Clinical trial NCT06169020: Developing Intelligent Wearable Algorithms
Official title: Design of an Intelligent Wearable to Assess Physical Activity and Health Related Outcomes - the DIWAH Study
Study type: INTERVENTIONAL
Overall status: ENROLLING_BY_INVITATION
Phase: NA
Conditions: Health Promotion
Keywords: Exercise
Physical activity
Wearable
Algorithm
Artificial Intelligence
Lead sponsor: Linnaeus University
Sponsor class: OTHER
First posted: 2023-12-13
Enrollment: 50
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: OTHER

Brief summary: Physical activity (PA) is one of the few behaviors that individuals can change on their own, incurring minimal costs while simultaneously yielding significant health benefits. Over the past decade, new methods have been developed to measure both physical activity and associated health outcomes, such as blood pressure. Notably, there has been an explosive development of so-called wearables, including smartwatches and activity trackers. Wearables are equipped with multiple sensors that measure various aspects of PA, such as steps and heart rate, as well as cardiovascular health indicators like blood pressure and oxygen saturation. Therefore, wearables can be viewed as Swiss army knives with many tools in one instrument. They are highly popular in the fitness industry, but their role in healthcare is appropriately limited. However, most wearables on the market have several disadvantages that make them unsuitable for use, even among healthy individuals.

Several studies have revealed that they do not produce reliable or valid data for metrics like pulse, steps, and PA-related energy expenditure. Furthermore, they are primarily designed for the fitness market, not for use within healthcare systems or as support for behavior change, and they have not been transparently evaluated. Additionally, the algorithms translating signals from sensors into interpretable outcomes are often trade secrets. Worse still, they are updated and modified at irregular intervals, making it challenging to compare outcomes over time. Other significant limitations include questionable patient confidentiality, as data is often uploaded to companies\&#39; cloud services.

While research monitors are more flexible and transparent compared to commercial wearables, they lack essential features for daily use that are crucial in healthcare environments, such as the ability to communicate with the user. Currently, both commercial and research monitors cannot assess PA on an individual level, as they only utilize a limited portion of the rich data collected. Therefore, it is not surprising that their implementation in clinical care remains a challenge.

Given the plethora of new products entering the market without documented validity, it is crucial to provide consumers, patients, healthcare professionals, and researchers with a transparent, evidence-based wearable. Against this backdrop, an interdisciplinary research group with the ambitious goal of developing and testing a high-functioning wearable tailored for use in healthcare-an e-physiotherapist (as opposed to commercial wearables targeting the fitness market-an \&#34;e-personal trainer\&#34;) have been formed. In this project, the focus is on measuring PA, blood pressure, and energy consumption, as they represent some of the most significant risk factors for mortality and morbidity, namely inactivity, hypertension, and obesity.

The overall goal of this project is to develop and validate AI-based algorithms for individually measuring various aspects of physical activity (PA), heart rate, energy expenditure, and blood pressure in laboratory settings as well as in everyday conditions. These algorithms represent a significant advancement compared to previous methods. In the case of PA metrics from accelerometry, current approaches rely on cut-points (threshold values) to define the intensity of PA. These cut-points are absolute, and individual variations in biology and biomechanics increase the risk of serious misclassification. To estimate intensity using heart rate, it is well-known that both resting heart rate and maximum heart rate are relative, requiring individual calibration for accurate measurements-essential even for accelerometry if one aims to measure PA on an individual level, a step not commonly taken today.

Furthermore, heart rate is influenced by factors beyond PA, such as emotions and medication. To address these issues, combining information from accelerometry (biomechanics) and heart rate (physiological response), enhancing the ability to identify individual intensity and energy expenditure of PA. In this project, artificial intelligence (AI) and machine learning (ML) will be employed to analyze the collected data and predict the intensity of PA. If the proposed method demonstrates the ability to measure PA and blood pressure at an individual level, the project will proceed. Our intention is to use AI/ML to combine PA information with blood pressure data, creating a self-learning system capable of suggesting an appropriate dose of PA to optimize blood pressure. This approach has not been studied yet, likely due to the complexity of obtaining and analyzing these data. However, the technology, processing power, and analysis tools are now available, making it timely to investigate its feasibility.

Detailed description: Globally, the life expectancy is gradually increasing, which leads to a demographic shift towards an increasing proportion of older people. For example, in Sweden, the number of individuals over the age of 75 will double during the next 50 years and the proportion will increase by more than 60% (Statistics Sweden, publicly available data). This means that more people will live longer and less people will be available to care for them. One of our major societal challenges to meet this demographic transition is to develop strategies for maintaining good health and quality of life in that age group and then when they inevitably become ill, how to treat them in the best possible way? One way forward is to capitalize on the opportunities the technological evolution have provided. In fact, Sweden have adopted a very ambitious goal as outlined in Vision2025 (www.ehalsa2025.se): In 2025, Sweden will be best in the world at using the opportunities offered by digitalization and eHealth to make it easier for people to achieve good and equal health and welfare, and to develop and strengthen their own resources for increased independence and participation in the life of society. However, digital technology alone is not sufficient to reach such a goal, other strategies that uses the technology to increase the health of individuals must also be present. One such strategy is to maintain a healthy level of physical activity through life. Physical activity is one of few behaviors that a human can change, on its own and to a low cost, that simultaneously produces significant physical and mental health benefits. To that end the ability to assess a person's habitual physical activity level, defined as "the hypothetical average around which that individual's physical activity varies" has for a long time been considered as the holy grail among physical activity researchers. For 30 years researchers have used accelerometers to objectively quantify free-living physical activity but in the last decade wearables have become hugely popular among the general population (Apple and Fitbit being the largest brands). Wearables, here defined as small portable devices with embedded sensors that measure health-related variables, claim to be able to collect long-term high-resolution data regarding health-related behaviour e.g., body posture, PA intensity, sleep, and much more. Wearables can also capture important aspects related to the cardiometabolic system such as heart rate, blood pressure, as well as support behaviour change. Thus, they can be viewed as a "Swiss army knife", since they provide a wide range of tools in one device. In theory, wearables hold a huge promise to be used to create evidence-based personalized PA in health promotion, disease prevention, and disease treatment. Compared to most research grade monitors the commercial wearables have several advantages that make them attractive for use in the healthcare system. They contain multiple sensors of relevance for the assessment of PA and related health outcomes, notably an accelerometer to detect biomechanical aspects of movement and an optical sensor to detect the physiological response to movement e.g., heart rate. Additional strengths of the commercial monitors compared to research grade monitors are that they are designed to be worn for a long time which gives an even better opportunity to assess the individual level of physical activity, especially among those the least active. However, during the last decade, there have been an explosion of different wearables and data processing methods without an established framework to evaluate their validity or reliability. This have caused great confusion driven by nonrobust device development and evaluation methods that do not reflect how they will be used in practice. Moreover, the divergence in summary estimates of physical activity within and between different brands prohibits an opportunity to pool data or to make direct comparisons between different studies. Other limitations that wearables have that makes them unsuitable for use, even among healthy individuals is that they are primarily designed for the fitness market and not for use within the health care system, they have not been evaluated in a transparent manner, they do not accurately capture physical activity among individuals with altered motorical pattern, e.g., elderly. Even though the hardware in most wearables are similar, the algorithms that translates the signals from the sensors to different interpretable outcomes (e.g., steps) are often proprietary and update with irregular intervals. Other important limitations include questionable patient confidentiality and data ownership. Neither the commercial nor research grade monitors can today assess physical activity at individual level since they make limited use of the wealth of data collected. If wearables can overcome these limitations, they hold much promise towards expanding the clinical repertoire of patient-specific measures, and they are considered an important tool for the future of precision health and personalized medicine. Considering the wealth of new wearable PA trackers entering the market without prior proof of validity it is fundamental to provide consumers, patients, health professionals and researchers with an open-source, evidence-based wearable with excellent validity and reliability.

Purpose and aims This project will start to merge the strengths of the commercial wearables with those of research grade monitors and modern data processing methods to overcome these limitations. The combination of information from accelerometry (biomechanics) and heart rate from the optical sensor (physiological response) improves the predictions regarding PA intensity and physical activity related energy expenditure on individual level. In addition, the optical sensor that are fitted in a wearable can estimate the blood pressure of an individual. The signals from the sensors will be analysed using artificial intelligence and machine learning to take better advantages of the rich data collected. Normally PA and health outcomes are being measured at different times, but wearables can collect data from multiple variables simultaneously and in real-time. This ability may provide novel details about the association between the physical activity behavior and the individuals clinical status. This have not yet been studied, most likely due to the complexity of acquiring and analyzing these data. However, the field have now moved to a point where the technology along with processing power and analytical tools exists. Thus, the time has come to explore the full possibility of combining real-time data on physical activity behaviour, health related outcomes and artificial intelligence. So that in the future citizens may be able to improve and maintain their health through device-based services and to make informed decisions that are based upon their personal health data.

The overarching purpose of DIWAH is to develop and validate artificial intelligence/machine learning based algorithms to assess physical activity and health related variables on individual level in real time for use within the health care system using open-source wearables.

This project will take the first steps by conducting a rigorous development and validation of the algorithms using a transparend phase-based framework. More specifically the project aim to develop algorithms for assessing:

* Physical activity
* Heart Rate
* Health related variables e.g., blood pressure and energy expenditure But also identify what underlying factors that improves the predictions such as age sex body composition and health related fitness levels.

Project description Study sample Around 50 apparently healthy adults (18-65 years, 50% females) will be recruited to this project. Recruitment will be done using a convenience sampling method from sport clubs, work sites and students at the University along with word of mouth.

There are no particular requirements to be included in the study except that the subjects must be apparently healthy and being able to jog for at least 30 minutes. Exclusion criteria includes known cardiovascular disease or being on a medication that influence the heart rate.

Study design Given that the hardware in all wearables are similar the focus is on developing as transparent algorithms as possible by following the framework outlined by Keadle et al. and simultaneously adhere to the expert recommendations of the INTERLIVE Network regarding HR monitoring. The Keadle-framework is inspired by drug development and suggests five phases: Mechanical signal testing, Laboratory testing, Semi-structured evaluation, Naturalistic validation, and Adoption. All of these phases will not be covered in the present project. The semi-structured evaluation phase is designed to identifies specific actives based on the signals from the sensors in the wearable, such as running or vacuuming. However, the physiological response and, thus, the health effects of physical activity would be similar for different kind of activities on the same intensity. For instance, the heart does not really care if one is running or shovelling snow if it is working with the same intensity. In addition, its ethically motivated to test the device in a patient population (phase five - Adoption) before the device provides valid data. Therefore, the semi-structured evaluation phase as well as the adoption phase will be omitted in the current project.

Phase 1 - Product Development Given that the hardware is similar in every wearable on the market, it is not of any importance exactly which specific brand of wearable will be used to develop and test our algorithms. The main idea with the first phase is to make sure that the wearable produces valid data and that the raw unprocessed data can be easily accessed. To this end two open-source wearables with hardware that meets the requirements for the assessment of physical activity (accelerometer), HR (optical sensor), health related variables and that also can communicate wireless with external devices using Bluetooth have been identified namely the Bangle.js 2 (https://www.espruino.com/Bangle.js2) and the Pinetime (https://www.pine64.org/pinetime/). Apps that will store the data locally at the devices and that allows for easy transfer to a external device for further processing and analysis of the data collected in phase 2 have been developed. Based on the outcome from phase 2 it is necessary before conducting phase 3 - naturalistic validation, to develop pre-processing methods, since the wearables memory capacity will not allow for storing the vast amount of raw data that will be collected during that phase. Thus, this phase will be an ongoing part of the entirety of the project.

Phase 2 - Laboratory development The goal of the phase 2 study is to develop and test algorithms to estimate the intensity of physical activity, energy cost of physical activity and blood pressure from the wearables signals under controlled laboratory conditions. In addition potential predictors that may increase the precision of the predictions will be investigated. The idea is that wearable data are collected at the same time as criterion measures, during a protocol from resting via walking and running at increasing speed until exhaustion. During the protocol they will be monitored using indirect calorimetry (Vyntus CPX, Vyaire Medical, Inc., Il, USA) and a heart rate monitor (Polar Electro Oy, Kempele, Finland) fitted around the chest, which fully comparable with a proper electrocardiogram (r\&amp;gt;0.99), simultaneously fitted with the wearable, a research grade accelerometer (Actigraph GT3+, Actigraphcorp, Pensacola, FL, USA) and a commercial wearable (Fit-Bit Sense, Fitbit international limited, Dublin, Ireland). This procedure will capture a continuous signal from the wearables at various physical activity intensities, from resting to maximal effort on individual level. This will allow to model that signal on group as well as on individual level, and to compare to the golden standard methods of indirect calorimetry and HR monitoring. Blood pressure data is goig to be collected using standard clinical monitors to compare an optical signal with that of a blood pressure meter.

Within the scope of the project, several several well-established health-related fitness tests will be conducted. For muscular strength, the hand-grip test will be used, and for flexibility, the sit and reach test. In addition, the timed up and go test (TUG) will be conducted \[18\]. Background data such as age, sex and body composition of the participants will also be collected. These tests are then used to explore the possibility to predict an individual's level of physical activity. The outcome of this phase is a set of models to count steps, and to detect the intensity of physical activity using the accelerometer, the optical sensor, the combination of the two, augmented with background data. It is also expected that a model to assess blood pressure using the wearables during rest is developed.

Phase 3 - Naturalistic validation The goal of the third phase of the project is to conduct a rigorous, independent validation of the algorithms in real-world conditions compared with gold standard measures. Thus after the development of algorithms in a controlled environment, the project will move on to test the wearable during free living. The same 50 participants (50% women, 18-65 years) from the second phase will ingest a dose of doubly labelled water (DLW), which is the golden standard to measure energy expenditure during free living with errors within ±3% from the true value as measured by respiratory chambers. Even if the DLW method is the golden standard, it is not often used as it is an expensive method involving enriching the body water of a subject with heavy hydrogen (2H) and heavy oxygen (18O), and then determining the difference in washout kinetics between both isotopes, which is being a function of carbon dioxide production, i.e., energy expenditure, using specialized equipment.

Based on the DLW kinetics, then it is possible to model the signals from the prototype wearable to find the best fit, i.e., how well the prototype assesses physical activity related energy expenditure. The outcome of this phase is expected to be an model that, based on the information provided by the wearable, assesses energy expenditure during free living conditions. The project also aim to determine the physical activity levels of the participants based on the information provided during phase two.

Data Analysis and Statistics In all phases this project will utilize the power of artificial intelligence, more specifically, various machine learning techniques to predict different aspects of physical activity, energy expenditure, and blood pressure on individual level. Machine learning automatically creates models based on data sets that have the potential of producing accurate predictions that control decisions in real-time without human interaction. The wearable(s) provide high-frequency raw data sampling from both accelerometry and optical sensor. Using this data for machine learning, provides good opportunities to indirectly measure physical activity and to individually advice healthy levels of physical activity. Several studies have tried to derive meaningful physical activity outcomes from accelerometer data collected via wrist-worn accelerometer. The studies based on machine learning aimed on the classification of physical activity types, e.g., sitting, standing, walking, running, etc., or PA intensity, e.g., sedentary, light, moderate, vigorous. They report a \&amp;gt;80% classification accuracy for activity type and a \&amp;gt;90% classification accuracy for intensity. Most of these studies use supervised machine learning approaches with labelled data, i.e., a ground truth is available, such as random forest, support vector machine, artificial neural network, and deep learning models. Only few studies use unsupervised machine learning models, such as hidden Semi-Markov models, and k-means clustering. Here, it is possible to create datasets with labelled data. However, since the overarching goal of this project is to develop a wearable that can adapt to individual users of the wearable, the learning approach must also be able to handle unlabelled data. To approach this issue reinforcement learning is suitable. Finally, since several background variables, including functional tests, have been collected they will be used to augment the models. Thus, the project will identify the best performing models to predict the physical activity intensity, frequency, and level from accelerometer and heart rate, and use that to predict energy expenditure and blood pressure. Best performing refers to both accuracy and explainability, and it is expected that different approaches excels in these two aspects. Moreover, based on more accurate measurements of physical activity and their accurate prediction using time-series data from accelerometers, an re-evaluation of the potential of functional tests as a point-in-time predictor for physical activity is going to be explored.

Eligibility criteria: Inclusion Criteria:

* Being able to jog for 30 consecutive minutes

Exclusion Criteria:

* Known heart condition

Primary outcomes:
measure: Energy expenditure
timeFrame: Testing of a single subject takes approximately 1,5 hours
measure: Physical activity intensity
timeFrame: Testing of a single subject takes approximately 1,5 hours
measure: Steps
timeFrame: Testing of a single subject takes approximately 1,5 hours
measure: Heart rate
timeFrame: Testing of a single subject takes approximately 1,5 hours
measure: Blood pressure
timeFrame: Testing of a single subject takes approximately 1,5 hours
measure: Free-living energy expenditure
timeFrame: The subjects will be monitored during approximately 12 days (10-14 days).
Secondary outcomes:
Other outcomes:
Locations:
city: Kalmar
country: Sweden
facility: Linneaus University
state: Kalmar Lan
Study officials:
References:
citation: Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, Carty C, Chaput JP, Chastin S, Chou R, Dempsey PC, DiPietro L, Ekelund U, Firth J, Friedenreich CM, Garcia L, Gichu M, Jago R, Katzmarzyk PT, Lambert E, Leitzmann M, Milton K, Ortega FB, Ranasinghe C, Stamatakis E, Tiedemann A, Troiano RP, van der Ploeg HP, Wari V, Willumsen JF. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020 Dec;54(24):1451-1462. doi: 10.1136/bjsports-2020-102955.
citation: Wright SP, Hall Brown TS, Collier SR, Sandberg K. How consumer physical activity monitors could transform human physiology research. Am J Physiol Regul Integr Comp Physiol. 2017 Mar 1;312(3):R358-R367. doi: 10.1152/ajpregu.00349.2016. Epub 2017 Jan 4.
citation: Hosanee M, Chan G, Welykholowa K, Cooper R, Kyriacou PA, Zheng D, Allen J, Abbott D, Menon C, Lovell NH, Howard N, Chan WS, Lim K, Fletcher R, Ward R, Elgendi M. Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring. J Clin Med. 2020 Mar 7;9(3):723. doi: 10.3390/jcm9030723.
citation: Aromatario O, Van Hoye A, Vuillemin A, Foucaut AM, Crozet C, Pommier J, Cambon L. How do mobile health applications support behaviour changes? A scoping review of mobile health applications relating to physical activity and eating behaviours. Public Health. 2019 Oct;175:8-18. doi: 10.1016/j.puhe.2019.06.011. Epub 2019 Jul 30.
citation: Bayoumy K, Gaber M, Elshafeey A, Mhaimeed O, Dineen EH, Marvel FA, Martin SS, Muse ED, Turakhia MP, Tarakji KG, Elshazly MB. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat Rev Cardiol. 2021 Aug;18(8):581-599. doi: 10.1038/s41569-021-00522-7. Epub 2021 Mar 4.
citation: Greiwe J, Nyenhuis SM. Wearable Technology and How This Can Be Implemented into Clinical Practice. Curr Allergy Asthma Rep. 2020 Jun 6;20(8):36. doi: 10.1007/s11882-020-00927-3.
citation: Peake JM, Kerr G, Sullivan JP. A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations. Front Physiol. 2018 Jun 28;9:743. doi: 10.3389/fphys.2018.00743. eCollection 2018.
citation: Bergman P. The number of repeated observations needed to estimate the habitual physical activity of an individual to a given level of precision. PLoS One. 2018 Feb 1;13(2):e0192117. doi: 10.1371/journal.pone.0192117. eCollection 2018.
citation: Bergman P, Hagstromer M. No one accelerometer-based physical activity data collection protocol can fit all research questions. BMC Med Res Methodol. 2020 Jun 3;20(1):141. doi: 10.1186/s12874-020-01026-7.
citation: Jensen MT, Treskes RW, Caiani EG, Casado-Arroyo R, Cowie MR, Dilaveris P, Duncker D, Di Rienzo M, Frederix I, De Groot N, Kolh PH, Kemps H, Mamas M, McGreavy P, Neubeck L, Parati G, Platonov PG, Schmidt-Trucksass A, Schuuring MJ, Simova I, Svennberg E, Verstrael A, Lumens J. ESC working group on e-cardiology position paper: use of commercially available wearable technology for heart rate and activity tracking in primary and secondary cardiovascular prevention-in collaboration with the European Heart Rhythm Association, European Association of Preventive Cardiology, Association of Cardiovascular Nursing and Allied Professionals, Patient Forum, and the Digital Health Committee. Eur Heart J Digit Health. 2021 Feb 8;2(1):49-59. doi: 10.1093/ehjdh/ztab011. eCollection 2021 Mar.
citation: Wong CK, Mentis HM, Kuber R. The bit doesn't fit: Evaluation of a commercial activity-tracker at slower walking speeds. Gait Posture. 2018 Jan;59:177-181. doi: 10.1016/j.gaitpost.2017.10.010. Epub 2017 Oct 9.
citation: Brage S, Brage N, Franks PW, Ekelund U, Wong MY, Andersen LB, Froberg K, Wareham NJ. Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure. J Appl Physiol (1985). 2004 Jan;96(1):343-51. doi: 10.1152/japplphysiol.00703.2003. Epub 2003 Sep 12.
citation: Keadle SK, Lyden KA, Strath SJ, Staudenmayer JW, Freedson PS. A Framework to Evaluate Devices That Assess Physical Behavior. Exerc Sport Sci Rev. 2019 Oct;47(4):206-214. doi: 10.1249/JES.0000000000000206.
citation: Muhlen JM, Stang J, Lykke Skovgaard E, Judice PB, Molina-Garcia P, Johnston W, Sardinha LB, Ortega FB, Caulfield B, Bloch W, Cheng S, Ekelund U, Brond JC, Grontved A, Schumann M. Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network. Br J Sports Med. 2021 Jul;55(14):767-779. doi: 10.1136/bjsports-2020-103148. Epub 2021 Jan 4.
citation: Gillinov S, Etiwy M, Wang R, Blackburn G, Phelan D, Gillinov AM, Houghtaling P, Javadikasgari H, Desai MY. Variable Accuracy of Wearable Heart Rate Monitors during Aerobic Exercise. Med Sci Sports Exerc. 2017 Aug;49(8):1697-1703. doi: 10.1249/MSS.0000000000001284.
citation: Oja, P. & Tuxworth, B. Eurofit for adults. Assessment of health-related fitness. Strasbourg: Council of Europe-UKK Institute, Tampere. (1995).
citation: Podsiadlo D, Richardson S. The timed "Up & Go": a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991 Feb;39(2):142-8. doi: 10.1111/j.1532-5415.1991.tb01616.x.
citation: Westerterp KR. Doubly labelled water assessment of energy expenditure: principle, practice, and promise. Eur J Appl Physiol. 2017 Jul;117(7):1277-1285. doi: 10.1007/s00421-017-3641-x. Epub 2017 May 15.
citation: Arvidsson D, Fridolfsson J, Borjesson M. Measurement of physical activity in clinical practice using accelerometers. J Intern Med. 2019 Aug;286(2):137-153. doi: 10.1111/joim.12908. Epub 2019 Apr 16.
citation: Liu F, Wanigatunga AA, Schrack JA. Assessment of Physical Activity in Adults Using Wrist Accelerometers. Epidemiol Rev. 2022 Jan 14;43(1):65-93. doi: 10.1093/epirev/mxab004.
citation: Rastogi T, Backes A, Schmitz S, Fagherazzi G, van Hees V, Malisoux L. Advanced analytical methods to assess physical activity behaviour using accelerometer raw time series data: a protocol for a scoping review. Syst Rev. 2020 Nov 7;9(1):259. doi: 10.1186/s13643-020-01515-2.
citation: Garatachea N, Torres Luque G, Gonzalez Gallego J. Physical activity and energy expenditure measurements using accelerometers in older adults. Nutr Hosp. 2010 Mar-Apr;25(2):224-30.
citation: Heesch KC, Hill RL, Aguilar-Farias N, van Uffelen JGZ, Pavey T. Validity of objective methods for measuring sedentary behaviour in older adults: a systematic review. Int J Behav Nutr Phys Act. 2018 Nov 26;15(1):119. doi: 10.1186/s12966-018-0749-2.
citation: Phillips LJ, Petroski GF, Markis NE. A Comparison of Accelerometer Accuracy in Older Adults. Res Gerontol Nurs. 2015 Sep-Oct;8(5):213-9. doi: 10.3928/19404921-20150429-03. Epub 2015 May 7.
citation: Sheng, B., Moosman, O. M., Del Pozo-Cruz, B., Del Pozo-Cruz, J., Alfonso-Rosa, R. M. &amp; Zhang, Y. A comparison of different machine learning algorithms, types and placements of activity monitors for physical activity classification. Measurement 154, 107480 (2020).
citation: Montoye AHK, Pivarnik JM, Mudd LM, Biswas S, Pfeiffer KA. Validation and Comparison of Accelerometers Worn on the Hip, Thigh, and Wrists for Measuring Physical Activity and Sedentary Behavior. AIMS Public Health. 2016 May 20;3(2):298-312. doi: 10.3934/publichealth.2016.2.298. eCollection 2016.
citation: Montoye AHK, Westgate BS, Fonley MR, Pfeiffer KA. Cross-validation and out-of-sample testing of physical activity intensity predictions with a wrist-worn accelerometer. J Appl Physiol (1985). 2018 May 1;124(5):1284-1293. doi: 10.1152/japplphysiol.00760.2017. Epub 2018 Jan 25.
citation: Ahmadi MN, Chowdhury A, Pavey T, Trost SG. Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation. PLoS One. 2020 May 20;15(5):e0233229. doi: 10.1371/journal.pone.0233229. eCollection 2020.
citation: Stewart T, Narayanan A, Hedayatrad L, Neville J, Mackay L, Duncan S. A Dual-Accelerometer System for Classifying Physical Activity in Children and Adults. Med Sci Sports Exerc. 2018 Dec;50(12):2595-2602. doi: 10.1249/MSS.0000000000001717.
citation: Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Sci Rep. 2018 May 21;8(1):7961. doi: 10.1038/s41598-018-26174-1.
citation: Narayanan A, Desai F, Stewart T, Duncan S, Mackay L. Application of Raw Accelerometer Data and Machine-Learning Techniques to Characterize Human Movement Behavior: A Systematic Scoping Review. J Phys Act Health. 2020 Mar 1;17(3):360-383. doi: 10.1123/jpah.2019-0088.
citation: Galán-Mercant, A., Ortiz, A., Herrera-Viedma, E., Tomas, M. T., Fernandes, B. &amp; Moral-Munoz, J. A. Assessing physical activity and functional fitness level using convolutional neural networks. Knowledge-Based Systems 185 (2019).
citation: Hamid A, Duncan MJ, Eyre ELJ, Jing Y. Predicting children's energy expenditure during physical activity using deep learning and wearable sensor data. Eur J Sport Sci. 2021 Jun;21(6):918-926. doi: 10.1080/17461391.2020.1789749. Epub 2020 Jul 16.
citation: van Kuppevelt D, Heywood J, Hamer M, Sabia S, Fitzsimons E, van Hees V. Segmenting accelerometer data from daily life with unsupervised machine learning. PLoS One. 2019 Jan 9;14(1):e0208692. doi: 10.1371/journal.pone.0208692. eCollection 2019.
citation: Jones PJ, Catt M, Davies MJ, Edwardson CL, Mirkes EM, Khunti K, Yates T, Rowlands AV. Feature selection for unsupervised machine learning of accelerometer data physical activity clusters - A systematic review. Gait Posture. 2021 Oct;90:120-128. doi: 10.1016/j.gaitpost.2021.08.007. Epub 2021 Aug 13.
citation: Hochberg I, Feraru G, Kozdoba M, Mannor S, Tennenholtz M, Yom-Tov E. Encouraging Physical Activity in Patients With Diabetes Through Automatic Personalized Feedback via Reinforcement Learning Improves Glycemic Control. Diabetes Care. 2016 Apr;39(4):e59-60. doi: 10.2337/dc15-2340. Epub 2016 Jan 28. No abstract available.
citation: Wijkman M, Carlsson M, Darwiche G, Nystrom FH. A pilot study of hypertension management using a telemedicine treatment approach. Blood Press Monit. 2020 Feb;25(1):18-21. doi: 10.1097/MBP.0000000000000413.