Physical Activity Monitoring Preferences in Adults With Bipolar Disorder

This report investigated physical activity (PA) monitoring preferences and problems among adults with bipolar disorder (BD).Methods: PARC2 study was conducted at the Western Psychiatric Institute and Clinic at the University of Pittsburgh. This secondary data analysis assessed three PA monitors; Body Media SW Pro Armband, Actigraph AM-7164, and Pedometer Omron HJ-720IT. PA monitors were worn simultaneously for 1 week. Participants reported preferences and problems (irritating, cumbersome, movement of the activity monitor, technical difficulties, and impaired functioning) encountered with each activity monitor.Results: Approximately 70% of the participants (n = 66) were middle-aged Caucasian women with a diagnosis of BD I and overweight. Sixty-six adults with BD wore all 3 monitors simultaneously. Twelve (18%) participants had no PA monitoring preference, 28 (42%) preferred the armband, 17 (26%) preferred the pedometer and 9 (14%) preferred the Actigraph. Activity monitoring preferences did not statistically differ by age, gender, race, BMI, diagnosis, or depressive and mania symptoms (p > 0.25). Two-thirds of the participants (64%) had at least one problem with at least one activity monitor. As far as problem categories, more than a quarter of participants reported irritation with the Armband (26%, n = 17) and movement of the pedometer (32%, n = 21). No statistically significant association was observed between activity monitoring preferences and problems (p = 0.72).Discussion: Adults with BD were willing to wear activity monitors even though problems were reported. Preference of physical activity monitors, in descending order, was the armband, pedometer, and Actigraph. One fifth of the adults with BD reported no preferences in activity monitors. The activity monitors selected for investigation included the “gold standard” in activity monitoring (Actigraph) worn at the waist as well as a research grade pedometer that is considerably more affordable, provides activity feedback in real-time, and may be a more feasible option for large scale studies.

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Commentary on ‘Physical Activity Monitoring in Patients with Peripheral Arterial Occlusive Disease: Validation of an Activity Monitor’

European Journal of Vascular and Endovascular Surgery ◽

10.1016/j.ejvs.2014.04.008 ◽

2014 ◽

Vol 48 (2) ◽

pp. 201

Author(s):

C. Oakley

Keyword(s):

Physical Activity ◽

Peripheral Arterial Occlusive Disease ◽

Activity Monitor ◽

Arterial Occlusive Disease ◽

Activity Monitoring ◽

Occlusive Disease ◽

Physical Activity Monitoring ◽

Peripheral Arterial

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Promise of Wearable Physical Activity Monitors in Oncology Practice

Journal of Oncology Practice ◽

10.1200/jop.2016.016857 ◽

2017 ◽

Vol 13 (2) ◽

pp. 82-89 ◽

Cited By ~ 34

Author(s):

Muhammad S. Beg ◽

Arjun Gupta ◽

Tyler Stewart ◽

Chad D. Rethorst

Keyword(s):

Physical Activity ◽

Relevant Information ◽

Activity Monitoring ◽

Chronic Obstructive ◽

Oncology Patient ◽

Activity Monitors ◽

Patients With Cancer ◽

Physical Activity Monitoring ◽

Physical Activity Monitors ◽

Oncology Practice

Commercially available physical activity monitors provide clinicians an opportunity to obtain oncology patient health measures to an unprecedented degree. These devices can provide objective and quantifiable measures of physical activity, which are not subject to errors or bias of self-reporting or shorter duration of formal testing. Prior work on so-called quantified-self data was based on older-generation, research-grade accelerometers, which laid the foundation for consumer-based physical activity monitoring devices to be validated as a feasible and reliable tool in patients with cancer. Physical activity monitors are being used in chronic conditions including chronic obstructive pulmonary disease, congestive heart failure, diabetes mellitus, and obesity. Differing demographics, compounded with higher symptom and treatment burdens in patients with cancer, imply that additional work is needed to understand the unique strengths and weaknesses of physical activity monitors in this population. Oncology programs can systematically implement these tools into their workflows in an adaptable and iterative manner. Translating large amounts of data collected from an individual physical activity monitoring device into clinically relevant information requires sophisticated data compilation and reduction. In this article, we summarize the characteristics of older- and newer-generation physical activity monitors, review the validation of physical activity monitors with respect to health-related quality-of-life assessments, and describe the current role of these devices for the practicing oncologist. We also highlight the challenges and next steps needed for physical activity monitors to provide relevant information that can change the current state of oncology practice.

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Physical Activity Monitoring in Extremely Obese Adolescents From the Teen-LABORATORIES Study

Journal of Physical Activity and Health ◽

10.1123/jpah.2013-0006 ◽

2015 ◽

Vol 12 (1) ◽

pp. 132-138 ◽

Cited By ~ 1

Author(s):

Renee M. Jeffreys ◽

Thomas H. Inge ◽

Todd M. Jenkins ◽

Wendy C. King ◽

Vedran Oruc ◽

...

Keyword(s):

Physical Activity ◽

Heart Rate ◽

Sedentary Behavior ◽

Activity Monitor ◽

Walk Test ◽

Female Age ◽

Activity Monitors ◽

Physical Activity Monitoring ◽

Sedentary Bouts ◽

Intermittent Activity

Background:The accuracy of physical activity (PA) monitors to discriminate between PA, sedentary behavior, and nonwear in extremely obese (EO) adolescents is unknown.Methods:Twenty-five subjects (9 male/16 female; age = 16.5 ± 2.0 y; BMI = 51 ± 8 kg/m2) wore 3 activity monitors (StepWatch [SAM], Actical [AC], Actiheart [AH]) during a 400-m walk test (400MWT), 2 standardized PA bouts of varying duration, and 1 sedentary bout.Results:For the 400MWT, percent error between observed and monitor-recorded steps was 5.5 ± 7.1% and 82.1 ± 38.6% for the SAM and AC steps, respectively (observed vs. SAM steps: −17.2 ± 22.2 steps; observed vs. AC steps: −264.5 ± 124.8 steps). All activity monitors were able to differentiate between PA and sedentary bouts, but only SAM steps and AH heart rate were significantly different between sedentary behavior and nonwear (P < .001). For all monitors, sedentary behavior was characterized by bouts of zero steps/counts punctuated by intermittent activity steps/counts; nonwear was represented almost exclusively by zero steps/counts.Conclusion:Of all monitors tested, the SAM was most accurate in terms of counting steps and differentiating levels of PA and thus, most appropriate for EO adolescents. The ability to accurately characterize PA intensity in EO adolescents critically depends on activity monitor selection.

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Big data vs accurate data in health research: Large-scale physical activity monitoring, smartphones, wearable devices and risk of unconscious bias

Medical Hypotheses ◽

10.1016/j.mehy.2018.07.015 ◽

2018 ◽

Vol 119 ◽

pp. 32-36 ◽

Cited By ~ 22

Author(s):

M.A. Brodie ◽

E.M. Pliner ◽

A. Ho ◽

Kalina Li ◽

Z. Chen ◽

...

Keyword(s):

Physical Activity ◽

Big Data ◽

Health Research ◽

Large Scale ◽

Wearable Devices ◽

Activity Monitoring ◽

Accurate Data ◽

Unconscious Bias ◽

Physical Activity Monitoring

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Physical Activity Monitoring in Patients with Peripheral Arterial Disease: Validation of an Activity Monitor

European Journal of Vascular and Endovascular Surgery ◽

10.1016/j.ejvs.2014.04.003 ◽

2014 ◽

Vol 48 (2) ◽

pp. 194-200 ◽

Cited By ~ 18

Author(s):

H.J.P. Fokkenrood ◽

N. Verhofstad ◽

M.M.L. van den Houten ◽

G.J. Lauret ◽

C. Wittens ◽

...

Keyword(s):

Physical Activity ◽

Peripheral Arterial Disease ◽

Arterial Disease ◽

Activity Monitor ◽

Activity Monitoring ◽

Physical Activity Monitoring ◽

Peripheral Arterial

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Sensing leg movement enhances wearable monitoring of energy expenditure

Nature Communications ◽

10.1038/s41467-021-24173-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Patrick Slade ◽

Mykel J. Kochenderfer ◽

Scott L. Delp ◽

Steven H. Collins

Keyword(s):

Physical Activity ◽

Energy Expenditure ◽

Large Scale ◽

Activity Monitoring ◽

Training Data ◽

Metabolic Energy ◽

Stair Climbing ◽

Time Varying ◽

Wearable System ◽

Physical Activity Monitoring

AbstractPhysical inactivity is the fourth leading cause of global mortality. Health organizations have requested a tool to objectively measure physical activity. Respirometry and doubly labeled water accurately estimate energy expenditure, but are infeasible for everyday use. Smartwatches are portable, but have significant errors. Existing wearable methods poorly estimate time-varying activity, which comprises 40% of daily steps. Here, we present a Wearable System that estimates metabolic energy expenditure in real-time during common steady-state and time-varying activities with substantially lower error than state-of-the-art methods. We perform experiments to select sensors, collect training data, and validate the Wearable System with new subjects and new conditions for walking, running, stair climbing, and biking. The Wearable System uses inertial measurement units worn on the shank and thigh as they distinguish lower-limb activity better than wrist or trunk kinematics and converge more quickly than physiological signals. When evaluated with a diverse group of new subjects, the Wearable System has a cumulative error of 13% across common activities, significantly less than 42% for a smartwatch and 44% for an activity-specific smartwatch. This approach enables accurate physical activity monitoring which could enable new energy balance systems for weight management or large-scale activity monitoring.

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