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Liu M, Zhou B, Rey VF, Bian S, Lukowicz P. iEat: automatic wearable dietary monitoring with bio-impedance sensing. Sci Rep 2024; 14:17873. [PMID: 39090160 PMCID: PMC11294556 DOI: 10.1038/s41598-024-67765-5] [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] [Received: 12/18/2023] [Accepted: 07/15/2024] [Indexed: 08/04/2024] Open
Abstract
Diet is an inseparable part of good health, from maintaining a healthy lifestyle for the general population to supporting the treatment of patients suffering from specific diseases. Therefore it is of great significance to be able to monitor people's dietary activity in their daily life remotely. While the traditional practices of self-reporting and retrospective analysis are often unreliable and prone to errors; sensor-based remote diet monitoring is therefore an appealing approach. In this work, we explore an atypical use of bio-impedance by leveraging its unique temporal signal patterns, which are caused by the dynamic close-loop circuit variation between a pair of electrodes due to the body-food interactions during dining activities. Specifically, we introduce iEat, a wearable impedance-sensing device for automatic dietary activity monitoring without the need for external instrumented devices such as smart utensils. By deploying a single impedance sensing channel with one electrode on each wrist, iEat can recognize food intake activities (e.g., cutting, putting food in the mouth with or without utensils, drinking, etc.) and food types from a defined category. The principle is that, at idle, iEat measures only the normal body impedance between the wrist-worn electrodes; while the subject is doing the food-intake activities, new paralleled circuits will be formed through the hand, mouth, utensils, and food, leading to consequential impedance variation. To quantitatively evaluate iEat in real-life settings, a food intake experiment was conducted in an everyday table-dining environment, including 40 meals performed by ten volunteers. With a lightweight, user-independent neural network model, iEat could detect four food intake-related activities with a macro F1 score of 86.4% and classify seven types of foods with a macro F1 score of 64.2%.
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Affiliation(s)
- Mengxi Liu
- German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany.
| | - Bo Zhou
- German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany
- Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Kaiserslautern, 67663, Germany
| | - Vitor Fortes Rey
- German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany
- Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Kaiserslautern, 67663, Germany
| | | | - Paul Lukowicz
- German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany
- Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Kaiserslautern, 67663, Germany
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Bennett JP, Cataldi D, Liu YE, Kelly NN, Quon BK, Gonzalez MC, Heymsfield SB, Shepherd JA. Variations in bioelectrical impedance devices impact raw measures comparisons and subsequent prediction of body composition using recommended estimation equations. Clin Nutr ESPEN 2024; 63:540-550. [PMID: 39047869 DOI: 10.1016/j.clnesp.2024.07.009] [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: 02/22/2024] [Revised: 07/03/2024] [Accepted: 07/09/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND & AIMS Bioelectrical impedance analysis (BIA) for body composition estimation is increasingly used in clinical and field settings to guide nutrition and training programs. Due to variations among BIA devices and the proprietary prediction equations used, studies have recommended the use of raw measures of resistance (R) and reactance (Xc) within population-specific equations to predict body composition. OBJECTIVE We compared raw measures from three BIA devices to assess inter-device variation and the impact of differences on body composition estimations. METHODS Raw R, Xc, impedance (Z) parameters were measured on a calibrated phantom and athletes using tetrapolar supine (BIASUP4), octapolar supine (BIASUP8), and octapolar standing (BIASTA8) devices. Measures of R and Xc were compared across devices and graphed using BIA vector analysis (BIVA) and raw parameters were entered into recommended athlete-specific equations for predicting fat-free mass (FFM) and appendicular lean soft tissue (ALST). Whole-body FFM and regional ALST were compared across devices and to a criterion five-compartment (5C) model and dual energy X-ray absorptiometry for ALST. RESULTS Data from 73 (23.2 ± 4.8 y) athletes were included in the analyses. Technical differences were observed between Z (range 12.2-50.1Ω) measures on the calibrated phantom. Differences in whole-body impedance were apparent due to posture (technological) and electrode placement (biological) factors. This resulted in raw measures for all three devices showing greater dehydration on BIVA compared to published norms for athletes using a separate BIA device. Compared to the 5C FFM, significant differences (p < 0.05) were observed on all three equations for BIASUP8 and BIASTA8, with constant error (CE) from -2.7 to -4.6 kg; no difference was observed for BIASUP4 or when device-specific algorithms were used. Published equations resulted in differences as large as 8.8 kg FFM among BIA devices. For ALST, even after a correction in the error of the published empirical equation, all three devices showed significant (p < 0.01) CE from -1.6 to -2.9 kg. CONCLUSIONS Raw bioimpedance measurements differ among devices due to technical, technological, and biological factors, limiting interchangeability of data across BIA systems. Professionals should be aware of these factors when purchasing systems, comparing data to published reference ranges, or when applying published empirical body composition prediction equations.
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Affiliation(s)
- Jonathan P Bennett
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA.
| | - Devon Cataldi
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Yong En Liu
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Nisa N Kelly
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Brandon K Quon
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Maria Cristina Gonzalez
- Graduate Program in Nutrition and Foods, Federal University of Pelotas, Rua Gomes Carneiro, 01- Centro, 96010-610, Pelotas, Brazil
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, Louisiana State University, 6400 Perkins Rd, Baton Rouge, LA, 70808, USA
| | - John A Shepherd
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
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Chang CY, Lenchik L, Blankemeier L, Chaudhari AS, Boutin RD. Biomarkers of Body Composition. Semin Musculoskelet Radiol 2024; 28:78-91. [PMID: 38330972 DOI: 10.1055/s-0043-1776430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
The importance and impact of imaging biomarkers has been increasing over the past few decades. We review the relevant clinical and imaging terminology needed to understand the clinical and research applications of body composition. Imaging biomarkers of bone, muscle, and fat tissues obtained with dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and ultrasonography are described.
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Affiliation(s)
- Connie Y Chang
- Division of Musculoskeletal Imaging and Intervention, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Leon Lenchik
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Louis Blankemeier
- Department of Electrical Engineering, Stanford University, Stanford, California
| | - Akshay S Chaudhari
- Department of Radiology and of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Robert D Boutin
- Department of Radiology, Stanford University School of Medicine, Stanford, California
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Bennett JP, Cataldi D, Liu YE, Kelly NN, Quon BK, Schoeller DA, Kelly T, Heymsfield SB, Shepherd JA. Development and validation of a rapid multicompartment body composition model using 3-dimensional optical imaging and bioelectrical impedance analysis. Clin Nutr 2024; 43:346-356. [PMID: 38142479 DOI: 10.1016/j.clnu.2023.12.009] [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: 05/08/2023] [Revised: 11/21/2023] [Accepted: 12/11/2023] [Indexed: 12/26/2023]
Abstract
BACKGROUND & AIMS The multicompartment approach to body composition modeling provides a more precise quantification of body compartments in healthy and clinical populations. We sought to develop and validate a simplified and accessible multicompartment body composition model using 3-dimensional optical (3DO) imaging and bioelectrical impedance analysis (BIA). METHODS Samples of adults and collegiate-aged student-athletes were recruited for model calibration. For the criterion multicompartment model (Wang-5C), participants received measures of scale weight, body volume (BV) via air displacement, total body water (TBW) via deuterium dilution, and bone mineral content (BMC) via dual energy x-ray absorptiometry. The candidate model (3DO-5C) used stepwise linear regression to derive surrogate measures of BV using 3DO, TBW using BIA, and BMC using demographics. Test-retest precision of the candidate model was assessed via root mean square error (RMSE). The 3DO-5C model was compared to criterion via mean difference, concordance correlation coefficient (CCC), and Bland-Altman analysis. This model was then validated using a separate dataset of 20 adults. RESULTS 67 (31 female) participants were used to build the 3DO-5C model. Fat-free mass (FFM) estimates from Wang-5C (60.1 ± 13.4 kg) and 3DO-5C (60.3 ± 13.4 kg) showed no significant mean difference (-0.2 ± 2.0 kg; 95 % limits of agreement [LOA] -4.3 to +3.8) and the CCC was 0.99 with a similar effect in fat mass that reflected the difference in FFM measures. In the validation dataset, the 3DO-5C model showed no significant mean difference (0.0 ± 2.5 kg; 95 % LOA -3.6 to +3.7) for FFM with almost perfect equivalence (CCC = 0.99) compared to the criterion Wang-5C. Test-retest precision (RMSE = 0.73 kg FFM) supports the use of this model for more frequent testing in order to monitor body composition change over time. CONCLUSIONS Body composition estimates provided by the 3DO-5C model are precise and accurate to criterion methods when correcting for field calibrations. The 3DO-5C approach offers a rapid, cost-effective, and accessible method of body composition assessment that can be used broadly to guide nutrition and exercise recommendations in athletic settings and clinical practice.
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Affiliation(s)
- Jonathan P Bennett
- Graduate Program in Human Nutrition, University of Hawai'i at Manoa, Agricultural Science Building, 1955 East-West Rd, Honolulu, HI, 96822, USA; Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Devon Cataldi
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Yong En Liu
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Nisa N Kelly
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Brandon K Quon
- Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA
| | - Dale A Schoeller
- Department of Nutritional Sciences, University of Wisconsin-Madison, 1415 Linden Drive, Madison, WI, 53706, USA
| | - Thomas Kelly
- Hologic Inc, 250 Campus Drive, Marlborough, MA, 01752, USA
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, Louisiana State University, 6400 Perkins Rd, Baton Rouge, LA, 70808, USA
| | - John A Shepherd
- Graduate Program in Human Nutrition, University of Hawai'i at Manoa, Agricultural Science Building, 1955 East-West Rd, Honolulu, HI, 96822, USA; Department of Epidemiology, University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA.
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Vallecillo-Bustos A, Compton AT, Swafford SH, Renna ME, Thorsen T, Stavres J, Graybeal AJ. The effect of postural orientation on body composition and total body water estimates produced by smartwatch bioelectrical impedance analysis: an intra- and inter-device evaluation. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2024; 15:89-98. [PMID: 39105154 PMCID: PMC11299788 DOI: 10.2478/joeb-2024-0010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Indexed: 08/07/2024]
Abstract
Advances in wearable technologies now allow modern smartwatches to collect body composition estimates through bioelectrical impedance techniques embedded within their design. However, this technique is susceptible to increased measurement error when postural changes alter body fluid distribution. The purpose of this study was to evaluate the effects of postural orientation on body composition and total body water (TBW) estimates produced by smartwatch bioelectrical impedance analysis (SWBIA) and determine its agreement with criterion measures. For this cross-sectional evaluation, 117 (age: 21.4±3.0 y; BMI: 25.3±5.7 kg/m2) participants (F:69, M:48) completed SWBIA measurements while in the seated, standing, and supine positions, then underwent criterion dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance spectroscopy (BIS) assessments. In the combined sample and females, body fat percent, fat mass, and fat-free mass using SWBIA were significantly different between the supine and standing positions (all p<0.001), though group level agreement with DXA was similar across positions. Supine SWBIA TBW estimates were significantly different between seated and standing estimates (all p≤0.026), but further analyses revealed that this was driven by the supine and seated differences observed in females (p=0.003). SWBIA TBW demonstrated similar group and individual level agreement with BIS across body positions with slight improvements observed during seated and supine assessments for females and males, respectively. SWBIA may demonstrate slight intra- and inter-device differences in body composition and TBW when measured across postural orientations, though further evaluations in external/clinical samples are necessary. While sex/position-specific guidelines may improve precision, these findings highlight the importance of standardized body positioning when using SWBIA.
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Affiliation(s)
| | - Abby T. Compton
- School of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA
| | - Sydney H. Swafford
- School of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA
| | - Megan E. Renna
- School of Psychology, University of Southern Mississippi, Hattiesburg, MS, USA
| | - Tanner Thorsen
- School of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA
| | - Jon Stavres
- School of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA
| | - Austin J. Graybeal
- School of Kinesiology and Nutrition, University of Southern Mississippi, Hattiesburg, MS, USA
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Cataldi D, Bennett JP, Wong MC, Quon BK, Liu YE, Kelly NN, Kelly T, Schoeller DA, Heymsfield SB, Shepherd JA. Accuracy and precision of multiple body composition methods and associations with muscle strength in athletes of varying hydration: The Da Kine Study. Clin Nutr 2024; 43:284-294. [PMID: 38104490 DOI: 10.1016/j.clnu.2023.11.040] [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: 06/20/2023] [Revised: 11/27/2023] [Accepted: 11/29/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Athletes vary in hydration status due to ongoing training regimes, diet demands, and extreme exertion. With water being one of the largest body composition compartments, its variation can cause misinterpretation of body composition assessments meant to monitor strength and training progress. In this study, we asked what accessible body composition approach could best quantify body composition in athletes with a variety of hydration levels. METHODS The Da Kine Study recruited collegiate and intramural athletes to undergo a variety of body composition assessments including air-displacement plethysmography (ADP), deuterium-oxide dilution (D2O), dual-energy X-ray absorptiometry (DXA), underwater-weighing (UWW), 3D-optical (3DO) imaging, and bioelectrical impedance (BIA). Each of these methods generated 2- or 3-compartment body composition estimates of fat mass (FM) and fat-free mass (FFM) and was compared to equivalent measures of the criterion 6-compartment model (6CM) that accounts for variance in hydration. Body composition by each method was used to predict abdominal and thigh strength, assessed by isokinetic/isometric dynamometry. RESULTS In total, 70 (35 female) athletes with a mean age of 21.8 ± 4.2 years were recruited. Percent hydration (Body Water6CM/FFM6CM) had substantial variation in both males (63-73 %) and females (58-78 %). ADP and DXA FM and FF M had moderate to substantial agreement with the 6C model (Lin's Concordance Coefficient [CCC] = 0.90-0.95) whereas the other measures had lesser agreement (CCC <0.90) with one exception of 3DO FFM in females (CCC = 0.91). All measures of FFM produced excellent precision with %CV < 1.0 %. However, FM measures in general had worse precision (% CV < 2.0 %). Increasing quartiles (significant p < 0.001 trend) of 6CM FFM resulted in increasing strength measures in males and females. Moreover, the stronger the agreement between the alternative methods to the 6CM, the more robust their correlation with strength, irrespective of hydration status. CONCLUSION The criterion 6CM showed the best association to strength regardless of the hydration status of the athletes for both males and females. Simpler methods showed high precision for both FM and FFM and those with the strongest agreement to the 6CM had the highest strength associations. SUMMARY BOX This study compared various body composition analysis methods in 70 athletes with varying states of hydration to the criterion 6-compartment model and assessed their relationship to muscle strength. The results showed that accurate and precise estimates of body composition can be determined in athletes, and a more accurate body composition measurement produces better strength estimates. The best laboratory-based techniques were air displacement plethysmography and dual-energy x-ray absorptiometry, while the commercial methods had moderate-poor agreement. Prioritizing accurate body composition assessment ensures better strength estimates in athletes.
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Affiliation(s)
- Devon Cataldi
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Jonathan P Bennett
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Michael C Wong
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Brandon K Quon
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Yong En Liu
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Nisa N Kelly
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA
| | - Thomas Kelly
- Hologic Inc, 250 Campus Dr, Marlborough, MA 01752, USA
| | - Dale A Schoeller
- Isotope Ratio Core Biotech Center and Nutritional Sciences, Henry Mall Madison, WI 53706, USA
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, 6400 Perkins Road, Baton Rouge, LA, 7080, USA
| | - John A Shepherd
- Department of Epidemiology, University of Hawaii Cancer Center, 701 Ilalo Street, Honolulu, HI, 96813, USA.
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Bioimpedance for assessing adiposity: The importance of comparisons. Am J Clin Nutr 2023; 117:639-640. [PMID: 36872024 DOI: 10.1016/j.ajcnut.2022.12.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 12/10/2023] [Indexed: 03/06/2023] Open
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Bennett JP, Liu YE, Kelly NN, Quon BK, Wong MC, McCarthy C, Heymsfield SB, Shepherd JA. Reply to Y Lu et al. Am J Clin Nutr 2023; 117:641-642. [PMID: 36872025 DOI: 10.1016/j.ajcnut.2023.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 03/06/2023] Open
Affiliation(s)
- Jonathan P Bennett
- From the Graduate Program in Human Nutrition, University of Hawai'i Manoa, Honolulu, HI, USA; The Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, HI, USA.
| | - Yong En Liu
- The Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, HI, USA
| | - Nisa N Kelly
- The Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, HI, USA
| | - Brandon K Quon
- The Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, HI, USA
| | - Michael C Wong
- From the Graduate Program in Human Nutrition, University of Hawai'i Manoa, Honolulu, HI, USA; The Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, HI, USA
| | - Cassidy McCarthy
- The Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA
| | - Steven B Heymsfield
- The Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA
| | - John A Shepherd
- From the Graduate Program in Human Nutrition, University of Hawai'i Manoa, Honolulu, HI, USA; The Department of Epidemiology, University of Hawai'i Cancer Center, Honolulu, HI, USA
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Brandner CF, Tinsley GM, Graybeal AJ. Smartwatch-based bioimpedance analysis for body composition estimation: precision and agreement with a 4-compartment model. Appl Physiol Nutr Metab 2023; 48:172-182. [PMID: 36462216 DOI: 10.1139/apnm-2022-0301] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Given that the prevalence of smartwatches has allowed them to become a hallmark in health monitoring, they are primed to provide accessible body composition estimations. The purpose of this study was to evaluate the precision and agreement of smartwatch-based bioimpedance analysis (SW-BIA) and multifrequency bioimpedance analysis (MFBIA) against a 4-compartment (4C) model criterion. A total of 186 participants (114 females) underwent body composition assessments necessary for a 4C model and SW-BIA and MFBIA. Values of total body water (TBW) from each device were compared with those obtained from bioimpedance spectroscopy. Precision was adequate though slightly lower for the smartwatch compared with other methods. No device demonstrated equivalence with the 4C model. Specifically, the SW-BIA overestimated and MFBIA underestimated body fat. MFBIA, but not SW-BIA, demonstrated equivalence for TBW. Overall error was higher for males using the smartwatch compared with females. While these findings do not invalidate the use of smartwatch-based estimates, clinicians should consider that there may be large errors relative to clinical measures. If this wearable device is intended to be used to monitor body composition change over time, these findings demonstrate the need for future research to evaluate its accuracy during follow-up testing.
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Affiliation(s)
- Caleb F Brandner
- School of Kinesiology & Nutrition, College of Education and Human Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA
| | - Grant M Tinsley
- Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX 79409, USA
| | - Austin J Graybeal
- School of Kinesiology & Nutrition, College of Education and Human Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA
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