publications

2024

1. Assessment of ownership of smart devices and the acceptability of digital health data sharing

   Md Mobashir Hasan Shandhi, Karnika Singh, Natasha Janson, and 6 more authors

   npj Digital Medicine 2024 7:1, Feb 2024

   Abs DOI

   Smart portable devices- smartphones and smartwatches- are rapidly being adopted by the general population, which has brought forward an opportunity to use the large volumes of physiological, behavioral, and activity data continuously being collected by these devices in naturalistic settings to perform research, monitor health, and track disease. While these data can serve to revolutionize health monitoring in research and clinical care, minimal research has been conducted to understand what motivates people to use these devices and their interest and comfort in sharing the data. In this study, we aimed to characterize the ownership and usage of smart devices among patients from an expansive academic health system in the southeastern US and understand their willingness to share data collected by the smart devices. We conducted an electronic survey of participants from an online patient advisory group around smart device ownership, usage, and data sharing. Out of the 3021 members of the online patient advisory group, 1368 (45%) responded to the survey, with 871 female (64%), 826 and 390 White (60%) and Black (29%) participants, respectively, and a slight majority (52%) age 58 and older. Most of the respondents (98%) owned a smartphone and the majority (59%) owned a wearable. In this population, people who identify as female, Hispanic, and Generation Z (age 18–25), and those completing higher education and having full-time employment, were most likely to own a wearable device compared to their demographic counterparts. 50% of smart device owners were willing to share and 32% would consider sharing their smart device data for research purposes. The type of activity data they are willing to share varies by gender, age, education, and employment. Findings from this study can be used to design both equitable and cost-effective digital health studies, leveraging personally-owned smartphones and wearables in representative populations, ultimately enabling the development of equitable digital health technologies.

2. Mealtime prediction using wearable insulin pump data to support diabetes management

   Baiying Lu, Yanjun Cui, Prajakta Belsare, and 3 more authors

   Scientific Reports 2024 14:1, Sep 2024

   Abs DOI

   Many patients with diabetes struggle with post-meal high blood glucose due to missed or untimely meal-related insulin doses. To address this challenge, our research aims to: (1) study mealtime patterns in patients with type 1 diabetes using wearable insulin pump data, and (2) develop personalized models for predicting future mealtimes to support timely insulin dose administration. Using two independent datasets with over 45,000 meal logs from 82 patients with diabetes, we find that the majority of people ( $∼$ 60%) have irregular and inconsistent mealtime patterns that change notably through the course of each day and across months in their own historical data. We also show the feasibility of predicting future mealtimes with personalized LSTM-based models that achieve an average F1 score of > 95% with less than 0.25 false positives per day. Our research lays the groundwork for developing a meal prediction system that can nudge patients with diabetes to administer bolus insulin doses before meal consumption to reduce the occurrence of post-meal high blood glucose.

2022

1. Investigating Temporal Patterns of Glycemic Control around Holidays

   Prajakta Belsare, Baiying Lu, Abigail Bartolome, and 1 more author

   Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, Sep 2022

   Abs DOI

   Maintaining good glycemic control is a central part of diabetes care. However, it can be a tedious task because many factors in daily living can affect glycemic control. To support management, a growing number of people living with diabetes are now being prescribed continuous glucose monitors (CGMs) for real-time tracking of their blood glucose levels. However, routine use of CGMs is also an invaluable source of patient-generated data for individual and population-level studies. Prior research has shown that festive periods such as holidays can be a notable contributor to overeating and weight gain. Thus, in this work, we sought to investigate patterns of glycemic control around the holidays, particularly Thanksgiving, Christmas, and New Year, by using 3-months of CGM data from 14 patients with Type 1 Diabetes. We leveraged clinically validated metrics for quantifying glycemic control from CGM data and well-established statistical tests to compare diabetes management on holiday weeks versus non-holiday weeks. Based on our analysis, we found that 86% of subjects (12 out of 14) had worse glycemic control (i.e., more ad-verse glycemic events) during holiday weeks compared to non-holiday weeks. This general trend was prevalent amongst most subjects, however, we also observed unique individual patterns of glycemic control. Our findings provide a basis for further research on temporal patterns in diabetes management and data-driven interventions to support patients and caregivers with maintaining good glycemic control all year round.

2021

1. Biosignal Compression Toolbox for Digital Biomarker Discovery

   Brinnae Bent, Baiying Lu, Juseong Kim, and 1 more author

   Sensors, Sep 2021

   Abs DOI

   A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.