Advancing Translational Research at the UMass Amherst Center for Personalized Health Monitoring

This is the moderator\u27s introductory presentation for the mini-symposium entitled Advancing Translational Research at the UMass Amherst Center for Personalized Health Monitoring, in which she discusses the mission of the Center, which is to advance life sciences research to improve human health

Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study

Background: Accelerometers objectively assess physical activity (PA) and are currently used in several large-scale epidemiological studies, but there is no consensus for processing the data. This study compared the impact of wear-time assessment methods and using either vertical (V)-axis or vector magnitude (VM) cut-points on accelerometer output. Methods: Participants (7,650 women, mean age 71.4 y) were mailed an accelerometer (ActiGraph GT3X+), instructed to wear it for 7 days, record dates and times the monitor was worn on a log, and return the monitor and log via mail. Data were processed using three wear-time methods (logs, Troiano or Choi algorithms) and V-axis or VM cut-points. Results: Using algorithms alone resulted in "mail-days" incorrectly identified as "wear-days" (27-79% of subjects had >7-days of valid data). Using only dates from the log and the Choi algorithm yielded: 1) larger samples with valid data than using log dates and times, 2) similar wear-times as using log dates and times, 3) more wear-time (V, 48.1 min more; VM, 29.5 min more) than only log dates and Troiano algorithm. Wear-time algorithm impacted sedentary time (~30-60 min lower for Troiano vs. Choi) but not moderate-to-vigorous (MV) PA time. Using V-axis cut-points yielded ~60 min more sedentary time and ~10 min less MVPA time than using VM cut-points. Conclusions: Combining log-dates and the Choi algorithm was optimal, minimizing missing data and researcher burden. Estimates of time in physical activity and sedentary behavior are not directly comparable between V-axis and VM cut-points. These findings will inform consensus development for accelerometer data processing in ongoing epidemiologic studies. Electronic supplementary material The online version of this article (doi:10.1186/1471-2458-14-1210) contains supplementary material, which is available to authorized users

Changes in Patient Reported Symptoms During the Natural Progression of Osteoarthritis

Background: Arthritis is the leading cause of disability among adults in the United States affecting twenty-one million adults[1]. In addition, osteoarthritis is the second most costly chronic condition in the U.S[2]. Physical activity is a challenge in all patients and is associated with fewer functional limitations and lower risk for developing illness[3]. Currently, there are no objective measures of physical activity in advanced knee OA. Objectives: The purpose of this study was to quantify patient-reported changes in pain and function during the natural progression of osteoarthritis at 3, 6, and 9 months, and to correlate these metrics with objective activity monitors. Methods: 50 patients who were undergoing non-operative management of OA were enrolled. Patients were seen at baseline, 3 months, 6 months, and 9 months. At each visit, basic demographics and patient-reported measures (SF-36, WOMAC, and Charlson Co-morbidity index) were recorded. In addition, patients wore ActiGraph and activPal activity monitors for 7 days following the visit. Results: The average age of the enrolled participants was 57 with 82% of participants being less than 65 years of age. Most participants were female (64%), and 80% of participants had 1 or fewer medical co-morbidities on the Charlson Co-morbidity Index. Only 4% of patients were using assistive devices. The average WOMAC pain score was 68 and did not change from one time period to the next. The average SF-36 PCS score was 38 and the MCS was 54, and neither changed over time. The average SF-36 PCS score in patients with a WOMAC pain score less than 80 was 36, while in those with a WOMAC pain score greater than 80 it was 42.5. In contrast, analyses of the activPal found a decline in activity over the time period. In the first 19 patients wearing the activPal who were analyzed, 12 of 19 increased sedentary time at 9 months by an average of 18%. In addition, 15 of 19 participants decreased minutes of moderate to vigorous physical activity (MVPA) at 9 months by an average of 26%. Conclusions: In our study of 50 participants with osteoarthritis, patient-reported function did not change over a 9-month period. However, preliminary activity data suggests a decline. Further work will correlate patient-reported measures to the objective measures recorded by activity monitors to determine if objective monitors are preferable to detect early changes in activity due to OA. [1] (CDC), Centers for Disease Control and Prevention. Prevalence of arthritis—United States, 1997. MMWR Morb Mortal Wkly Rep 2001. May 4; 50:334-6. [2] Druss BG, Marcus SC, Olfson M, Pincus HA. The most expensive medical conditions in America. Health Affairs. 2002; 21:105-11. [3] Centers for Disease Control and Prevention (CDC). Physical activity among adults with a disability—United States, 2005. MMWR Morb Mortal Wkly Rep 2007. Oct 5;56(39):1021-4

A Comparison of Wrist and Hip Accelerometer Output at Different Walking Speeds

Physical activity has been objectively measured using hip-worn accelerometers for decades. However, wrist-worn accelerometers are currently used in large-scale studies. Differences in wrist and hip dynamics during locomotion may affect monitor output, which may impact how prediction models are built. PURPOSE: To compare ActiGraph™ wrist and hip accelerations (g’s) at varying locomotion speeds. METHODS: Participants (N = 7) wore ActiGraph™ GT3X+ accelerometers on the dominant wrist and hip (sampling rate 80Hz). They performed three 5-minute trials at self-paced (SP), slow (SL), and fast (F) over-ground walking speeds. Mean and standard deviation of the vector magnitude (VM) were calculated from two 20-s data windows per condition. Linear mixed-effects models were used to determine if the relationship was different between speed and vector VM at the hip and wrist. RESULTS: Significant differences were found between the slopes (speed vs VM) of the hip m = 0.052 (95% CI: 0.033, 0.103) compared to the wrist m = 0.195 (95% CI: 0.160, 0.230) p\u3c0.001. DISCUSSION: The results show that ActiGraph™ wrist and hip accelerations (g’s) differ at varying locomotion speeds. There is a curvilinear increase in VM at the wrist as locomotion speed increases, whereas there is a linear increase in VM at the hip as locomotion speed increases. The pattern of change of wrist VM is different and more variable between subjects compared to hip VM, which may impact measurement error and model development. Additionally, wrist VM is more responsive to changes in speed than hip VM, suggesting that a wrist worn accelerometer may be more sensitive to locomotion intensity

Validity of the relative percent concept for equating training intensity

The validity of the relative percent concept for equating training intensity was investigated using the point of metabolic acidosis (anaerobic threshold) as the criterion variable. Percent oxygen uptake, heart rate and metabolic acidosis were determined at 60, 70, and 80% of max heart rate on a bicycle ergometer test ( N =31). At 60 and 70% of max heart rate only one individual was definitely in metabolic acidosis. At 80% max heart rate 17 subjects were working at a level above the point of metabolic acidosis while 14 were working at or below this point. Thus, it was suggested that even if subjects are exercising at the same relative percent maximum HR, the metabolic stress using metabolic acidosis as the criterion is not constant across subjects. It was further shown that the regression of percent O 2 max on percent max HR was a spurious one resulting in poor prediction of individual O 2 values. The data presented suggest that the relative percent concept for equating exercise or subsequent training intensity needs careful re-evaluation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47421/1/421_2004_Article_BF00421445.pd

Comparison of blood lipids, lipoproteins, anthropometric measures, and resting and exercise cardiovascular responses in children, 6-7 years old

The purpose of this investigation was to determine sex differences and interrelationships in anthropometric, blood lipids and lipoproteins, steady rate and maximal bicycle ergometric measures in boys (N = 38) and girls (N= 28) ages 6 to 7 years. After adjusting for a significantly (P P -1 whereas no differences (P> 0.05) existed in preexercise and maximal heart rates. Multiple regression analyses resulted in weak but significant (P P r = 0.46) was obtained for the girls. These data indicate that sex differences exist for selected ergometric, anthropometric, and blood lipid and lipoprotein measures as early as 6 years. Also, the association among blood lipid and lipoprotein measures may differ between boys and girls.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/24221/1/0000480.pd

Utilization and Harmonization of Adult Accelerometry Data: Review and Expert Consensus.

PURPOSE: This study aimed to describe the scope of accelerometry data collected internationally in adults and to obtain a consensus from measurement experts regarding the optimal strategies to harmonize international accelerometry data. METHODS: In March 2014, a comprehensive review was undertaken to identify studies that collected accelerometry data in adults (sample size, n ≥ 400). In addition, 20 physical activity experts were invited to participate in a two-phase Delphi process to obtain consensus on the following: unique research opportunities available with such data, additional data required to address these opportunities, strategies for enabling comparisons between studies/countries, requirements for implementing/progressing such strategies, and value of a global repository of accelerometry data. RESULTS: The review identified accelerometry data from more than 275,000 adults from 76 studies across 36 countries. Consensus was achieved after two rounds of the Delphi process; 18 experts participated in one or both rounds. The key opportunities highlighted were the ability for cross-country/cross-population comparisons and the analytic options available with the larger heterogeneity and greater statistical power. Basic sociodemographic and anthropometric data were considered a prerequisite for this. Disclosure of monitor specifications and protocols for data collection and processing were deemed essential to enable comparison and data harmonization. There was strong consensus that standardization of data collection, processing, and analytical procedures was needed. To implement these strategies, communication and consensus among researchers, development of an online infrastructure, and methodological comparison work were required. There was consensus that a global accelerometry data repository would be beneficial and worthwhile. CONCLUSIONS: This foundational resource can lead to implementation of key priority areas and identification of future directions in physical activity epidemiology, population monitoring, and burden of disease estimates.This work, and authors involved in this work were supported by the UK Medical Research Council (grants MC_UU_12015/3 and MRC Centenary Award to KWi, SB); the British Heart Foundation (grant FS/12/58/29709 to KWi); the Australian Heart Foundation (grant PH 12B 7054 to GNH); the Australian National Health and Medical Research Council (Fellowship to NO; Program grant to NO; NHMRC Centre for Research Excellence Grant in the Translational Science of Sedentary Behaviour APP1041056 to GNH, NO, DD); an Australian Postgraduate Award (to SS); The Coca-Cola Company, Body Media, U.S. National Institutes of Health, and Technogym (to SB); MRC, Chartered Society of Physiotherapy, EPSRC, Greater Manchester Academic Health Science Network (to MG); Australian Research Council (Future Fellowship: FT100100918 to DD).This is the final published version. It first appeared at http://dx.doi.org/10.1249/MSS.000000000000066

Simple to Complex Modeling of Breathing Volume Using a Motion Sensor

Purpose—To compare simple and complex modeling techniques to estimate categories of low, medium, and high ventilation (VE) from ActiGraph™ activity counts. Methods—Vertical axis ActiGraph™ GT1M activity counts, oxygen consumption and VE were measured during treadmill walking and running, sports, household chores and labor-intensive employment activities. Categories of low (\u3c19.3 l/min), medium (19.3 to 35.4 l/min) and high (\u3e35.4 l/min) VEs were derived from activity intensity classifications (light \u3c2.9 METs, moderate 3.0 to 5.9 METs and vigorous \u3e6.0 METs). We examined the accuracy of two simple techniques (multiple regression and activity count cut-point analyses) and one complex (random forest technique) modeling technique in predicting VE from activity counts. Results—Prediction accuracy of the complex random forest technique was marginally better than the simple multiple regression method. Both techniques accurately predicted VE categories almost 80% of the time. The multiple regression and random forest techniques were more accurate (85 to 88%) in predicting medium VE. Both techniques predicted the high VE (70 to 73%) with greater accuracy than low VE (57 to 60%). Actigraph™ cut-points for light, medium and high VEs were \u3c1381, 1381 to 3660 and \u3e3660 cpm. Conclusions—There were minor differences in prediction accuracy between the multiple regression and the random forest technique. This study provides methods to objectively estimate VE categories using activity monitors that can easily be deployed in the field. Objective estimates of VE should provide a better understanding of the dose–response relationship between internal exposure to pollutants and disease