1
|
Brand YE, Kluge F, Palmerini L, Paraschiv-Ionescu A, Becker C, Cereatti A, Maetzler W, Sharrack B, Vereijken B, Yarnall AJ, Rochester L, Del Din S, Muller A, Buchman AS, Hausdorff JM, Perlman O. Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist-Worn Accelerometer Data: Development and Validation of ElderNet. RESEARCH SQUARE 2024:rs.3.rs-4102403. [PMID: 38559043 PMCID: PMC10980143 DOI: 10.21203/rs.3.rs-4102403/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Progressive gait impairment is common in aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1,000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised gait detection method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults.
Collapse
|
2
|
Lv C, Guo W, Yin X, Liu L, Huang X, Li S, Zhang L. Innovative applications of artificial intelligence during the COVID-19 pandemic. INFECTIOUS MEDICINE 2024; 3:100095. [PMID: 38586543 PMCID: PMC10998276 DOI: 10.1016/j.imj.2024.100095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/16/2023] [Accepted: 02/18/2024] [Indexed: 04/09/2024]
Abstract
The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.
Collapse
Affiliation(s)
- Chenrui Lv
- Huazhong Agricultural University, Wuhan 430070, China
| | - Wenqiang Guo
- Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Huazhong Agricultural University, Wuhan 430070, China
| | - Liu Liu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research, Shanghai 200001, China
| | - Xinlei Huang
- Huazhong Agricultural University, Wuhan 430070, China
| | - Shimin Li
- Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Huazhong Agricultural University, Wuhan 430070, China
| |
Collapse
|
3
|
Mayorga-Vega D, Casado-Robles C, Guijarro-Romero S, Viciana J. Criterion-Related Validity of Consumer-Wearable Activity Trackers for Estimating Steps in Primary Schoolchildren under Controlled Conditions: Fit-Person Study. J Sports Sci Med 2024; 23:79-96. [PMID: 38455433 PMCID: PMC10915616 DOI: 10.52082/jssm.2024.79] [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: 10/23/2023] [Accepted: 12/12/2023] [Indexed: 03/09/2024]
Abstract
The purposes were to examine the criterion-related validity of the steps estimated by consumer-wearable activity trackers (wrist-worn activity trackers: Fitbit Ace 2, Garmin Vivofit Jr, and Xiomi Mi Band 5; smartphone applications: Pedometer, Pedometer Pacer Health, and Google Fit/Apple Health) and their comparability in primary schoolchildren under controlled conditions. An initial sample of 66 primary schoolchildren (final sample = 56; 46.4% females), aged 9-12 years old (mean = 10.4 ± 1.0 years), wore three wrist-worn activity trackers (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on their non-dominant wrist and had three applications in two smartphones (Pedometer, Pedometer Pacer Health, and Google Fit/Apple Health for Android/iOS installed in Samsung Galaxy S20+/iPhone 11 Pro Max) in simulated front trouser pockets. Primary schoolchildren's steps estimated by the consumer-wearable activity trackers and the video-based counting independently by two researchers (gold standard) were recorded while they performed a 200-meter course in slow, normal and brisk pace walking, and running conditions. Results showed that the criterion-related validity of the step scores estimated by the three Samsung applications and the Garmin Vivofit Jr 2 were good-excellent in the four walking/running conditions (e.g., MAPE = 0.6-2.3%; lower 95% CI of the ICC = 0.81-0.99), as well as being comparable. However, the Apple applications, Fitbit Ace 2, and Xiaomi Mi Band 5 showed poor criterion-related validity and comparability on some walking/running conditions (e.g., lower 95% CI of the ICC < 0.70). Although, as in real life primary schoolchildren also place their smartphones in other parts (e.g., schoolbags, hands or even somewhere away from the body), the criterion-related validity of the Garmin Vivofit Jr 2 potentially would be considerably higher than that of the Samsung applications. The findings of the present study highlight the potential of the Garmin Vivofit Jr 2 for monitoring primary schoolchildren's steps under controlled conditions.
Collapse
Affiliation(s)
- Daniel Mayorga-Vega
- Departamento de Didáctica de las Lenguas, las Artes y el Deporte, Facultad de Ciencias de la Educación, Universidad de Málaga, Málaga, Spain
| | | | - Santiago Guijarro-Romero
- Department of Didactic of Musical, Plastic and Corporal Expression, University of Valladolid, Valladolid, Spain
| | - Jesús Viciana
- Department of Physical Education and Sport, University of Granada, Granada, Spain
| |
Collapse
|
4
|
Leung W, Shi L, Jung J. Are individuals with disabilities using wearable devices? A secondary data analysis of 2017 BRFSS. Disabil Rehabil Assist Technol 2024; 19:131-138. [PMID: 35511679 DOI: 10.1080/17483107.2022.2071485] [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: 04/18/2021] [Accepted: 04/21/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE The study aims to investigate the prevalence of individuals with disabilities who reported using wearable devices, to examine the association between wearable device usage and disability status, and to determine the characteristic of individuals with disabilities associated with wearable device usage using the 2017 Behavioural Risk Factor Surveillance System (BRFSS) through secondary data analysis. MATERIALS AND METHODS Data from the 2017 BRFSS of eight states were used in the analysis. Descriptive analysis, chi-square analysis, and multivariable logistic regressions were performed. Subsample analyses were also conducted for individuals with disabilities and different types of disability, including visual impairments, hearing impairments, cognitive disability, independent living disability, self-care disability, and mobility disability on wearable device usage. RESULTS 14.6% (95% CI [11.7, 17.5]) of participants with disabilities were wearable device users. Individuals with disabilities were .63 (95% CI [.48, .83], p < 0.001) and .67 (95% CI [.50, .90], p = 0.007) times the odds of individuals without disabilities in using wearable devices, respectively, according to unadjusted and adjusted logistic regression. Individuals with mobility disability were less likely to utilise wearable devices than their counterparts. Among individuals with disabilities, those who were age 65 years or older had a lower odds of using wearable devices (OR = .55, 95% CI [0.35, 0.85), p = 0.007). CONCLUSION Individuals with disabilities are using wearable devices in collecting various health-related information. Further research is needed to determine reasons why individuals with disabilities are not using wearable devices and how individuals with disabilities are using wearable devices.IMPLICATIONS FOR REHABILITATIONWearable devices can track various health-related information such as physical activity levels, sleep patterns, calories intakes, and chronic health conditions.Using nationally represent data, individuals with disabilities have access and utilise wearable devices in free living setting.Compare to individuals without disabilities, individuals with disabilities are less likely to utilise wearable devices in free living setting.Further research is needed to determine the accessibility of wearable devices for individuals with disabilities and its usage in rehabilitation setting.
Collapse
Affiliation(s)
- Willie Leung
- Department of Health Sciences and Human Performance, College of Natural and Health Sciences, The University of Tampa, Tampa, FL, USA
| | - Lu Shi
- Helath and Management Policy Program, School of Social and Behavioral Health Science, College of Public Health and Human Science, 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
|
5
|
McCarthy M, Jevotovsky D, Mann D, Veerubhotla A, Muise E, Whiteson J, Rizzo JR. Implementing Remote Patient Monitoring of Physical Activity in Clinical Practice. Rehabil Nurs 2023; 48:209-215. [PMID: 37723623 PMCID: PMC10840984 DOI: 10.1097/rnj.0000000000000435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/20/2023]
Abstract
PURPOSE Remote patient monitoring (RPM) is a tool for patients to share data collected outside of office visits. RPM uses technology and the digital transmission of data to inform clinician decision-making in patient care. Using RPM to track routine physical activity is feasible to operationalize, given contemporary consumer-grade devices that can sync to the electronic health record. Objective monitoring through RPM can be more reliable than patient self-reporting for physical activity. DESIGN AND METHODS This article reports on four pilot studies that highlight the utility and practicality of RPM for physical activity monitoring in outpatient clinical care. Settings include endocrinology, cardiology, neurology, and pulmonology settings. RESULTS The four pilot use cases discussed demonstrate how RPM is utilized to monitor physical activity, a shift that has broad implications for prediction, prevention, diagnosis, and management of chronic disease and rehabilitation progress. CLINICAL RELEVANCE If RPM for physical activity is to be expanded, it will be important to consider that certain populations may face challenges when accessing digital health services. CONCLUSION RPM technology provides an opportunity for clinicians to obtain objective feedback for monitoring progress of patients in rehabilitation settings. Nurses working in rehabilitation settings may need to provide additional patient education and support to improve uptake.
Collapse
Affiliation(s)
- Margaret McCarthy
- Rory Meyers College of Nursing, New York University, New York, NY, USA
| | | | - Devin Mann
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Akhila Veerubhotla
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Jonathan Whiteson
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, New York, NY, USA
| | - John Ross Rizzo
- Department of Rehabilitation Medicine, NYU Grossman School of Medicine, New York, NY, USA
| |
Collapse
|
6
|
Qiao S, Khushf G, Li X, Zhang J, Olatosi B. Developing an ethical framework-guided instrument for assessing bias in EHR-based Big Data studies: a research protocol. BMJ Open 2023; 13:e070870. [PMID: 37591640 PMCID: PMC10441074 DOI: 10.1136/bmjopen-2022-070870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 07/03/2023] [Indexed: 08/19/2023] Open
Abstract
INTRODUCTION The emergence of Big Data health research has exponentially advanced the fields of medicine and public health but has also faced many ethical challenges. One of most worrying but still under-researched aspects of the ethical issues is the risk of potential biases in data sets (eg, electronic health records (EHR) data) as well as in the data curation and acquisition cycles. This study aims to develop, refine and pilot test an ethical framework-guided instrument for assessing bias in Big Data research using EHR data sets. METHODS AND ANALYSIS Ethical analysis and instrument development (ie, the EHR bias assessment guideline) will be implemented through an iterative process composed of literature/policy review, content analysis and interdisciplinary dialogues and discussion. The ethical framework and EHR bias assessment guideline will be iteratively refined and integrated with preliminary summaries of results in a way that informs subsequent research. We will engage data curators, end-user researchers, healthcare workers and patient representatives throughout all iterative cycles using various formats including in-depth interviews of key stakeholders, panel discussions and charrette workshops. The developed EHR bias assessment guideline will be pilot tested in an existing National Institutes of Health (NIH) funded Big Data HIV project (R01AI164947). ETHICS AND DISSEMINATION The study was approved by Institutional Review Boards at the University of South Carolina (Pro00122501). Informed consent will be provided by the participants in the in-depth interviews. Study findings will be disseminated with key stakeholders, presented at relevant workshops and academic conferences, and published in peer-reviewed journals.
Collapse
Affiliation(s)
- Shan Qiao
- Health Promotion Education and Behavior, University of South Carolina, Columbia, South Carolina, USA
| | - George Khushf
- Department of Philosophy, University of South Carolina, Columbia, South Carolina, USA
| | - Xiaoming Li
- Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - Jiajia Zhang
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, South Carolina College of Pharmacy - University of South Carolina Campus, Columbia, South Carolina, USA
| | - Bankole Olatosi
- Health Services, Policy and Management, University of South Carolina Arnold School of Public Health, Columbia, South Carolina, USA
| |
Collapse
|
7
|
Mayorga-Vega D, Casado-Robles C, Guijarro-Romero S, Viciana J. Validity of activity wristbands for estimating daily physical activity in primary schoolchildren under free-living conditions: School-Fit study. Front Public Health 2023; 11:1211237. [PMID: 37554735 PMCID: PMC10405174 DOI: 10.3389/fpubh.2023.1211237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/10/2023] [Indexed: 08/10/2023] Open
Abstract
Introduction The use of activity wristbands to monitor and promote schoolchildren's physical activity (PA) is increasingly widespread. However, their validity has not been sufficiently studied, especially among primary schoolchildren. Consequently, the main purpose was to examine the validity of the daily steps and moderate-to-vigorous PA (MVPA) scores estimated by the activity wristbands Fitbit Ace 2, Garmin Vivofit Jr 2, and the Xiaomi Mi Band 5 in primary schoolchildren under free-living conditions. Materials and methods An initial sample of 67 schoolchildren (final sample = 62; 50% females), aged 9-12 years old (mean = 10.4 ± 1.0 years), participated in the present study. Each participant wore three activity wristbands (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on his/her non-dominant wrist and a research-grade accelerometer (ActiGraph wGT3X-BT) on his/her hip as the reference standard (number of steps and time in MVPA) during the waking time of one day. Results Results showed that the validity of the daily step scores estimated by the Garmin Vivofit Jr 2 and Xiaomi Mi Band 5 were good and acceptable (e.g., MAPE = 9.6/11.3%, and lower 95% IC of ICC = 0.87/0.73), respectively, as well as correctly classified schoolchildren as meeting or not meeting the daily 10,000/12,000-step-based recommendations, obtaining excellent/good and good/acceptable results (e.g., Garmin Vivofit Jr 2, k = 0.75/0.62; Xiaomi Mi Band 5, k = 0.73/0.53), respectively. However, the Fitbit Ace 2 did not show an acceptable validity (e.g., daily steps: MAPE = 21.1%, and lower 95% IC of ICC = 0.00; step-based recommendations: k = 0.48/0.36). None of the three activity wristbands showed an adequate validity for estimating daily MVPA (e.g., MAPE = 36.6-90.3%, and lower 95% IC of ICC = 0.00-0.41) and the validity for the MVPA-based recommendation tended to be considerably lower (e.g., k = -0.03-0.54). Conclusions The activity wristband Garmin Vivofit Jr 2 obtained the best validity for monitoring primary schoolchildren's daily steps, offering a feasible alternative to the research-grade accelerometers. Furthermore, this activity wristband could be used during PA promotion programs to provide accurate feedback to primary schoolchildren to ensure their accomplishment with the PA recommendations.
Collapse
Affiliation(s)
- Daniel Mayorga-Vega
- Departamento de Didáctica de las Lenguas, las Artes y el Deporte, Facultad de Ciencias de la Educación, Universidad de Málaga, Málaga, Spain
| | | | - Santiago Guijarro-Romero
- Department of Didactic of Musical, Plastic and Corporal Expression, University of Valladolid, Valladolid, Spain
| | - Jesús Viciana
- Department of Physical Education and Sport, University of Granada, Granada, Spain
| |
Collapse
|
8
|
Grant S, Tonkin E, Craddock I, Blom A, Holmes M, Judge A, Masullo A, Perello Nieto M, Song H, Whitehouse M, Flach P, Gooberman-Hill R. Toward Enhanced Clinical Decision Support for Patients Undergoing a Hip or Knee Replacement: Focus Group and Interview Study With Surgeons. JMIR Perioper Med 2023; 6:e36172. [PMID: 37093626 PMCID: PMC10167586 DOI: 10.2196/36172] [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: 01/07/2022] [Revised: 11/14/2022] [Accepted: 02/16/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND The current assessment of recovery after total hip or knee replacement is largely based on the measurement of health outcomes through self-report and clinical observations at follow-up appointments in clinical settings. Home activity-based monitoring may improve assessment of recovery by enabling the collection of more holistic information on a continuous basis. OBJECTIVE This study aimed to introduce orthopedic surgeons to time-series analyses of patient activity data generated from a platform of sensors deployed in the homes of patients who have undergone primary total hip or knee replacement and understand the potential role of these data in postoperative clinical decision-making. METHODS Orthopedic surgeons and registrars were recruited through a combination of convenience and snowball sampling. Inclusion criteria were a minimum required experience in total joint replacement surgery specific to the hip or knee or familiarity with postoperative recovery assessment. Exclusion criteria included a lack of specific experience in the field. Of the 9 approached participants, 6 (67%) orthopedic surgeons and 3 (33%) registrars took part in either 1 of 3 focus groups or 1 of 2 interviews. Data were collected using an action-based approach in which stimulus materials (mock data visualizations) provided imaginative and creative interactions with the data. The data were analyzed using a thematic analysis approach. RESULTS Each data visualization was presented sequentially followed by a discussion of key illustrative commentary from participants, ending with a summary of key themes emerging across the focus group and interview data set. CONCLUSIONS The limitations of the evidence are as follows. The data presented are from 1 English hospital. However, all data reflect the views of surgeons following standard national approaches and training. Although convenience sampling was used, participants' background, skills, and experience were considered heterogeneous. Passively collected home monitoring data offered a real opportunity to more objectively characterize patients' recovery from surgery. However, orthopedic surgeons highlighted the considerable difficulty in navigating large amounts of complex data within short medical consultations with patients. Orthopedic surgeons thought that a proposed dashboard presenting information and decision support alerts would fit best with existing clinical workflows. From this, the following guidelines for system design were developed: minimize the risk of misinterpreting data, express a level of confidence in the data, support clinicians in developing relevant skills as time-series data are often unfamiliar, and consider the impact of patient engagement with data in the future. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1136/bmjopen-2018-021862.
Collapse
Affiliation(s)
- Sabrina Grant
- Musculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom
| | - Emma Tonkin
- Digital Health, Faculty of Engineering, Bristol, United Kingdom
| | - Ian Craddock
- Digital Health, Faculty of Engineering, Bristol, United Kingdom
| | - Ashley Blom
- Faculty of Medicine, Dentistry and Health, University of Sheffield, Sheffield, United Kingdom
| | - Michael Holmes
- Digital Health, Faculty of Engineering, Bristol, United Kingdom
| | - Andrew Judge
- Musculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom
| | | | | | - Hao Song
- Digital Health, Faculty of Engineering, Bristol, United Kingdom
| | - Michael Whitehouse
- Musculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom
| | - Peter Flach
- Intelligent Systems Laboratory, Department of Computer Science, University of Bristol, Bristol, United Kingdom
| | - Rachael Gooberman-Hill
- Musculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom
| |
Collapse
|
9
|
Christensen JC, Stanley EC, Oro EG, Carlson HB, Naveh YY, Shalita R, Teitz LS. The validity and reliability of the OneStep smartphone application under various gait conditions in healthy adults with feasibility in clinical practice. J Orthop Surg Res 2022; 17:417. [PMID: 36104792 PMCID: PMC9476593 DOI: 10.1186/s13018-022-03300-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 08/22/2022] [Indexed: 11/10/2022] Open
Abstract
Objective Primary purpose of this study was to determine the validity and reliability of the OneStep smartphone application in healthy adults. Secondary purpose was to determine the feasibility of measuring gait dysfunction, limitation in spatiotemporal characteristics, longitudinally in patients following total hip or knee arthroplasty.
Methods First objective, 20 healthy adults (mean age, 42.3 ± 19.7 years; 60% males; mean body mass index, 29.0 ± 5.2 kg/m2) underwent gait analysis under four gait conditions (self-selected gait speed, fixed gait speed at 0.8 m/s, fixed gait speed at 2.0 m/s and self-selected gait speed with dual task) for the validity and reliability of the smartphone to the motion laboratory. Reliability was determined by intraclass correlation coefficients. Validity was determined by Pearson correlations. Agreement was assessed by the Bland–Altman method. Second objective, 12 additional patients with total hip or knee arthroplasty (mean age, 58.7 ± 6.5 years; 58% males; mean body mass index, 28.9 ± 5.8 kg/m2) were measured at 2- and 10 weeks postoperatively. The smartphone application was used to evaluate change in gait dysfunction over time within the patients’ own environment using paired t test.
Results The smartphone application demonstrated moderate-to-excellent intraclass correlation coefficients for reliability between-system (ICC range, 0.56–0.99), -limb (ICC range, 0.62–0.99) and -device (ICC range, 0.61–0.96) for gait analysis of healthy adults. Pearson correlations were low-to-very high between methods (r range, 0.45–0.99). Bland–Altman analysis revealed relative underestimation of spatiotemporal variables by the smartphone application compared to the motion system. For patients following total hip or knee arthroplasty, gait analysis using the OneStep application demonstrated significant improvement (p < 0.001, Cohen’s d > 0.95) in gait dysfunction between 2- and 10 weeks postoperatively. Conclusion The smartphone application can be a valid, reliable and feasible alternative to motion laboratories in evaluating deficits in gait dysfunction in various environments and clinical settings.
Collapse
|
10
|
Variability in Physical Inactivity Responses of University Students during COVID-19 Pandemic: A Monitoring of Daily Step Counts Using a Smartphone Application. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19041958. [PMID: 35206149 PMCID: PMC8871971 DOI: 10.3390/ijerph19041958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/27/2022] [Accepted: 02/07/2022] [Indexed: 02/01/2023]
Abstract
This study investigated the changes in physical inactivity of university students during the COVID-19 pandemic, with reference to their academic calendar. We used the daily step counts recorded by a smartphone application (iPhone Health App) from April 2020 to January 2021 (287 days) for 603 participants. The data for 287 days were divided into five periods based on their academic calendar. The median value of daily step counts across each period was calculated. A k-means clustering analysis was performed to classify the 603 participants into subgroups to demonstrate the variability in the physical inactivity responses. The median daily step counts, with a 7-day moving average, dramatically decreased from 5000 to 2000 steps/day in early April. It remained at a lower level (less than 2000 steps/day) during the first semester, then increased to more than 5000 steps/day at the start of summer vacation. The clustering analysis demonstrated the variability in physical inactivity responses. The inactive students did not recover daily step counts throughout the year. Consequently, promoting physical activity is recommended for inactive university students over the course of the whole semester.
Collapse
|
11
|
Lorenzi LJ, Belo LF, Frohlich DM, Dourado VZ, Castro PC, Gomes GADO. Factors related to the adoption and adherence of physical activity mobile applications by older people: a scoping review protocol. BMJ Open 2021; 11:e052414. [PMID: 34625417 PMCID: PMC8504356 DOI: 10.1136/bmjopen-2021-052414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Ageing is a natural process marked by physiological changes and declines in functional capacity. One strategy to encourage healthy habits in older people is the use of applications on mobile devices to promote physical activity (PA). An immediate challenge is for these applications to be accessible to older people themselves, while a second challenge is to retain their interest and engagement in connection with PA itself. Therefore, the purpose of this review is to map the factors related to the adoption and adherence of PA mobile applications by older people. METHODS AND ANALYSIS Five databases will be searched where articles and reviews, available between 2010 and present, in English, Portuguese or Spanish, at full text, will be included. In addition, two additional strategies will be performed, including grey literature. The search terms adoption, adherence, factors, mobile application, PA, older people and other terms related to them will be used in the search strategy. This review will include studies that identify factors related to the adoption and adherence to PA mobile applications by people over 60 years. The selection of studies will be carried out by two reviewers in five stages: identification of studies and duplicate removal; pilot test; selection by reading abstracts; inclusion by reading the full text and search in additional sources. Disagreements will be resolved by a third reviewer. Data will be extracted using a data extraction tool. Quantitative data will be described in a narrative manner and qualitative data will be categorised through inductive thematic analysis. ETHICS AND DISSEMINATION Ethical approval is not required for this scoping review. Plans for the dissemination of the review include the presentation of the results at relevant scientific conferences and the submission and publication in significant journals.
Collapse
|
12
|
Criterion Validity of iOS and Android Applications to Measure Steps and Distance in Adults. TECHNOLOGIES 2021. [DOI: 10.3390/technologies9030055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The growing popularity of physical activity (PA) applications (apps) in recent years and the vast amounts of data that they generate present attractive possibilities for surveillance. However, measurement accuracy is indispensable when tracking PA variables to provide meaningful measures of PA. The purpose of this study was to examine the steps and distance criterion validity of freeware accelerometer-based PA smartphone apps, during incremental-intensity treadmill walking and jogging. Thirty healthy adults (25.9 ± 5.7 years) participated in this cross-sectional study. They were fitted with two smartphones (one with Android and one with iOS operating systems), each one simultaneously running four different apps (i.e., Runtastic Pedometer, Accupedo, Pacer, and Argus). They walked and jogged for 5 min at each of the predefined speeds of 4.8, 6.0, and 8.4 km/h on a treadmill, and two researchers counted every step taken during trials with a digital tally counter. Validity was evaluated by comparing each app with the criterion measure using repeated-measures analysis of variance (ANOVA), mean absolute percentage errors (MAPEs), and Bland–Altman plots. For step count, Android apps performed slightly more accurately that iOS apps; nevertheless, MAPEs were generally low for all apps (<5%) and accuracy increased at higher speeds. On the other hand, errors were significantly higher for distance estimation (>10%). The findings suggest that accelerometer-based apps are accurate tools for counting steps during treadmill walking and jogging and could be considered suitable for use as an outcome measure within a clinical trial. However, none of the examined apps was suitable for measuring distance.
Collapse
|
13
|
Gopal DP, Chetty U, O'Donnell P, Gajria C, Blackadder-Weinstein J. Implicit bias in healthcare: clinical practice, research and decision making. Future Healthc J 2021; 8:40-48. [PMID: 33791459 DOI: 10.7861/fhj.2020-0233] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Bias is the evaluation of something or someone that can be positive or negative, and implicit or unconscious bias is when the person is unaware of their evaluation. This is particularly relevant to policymaking during the coronavirus pandemic and racial inequality highlighted during the support for the Black Lives Matter movement. A literature review was performed to define bias, identify the impact of bias on clinical practice and research as well as clinical decision making (cognitive bias). Bias training could bridge the gap from the lack of awareness of bias to the ability to recognise bias in others and within ourselves. However, there are no effective debiasing strategies. Awareness of implicit bias must not deflect from wider socio-economic, political and structural barriers as well ignore explicit bias such as prejudice.
Collapse
Affiliation(s)
- Dipesh P Gopal
- Barts and The London School of Medicine and Dentistry, London, UK
| | | | | | | | | |
Collapse
|
14
|
Caputo EL, Feter N, Alberton CL, Leite JS, Rodrigues AN, Dumith SDC, Silva MCD. Reliability of a smartphone application to measure physical activity. Res Sports Med 2021; 30:264-271. [PMID: 33719802 DOI: 10.1080/15438627.2021.1899919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
The aim of this study was to evaluate how accurate is a smartphone app to measure a physical activity parameter (steps). Physical Education undergraduate students (n = 46), both male and female, were recruited. A tally counter, a validated device (Xiaomi Mi Band 2®) and My Active Life app were used to perform the steps count. Each participant took three low-intensity treadmill walks (5 km h-1), with a number of target steps (500-, 1000- and 1500-steps walk). Visual agreement analyses was performed through Bland-Altman plots. There was no significant interaction between steps walks and device during treadmill walking test (F(2,84) = 3.854; p = 0.07). Differences in steps measured by Mi Band were not different from 0 in 500-steps walk (p = 0.243) and 1000-steps walk (p = 0.350), and in My Active Life in 500-steps walk (p = 0.177) and 1500-steps walk (p = 0.221). Bland-Altman analyses indicated an acceptable agreement between My active Life app and Mi Band devices for 1000-steps walk (-359.01; 310.43) and 1500-steps walk (-572.97; 377.11). In conclusion, My Active Life app showed accuracy in measuring total steps, in longer walking activities (e.g. higher than 1000 steps), and can be used on a daily basis and in research setting.
Collapse
Affiliation(s)
- Eduardo L Caputo
- Postgraduate Program in Physical Education, Federal University of Pelotas, Pelotas, Brazil
| | - Natan Feter
- Postgraduate Program in Physical Education, Federal University of Pelotas, Pelotas, Brazil
| | - Cristine L Alberton
- Postgraduate Program in Physical Education, Federal University of Pelotas, Pelotas, Brazil
| | - Jayne S Leite
- Postgraduate Program in Public Health, Federal University of Rio Grande, Rio Grande, Brazil
| | - Alysson N Rodrigues
- Graduate Program in Computer Science, Federal University of Pelotas, Pelotas, Brazil
| | - Samuel de C Dumith
- Postgraduate Program in Public Health, Federal University of Rio Grande, Rio Grande, Brazil
| | - Marcelo C da Silva
- Postgraduate Program in Physical Education, Federal University of Pelotas, Pelotas, Brazil
| |
Collapse
|
15
|
Johnston W, Judice PB, Molina García P, Mühlen JM, Lykke Skovgaard E, Stang J, Schumann M, Cheng S, Bloch W, Brønd JC, Ekelund U, Grøntved A, Caulfield B, Ortega FB, Sardinha LB. Recommendations for determining the validity of consumer wearable and smartphone step count: expert statement and checklist of the INTERLIVE network. Br J Sports Med 2020; 55:780-793. [PMID: 33361276 PMCID: PMC8273687 DOI: 10.1136/bjsports-2020-103147] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2020] [Indexed: 01/06/2023]
Abstract
Consumer wearable and smartphone devices provide an accessible means to objectively measure physical activity (PA) through step counts. With the increasing proliferation of this technology, consumers, practitioners and researchers are interested in leveraging these devices as a means to track and facilitate PA behavioural change. However, while the acceptance of these devices is increasing, the validity of many consumer devices have not been rigorously and transparently evaluated. The Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives to develop best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice consumer wearable and smartphone step counter validation protocol. A two-step process was used to aggregate data and form a scientific foundation for the development of an optimal and feasible validation protocol: (1) a systematic literature review and (2) additional searches of the wider literature pertaining to factors that may introduce bias during the validation of these devices. The systematic literature review process identified 2897 potential articles, with 85 articles deemed eligible for the final dataset. From the synthesised data, we identified a set of six key domains to be considered during design and reporting of validation studies: target population, criterion measure, index measure, validation conditions, data processing and statistical analysis. Based on these six domains, a set of key variables of interest were identified and a 'basic' and 'advanced' multistage protocol for the validation of consumer wearable and smartphone step counters was developed. The INTERLIVE consortium recommends that the proposed protocol is used when considering the validation of any consumer wearable or smartphone step counter. Checklists have been provided to guide validation protocol development and reporting. The network also provide guidance for future research activities, highlighting the imminent need for the development of feasible alternative 'gold-standard' criterion measures for free-living validation. Adherence to these validation and reporting standards will help ensure methodological and reporting consistency, facilitating comparison between consumer devices. Ultimately, this will ensure that as these devices are integrated into standard medical care, consumers, practitioners, industry and researchers can use this technology safely and to its full potential.
Collapse
Affiliation(s)
- William Johnston
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.,School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Pedro B Judice
- Centro de Investigação em Desporto, Educação Física e Exercício e Saúde, CIDEFES, Universidade Lusófona, Lisbon, Portugal.,Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Cruz-Quebrada, Portugal
| | - Pablo Molina García
- PROFITH (PROmoting FITness and Health through physical activity) Research Group, Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
| | - Jan M Mühlen
- Institute of Cardiovascular Research and Sports Medicine, Department of Molecular and Cellular Sports Medicine, German Sport University, Cologne, Germany
| | - Esben Lykke Skovgaard
- Department of Sports Science and Clinical Biomechanics, Research Unit for Exercise Epidemiology, Centre of Research in Childhood Health, University of Southern Denmark, Odense M, Denmark
| | - Julie Stang
- Department of Sport Medicine, Norwegian School of Sport Sciences, Oslo, Norway
| | - Moritz Schumann
- Institute of Cardiovascular Research and Sports Medicine, Department of Molecular and Cellular Sports Medicine, German Sport University, Cologne, Germany.,Exercise Translational Medicine Centre, the Key Laboratory of Systems Biomedicine, Ministry of Education, and Exercise, Health and Technology Centre, Department of Physical Education, Shanghai Jiao Tong University, Shanghai, China
| | - Shulin Cheng
- Institute of Cardiovascular Research and Sports Medicine, Department of Molecular and Cellular Sports Medicine, German Sport University, Cologne, Germany.,Exercise Translational Medicine Centre, the Key Laboratory of Systems Biomedicine, Ministry of Education, and Exercise, Health and Technology Centre, Department of Physical Education, Shanghai Jiao Tong University, Shanghai, China
| | - Wilhelm Bloch
- Institute of Cardiovascular Research and Sports Medicine, Department of Molecular and Cellular Sports Medicine, German Sport University, Cologne, Germany
| | - Jan Christian Brønd
- Department of Sports Science and Clinical Biomechanics, Research Unit for Exercise Epidemiology, Centre of Research in Childhood Health, University of Southern Denmark, Odense M, Denmark
| | - Ulf Ekelund
- Department of Sport Medicine, Norwegian School of Sport Sciences, Oslo, Norway
| | - Anders Grøntved
- Department of Sports Science and Clinical Biomechanics, Research Unit for Exercise Epidemiology, Centre of Research in Childhood Health, University of Southern Denmark, Odense M, Denmark
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.,School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Francisco B Ortega
- PROFITH (PROmoting FITness and Health through physical activity) Research Group, Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
| | - Luis B Sardinha
- Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Cruz-Quebrada, Portugal
| |
Collapse
|
16
|
Wang Y, Gangwani R, Kannan L, Schenone A, Wang E, Bhatt T. Can Smartphone-Derived Step Data Predict Laboratory-Induced Real-Life Like Fall-Risk in Community- Dwelling Older Adults? Front Sports Act Living 2020; 2:73. [PMID: 33345064 PMCID: PMC7739785 DOI: 10.3389/fspor.2020.00073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 05/20/2020] [Indexed: 11/13/2022] Open
Abstract
Background: As age progresses, decline in physical function predisposes older adults to high fall-risk, especially on exposure to environmental perturbations such as slips and trips. However, there is limited evidence of association between daily community ambulation, an easily modifiable factor of physical activity (PA), and fall-risk. Smartphones, equipped with accelerometers, can quantify, and display daily ambulation-related PA simplistically in terms of number of steps. If any association between daily steps and fall-risks is established, smartphones due to its convenience and prevalence could provide health professionals with a meaningful outcome measure, in addition to existing clinical measurements, to identify older adults at high fall-risk. Objective: This study aimed to explore whether smartphone-derived step data during older adults' community ambulation alone or together with commonly used clinical fall-risk measurements could predict falls following laboratory-induced real-life like slips and trips. Relationship between step data and PA questionnaire and clinical fall-risk assessments were examined as well. Methods: Forty-nine community-dwelling older adults (age 60-90 years) completed Berg Balance Scale (BBS), Activities-specific Balance Confidence scale (ABC), Timed Up-and-Go (TUG), and Physical Activity Scale for the Elderly (PASE). One-week and 1-month smartphone steps data were retrieved. Participants' 1-year fall history was noted. All participants' fall outcomes to laboratory-induced slip-and-trip perturbations were recorded. Logistic regression was performed to identify a model that best predicts laboratory falls. Pearson correlations examined relationships between study variables. Results: A model including age, TUG, and fall history significantly predicted laboratory falls with a sensitivity of 94.3%, specificity of 58.3%, and an overall accuracy of 85.1%. Neither 1-week nor 1-month steps data could predict laboratory falls. One-month steps data significantly positively correlated with BBS (r = 0.386, p = 0.006) and ABC (r = 0.369, p = 0.012), and negatively correlated with fall history (r p = -0.293, p = 0.041). Conclusion: Older participants with fall history and higher TUG scores were more likely to fall in the laboratory. No association between smartphone steps data and laboratory fall-risk was established in our study population of healthy community-dwelling older adults which calls for further studies on varied populations. Although modest, results do reveal a relationship between steps data and functional balance deficits and fear of falls.
Collapse
Affiliation(s)
- Yiru Wang
- Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States
| | - Rachana Gangwani
- Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States.,MS Program in Rehabilitation Sciences, Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States
| | - Lakshmi Kannan
- Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States.,Ph.D. Program in Rehabilitation Sciences, Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States
| | - Alison Schenone
- Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States
| | - Edward Wang
- Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States
| | - Tanvi Bhatt
- Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States
| |
Collapse
|
17
|
Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17249314. [PMID: 33322833 PMCID: PMC7764011 DOI: 10.3390/ijerph17249314] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/26/2020] [Accepted: 12/09/2020] [Indexed: 12/29/2022]
Abstract
Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3–38.2% during overground walking, 48.2–861.2% during ADLs, and 11.2–47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.
Collapse
|
18
|
Kim J, Tam S. Data Integration by Combining Big Data and Survey Sample Data for Finite Population Inference. Int Stat Rev 2020. [DOI: 10.1111/insr.12434] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Jae‐Kwang Kim
- Department of Statistics Iowa State University Ames Iowa USA
| | - Siu‐Ming Tam
- Methodology Division, Australian Bureau of Statistics, Canberra and School of Mathematics and Statistics University of Wollongong Wollongong New South Wales Australia
| |
Collapse
|
19
|
Pliner EM, Dukes AA, Beschorner KE, Mahboobin A. Effects of Student Interests on Engagement and Performance in Biomechanics. J Appl Biomech 2020; 36:360-367. [PMID: 32963129 DOI: 10.1123/jab.2020-0029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 06/07/2020] [Accepted: 07/09/2020] [Indexed: 11/18/2022]
Abstract
There is a need for pedagogical techniques that increase student engagement among underrepresented groups in engineering. Relating engineering content to student interests, particularly through biomechanics applications, shows promise toward engaging a diverse group of students. This study investigates the effects of student interests on engagement and performance in 10th grade students enrolled in a summer program for students underrepresented in the science, technology, engineering, and mathematics fields. The authors assessed the effects of interest-tailored lectures on student engagement and performance in a 5-week program with bioengineering workshops, focusing on the delivery of biomechanics content. A total of 31 students received interest-tailored lectures (intervention) and 23 students received only generic lectures (control) in biomechanics. In addition, the authors assessed the effects of teaching method (lecture, classroom activities, and laboratory tours) on student engagement. The authors found interest-tailored lectures to significantly increase student engagement in lecture compared with generic lectures. Students that received interest-tailored lectures had an insignificant, but meaningful 5% increase in student performance. Students rated laboratory tours higher in engagement than other teaching methods. This study provides detailed examples that can directly assist student teaching and outreach in biomechanics. Furthermore, the pedagogical techniques in this study can be used to increase engagement of underrepresented students in engineering.
Collapse
|
20
|
Job M, Dottor A, Viceconti A, Testa M. Ecological Gait as a Fall Indicator in Older Adults: A Systematic Review. THE GERONTOLOGIST 2020; 60:e395-e412. [PMID: 31504484 DOI: 10.1093/geront/gnz113] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Falls represent a major threat for elders, affecting their life quality and expectancy. Clinical tests and questionnaires showed low diagnostic value with respect to fall risk. Modern sensor technology allows in-home gait assessments, with the possibility to register older adults' ecological mobility and, potentially, to improve accuracy in determining fall risk. Hence, we studied the correlation between standardized assessments and ecological gait measures, comparing their ability to identify fall risk and predict prospective falls. RESEARCH DESIGN AND METHOD A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement guidelines. RESULTS From a total of 938 studies screened, nine articles with an observational study design were included. Evidence from selected works was subcategorized in (i) correlations between ecological and clinical measures and comparative statistics of (ii) prospective fall prediction and (iii) fall risk identification. A large number of correlations were observed between single ecological gait assessments and multiple clinical fall risk evaluations. Moreover, the combination of daily-life features and clinical tests outcomes seemed to improve diagnostic accuracy in fall risk identification and fall prediction. However, it was not possible to understand the extent of this enhancement due to the high variability in models' parameters. DISCUSSION AND IMPLICATIONS Evidence suggested that sensor-based ecological assessments of gait could boost diagnostic accuracy of fall risk measurement protocols if used in combination with clinical tests. Nevertheless, further studies are needed to understand what ecological features of gait should be considered and to standardize models' definition.
Collapse
Affiliation(s)
- Mirko Job
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Campus of Savona, Italy.,Rehabilitation and Engineering Laboratory (REHElab), University of Genoa, Campus of Savona, Italy
| | - Alberto Dottor
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Campus of Savona, Italy
| | - Antonello Viceconti
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Campus of Savona, Italy
| | - Marco Testa
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Campus of Savona, Italy.,Rehabilitation and Engineering Laboratory (REHElab), University of Genoa, Campus of Savona, Italy
| |
Collapse
|
21
|
Indraratna P, Tardo D, Yu J, Delbaere K, Brodie M, Lovell N, Ooi SY. Mobile Phone Technologies in the Management of Ischemic Heart Disease, Heart Failure, and Hypertension: Systematic Review and Meta-Analysis. JMIR Mhealth Uhealth 2020; 8:e16695. [PMID: 32628615 PMCID: PMC7381017 DOI: 10.2196/16695] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 03/30/2020] [Accepted: 04/08/2020] [Indexed: 12/20/2022] Open
Abstract
Background Cardiovascular disease (CVD) remains the leading cause of death worldwide. Mobile phones have become ubiquitous in most developed societies. Smartphone apps, telemonitoring, and clinician-driven SMS allow for novel opportunities and methods in managing chronic CVD, such as ischemic heart disease, heart failure, and hypertension, and in the conduct and support of cardiac rehabilitation. Objective A systematic review was conducted using seven electronic databases, identifying all relevant randomized control trials (RCTs) featuring a mobile phone intervention (MPI) used in the management of chronic CVD. Outcomes assessed included mortality, hospitalizations, blood pressure (BP), and BMI. Methods Electronic data searches were performed using seven databases from January 2000 to June 2019. Relevant articles were reviewed and analyzed. Meta-analysis was performed using standard techniques. The odds ratio (OR) was used as a summary statistic for dichotomous variables. A random effect model was used. Results A total of 26 RCTs including 6713 patients were identified and are described in this review, and 12 RCTs were included in the meta-analysis. In patients with heart failure, MPIs were associated with a significantly lower rate of hospitalizations (244/792, 30.8% vs 287/803, 35.7%; n=1595; OR 0.77, 95% CI 0.62 to 0.97; P=.03; I2=0%). In patients with hypertension, patients exposed to MPIs had a significantly lower systolic BP (mean difference 4.3 mm Hg; 95% CI −7.8 to −0.78 mm Hg; n=2023; P=.02). Conclusions The available data suggest that MPIs may have a role as a valuable adjunct in the management of chronic CVD.
Collapse
Affiliation(s)
- Praveen Indraratna
- Department of Cardiology, Prince of Wales Hospital, Sydney, Australia.,Prince of Wales Clinical School, The University of New South Wales, Sydney, Australia
| | - Daniel Tardo
- Department of Medicine, St Vincent's Hospital, Sydney, Australia.,Faculty of Medicine, The University of Notre Dame, Sydney, Australia
| | - Jennifer Yu
- Department of Cardiology, Prince of Wales Hospital, Sydney, Australia.,Prince of Wales Clinical School, The University of New South Wales, Sydney, Australia
| | - Kim Delbaere
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, Sydney, Australia.,School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
| | - Matthew Brodie
- Falls, Balance and Injury Research Centre, Neuroscience Research Australia, Sydney, Australia.,Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, Australia
| | - Nigel Lovell
- Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, Australia
| | - Sze-Yuan Ooi
- Department of Cardiology, Prince of Wales Hospital, Sydney, Australia.,Prince of Wales Clinical School, The University of New South Wales, Sydney, Australia
| |
Collapse
|
22
|
Adamakis M. Criterion validity of wearable monitors and smartphone applications to measure physical activity energy expenditure in adolescents. SPORT SCIENCES FOR HEALTH 2020. [DOI: 10.1007/s11332-020-00654-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
|
23
|
Rigamonti L, Albrecht UV, Lutter C, Tempel M, Wolfarth B, Back DA. Potentials of Digitalization in Sports Medicine: A Narrative Review. Curr Sports Med Rep 2020; 19:157-163. [PMID: 32282462 DOI: 10.1249/jsr.0000000000000704] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Digital transformation is becoming increasingly common in modern life and sports medicine, like many other medical disciplines, it is strongly influenced and impacted by this rapidly changing field. This review aims to give a brief overview of the potential that digital technologies can have for health care providers and patients in the clinical practice of sports medicine. We will focus on mobile applications, wearables, smart devices, intelligent machines, telemedicine, artificial intelligence, big data, system interoperability, virtual reality, augmented reality, exergaming, or social networks. While some technologies are already used in current medical practice, others still have undiscovered potential. Due to the diversity and ever changing nature of this field, we will briefly review multiple areas in an attempt to give readers some general exposure to the landscape instead of a thorough, deep review of one topic. Further research will be necessary to show how digitalization applications could best be used for patient treatments.
Collapse
Affiliation(s)
- Lia Rigamonti
- Center of Sport Medicine, Department of Sport and Health Science, University of Potsdam, University Outpatient Clinic, Potsdam, GERMANY
| | - Urs-Vito Albrecht
- Hannover Medical School, Peter L Reichertz Institute for Medical Informatics, Hannover, GERMANY
| | - Christoph Lutter
- Department of Orthopedic and Trauma Surgery, Sports Orthopedics and Sports Medicine, Klinikum Bamberg, Bamberg, GERMANY
| | - Mathias Tempel
- Department of Sports Medicine, Humboldt University, Charité University Medicine Berlin, Berlin, GERMANY
| | - Bernd Wolfarth
- Department of Sports Medicine, Humboldt University, Charité University Medicine Berlin, Berlin, GERMANY
| | | | | |
Collapse
|
24
|
Affiliation(s)
- Chad Cook
- Department of Orthopaedics, Duke University, Durham, NC, USA
| | | |
Collapse
|
25
|
Bauer M, Glenn T, Geddes J, Gitlin M, Grof P, Kessing LV, Monteith S, Faurholt-Jepsen M, Severus E, Whybrow PC. Smartphones in mental health: a critical review of background issues, current status and future concerns. Int J Bipolar Disord 2020; 8:2. [PMID: 31919635 PMCID: PMC6952480 DOI: 10.1186/s40345-019-0164-x] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/24/2019] [Indexed: 02/06/2023] Open
Abstract
There has been increasing interest in the use of smartphone applications (apps) and other consumer technology in mental health care for a number of years. However, the vision of data from apps seamlessly returned to, and integrated in, the electronic medical record (EMR) to assist both psychiatrists and patients has not been widely achieved, due in part to complex issues involved in the use of smartphone and other consumer technology in psychiatry. These issues include consumer technology usage, clinical utility, commercialization, and evolving consumer technology. Technological, legal and commercial issues, as well as medical issues, will determine the role of consumer technology in psychiatry. Recommendations for a more productive direction for the use of consumer technology in psychiatry are provided.
Collapse
Affiliation(s)
- Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany.
| | - Tasha Glenn
- ChronoRecord Association, Fullerton, CA, USA
| | - John Geddes
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Michael Gitlin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Paul Grof
- Mood Disorders Center of Ottawa, Ottawa, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Lars V Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Scott Monteith
- Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, USA
| | - Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Emanuel Severus
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany
| | - Peter C Whybrow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| |
Collapse
|
26
|
Stienen MN, Gautschi OP, Staartjes VE, Maldaner N, Sosnova M, Ho AL, Veeravagu A, Desai A, Zygourakis CC, Park J, Regli L, Ratliff JK. Reliability of the 6-minute walking test smartphone application. J Neurosurg Spine 2019; 31:786-793. [PMID: 31518975 DOI: 10.3171/2019.6.spine19559] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 06/05/2019] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Objective functional measures such as the 6-minute walking test (6WT) are increasingly applied to evaluate patients with degenerative diseases of the lumbar spine before and after (surgical) treatment. However, the traditional 6WT is cumbersome to apply, as it requires specialized in-hospital infrastructure and personnel. The authors set out to compare 6-minute walking distance (6WD) measurements obtained with a newly developed smartphone application (app) and those obtained with the gold-standard distance wheel (DW). METHODS The authors developed a free iOS- and Android-based smartphone app that allows patients to measure the 6WD in their home environment using global positioning system (GPS) coordinates. In a laboratory setting, the authors obtained 6WD measurements over a range of smartphone models, testing environments, and walking patterns and speeds. The main outcome was the relative measurement error (rME; in percent of 6WD), with |rME| < 7.5% defined as reliable. The intraclass correlation coefficient (ICC) for agreement between app- and DW-based 6WD was calculated. RESULTS Measurements (n = 406) were reliable with all smartphone types in neighborhood, nature, and city environments (without high buildings), as well as with unspecified, straight, continuous, and stop-and-go walking patterns (ICC = 0.97, 95% CI 0.97-0.98, p < 0.001). Measurements were unreliable indoors, in city areas with high buildings, and for predominantly rectangular walking courses. Walking speed had an influence on the ME, with worse accuracy (2% higher rME) for every kilometer per hour slower walking pace (95% CI 1.4%-2.5%, p < 0.001). Mathematical adjustment of the app-based 6WD for velocity-dependent error mitigated the rME (p < 0.011), attenuated velocity dependence (p = 0.362), and had a positive effect on accuracy (ICC = 0.98, 95% CI 0.98-0.99, p < 0.001). CONCLUSIONS The new, free, spine-specific 6WT smartphone app measures the 6WD conveniently by using GPS coordinates, empowering patients to independently determine their functional status before and after (surgical) treatment. Measurements of 6WD obtained for the target population under the recommended circumstances are highly reliable.
Collapse
Affiliation(s)
- Martin N Stienen
- 1Department of Neurosurgery, University Hospital Zurich and Clinical Neuroscience Center, University of Zurich, Switzerland
- 2Department of Neurosurgery, Stanford University Hospital and Clinics, Stanford, California
| | - Oliver P Gautschi
- 3Neurological and Spinal Surgery Centre, Hirslanden Klinik St. Anna, Lucerne; and
| | - Victor E Staartjes
- 1Department of Neurosurgery, University Hospital Zurich and Clinical Neuroscience Center, University of Zurich, Switzerland
| | - Nicolai Maldaner
- 4Department of Neurosurgery, Kantonsspital St. Gallen, Switzerland
| | - Marketa Sosnova
- 4Department of Neurosurgery, Kantonsspital St. Gallen, Switzerland
| | - Allen L Ho
- 2Department of Neurosurgery, Stanford University Hospital and Clinics, Stanford, California
| | - Anand Veeravagu
- 2Department of Neurosurgery, Stanford University Hospital and Clinics, Stanford, California
| | - Atman Desai
- 2Department of Neurosurgery, Stanford University Hospital and Clinics, Stanford, California
| | - Corinna C Zygourakis
- 2Department of Neurosurgery, Stanford University Hospital and Clinics, Stanford, California
| | - Jon Park
- 2Department of Neurosurgery, Stanford University Hospital and Clinics, Stanford, California
| | - Luca Regli
- 1Department of Neurosurgery, University Hospital Zurich and Clinical Neuroscience Center, University of Zurich, Switzerland
| | - John K Ratliff
- 2Department of Neurosurgery, Stanford University Hospital and Clinics, Stanford, California
| |
Collapse
|
27
|
Silsupadol P, Prupetkaew P, Kamnardsiri T, Lugade V. Smartphone-Based Assessment of Gait During Straight Walking, Turning, and Walking Speed Modulation in Laboratory and Free-Living Environments. IEEE J Biomed Health Inform 2019; 24:1188-1195. [PMID: 31329138 DOI: 10.1109/jbhi.2019.2930091] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
As turns and walking speed modulation are crucial for functional mobility, development of a field-based tool to objectively evaluate non-steady-state gait is essential. This study aimed to quantify spatiotemporal gait using three Android smartphones during steady-state walking, turns, and gait speed modulation in laboratory and free-living environments. In total, 24 adults ambulated along a 10-m walkway in both environments under seven conditions: straight walking, 90° left or right turn, and modulating gait speed from usual-slow, usual-fast, slow-fast, and fast-slow. Two smartphones were attached to the body, with another phone placed in a shoulder bag. Gait velocity, step time, step length, cadence, and symmetry were computed from smartphone-based tri-axial accelerometers and validated with motion capture and video, in laboratory and free-living environments, respectively. Validity was assessed using Pearson's correlation and Bland-Altman analysis. Gait velocity results revealed moderate to very high validity across all walking conditions, smartphone models, smartphone locations, and environments. Correlations for gait velocity ranged between 0.87-0.91 and 0.79-0.83 for straight walking, 0.86-0.95 and 0.86-0.89 for turning, and 0.51-0.90 and 0.67-0.89 for speed modulation trials, in laboratory and free-living environments, respectively. Step time, step length, and cadence demonstrated high to very high correlations for straight walking and turns. However, symmetry results revealed high correlations only during straight walking in the laboratory. Conditions that included slow walking showed negligible to moderate validity with a high bias. In conclusion, smartphones can be employed as field-based devices to assess steady-state walking, turning, and speed modulation across environment, model, and placement when walking faster than 0.5 m/s.
Collapse
|
28
|
Godfrey A, Brodie M, van Schooten KS, Nouredanesh M, Stuart S, Robinson L. Inertial wearables as pragmatic tools in dementia. Maturitas 2019; 127:12-17. [PMID: 31351515 DOI: 10.1016/j.maturitas.2019.05.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/22/2019] [Accepted: 05/23/2019] [Indexed: 01/02/2023]
Abstract
Dementia is a critically important issue due to its wide impact on health services as well as its personal and societal costs. Limitations exist for current dementia protocols, and there are calls to introduce modern technology that facilitates the addition of digital biomarkers to routine clinical practice. Wearable technology (wearables) are nearly ubiquitous in everyday life, gathering discrete and continuous digital data on habitual activities, but their utility in modern medicine remains low. Due to advances in data analytics, wearables are now commonly discussed as pragmatic tools to aid the diagnosis and treatment of a range of neurological disorders. Inertial sensor-based wearables are one such technology; they offer a low-cost approach to quantify routine movements that are fundamental to normal activities of daily living, most notably postural control and gait. Here, we provide a narrative review of how wearables are providing useful postural control and gait data to facilitate the capture of digital markers to aid dementia research. We outline the history of wearables, from their humble beginnings to their current use beyond the clinic, and explore their integration into modern systems, as well as the ongoing standardisation and regulatory efforts to integrate their use in clinical trials.
Collapse
Affiliation(s)
- A Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle, UK.
| | - M Brodie
- Falls Balance & Injury Research Centre, Neuroscience Research Australia, NSW, Australia; Graduate School of Biomedical Engineering, University of New South Wales, NSW, Australia
| | - K S van Schooten
- Neuroscience Research Australia, University of New South Wales, Sydney, Australia; School of Public Health and Community Medicine, University of New South Wales, NSW, Australia
| | - M Nouredanesh
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Canada
| | - S Stuart
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - L Robinson
- Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK
| |
Collapse
|
29
|
Chaput JP. Accuracy and inequalities in physical activity research. LANCET GLOBAL HEALTH 2019; 7:e185. [DOI: 10.1016/s2214-109x(18)30512-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 11/02/2018] [Indexed: 01/06/2023]
|
30
|
Lai-Hung Wong G, Sung JJY. The emerging role of big data in gastroenterology and hepatology. J Gastroenterol Hepatol 2019; 34:307-308. [PMID: 30698322 DOI: 10.1111/jgh.14606] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Grace Lai-Hung Wong
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.,Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Joseph Jao-Yiu Sung
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.,Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| |
Collapse
|
31
|
Strain T, Wijndaele K, Brage S. Physical Activity Surveillance Through Smartphone Apps and Wearable Trackers: Examining the UK Potential for Nationally Representative Sampling. JMIR Mhealth Uhealth 2019; 7:e11898. [PMID: 30694198 PMCID: PMC6371078 DOI: 10.2196/11898] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 09/19/2018] [Accepted: 09/22/2018] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Smartphones and wearable activity trackers present opportunities for large-scale physical activity (PA) surveillance that overcome some limitations of questionnaires or researcher-administered devices. However, it remains unknown whether current users of such technologies are representative of the UK population. OBJECTIVE The objective of this study was to investigate potential sociodemographic biases in individuals using, or with the potential to use, smartphone apps or wearable activity trackers for PA surveillance in the United Kingdom. METHODS We used data of adults (aged ≥16 years) from two nationally representative surveys. Using the UK-wide 2018 Ofcom Technology Tracker (unweighted N=3688), we derived mutually adjusted odds ratios (ORs; 95% CI) of personal use or household ownership of a smartwatch or fitness tracker and personal use of a smartphone by age, sex, social grade, activity- or work-limiting disability, urban or rural, and home nation. Using the 2016 Health Survey for England (unweighted N=4539), we derived mutually adjusted ORs of the use of wearable trackers or websites or smartphone apps for weight management. The explanatory variables were age, sex, PA, deprivation, and body mass index (BMI). Furthermore, we stratified these analyses by BMI, as these questions were asked in the context of weight management. RESULTS Smartphone use was the most prevalent of all technology outcomes, with 79.01% (weighted 2085/2639) of the Technology Tracker sample responding affirmatively. All other outcomes were <30% prevalent. Age ≥65 years was the strongest inverse correlate of all outcomes (eg, OR 0.03, 95% CI 0.02-0.05 for smartphone use compared with those aged 16-44 years). In addition, lower social grade and activity- or work-limiting disability were inversely associated with all Technology Tracker outcomes. Physical inactivity and male sex were inversely associated with both outcomes assessed in the Health Survey for England; higher levels of deprivation were only inversely associated with websites or phone apps used for weight management. The conclusions did not differ meaningfully in the BMI-stratified analyses, except for deprivation that showed stronger inverse associations with website or phone app use in the obese. CONCLUSIONS The sole use of PA data from wearable trackers or smartphone apps for UK national surveillance is premature, as those using these technologies are more active, younger, and more affluent than those who do not.
Collapse
Affiliation(s)
- Tessa Strain
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Katrien Wijndaele
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Søren Brage
- MRC Epidemiology Unit, Institute of Metabolic Science, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|