Integrating, Harmonizing, and Curating Studies With High-Frequency and Hourly Physiological Data: Proof of Concept from Seven Traumatic Brain Injury Data Sets.

Research in severe traumatic brain injury (TBI) has historically been limited by studies with relatively small sample sizes that result in low power to detect small, yet clinically meaningful outcomes. Data sharing and integration from existing sources hold promise to yield larger more robust sample sizes that improve the potential signal and generalizability of important research questions. However, curation and harmonization of data of different types and of disparate provenance is challenging. We report our approach and experience integrating multiple TBI data sets containing collected physiological data, including both expected and unexpected challenges encountered in the integration process. Our harmonized data set included data on 1536 patients from the Citicoline Brain Injury Treatment Trial (COBRIT), Effect of erythropoietin and transfusion threshold on neurological recovery after traumatic brain injury: a randomized clinical trial (EPO Severe TBI), BEST-TRIP, Progesterone for the Treatment of Traumatic Brain Injury III Clinical Trial (ProTECT III), Transforming Research and Clinical Knowledge in Traumatic brain Injury (TRACK-TBI), Brain Oxygen Optimization in Severe Traumatic Brain Injury Phase-II (BOOST-2), and Ben Taub General Hospital (BTGH) Research Database studies. We conclude with process recommendations for data acquisition for future prospective studies to aid integration of these data with existing studies. These recommendations include using common data elements whenever possible, a standardized recording system for labeling and timing of high-frequency physiological data, and secondary use of studies in systems such as Federal Interagency Traumatic Brain Injury Research Informatics System (FITBIR), to engage investigators who collected the original data.

Cross-sectional and Prospective Associations of Rest-Activity Rhythms With Body Mass Index in Older Men: A Novel Analysis Using Harmonic Hidden Markov Models.

Growing evidence supports a role for rest-activity rhythms (RARs) in metabolic health. Epidemiological studies in adolescents and young adults showed that RAR characteristics consistent with weakened rhythmicity were associated with obesity. However, studies in older adults are lacking. The objective of this study was to examine the cross-sectional and prospective associations between RAR and obesity in older men using the Harmonic Hidden Markov Model (HHMM), a novel analytical approach with several advantages over conventional methods for characterizing RAR. The analysis included nearly 3,000 participants in the Osteoporotic Fractures in Men study with 5-day 24-h actigraphy data. The strength of RAR was measured by rhythmic index (RI), a scaled value between 0 and 1 with higher values indicating better RAR. Multiple linear and logistic regression adjusting for multiple confounders were performed to examine the RI in relation to body mass index (BMI) and obesity status at baseline and after ~3.5 years of follow-up. We showed that the HHMM can derive both meaningful visual profile and quantifier of RAR. A lower RI was associated with higher BMI and obesity at baseline, and an elevated likelihood for developing obesity over follow-up. Specifically, when compared with men in the highest quartile of RI, those in the lowest quartile on average had a higher BMI (ß [95% confidence interval (CI)], 1.76 [1.39, 2.13]) and were more likely to be obese at baseline (odds ratio (OR) [95% CI], 2.63 [2.03, 3.43]). Moreover, among nonobese men at baseline, those in the lowest quartile of RI were 2.06 times (OR [95% CI], 2.06 [1.02, 4.27]) more likely to develop obesity over follow-up when compared with those in the highest quartile. In conclusion, our study demonstrated the utility of HHMM in characterizing RAR and showed that rhythmicity strength was associated with BMI and risk of obesity in older men.