Validating and Testing Wearable Sensors to Assess Physical Activity and Sedentary Behavior in the Center for Personalized Health Monitoring

As part of the mini-symposium entitled Creating Devices for Personalized Health Monitoring, Dr. Freedson highlights her group’s research on the calibration and validation of wearable physical activity sensors and how these sensors are used to examine the relationship between physical activity dose and health-related responses. She also discusses research pertaining to sleep monitoring sensors conducted by Dr. Rebecca Spencer in the Department of Psychology

Assessing Sedentary Behavior and Physical Activity with Wearable Sensors

As part of the mini-symposium entitled Physical Activity, Sedentary Behavior and Function in Individuals with Knee and Hip Osteoarthritis: Clinical Observations and Opportunities for Future Research, Dr. Freedson highlights her group\u27s research on using wearable monitors to objectively quantify changes in physical activity and sedentary behavior in individuals with moderate to severe knee and/or hip osteoarthritis

Comparison of Raw Acceleration from the GENEA and ActiGraph™ GT3X+ Activity Monitors

Purpose: To compare raw acceleration output of the ActiGraph™ GT3X+ and GENEA activity monitors. Methods: A GT3X+ and GENEA were oscillated in an orbital shaker at frequencies ranging from 0.7 to 4.0 Hz (ten 2-min trials/frequency) on a fixed radius of 5.08 cm. Additionally, 10 participants (age = 23.8 ± 5.4 years) wore the GT3X+ and GENEA on the dominant wrist and performed treadmill walking (2.0 and 3.5 mph) and running (5.5 and 7.5 mph) and simulated free-living activities (computer work, cleaning a room, vacuuming and throwing a ball) for 2-min each. A linear mixed model was used to compare the mean triaxial vector magnitude (VM) from the GT3X+ and GENEA at each oscillation frequency. For the human testing protocol, random forest machine-learning technique was used to develop two models using frequency domain (FD) and time domain (TD) features for each monitor. We compared activity type recognition accuracy between the GT3X+ and GENEA when the prediction model was fit using one monitor and then applied to the other. Z-statistics were used to compare the proportion of accurate predictions from the GT3X+ and GENEA for each model. Results: GENEA produced significantly higher (p \u3c 0.05, 3.5 to 6.2%) mean VM than GT3X+ at all frequencies during shaker testing. Training the model using TD input features on the GENEA and applied to GT3X+ data yielded significantly lower (p \u3c 0.05) prediction accuracy. Prediction accuracy was not compromised when interchangeably using FD models between monitors. Conclusions: It may be inappropriate to apply a model developed on the GENEA to predict activity type using GT3X+ data when input features are TD attributes of raw acceleration

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

The Physical Activity Tracker Testing in Youth (P.A.T.T.Y.) Study: Content Analysis and Children\u27s Perceptions

Background: Activity trackers are widely used by adults and several models are now marketed for children. Objective: The aims of this study were to (1) perform a content analysis of behavioral change techniques (BCTs) used by three commercially available youth-oriented activity trackers and (2) obtain feedback describing children’s perception of these devices and the associated websites. Methods: A content analysis recorded the presence of 36 possible BCTs for the MovBand (MB), Sqord (SQ), and Zamzee (ZZ) activity trackers. In addition, 16 participants (mean age 8.6 years [SD 1.6]; 50% female [8/16]) received all three trackers and were oriented to the devices and websites. Participants were instructed to wear the trackers on 4 consecutive days and spend ≥10 min/day on each website. A cognitive interview and survey were administered when the participant returned the devices. Qualitative data analysis was used to analyze the content of the cognitive interviews. Chi-square analyses were used to determine differences in behavioral monitoring and social interaction features between websites. Results: The MB, SQ, and ZZ devices or websites included 8, 15, and 14 of the possible 36 BCTs, respectively. All of the websites had a behavioral monitoring feature (charts for tracking activity), but the percentage of participants indicating that they “liked” those features varied by website (MB: 8/16, 50%; SQ: 6/16, 38%; ZZ: 11/16, 69%). Two websites (SQ and ZZ) included an “avatar” that the user could create to represent themselves on the website. Participants reported that they “liked” creating and changing their avatar (SQ: 12/16, 75%, ZZ: 15/16, 94%), which was supported by the qualitative analyses of the cognitive interviews. Most participants (75%) indicated that they would want to wear the devices more if their friends were wearing a tracker. No significant differences were observed between SQ and ZZ devices in regards to liking or use of social support interaction features (P=.21 to .37). Conclusions: The websites contained several BCTs consistent with previously identified strategies. Children “liked” the social aspects of the websites more than the activity tracking features. Developers of commercial activity trackers for youth may benefit from considering a theoretical perspective during the website design process

The Feasibility of Reducing and Measuring Sedentary Time among Overweight, Non-Exercising Office Workers

This study examined the feasibility of reducing free-living sedentary time (ST) and the convergent validity of various tools to measure ST. Twenty overweight/obese participants wore the activPAL (AP) (criterion measure) and ActiGraph (AG; 100 and 150 count/minute cut-points) for a 7-day baseline period. Next, they received a simple intervention targeting free-living ST reductions (7-day intervention period). ST was measured using two questionnaires following each period. ST significantly decreased from 67% of wear time (baseline period) to 62.7% of wear time (intervention period) according to AP (n = 14, P < 0.01). No other measurement tool detected a reduction in ST. The AG measures were more accurate (lower bias) and more precise (smaller confidence intervals) than the questionnaires. Participants reduced ST by ~5%, which is equivalent to a 48_min reduction over a 16-hour waking day. These data describe ST measurement properties from wearable monitors and self-report tools to inform sample-size estimates for future ST interventions

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

Youth Oriented Activity Trackers: Comprehensive Laboratory- and Field-Based Validation

Background: Commercial activity trackers are growing in popularity among adults and some are beginning to be marketed to children. There is, however, a paucity of independent research examining the validity of these devices to detect physical activity of different intensity levels. Objectives: The purpose of this study was to determine the validity of the output from 3 commercial youth-oriented activity trackers in 3 phases: (1) orbital shaker, (2) structured indoor activities, and (3) 4 days of free-living activity. Methods: Four units of each activity tracker (Movband [MB], Sqord [SQ], and Zamzee [ZZ]) were tested in an orbital shaker for 5-minutes at three frequencies (1.3, 1.9, and 2.5 Hz). Participants for Phase 2 (N=14) and Phase 3 (N=16) were 6-12 year old children (50% male). For Phase 2, participants completed 9 structured activities while wearing each tracker, the ActiGraph GT3X+ (AG) research accelerometer, and a portable indirect calorimetry system to assess energy expenditure (EE). For Phase 3, participants wore all 4 devices for 4 consecutive days. Correlation coefficients, linear models, and non-parametric statistics evaluated the criterion and construct validity of the activity tracker output. Results: Output from all devices was significantly associated with oscillation frequency (r=.92-.99). During Phase 2, MB and ZZ only differentiated sedentary from light intensity (PPr=.76, .86, and .59 for the MB, SQ, and ZZ, respectively). Conclusions: Across study phases, the SQ demonstrated stronger validity than the MB and ZZ. The validity of youth-oriented activity trackers may directly impact their effectiveness as behavior modification tools, demonstrating a need for more research on such devices

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