glasgow Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
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13
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De Calheiros Velozo J, Habets J, George SV, Niemeijer K, Minaeva O, Hagemann N, Herff C, Kuppens P, Rintala A, Vaessen T, Riese H, Delespaul P. Designing daily-life research combining experience sampling method with parallel data. Psychol Med 2024; 54:98-107. [PMID: 36039768 DOI: 10.1017/s0033291722002367] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Ambulatory monitoring is gaining popularity in mental and somatic health care to capture an individual's wellbeing or treatment course in daily-life. Experience sampling method collects subjective time-series data of patients' experiences, behavior, and context. At the same time, digital devices allow for less intrusive collection of more objective time-series data with higher sampling frequencies and for prolonged sampling periods. We refer to these data as parallel data. Combining these two data types holds the promise to revolutionize health care. However, existing ambulatory monitoring guidelines are too specific to each data type, and lack overall directions on how to effectively combine them. METHODS Literature and expert opinions were integrated to formulate relevant guiding principles. RESULTS Experience sampling and parallel data must be approached as one holistic time series right from the start, at the study design stage. The fluctuation pattern and volatility of the different variables of interest must be well understood to ensure that these data are compatible. Data have to be collected and operationalized in a manner that the minimal common denominator is able to answer the research question with regard to temporal and disease severity resolution. Furthermore, recommendations are provided for device selection, data management, and analysis. Open science practices are also highlighted throughout. Finally, we provide a practical checklist with the delineated considerations and an open-source example demonstrating how to apply it. CONCLUSIONS The provided considerations aim to structure and support researchers as they undertake the new challenges presented by this exciting multidisciplinary research field.
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Affiliation(s)
| | - Jeroen Habets
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Sandip V George
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Koen Niemeijer
- Department of Psychology and Educational Sciences, Research Group of Quantitative Psychology and Individual Differences, KU Leuven, Leuven, Belgium
| | - Olga Minaeva
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Noëmi Hagemann
- Department of Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
| | - Christian Herff
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Peter Kuppens
- Department of Psychology and Educational Sciences, Research Group of Quantitative Psychology and Individual Differences, KU Leuven, Leuven, Belgium
| | - Aki Rintala
- Department of Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
- Faculty of Social and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - Thomas Vaessen
- Department of Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
- Department of Neurosciences, Mind Body Research, KU Leuven, Leuven, Belgium
| | - Harriëtte Riese
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Philippe Delespaul
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
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14
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Zhang X, Lewis S, Carter LA, Chen X, Zhou J, Wang X, Bucci S. Evaluating a smartphone-based symptom self-monitoring app for psychosis in China (YouXin): A non-randomised validity and feasibility study with a mixed-methods design. Digit Health 2024; 10:20552076231222097. [PMID: 38188856 PMCID: PMC10768587 DOI: 10.1177/20552076231222097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/05/2023] [Indexed: 01/09/2024] Open
Abstract
Background Psychosis causes a significant burden globally, including in China, where limited mental health resources hinder access to care. Smartphone-based remote monitoring offers a promising solution. This study aimed to assess the validity, feasibility, acceptability, and safety of a symptom self-monitoring smartphone app, YouXin, for people with psychosis in China. Methods A pre-registered non-randomised validity and feasibility study with a mixed-methods design. Participants with psychosis were recruited from a major tertiary psychiatric hospital in Beijing, China. Participants utilised the YouXin app to self-monitor psychosis and mood symptoms for four weeks. Feasibility outcomes were recruitment, retention and outcome measures completeness. Active symptom monitoring (ASM) validity was tested against corresponding clinical assessments (PANSS and CDS) using Spearman correlation. Ten participants completed qualitative interviews at study end to explore acceptability of the app and trial procedures. Results Feasibility parameters were met. The target recruitment sample of 40 participants was met, with 82.5% completing outcome measures, 60% achieving acceptable ASM engagement (completing >33% of all prompts), and 33% recording sufficient passive monitoring data to extract mobility indicators. Five ASM domains (hallucinations, suspiciousness, guilt feelings, delusions, grandiosity) achieved moderate correlation with clinical assessment. Both quantitative and qualitative evaluation showed high acceptability of YouXin. Clinical measurements indicated no symptom and functional deterioration. No adverse events were reported, suggesting YouXin is safe to use in this clinical population. Conclusions The trial feasibility, acceptability and safety parameters were met and a powered efficacy study is indicated. However, refinements are needed to improve ASM validity and increase passive monitoring data completeness.
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Affiliation(s)
- Xiaolong Zhang
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Shôn Lewis
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Lesley-Anne Carter
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Xu Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jiaojiao Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xingyu Wang
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
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15
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Chow PI, Roller DG, Boukhechba M, Shaffer KM, Ritterband LM, Reilley MJ, Le TM, Kunk PR, Bauer TW, Gioeli DG. Mobile sensing to advance tumor modeling in cancer patients: A conceptual framework. Internet Interv 2023; 34:100644. [PMID: 38099095 PMCID: PMC10719510 DOI: 10.1016/j.invent.2023.100644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 06/28/2023] [Accepted: 07/07/2023] [Indexed: 12/17/2023] Open
Abstract
As mobile and wearable devices continue to grow in popularity, there is strong yet unrealized potential to harness people's mobile sensing data to improve our understanding of their cellular and biologically-based diseases. Breakthrough technical innovations in tumor modeling, such as the three dimensional tumor microenvironment system (TMES), allow researchers to study the behavior of tumor cells in a controlled environment that closely mimics the human body. Although patients' health behaviors are known to impact their tumor growth through circulating hormones (cortisol, melatonin), capturing this process is a challenge to rendering realistic tumor models in the TMES or similar tumor modeling systems. The goal of this paper is to propose a conceptual framework that unifies researchers from digital health, data science, oncology, and cellular signaling, in a common cause to improve cancer patients' treatment outcomes through mobile sensing. In support of our framework, existing studies indicate that it is feasible to use people's mobile sensing data to approximate their underlying hormone levels. Further, it was found that when cortisol is cycled through the TMES based on actual patients' cortisol levels, there is a significant increase in pancreatic tumor cell growth compared to when cortisol levels are at normal healthy levels. Taken together, findings from these studies indicate that continuous monitoring of people's hormone levels through mobile sensing may improve experimentation in the TMES, by informing how hormones should be introduced. We hope our framework inspires digital health researchers in the psychosocial sciences to consider how their expertise can be applied to advancing outcomes across levels of inquiry, from behavioral to cellular.
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Affiliation(s)
- Philip I. Chow
- Department of Psychiatry and Neurobehavioral Sciences, Center for Behavioral Health and Technology, University of Virginia, USA
- Cancer Center, University of Virginia, USA
| | - Devin G. Roller
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, USA
| | - Mehdi Boukhechba
- Department of Engineering Systems and Environment, University of Virginia, USA
- Janssen Pharmaceutical Companies of Johnson & Johnson, USA
| | - Kelly M. Shaffer
- Department of Psychiatry and Neurobehavioral Sciences, Center for Behavioral Health and Technology, University of Virginia, USA
| | - Lee M. Ritterband
- Department of Psychiatry and Neurobehavioral Sciences, Center for Behavioral Health and Technology, University of Virginia, USA
- Cancer Center, University of Virginia, USA
| | | | - Tri M. Le
- Department of Medicine, University of Virginia, USA
| | - Paul R. Kunk
- Department of Medicine, University of Virginia, USA
| | - Todd W. Bauer
- Department of Surgery, University of Virginia, USA
- Cancer Center, University of Virginia, USA
| | - Daniel G. Gioeli
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, USA
- Cancer Center, University of Virginia, USA
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Torres WO, Abbott ME, Wang Y, Stuart HS. Skin Sensitivity Assessment Using Smartphone Haptic Feedback. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:216-221. [PMID: 38059068 PMCID: PMC10697294 DOI: 10.1109/ojemb.2023.3328502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/21/2023] [Accepted: 10/25/2023] [Indexed: 12/08/2023] Open
Abstract
Goal: This work presents a smartphone application to assess cutaneous sensory perception by establishing Vibrational Perception Thresholds (VPTs). Cutaneous sensory perception diagnostics allow for the early detection and symptom tracking of tactile dysfunction. However, lack of access to healthcare and the limited frequency of current screening tools can leave skin sensation impairments undiscovered or unmonitored. Methods: A 23-participant cross-sectional study in subjects with a range of finger sensation tests Smartphone Established VPTs (SE-VPTs) by varying device vibrational intensity. These are compared against monofilament test scores, a clinical measure of skin sensitivity. Results: We find a strong positive correlation between SE-VPTs and monofilament scores ([Formula: see text] = 0.86, p = 1.65e-07). Conclusions: These results demonstrate the feasibility of using a smartphone as a skin sensation screening tool.
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Affiliation(s)
- Wilson O. Torres
- Embodied Dexterity Group, Department of Mechanical EngineeringUniversity of California BerkeleyBerkeleyCA94720USA
| | - Michael E. Abbott
- Embodied Dexterity Group, Department of Mechanical EngineeringUniversity of California BerkeleyBerkeleyCA94720USA
| | - Yuqing Wang
- Embodied Dexterity Group, Department of Mechanical EngineeringUniversity of California BerkeleyBerkeleyCA94720USA
| | - Hannah S. Stuart
- Embodied Dexterity Group, Department of Mechanical EngineeringUniversity of California BerkeleyBerkeleyCA94720USA
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Marciano L, Vocaj E, Bekalu MA, La Tona A, Rocchi G, Viswanath K. The Use of Mobile Assessments for Monitoring Mental Health in Youth: Umbrella Review. J Med Internet Res 2023; 25:e45540. [PMID: 37725422 PMCID: PMC10548333 DOI: 10.2196/45540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/12/2023] [Accepted: 07/06/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND Improving mental health in youth is a major concern. Future approaches to monitor and intervene in youth mental health problems should rely on mobile tools that allow for the daily monitoring of mental health both actively (eg, using ecological momentary assessments [EMAs]) and passively (eg, digital phenotyping) by capturing individuals' data. OBJECTIVE This umbrella review aims to (1) report the main characteristics of existing reviews on mental health and young people, including mobile approaches to mental health; (2) describe EMAs and trace data and the mental health conditions investigated; (3) report the main results; and (4) outline promises, limitations, and directions for future research. METHODS A systematic literature search was carried out in 9 scientific databases (Communication & Mass Media Complete, Psychology and Behavioral Sciences Collection, PsycINFO, CINAHL, ERIC, MEDLINE, the ProQuest Sociology Database, Web of Science, and PubMed) on January 30, 2022, coupled with a hand search and updated in July 2022. We included (systematic) reviews of EMAs and trace data in the context of mental health, with a specific focus on young populations, including children, adolescents, and young adults. The quality of the included reviews was evaluated using the AMSTAR (Assessment of Multiple Systematic Reviews) checklist. RESULTS After the screening process, 30 reviews (published between 2016 and 2022) were included in this umbrella review, of which 21 (70%) were systematic reviews and 9 (30%) were narrative reviews. The included systematic reviews focused on symptoms of depression (5/21, 24%); bipolar disorders, schizophrenia, or psychosis (6/21, 29%); general ill-being (5/21, 24%); cognitive abilities (2/21, 9.5%); well-being (1/21, 5%); personality (1/21, 5%); and suicidal thoughts (1/21, 5%). Of the 21 systematic reviews, 15 (71%) summarized studies that used mobile apps for tracing, 2 (10%) summarized studies that used them for intervention, and 4 (19%) summarized studies that used them for both intervention and tracing. Mobile tools used in the systematic reviews were smartphones only (8/21, 38%), smartphones and wearable devices (6/21, 29%), and smartphones with other tools (7/21, 33%). In total, 29% (6/21) of the systematic reviews focused on EMAs, including ecological momentary interventions; 33% (7/21) focused on trace data; and 38% (8/21) focused on both. Narrative reviews mainly focused on the discussion of issues related to digital phenotyping, existing theoretical frameworks used, new opportunities, and practical examples. CONCLUSIONS EMAs and trace data in the context of mental health assessments and interventions are promising tools. Opportunities (eg, using mobile approaches in low- and middle-income countries, integration of multimodal data, and improving self-efficacy and self-awareness on mental health) and limitations (eg, absence of theoretical frameworks, difficulty in assessing the reliability and effectiveness of such approaches, and need to appropriately assess the quality of the studies) were further discussed. TRIAL REGISTRATION PROSPERO CRD42022347717; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347717.
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Affiliation(s)
- Laura Marciano
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
| | - Emanuela Vocaj
- Lombard School of Cognitive-Neuropsychological Psychotherapy, Pavia, Italy
| | - Mesfin A Bekalu
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
| | - Antonino La Tona
- Dipartimento di Scienze Umane e Sociali, Università degli Studi di Bergamo, Bergamo, Italy
| | - Giulia Rocchi
- Department of Dynamic, Clinical Psychology and Health Studies, Sapienza University, Rome, Italy
| | - Kasisomayajula Viswanath
- Lee Kum Sheung Center for Health and Happiness, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Dana Farber Cancer Institute, Boston, MA, United States
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Zhang X, Lewis S, Carter LA, Bucci S. A Digital System (YouXin) to Facilitate Self-Management by People With Psychosis in China: Protocol for a Nonrandomized Validity and Feasibility Study With a Mixed Methods Design. JMIR Res Protoc 2023; 12:e45170. [PMID: 37698905 PMCID: PMC10523209 DOI: 10.2196/45170] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Psychosis is one of the most disabling mental health conditions and causes significant personal, social, and economic burden. Accurate and timely symptom monitoring is critical to offering prompt and time-sensitive clinical services. Digital health is a promising solution for the barriers encountered by conventional symptom monitoring approaches, including accessibility, the ecological validity of assessments, and recall bias. However, to date, there has been no digital health technology developed to support self-management for people with psychosis in China. OBJECTIVE We report the study protocol to evaluate the validity, feasibility, acceptability, usability, and safety of a symptom self-monitoring smartphone app (YouXin; Chinese name ) for people with psychosis in China. METHODS This is a nonrandomized validity and feasibility study with a mixed methods design. The study was approved by the University of Manchester and Beijing Anding Hospital Research Ethics Committee. YouXin is a smartphone app designed to facilitate symptom self-monitoring for people with psychosis. YouXin has 2 core functions: active monitoring of symptoms (ie, smartphone survey) and passive monitoring of behavioral activity (ie, passive data collection via embedded smartphone sensors). The development process of YouXin utilized a systematic coproduction approach. A series of coproduction consultation meetings was conducted by the principal researcher with service users and clinicians to maximize the usability and acceptability of the app for end users. Participants with psychosis aged 16 years to 65 years were recruited from Beijing Anding Hospital, Beijing, China. All participants were invited to use the YouXin app to self-monitor symptoms for 4 weeks. At the end of the 4-week follow-up, we invited participants to take part in a qualitative interview to explore the acceptability of the app and trial procedures postintervention. RESULTS Recruitment to the study was initiated in August 2022. Of the 47 participants who were approached for the study from August 2022 to October 2022, 41 participants agreed to take part in the study. We excluded 1 of the 41 participants for not meeting the inclusion criteria, leaving a total of 40 participants who began the study. As of December 2022, 40 participants had completed the study, and the recruitment was complete. CONCLUSIONS This study is the first to develop and test a symptom self-monitoring app specifically designed for people with psychosis in China. If the study shows the feasibility of YouXin, a potential future direction is to integrate the app into clinical workflows to facilitate digital mental health care for people with psychosis in China. This study will inform improvements to the app, trial procedures, and implementation strategies with this population. Moreover, the findings of this trial could lead to optimization of digital health technologies designed for people with psychosis in China. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/45170.
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Affiliation(s)
- Xiaolong Zhang
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Shôn Lewis
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Lesley-Anne Carter
- Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
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Washington P. Personalized Machine Learning using Passive Sensing and Ecological Momentary Assessments for Meth Users in Hawaii: A Research Protocol. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.24.23294587. [PMID: 37662253 PMCID: PMC10473804 DOI: 10.1101/2023.08.24.23294587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background Artificial intelligence (AI)-powered digital therapies which detect meth cravings delivered on consumer devices have the potential to reduce these disparities by providing remote and accessible care solutions to Native Hawaiians, Filipinos, and Pacific Islanders (NHFPI) communities with limited care solutions. However, NHFPI are fully understudied with respect to digital therapeutics and AI health sensing despite using technology at the same rates as other races. Objective We seek to fulfill two research aims: (1) Understand the feasibility of continuous remote digital monitoring and ecological momentary assessments (EMAs) in NHFPI in Hawaii by curating a novel dataset of longitudinal FitBit biosignals with corresponding craving and substance use labels. (2) Develop personalized AI models which predict meth craving events in real time using wearable sensor data. Methods We will develop personalized AI/ML (artificial intelligence/machine learning) models for meth use and craving prediction in 40 NHFPI individuals by curating a novel dataset of real-time FitBit biosensor readings and corresponding participant annotations (i.e., raw self-reported substance use data) of their meth use and cravings. In the process of collecting this dataset, we will glean insights about cultural and other human factors which can challenge the proper acquisition of precise annotations. With the resulting dataset, we will employ self-supervised learning (SSL) AI approaches, which are a new family of ML methods that allow a neural network to be trained without labels by being optimized to make predictions about the data itself. The inputs to the proposed AI models are FitBit biosensor readings and the outputs are predictions of meth use or craving. This paradigm is gaining increased attention in AI for healthcare. Conclusions We expect to develop models which significantly outperform traditional supervised methods by fine-tuning to an individual subject's data. Such methods will enable AI solutions which work with the limited data available from NHFPI populations and which are inherently unbiased due to their personalized nature. Such models can support future AI-powered digital therapeutics for substance abuse.
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Knights J, Shen J, Mysliwiec V, DuBois H. Associations of smartphone usage patterns with sleep and mental health symptoms in a clinical cohort receiving virtual behavioral medicine care: a retrospective study. SLEEP ADVANCES : A JOURNAL OF THE SLEEP RESEARCH SOCIETY 2023; 4:zpad027. [PMID: 37485313 PMCID: PMC10359037 DOI: 10.1093/sleepadvances/zpad027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 06/15/2023] [Indexed: 07/25/2023]
Abstract
Study Objectives We sought to develop behavioral sleep measures from passively sensed human-smartphone interactions and retrospectively evaluate their associations with sleep disturbance, anxiety, and depressive symptoms in a large cohort of real-world patients receiving virtual behavioral medicine care. Methods Behavioral sleep measures from smartphone data were developed: daily longest period of smartphone inactivity (inferred sleep period [ISP]); 30-day expected period of inactivity (expected sleep period [ESP]); regularity of the daily ISP compared to the ESP (overlap percentage); and smartphone usage during inferred sleep (disruptions, wakefulness during sleep period). These measures were compared to symptoms of sleep disturbance, anxiety, and depression using linear mixed-effects modeling. More than 2300 patients receiving standard-of-care virtual mental healthcare across more than 111 000 days were retrospectively analyzed. Results Mean ESP duration was 8.4 h (SD = 2.3), overlap percentage 75% (SD = 18%) and disrupted time windows 4.85 (SD = 3). There were significant associations between overlap percentage (p < 0.001) and disruptions (p < 0.001) with sleep disturbance symptoms after accounting for demographics. Overlap percentage and disruptions were similarly associated with anxiety and depression symptoms (all p < 0.001). Conclusions Smartphone behavioral measures appear useful to longitudinally monitor sleep and benchmark depressive and anxiety symptoms in patients receiving virtual behavioral medicine care. Patterns consistent with better sleep practices (i.e. greater regularity of ISP, fewer disruptions) were associated with lower levels of reported sleep disturbances, anxiety, and depression.
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Affiliation(s)
- Jonathan Knights
- Corresponding author. Jonathan Knights, Department of Applied Science, SonderMind, 3000 Lawrence St, Denver, CO 80205, USA.
| | - Jacob Shen
- At time of submission: Mindstrong Health, Menlo Park, CA, USA
| | - Vincent Mysliwiec
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Holly DuBois
- At time of submission: Mindstrong Health, Menlo Park, CA, USA
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21
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Wiedermann M, Rose AH, Maier BF, Kolb JJ, Hinrichs D, Brockmann D. Evidence for positive long- and short-term effects of vaccinations against COVID-19 in wearable sensor metrics. PNAS NEXUS 2023; 2:pgad223. [PMID: 37497048 PMCID: PMC10368316 DOI: 10.1093/pnasnexus/pgad223] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/26/2023] [Accepted: 06/26/2023] [Indexed: 07/28/2023]
Abstract
Vaccines are among the most powerful tools to combat the COVID-19 pandemic. They are highly effective against infection and substantially reduce the risk of severe disease, hospitalization, ICU admission, and death. However, their potential for attenuating long-term changes in personal health and health-related wellbeing after a SARS-CoV-2 infection remains a subject of debate. Such effects can be effectively monitored at the individual level by analyzing physiological data collected by consumer-grade wearable sensors. Here, we investigate changes in resting heart rate, daily physical activity, and sleep duration around a SARS-CoV-2 infection stratified by vaccination status. Data were collected over a period of 2 years in the context of the German Corona Data Donation Project with around 190,000 monthly active participants. Compared to their unvaccinated counterparts, we find that vaccinated individuals, on average, experience smaller changes in their vital data that also return to normal levels more quickly. Likewise, extreme changes in vitals during the acute phase of the disease occur less frequently in vaccinated individuals. Our results solidify evidence that vaccines can mitigate long-term detrimental effects of SARS-CoV-2 infections both in terms of duration and magnitude. Furthermore, they demonstrate the value of large-scale, high-resolution wearable sensor data in public health research.
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Affiliation(s)
| | - Annika H Rose
- Computational Epidemiology Group, Robert Koch Institute, 13353 Berlin, Germany
- Institute for Theoretical Biology and Integrated Research Institute for the Life-Sciences, Humboldt University of Berlin, 10115 Berlin, Germany
| | - Benjamin F Maier
- Computational Epidemiology Group, Robert Koch Institute, 13353 Berlin, Germany
- DTU Compute, Technical University of Denmark, Kongens Lyngby 2800, Denmark
- Copenhagen Center for Social Data Science, University of Copenhagen, Copenhagen 1353, Denmark
| | - Jakob J Kolb
- Computational Epidemiology Group, Robert Koch Institute, 13353 Berlin, Germany
- Institute for Theoretical Biology and Integrated Research Institute for the Life-Sciences, Humboldt University of Berlin, 10115 Berlin, Germany
| | - David Hinrichs
- Computational Epidemiology Group, Robert Koch Institute, 13353 Berlin, Germany
- Institute for Theoretical Biology and Integrated Research Institute for the Life-Sciences, Humboldt University of Berlin, 10115 Berlin, Germany
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Shausan A, Nazarathy Y, Dyda A. Emerging data inputs for infectious diseases surveillance and decision making. Front Digit Health 2023; 5:1131731. [PMID: 37082524 PMCID: PMC10111015 DOI: 10.3389/fdgth.2023.1131731] [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: 12/26/2022] [Accepted: 03/20/2023] [Indexed: 04/07/2023] Open
Abstract
Infectious diseases create a significant health and social burden globally and can lead to outbreaks and epidemics. Timely surveillance for infectious diseases is required to inform both short and long term public responses and health policies. Novel data inputs for infectious disease surveillance and public health decision making are emerging, accelerated by the COVID-19 pandemic. These include the use of technology-enabled physiological measurements, crowd sourcing, field experiments, and artificial intelligence (AI). These technologies may provide benefits in relation to improved timeliness and reduced resource requirements in comparison to traditional methods. In this review paper, we describe current and emerging data inputs being used for infectious disease surveillance and summarize key benefits and limitations.
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Affiliation(s)
- Aminath Shausan
- School of Public Health, The University of Queensland, Brisbane, QLD, Australia
- School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia
| | - Yoni Nazarathy
- School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia
| | - Amalie Dyda
- School of Public Health, The University of Queensland, Brisbane, QLD, Australia
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23
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Lee K, Cheongho Lee T, Yefimova M, Kumar S, Puga F, Azuero A, Kamal A, Bakitas MA, Wright AA, Demiris G, Ritchie CS, Pickering CE, Nicholas Dionne-Odom J. Using Digital phenotyping to understand health-related outcomes: A scoping review. Int J Med Inform 2023; 174:105061. [PMID: 37030145 DOI: 10.1016/j.ijmedinf.2023.105061] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/10/2023] [Accepted: 03/24/2023] [Indexed: 04/01/2023]
Abstract
BACKGROUND Digital phenotyping may detect changes in health outcomes and potentially lead to proactive measures to mitigate health declines and avoid major medical events. While health-related outcomes have traditionally been acquired through self-report measures, those approaches have numerous limitations, such as recall bias, and social desirability bias. Digital phenotyping may offer a potential solution to these limitations. OBJECTIVES The purpose of this scoping review was to identify and summarize how passive smartphone data are processed and evaluated analytically, including the relationship between these data and health-related outcomes. METHODS A search of PubMed, Scopus, Compendex, and HTA databases was conducted for all articles in April 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines. RESULTS A total of 40 articles were included and went through an analysis based on data collection approaches, feature extraction, data analytics, behavioral markers, and health-related outcomes. This review demonstrated a layer of features derived from raw sensor data that can then be integrated to estimate and predict behaviors, emotions, and health-related outcomes. Most studies collected data from a combination of sensors. GPS was the most used digital phenotyping data. Feature types included physical activity, location, mobility, social activity, sleep, and in-phone activity. Studies involved a broad range of the features used: data preprocessing, analysis approaches, analytic techniques, and algorithms tested. 55% of the studies (n = 22) focused on mental health-related outcomes. CONCLUSION This scoping review catalogued in detail the research to date regarding the approaches to using passive smartphone sensor data to derive behavioral markers to correlate with or predict health-related outcomes. Findings will serve as a central resource for researchers to survey the field of research designs and approaches performed to date and move this emerging domain of research forward towards ultimately providing clinical utility in patient care.
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Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. J Biomed Inform 2023; 138:104278. [PMID: 36586498 DOI: 10.1016/j.jbi.2022.104278] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022]
Abstract
Many studies have used Digital Phenotyping of Mental Health (DPMH) to complement classic methods of mental health assessment and monitoring. This research area proposes innovative methods that perform multimodal sensing of multiple situations of interest (e.g., sleep, physical activity, mobility) to health professionals. In this paper, we present a Systematic Literature Review (SLR) to recognize, characterize and analyze the state of the art on DPMH using multimodal sensing of multiple situations of interest to professionals. We searched for studies in six digital libraries, which resulted in 1865 retrieved published papers. Next, we performed a systematic process of selecting studies based on inclusion and exclusion criteria, which selected 59 studies for the data extraction phase. First, based on the analysis of the extracted data, we describe an overview of this field, then presenting characteristics of the selected studies, the main mental health topics targeted, the physical and virtual sensors used, and the identified situations of interest. Next, we outline answers to research questions, describing the context data sources used to detect situations, the DPMH workflow used for multimodal sensing of situations, and the application of DPMH solutions in the mental health assessment and monitoring process. In addition, we recognize trends presented by DPMH studies, such as the design of solutions for high-level information recognition, association of features with mental states/disorders, classification of mental states/disorders, and prediction of mental states/disorders. We also recognize the main open issues in this research area. Based on the results of this SLR, we conclude that despite the potential and continuous evolution for using these solutions as medical decision support tools, this research area needs more work to overcome technology and methodological rigor issues to adopt proposed solutions in real clinical settings.
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Affiliation(s)
- Ivan Moura
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil.
| | - Ariel Teles
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil; Federal Institute of Maranhão, Brazil
| | - Davi Viana
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Jean Marques
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Luciano Coutinho
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Francisco Silva
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
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Leung T, Vuillerme N. The Use of Passive Smartphone Data to Monitor Anxiety and Depression Among College Students in Real-World Settings: Protocol for a Systematic Review. JMIR Res Protoc 2022; 11:e38785. [PMID: 36515983 PMCID: PMC9798267 DOI: 10.2196/38785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 08/01/2022] [Accepted: 08/23/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND College students are particularly at risk of depression and anxiety. These disorders have a serious impact on public health and affect patients' daily lives. The potential for using smartphones to monitor these mental conditions, providing passively collected physiological and behavioral data, has been reported among the general population. However, research on the use of passive smartphone data to monitor anxiety and depression among specific populations of college students has never been reviewed. OBJECTIVE This review's objectives are (1) to provide an overview of the use of passive smartphone data to monitor depression and anxiety among college students, given their specific type of smartphone use and living setting, and (2) to evaluate the different methods used to assess those smartphone data, including their strengths and limitations. METHODS This review will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two independent investigators will review English-language, full-text, peer-reviewed papers extracted from PubMed and Web of Science that measure passive smartphone data and levels of depression or anxiety among college students. A preliminary search was conducted in February 2022 as a proof of concept. RESULTS Our preliminary search identified 115 original articles, 8 of which met our eligibility criteria. Our planned full study will include an article selection flowchart, tables, and figures representing the main information extracted on the use of passive smartphone data to monitor anxiety and depression among college students. CONCLUSIONS The planned review will summarize the published research on using passive smartphone data to monitor anxiety and depression among college students. The review aims to better understand whether and how passive smartphone data are associated with indicators of depression and anxiety among college students. This could be valuable in order to provide a digital solution for monitoring mental health issues in this specific population by enabling easier identification and follow-up of the patients. TRIAL REGISTRATION PROSPERO CRD42022316263; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=316263. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/38785.
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Affiliation(s)
| | - Nicolas Vuillerme
- AGEIS, Université Grenoble Alpes, Grenoble, France.,LabCom Telecom4Health, Orange Labs & Université Grenoble Alpes, CNRS, Inria, Grenoble INP-UGA, Grenoble, France.,Institut Universitaire de France, Paris, France
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26
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Ross G, Zhao Y, Bosman A, Geballa-Koukoula A, Zhou H, Elliott C, Nielen M, Rafferty K, Salentijn G. Data handling and ethics of emerging smartphone-based (bio)sensors – Part 1: Best practices and current implementation. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Koutsouleris N, Hauser TU, Skvortsova V, De Choudhury M. From promise to practice: towards the realisation of AI-informed mental health care. THE LANCET DIGITAL HEALTH 2022; 4:e829-e840. [DOI: 10.1016/s2589-7500(22)00153-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/14/2022] [Accepted: 07/27/2022] [Indexed: 11/07/2022]
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The promise of a model-based psychiatry: building computational models of mental ill health. Lancet Digit Health 2022; 4:e816-e828. [PMID: 36229345 PMCID: PMC9627546 DOI: 10.1016/s2589-7500(22)00152-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/05/2022] [Accepted: 07/27/2022] [Indexed: 11/07/2022]
Abstract
Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.
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29
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Zhang X, Yang Y, Cao J, Qi Z, Li G. Point-of-care CRISPR/Cas biosensing technology: A promising tool for preventing the possible COVID-19 resurgence caused by contaminated cold-chain food and packaging. FOOD FRONTIERS 2022; 4:FFT2176. [PMID: 36712576 PMCID: PMC9874772 DOI: 10.1002/fft2.176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/1912] [Revised: 12/12/1912] [Accepted: 12/12/1912] [Indexed: 02/01/2023] Open
Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused great public health concern and has been a global threat due to its high transmissibility and morbidity. Although the SARS-CoV-2 transmission mainly relies on the person-to-person route through the respiratory droplets, the possible transmission through the contaminated cold-chain food and packaging to humans has raised widespread concerns. This review discussed the possibility of SARS-CoV-2 transmission via the contaminated cold-chain food and packaging by tracing the occurrence, the survival of SARS-CoV-2 in the contaminated cold-chain food and packaging, as well as the transmission and outbreaks related to the contaminated cold-chain food and packaging. Rapid, accurate, and reliable diagnostics of SARS-CoV-2 is of great importance for preventing and controlling the COVID-19 resurgence. Therefore, we summarized the recent advances on the emerging clustered regularly interspaced short palindromic repeats (CRISPR)/Cas system-based biosensing technology that is promising and powerful for preventing the possible COVID-19 resurgence caused by the contaminated cold-chain food and packaging during the COVID-19 pandemic, including CRISPR/Cas system-based biosensors and their integration with portable devices (e.g., smartphone, lateral flow assays, microfluidic chips, and nanopores). Impressively, this review not only provided an insight on the possibility of SARS-CoV-2 transmission through the food supply chain, but also proposed the future opportunities and challenges on the development of CRISPR/Cas system-based detection methods for the diagnosis of SARS-CoV-2.
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Affiliation(s)
- Xianlong Zhang
- Food safety and Quality Control Innovation team, Department of Food Science and EngineeringSchool of Food and Biological Engineering, Shaanxi University of Science and TechnologyXi'an710021China
| | - Yan Yang
- Food safety and Quality Control Innovation team, Department of Food Science and EngineeringSchool of Food and Biological Engineering, Shaanxi University of Science and TechnologyXi'an710021China
| | - Juanjuan Cao
- Food safety and Quality Control Innovation team, Department of Food Science and EngineeringSchool of Food and Biological Engineering, Shaanxi University of Science and TechnologyXi'an710021China
| | - Zihe Qi
- Food safety and Quality Control Innovation team, Department of Food Science and EngineeringSchool of Food and Biological Engineering, Shaanxi University of Science and TechnologyXi'an710021China
| | - Guoliang Li
- Food safety and Quality Control Innovation team, Department of Food Science and EngineeringSchool of Food and Biological Engineering, Shaanxi University of Science and TechnologyXi'an710021China
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Parker H, Burkart S, Reesor-Oyer L, Smith MT, Dugger R, von Klinggraeff L, Weaver RG, Beets MW, Armstrong B. Feasibility of Measuring Screen Time, Activity, and Context Among Families With Preschoolers: Intensive Longitudinal Pilot Study. JMIR Form Res 2022; 6:e40572. [PMID: 36173677 PMCID: PMC9562053 DOI: 10.2196/40572] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 11/23/2022] Open
Abstract
Background Digital media has made screen time more available across multiple contexts, but our understanding of the ways children and families use digital media has lagged behind the rapid adoption of this technology. Objective This study evaluated the feasibility of an intensive longitudinal data collection protocol to objectively measure digital media use, physical activity, sleep, sedentary behavior, and socioemotional context among caregiver-child dyads. This paper also describes preliminary convergent validity of ecological momentary assessment (EMA) measures and preliminary agreement between caregiver self-reported phone use and phone use collected from passive mobile sensing. Methods Caregivers and their preschool-aged child (3-5 years) were recruited to complete a 30-day assessment protocol. Within 30-days, caregivers completed 7 days of EMA to measure child behavior problems and caregiver stress. Caregivers and children wore an Axivity AX3 (Newcastle Upon Tyne) accelerometer to assess physical activity, sedentary behavior, and sleep. Phone use was assessed via passive mobile sensing; we used Chronicle for Android users and screenshots of iOS screen time metrics for iOS users. Participants were invited to complete a second 14-day protocol approximately 3-12 months after their first assessment. We used Pearson correlations to examine preliminary convergent validity between validated questionnaire measures of caregiver psychological functioning, child behavior, and EMA items. Root mean square errors were computed to examine the preliminary agreement between caregiver self-reported phone use and objective phone use. Results Of 110 consenting participants, 105 completed all protocols (105/110, 95.5% retention rate). Compliance was defined a priori as completing ≥70%-75% of each protocol task. There were high compliance rates for passive mobile sensing for both Android (38/40, 95%) and iOS (64/65, 98%). EMA compliance was high (105/105, 100%), but fewer caregivers and children were compliant with accelerometry (62/99, 63% and 40/100, 40%, respectively). Average daily phone use was 383.4 (SD 157.0) minutes for Android users and 354.7 (SD 137.6) minutes for iOS users. There was poor agreement between objective and caregiver self-reported phone use; root mean square errors were 157.1 and 81.4 for Android and iOS users, respectively. Among families who completed the first assessment, 91 re-enrolled to complete the protocol a second time, approximately 7 months later (91/105, 86.7% retention rate). Conclusions It is feasible to collect intensive longitudinal data on objective digital media use simultaneously with accelerometry and EMA from an economically and racially diverse sample of families with preschool-aged children. The high compliance and retention of the study sample are encouraging signs that these methods of intensive longitudinal data collection can be completed in a longitudinal cohort study. The lack of agreement between self-reported and objectively measured mobile phone use highlights the need for additional research using objective methods to measure digital media use. International Registered Report Identifier (IRRID) RR2-36240
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Affiliation(s)
- Hannah Parker
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Sarah Burkart
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Layton Reesor-Oyer
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Michal T Smith
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Roddrick Dugger
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Lauren von Klinggraeff
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - R Glenn Weaver
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Michael W Beets
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Bridget Armstrong
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
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Baumeister H, Garatva P, Pryss R, Ropinski T, Montag C. Digitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung? PSYCHOLOGISCHE RUNDSCHAU 2022. [DOI: 10.1026/0033-3042/a000609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Zusammenfassung. Digitale Phänotypisierung stellt einen neuen, leistungsstarken Ansatz zur Realisierung psychodiagnostischer Aufgaben in vielen Bereichen der Psychologie und Medizin dar. Die Grundidee besteht aus der Nutzung digitaler Spuren aus dem Alltag, um deren Vorhersagekraft für verschiedenste Anwendungsmöglichkeiten zu überprüfen und zu nutzen. Voraussetzungen für eine erfolgreiche Umsetzung sind elaborierte Smart Sensing Ansätze sowie Big Data-basierte Extraktions- (Data Mining) und Machine Learning-basierte Analyseverfahren. Erste empirische Studien verdeutlichen das hohe Potential, aber auch die forschungsmethodischen sowie ethischen und rechtlichen Herausforderungen, um über korrelative Zufallsbefunde hinaus belastbare Befunde zu gewinnen. Hierbei müssen rechtliche und ethische Richtlinien sicherstellen, dass die Erkenntnisse in einer für Einzelne und die Gesellschaft als Ganzes wünschenswerten Weise genutzt werden. Für die Psychologie als Lehr- und Forschungsdomäne bieten sich durch Digitale Phänotypisierung vielfältige Möglichkeiten, die zum einen eine gelebte Zusammenarbeit verschiedener Fachbereiche und zum anderen auch curriculare Erweiterungen erfordern. Die vorliegende narrative Übersicht bietet eine theoretische, nicht-technische Einführung in das Forschungsfeld der Digitalen Phänotypisierung, mit ersten empirischen Befunden sowie einer Diskussion der Möglichkeiten und Grenzen sowie notwendigen Handlungsfeldern.
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Affiliation(s)
- Harald Baumeister
- Abteilung für Klinische Psychologie und Psychotherapie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
| | - Patricia Garatva
- Abteilung für Klinische Psychologie und Psychotherapie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
| | - Rüdiger Pryss
- Institut für Klinische Epidemiologie und Biometrie, Universität Würzburg, Deutschland
| | - Timo Ropinski
- Arbeitsgruppe Visual Computing, Institut für Medieninformatik, Universität Ulm, Deutschland
| | - Christian Montag
- Abteilung für Molekulare Psychologie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
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Ren B, Xia CH, Gehrman P, Barnett I, Satterthwaite T. Measuring Daily Activity Rhythms in Young Adults at Risk of Affective Instability Using Passively Collected Smartphone Data: Observational Study. JMIR Form Res 2022; 6:e33890. [PMID: 36103225 PMCID: PMC9520392 DOI: 10.2196/33890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 01/18/2022] [Accepted: 07/19/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Irregularities in circadian rhythms have been associated with adverse health outcomes. The regularity of rhythms can be quantified using passively collected smartphone data to provide clinically relevant biomarkers of routine. OBJECTIVE This study aims to develop a metric to quantify the regularity of activity rhythms and explore the relationship between routine and mood, as well as demographic covariates, in an outpatient psychiatric cohort. METHODS Passively sensed smartphone data from a cohort of 38 young adults from the Penn or Children's Hospital of Philadelphia Lifespan Brain Institute and Outpatient Psychiatry Clinic at the University of Pennsylvania were fitted with 2-state continuous-time hidden Markov models representing active and resting states. The regularity of routine was modeled as the hour-of-the-day random effects on the probability of state transition (ie, the association between the hour-of-the-day and state membership). A regularity score, Activity Rhythm Metric, was calculated from the continuous-time hidden Markov models and regressed on clinical and demographic covariates. RESULTS Regular activity rhythms were associated with longer sleep durations (P=.009), older age (P=.001), and mood (P=.049). CONCLUSIONS Passively sensed Activity Rhythm Metrics are an alternative to existing metrics but do not require burdensome survey-based assessments. Low-burden, passively sensed metrics based on smartphone data are promising and scalable alternatives to traditional measurements.
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Affiliation(s)
- Benny Ren
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Cedric Huchuan Xia
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Philip Gehrman
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
- Michael J Crescenz VA Medical Center, Philadelphia, PA, United States
| | - Ian Barnett
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Theodore Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
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Gopalakrishnan A, Venkataraman R, Gururajan R, Zhou X, Genrich R. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review. PeerJ Comput Sci 2022; 8:e1042. [PMID: 36092018 PMCID: PMC9455148 DOI: 10.7717/peerj-cs.1042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Mental health issues are a serious consequence of the COVID-19 pandemic, influencing about 700 million people worldwide. These physiological issues need to be consistently observed on the people through non-invasive devices such as smartphones, and fitness bands in order to remove the burden of having the conciseness of continuously being monitored. On the other hand, technological improvements have enhanced the abilities and roles of conventional mobile phones from simple communication to observations and improved accessibility in terms of size and price may reflect growing familiarity with the smartphone among a vast number of consumers. As a result of continuous monitoring, together with various embedded sensors in mobile phones, raw data can be converted into useful information about the actions and behaviors of the consumers. Thus, the aim of this comprehensive work concentrates on the literature work done so far in the prediction of mental health issues via passive monitoring data from smartphones. This study also explores the way users interact with such self-monitoring technologies and what challenges they might face. We searched several electronic databases (PubMed, IEEE Xplore, ACM Digital Libraries, Soups, APA PsycInfo, and Mendeley Data) for published studies that are relevant to focus on the topic and English language proficiency from January 2015 to December 2020. We identified 943 articles, of which 115 articles were eligible for this scoping review based on the predetermined inclusion and exclusion criteria carried out manually. These studies provided various works regarding smartphones for health monitoring such as Physical activity (26.0 percent; 30/115), Mental health analysis (27.8 percent; 32/115), Student specific monitoring (15.6 percent; 18/115) are the three analyses carried out predominantly.
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Affiliation(s)
- Abinaya Gopalakrishnan
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Revathi Venkataraman
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
| | - Raj Gururajan
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Rohan Genrich
- School of Business, University of Southern Queensland, Toowoomba, Australia
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Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. SENSORS 2022; 22:s22103893. [PMID: 35632301 PMCID: PMC9147201 DOI: 10.3390/s22103893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/16/2022] [Accepted: 05/19/2022] [Indexed: 12/10/2022]
Abstract
Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.
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Qirtas MM, Zafeiridi E, Pesch D, White EB. Loneliness and Social Isolation Detection Using Passive Sensing Techniques: Scoping Review. JMIR Mhealth Uhealth 2022; 10:e34638. [PMID: 35412465 PMCID: PMC9044142 DOI: 10.2196/34638] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 02/21/2022] [Accepted: 02/25/2022] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Loneliness and social isolation are associated with multiple health problems, including depression, functional impairment, and death. Mobile sensing using smartphones and wearable devices, such as fitness trackers or smartwatches, as well as ambient sensors, can be used to acquire data remotely on individuals and their daily routines and behaviors in real time. This has opened new possibilities for the early detection of health and social problems, including loneliness and social isolation. OBJECTIVE This scoping review aimed to identify and synthesize recent scientific studies that used passive sensing techniques, such as the use of in-home ambient sensors, smartphones, and wearable device sensors, to collect data on device users' daily routines and behaviors to detect loneliness or social isolation. This review also aimed to examine various aspects of these studies, especially target populations, privacy, and validation issues. METHODS A scoping review was undertaken, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Studies on the topic under investigation were identified through 6 databases (IEEE Xplore, Scopus, ACM, PubMed, Web of Science, and Embase). The identified studies were screened for the type of passive sensing detection methods for loneliness and social isolation, targeted population, reliability of the detection systems, challenges, and limitations of these detection systems. RESULTS After conducting the initial search, a total of 40,071 papers were identified. After screening for inclusion and exclusion criteria, 29 (0.07%) studies were included in this scoping review. Most studies (20/29, 69%) used smartphone and wearable technology to detect loneliness or social isolation, and 72% (21/29) of the studies used a validated reference standard to assess the accuracy of passively collected data for detecting loneliness or social isolation. CONCLUSIONS Despite the growing use of passive sensing technologies for detecting loneliness and social isolation, some substantial gaps still remain in this domain. A population heterogeneity issue exists among several studies, indicating that different demographic characteristics, such as age and differences in participants' behaviors, can affect loneliness and social isolation. In addition, despite extensive personal data collection, relatively few studies have addressed privacy and ethical issues. This review provides uncertain evidence regarding the use of passive sensing to detect loneliness and social isolation. Future research is needed using robust study designs, measures, and examinations of privacy and ethical concerns.
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Affiliation(s)
- Malik Muhammad Qirtas
- School of Computer Science & Information Technology, University College Cork, Cork, Ireland
| | - Evi Zafeiridi
- School of Computer Science & Information Technology, University College Cork, Cork, Ireland
| | - Dirk Pesch
- School of Computer Science & Information Technology, University College Cork, Cork, Ireland
| | - Eleanor Bantry White
- School of Computer Science & Information Technology, University College Cork, Cork, Ireland
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Huang EJ, Yan K, Onnela JP. Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:2618. [PMID: 35408232 PMCID: PMC9002497 DOI: 10.3390/s22072618] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/03/2022] [Accepted: 03/26/2022] [Indexed: 02/01/2023]
Abstract
Physical activity patterns can reveal information about one's health status. Built-in sensors in a smartphone, in comparison to a patient's self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the movelet method to classify the activities performed using smartphone accelerometer and gyroscope data, which measure a phone's acceleration and angular velocity, respectively. The movelet method constructs a personalized dictionary for each participant using training data and classifies activities in new data with the dictionary. Our results show that this method has the advantages of being interpretable and transparent. A unique aspect of our movelet application involves extracting unique information, optimally, from multiple sensors. In comparison to single-sensor applications, our approach jointly incorporates the accelerometer and gyroscope sensors with the movelet method. Our findings show that combining data from the two sensors can result in more accurate activity recognition than using each sensor alone. In particular, the joint-sensor method reduces errors of the gyroscope-only method in differentiating between standing and sitting. It also reduces errors in the accelerometer-only method when classifying vigorous activities.
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Affiliation(s)
- Emily J Huang
- Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC 27106, USA
| | - Kebin Yan
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
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Dirgová Luptáková I, Kubovčík M, Pospíchal J. Wearable Sensor-Based Human Activity Recognition with Transformer Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:1911. [PMID: 35271058 PMCID: PMC8914677 DOI: 10.3390/s22051911] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 02/22/2022] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
Computing devices that can recognize various human activities or movements can be used to assist people in healthcare, sports, or human-robot interaction. Readily available data for this purpose can be obtained from the accelerometer and the gyroscope built into everyday smartphones. Effective classification of real-time activity data is, therefore, actively pursued using various machine learning methods. In this study, the transformer model, a deep learning neural network model developed primarily for the natural language processing and vision tasks, was adapted for a time-series analysis of motion signals. The self-attention mechanism inherent in the transformer, which expresses individual dependencies between signal values within a time series, can match the performance of state-of-the-art convolutional neural networks with long short-term memory. The performance of the proposed adapted transformer method was tested on the largest available public dataset of smartphone motion sensor data covering a wide range of activities, and obtained an average identification accuracy of 99.2% as compared with 89.67% achieved on the same data by a conventional machine learning method. The results suggest the expected future relevance of the transformer model for human activity recognition.
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Goulding EH, Dopke CA, Rossom RC, Michaels T, Martin CR, Ryan C, Jonathan G, McBride A, Babington P, Bernstein M, Bank A, Garborg CS, Dinh JM, Begale M, Kwasny MJ, Mohr DC. A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Empirical and Theoretical Framework, Intervention Design, and Study Protocol for a Randomized Controlled Trial. JMIR Res Protoc 2022; 11:e30710. [PMID: 35188473 PMCID: PMC8902672 DOI: 10.2196/30710] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 11/27/2021] [Accepted: 11/30/2021] [Indexed: 12/18/2022] Open
Abstract
Background Bipolar disorder is a severe mental illness with high morbidity and mortality rates. Even with pharmacological treatment, frequent recurrence of episodes, long episode durations, and persistent interepisode symptoms are common and disruptive. Combining psychotherapy with pharmacotherapy improves outcomes; however, many individuals with bipolar disorder do not receive psychotherapy. Mental health technologies can increase access to self-management strategies derived from empirically supported bipolar disorder psychotherapies while also enhancing treatment by delivering real-time assessments, personalized feedback, and provider alerts. In addition, mental health technologies provide a platform for self-report, app use, and behavioral data collection to advance understanding of the longitudinal course of bipolar disorder, which can then be used to support ongoing improvement of treatment. Objective A description of the theoretical and empirically supported framework, design, and protocol for a randomized controlled trial (RCT) of LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder, is provided to facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar disorder. The goal of the trial is to determine the effectiveness of LiveWell for reducing relapse risk and symptom burden as well as improving quality of life (QOL) while simultaneously clarifying behavioral targets involved in staying well and better characterizing the course of bipolar disorder and treatment response. Methods The study is a single-blind RCT (n=205; 2:3 ratio of usual care vs usual care plus LiveWell). The primary outcome is the time to relapse. Secondary outcomes are percentage time symptomatic, symptom severity, and QOL. Longitudinal changes in target behaviors proposed to mediate the primary and secondary outcomes will also be determined, and their relationships with the outcomes will be assessed. A database of clinical status, symptom severity, real-time self-report, behavioral sensor, app use, and personalized content will be created to better predict treatment response and relapse risk. Results Recruitment and screening began in March 2017 and ended in April 2019. Follow-up ended in April 2020. The results of this study are expected to be published in 2022. Conclusions This study will examine whether LiveWell reduces relapse risk and symptom burden and improves QOL for individuals with bipolar disorder by increasing access to empirically supported self-management strategies. The role of selected target behaviors (medication adherence, sleep duration, routine, and management of signs and symptoms) in these outcomes will also be examined. Simultaneously, a database will be created to initiate the development of algorithms to personalize and improve treatment for bipolar disorder. In addition, we hope that this description of the theoretical and empirically supported framework, intervention design, and study protocol for the RCT of LiveWell will facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar and other mental health disorders. Trial Registration ClinicalTrials.gov NCT03088462; https://www.clinicaltrials.gov/ct2/show/NCT03088462 International Registered Report Identifier (IRRID) DERR1-10.2196/30710
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Affiliation(s)
- Evan H Goulding
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Cynthia A Dopke
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | | | - Tania Michaels
- Department of Psychiatry, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Clair R Martin
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Chloe Ryan
- Carolina Outreach, Durham, NC, United States
| | - Geneva Jonathan
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Alyssa McBride
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Pamela Babington
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Mary Bernstein
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Andrew Bank
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - C Spencer Garborg
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | | | | | - Mary J Kwasny
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - David C Mohr
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
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Mendes JPM, Moura IR, Van de Ven P, Viana D, Silva FJS, Coutinho LR, Teixeira S, Rodrigues JJPC, Teles AS. Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review. J Med Internet Res 2022; 24:e28735. [PMID: 35175202 PMCID: PMC8895287 DOI: 10.2196/28735] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 06/20/2021] [Accepted: 12/23/2021] [Indexed: 12/12/2022] Open
Abstract
Background Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients’ interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies. Objective This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective. Methods We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends. Results A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal. Conclusions This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.
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Affiliation(s)
- Jean P M Mendes
- Laboratory of Intelligent Distributed Systems, Federal University of Maranhão, São Luís, Brazil
| | - Ivan R Moura
- Laboratory of Intelligent Distributed Systems, Federal University of Maranhão, São Luís, Brazil
| | - Pepijn Van de Ven
- Health Research Institute, University of Limerick, Limerick, Ireland
| | - Davi Viana
- Laboratory of Intelligent Distributed Systems, Federal University of Maranhão, São Luís, Brazil
| | - Francisco J S Silva
- Laboratory of Intelligent Distributed Systems, Federal University of Maranhão, São Luís, Brazil
| | - Luciano R Coutinho
- Laboratory of Intelligent Distributed Systems, Federal University of Maranhão, São Luís, Brazil
| | - Silmar Teixeira
- NeuroInovation & Technological Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil
| | - Joel J P C Rodrigues
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China.,Instituto de Telecomunicações, Covilhã, Portugal
| | - Ariel Soares Teles
- Laboratory of Intelligent Distributed Systems, Federal University of Maranhão, São Luís, Brazil.,NeuroInovation & Technological Laboratory, Federal University of Delta do Parnaíba, Parnaíba, Brazil.,Federal Institute of Maranhão, Araioses, Brazil
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Valentine L, D’Alfonso S, Lederman R. Recommender systems for mental health apps: advantages and ethical challenges. AI & SOCIETY 2022; 38:1-12. [PMID: 35068708 PMCID: PMC8761504 DOI: 10.1007/s00146-021-01322-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 11/08/2021] [Indexed: 11/30/2022]
Abstract
Recommender systems assist users in receiving preferred or relevant services and information. Using such technology could be instrumental in addressing the lack of relevance digital mental health apps have to the user, a leading cause of low engagement. However, the use of recommender systems for digital mental health apps, particularly those driven by personal data and artificial intelligence, presents a range of ethical considerations. This paper focuses on considerations particular to the juncture of recommender systems and digital mental health technologies. While separate bodies of work have focused on these two areas, to our knowledge, the intersection presented in this paper has not yet been examined. This paper identifies and discusses a set of advantages and ethical concerns related to incorporating recommender systems into the digital mental health (DMH) ecosystem. Advantages of incorporating recommender systems into DMH apps are identified as (1) a reduction in choice overload, (2) improvement to the digital therapeutic alliance, and (3) increased access to personal data & self-management. Ethical challenges identified are (1) lack of explainability, (2) complexities pertaining to the privacy/personalization trade-off and recommendation quality, and (3) the control of app usage history data. These novel considerations will provide a greater understanding of how DMH apps can effectively and ethically implement recommender systems.
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Affiliation(s)
- Lee Valentine
- Orygen, Parkville, VIC 3052 Australia
- Centre for Youth Mental Health, University of Melbourne, Parkville, VIC 3010 Australia
| | - Simon D’Alfonso
- School of Computing and Information Systems, Faculty of Engineering and Information Technology, University of Melbourne, Parkville, VIC 3010 Australia
| | - Reeva Lederman
- School of Computing and Information Systems, Faculty of Engineering and Information Technology, University of Melbourne, Parkville, VIC 3010 Australia
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Costa A, Milne R. Understanding 'passivity' in digital health through imaginaries and experiences of coronavirus disease 2019 contact tracing apps. BIG DATA & SOCIETY 2022; 9:20539517221091138. [PMID: 36819735 PMCID: PMC7614187 DOI: 10.1177/20539517221091138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Growing interest is being directed to the health applications of so-called 'passive data' collected through wearables and sensors without active input by users. High promises are attached to passive data and their potential to unlock new insights into health and illness, but as researchers and commentators have noted, this mode of data gathering also raises fundamental questions regarding the subject's agency, autonomy and privacy. To explore how these tensions are negotiated in practice, we present and discuss findings from an interview study with 30 members of the public in the UK and Italy, which examined their views and experiences of the coronavirus disease 2019 contact tracing apps as a large-scale, high-impact example of digital health technology using passive data. We argue that, contrary to what the phrasing 'passive data' suggests, passivity is not a quality of specific modes of data collection but is contingent on the very practices that the technology is supposed to unobtrusively capture.
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Affiliation(s)
- Alessia Costa
- Wellcome Connecting Science, Engagement and Society, Cambridgeshire, Hinxton, UK
| | - Richard Milne
- Wellcome Connecting Science, Engagement and Society, Cambridgeshire, Hinxton, UK
- Kavli Centre for Ethics, Science and the Public, Faculty of Education, University of Cambridge
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Bettis AH, Burke TA, Nesi J, Liu RT. Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review. Clin Psychol Sci 2022; 10:3-26. [PMID: 35174006 PMCID: PMC8846444 DOI: 10.1177/21677026211011982] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
The ability to regulate emotions in response to stress is central to healthy development. While early research in emotion regulation predominantly employed static, self-report measurement, the past decade has seen a shift in focus toward understanding the dynamic nature of regulation processes. This is reflected in recent refinements in the definition of emotion regulation, which emphasize the importance of the ability to flexibly adapt regulation efforts across contexts. The latest proliferation of digital technologies employed in mental health research offers the opportunity to capture the state- and context-sensitive nature of emotion regulation. In this conceptual review, we examine the use of digital technologies (ecological momentary assessment; wearable and smartphone technology, physical activity, acoustic data, visual data, and geo-location; smart home technology; virtual reality; social media) in the assessment of emotion regulation and describe their application to interventions. We also discuss challenges and ethical considerations, and outline areas for future research.
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Affiliation(s)
| | | | | | - Richard T Liu
- Harvard Medical School
- Massachusetts General Hospital
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43
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Peyroteo M, Ferreira IA, Elvas LB, Ferreira JC, Lapão LV. Remote Monitoring Systems for Patients With Chronic Diseases in Primary Health Care: Systematic Review. JMIR Mhealth Uhealth 2021; 9:e28285. [PMID: 34932000 PMCID: PMC8734917 DOI: 10.2196/28285] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 05/25/2021] [Accepted: 10/08/2021] [Indexed: 01/19/2023] Open
Abstract
Background The digital age, with digital sensors, the Internet of Things (IoT), and big data tools, has opened new opportunities for improving the delivery of health care services, with remote monitoring systems playing a crucial role and improving access to patients. The versatility of these systems has been demonstrated during the current COVID-19 pandemic. Health remote monitoring systems (HRMS) present various advantages such as the reduction in patient load at hospitals and health centers. Patients that would most benefit from HRMS are those with chronic diseases, older adults, and patients that experience less severe symptoms recovering from SARS-CoV-2 viral infection. Objective This paper aimed to perform a systematic review of the literature of HRMS in primary health care (PHC) settings, identifying the current status of the digitalization of health processes, remote data acquisition, and interactions between health care personnel and patients. Methods A systematic literature review was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify articles that explored interventions with HRMS in patients with chronic diseases in the PHC setting. Results The literature review yielded 123 publications, 18 of which met the predefined inclusion criteria. The selected articles highlighted that sensors and wearables are already being used in multiple scenarios related to chronic disease management at the PHC level. The studies focused mostly on patients with diabetes (9/26, 35%) and cardiovascular diseases (7/26, 27%). During the evaluation of the implementation of these interventions, the major difficulty that stood out was the integration of information into already existing systems in the PHC infrastructure and in changing working processes of PHC professionals (83%). Conclusions The PHC context integrates multidisciplinary teams and patients with often complex, chronic pathologies. Despite the theoretical framework, objective identification of problems, and involvement of stakeholders in the design and implementation processes, these interventions mostly fail to scale up. Despite the inherent limitations of conducting a systematic literature review, the small number of studies in the PHC context is a relevant limitation. This study aimed to demonstrate the importance of matching technological development to the working PHC processes in interventions regarding the use of sensors and wearables for remote monitoring as a source of information for chronic disease management, so that information with clinical value is not lost along the way.
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Affiliation(s)
- Mariana Peyroteo
- NOVA School of Science and Technology, NOVA University of Lisbon, Setúbal, Portugal.,Inov Inesc Inovação, Instituto de Novas Tecnologias, Lisbon, Portugal
| | - Inês Augusto Ferreira
- Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisbon, Portugal.,School of Biology, St Leonard's Postgraduate College, The University of St Andrews, St Andrews, United Kingdom
| | - Luís Brito Elvas
- Inov Inesc Inovação, Instituto de Novas Tecnologias, Lisbon, Portugal.,Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisbon, Portugal
| | - João Carlos Ferreira
- Inov Inesc Inovação, Instituto de Novas Tecnologias, Lisbon, Portugal.,Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisbon, Portugal
| | - Luís Velez Lapão
- Unidade de Investigação e Desenvolvimento em Engenharia Mecanica e Industrial, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Setúbal, Portugal.,Comprehensive Health Research Center, NOVA Medical School, NOVA University of Lisbon, Lisboa, Portugal
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44
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Ma Z, Wang J, Li J, Jia Y. The association between obesity and problematic smartphone use among school-age children and adolescents: a cross-sectional study in Shanghai. BMC Public Health 2021; 21:2067. [PMID: 34763684 PMCID: PMC8581960 DOI: 10.1186/s12889-021-12124-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 10/28/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The study aimed to investigate the association between content-based problematic smartphone use and obesity in school-age children and adolescents, including variations in the association by educational stage and sex. METHODS Two-stage non-probability sampling was used to recruit 8419 participants from nineteen primary schools, five middle schools, and thirteen high schools in Shanghai in December 2017. Obesity was identified by body mass index (BMI), which was obtained from the school physical examination record, while problematic smartphone use was measured by the Revised Problematic Smartphone Use Classification Scale as the independent variable. RESULTS The rates of obesity varied with educational stages, while problematic smartphone use increased with educational stages. Male students reported higher obesity rates (37.1%vs19.4%, P < 0.001) and greater problematic smartphone use scores (25.65 ± 10.37 vs 22.88 ± 8.94, P < 0.001) than female students. Problematic smartphone use for entertainment (smartphone users addicted to entertainment games, music, videos, novels and other applications) was positively associated to obesity status for primary school [odds ratio (OR), 1.030; 95% confidence interval (95% CI), 1.005-1.057] and high school students (OR, 1.031; 95% CI, 1.004-1.059). For female students, problematic smartphone use for entertainment was positively associated with obesity status (OR, 1.046; 95% CI, 1.018-1.075). CONCLUSIONS Problematic smartphone use may be associated with obesity in children and adolescents. The association differed based on the educational stage and sex, and the difference possessed dimensional specificity.
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Affiliation(s)
- Zhicong Ma
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, Fudan University, Shanghai, 200032, China
| | - Jiangqi Wang
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, Fudan University, Shanghai, 200032, China
| | - Jiang Li
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, Fudan University, Shanghai, 200032, China
- Health Communication Institute, Fudan University, Shanghai, 200032, China
| | - Yingnan Jia
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, Fudan University, Shanghai, 200032, China.
- Health Communication Institute, Fudan University, Shanghai, 200032, China.
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45
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Müller SR, Chen XL, Peters H, Chaintreau A, Matz SC. Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Sci Rep 2021; 11:14007. [PMID: 34234186 PMCID: PMC8263566 DOI: 10.1038/s41598-021-93087-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 06/21/2021] [Indexed: 11/25/2022] Open
Abstract
Depression is one of the most common mental health issues in the United States, affecting the lives of millions of people suffering from it as well as those close to them. Recent advances in research on mobile sensing technologies and machine learning have suggested that a person's depression can be passively measured by observing patterns in people's mobility behaviors. However, the majority of work in this area has relied on highly homogeneous samples, most frequently college students. In this study, we analyse over 57 million GPS data points to show that the same procedure that leads to high prediction accuracy in a homogeneous student sample (N = 57; AUC = 0.82), leads to accuracies only slightly higher than chance in a U.S.-wide sample that is heterogeneous in its socio-demographic composition as well as mobility patterns (N = 5,262; AUC = 0.57). This pattern holds across three different modelling approaches which consider both linear and non-linear relationships. Further analyses suggest that the prediction accuracy is low across different socio-demographic groups, and that training the models on more homogeneous subsamples does not substantially improve prediction accuracy. Overall, the findings highlight the challenge of applying mobility-based predictions of depression at scale.
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Affiliation(s)
- Sandrine R Müller
- Data Science Institute, Columbia University, New York, USA.
- Department of Psychology, Bielefeld University, Bielefeld, Germany.
| | - Xi Leslie Chen
- Computer Science Department, Columbia University, New York, USA
| | - Heinrich Peters
- Columbia Business School, Columbia University, New York, USA
| | | | - Sandra C Matz
- Columbia Business School, Columbia University, New York, USA
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46
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Abo-Tabik M, Benn Y, Costen N. Are Machine Learning Methods the Future for Smoking Cessation Apps? SENSORS 2021; 21:s21134254. [PMID: 34206167 PMCID: PMC8271573 DOI: 10.3390/s21134254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/07/2021] [Accepted: 06/16/2021] [Indexed: 11/16/2022]
Abstract
Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.
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Affiliation(s)
- Maryam Abo-Tabik
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK;
| | - Yael Benn
- Department of Psychology, Manchester Metropolitan University, Manchester M15 6GX, UK
- Correspondence: (Y.B.); (N.C.)
| | - Nicholas Costen
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK;
- Correspondence: (Y.B.); (N.C.)
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47
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Wetzel B, Pryss R, Baumeister H, Edler JS, Gonçalves ASO, Cohrdes C. "How Come You Don't Call Me?" Smartphone Communication App Usage as an Indicator of Loneliness and Social Well-Being across the Adult Lifespan during the COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6212. [PMID: 34201322 PMCID: PMC8227237 DOI: 10.3390/ijerph18126212] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/01/2021] [Accepted: 06/03/2021] [Indexed: 01/08/2023]
Abstract
Loneliness and lack of social well-being are associated with adverse health outcomes and have increased during the COVID-19 pandemic. Smartphone communication data have been suggested to help monitor loneliness, but this requires further evidence. We investigated the informative value of smartphone communication app data for predicting subjective loneliness and social well-being in a sample of 364 participants ranging from 18 to 78 years of age (52.2% female; mean age = 42.54, SD = 13.22) derived from the CORONA HEALTH APP study from July to December 2020 in Germany. The participants experienced relatively high levels of loneliness and low social well-being during the time period characterized by the COVID-19 pandemic. Apart from positive associations with phone call use times, smartphone communication app use was associated with social well-being and loneliness only when considering the age of participants. Younger participants with higher use times tended to report less social well-being and higher loneliness, while the opposite association was found for older adults. Thus, the informative value of smartphone communication use time was rather small and became evident only in consideration of age. The results highlight the need for further investigations and the need to address several limitations in order to draw conclusions at the population level.
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Affiliation(s)
- Britta Wetzel
- Mental Health Research Unit, Department of Epidemiology and Health Monitoring, Robert Koch Institute, 12101 Berlin, Germany; (B.W.); (J.-S.E.)
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-University of Würzburg, 97080 Würzburg, Germany;
| | - Harald Baumeister
- Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany;
| | - Johanna-Sophie Edler
- Mental Health Research Unit, Department of Epidemiology and Health Monitoring, Robert Koch Institute, 12101 Berlin, Germany; (B.W.); (J.-S.E.)
| | | | - Caroline Cohrdes
- Mental Health Research Unit, Department of Epidemiology and Health Monitoring, Robert Koch Institute, 12101 Berlin, Germany; (B.W.); (J.-S.E.)
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Using Graph Representation Learning to Predict Salivary Cortisol Levels in Pancreatic Cancer Patients. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2021; 5:401-419. [DOI: 10.1007/s41666-021-00098-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 02/25/2021] [Accepted: 03/30/2021] [Indexed: 10/21/2022]
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49
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Poudyal A, van Heerden A, Hagaman A, Islam C, Thapa A, Maharjan SM, Byanjankar P, Kohrt BA. What Does Social Support Sound Like? Challenges and Opportunities for Using Passive Episodic Audio Collection to Assess the Social Environment. Front Public Health 2021; 9:633606. [PMID: 33855008 PMCID: PMC8039317 DOI: 10.3389/fpubh.2021.633606] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 03/01/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The social environment, comprised of social support, social burden, and quality of interactions, influences a range of health outcomes, including mental health. Passive audio data collection on mobile phones (e.g., episodic recording of the auditory environment without requiring any active input from the phone user) enables new opportunities to understand the social environment. We evaluated the use of passive audio collection on mobile phones as a window into the social environment while conducting a study of mental health among adolescent and young mothers in Nepal. Methods: We enrolled 23 adolescent and young mothers who first participated in qualitative interviews to describe their social support and identify sounds potentially associated with that support. Then, episodic recordings were collected for 2 weeks from the mothers using an app to record 30 s of audio every 15 min from 4 A.M. to 9 P.M. Audio data were processed and classified using a pretrained model. Each classification category was accompanied by an estimated accuracy score. Manual validation of the machine-predicted speech and non-speech categories was done for accuracy. Results: In qualitative interviews, mothers described a range of positive and negative social interactions and the sounds that accompanied these. Potential positive sounds included adult speech and laughter, infant babbling and laughter, and sounds from baby toys. Sounds characterizing negative stimuli included yelling, crying, screaming by adults and crying by infants. Sounds associated with social isolation included silence and TV or radio noises. Speech comprised 43% of all passively recorded audio clips (n = 7,725). Manual validation showed a 23% false positive rate and 62% false-negative rate for speech, demonstrating potential underestimation of speech exposure. Other common sounds were music and vehicular noises. Conclusions: Passively capturing audio has the potential to improve understanding of the social environment. However, a pre-trained model had the limited accuracy for identifying speech and lacked categories allowing distinction between positive and negative social interactions. To improve the contribution of passive audio collection to understanding the social environment, future work should improve the accuracy of audio categorization, code for constellations of sounds, and combine audio with other smartphone data collection such as location and activity.
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Affiliation(s)
- Anubhuti Poudyal
- Division of Global Mental Health, Department of Psychiatry and Behavioral Sciences, George Washington School of Medicine and Health Sciences, Washington, DC, United States
| | - Alastair van Heerden
- Human and Social Development, Human Sciences Research Council, Pietermaritzburg, South Africa.,Medical Research Council/Wits Developmental Pathways for Health Research Unit, Department of Pediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ashley Hagaman
- Department of Social and Behavioral Sciences, Yale School of Public Health, Yale University, New Haven, CT, United States.,Center for Methods in Implementation and Prevention Science, Yale School of Public Health, Yale University, New Haven, CT, United States
| | - Celia Islam
- George Washington School of Medicine and Health Sciences, Washington, DC, United States
| | - Ada Thapa
- Division of Global Mental Health, Department of Psychiatry and Behavioral Sciences, George Washington School of Medicine and Health Sciences, Washington, DC, United States
| | | | - Prabin Byanjankar
- Transcultural Psychosocial Organization (TPO) Nepal, Kathmandu, Nepal
| | - Brandon A Kohrt
- Division of Global Mental Health, Department of Psychiatry and Behavioral Sciences, George Washington School of Medicine and Health Sciences, Washington, DC, United States
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50
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Doarn CR. A New Beginning. Telemed J E Health 2021; 27:115. [PMID: 33600266 DOI: 10.1089/tmj.2021.29052.crd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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