BackgroundThe New York Heart Association (NYHA) functional classification system has poor inter-rater reproducibility. A previously published pilot study showed a statistically significant difference between the daily step counts of heart failure (with reduced ejection fraction) patients classified as NYHA functional class II and III as measured by wrist-worn activity monitors. However, the study’s small sample size severely limits scientific confidence in the generalizability of this finding to a larger heart failure (HF) population.ObjectiveThis study aimed to validate the pilot study on a larger sample of patients with HF with reduced ejection fraction (HFrEF) and attempt to characterize the step count distribution to gain insight into a more objective method of assessing NYHA functional class.MethodsWe repeated the analysis performed during the pilot study on an independently recorded dataset comprising a total of 50 patients with HFrEF (35 NYHA II and 15 NYHA III) patients. Participants were monitored for step count with a Fitbit Flex for a period of 2 weeks in a free-living environment.ResultsComparing group medians, patients exhibiting NYHA class III symptoms had significantly lower recorded 2-week mean daily total step count (3541 vs 5729 [steps], P=.04), lower 2-week maximum daily total step count (10,792 vs 5904 [steps], P=.03), lower 2-week recorded mean daily mean step count (4.0 vs 2.5 [steps/minute], P=.04,), and lower 2-week mean and 2-week maximum daily per minute step count maximums (88.1 vs 96.1 and 111.0 vs 123.0 [steps/minute]; P=.02 and .004, respectively).ConclusionsPatients with NYHA II and III symptoms differed significantly by various aggregate measures of free-living step count including the (1) mean and (2) maximum daily total step count as well as by the (3) mean of daily mean step count and by the (4) mean and (5) maximum of the daily per minute step count maximum. These findings affirm that the degree of exercise intolerance of NYHA II and III patients as a group is quantifiable in a replicable manner. This is a novel and promising finding that suggests the existence of a possible, completely objective measure of assessing HF functional class, something which would be a great boon in the continuing quest to improve patient outcomes for this burdensome and costly disease.

This paper evaluates the relation between Alcohol Withdrawal Syndrome tremors in the left and right hands of patients. By analyzing 122 recordings from 61 patients in emergency departments, we found a weak relationship between the left and right hand tremor frequencies (correlation coefficient of 0.63). We found a much stronger relationship between the expert physician tremor ratings (on CIWA-Ar 0-7 scale) of the two hands, with a correlation coefficient of 0.923. Next, using a smartphone to collect the tremor data and using a previously developed model for obtaining estimated tremor ratings, we also found a strong correlation (correlation coefficient of 0.852) between the estimates of each hand. Finally, we evaluated different methods of combining the data from the two hands for obtaining a single tremor rating estimate, and found that simply averaging the tremor ratings of the two hands results in the lowest tremor estimate error (an RMSE of 0.977). Looking at the frequency dependence of this error, we found that higher frequency tremors had a much lower estimation error (an RMSE of 1.102 for tremors with frequencies in the 3-6Hz range as compared to 0.625 for tremors with frequencies in the 7-10Hz range).

In this paper, we propose a signal processing method of assessing the severity tremors caused by alcohol withdrawal (AW) syndrome. We have developed an iOS application to calculate the Clinical Institute Withdrawal Assessment (CIWA) score which captures iPod movements using the built-in accelerometer in order to reliably estimate the tremor severity component of the score. We report on the characteristics of AW tremor, the accuracy of electronic assessment of tremor compared to expert clinician assessment, and the potential for using signal processing assessment to differentiate factitious from real tremor in patients seen in the emergency department, as well as in nurses mimicking a tremor. Our preliminary results are based on 84 recordings from 61 subjects (49 patients, 12 nurses). In general we found a linear relationship between energy measured by the accelerometer (in the 4.4-10 Hz range) and the expert rating of tremor severity. Additionally, we demonstrate that 75% of the recordings from patients with actual AW syndrome had a mean peak frequency higher than 7 Hz whereas only 17% of the nurses' factitious tremors were above 7 Hz, suggesting that tremor above 7 Hz could be a potential discriminator of real versus factitious tremors.

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