Publications

@article{nokey,

title = {A novel conceptual model of trauma-informed care for patients with post-acute sequelae of SARS-CoV-2 illness (PASC)},

author = {Candace W Burton and Charles A Downs and Thomas Hughes and Natalie Lambert and Heather L Abrahim and Maria Giraldo Herrera and Yong Huang and Amir Rahmani and Jung-Ah Lee and Rana Chakraborty and Melissa D Pinto},

url = {https://pubmed.ncbi.nlm.nih.gov/36036199/},

doi = {10.1111/jan.15426},

year  = {2022},

date = {2022-11-01},

journal = {Journal of advanced nursing},

volume = {78},

number = {11},

pages = {3618-3628},

abstract = {Aim: This paper proposes a novel, trauma-informed, conceptual model of care for Post-Acute Sequelae of COVID-19 illness (PASC).

Design: This paper describes essential elements, linkages and dimensions of the model that affect PASC patient experiences and the potential impact of trauma-informed care on outcomes.

Data sources: PASC is a consequence of the global pandemic, and a new disease of which little is known. Our model was derived from the limited available studies, expert clinical experience specific to PASC survivors and publicly available social media narratives authored by PASC survivors.

Implications for nursing: The model provides a critical and novel framework for the understanding and care of persons affected by PASC. This model is aimed at the provision of nursing care, with the intention of reducing the traumatic impacts of the uncertain course of this disease, a lack of defined treatment options and difficulties in seeking care. The use of a trauma-informed care approach to PASC patients can enhance nurses' ability to remediate and ameliorate both the traumatic burden of and the symptoms and experience of the illness.

Conclusion: Applying a trauma-informed perspective to care of PASC patients can help to reduce the overall burden of this complex condition. Owing to the fundamentally holistic perspective of the nursing profession, nurses are best positioned to implement care that addresses multiple facets of the PASC experience.

Impact: The proposed model specifically addresses the myriad ways in which PASC may affect physical as well as mental and psychosocial dimensions of health. The model particularly seeks to suggest means of supporting patients who have already experienced a life-threatening illness and are now coping with its long-term impact. Since the scope of this impact is not yet defined, trauma-informed care for PASC patients is likely to reduce the overall health and systems burdens of this complex condition.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shahhosseini2022Online,

title = {Online Learning for Orchestration of Inference in Multi-user End-edge-cloud Networks},

author = {Sina Shahhosseini and DongJoo Seo and Anil Kanduri and Tianyi Hu and Sung-Soo Lim and Bryan Donyanavard and Amir M. Rahmani and and Nikil Dutt},

url = {https://dl.acm.org/doi/full/10.1145/3520129},

doi = {10.1145/3520129},

year  = {2022},

date = {2022-11-01},

urldate = {2022-11-01},

journal = {ACM Transactions on Embedded Computing Systems},

volume = {21},

number = {73},

pages = {1–25},

abstract = {Deep-learning-based intelligent services have become prevalent in cyber-physical applications, including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protection, responsiveness, and reliability. Resource-constrained end-devices must be carefully managed to meet the latency and energy requirements of computationally intensive deep learning services. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). However, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy. We demonstrate the effectiveness of our solution for edge devices in an end-edge-cloud system and evaluate with a real-setup implementation using multiple AWS and ARM core configurations. Our solution provides 35% speedup in the average response time compared to the state-of-the-art with less than 0.9% accuracy reduction, demonstrating the promise of our online learning framework for orchestrating DL inference in end-edge-cloud systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{huang2021covid,

title = {COVID symptoms, symptom clusters, and predictors for becoming a long-hauler: looking for clarity in the haze of the pandemic},

author = {Yong Huang and Melissa D Pinto and Jessica L Borelli and Milad Asgari Mehrabadi and Heather Abrihim and Nikil Dutt and Natalie Lambert and Erika L Nurmi and Rana Chakraborty and Amir M Rahmani and Charles A. Downs},

url = {https://journals.sagepub.com/doi/full/10.1177/10547738221125632},

doi = {10.1177/10547738221125632},

year  = {2022},

date = {2022-09-01},

urldate = {2021-01-01},

journal = {Clinical Nursing Research},

volume = {31},

number = {8},

publisher = {Cold Spring Harbor Laboratory Press},

abstract = {Post-acute sequelae of SARS-CoV-2 (PASC) is defined as persistent symptoms after apparent recovery from acute COVID-19 infection, also known as COVID-19 long-haul. We performed a retrospective review of electronic health records (EHR) from the University of California COvid Research Data Set (UC CORDS), a de-identified EHR of PCR-confirmed SARS-CoV-2-positive patients in California. The purposes were to (1) describe the prevalence of PASC, (2) describe COVID-19 symptoms and symptom clusters, and (3) identify risk factors for PASC. Data were subjected to non-negative matrix factorization to identify symptom clusters, and a predictive model of PASC was developed. PASC prevalence was 11% (277/2,153), and of these patients, 66% (183/277) were considered asymptomatic at days 0–30. Five PASC symptom clusters emerged and specific symptoms at days 0–30 were associated with PASC. Women were more likely than men to develop PASC, with all age groups and ethnicities represented. PASC is a public health priority.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lambert2021covid,

title = {The other COVID-19 survivors: Timing, duration, and health impact of post-acute sequelae of SARS-CoV-2 infection},

author = {Natalie Lambert and Survivor Corps and Sarah A El-Azab and Nathan S Ramrakhiani and Anthony Barisano and Lu Yu and Kaitlyn Taylor and Alvaro Esperanca and Charles A Downs and Heather L Abrahim and Thomas Hughes and Amir M. Rahmani and Jessica L Borelli and Rana Chakraborty and Melissa D. Pinto},

url = {https://pubmed.ncbi.nlm.nih.gov/36181315/},

doi = {10.1111/jocn.16541},

year  = {2022},

date = {2022-09-01},

urldate = {2021-01-01},

journal = {Journal of Clinical Nursing},

publisher = {Cold Spring Harbor Laboratory Press},

abstract = {Aims and objectives: To determine the frequency, timing, and duration of post-acute sequelae of SARS-CoV-2 infection (PASC) and their impact on health and function.

Background: Post-acute sequelae of SARS-CoV-2 infection is an emerging major public health problem that is poorly understood and has no current treatment or cure. PASC is a new syndrome that has yet to be fully clinically characterised.

Design: Descriptive cross-sectional survey (n = 5163) was conducted from online COVID-19 survivor support groups who reported symptoms for more than 21 days following SARS-CoV-2 infection.

Methods: Participants reported background demographics and the date and method of their covid diagnosis, as well as all symptoms experienced since onset of covid in terms of the symptom start date, duration, and Likert scales measuring three symptom-specific health impacts: pain and discomfort, work impairment, and social impairment. Descriptive statistics and measures of central tendencies were computed for participant demographics and symptom data.

Results: Participants reported experiencing a mean of 21 symptoms (range 1-93); fatigue (79.0%), headache (55.3%), shortness of breath (55.3%) and difficulty concentrating (53.6%) were the most common. Symptoms often remitted and relapsed for extended periods of time (duration M = 112 days), longest lasting symptoms included the inability to exercise (M = 106.5 days), fatigue (M = 101.7 days) and difficulty concentrating, associated with memory impairment (M = 101.1 days). Participants reported extreme pressure at the base of the head, syncope, sharp or sudden chest pain, and "brain pressure" among the most distressing and impacting daily life.

Conclusions: Post-acute sequelae of SARS-CoV-2 infection can be characterised by a wide range of symptoms, many of which cause moderate-to-severe distress and can hinder survivors' overall well-being.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {Affect Dynamics, Affect Variability, COVID-19, Objective Sleep Outcomes, Sleep},

pubstate = {published},

tppubtype = {article}

}

@article{Sarhaddi2022Trends,

title = {Trends in Heart Rate and Heart Rate Variability During Pregnancy and the 3-Month Postpartum Period: Continuous Monitoring in a Free-living Context},

author = {Fatemeh Sarhaddi and Iman Azimi and Anna Axelin and Hannakaisa Niela-Vilen and Pasi Liljeberg and Amir M Rahmani},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9206203/},

doi = {10.2196/33458},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

journal = {JMIR mHealth and uHealth},

volume = {10},

number = {6},

pages = {e33458},

abstract = {Background: Heart rate variability (HRV) is a noninvasive method that reflects the regulation of the autonomic nervous system. Altered HRV is associated with adverse mental or physical health complications. The autonomic nervous system also has a central role in physiological adaption during pregnancy, causing normal changes in HRV.

Objective: The aim of this study was to assess trends in heart rate (HR) and HRV parameters as a noninvasive method for remote maternal health monitoring during pregnancy and 3-month postpartum period.

Methods: A total of 58 pregnant women were monitored using an Internet of Things–based remote monitoring system during pregnancy and 3-month postpartum period. Pregnant women were asked to continuously wear Gear Sport smartwatch to monitor their HR and HRV extracted from photoplethysmogram (PPG) signals. In addition, a cross-platform mobile app was used to collect background and delivery-related information. We analyzed PPG signals collected during the night and discarded unreliable signals by applying a PPG quality assessment method to the collected signals. HR, HRV, and normalized HRV parameters were extracted from reliable signals. The normalization removed the effect of HR changes on HRV trends. Finally, we used hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters.

Results: HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P=.006). Time-domain HRV parameters, average normal interbeat intervals (IBIs; average normal IBIs [AVNN]), SD of normal IBIs (SDNN), root mean square of the successive difference of normal IBIs (RMSSD), normalized SDNN, and normalized RMSSD decreased significantly during the second trimester (P<.001). Then, AVNN, SDNN, RMSSD, and normalized SDNN increased significantly during the third trimester (with P=.002, P<.001, P<.001, and P<.001, respectively). Some of the frequency-domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF, decreased significantly during the second trimester (with P<.001, P<.001, and P=.003, respectively), and HF increased significantly during the third trimester (P=.007). In the postpartum period, normalized RMSSD decreased (P=.01), and the LF to HF ratio (LF/HF) increased significantly (P=.004).

Conclusions: Our study indicates the physiological changes during pregnancy and the postpartum period. We showed that HR increased and HRV parameters decreased as pregnancy proceeded, and the values returned to normal after delivery. Moreover, our results show that HR started to decrease, whereas time-domain HRV parameters and HF started to increase during the third trimester. The results also indicated that age was significantly associated with HRV parameters during pregnancy and postpartum period, whereas education level was associated with HRV parameters during the third trimester. In addition, our results demonstrate the possibility of continuous HRV monitoring in everyday life settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jimah2022Micro,

title = {A Micro-Level Analysis of Physiological Responses to COVID-19: Continuous Monitoring of Pregnant Women in California},

author = {Tamara Jimah and Priscilla Kehoe and Holly Borg and Pamela Pimentel and Amir M. Rahmani and Nikil Dutt and Yuqing Guo},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021503/},

doi = {10.3389/fpubh.2022.808763},

year  = {2022},

date = {2022-04-01},

urldate = {2022-04-01},

journal = {Frontiers in Public Health},

volume = {10},

pages = {808763},

abstract = {Continuous monitoring of perinatal women in a descriptive case study allowed us the opportunity to examine the time during which the COVID-19 infection led to physiological changes in two low-income pregnant women. An important component of this study was the use of a wearable sensor device, the Oura ring, to monitor and record vital physiological parameters during sleep. Two women in their second and third trimesters, respectively, were selected based on a positive COVID-19 diagnosis. Both women were tested using the polymerase chain reaction method to confirm the presence of the virus during which time we were able to collect these physiological data. In both cases, we observed 3–6 days of peak physiological changes in resting heart rate (HR), heart rate variability (HRV), and respiratory rate (RR), as well as sleep surrounding the onset of COVID-19 symptoms. The pregnant woman in her third trimester showed a significant increase in resting HR (p = 0.006) and RR (p = 0.048), and a significant decrease in HRV (p = 0.027) and deep sleep duration (p = 0.029). She reported experiencing moderate COVID-19 symptoms and did not require hospitalization. At 38 weeks of gestation, she had a normal delivery and gave birth to a healthy infant. The participant in her second trimester showed similar physiological changes during the 3-day peak period. Importantly, these changes appeared to return to the pre-peak levels. Common symptoms reported by both cases included loss of smell and nasal congestion, with one losing her sense of taste. Results suggest the potential to use the changes in cardiorespiratory responses and sleep for real-time monitoring of health and well-being during pregnancy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A novel wireless ECG system for prolonged monitoring of multiple zebrafish for heart disease and drug screening studies},

author = {Tai Le and Jimmy Zhang and Anh H.Nguyen and Ramses Seferino Trigo Torres and Khuong Vo and Nikil Dutt and Juhyun Lee and Yonghe Ding and Xiaolei Xu and Michael P.H. Lau and HungCao},

url = {https://www.sciencedirect.com/science/article/pii/S0956566321008459},

doi = {10.1016/j.bios.2021.113808},

year  = {2022},

date = {2022-02-01},

journal = {Biosensors and Bioelectronics},

volume = {197},

abstract = {Zebrafish and their mutant lines have been extensively used in cardiovascular studies. In the current study, the novel system, Zebra II, is presented for prolonged electrocardiogram (ECG) acquisition and analysis for multiple zebrafish within controllable working environments. The Zebra II is composed of a perfusion system, apparatuses, sensors, and an in-house electronic system. First, the Zebra II is validated in comparison with a benchmark system, namely iWORX, through various experiments. The validation displayed comparable results in terms of data quality and ECG changes in response to drug treatment. The effects of anesthetic drugs and temperature variation on zebrafish ECG were subsequently investigated in experiments that need real-time data assessment. The Zebra II's capability of continuous anesthetic administration enabled prolonged ECG acquisition up to 1 h compared to that of 5 min in existing systems. The novel, cloud-based, automated analysis with data obtained from four fish further provided a useful solution for combinatorial experiments and helped save significant time and effort. The system showed robust ECG acquisition and analytics for various applications including arrhythmia in sodium induced sinus arrest, temperature-induced heart rate variation, and drug-induced arrhythmia in Tg(SCN5A-D1275N) mutant and wildtype fish. The multiple channel acquisition also enabled the implementation of randomized controlled trials on zebrafish models. The developed ECG system holds promise and solves current drawbacks in order to greatly accelerate drug screening applications and other cardiovascular studies using zebrafish.},

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{borelli2021barreras,

title = {Barreras y Soluciones: Lessons learned from integrating research-based clinical techniques into a community agency serving low-income Latinx immigrant families},

author = {Jessica L Borelli and Breana R Cervantes and Hannah K Hecht and Christina M Marquez and Rosy DePrado and Gina Torres and Araceli Robles and Noraima Chirinos and Francisca Leal and Gloria Itzel Montiel and Melissa Pedroza and Nancy Guerra},

url = {https://onlinelibrary.wiley.com/doi/full/10.1111/famp.12712},

doi = {10.1111/famp.12712},

year  = {2021},

date = {2021-08-01},

urldate = {2021-01-01},

journal = {Family Process},

volume = {61},

number = {1},

pages = {108-129},

publisher = {Wiley Online Library},

abstract = {Barriers facing effective science-to-practice translation have led scholars to conduct early-stage intervention research within community organizations. We describe our experiences developing a manualized parent–youth attachment-based group therapy intervention within a community health organization dedicated to serving low-income Latinx immigrant families, Latino Health Access (LHA), in which services are rendered by trained community workers (promotores). By conducting a qualitative analysis of interviews with all members of this academic–community partnership (research [Principal Investigator, student researchers] and community agency team members [Administrators, promotores]), we discuss the challenges and opportunities that this collaboration has generated. The results led both the research and community teams to question assumptions about the basic skills, values, and attitudes that underlie the integration of science and practice. We will share the insights that have helped to promote connection and understanding among the stakeholders and the efforts made to support the progress and successes of developing community interventions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bate2021my,

title = {Be My Safe Haven: Parent--Child Relationships and Emotional Health During COVID-19},

author = {Jordan Bate and Phoebe T Pham and Jessica L Borelli},

url = {https://pubmed.ncbi.nlm.nih.gov/34283892/},

doi = {10.1093/jpepsy/jsab046},

year  = {2021},

date = {2021-07-01},

urldate = {2021-01-01},

journal = {Journal of pediatric psychology},

volume = {46},

number = {6},

pages = {624--634},

publisher = {Oxford University Press},

abstract = {Objective: Since March 2020, millions of children have been confined to their homes and restricted from in-person activities, radically changing the dynamics of parent-child relationships. This study examines the association between coronavirus disease 2019 (COVID-19) impact and the mental health of parents and school-aged children; specifically, whether qualities of the parent-child relationship moderated the relationship between parents' emotional health (EH) and children's emotional and behavioral health (EBH).

Methods: Data from this Internet-based study of a community sample were collected in March-May 2020. Parents (N = 158, 92.4% White, 96.2% female) reported on COVID-19 impacts, their own EH, perceptions of their relationship with their eldest child between 6 and 12 years-old, and the EBH of that child.

Results: Responses to questions about COVID-19 impact were assigned weighted values and used to create a COVID-19 impact scale. Hierarchical linear regressions revealed that greater COVID-19 impact was associated with greater parents' EH issues only, and parents' EH was a significant positive predictor of children's EBH. Positive qualities and conflict in the parent-child relationship moderated the link between parents' and children's EH. At higher levels of relationship conflict and lower levels of positivity, there were stronger positive associations between parents' and children's EH. Parent-child relationship quality did not moderate the association between parents' EH and children's behavioral health (BH).

Conclusions: These cross-sectional study results suggest that beyond focusing on symptom management, families may benefit from supports targeting the parent-child relationship. Insights and implications for practitioners are discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {SEAMS: Self-optimizing Runtime Manager for Approximate Memory Hierarchies},

author = {Biswadip Maity and Bryan Donyanavard and Anmol Surhonne and Amir M. Rahmani and Andreas Herkersdorf and Nikil Dutt},

url = {https://dl.acm.org/doi/fullHtml/10.1145/3466875},

doi = {10.1145/3466875},

year  = {2021},

date = {2021-07-01},

urldate = {2021-07-01},

journal = {ACM Transactions on Embedded Computing Systems (ACM-TECS)},

volume = {20},

number = {5},

abstract = {Memory approximation techniques are commonly limited in scope, targeting individual levels of the memory hierarchy. Existing approximation techniques for a full memory hierarchy determine optimal configurations at design-time provided a goal and application. Such policies are rigid: they cannot adapt to unknown workloads and must be redesigned for different memory configurations and technologies. We propose SEAMS: the first self-optimizing runtime manager for coordinating configurable approximation knobs across all levels of the memory hierarchy. SEAMS continuously updates and optimizes its approximation management policy throughout runtime for diverse workloads. SEAMS optimizes the approximate memory configuration to minimize energy consumption without compromising the quality threshold specified by application developers. SEAMS can (1) learn a policy at runtime to manage variable application quality of service (QoS) constraints, (2) automatically optimize for a target metric within those constraints, and (3) coordinate runtime decisions for interdependent knobs and subsystems. We demonstrate SEAMS’ ability to efficiently provide functions (1)–(3) on a RISC-V Linux platform with approximate memory segments in the on-chip cache and main memory. We demonstrate SEAMS’ ability to save up to 37% energy in the memory subsystem without any design-time overhead. We show SEAMS’ ability to reduce QoS violations by 75% with <5% additional energy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{montgomery2021curating,

title = {Curating the Digital Mental Health Landscape With a Guide to Behavioral Health Apps: A County-Driven Resource},

author = {Robert M Montgomery and Lamar Brandysky and Martha Neary and Elizabeth Eikey and Gloria Mark and Margaret Schneider and Nicole A Stadnick and Kai Zheng and Dana B Mukamel and Dara H Sorkin and Stephen M Schueller},

url = {https://pubmed.ncbi.nlm.nih.gov/34030454/},

doi = {10.1176/appi.ps.202000803},

year  = {2021},

date = {2021-05-01},

urldate = {2021-01-01},

journal = {Psychiatric Services},

volume = {72},

number = {10},

pages = {1229-1232},

publisher = {Am Psychiatric Assoc},

abstract = {With more than 10,000 mental health apps available, consumers and clinicians who want to adopt such tools can be overwhelmed by the multitude of options and lack of clear evaluative standards. Despite the increasing prevalence of curated lists, or app guides, challenges remain. Organizations providing mental health services to consumers have an opportunity to address these challenges by producing guides that meet relevant standards of quality and are tailored to local needs. This column summarizes an example of the collaborative process of app guide development in a publicly funded mental health service context and highlights opportunities and barriers identified through the process.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mehrabadi2021causality,

title = {The causality inference of public interest in restaurants and bars on daily COVID-19 cases in the United States: Google Trends analysis},

author = {Milad Asgari Mehrabadi and Nikil Dutt and Amir M Rahmani},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8025919/},

doi = {10.2196/22880},

year  = {2021},

date = {2021-04-01},

urldate = {2021-04-01},

journal = {JMIR public health and surveillance},

volume = {7},

number = {4},

pages = {e22880},

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

abstract = {Background: The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak.

Objective: The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project.

Methods: To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends.

Results: Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test.

Conclusions: Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rodrigues2021digital,

title = {Digital Health-Enabled Community-Centered Care (D-CCC): A Scalable Model to Empower Future Community Health Workers utilizing Human-in-the-Loop AI},

author = {Sarah M Rodrigues and Anil Kanduri and Adeline M Nyamathi and Nikil Dutt and Pramod P Khargonekar and Amir M Rahmani},

url = {https://pubmed.ncbi.nlm.nih.gov/35384853/},

doi = {10.2196/29535},

year  = {2021},

date = {2021-04-01},

urldate = {2021-01-01},

journal = {JMIR Formative Research},

volume = {6},

number = {4},

pages = {e29535},

publisher = {Cold Spring Harbor Laboratory Press},

abstract = {Digital health-enabled community-centered care (D-CCC) represents a pioneering vision for the future of community-centered care. D-CCC aims to support and amplify the digital footprint of community health workers through a novel artificial intelligence-enabled closed-loop digital health platform designed for, and with, community health workers. By focusing digitalization at the level of the community health worker, D-CCC enables more timely, supported, and individualized community health worker-delivered interventions. D-CCC has the potential to move community-centered care into an expanded, digitally interconnected, and collaborative community-centered health and social care ecosystem of the future, grounded within a robust and digitally empowered community health workforce.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}