1
|
Buendia R, Karpefors M, Folkvaljon F, Hunter R, Sillen H, Luu L, Docherty K, Cowie MR. Wearable Sensors to Monitor Physical Activity in Heart Failure Clinical Trials: State-of-the-Art Review. J Card Fail 2024; 30:703-716. [PMID: 38452999 DOI: 10.1016/j.cardfail.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/24/2024] [Accepted: 01/30/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Estimation of the effects that drugs or other interventions have on patients' symptoms and functions is crucial in heart failure trials. Traditional symptoms and functions clinical outcome assessments have important limitations. Actigraphy may help to overcome these limitations due to its objective nature and the potential for continuous recording of data. However, actigraphy is not currently accepted as clinically relevant by key stakeholders. METHODS AND RESULTS In this state-of-the-art study, the key aspects to consider when implementing actigraphy in heart failure trials are discussed. They include which actigraphy-derived measures should be considered, how to build endpoints using them, how to measure and analyze them, and how to handle the patients' and sites' logistics of integrating devices into trials. A comprehensive recommendation based on the current evidence is provided. CONCLUSION Actigraphy is technically feasible in clinical trials involving heart failure, but successful implementation and use to demonstrate clinically important differences in physical functioning with drug or other interventions require careful consideration of many design choices.
Collapse
Affiliation(s)
- Ruben Buendia
- Data Science, Late-Stage Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
| | - Martin Karpefors
- Data Science, Late-Stage Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Folke Folkvaljon
- Patient Centered Science, BioPharmaceuticals Business, AstraZeneca, Gothenburg, Sweden
| | - Robert Hunter
- Regulatory, Late-Stage Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Luton, UK
| | | | - Long Luu
- Digital Health R&D, AstraZeneca, Gaithersburg, MD, US
| | - Kieran Docherty
- British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - Martin R Cowie
- Late-Stage Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Boston, MA, US
| |
Collapse
|
2
|
Jin JQ, Hong J, Elhage KG, Braun M, Spencer RK, Chung M, Yeroushalmi S, Hadeler E, Mosca M, Bartholomew E, Hakimi M, Davis MS, Thibodeaux Q, Wu D, Kahlon A, Dhaliwal P, Mathes EF, Dhaliwal N, Bhutani T, Liao W. Development of SkinTracker, an integrated dermatology mobile app and web portal enabling remote clinical research studies. Front Digit Health 2023; 5:1228503. [PMID: 37744686 PMCID: PMC10516539 DOI: 10.3389/fdgth.2023.1228503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction In-person dermatology clinical research studies often face recruitment and participation challenges due to travel-, time-, and cost-associated barriers. Studies incorporating virtual/asynchronous formats can potentially enhance research subject participation and satisfaction, but few mobile health tools are available to enable remote study conduct. We developed SkinTracker, a patient-facing mobile app and researcher-facing web platform, that enables longitudinal collection of skin photos, patient reported outcomes, and biometric health and environmental data. Methods Eight design thinking sessions including dermatologists, clinical research staff, software engineers, and graphic designers were held to create the components of SkinTracker. Following iterative prototyping, SkinTracker was piloted across six adult and four pediatric subjects with atopic dermatitis (AD) of varying severity levels to test and provide feedback on SkinTracker for six months. Results The SkinTracker app enables collection of informed consent for study participation, baseline medical history, standardized skin photographs, patient-reported outcomes (e.g., Patient Oriented Eczema Measure (POEM), Pruritus Numerical Rating Scale (NRS), Dermatology Life Quality Index (DLQI)), medication use, adverse events, voice diary to document qualitative experiences, chat function for communication with research team, environmental and biometric data such as exercise and sleep metrics through integration with an Apple Watch. The researcher web portal allows for management and visualization of subject enrollment, skin photographs for examination and severity scoring, survey completion, and other patient modules. The pilot study requested that subjects complete surveys and photographs on a weekly to monthly basis via the SkinTracker app. Afterwards, participants rated their experience in a 7-item user experience survey covering app function, design, and desire for participation in future studies using SkinTracker. Almost all subjects agreed or strongly agreed that SkinTracker enabled more convenient participation in skin research studies compared to an in-person format. Discussion To our knowledge, SkinTracker is one of the first integrated app- and web-based platforms allowing collection and management of data commonly obtained in clinical research studies. SkinTracker enables detailed, frequent capture of data that may better reflect the fluctuating course of conditions such as AD, and can be modularly customized for different skin conditions to improve dermatologic research participation and patient access.
Collapse
Affiliation(s)
- Joy Q. Jin
- School of Medicine, University of California, San Francisco, San Francisco, CA, United States
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Julie Hong
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Kareem G. Elhage
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Mitchell Braun
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Riley K. Spencer
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Mimi Chung
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Samuel Yeroushalmi
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Edward Hadeler
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Megan Mosca
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Erin Bartholomew
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Marwa Hakimi
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Mitchell S. Davis
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Quinn Thibodeaux
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - David Wu
- School of Medicine, University of California, San Francisco, San Francisco, CA, United States
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | | | | | - Erin F. Mathes
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | | | - Tina Bhutani
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| | - Wilson Liao
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
| |
Collapse
|
3
|
Mishra SR, Dempsey W, Klasnja P. A Text Messaging Intervention for Priming the Affective Rewards of Exercise in Adults: Protocol for a Microrandomized Trial. JMIR Res Protoc 2023; 12:e46560. [PMID: 37656493 PMCID: PMC10504629 DOI: 10.2196/46560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 05/19/2023] [Accepted: 06/05/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Physical activity is a critical target for health interventions, but effective interventions remain elusive. A growing body of work suggests that interventions targeting affective attitudes toward physical activity may be more effective for sustaining activity long term than those that rely on cognitive constructs alone, such as goal setting and self-monitoring. Anticipated affective response in particular is a promising target for intervention. OBJECTIVE We will evaluate the efficacy of an SMS text messaging intervention that manipulates anticipated affective response to exercise to promote physical activity. We hypothesize that reminding users of a positive postexercise affective state before their planned exercise sessions will increase their calories burned during this exercise session. We will deploy 2 forms of affective SMS text messages to explore the design space: low-reflection messages written by participants for themselves and high-reflection prompts that require users to reflect and respond. We will also explore the effect of the intervention on affective attitudes toward exercise. METHODS A total of 120 individuals will be enrolled in a 9-week microrandomized trial testing affective messages that remind users about feeling good after exercise (40% probability), control reminders (30% probability), or no message (30% probability). Two types of affective SMS text messages will be deployed: one requiring a response and the other in a read-only format. Participants will write the read-only messages themselves to ensure that the messages accurately reflect the participants' anticipated postexercise affective state. Affective attitudes toward exercise and intrinsic motivation for exercise will be measured at the beginning and end of the study. The weighted and centered least squares method will be used to analyze the effect of delivering the intervention versus not on calories burned over 4 hours around the time of the planned activity, measured by the Apple Watch. Secondary analyses will include the effect of the intervention on step count and active minutes, as well as an investigation of the effects of the intervention on affective attitudes toward exercise and intrinsic motivation for exercise. Participants will be interviewed to gain qualitative insights into intervention impact and acceptability. RESULTS Enrollment began in May 2023, with 57 participants enrolled at the end of July 2023. We anticipate enrolling 120 participants. CONCLUSIONS This study will provide early evidence about the effect of a repeated manipulation of anticipated affective response to exercise. The use of 2 different types of messages will yield insight into optimal design strategies for improving affective attitudes toward exercise. TRIAL REGISTRATION ClinicalTrials.gov NCT05582369; https://classic.clinicaltrials.gov/ct2/show/NCT05582369. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/46560.
Collapse
Affiliation(s)
- Sonali R Mishra
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Walter Dempsey
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Predrag Klasnja
- School of Information, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
4
|
Durán Vega HC, Lopez Echaury A, Flores E. The Energy a Plastic Surgeon Expends during Liposuction. PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN 2023; 11:e5001. [PMID: 37250835 PMCID: PMC10212613 DOI: 10.1097/gox.0000000000005001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 03/29/2023] [Indexed: 05/31/2023]
Abstract
It is generally accepted that liposuction requires a significant amount of energy from surgeons. This procedure involves the use of specialized equipment and techniques to remove fat cells from the body, which can be physically demanding for surgeons. The amount of effort required for liposuction must be evaluated in terms of energy consumption. Our goal was to conduct a study to record the energy that the surgeon uses during liposuction and correlate these results with the volume of fat obtained as well as other variables. Methods A series of cases was carried out from April 2022 to November 1, 2022, in three different plastic surgery centers. Three plastic surgeons recorded the procedures using an Apple Watch, choosing from among Apple Watch training options and free indoor walking. The surgeon then concluded the registration at the time of finishing the surgery and removed the surgical gloves and gowns. Results Complete data were obtained for 63 patients. The average fat obtained per 1 kcal of energy was 6.14 cm3 of fat, and 160 cal to obtain 1 cm3 of fat by liposuction. Other data that demonstrated statistically significant correlations were fat volume versus average pace (km), total fat volume versus average heart rate, fat volume versus surgical time, and fat volume versus distance. Conclusions Liposuction is a surgical procedure that requires considerable effort. This study demonstrates the amount of energy required for regular liposuction. Compared with other single procedures, three times more energy is required to complete liposuction.
Collapse
|
5
|
Lui GY, Loughnane D, Polley C, Jayarathna T, Breen PP. The Apple Watch for Monitoring Mental Health-Related Physiological Symptoms: Literature Review. JMIR Ment Health 2022; 9:e37354. [PMID: 36069848 PMCID: PMC9494213 DOI: 10.2196/37354] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 07/29/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND An anticipated surge in mental health service demand related to COVID-19 has motivated the use of novel methods of care to meet demand, given workforce limitations. Digital health technologies in the form of self-tracking technology have been identified as a potential avenue, provided sufficient evidence exists to support their effectiveness in mental health contexts. OBJECTIVE This literature review aims to identify current and potential physiological or physiologically related monitoring capabilities of the Apple Watch relevant to mental health monitoring and examine the accuracy and validation status of these measures and their implications for mental health treatment. METHODS A literature review was conducted from June 2021 to July 2021 of both published and gray literature pertaining to the Apple Watch, mental health, and physiology. The literature review identified studies validating the sensor capabilities of the Apple Watch. RESULTS A total of 5583 paper titles were identified, with 115 (2.06%) reviewed in full. Of these 115 papers, 19 (16.5%) were related to Apple Watch validation or comparison studies. Most studies showed that the Apple Watch could measure heart rate acceptably with increased errors in case of movement. Accurate energy expenditure measurements are difficult for most wearables, with the Apple Watch generally providing the best results compared with peers, despite overestimation. Heart rate variability measurements were found to have gaps in data but were able to detect mild mental stress. Activity monitoring with step counting showed good agreement, although wheelchair use was found to be prone to overestimation and poor performance on overground tasks. Atrial fibrillation detection showed mixed results, in part because of a high inconclusive result rate, but may be useful for ongoing monitoring. No studies recorded validation of the Sleep app feature; however, accelerometer-based sleep monitoring showed high accuracy and sensitivity in detecting sleep. CONCLUSIONS The results are encouraging regarding the application of the Apple Watch in mental health, particularly as heart rate variability is a key indicator of changes in both physical and emotional states. Particular benefits may be derived through avoidance of recall bias and collection of supporting ecological context data. However, a lack of methodologically robust and replicated evidence of user benefit, a supportive health economic analysis, and concerns about personal health information remain key factors that must be addressed to enable broader uptake.
Collapse
Affiliation(s)
- Gough Yumu Lui
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia
| | | | - Caitlin Polley
- Electrical and Electronic Engineering, School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW, Australia
| | - Titus Jayarathna
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia
| | - Paul P Breen
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia.,Translational Health Research Institute, Western Sydney University, Penrith, NSW, Australia
| |
Collapse
|
6
|
Chevance G, Golaszewski NM, Tipton E, Hekler EB, Buman M, Welk GJ, Patrick K, Godino JG. Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis. JMIR Mhealth Uhealth 2022; 10:e35626. [PMID: 35416777 PMCID: PMC9047731 DOI: 10.2196/35626] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/27/2022] [Accepted: 02/10/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge. OBJECTIVE The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits. METHODS We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps. RESULTS A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of -2.99 beats per minute (k comparison=74), -2.77 kcal per minute (k comparison=29), and -3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: -23.99 to 18.01, -12.75 to 7.41, and -13.07 to 6.86, respectively). CONCLUSIONS Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.
Collapse
Affiliation(s)
| | - Natalie M Golaszewski
- Herbert Wertheim School of Public Health and Longevity Science, University of California, San Diego, La Jolla, CA, United States
- Center for Wireless & Population Health Systems, University of California, San Diego, La Jolla, CA, United States
| | - Elizabeth Tipton
- Department of Statistics, Northwestern University, Evanston, IL, United States
| | - Eric B Hekler
- Herbert Wertheim School of Public Health and Longevity Science, University of California, San Diego, La Jolla, CA, United States
- Center for Wireless & Population Health Systems, University of California, San Diego, La Jolla, CA, United States
- Exercise and Physical Activity Resource Center, University of California, San Diego, La Jolla, CA, United States
| | - Matthew Buman
- School of Nutrition & Health Promotion, Arizona State University, Phoenix, AZ, United States
| | - Gregory J Welk
- Department of Kinesiology, Iowa State University, Ames, IA, United States
| | - Kevin Patrick
- Center for Wireless & Population Health Systems, University of California, San Diego, La Jolla, CA, United States
| | - Job G Godino
- Herbert Wertheim School of Public Health and Longevity Science, University of California, San Diego, La Jolla, CA, United States
- Center for Wireless & Population Health Systems, University of California, San Diego, La Jolla, CA, United States
- Exercise and Physical Activity Resource Center, University of California, San Diego, La Jolla, CA, United States
- Laura Rodriguez Research Institute, Family Health Centers of San Diego, San Diego, CA, United States
| |
Collapse
|
7
|
De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, Lavelle G, Matcham F, Pace A, Mohr DC, Dobson R, Hotopf M. Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ Digit Med 2022; 5:3. [PMID: 35017634 PMCID: PMC8752685 DOI: 10.1038/s41746-021-00548-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/28/2021] [Indexed: 12/27/2022] Open
Abstract
The use of digital tools to measure physiological and behavioural variables of potential relevance to mental health is a growing field sitting at the intersection between computer science, engineering, and clinical science. We summarised the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and identified leading digital signals for depression. Medical and computer science databases were searched between January 2007 and November 2019. Published studies linking depression and objective behavioural data obtained from smartphone and wearable device sensors in adults with unipolar depression and healthy subjects were included. A descriptive approach was taken to synthesise study methodologies. We included 51 studies and found threats to reproducibility and transparency arising from failure to provide comprehensive descriptions of recruitment strategies, sample information, feature construction and the determination and handling of missing data. The literature is characterised by small sample sizes, short follow-up duration and great variability in the quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Machine learning models found the predictive value of aggregated features. Given the pitfalls in the combined literature, these results should be taken purely as a starting point for hypothesis generation. Since this research is ultimately aimed at informing clinical practice, we recommend improvements in reporting standards including consideration of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the reporting of potential bias in studies with numerous features.
Collapse
Affiliation(s)
- Valeria De Angel
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK.
| | - Serena Lewis
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Psychology, University of Bath, Bath, UK
| | - Katie White
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carolin Oetzmann
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Daniel Leightley
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Emanuela Oprea
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Grace Lavelle
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alice Pace
- Chelsea And Westminster Hospital NHS Foundation Trust, London, UK
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Richard Dobson
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, 16 De Crespigny Park, London, SE5 8AF, UK
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
| |
Collapse
|
8
|
Leung W, Case L, Sung MC, Jung J. A meta-analysis of Fitbit devices: same company, different models, different validity evidence. J Med Eng Technol 2021; 46:102-115. [PMID: 34881682 DOI: 10.1080/03091902.2021.2006350] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Fitbit devices are among the most commonly used physical activity devices used by the general public. Multiple studies have examined the validity evidence of Fitbit devices of estimating energy expenditure during physical activity compared to criterion references. However, the literature lacks objective, summary validity evidence that supports the use of various models of Fitbit devices. Therefore, this study aims (a) to examine the validity evidence among the various models of Fitbit devices and (b) to investigate the influence of several device factors on the validity evidence of Fitbit models using meta-analysis. A total of 402 articles were identified through five databases. Upon review of the articles, 29 studies were included in the meta-analysis. Seven different moderator variables, including Fitbit model, device placement, type of device, heart rate capability, release year of devices, activity types and sedentary activity, were identified and included in the meta-analysis to examine their impact on the validity evidence of Fitbit devices. The summarised validity coefficient of energy expenditure during physical activity estimated by Fitbit devices and measured by criterion references was r=.64 (k = 29, 95% CI [.59, .69], p<.001). Fitbit model was not found to be a significant factor impacting validity evidence of Fitbit devices, but heart rate capability, activity types and sedentary activity were found to be significant factors impacting validity evidence. This study found that not all Fitbit models have a similar ability in estimating energy expenditure during physical activity. Continued research is needed in examining the validity evidence of Fitbit devices, especially considering some factors may affect the validity evidence in measuring energy expenditure during physical activity.
Collapse
Affiliation(s)
- Willie Leung
- Department of Health Sciences & Human Performance, College of Natural and Health Sciences, The University of Tampa, Tampa, FL, USA
| | - Layne Case
- Department of Physical Education, College of Education, University of South Carolina, Columbia, SC, USA
| | - Ming-Chih Sung
- Kinesiology, School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Jaehun Jung
- Department of Health & Human Performance, College of Education and Human Development, Northwestern State University of Louisiana, Natchitoches, LA, USA
| |
Collapse
|
9
|
Kwon S, Kim Y, Bai Y, Burns RD, Brusseau TA, Byun W. Validation of the Apple Watch for Estimating Moderate-to-Vigorous Physical Activity and Activity Energy Expenditure in School-Aged Children. SENSORS 2021; 21:s21196413. [PMID: 34640733 PMCID: PMC8512453 DOI: 10.3390/s21196413] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/08/2021] [Accepted: 09/19/2021] [Indexed: 12/18/2022]
Abstract
The Apple Watch is one of the most popular wearable devices designed to monitor physical activity (PA). However, it is currently unknown whether the Apple Watch accurately estimates children’s free-living PA. Therefore, this study assessed the concurrent validity of the Apple Watch 3 in estimating moderate-to-vigorous physical activity (MVPA) time and active energy expenditure (AEE) for school-aged children under a simulated and a free-living condition. Twenty elementary school students (Girls: 45%, age: 9.7 ± 2.0 years) wore an Apple Watch 3 device on their wrist and performed prescribed free-living activities in a lab setting. A subgroup of participants (N = 5) wore the Apple Watch for seven consecutive days in order to assess the validity in free-living condition. The K5 indirect calorimetry (K5) and GT3X+ were used as the criterion measure under simulated free-living and free-living conditions, respectively. Mean absolute percent errors (MAPE) and Bland-Altman (BA) plots were conducted to assess the validity of the Apple Watch 3 compared to those from the criterion measures. Equivalence testing determined the statistical equivalence between the Apple Watch and K5 for MVPA time and AEE. The Apple Watch provided comparable estimates for MVPA time (mean bias: 0.3 min, p = 0.91, MAPE: 1%) and for AEE (mean bias: 3.8 kcal min, p = 0.75, MAPE: 4%) during the simulated free-living condition. The BA plots indicated no systematic bias for the agreement in MVPA and AEE estimates between the K5 and Apple Watch 3. However, the Apple Watch had a relatively large variability in estimating AEE in children. The Apple Watch was statistically equivalent to the K5 within ±17.7% and ±20.8% for MVPA time and AEE estimates, respectively. Our findings suggest that the Apple Watch 3 has the potential to be used as a PA assessment tool to estimate MVPA in school-aged children.
Collapse
Affiliation(s)
- Sunku Kwon
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (Y.B.); (R.D.B.); (T.A.B.)
| | - Youngwon Kim
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong 999077, China;
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SL, UK
| | - Yang Bai
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (Y.B.); (R.D.B.); (T.A.B.)
| | - Ryan D. Burns
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (Y.B.); (R.D.B.); (T.A.B.)
| | - Timothy A. Brusseau
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (Y.B.); (R.D.B.); (T.A.B.)
| | - Wonwoo Byun
- Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA; (S.K.); (Y.B.); (R.D.B.); (T.A.B.)
- Correspondence: ; Tel.: +1-801-583-1119
| |
Collapse
|
10
|
Claudel SE, Tamura K, Troendle J, Andrews MR, Ceasar JN, Mitchell VM, Vijayakumar N, Powell-Wiley TM. Comparing Methods to Identify Wear-Time Intervals for Physical Activity With the Fitbit Charge 2. J Aging Phys Act 2021; 29:529-535. [PMID: 33326935 PMCID: PMC8493649 DOI: 10.1123/japa.2020-0059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 07/22/2020] [Accepted: 08/26/2020] [Indexed: 01/28/2023]
Abstract
There is no established method for processing data from commercially available physical activity trackers. This study aims to develop a standardized approach to defining valid wear time for use in future interventions and analyses. Sixteen African American women (mean age = 62.1 years and mean body mass index = 35.5 kg/m2) wore the Fitbit Charge 2 for 20 days. Method 1 defined a valid day as ≥10-hr wear time with heart rate data. Method 2 removed minutes without heart rate data, minutes with heart rate ≤ mean - 2 SDs below mean and ≤2 steps, and nighttime. Linear regression modeled steps per day per week change. Using Method 1 (n = 292 person-days), participants had 20.5 (SD = 4.3) hr wear time per day compared with 16.3 (SD = 2.2) hr using Method 2 (n = 282) (p < .0001). With Method 1, participants took 7,436 (SD = 3,543) steps per day compared with 7,298 (SD = 3,501) steps per day with Method 2 (p = .64). The proposed algorithm represents a novel approach to standardizing data generated by physical activity trackers. Future studies are needed to improve the accuracy of physical activity data sets.
Collapse
|
11
|
Leung W, Case L, Jung J, Yun J. Factors associated with validity of consumer-oriented wearable physical activity trackers: a meta-analysis. J Med Eng Technol 2021; 45:223-236. [PMID: 33750250 DOI: 10.1080/03091902.2021.1893395] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The purposes of this study were to examine (1) the strength of the criterion validity evidence of various consumer-oriented wearable physical activity trackers, (2) the influence of brands of consumer-oriented wearable physical activity on validity evidence and (3) factors that may contribute to differences in the strength of the criterion validity evidence. A total of 589 articles were identified through four databases. Pairs of researchers reviewed the articles to determine eligibility. A total of 29 studies with 96 validity coefficients were included in the meta-analysis. Five different moderators, including the brands of physical activity trackers, placement of devices, type of activities (ambulatory vs. lifestyle activities), population, and release year, were analysed to examine which factors impact the validity evidence. The summarised validity coefficient between activity trackers and energy expenditure ranged from r = .41 to r = .91. Moderator analyses revealed that the brand, placement of the device, and population significantly impact the magnitude of the validity evidence, while the type of activity and release year of the devices do not. Device brand, population, andplacement are each factor that significantly affects the validity coefficientsbetween consumer-oriented wearable physical activity trackers. Efforts should be made to improve the accuracy of these devices to maintain the credibility of the research and the trust of consumers.
Collapse
Affiliation(s)
- Willie Leung
- Kinesiology, School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Layne Case
- Kinesiology, School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Jaehun Jung
- Department of Health and Human Performance, College of Education and Human Development, Northwestern State University of Louisiana, Natchitoches, LA, USA
| | - Joonkoo Yun
- Department of Kinesiology, College of Health and Human Performance, Eastern Carolina University, Greenville, NC, USA
| |
Collapse
|
12
|
Fuller D, Colwell E, Low J, Orychock K, Tobin MA, Simango B, Buote R, Van Heerden D, Luan H, Cullen K, Slade L, Taylor NGA. Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review. JMIR Mhealth Uhealth 2020; 8:e18694. [PMID: 32897239 PMCID: PMC7509623 DOI: 10.2196/18694] [Citation(s) in RCA: 218] [Impact Index Per Article: 54.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 06/22/2020] [Accepted: 06/25/2020] [Indexed: 12/27/2022] Open
Abstract
Background Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures. Objective The purpose of this systematic review was to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure. Methods We identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only articles published in the English language up to May 2019 were considered. Studies were excluded if they did not identify the device used and if they did not examine the validity or reliability of the device. Studies involving the general population and all special populations were included. We operationalized validity as criterion validity (as compared with other measures) and construct validity (degree to which the device is measuring what it claims). Reliability measures focused on intradevice and interdevice reliability. Results We included 158 publications examining nine different commercial wearable device brands. Fitbit was by far the most studied brand. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate and Fitbit tending toward underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand. Conclusions Commercial wearable devices are accurate for measuring steps and heart rate in laboratory-based settings, but this varies by the manufacturer and device type. Devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.
Collapse
Affiliation(s)
- Daniel Fuller
- School of Human Kinetics and Recreation, Memorial University, St. John's, NL, Canada.,Department of Computer Science, Memorial University, St. John's, NL, Canada.,Division of Community Health and Humanities, Faculty of Medicine, Memorial University, St. John's, NL, Canada
| | - Emily Colwell
- School of Human Kinetics and Recreation, Memorial University, St. John's, NL, Canada
| | - Jonathan Low
- School of Human Kinetics and Recreation, Memorial University, St. John's, NL, Canada
| | - Kassia Orychock
- School of Human Kinetics and Recreation, Memorial University, St. John's, NL, Canada
| | | | - Bo Simango
- School of Human Kinetics and Recreation, Memorial University, St. John's, NL, Canada
| | - Richard Buote
- Faculty of Medicine, Memorial University, St. John's, NL, Canada
| | | | - Hui Luan
- Department of Geography, University of Oregon, Eugene, OR, United States
| | - Kimberley Cullen
- School of Human Kinetics and Recreation, Memorial University, St. John's, NL, Canada.,Division of Community Health and Humanities, Faculty of Medicine, Memorial University, St. John's, NL, Canada
| | - Logan Slade
- Faculty of Medicine, Memorial University, St. John's, NL, Canada
| | - Nathan G A Taylor
- School of Health Administration, Dalhousie University, Halifax, NS, Canada
| |
Collapse
|
13
|
Van Hooren B, Goudsmit J, Restrepo J, Vos S. Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements. J Sports Sci 2019; 38:214-230. [PMID: 31795815 DOI: 10.1080/02640414.2019.1690960] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Injuries and lack of motivation are common reasons for discontinuation of running. Real-time feedback from wearables can reduce discontinuation by reducing injury risk and improving performance and motivation. There are however several limitations and challenges with current real-time feedback approaches. We discuss these limitations and challenges and provide a framework to optimise real-time feedback for reducing injury risk and improving performance and motivation. We first discuss the reasons why individuals run and propose that feedback targeted to these reasons can improve motivation and compliance. Secondly, we review the association of running technique and running workload with injuries and performance and we elaborate how real-time feedback on running technique and workload can be applied to reduce injury risk and improve performance and motivation. We also review different feedback modalities and motor learning feedback strategies and their application to real-time feedback. Briefly, the most effective feedback modality and frequency differ between variables and individuals, but a combination of modalities and mixture of real-time and delayed feedback is most effective. Moreover, feedback promoting perceived competence, autonomy and an external focus can improve motivation, learning and performance. Although the focus is on wearables, the challenges and practical applications are also relevant for laboratory-based gait retraining.
Collapse
Affiliation(s)
- Bas Van Hooren
- School of Sport Studies, Fontys University of Applied Sciences, Eindhoven, The Netherlands.,Department of Nutrition and Movement Sciences, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Jos Goudsmit
- School of Sport Studies, Fontys University of Applied Sciences, Eindhoven, The Netherlands.,Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Juan Restrepo
- Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Steven Vos
- School of Sport Studies, Fontys University of Applied Sciences, Eindhoven, The Netherlands.,Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands
| |
Collapse
|