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Article ; Online: Impact of Daily Physical Activity as Measured by Commonly Available Wearables on Mealtime Glucose Control in Type 1 Diabetes.

Ozaslan, Basak / Patek, Stephen D / Breton, Marc D

2020 Volume 22, Issue 10, Page(s) 742–748

Abstract: Objective: ...

Abstract	Objective:
MeSH term(s)	Blood Glucose ; Blood Glucose Self-Monitoring ; Diabetes Mellitus, Type 1/drug therapy ; Exercise ; Glycemic Control ; Humans ; Insulin/therapeutic use ; Meals ; Postprandial Period ; Retrospective Studies ; Wearable Electronic Devices
Chemical Substances	Blood Glucose ; Insulin
Language	English
Publishing date	2020-03-17
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1452816-2
ISSN	1557-8593 ; 1520-9156
ISSN (online)	1557-8593
ISSN	1520-9156
DOI	10.1089/dia.2019.0517
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Article ; Online: A Multiple Hypothesis Approach to Estimating Meal Times in Individuals With Type 1 Diabetes.

Corbett, John P / Breton, Marc D / Patek, Stephen D

Journal of diabetes science and technology

2019 Volume 15, Issue 1, Page(s) 141–146

Abstract: Introduction: It is important to have accurate information regarding when individuals with type 1 diabetes have eaten and taken insulin to reconcile those events with their blood glucose levels throughout the day. Insulin pumps and connected insulin ... ...

Abstract	Introduction: It is important to have accurate information regarding when individuals with type 1 diabetes have eaten and taken insulin to reconcile those events with their blood glucose levels throughout the day. Insulin pumps and connected insulin pens provide records of when the user injected insulin and how many carbohydrates were recorded, but it is often unclear when meals occurred. This project demonstrates a method to estimate meal times using a multiple hypothesis approach. Methods: When an insulin dose is recorded, multiple hypotheses were generated describing variations of when the meal in question occurred. As postprandial glucose values informed the model, the posterior probability of the truth of each hypothesis was evaluated, and from these posterior probabilities, an expected meal time was found. This method was tested using simulation and a clinical data set ( Results: For the simulation data set, meals were estimated with an average error of -0.77 (±7.94) minutes when uniform priors were used and -0.99 (±8.55) and -0.88 (±7.84) for normally distributed priors ( Conclusion: This technique could be used to help advise physicians about the meal time insulin dosing behaviors of their patients and potentially influence changes in their treatment strategy.
MeSH term(s)	Blood Glucose ; Cross-Over Studies ; Diabetes Mellitus, Type 1/drug therapy ; Humans ; Insulin ; Meals ; Postprandial Period
Chemical Substances	Blood Glucose ; Insulin
Language	English
Publishing date	2019-10-22
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1932-2968
ISSN (online)	1932-2968
DOI	10.1177/1932296819883267
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Article ; Online: Automatically accounting for physical activity in insulin dosing for type 1 diabetes.

Ozaslan, Basak / Patek, Stephen D / Fabris, Chiara / Breton, Marc D

Computer methods and programs in biomedicine

2020 Volume 197, Page(s) 105757

Abstract: Background and objective: Type 1 diabetes is a disease characterized by lifelong insulin administration to compensate for the autoimmune destruction of insulin-producing pancreatic beta-cells. Optimal insulin dosing presents a challenge for individuals ... ...

Abstract	Background and objective: Type 1 diabetes is a disease characterized by lifelong insulin administration to compensate for the autoimmune destruction of insulin-producing pancreatic beta-cells. Optimal insulin dosing presents a challenge for individuals with type 1 diabetes, as the amount of insulin needed for optimal blood glucose control depends on each subject's varying needs. In this context, physical activity represents one of the main factors altering insulin requirements and complicating treatment decisions. This work aims to develop and test in simulation a data-driven method to automatically incorporate physical activity into daily treatment decisions to optimize mealtime glycemic control in individuals with type 1 diabetes. Methods: We leveraged glucose, insulin, meal and physical activity data collected from twenty-three individuals to develop a method that (i) tracks and quantifies the accumulated glycemic impact from daily physical activity in real-time, (ii) extracts an individualized routine physical activity profile, and (iii) adjusts insulin doses according to the prolonged changes in insulin needs due to deviations in daily physical activity in a personalized manner. We used the data replay simulation framework developed at the University of Virginia to "re-simulate" the clinical data and estimate the performances of the new decision support system for physical activity informed insulin dosing against standard insulin dosing. The paired t-test is used to compare the performances of dosing methods with p < 0.05 as the significance threshold. Results: Simulation results show that, compared with standard dosing, the proposed physical-activity informed insulin dosing could result in significantly less time spent in hypoglycemia (15.3± 8% vs. 11.1± 4%, p = 0.007) and higher time spent in the target glycemic range (66.1± 11.7% vs. 69.6± 12.2%, p < 0.01) and no significant difference in the time spent above the target range(26.6± 1.4 vs. 27.4± 0.1, p = 0.5). Conclusions: Integrating daily physical activity, as measured by the step count, into insulin dose calculations has the potential to improve blood glucose control in daily life with type 1 diabetes.
MeSH term(s)	Blood Glucose ; Diabetes Mellitus, Type 1/drug therapy ; Exercise ; Humans ; Hypoglycemic Agents/therapeutic use ; Insulin/therapeutic use ; Insulin Infusion Systems
Chemical Substances	Blood Glucose ; Hypoglycemic Agents ; Insulin
Language	English
Publishing date	2020-09-21
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2020.105757
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Article ; Online: Artificial intelligence and diabetes technology: A review.

Gautier, Thibault / Ziegler, Leah B / Gerber, Matthew S / Campos-Náñez, Enrique / Patek, Stephen D

Metabolism: clinical and experimental

2021 Volume 124, Page(s) 154872

Abstract: Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability ...

Abstract	Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.
MeSH term(s)	Algorithms ; Artificial Intelligence ; Blood Glucose Self-Monitoring/instrumentation ; Diabetes Mellitus/blood ; Diabetes Mellitus/diagnosis ; Diabetes Mellitus/drug therapy ; Humans ; Insulin Infusion Systems ; Machine Learning
Language	English
Publishing date	2021-09-01
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	80230-x
ISSN	1532-8600 ; 0026-0495
ISSN (online)	1532-8600
ISSN	0026-0495
DOI	10.1016/j.metabol.2021.154872
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Article: Effect of an Automated Advice Algorithm (CloudConnect) on Adolescent-Parent Diabetes-Specific Communication and Glycemic Management: A Randomized Trial.

DeBoer, Mark D / Valdez, Rupa / Corbett, John P / Krauthause, Katie / Wakeman, Christian A / Luke, Alexander S / Oliveri, Mary C / Cherñavvsky, Daniel R / Patek, Stephen D

Diabetes therapy : research, treatment and education of diabetes and related disorders

2023 Volume 14, Issue 5, Page(s) 899–913

Abstract: Introduction: Because adolescence is a time of difficult management of Type 1 diabetes (T1D) in part from adolescent-parent shared responsibility of T1D management, our objective was to assess the effects of a decision support system (DSS) CloudConnect ... ...

Abstract	Introduction: Because adolescence is a time of difficult management of Type 1 diabetes (T1D) in part from adolescent-parent shared responsibility of T1D management, our objective was to assess the effects of a decision support system (DSS) CloudConnect on T1D-related communication between adolescents and their parents and on glycemic management. Methods: We followed 86 participants including 43 adolescents with T1D (not on automated insulin delivery systems, AID) and their parents/care-giver for a 12-week intervention of UsualCare + CGM or CloudConnect, which included a Weekly Report of automated T1D advice, including insulin dose adjustments, based on data from continuous glucose monitors (CGM), Fitbit and insulin use. Primary outcome was T1D-specific communication and secondary outcomes were hemoglobin A1c, time-in-target range (TIR) 70-180 mg/dl, and additional psychosocial scales. Results: Adolescents and parents reported a similar amount of T1D-related communication in both the UsualCare + CGM or CloudConnect groups and had similar levels of final HbA1c. Overall blood glucose time in range 70-180 mg/dl and time below 70 mg/dl were not different between groups. Parents but not children in the CloudConnect group reported less T1D-related conflict; however, compared to the UsualCare + CGM group, adolescents and parents in the CloudConnect reported a more negative tone of T1D-related communication. Adolescent-parent pairs in the CloudConnect group reported more frequent changes in insulin dose. There were no differences in T1D quality of life between groups. Conclusions: While feasible, the CloudConnect DSS system did not increase T1D communication or provide improvements in glycemic management. Further efforts are needed to improve T1D management in adolescents with T1D not on AID systems.
Language	English
Publishing date	2023-04-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	2566702-6
ISSN	1869-6961 ; 1869-6953
ISSN (online)	1869-6961
ISSN	1869-6953
DOI	10.1007/s13300-023-01401-9
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Article ; Online: Real-world walking in multiple sclerosis: Separating capacity from behavior.

Engelhard, Matthew M / Patek, Stephen D / Lach, John C / Goldman, Myla D

Gait & posture

2017 Volume 59, Page(s) 211–216

Abstract: Background: Habitual physical activity (HPA) measurement addresses the impact of MS on real-world walking, yet its interpretation is confounded by the competing influences of MS-associated walking capacity and physical activity behaviors.: Objective: ...

Abstract	Background: Habitual physical activity (HPA) measurement addresses the impact of MS on real-world walking, yet its interpretation is confounded by the competing influences of MS-associated walking capacity and physical activity behaviors. Objective: To develop specific measures of MS-associated walking capacity through statistically sophisticated HPA analysis, thereby more precisely defining the real-world impact of disease. Methods: Eighty-eight MS and 38 control subjects completed timed walks and patient-reported outcomes in clinic, then wore an accelerometer for 7days. HPA was analyzed with several new statistics, including the maximum step rate (MSR) and habitual walking step rate (HWSR), along with conventional methods, including average daily steps. HPA statistics were validated using clinical walking outcomes. Results: The six-minute walk (6MW) step rate correlated most strongly with MSR (r=0.863, p<10 Conclusions: Conventional HPA statistics are poor measures of capacity due to variability in activity behaviors. The MSR and HWSR are valid, specific measures of real-world capacity which capture subjects' highest step rate and preferred step rate, respectively.
MeSH term(s)	Adult ; Disability Evaluation ; Exercise ; Female ; Humans ; Male ; Middle Aged ; Multiple Sclerosis/classification ; Multiple Sclerosis/diagnosis ; Walking ; Young Adult
Language	English
Publishing date	2017-10-16
Publishing country	England
Document type	Journal Article
ZDB-ID	1162323-8
ISSN	1879-2219 ; 0966-6362
ISSN (online)	1879-2219
ISSN	0966-6362
DOI	10.1016/j.gaitpost.2017.10.015
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Zs.A 3804: Show issues

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Article ; Online: Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?

Fabris, Chiara / Patek, Stephen D / Breton, Marc D

Journal of diabetes science and technology

2015 Volume 10, Issue 1, Page(s) 50–59

Abstract: Background: The risk of hypo- and hyperglycemia has been assessed for years by computing the well-known low blood glucose index (LBGI) and high blood glucose index (HBGI) on sparse self-monitoring blood glucose (SMBG) readings. These metrics have been ... ...

Abstract	Background: The risk of hypo- and hyperglycemia has been assessed for years by computing the well-known low blood glucose index (LBGI) and high blood glucose index (HBGI) on sparse self-monitoring blood glucose (SMBG) readings. These metrics have been shown to be predictive of future glycemic events and clinically relevant cutoff values to classify the state of a patient have been defined, but their application to continuous glucose monitoring (CGM) profiles has not been validated yet. The aim of this article is to explore the relationship between CGM-based and SMBG-based LBGI/HBGI, and provide a guideline to follow when these indices are computed on CGM time series. Methods: Twenty-eight subjects with type 1 diabetes mellitus (T1DM) were monitored in daily-life conditions for up to 4 weeks with both SMBG and CGM systems. Linear and nonlinear models were considered to describe the relationship between risk indices evaluated on SMBG and CGM data. Results: LBGI values obtained from CGM did not match closely SMBG-based values, with clear underestimation especially in the low risk range, and a linear transformation performed best to match CGM-based LBGI to SMBG-based LBGI. For HBGI, a linear model with unitary slope and no intercept was reliable, suggesting that no correction is needed to compute this index from CGM time series. Conclusions: Alternate versions of LBGI and HBGI adapted to the characteristics of CGM signals have been proposed that enable extending results obtained for SMBG data and using clinically relevant cutoff values previously defined to promptly classify the glycemic condition of a patient.
MeSH term(s)	Adult ; Blood Glucose/analysis ; Blood Glucose Self-Monitoring/methods ; Blood Glucose Self-Monitoring/standards ; Diabetes Mellitus, Type 1/blood ; Female ; Humans ; Infusion Pumps, Implantable ; Insulin Infusion Systems ; Male ; Middle Aged ; Risk
Chemical Substances	Blood Glucose
Language	English
Publishing date	2015-08-14
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	1932-2968
ISSN (online)	1932-2968
DOI	10.1177/1932296815599177
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Article ; Online: Remotely engaged: Lessons from remote monitoring in multiple sclerosis.

Engelhard, Matthew M / Patek, Stephen D / Sheridan, Kristina / Lach, John C / Goldman, Myla D

International journal of medical informatics

2017 Volume 100, Page(s) 26–31

Abstract: Objectives: Evaluate web-based patient-reported outcome (wbPRO) collection in MS subjects in terms of feasibility, reliability, adherence, and subject-perceived benefits; and quantify the impact of MS-related symptoms on perceived well-being.: Methods! ...

Abstract	Objectives: Evaluate web-based patient-reported outcome (wbPRO) collection in MS subjects in terms of feasibility, reliability, adherence, and subject-perceived benefits; and quantify the impact of MS-related symptoms on perceived well-being. Methods: Thirty-one subjects with MS completed wbPROs targeting MS-related symptoms over six months using a customized web portal. Demographics and clinical outcomes were collected in person at baseline and six months. Results: Approximately 87% of subjects completed wbPROs without assistance, and wbPROs strongly correlated with standard PROs (r>0.91). All wbPROs were completed less frequently in the second three months (p<0.05). Frequent wbPRO completion was significantly correlated with higher step on the Expanded Disability Status Scale (EDSS) (p=0.026). Nearly 52% of subjects reported improved understanding of their disease, and approximately 16% wanted individualized wbPRO content. Over half (63.9%) of perceived well-being variance was explained by MS symptoms, notably depression (r Conclusions: wbPRO collection was feasible and reliable. More disabled subjects had higher completion rates, yet most subjects failed requirements in the second three months. Remote monitoring has potential to improve patient-centered care and communication between patient and provider, but tailored PRO content and other innovations are needed to combat declining adherence.
Language	English
Publishing date	2017-04
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	1466296-6
ISSN	1872-8243 ; 1386-5056
ISSN (online)	1872-8243
ISSN	1386-5056
DOI	10.1016/j.ijmedinf.2017.01.006
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Article ; Online: Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring.

Kovatchev, Boris P / Patek, Stephen D / Ortiz, Edward Andrew / Breton, Marc D

Diabetes technology & therapeutics

2015 Volume 17, Issue 3, Page(s) 177–186

Abstract: Background: The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical ... ...

Abstract	Background: The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes. Materials and methods: A combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to "replay" real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows. Results: In all six scenarios, the occurrence of hypoglycemia (frequency of BG levels ≤50 mg/dL and BG levels ≤39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD =10%. Similarly, hyperglycemia (frequency of BG levels ≥250 mg/dL and BG levels ≥400 mg/dL) increased and displayed an abrupt slope change at MARD=10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively. Conclusions: Using CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD=10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.
MeSH term(s)	Adult ; Aged ; Blood Glucose/analysis ; Blood Glucose Self-Monitoring/methods ; Blood Glucose Self-Monitoring/standards ; Computer Simulation/statistics & numerical data ; Databases, Factual ; Diabetes Mellitus/blood ; Diabetes Mellitus/drug therapy ; Female ; Humans ; Hyperglycemia/blood ; Hyperglycemia/diagnosis ; Hypoglycemia/blood ; Hypoglycemia/diagnosis ; Hypoglycemic Agents/administration & dosage ; Insulin/administration & dosage ; Insulin Infusion Systems ; Male ; Meals ; Middle Aged ; Monitoring, Physiologic/instrumentation ; Reproducibility of Results ; Retrospective Studies ; Signal Processing, Computer-Assisted
Chemical Substances	Blood Glucose ; Hypoglycemic Agents ; Insulin
Language	English
Publishing date	2015-03
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1452816-2
ISSN	1557-8593 ; 1520-9156
ISSN (online)	1557-8593
ISSN	1520-9156
DOI	10.1089/dia.2014.0272
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Article ; Online: Toward an agent-based patient-physician model for the adoption of continuous glucose monitoring technology.

Verella, J Tipan / Patek, Stephen D

Journal of diabetes science and technology

2009 Volume 3, Issue 2, Page(s) 353–362

Abstract: Health care is a major component of the U.S. economy, and tremendous research and development efforts are directed toward new technologies in this arena. Unfortunately few tools exist for predicting outcomes associated with new medical products, ... ...

Abstract	Health care is a major component of the U.S. economy, and tremendous research and development efforts are directed toward new technologies in this arena. Unfortunately few tools exist for predicting outcomes associated with new medical products, including whether new technologies will find widespread use within the target population. Questions of technology adoption are rife within the diabetes technology community, and we particularly consider the long-term prognosis for continuous glucose monitoring (CGM) technology. We present an approach to the design and analysis of an agent model that describes the process of CGM adoption among patients with type 1 diabetes mellitus (T1DM), their physicians, and related stakeholders. We particularly focus on patient-physician interactions, with patients discovering CGM technology through word-of-mouth communication and through advertising, applying pressure to their physicians in the context of CGM device adoption, and physicians, concerned about liability, looking to peers for a general level of acceptance of the technology before recommending CGM to their patients. Repeated simulation trials of the agent-based model show that the adoption process reflects the heterogeneity of the adopting community. We also find that the effect of the interaction between patients and physicians is agents. Each physician, say colored by the nature of the environment as defined by the model parameters. We find that, by being able to represent the diverse perspectives of different types of stakeholders, agent-based models can offer useful insights into the adoption process. Models of this sort may eventually prove to be useful in helping physicians, other health care providers, patient advocacy groups, third party payers, and device manufacturers understand the impact of their decisions about new technologies.
MeSH term(s)	Blood Glucose/analysis ; Blood Glucose Self-Monitoring/instrumentation ; Diabetes Mellitus, Type 1/blood ; Diffusion of Innovation ; Humans ; Physician-Patient Relations
Chemical Substances	Blood Glucose
Language	English
Publishing date	2009-03-01
Publishing country	United States
Document type	Journal Article
ISSN	1932-2968
ISSN (online)	1932-2968
DOI	10.1177/193229680900300217
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