Physical activity, sleep and cardiovascular health data for 50,000 individuals from the MyHeart Counts Study.

Studies have established the importance of physical activity and fitness for long-term cardiovascular health, yet limited data exist on the association between objective, real-world large-scale physical activity patterns, fitness, sleep, and cardiovascular health primarily due to difficulties in collecting such datasets. We present data from the MyHeart Counts Cardiovascular Health Study, wherein participants contributed data via an iPhone application built using Apple's ResearchKit framework and consented to make this data available freely for further research applications. In this smartphone-based study of cardiovascular health, participants recorded daily physical activity, completed health questionnaires, and performed a 6-minute walk fitness test.

The 24-Hour Activity Cycle: A New Paradigm for Physical Activity.

The physiologic mechanisms by which the four activities of sleep, sedentary behavior, light-intensity physical activity, and moderate-to-vigorous physical activity (MVPA) affect health are related, but these relationships have not been well explored in adults. Research studies have commonly evaluated how time spent in one activity affects health. Because one can only increase time in one activity by decreasing time in another, such studies cannot determine the extent that a health benefit is due to one activity versus due to reallocating time among the other activities.

Feasibility of Obtaining Measures of Lifestyle From a Smartphone App: The MyHeart Counts Cardiovascular Health Study.

Importance: Studies have established the importance of physical activity and fitness, yet limited data exist on the associations between objective, real-world physical activity patterns, fitness, sleep, and cardiovascular health.

Objectives: To assess the feasibility of obtaining measures of physical activity, fitness, and sleep from smartphones and to gain insights into activity patterns associated with life satisfaction and self-reported disease.

Design, Setting, And Participants: The MyHeart Counts smartphone app was made available in March 2015, and prospective participants downloaded the free app between March and October 2015.

Activity Recognition in Youth Using Single Accelerometer Placed at Wrist or Ankle.

Purpose: State-of-the-art methods for recognizing human activity using raw data from body-worn accelerometers have primarily been validated with data collected from adults. This study applies a previously available method for activity classification using wrist or ankle accelerometer to data sets collected from both adults and youth.

Methods: An algorithm for detecting activity from wrist-worn accelerometers, originally developed using data from 33 adults, is tested on a data set of 20 youth (age, 13 ± 1.

Twenty-four Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices.

Unlabelled: Getting enough sleep, exercising, and limiting sedentary activities can greatly contribute to disease prevention and overall health and longevity. Measuring the full 24-h activity cycle-sleep, sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA)-may now be feasible using small wearable devices.

Purpose: This study compared nine devices for accuracy in a 24-h activity measurement.

Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies.

Background: There is increasing interest in using smartphones as stand-alone physical activity monitors via their built-in accelerometers, but there is presently limited data on the validity of this approach.

Objective: The purpose of this work was to determine the validity and reliability of 3 Android smartphones for measuring physical activity among midlife and older adults.

Methods: A laboratory (study 1) and a free-living (study 2) protocol were conducted.

Activity recognition using a single accelerometer placed at the wrist or ankle.

Purpose: Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities.

Estimating activity and sedentary behavior from an accelerometer on the hip or wrist.

Purpose: Previously, the National Health and Examination Survey measured physical activity with an accelerometer worn on the hip for 7 d but recently changed the location of the monitor to the wrist. This study compared estimates of physical activity intensity and type with an accelerometer on the hip versus the wrist.

Methods: Healthy adults (n = 37) wore triaxial accelerometers (Wockets) on the hip and dominant wrist along with a portable metabolic unit to measure energy expenditure during 20 activities.

Using Wearable Activity Type Detection to Improve Physical Activity Energy Expenditure Estimation.

Accurate, real-time measurement of energy expended during everyday activities would enable development of novel health monitoring and wellness technologies. A technique using three miniature wearable accelerometers is presented that improves upon state-of-the-art energy expenditure (EE) estimation. On a dataset acquired from 24 subjects performing gym and household activities, we demonstrate how knowledge of activity type, which can be automatically inferred from the accelerometer data, can improve EE estimates by more than 15% when compared to the best estimates from other methods.