Identifying Unique Subgroups of Individuals With Stroke Using Heart Rate and Steps to Characterize Physical Activity.

Background Low physical activity (PA) is associated with poor health outcomes after stroke. Step counts are a common metric of PA; however, other physiologic signals (eg, heart rate) may help to identify subgroups of individuals poststroke at varying levels of risk of poor health outcomes. Here, we aimed to identify clinically relevant subgroups of individuals poststroke based on PA profiles that leverage multiple data sources, including step count and heart rate data, from wearable devices. Methods and Results Seventy individuals poststroke participated. Participants wore a Fitbit Inspire 2 for 1 year and completed clinical assessments. We defined a group-based steps-per-minute threshold and an individual heart rate threshold to categorize each minute of PA into 1 of 4 states: high steps/high heart rate, low steps/low heart rate, high steps/low heart rate, and low steps/high heart rate. We used the proportion of time spent in each state along with steps per day, sedentary time, mean steps among minutes with high steps and high heart rate, and resting heart rate in a k-means clustering algorithm to identify subgroups and compared Activity Measure for Post-Acute Care Mobility T Score, Stroke Impact Scale, and gait speed among subgroups. We identified 3 subgroups, Active (n=8), Sedentary (n=29), and Deconditioned (n=33), which differed significantly on all clustering variables except resting heart rate. We observed significant differences in Activity Measure for Post-Acute Care Mobility T scores between subgroups, with the Deconditioned subgroup exhibiting the lowest score. Conclusions Quantifying PA with heart rate and step count using readily available wearable devices can identify clinically meaningful subgroups of individuals poststroke.

Reliability of low mass toenail samples as biomarkers of chronic metal exposure.

BACKGROUND: Toenails are a promising matrix for chronic metal exposure assessment, but there are currently no standard methods for collection and analysis. Questions remain about sample mass requirements and the extent to which metals measured in this matrix are representative of chronic body burden. OBJECTIVE: This study proposes a method to maximize sample conservation for toenail metals analysis using inductively coupled plasma mass spectrometry (ICP-MS). We demonstrate the reliability of an ~25 mg toenail sample (typically 1-2 clippings) for metals analysis and evaluate the intra-individual variability of multiple metals in this matrix over time in men from the Gulf Long-term Follow-up (GuLF) Study. METHODS: Toenail samples from 123 GuLF Study participants were collected at two visits 3 years apart and analyzed for 18 elements using ICP-MS. Participants with samples exceeding 200 mg at the first visit (n = 29) were selected for triplicate sub-sample analysis. Kendall's coefficient of concordance (W) was used to assess sub-sample reliability and Spearman's correlation coefficients (ρ) were used to evaluate fluctuations in elemental concentrations over time. RESULTS: Results were not reported for Cd, Co, Mo, Sb, and V (detected in <60% of the samples). There was strong agreement among triplicate samples (Kendall's W: 0.72 (Cu)-0.90 (Cu)) across all elements evaluated, moderate correlations of elemental concentrations (Spearman's ρ: 0.21-0.42) over 3 years for As, Ca, Cr, Fe, Pb, Mn, and Zn, and strong correlations (>0.50) for Se, Cu, and Hg. IMPACT STATEMENT: This toenail reliability study found that a low-mass (~25 mg) toenail sample (1-2 clippings) is suitable for the determination of most elements using ICP-MS and helps to increase the analytical capacity of limited toenail biospecimens collected in cohort studies. The results highlight differences in the suitability of toenails for chronic metal exposure assessment by element and underscore the need to consider intra-person variability, especially when comparing results across studies. We also provide recommendations for analytical standardization and the partitioning of the total collected toenail sample into multiple analytic sub-samples for future studies using toenail biospecimen for multiple assays.

Fingerprinting walking using wrist-worn accelerometers.

BACKGROUND: Identifying an individual from accelerometry data collected during walking without reliance on step-cycle detection has not been achieved with high accuracy. RESEARCH QUESTION: We propose an open-source reproducible method to: (1) create a unique, person-specific "walking fingerprint" from a sample of un-landmarked high-resolution data collected by a wrist-worn accelerometer; and (2) predict who an individual is from their walking fingerprint. METHODS: Accelerometry data were collected during walking from 32 individuals (23-52 y.o., 19 females) for at least 380 s each. For this study's purpose, data are not landmarked, nor synchronized. Individual walking fingerprints were created by: (1) partitioning the accelerometer time series in adjacent, non-overlapping one-second intervals; (2) transforming all one-second interval data for a given individual into a three-dimensional (3D) image obtained by plotting each one-second interval time series by the lagged time series for a series of lags; (3) partitioning these resulting participant-specific 3D images into a grid of cells; and (4) identifying the combinations of cells (areas in the 3D image) that best predict the individual. For every participant, the first 200 s of data were used as training and the last 180 s as testing. This approach does not use segmentation methods for individual strides, which reduces dependence on complementary algorithms and increases its generalizability. RESULTS: The method correctly identified 100 % of the participants in the test data and highlighted unique features of walking that characterize the individuals. SIGNIFICANCE: Predicting the identity of an individual from their walking pattern has immediate implications that can complement or replace those of actual fingerprinting, voice, and image recognition. Furthermore, as walking may change with age or disease burden, individual walking fingerprints may be used as biomarkers of change in health status with potential clinical and epidemiologic implications.

Evaluation of Intensive Telephonic Nutritional and Lifestyle Counseling to Enhance Outcomes of Bariatric Surgery.

BACKGROUND: To determine the impact of an intensive perioperative nutritional and lifestyle support protocol on long-term outcomes of bariatric surgery. METHODS: A retrospective observational study was conducted of 955 patients who underwent gastric bypass surgery between 2005 and 2015. Patients were divided into two cohorts: (1) 2005 through August 2013: these 767 patients were required to participate in the intensive telephone-based nutritional support program from 8 weeks preoperative through 44 weeks postoperative; (2) after August 2013, the program was discontinued and 188 patients did not have intensive telephonic nutritional support. Inverse probability weighting was used to obtain weight loss estimates at 1 and 3 years postoperative. Time-to-event analyses were used to investigate hospitalization rates postoperative. Poisson models were used to investigate healthcare utilization. RESULTS: Patients who participated in the program exhibited 1.97% (95% CI 0.7, 3.3) greater %TWL at 1 year and 2.2% (95% CI -0.3, 4.1) greater %TWL at 3 years postoperative than patients who did not participate. Secondary analyses indicated participation in the program was associated with 44% shorter time to first hospitalization postoperative (p < 0.001). CONCLUSIONS: In this health system, intensive nutritional support was associated with greater weight loss at 1 and 3 years postoperative and higher hospitalization rates.

Investigating Bias from Missing Data in an Electronic Health Records-Based Study of Weight Loss After Bariatric Surgery.

PURPOSE: Missing data is common in electronic health records (EHR)-based obesity research. To avoid bias, it is critical to understand mechanisms that underpin missingness. We conducted a survey among bariatric surgery patients in three integrated health systems to (i) investigate predictors of disenrollment and (ii) examine differences in weight between disenrollees and enrollees at 5 years. MATERIALS AND METHODS: We identified 2883 patients who had bariatric surgery between 11/2013 and 08/2014. Patients who disenrolled before their 5-year anniversary were invited to participate in a survey to ascertain reasons for disenrollment and current weight. Logistic regression was used to investigate predictors of disenrollment. Five-year percent weight change distributions were estimated using inverse-probability weighting to adjust for (un)availability of EHR weight data at 5 years among enrollees and survey (non-)response among disenrollees. RESULTS: Among 536 disenrolled patients, 104 (19%) completed the survey. Among 2347 patients who maintained enrollment, 384 (16%) had no weight measurement in the EHR near 5 years. Insurance, age, Hispanic ethnicity, and site predicted disenrollment. Disenrollees had slightly greater weight loss than enrollees. CONCLUSION: We found little evidence of weight loss differences by enrollment status. Collecting information through surveys can be an effective tool to investigate and adjust for missingness in EHR-based studies.