PROJECTS

@article{Mousavi2022,

title = {Sleep Patterns and Affect Dynamics Among College Students During the COVID-19 Pandemic: Intensive Longitudinal Study},

author = {Zahra Mousavi and Katharine Simon and Alex Rivera and Asal Yunusova and Sirui Hu and Sina Labbaf and Salar Jafarlou and Nikil Dutt and Ramesh Jain and Amir M. Rahmani and Jessica Borelli},

doi = {10.2196/33964},

issn = {2561-326X},

year  = {2022},

date = {2022-08-05},

urldate = {2022-08-05},

journal = {JMIR Formative Research},

volume = {6},

number = {8},

pages = {e33964},

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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being},

author = {Amir M Rahmani and Jocelyn Lai and Salar Jafarlou and Iman Azimi and Asal Yunusova and Alex Rivera and Sina Labbaf and Arman Anzanpour and Nikil Dutt and Ramesh Jain and Jessica Borelli},

url = {https://www.frontiersin.org/articles/10.3389/fdgth.2022.933587/full},

doi = {10.3389/fdgth.2022.933587},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Frontiers in Digital Health},

volume = {4},

abstract = {Current digital mental healthcare solutions conventionally take on a reactive approach, requiring individuals to self-monitor and document existing symptoms. These solutions are unable to provide comprehensive, wrap-around, customized treatments that capture an individual’s holistic mental health model as it unfolds over time. Recognizing that each individual requires personally tailored mental health treatment, we introduce the notion of Personalized Mental Health Navigation (MHN): a cybernetic goal-based system that deploys a continuous loop of monitoring, estimation, and guidance to steer the individual towards mental flourishing. We present the core components of MHN that are premised on the importance of addressing an individual’s personal mental health state. Moreover, we provide an overview of the existing physical health navigation systems and highlight the requirements and challenges of deploying the navigational approach to the mental health domain.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2201-11230,

title = {Objective Prediction of Tomorrow's Affect Using Multi-Modal Physiological 

 Data and Personal Chronicles: A Study of Monitoring College Student 

 Well-being in 2020},

author = {Salar Jafarlou and Jocelyn Lai and Zahra Mousavi and Sina Labbaf and Ramesh C. Jain and Nikil D. Dutt and Jessica L. Borelli and Amir M. Rahmani},

url = {https://arxiv.org/abs/2201.11230},

year  = {2022},

date = {2022-01-01},

journal = {CoRR},

volume = {abs/2201.11230},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{yunusova2021assessing,

title = {Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study},

author = {Asal Yunusova and Jocelyn Lai and Alexander P Rivera and Sirui Hu and Sina Labbaf and Amir M Rahmani and Nikil Dutt and Ramesh C Jain and Jessica L Borelli},

url = {https://pubmed.ncbi.nlm.nih.gov/33513124/},

doi = {10.2196/25775},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {JMIR Research Protocols},

volume = {10},

number = {3},

pages = {e25775},

publisher = {JMIR Publications Inc., Toronto, Canada},

abstract = {Background: Individuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation.

Objective: In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health.

Methods: We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months.

Results: Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19-related stress assessments.

Conclusions: Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lai2021using,

title = {Using Multimodal Assessments to Capture Personalized Contexts of College Student Well-being in 2020: Case Study},

author = {Jocelyn Lai and Amir Rahmani and Asal Yunusova and Alexander P Rivera and Sina Labbaf and Sirui Hu and Nikil Dutt and Ramesh Jain and Jessica L Borelli and others},

year  = {2021},

date = {2021-01-01},

journal = {JMIR formative research},

volume = {5},

number = {5},

pages = {e26186},

publisher = {JMIR Publications Inc., Toronto, Canada},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{borelli2020nine,

title = {Nine Months in the Life of a College Student During 2020: A Case Study Using Multi-Modal Assessments to Capture Personalized Contexts of Well-being},

author = {Jessica Borelli and Jocelyn Lai and Asal Yunusova and Alex P Rivera and Sina Labbaf and Sirui Hu and Nikil D Dutt and Ramesh C Jain and Amir Rahmani},

year  = {2020},

date = {2020-01-01},

publisher = {PsyArXiv},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2305-00111,

title = {Active Reinforcement Learning for Personalized Stress Monitoring in 

 Everyday Settings},

author = {Ali Tazarv and Sina Labbaf and Amir M. Rahmani and Nikil D. Dutt and Marco Levorato},

url = {https://doi.org/10.48550/arXiv.2305.00111},

doi = {10.48550/arXiv.2305.00111},

year  = {2023},

date = {2023-01-01},

journal = {CoRR},

volume = {abs/2305.00111},

keywords = {},

pubstate = {published},

tppubtype = {article}

}