Paul Hibbing

Effects of singing on voice, respiratory control and quality of life in persons with Parkinson’s disease

Abstract Purpose Interventions focused on singing may provide additional benefits to established voice and respiratory therapies, due to their greater emphasis on the respiratory muscle control system in those with Parkinson’s disease (PD) progresses. The purpose of this study was to examine if singing can improve voice, respiratory pressure and quality of life (QOL) in persons with PD. Methods This pilot study measured the effects of a singing intervention in 27 participants with PD. Participants were assigned to a high (met twice weekly) or low (met once weekly) dosage group. Voice, respiratory and QOL measures were recorded before and after an 8-week singing intervention. Sessions were led by board-certified music therapists and included a series of vocal and articulation exercises and group singing. Results Both groups demonstrated significant improvements in maximum inspiratory and expiratory pressure, as well as phonation time. While other voice measures improved, they did not reach statistical significance. Voice QOL and whole health QOL also significantly improved. Conclusion These results suggest singing may be a beneficial and engaging treatment choice for improving and maintaining vocal function and respiratory pressure in persons with PD. Implications for Rehabilitation In a small sample, group singing proved beneficial for improving voice and respiratory impairment in persons with Parkinson’s disease. Completing group singing one time per week for 8 weeks was as effective as completing group singing two times per week for 8 weeks in persons with Parkinson’s disease. Group singing is an effective means of improving overall quality of life in persons with Parkinson’s disease.

Calibration and Validation of the Youth Activity Profile: The FLASHE Study

INTRODUCTION This study describes the calibration and validity of the Youth Activity Profile (YAP) for use in the National Cancer Institutes Family Life, Activity, Sun, Health, and Eating (FLASHE) study. The calibrated YAP was designed to estimate minutes of moderate to vigorous physical activity (MVPA) and sedentary behavior (SB). METHODS The YAP was calibrated/validated in adolescents (aged 12-17 years) using cross-sectional data from the FLASHE study. Participants wore a GT3X+ ActiGraph on the dominant wrist for 7 days and then completed the YAP. Calibration was conducted for school (n=118); out of school (n=119); weekend (n=61); and SB (n=116) subsections of the YAP and by regressing percentage time in MVPA/SB (%MVPA/%SB) on each respective YAP subsection score, age, and the interaction between these two. The final algorithms were applied to independent samples (n=39-51) to examine validity (median absolute percentage error, equivalence testing). RESULTS The final algorithms explained 15% (school); 16% (out of school); and 12% (weekend) of the variability in GT3X+ %MVPA and 7% of the variability in GT3X+ %SB. The calibrated algorithms were applied to independent samples and predicted GT3X+ minutes of MVPA/SB, with median absolute percentage error values ranging from 12.5% (SB section) to 32.5% (weekend section). Predicted values obtained from the YAP were within 10%-20% of those produced by the GT3X+. CONCLUSIONS The YAP-predicted minutes of MVPA/SB resulted in similar group estimates obtained from an objective measure. The YAP offers good utility for large-scale research projects to characterize PA/SB levels among groups of youth.

Surveillance of Youth Physical Activity and Sedentary Behavior With Wrist Accelerometry

INTRODUCTION Accurate tracking of physical activity (PA) and sedentary behavior (SB) is important to advance public health, but little is known about how to interpret wrist-worn accelerometer data. This study compares youth estimates of SB and moderate to vigorous PA (MVPA) obtained using raw and count-based processing methods. METHODS Data were collected between April and October 2014 for the National Cancer Institutes Family Life, Activity, Sun, Health, and Eating Study: a cross-sectional Internet-based study of youth/family cancer prevention behaviors. A subsample of 628 adolescents (aged 12-17 years) wore the ActiGraph GT3X+ on the wrist for 7 days. In 2015-2016, SB and MVPA time were calculated from raw data using R-package GGIR and from activity counts data using published cutpoints (Crouter and Chandler). Estimates were compared across age, sex, and weight status to examine the impact of processing methods on behavioral outcomes. RESULTS ActiGraph data were available for 408 participants. Large differences in SB and MVPA time were observed between processing methods, but age and gender patterns were similar. Younger children (aged 12-14 years) had lower sedentary time and greater MVPA time (p-values <0.05) than older children (aged 15-17 years), consistent across methods. The proportion of youth with ≥60 minutes of MVPA/day was highest with the Crouter methods (~50%) and lowest with GGIR (~0%). CONCLUSIONS Conclusions about youth PA and SB are influenced by the wrist-worn accelerometer data processing method. Efforts to harmonize processing methods are needed to promote standardization and facilitate reporting of monitor-based PA data.

Comparative evaluation of heart rate-based monitors: Apple Watch vs Fitbit Charge HR

ABSTRACT The purpose of this investigation was to examine the validity of energy expenditure (EE), steps, and heart rate measured with the Apple Watch 1 and Fitbit Charge HR. Thirty-nine healthy adults wore the two monitors while completing a semi-structured activity protocol consisting of 20 minutes of sedentary activity, 25 minutes of aerobic exercise, and 25 minutes of light intensity physical activity. Criterion measures were obtained from an Oxycon Mobile for EE, a pedometer for steps, and a Polar heart rate strap worn on the chest for heart rate. For estimating whole-trial EE, the mean absolute percent error (MAPE) from Fitbit Charge HR (32.9%) was more than twice that of Apple Watch 1 (15.2%). This trend was consistent for the individual conditions. Both monitors accurately assessed steps during aerobic activity (MAPEApple: 6.2%; MAPEFitbit: 9.4%) but overestimated steps in light physical activity. For heart rate, Fitbit Charge HR produced its smallest MAPE in sedentary behaviors (7.2%), followed by aerobic exercise (8.4%), and light activity (10.1%). The Apple Watch 1 had stronger validity than the Fitbit Charge HR for assessing overall EE and steps during aerobic exercise. The Fitbit Charge HR provided heart rate estimates that were statistically equivalent to Polar monitor.

Lab-based validation of different data processing methods for wrist-worn ActiGraph accelerometers in young adults

The wrist is increasingly being used as the preferred site for objectively assessing physical activity but the relative accuracy of processing methods for wrist data has not been determined. OBJECTIVE This study evaluates the validity of four processing methods for wrist-worn ActiGraph (AG) data against energy expenditure (EE) measured using a portable metabolic analyzer (OM; Oxycon mobile) and the Compendium of physical activity. APPROACH Fifty-one adults (ages 18-40) completed 15 activities ranging from sedentary to vigorous in a laboratory setting while wearing an AG and the OM. Estimates of EE and categorization of activity intensity were obtained from the AG using a linear method based on Hildebrand cutpoints (HLM), a non-linear modification of this method (HNLM), and two methods developed by Staudenmayer based on a Linear Model (SLM) and using random forest (SRF). Estimated EE and classification accuracy were compared to the OM and Compendium using Bland-Altman plots, equivalence testing, mean absolute percent error (MAPE), and Kappa statistics. MAIN RESULTS Overall, classification agreement with the Compendium was similar across methods ranging from a Kappa of 0.46 (HLM) to 0.54 (HNLM). However, specificity and sensitivity varied by method and intensity, ranging from a sensitivity of 0% (HLM for sedentary) to a specificity of ~99% for all methods for vigorous. None of the methods was significantly equivalent to the OM (p  >  0.05). SIGNIFICANCE Across activities, none of the methods evaluated had a high level of agreement with criterion measures. Additional research is needed to further refine the accuracy of processing wrist-worn accelerometer data.

Therapeutic singing as an early intervention for swallowing in persons with Parkinson's disease.

OBJECTIVE For persons with Parkinsons disease (PD), secondary motor symptoms such as swallow impairment impact the quality of life and are major contributors to mortality. There is a present need for therapeutic interventions aimed at improving swallow function during the early stages of PD. The purpose of this pilot study was to examine the effects of a group therapeutic singing intervention on swallowing in persons with PD with no significant dysphagia symptoms. DESIGN Cohort study. SETTING University in the United States. PARTICIPANTS Twenty-four participants with PD. INTERVENTION Eight weeks of group therapeutic singing. MAIN OUTCOME MEASURES Electromyography (EMG) was used to assess muscle activity associated with swallow pre and post the group singing intervention. Swallow quality of life (SWAL-QOL) and the Unified Parkinsons Disease Rating Scale (UPDRS) were also obtained pre- and post-intervention. RESULTS Participants reported minimal difficulty with swallowing, yet results revealed a significant increase in EMG outcome measures, as well as significant improvement in UPDRS total and UPDRS motor scores. No significant differences were revealed for SWAL-QOL. CONCLUSION Increases in EMG timing measures may suggest that group singing results in the prolongation of laryngeal elevation, protecting the airway from foreign material for longer periods of time during swallow. Combined with the improvement in UPDRS clinical measures, therapeutic singing may be an engaging early intervention strategy to address oropharyngeal dysphagia while also benefiting additional clinical symptoms of PD.

Estimating Energy Expenditure with ActiGraph GT9X Inertial Measurement Unit

Purpose The purpose of this study was to explore whether gyroscope and magnetometer data from the ActiGraph GT9X improved accelerometer-based predictions of energy expenditure (EE). Methods Thirty participants (mean ± SD: age, 23.0 ± 2.3 yr; body mass index, 25.2 ± 3.9 kg·m−2) volunteered to complete the study. Participants wore five GT9X monitors (right hip, both wrists, and both ankles) while performing 10 activities ranging from rest to running. A Cosmed K4b2 was worn during the trial, as a criterion measure of EE (30-s averages) expressed in METs. Triaxial accelerometer data (80 Hz) were converted to milli-G using Euclidean norm minus one (ENMO; 1-s epochs). Gyroscope data (100 Hz) were expressed as a vector magnitude (GVM) in degrees per second (1-s epochs) and magnetometer data (100 Hz) were expressed as direction changes per 5 s. Minutes 4–6 of each activity were used for analysis. Three two-regression algorithms were developed for each wear location: 1) ENMO, 2) ENMO and GVM, and 3) ENMO, GVM, and direction changes. Leave-one-participant-out cross-validation was used to evaluate the root mean square error (RMSE) and mean absolute percent error (MAPE) of each algorithm. Results Adding gyroscope to accelerometer-only algorithms resulted in RMSE reductions between 0.0 METs (right wrist) and 0.17 METs (right ankle), and MAPE reductions between 0.1% (right wrist) and 6.0% (hip). When direction changes were added, RMSE changed by ⩽0.03 METs and MAPE by ⩽0.21%. Conclusions The combined use of gyroscope and accelerometer at the hip and ankles improved individual-level prediction of EE compared with accelerometer only. For the wrists, adding gyroscope produced negligible changes. The magnetometer did not meaningfully improve estimates for any algorithms.

The influence of moving with music on motor cortical activity

Although there is a growing interest in using music to improve movement performance in various populations, there remains a need to better understand how music influences motor cortical activity. Listening to music is tightly linked to neural processes within the motor cortex and can modulate motor cortical activity in healthy young adult (HYAs). There is limited evidence regarding how moving to music modulates motor cortical activity. Thus, the purpose of this study was to explore the influence of moving to music on motor cortical activity in HYAs. Electroencephalography was collected while 32 HYAs tapped their index finger in time with a tone and with two contrasting music styles. Two movement rates were presented for each condition. Power spectra were obtained from data collected over the primary sensorimotor region and supplemental motor area and were compared between conditions. Results revealed a significant difference between both music conditions and the tone only condition for both the regions. For both music styles, power was increased in the beta band for low movement rates and increased in the alpha band for high movement rates. A secondary analysis determining the effect of music experience on motor cortical activity revealed a significant difference between musicians and non-musicians. Power in the beta band was increased across all conditions. The results of this study provide the initial step towards a more complete understanding of the neurophysiological underpinnings of music on movement performance which may inform future studies and therapeutic strategies.

Domain agnostic online semantic segmentation for multi-dimensional time series

AbstractUnsupervised semantic segmentation in the time series domain is a much studied problem due to its potential to detect unexpected regularities and regimes in poorly understood data. However, the current techniques have several shortcomings, which have limited the adoption of time series semantic segmentation beyond academic settings for four primary reasons. First, most methods require setting/learning many parameters and thus may have problems generalizing to novel situations. Second, most methods implicitly assume that all the data is segmentable and have difficulty when that assumption is unwarranted. Thirdly, many algorithms are only defined for the single dimensional case, despite the ubiquity of multi-dimensional data. Finally, most research efforts have been confined to the batch case, but online segmentation is clearly more useful and actionable. To address these issues, we present a multi-dimensional algorithm, which is domain agnostic, has only one, easily-determined parameter, and can handle data streaming at a high rate. In this context, we test the algorithm on the largest and most diverse collection of time series datasets ever considered for this task and demonstrate the algorithm’s superiority over current solutions.