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Feasibility and Performance Test of a Real-Time Sensor-Informed Context-Sensitive Ecological Momentary Assessment to Capture Physical Activity

Feasibility and Performance Test of a Real-Time Sensor-Informed Context-Sensitive Ecological Momentary Assessment to Capture Physical Activity

Methods of physical activity assessment that collect real-time self-report data about activities and contexts during key moments of the day, such as when an accelerometer is removed, or immediately after a bout of physical activity, have the potential to yield information that an accelerometer cannot. Ecological momentary assessment (EMA) is one such real-time self-report data capture method, which elicits responses to electronic surveys throughout the course of daily life [14,15].

Genevieve Fridlund Dunton, Eldin Dzubur, Stephen Intille

J Med Internet Res 2016;18(6):e106

Do Measures of Real-World Physical Behavior Provide Insights Into the Well-Being and Physical Function of Cancer Survivors? Cross-Sectional Analysis

Do Measures of Real-World Physical Behavior Provide Insights Into the Well-Being and Physical Function of Cancer Survivors? Cross-Sectional Analysis

To do so, we leveraged data from 2 previous studies of individuals who had completed cancer treatment to test whether an array of digital measures of real-world physical behavior, measured with a wearable accelerometer over a 1-week period, were related to self-reported and performance measures of physical function. First, we examined associations between real-world physical behavior and self-reported well-being and physical function.

Shelby L Bachman, Emma Gomes, Suvekshya Aryal, David Cella, Ieuan Clay, Kate Lyden, Heather J Leach

JMIR Cancer 2024;10:e53180

The Dosing of Mobile-Based Just-in-Time Adaptive Self-Management Prompts for Caregivers: Preliminary Findings From a Pilot Microrandomized Study

The Dosing of Mobile-Based Just-in-Time Adaptive Self-Management Prompts for Caregivers: Preliminary Findings From a Pilot Microrandomized Study

For each day during the home monitoring period, those caregivers in the JITAI arm had a 50-50 chance to receive personalized JITAI messages derived from sensor data (eg, accelerometer-based estimates of physical activity and sleep duration) and the daily self-reported ratings of HRQOL (caregiver strain, anxiety, and depression) via the study app, while the control arm did not receive any JITAI messages.

Jitao Wang, Zhenke Wu, Sung Won Choi, Srijan Sen, Xinghui Yan, Jennifer A Miner, Angelle M Sander, Angela K Lyden, Jonathan P Troost, Noelle E Carlozzi

JMIR Form Res 2023;7:e43099

The Surveillance of Physical Activity, Sedentary Behavior, and Sleep: Protocol for the Development and Feasibility Evaluation of a Novel Measurement System

The Surveillance of Physical Activity, Sedentary Behavior, and Sleep: Protocol for the Development and Feasibility Evaluation of a Novel Measurement System

An easily attachable Conformité Européenne–approved triaxial accelerometer (SENSmotion Plus, SENS Innovation Ap S) will be used in the development of the Sur PASS system. The SENSmotion Plus is a discrete, lightweight accelerometer (47 mm length × 22 mm breadth × 4.5 mm thickness; 7 grams), which is waterproof and has a memory capacity of approximately 4 days when sampling at 25 Hz.

Patrick Crowley, Erika Ikeda, Sheikh Mohammed Shariful Islam, Rasmus Kildedal, Sandra Schade Jacobsen, Jon Roslyng Larsen, Peter J Johansson, Pasan Hettiarachchi, Mette Aadahl, Paul Jarle Mork, Leon Straker, Emmanuel Stamatakis, Andreas Holtermann, Nidhi Gupta

JMIR Res Protoc 2022;11(6):e35697

Effectiveness of a Smartwatch App in Detecting Induced Falls: Observational Study

Effectiveness of a Smartwatch App in Detecting Induced Falls: Observational Study

The frequency of the smartwatch accelerometer is 2 k Hz with the algorithm of the app collecting data every 0.01 seconds. The algorithm follows strict rules for the three phases of a fall, as shown in Figure 1. The algorithm was supplied by My Medic Watch. T1 is defined as the time during which the smartwatch is moving toward the earth (fall time) recording a low acceleration, lower than 1 G.

Bruce Brew, Steven G Faux, Elizabeth Blanchard

JMIR Form Res 2022;6(3):e30121

Respiration Rate Estimation Based on Independent Component Analysis of Accelerometer Data: Pilot Single-Arm Intervention Study

Respiration Rate Estimation Based on Independent Component Analysis of Accelerometer Data: Pilot Single-Arm Intervention Study

For these reasons, in this study, the number of respirations was calculated using data from an accelerometer sensor passed to a smartphone, which was suitable for monitoring the respiration rate in daily life. The accelerometer sensor has three axes and has sensitivity related to the degree of inclination and to the direction in which it is resting [8]. Therefore, axial correction is required to use the values from the accelerometer sensor.

JeeEun Lee, Sun K Yoo

JMIR Mhealth Uhealth 2020;8(8):e17803

Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study

Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study

The smart watch included a triaxial accelerometer and a triaxial gyroscope. The sampling rate of the smart watch is advertised as 25 Hz, but our results show that the actual sampling rate on average was 23.8 Hz. For the smart watch, we developed a Tizen app that recorded accelerometer and gyroscope sensor data. Participants rated their current mood state using PANAS [21] on a 7-inch tablet. PANAS contains 10 adjectives for positive (eg, joy) and 10 adjectives for negative feelings (eg, anxiety).

Juan Carlos Quiroz, Elena Geangu, Min Hooi Yong

JMIR Ment Health 2018;5(3):e10153

Sleep Quality Prediction From Wearable Data Using Deep Learning

Sleep Quality Prediction From Wearable Data Using Deep Learning

Traditional prediction models applied to activity raw accelerometer data (eg, logistic regression) suffer from at least 2 key limitations: (1) They are not robust enough to learn useful patterns from noisy raw accelerometer output. As a result, existing methods for classification and analysis of physical activity rely on extracting higher-level features that can be fed into prediction models [34].

Aarti Sathyanarayana, Shafiq Joty, Luis Fernandez-Luque, Ferda Ofli, Jaideep Srivastava, Ahmed Elmagarmid, Teresa Arora, Shahrad Taheri

JMIR Mhealth Uhealth 2016;4(4):e125

Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review

Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review

Earlier studies commonly used uniaxial accelerometer devices that measure only vertical axis acceleration in g-units, corresponding to the acceleration due to gravity (9.81 m/s2) [10-13]. However, in recent decades, triaxial accelerometers operating in 3 orthogonal dimensions (ax, ay, and az) have gained preference due to their ability to offer a broad coverage, thus providing a more comprehensive understanding of overall human activity [14].

Ya-Ting Liang, Charlotte Wang, Chuhsing Kate Hsiao

J Med Internet Res 2024;26:e59497