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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 35671

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Special Issue Editors

Prof. Dr. Malcolm Granat

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Guest Editor

Health and Rehabilitation Sciences, School of Health Sciences, University of Salford, Manchester, England
Interests: Physical behaviour;accelerometry;patterns of physical behaviour; real-world digital outcomes; event-based analysis; body-worn sensors

Prof. Dr. Andreas Holtermann

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Guest Editor

National Research Centre for the Working Environment, Copenhagen, Denmark.
Interests: occupational physical activity; physical behaviour; accelerometry; heart rate monitoring;posture;physical behaviour classifications
Special Issues, Collections and Topics in MDPI journals

Dr. Kate Lyden

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Guest Editor

Adjunct Faculty Kinesiology Department, University of Massachusetts Amherst, Amherst, MA, USA
Interests: actigraphy; physical activity; sedentary behaviour; sleep; physical function; patterns of physical behaviour; real-world data; digital biomarker

Special Issue Information

The understanding and quantification of free-living physical behaviours are important not only for determining the relationship between these behaviours and health but also in planning interventions and developing public health messages. Physical behaviour comprises free-living activities whose patterns reflect the response to a range of factors and constraints. These can be external influences (e.g., from vocational or environmental), health-related, or free choice. It is proposed that the type of activities (with their associated movements) and the choice of when these are performed, i.e., their patterns in time, are the building blocks of free-living physical behaviour. Using a range of body-worn and environmental sensors, we can now robustly classify an increasing number of these activities (lying, sitting, standing, walking, car transportation, cycling etc.). Using a range of sophisticated techniques, we are also able to quantify the pattern of these activities. Quantifying and understanding these patterns provide insights into behaviour on an individual and population level, and this will have a transformational impact on both epidemiological studies and clinical research.

Prof. Malcolm Granat
Guest Editor

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Keywords

physical behaviour
physical activity
sedentary behaviour
free-living
postural transitions
accelerometers
gyroscopes
barometers
magnetometer validation
machine learning

Published Papers (13 papers)

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Editorial

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4 pages, 185 KiB

Open AccessEditorial

Sensors for Human Physical Behaviour Monitoring

by Malcolm Granat, Andreas Holtermann and Kate Lyden

Sensors 2023, 23(8), 4091; https://0-doi-org.brum.beds.ac.uk/10.3390/s23084091 - 19 Apr 2023

Viewed by 1023

Abstract

The understanding and measurement of physical behaviours that occur in everyday life are essential not only for determining their relationship with health, but also for interventions, physical activity monitoring/surveillance of the population and specific groups, drug development, and developing public health guidelines and [...] Read more.

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

Research

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14 pages, 1752 KiB

Open AccessArticle

Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm

by Benjamin D. Maylor, Charlotte L. Edwardson, Paddy C. Dempsey, Matthew R. Patterson, Tatiana Plekhanova, Tom Yates and Alex V. Rowlands

Sensors 2022, 22(24), 9984; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249984 - 18 Dec 2022

Cited by 6 | Viewed by 2675

Abstract

Stepping-based targets such as the number of steps per day provide an intuitive and commonly used method of prescribing and self-monitoring physical activity goals. Physical activity surveillance is increasingly being obtained from wrist-worn accelerometers. However, the ability to derive stepping-based metrics from this wear location still lacks validation and open-source methods. This study aimed to assess the concurrent validity of two versions (1. original and 2. optimized) of the Verisense step-count algorithm at estimating step-counts from wrist-worn accelerometry, compared with steps from the thigh-worn activPAL as the comparator. Participants (n = 713), across three datasets, had >24 h continuous concurrent accelerometry wear on the non-dominant wrist and thigh. Compared with activPAL, total daily steps were overestimated by 913 ± 141 (mean bias ± 95% limits of agreement) and 742 ± 150 steps/day with Verisense algorithms 1 and 2, respectively, but moderate-to-vigorous physical activity (MVPA) steps were underestimated by 2207 ± 145 and 1204 ± 103 steps/day in Verisense algorithms 1 and 2, respectively. In summary, the optimized Verisense algorithm was more accurate in detecting total and MVPA steps. Findings highlight the importance of assessing algorithm performance beyond total step count, as not all steps are equal. The optimized Verisense open-source algorithm presents acceptable accuracy for derivation of stepping-based metrics from wrist-worn accelerometry. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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9 pages, 1907 KiB

Open AccessArticle

Measuring Foot Abduction Brace Wear Time Using a Single 3-Axis Accelerometer

by Benjamin Griffiths, Natan Silver, Malcolm H. Granat and Ehud Lebel

Sensors 2022, 22(7), 2433; https://doi.org/10.3390/s22072433 - 22 Mar 2022

Cited by 2 | Viewed by 1427

Abstract

The recommended treatment for idiopathic congenital clubfoot deformity involves a series of weekly castings, surgery, and a period of bracing using a foot abduction brace (FAB). Depending on the age of the child, the orthotic should be worn for periods that reduce in duration as the child develops. Compliance is vital to achieve optimal functional outcomes and reduce the likelihood of reoccurrence, deformity, or the need for future surgery. However, compliance is typically monitored by self-reporting, which is time-consuming to implement and lacks accuracy. This study presents a novel method for objectively monitoring FAB wear using a single 3-axis accelerometer. Eleven families mounted an accelerometer on their infant’s FAB for up to seven days. Parents were also given a physical diary that was used to record the daily application and removal of the orthotic in line with their treatment. Both methods produced very similar measurements of wear that visually aligned with the movement measured by the accelerometer. Bland Altman plots showed a −0.55-h bias in the diary measurements and the limits of agreement ranging from −2.96 h to 1.96 h. Furthermore, the Cohens Kappa coefficient for the entire dataset was 0.88, showing a very high level of agreement. The method provides an advantage over existing objective monitoring solutions as it can be easily applied to existing FABs, preventing the need for bespoke monitoring devices. The novel method can facilitate increased research into FAB compliance and help enable FAB monitoring in clinical practice. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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10 pages, 1534 KiB

Open AccessArticle

Defining Continuous Walking Events in Free-Living Environments: Mind the Gap

by Abolanle R. Gbadamosi, Benjamin N. Griffiths, Alexandra M. Clarke-Cornwell and Malcolm H. Granat

Sensors 2022, 22(5), 1720; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051720 - 22 Feb 2022

Cited by 1 | Viewed by 1924

Abstract

In free-living environments, continuous walking can be challenging to achieve without encountering interruptions, making it difficult to define a continuous walking event. While limited research has been conducted to define a continuous walking event that accounts for interruptions, no method has considered the intensity change caused by these interruptions, which is crucial for achieving the associated health outcomes. A sample of 24 staff members at the University of Salford were recruited. The participants wore an accelerometer-based device (activPAL™) for seven days continuously and completed an activity diary, to explore a novel methodological approach of combining short interruptions of time between walking events based on an average walking cadence. The definition of moderate-to-vigorous physical activity (MVPA) used was a minimum walking cadence of either 76, 100, or 109 steps/min. The average daily time spent in MVPA increased from 75.2 ± 32.6 min to 86.5 ± 37.4 min using the 76 steps/min, 48.3 ± 29.5 min to 53.0 ± 33.3 min using the 100 steps/min threshold, and 31.4 ± 20.5 min to 33.9 ± 22.6 min using the 109 steps/min threshold; the difference before grouping and after grouping was statistically significant (p < 0.001). This novel method will enable future analyses of the associations between continuous walking and health-related outcomes. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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11 pages, 1318 KiB

Open AccessArticle

Inertial Sensor Algorithm to Estimate Walk Distance

by Vrutangkumar V. Shah, Carolin Curtze, Kristen Sowalsky, Ishu Arpan, Martina Mancini, Patricia Carlson-Kuhta, Mahmoud El-Gohary, Fay B. Horak and James McNames

Sensors 2022, 22(3), 1077; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031077 - 29 Jan 2022

Cited by 7 | Viewed by 3279

Abstract

The “total distance walked” obtained during a standardized walking test is an integral component of physical fitness and health status tracking in a range of consumer and clinical applications. Wearable inertial sensors offer the advantages of providing accurate, objective, and reliable measures of gait while streamlining walk test administration. The aim of this study was to develop an inertial sensor-based algorithm to estimate the total distance walked using older subjects with impaired fasting glucose (Study I), and to test the generalizability of the proposed algorithm in patients with Multiple Sclerosis (Study II). All subjects wore two inertial sensors (Opals by Clario-APDM Wearable Technologies) on their feet. The walking distance algorithm was developed based on 108 older adults in Study I performing a 400 m walk test along a 20 m straight walkway. The validity of the algorithm was tested using a 6-minute walk test (6MWT) in two sub-studies of Study II with different lengths of a walkway, 15 m (Study II-A, n = 24) and 20 m (Study II-B, n = 22), respectively. The start and turn around points were marked with lines on the floor while smaller horizontal lines placed every 1 m served to calculate the manual distance walked (ground truth). The proposed algorithm calculates the forward distance traveled during each step as the change in the horizontal position from each foot-flat period to the subsequent foot-flat period. The total distance walked is then computed as the sum of walk distances for each stride, including turns. The proposed algorithm achieved an average absolute error rate of 1.92% with respect to a fixed 400 m distance for Study I. The same algorithm achieved an absolute error rate of 4.17% and 3.21% with respect to an averaged manual distance for 6MWT in Study II-A and Study II-B, respectively. These results demonstrate the potential of an inertial sensor-based algorithm to estimate a total distance walked with good accuracy with respect to the manual, clinical standard. Further work is needed to test the generalizability of the proposed algorithm with different administrators and populations, as well as larger diverse cohorts. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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14 pages, 3310 KiB

Open AccessArticle

A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees

by Benjamin Griffiths, Laura Diment and Malcolm H. Granat

Sensors 2021, 21(22), 7458; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227458 - 10 Nov 2021

Cited by 8 | Viewed by 2390

Abstract

There are currently limited data on how prosthetic devices are used to support lower-limb prosthesis users in their free-living environment. Possessing the ability to monitor a patient’s physical behaviour while using these devices would enhance our understanding of the impact of different prosthetic products. The current approaches for monitoring human physical behaviour use a single thigh or wrist-worn accelerometer, but in a lower-limb amputee population, we have the unique opportunity to embed a device within the prosthesis, eliminating compliance issues. This study aimed to develop a model capable of accurately classifying postures (sitting, standing, stepping, and lying) by using data from a single shank-worn accelerometer. Free-living posture data were collected from 14 anatomically intact participants and one amputee over three days. A thigh worn activity monitor collected labelled posture data, while a shank worn accelerometer collected 3-axis acceleration data. Postures and the corresponding shank accelerations were extracted in window lengths of 5–180 s and used to train several machine learning classifiers which were assessed by using stratified cross-validation. A random forest classifier with a 15 s window length provided the highest classification accuracy of 93% weighted average F-score and between 88 and 98% classification accuracy across all four posture classes, which is the best performance achieved to date with a shank-worn device. The results of this study show that data from a single shank-worn accelerometer with a machine learning classification model can be used to accurately identify postures that make up an individual’s daily physical behaviour. This opens up the possibility of embedding an accelerometer-based activity monitor into the shank component of a prosthesis to capture physical behaviour information in both above and below-knee amputees. The models and software used in this study have been made open source in order to overcome the current restrictions of applying activity monitoring methods to lower-limb prosthesis users. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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11 pages, 1286 KiB

Open AccessCommunication

Day-to-Day Variability and Year-to-Year Reproducibility of Accelerometer-Measured Free-Living Sit-to-Stand Transitions Volume and Intensity among Community-Dwelling Older Adults

by Antti Löppönen, Laura Karavirta, Erja Portegijs, Kaisa Koivunen, Taina Rantanen, Taija Finni, Christophe Delecluse, Evelien Van Roie and Timo Rantalainen

Sensors 2021, 21(18), 6068; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186068 - 10 Sep 2021

Cited by 7 | Viewed by 2673

Abstract

(1) Background: The purpose of this study was to evaluate the day-to-day variability and year-to-year reproducibility of an accelerometer-based algorithm for sit-to-stand (STS) transitions in a free-living environment among community-dwelling older adults. (2) Methods: Free-living thigh-worn accelerometry was recorded for three to seven days in 86 (women n = 55) community-dwelling older adults, on two occasions separated by one year, to evaluate the long-term consistency of free-living behavior. (3) Results: Year-to-year intraclass correlation coefficients (ICC) for the number of STS transitions were 0.79 (95% confidence interval, 0.70–0.86, p < 0.001), for mean angular velocity—0.81 (95% ci, 0.72–0.87, p < 0.001), and maximal angular velocity—0.73 (95% ci, 0.61–0.82, p < 0.001), respectively. Day-to-day ICCs were 0.63–0.72 for number of STS transitions (95% ci, 0.49–0.81, p < 0.001) and for mean angular velocity—0.75–0.80 (95% ci, 0.64–0.87, p < 0.001). Minimum detectable change (MDC) was 20.1 transitions/day for volume, 9.7°/s for mean intensity, and 31.7°/s for maximal intensity. (4) Conclusions: The volume and intensity of STS transitions monitored by a thigh-worn accelerometer and a sit-to-stand transitions algorithm are reproducible from day to day and year to year. The accelerometer can be used to reliably study STS transitions in free-living environments, which could add value to identifying individuals at increased risk for functional disability. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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13 pages, 2092 KiB

Open AccessArticle

Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model

by Brian Russell, Andrew McDaid, William Toscano and Patria Hume

Sensors 2021, 21(16), 5442; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165442 - 12 Aug 2021

Cited by 6 | Viewed by 2542

Abstract

Aim: To determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run. Methods: A field-based protocol of continuous fatigue repeated hourly induced physical (~45 min) and cognitive (~10 min) fatigue on one healthy participant. The physical load was a 3.8 km, 200 m vertical gain, trail run, with acceleration and electrocardiogram (ECG) data collected using a single sensor. Cognitive load was a Multi Attribute Test Battery (MATB) and separate assessment battery included the Finger Tap Test (FTT), Stroop, Trail Making A and B, Spatial Memory, Paced Visual Serial Addition Test (PVSAT), and a vertical jump. A fatigue prediction model was implemented using a Convolutional Neural Network (CNN). Results: When the fatigue test battery results were compared for sensitivity to the protocol load, FTT right hand (R² 0.71) and Jump Height (R² 0.78) were the most sensitive while the other tests were less sensitive (R² values Stroop 0.49, Trail Making A 0.29, Trail Making B 0.05, PVSAT 0.03, spatial memory 0.003). The best prediction results were achieved with a rolling average of 200 predictions (102.4 s), during set activity types, mean absolute error for ‘walk up’ (MAE200 12.5%), and range of absolute error for ‘run down’ (RAE200 16.7%). Conclusions: We were able to measure cognitive and physical fatigue using a single wearable sensor during a practical field protocol, including contextual factors in conjunction with a neural network model. This research has practical application to fatigue research in the field. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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12 pages, 2620 KiB

Open AccessArticle

Validity of a Non-Proprietary Algorithm for Identifying Lying Down Using Raw Data from Thigh-Worn Triaxial Accelerometers

by Pasan Hettiarachchi, Katarina Aili, Andreas Holtermann, Emmanuel Stamatakis, Magnus Svartengren and Peter Palm

Sensors 2021, 21(3), 904; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030904 - 29 Jan 2021

Cited by 16 | Viewed by 2480

Abstract

Body postural allocation during daily life is important for health, and can be assessed with thigh-worn accelerometers. An algorithm based on sedentary bouts from the proprietary ActivePAL software can detect lying down from a single thigh-worn accelerometer using rotations of the thigh. However, it is not usable across brands of accelerometers. This algorithm has the potential to be refined. Aim: To refine and assess the validity of an algorithm to detect lying down from raw data of thigh-worn accelerometers. Axivity-AX3 accelerometers were placed on the thigh and upper back (reference) on adults in a development dataset (n = 50) and a validation dataset (n = 47) for 7 days. Sedentary time from the open Acti4-algorithm was used as input to the algorithm. In addition to the thigh-rotation criterion in the existing algorithm, two criteria based on standard deviation of acceleration and a time duration criterion of sedentary bouts were added. The mean difference (95% agreement-limits) between the total identified lying time/day, between the refined algorithm and the reference was +2.9 (−135,141) min in the development dataset and +6.5 (−145,159) min in the validation dataset. The refined algorithm can be used to estimate lying time in studies using different accelerometer brands. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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19 pages, 9010 KiB

Open AccessArticle

Night-Time Monitoring System (eNightLog) for Elderly Wandering Behavior

by James Chung-Wai Cheung, Eric Wing-Cheong Tam, Alex Hing-Yin Mak, Tim Tin-Chun Chan, Will Po-Yan Lai and Yong-Ping Zheng

Sensors 2021, 21(3), 704; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030704 - 20 Jan 2021

Cited by 18 | Viewed by 4372

Abstract

Wandering is a common behavioral disorder in the community-dwelling elderly. More than two-thirds of caregivers believe that wandering would cause falls. While physical restraint is a common measure to address wandering, it could trigger challenging behavior in approximately 80% of the elderly with dementia. This study aims to develop a virtual restraint using a night monitoring system (eNightLog) to provide a safe environment for the elderly and mitigate the caregiver burden. The eNightLog system consisted of remote sensors, including a near infra-red 3D time-of-flight sensor and ultrawideband sensors. An alarm system was controlled by customized software and algorithm based on the respiration rate and body posture of the elderly. The performance of the eNightLog system was evaluated in both single and double bed settings by comparing to that of a pressure mat and an infrared fence system, under simulated bed-exiting scenarios. The accuracy and precision for the three systems were 99.0%, 98.8%, 85.9% and 99.2%, 97.8%, 78.6%, respectively. With higher accuracy, precision, and a lower false alarm rate, eNightLog demonstrated its potential as an alternative to physical restraint to remedy the workload of the caregivers and the psychological impact of the elderly. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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14 pages, 1205 KiB

Open AccessArticle

Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children

by Matthew N. Ahmadi, Toby G. Pavey and Stewart G. Trost

Sensors 2020, 20(16), 4364; https://0-doi-org.brum.beds.ac.uk/10.3390/s20164364 - 05 Aug 2020

Cited by 33 | Viewed by 3975

Abstract

Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hip and non-dominant wrist. Video-based direct observation was used to categorize the children’s movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%–86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children’s movement behaviors under real-world conditions. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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Review

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44 pages, 1047 KiB

Open AccessReview

Reported Outcome Measures in Studies of Real-World Ambulation in People with a Lower Limb Amputation: A Scoping Review

by Mirjam Mellema and Terje Gjøvaag

Sensors 2022, 22(6), 2243; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062243 - 14 Mar 2022

Cited by 3 | Viewed by 2855

Abstract

Background: The rapidly increasing use of wearable technology to monitor free-living ambulatory behavior demands to address to what extent the chosen outcome measures are representative for real-world situations. This scoping review aims to provide an overview of the purpose of use of wearable activity monitors in people with a Lower Limb Amputation (LLA) in the real world, to identify the reported outcome measures, and to evaluate to what extent the reported outcome measures capture essential information from real-world ambulation of people with LLA. Methods: The literature search included a search in three databases (MEDLINE, CINAHL, and EMBASE) for articles published between January 1999 and January 2022, and a hand-search. Results and conclusions: 98 articles met the inclusion criteria. According to the included studies’ main objective, the articles were classified into observational (n = 46), interventional (n = 34), algorithm/method development (n = 12), and validity/feasibility studies (n = 6). Reported outcome measures were grouped into eight categories: step count (reported in 73% of the articles), intensity of activity/fitness (31%), type of activity/body posture (27%), commercial scores (15%), prosthetic use and fit (11%), gait quality (7%), GPS (5%), and accuracy (4%). We argue that researchers should be more careful with choosing reliable outcome measures, in particular, regarding the frequently used category step count. However, the contemporary technology is limited in providing a comprehensive picture of real-world ambulation. The novel knowledge from this review should encourage researchers and developers to engage in debating and defining the framework of ecological validity in rehabilitation sciences, and how this framework can be utilized in the development of wearable technologies and future studies of real-world ambulation in people with LLA. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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Other

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21 pages, 3776 KiB

Open AccessProtocol

Companion: A Pilot Randomized Clinical Trial to Test an Integrated Two-Way Communication and Near-Real-Time Sensing System for Detecting and Modifying Daily Inactivity among Adults >60 Years—Design and Protocol

by Diego Arguello, Ethan Rogers, Grant H. Denmark, James Lena, Troy Goodro, Quinn Anderson-Song, Gregory Cloutier, Charles H. Hillman, Arthur F. Kramer, Carmen Castaneda-Sceppa and Dinesh John

Sensors 2023, 23(4), 2221; https://0-doi-org.brum.beds.ac.uk/10.3390/s23042221 - 16 Feb 2023

Cited by 2 | Viewed by 2070

Abstract

Supervised personal training is most effective in improving the health effects of exercise in older adults. Yet, low frequency (60 min, 1–3 sessions/week) of trainer contact limits influence on behavior change outside sessions. Strategies to extend the effect of trainer contact outside of supervision and that integrate meaningful and intelligent two-way communication to provide complex and interactive problem solving may motivate older adults to “move more and sit less” and sustain positive behaviors to further improve health. This paper describes the experimental protocol of a 16-week pilot RCT (N = 46) that tests the impact of supplementing supervised exercise (i.e., control) with a technology-based behavior-aware text-based virtual “Companion” that integrates a human-in-the-loop approach with wirelessly transmitted sensor-based activity measurement to deliver behavior change strategies using socially engaging, contextually salient, and tailored text message conversations in near-real-time. Primary outcomes are total-daily and patterns of habitual physical behaviors after 16 and 24 weeks. Exploratory analyses aim to understand Companion’s longitudinal behavior effects, its user engagement and relationship to behavior, and changes in cardiometabolic and cognitive outcomes. Our findings may allow the development of a more scalable hybrid AI Companion to impact the ever-growing public health epidemic of sedentariness contributing to poor health outcomes, reduced quality of life, and early death. Full article

(This article belongs to the Special Issue Sensors for Human Physical Behaviour Monitoring)

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