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Labbaf S, Abbasian M, Azimi I, Dutt N, Rahmani AM. ZotCare: a flexible, personalizable, and affordable mhealth service provider. Front Digit Health 2023; 5:1253087. [PMID: 37781455 PMCID: PMC10539601 DOI: 10.3389/fdgth.2023.1253087] [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: 07/04/2023] [Accepted: 09/04/2023] [Indexed: 10/03/2023] Open
Abstract
The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHealth) applications, which present significant technical challenges for researchers. While existing mHealth solutions have made progress in addressing some of these challenges, they often fall short in terms of time-to-use, affordability, and flexibility for personalization and adaptation. ZotCare aims to address these limitations by offering ready-to-use and flexible services, providing researchers with an accessible, cost-effective, and adaptable solution for their mHealth studies. This article focuses on ZotCare's service orchestration and highlights its capabilities in creating a programmable environment for mHealth research. Additionally, we showcase several successful research use cases that have utilized ZotCare, both in the past and in ongoing projects. Furthermore, we provide resources and information for researchers who are considering ZotCare as their mHealth research solution.
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Affiliation(s)
- Sina Labbaf
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Mahyar Abbasian
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Iman Azimi
- Institute for Future Health, University of California, Irvine, Irvine, CA, United States
| | - Nikil Dutt
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
- Institute for Future Health, University of California, Irvine, Irvine, CA, United States
| | - Amir M. Rahmani
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
- Institute for Future Health, University of California, Irvine, Irvine, CA, United States
- School of Nursing, University of California, Irvine, Irvine, CA, United States
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Jafarlou S, Lai J, Azimi I, Mousavi Z, Labbaf S, Jain RC, Dutt N, Borelli JL, Rahmani A. Objective Prediction of Next-Day's Affect Using Multimodal Physiological and Behavioral Data: Algorithm Development and Validation Study. JMIR Form Res 2023; 7:e39425. [PMID: 36920456 PMCID: PMC10131982 DOI: 10.2196/39425] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 12/08/2022] [Accepted: 12/08/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Affective states are important aspects of healthy functioning; as such, monitoring and understanding affect is necessary for the assessment and treatment of mood-based disorders. Recent advancements in wearable technologies have increased the use of such tools in detecting and accurately estimating mental states (eg, affect, mood, and stress), offering comprehensive and continuous monitoring of individuals over time. OBJECTIVE Previous attempts to model an individual's mental state relied on subjective measurements or the inclusion of only a few objective monitoring modalities (eg, smartphones). This study aims to investigate the capacity of monitoring affect using fully objective measurements. We conducted a comparatively long-term (12-month) study with a holistic sampling of participants' moods, including 20 affective states. METHODS Longitudinal physiological data (eg, sleep and heart rate), as well as daily assessments of affect, were collected using 3 modalities (ie, smartphone, watch, and ring) from 20 college students over a year. We examined the difference between the distributions of data collected from each modality along with the differences between their rates of missingness. Out of the 20 participants, 7 provided us with 200 or more days' worth of data, and we used this for our predictive modeling setup. Distributions of positive affect (PA) and negative affect (NA) among the 7 selected participants were observed. For predictive modeling, we assessed the performance of different machine learning models, including random forests (RFs), support vector machines (SVMs), multilayer perceptron (MLP), and K-nearest neighbor (KNN). We also investigated the capability of each modality in predicting mood and the most important features of PA and NA RF models. RESULTS RF was the best-performing model in our analysis and performed mood and stress (nervousness) prediction with ~81% and ~72% accuracy, respectively. PA models resulted in better performance compared to NA. The order of the most important modalities in predicting PA and NA was the smart ring, phone, and watch, respectively. SHAP (Shapley Additive Explanations) analysis showed that sleep and activity-related features were the most impactful in predicting PA and NA. CONCLUSIONS Generic machine learning-based affect prediction models, trained with population data, outperform existing methods, which use the individual's historical information. Our findings indicated that our mood prediction method outperformed the existing methods. Additionally, we found that sleep and activity level were the most important features for predicting next-day PA and NA, respectively.
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Affiliation(s)
- Salar Jafarlou
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, United States
| | - Jocelyn Lai
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Iman Azimi
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, United States.,Institute for Future Health, University of California, Irvine, Irvine, CA, United States
| | - Zahra Mousavi
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Sina Labbaf
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, United States
| | - Ramesh C Jain
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, United States.,Institute for Future Health, University of California, Irvine, Irvine, CA, United States
| | - Nikil Dutt
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, United States
| | - Jessica L Borelli
- Department of Cognitive Science, University of California, Irvine, Irvine, CA, United States
| | - Amir Rahmani
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, United States.,Institute for Future Health, University of California, Irvine, Irvine, CA, United States.,School of Nursing, University of California, Irvine, Irvine, CA, United States
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Valenzuela RLG, Velasco RIB, Jorge MPPC. Impact of COVID-19 pandemic on sleep of undergraduate students: A systematic literature review. Stress Health 2023; 39:4-34. [PMID: 35699687 DOI: 10.1002/smi.3171] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 05/11/2022] [Accepted: 06/02/2022] [Indexed: 02/05/2023]
Abstract
The 2019 coronavirus pandemic forced the shift to distance education aggravating mental and physical vulnerabilities of undergraduate students, including sleep. This review aims to describe sleep problem rates and prevalence, sleep pattern disruption, sleep duration, sleep quality, insomnia symptoms, psychological and socio-economic factors affecting sleep of undergraduates in 22 countries. A systematic search for articles published from 2020 to 2021 using 'COVID-19,' 'Coronavirus,' 'Pandemic,' 'Sleep,' 'Mental Health,' and 'Students' from PubMed, Scopus, and Cochrane yielded 2550 articles, where 72 were included. Selection criteria were: English full-text available articles, undergraduates and not postgraduates, reported sleep outcomes, and participants not from allied health courses. Risk of bias was assessed using various Joanna Briggs Institute checklists and outcomes were descriptively synthesized. Prevalence of sleep problems was notable, while longitudinal studies showed increased rates. There was significantly increased sleep duration, and sleep pattern disruption during lockdowns. Several psychological, behavioural, environmental, demographic, and socio-economic factors were found to be associated with sleep changes. These highlight the pandemic's impact on sleep of undergraduate students and reveal opportunities for institutions to intervene with policies and programs to promote the well-being of undergraduates. Limitations include recall bias and underrepresentation of other countries. This study is self-funded with registration number RGAO-2021-0071.
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Affiliation(s)
| | | | - Manuel Peter Paul C Jorge
- College of Medicine, University of the Philippines Manila, Manila, Philippines.,Department of Physiology, College of Medicine, University of the Philippines Manila, Manila, Philippines
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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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Mousavi ZA, Lai J, Simon K, Rivera AP, Yunusova A, Hu S, Labbaf S, Jafarlou S, Dutt ND, Jain RC, Rahmani AM, Borelli JL. Sleep Patterns and Affect Dynamics Among College Students During the COVID-19 Pandemic: Intensive Longitudinal Study. JMIR Form Res 2022; 6:e33964. [PMID: 35816447 PMCID: PMC9359303 DOI: 10.2196/33964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 05/24/2022] [Accepted: 06/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background
Sleep disturbance is a transdiagnostic risk factor that is so prevalent among young adults that it is considered a public health epidemic, which has been exacerbated by the COVID-19 pandemic. Sleep may contribute to mental health via affect dynamics. Prior literature on the contribution of sleep to affect is largely based on correlational studies or experiments that do not generalize to the daily lives of young adults. Furthermore, the literature examining the associations between sleep variability and affect dynamics remains scant.
Objective
In an ecologically valid context, using an intensive longitudinal design, we aimed to assess the daily and long-term associations between sleep patterns and affect dynamics among young adults during the COVID-19 pandemic.
Methods
College student participants (N=20; female: 13/20, 65%) wore an Oura ring (Ōura Health Ltd) continuously for 3 months to measure sleep patterns, such as average and variability in total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, and sleep onset latency (SOL), resulting in 1173 unique observations. We administered a daily ecological momentary assessment by using a mobile health app to evaluate positive affect (PA), negative affect (NA), and COVID-19 worry once per day.
Results
Participants with a higher sleep onset latency (b=−1.09, SE 0.36; P=.006) and TST (b=−0.15, SE 0.05; P=.008) on the prior day had lower PA on the next day. Further, higher average TST across the 3-month period predicted lower average PA (b=−0.36, SE 0.12; P=.009). TST variability predicted higher affect variability across all affect domains. Specifically, higher variability in TST was associated higher PA variability (b=0.09, SE 0.03; P=.007), higher negative affect variability (b=0.12, SE 0.05; P=.03), and higher COVID-19 worry variability (b=0.16, SE 0.07; P=.04).
Conclusions
Fluctuating sleep patterns are associated with affect dynamics at the daily and long-term scales. Low PA and affect variability may be potential pathways through which sleep has implications for mental health.
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Affiliation(s)
- Zahra Avah Mousavi
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Jocelyn Lai
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Katharine Simon
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Alexander P Rivera
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Asal Yunusova
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Sirui Hu
- Department of Economics, University of California, Irvine, Irvine, CA, United States
| | - Sina Labbaf
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Salar Jafarlou
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Nikil D Dutt
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Ramesh C Jain
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Amir M Rahmani
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
- School of Nursing, University of California, Irvine, Irvine, CA, United States
| | - Jessica L Borelli
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
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