All Publications

  • CardinalKit: open-source standards-based, interoperable mobile development platform to help translate the promise of digital health. JAMIA open Aalami, O., Hittle, M., Ravi, V., Griffin, A., Schmiedmayer, P., Shenoy, V., Gutierrez, S., Venook, R. 2023; 6 (3): ooad044


    Smartphone devices capable of monitoring users' health, physiology, activity, and environment revolutionize care delivery, medical research, and remote patient monitoring. Such devices, laden with clinical-grade sensors and cloud connectivity, allow clinicians, researchers, and patients to monitor health longitudinally, passively, and persistently, shifting the paradigm of care and research from low-resolution, intermittent, and discrete to one of persistent, continuous, and high resolution. The collection, transmission, and storage of sensitive health data using mobile devices presents unique challenges that serve as significant barriers to entry for care providers and researchers alike. Compliance with standards like HIPAA and GDPR requires unique skills and practices. These requirements make off-the-shelf technologies insufficient for use in the digital health space. As a result, budget, timeline, talent, and resource constraints are the largest barriers to new digital technologies. The CardinalKit platform is an open-source project addressing these challenges by focusing on reducing these barriers and accelerating the innovation, adoption, and use of digital health technologies. CardinalKit provides a mobile template application and web dashboard to enable an interoperable foundation for developing digital health applications. We demonstrate the applicability of CardinalKit to a wide variety of digital health applications across 18 innovative digital health prototypes.

    View details for DOI 10.1093/jamiaopen/ooad044

    View details for PubMedID 37485467

    View details for PubMedCentralID PMC10356573

  • Population-Based Estimates for the Prevalence of Multiple Sclerosis in the United States by Race, Ethnicity, Age, Sex, and Geographic Region. JAMA neurology Hittle, M., Culpepper, W. J., Langer-Gould, A., Marrie, R. A., Cutter, G. R., Kaye, W. E., Wagner, L., Topol, B., LaRocca, N. G., Nelson, L. M., Wallin, M. T. 2023


    Importance: Racial, ethnic, and geographic differences in multiple sclerosis (MS) are important factors to assess when determining the disease burden and allocating health care resources.Objective: To calculate the US prevalence of MS in Hispanic, non-Hispanic Black (hereafter referred to as Black), and non-Hispanic White individuals (hereafter referred to as White) stratified by age, sex, and region.Design, Setting, and Participants: A validated algorithm was applied to private, military, and public (Medicaid and Medicare) administrative health claims data sets to identify adult cases of MS between 2008 and 2010. Data analysis took place between 2019 and 2022. The 3-year cumulative prevalence overall was determined in each data set and stratified by age, sex, race, ethnicity, and geography. The insurance pools included 96 million persons from 2008 to 2010. Insurance and stratum-specific estimates were applied to the 2010 US Census data and the findings combined to calculate the 2010 prevalence of MS cumulated over 10 years. No exclusions were made if a person met the algorithm criteria.Main Outcomes and Measurements: Prevalence of MS per 100 000 US adults stratified by demographic group and geography. The 95% CIs were approximated using a binomial distribution.Results: A total of 744 781 persons 18 years and older were identified with MS with 564 426 cases (76%) in females and 180 355 (24%) in males. The median age group was 45 to 54 years, which included 229 216 individuals (31%), with 101 271 aged 18 to 24 years (14%), 158 997 aged 35 to 44 years (21%), 186 758 aged 55 to 64 years (25%), and 68 539 individuals (9%) who were 65 years or older. White individuals were the largest group, comprising 577 725 cases (77%), with 80 276 Black individuals (10%), 53 456 Hispanic individuals (7%), and 33 324 individuals (4%) in the non-Hispanic other category. The estimated 2010 prevalence of MS per 100 000 US adults cumulated over 10 years was 161.2 (95% CI, 159.8-162.5) for Hispanic individuals (regardless of race), 298.4 (95% CI, 296.4-300.5) for Black individuals, 374.8 (95% CI, 373.8-375.8) for White individuals, and 197.7 (95% CI, 195.6-199.9) for individuals from non-Hispanic other racial and ethnic groups. During the same time period, the female to male ratio was 2.9 overall. Age stratification in each of the racial and ethnic groups revealed the highest prevalence of MS in the 45- to 64-year-old age group, regardless of racial and ethnic classification. With each degree of latitude, MS prevalence increased by 16.3 cases per 100 000 (95% CI, 12.7-19.8; P<.001) in the unadjusted prevalence estimates, and 11.7 cases per 100 000 (95% CI, 7.4-16.1; P<.001) in the direct adjusted estimates. The association of latitude with prevalence was strongest in women, Black individuals, and older individuals.Conclusions and Relevance: This study found that White individuals had the highest MS prevalence followed by Black individuals, individuals from other non-Hispanic racial and ethnic groups, and Hispanic individuals. Inconsistent racial and ethnic classifications created heterogeneity within groups. In the United States, MS affects diverse racial and ethnic groups. Prevalence of MS increases significantly and nonuniformly with latitude in the United States, even when adjusted for race, ethnicity, age, and sex. These findings are important for clinicians, researchers, and policy makers.

    View details for DOI 10.1001/jamaneurol.2023.1135

    View details for PubMedID 37184850

  • Research gaps and opportunities in precision nutrition: an NIH workshop report. The American journal of clinical nutrition Lee, B. Y., Ordovas, J. M., Parks, E. J., Anderson, C. A., Barabasi, A., Clinton, S. K., de la Haye, K., Duffy, V. B., Franks, P. W., Ginexi, E. M., Hammond, K. J., Hanlon, E. C., Hittle, M., Ho, E., Horn, A. L., Isaacson, R. S., Mabry, P. L., Malone, S., Martin, C. K., Mattei, J., Meydani, S. N., Nelson, L. M., Neuhouser, M. L., Parent, B., Pronk, N. P., Roche, H. M., Saria, S., Scheer, F. A., Segal, E., Sevick, M. A., Spector, T. D., Van Horn, L. B., Varady, K. A., Voruganti, V. S., Martinez, M. F. 2022


    Precision nutrition is an emerging concept that aims to develop nutrition recommendations tailored to different people's circumstances and biological characteristics. Responses to dietary change and the resulting health outcomes from consuming different diets may vary significantly between people based on interactions between their genetic backgrounds, physiology, microbiome, underlying health status, behaviors, social influences, and environmental exposures. On January 11-12, 2021, the National Institutes of Health convened a workshop entitled "Precision Nutrition: Research Gaps and Opportunities" to bring together experts to discuss the issues involved in better understanding and addressing precision nutrition. The Workshop proceeded in three parts: Part I covered many aspects of genetics and physiology that mediate the links between nutrient intake and health conditions such as cardiovascular disease, Alzheimer's disease, and cancer. Part II reviewed potential contributors to interindividual variability in dietary exposures and responses such as baseline nutritional status, circadian rhythm/sleep, environmental exposures, sensory properties of food, stress, inflammation, and the social determinants of health. Part III presented the need for systems approaches, with new methods and technologies that can facilitate the study and implementation of precision nutrition, and workforce development needed to create a new generation of researchers. The workshop concluded that much research will be needed before more precise nutrition recommendations can be achieved. This includes better understanding and accounting for variables such as age, sex, ethnicity, medical history, genetics, and social and environmental factors. The advent of new methods and technologies and the availability of considerably more data bring tremendous opportunity. However, the field must proceed with appropriate levels of caution and make sure the factors listed above are all considered, and systems approaches, and methods are incorporated. It will be important to develop and train an expanded workforce with the goal of reducing health disparities and improving precision nutritional advice for all Americans.

    View details for DOI 10.1093/ajcn/nqac237

    View details for PubMedID 36055772

  • Assessment of the Frequency and Variety of Persistent Symptoms Among Patients With COVID-19: A Systematic Review. JAMA network open Nasserie, T., Hittle, M., Goodman, S. N. 2021; 4 (5): e2111417


    Importance: Infection with COVID-19 has been associated with long-term symptoms, but the frequency, variety, and severity of these complications are not well understood. Many published commentaries have proposed plans for pandemic control that are primarily based on mortality rates among older individuals without considering long-term morbidity among individuals of all ages. Reliable estimates of such morbidity are important for patient care, prognosis, and development of public health policy.Objective: To conduct a systematic review of studies examining the frequency and variety of persistent symptoms after COVID-19 infection.Evidence Review: A search of PubMed and Web of Science was conducted to identify studies published from January 1, 2020, to March 11, 2021, that examined persistent symptoms after COVID-19 infection. Persistent symptoms were defined as those persisting for at least 60 days after diagnosis, symptom onset, or hospitalization or at least 30 days after recovery from the acute illness or hospital discharge. Search terms included COVID-19, SARS-CoV-2, coronavirus, 2019-nCoV, long-term, after recovery, long-haul, persistent, outcome, symptom, follow-up, and longitudinal. All English-language articles that presented primary data from cohort studies that reported the prevalence of persistent symptoms among individuals with SARS-CoV-2 infection and that had clearly defined and sufficient follow-up were included. Case reports, case series, and studies that described symptoms only at the time of infection and/or hospitalization were excluded. A structured framework was applied to appraise study quality.Findings: A total of 1974 records were identified; of those, 1247 article titles and abstracts were screened. After removal of duplicates and exclusions, 92 full-text articles were assessed for eligibility; 47 studies were deemed eligible, and 45 studies reporting 84 clinical signs or symptoms were included in the systematic review. Of 9751 total participants, 5266 (54.0%) were male; 30 of 45 studies reported mean or median ages younger than 60 years. Among 16 studies, most of which comprised participants who were previously hospitalized, the median proportion of individuals experiencing at least 1 persistent symptom was 72.5% (interquartile range [IQR], 55.0%-80.0%). Individual symptoms occurring most frequently included shortness of breath or dyspnea (26 studies; median frequency, 36.0%; IQR, 27.6%-50.0%), fatigue or exhaustion (25 studies; median frequency, 40.0%; IQR, 31.0%-57.0%), and sleep disorders or insomnia (8 studies; median 29.4%, IQR, 24.4%-33.0%). There were wide variations in the design and quality of the studies, which had implications for interpretation and often limited direct comparability and combinability. Major design differences included patient populations, definitions of time zero (ie, the beginning of the follow-up interval), follow-up lengths, and outcome definitions, including definitions of illness severity.Conclusions and Relevance: This systematic review found that COVID-19 symptoms commonly persisted beyond the acute phase of infection, with implications for health-associated functioning and quality of life. Current studies of symptom persistence are highly heterogeneous, and future studies need longer follow-up, improved quality, and more standardized designs to reliably quantify risks.

    View details for DOI 10.1001/jamanetworkopen.2021.11417

    View details for PubMedID 34037731

  • 7th International Conference on Neurology and Epidemiology (ICNE), Virtual Conference, March 19-20, 2021. Neuroepidemiology 2021; 55 Suppl 1: 1-106

    View details for DOI 10.1159/000515315

    View details for PubMedID 33725691

  • The timed 25-foot walk in a large cohort of multiple sclerosis patients. Multiple sclerosis (Houndmills, Basingstoke, England) Kalinowski, A., Cutter, G., Bozinov, N., Hinman, J. A., Hittle, M., Motl, R., Odden, M., Nelson, L. M. 2021: 13524585211017013


    The timed 25-foot walk (T25FW) is a key clinical outcome measure in multiple sclerosis patient management and clinical research.To evaluate T25FW performance and factors associated with its change in the Multiple Sclerosis Outcome Assessments Consortium (MSOAC) Placebo Database (n = 2465).We created confirmed disability progression (CDP) variables for T25FW and Expanded Disability Status Scale (EDSS) outcomes. We used intraclass correlation coefficients (ICCs) and Bland Altman plots to evaluate reliability. We evaluated T25FW changes and predictive validity using a mixed-effects model, survival analysis, and nested case-control analysis.The mean baseline score for the T25FW in this study population was 9.2 seconds, median = 6.1 (standard deviation = 11.0, interquartile range (IQR) = 4.8, 9.0). The T25FW measure demonstrated excellent test-retest reliability (ICC = 0.98). Walk times increased with age, disability, disease type, and disease duration; relapses were not associated with an increase. Patients with T25FW progression had a faster time to EDSS-CDP compared to those without (hazards ratio (HR): 2.6; confidence interval (CI): 2.2, 3.1). Changes in the T25FW were more likely to precede changes in EDSS.This research confirms the association of the T25FW with disability and provides some evidence of predictive validity. Our findings support the continued use of the T25FW in clinical practice and clinical trials.

    View details for DOI 10.1177/13524585211017013

    View details for PubMedID 34100297

  • Development and Internal Validation of a Multivariable Prediction Model for Individual Episodic Migraine Attacks Based on Daily Trigger Exposures. Headache Holsteen, K. K., Hittle, M., Barad, M., Nelson, L. M. 2020


    OBJECTIVE: To develop and internally validate a multivariable predictive model for days with new-onset migraine headaches based on patient self-prediction and exposure to common trigger factors.BACKGROUND: Accurate real-time forecasting of one's daily risk of migraine attack could help episodic migraine patients to target preventive medications for susceptible time periods and help decrease the burden of disease. Little is known about the predictive utility of common migraine trigger factors.METHODS: We recruited adults with episodic migraine through online forums to participate in a 90-day prospective daily-diary cohort study conducted through a custom research application for iPhone. Every evening, participants answered questions about migraine occurrence and potential predictors including stress, sleep, caffeine and alcohol consumption, menstruation, and self-prediction. We developed and estimated multivariable multilevel logistic regression models for the risk of a new-onset migraine day vs a healthy day and internally validated the models using repeated cross-validation.RESULTS: We had 178 participants complete the study and qualify for the primary analysis which included 1870 migraine events. We found that a decrease in caffeine consumption, higher self-predicted probability of headache, a higher level of stress, and times within 2days of the onset of menstruation were positively associated with next-day migraine risk. The multivariable model predicted migraine risk only slightly better than chance (within-person C-statistic: 0.56, 95% CI: 0.54, 0.58).CONCLUSIONS: In this study, episodic migraine attacks were not predictable based on self-prediction or on self-reported exposure to common trigger factors. Improvements in accuracy and breadth of data collection are needed to build clinically useful migraine prediction models.

    View details for DOI 10.1111/head.13960

    View details for PubMedID 33022773

  • Sleep monitoring with the Apple Watch: comparison to a clinically validated actigraph F1000Research Roomkham, S., Hittle, M., Cheung, J., Lovell, D., Mignot, E., Perrin, D. 2019