This paper is in the following e-collection/theme issue:

mHealth for Wellness, Behavior Change and Prevention (2871) mHealth for Data Collection and Research (955) Wearable Devices and Sensors (888) Innovations and Technology for Healthy Eating Education (414) Quantified Self and Wellness (77) Mobile Health (mhealth) (2939) Development of Novel Medical Devices and Innovations for Existing Devices (80)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56083, first published .
The Association of Macronutrient Consumption and BMI to Exhaled Carbon Dioxide in Lumen Users: Retrospective Real-World Study

The Association of Macronutrient Consumption and BMI to Exhaled Carbon Dioxide in Lumen Users: Retrospective Real-World Study

The Association of Macronutrient Consumption and BMI to Exhaled Carbon Dioxide in Lumen Users: Retrospective Real-World Study

Authors of this article:

Shlomo Yeshurun1 Author Orcid Image ;   Tomer Cramer1 Author Orcid Image ;   Daniel Souroujon1, 2 Author Orcid Image ;   Merav Mor1 Author Orcid Image
Article Authors Cited by Tweetations (3) Metrics

    Original Paper

    1Metaflow Ltd, Tel-Aviv, Israel

    2School of Public Health, Tel Aviv University, Tel-Aviv, Israel

    Corresponding Author:

    Shlomo Yeshurun, PhD

    Metaflow Ltd

    HaArba’a St 30

    Tel-Aviv, 6473926

    Israel

    Phone: 972 37684062

    Email: shlomoyesh@gmail.com


    Background: Metabolic flexibility is the ability of the body to rapidly switch between fuel sources based on their accessibility and metabolic requirements. High metabolic flexibility is associated with improved health outcomes and a reduced risk of several metabolic disorders. Metabolic flexibility can be improved through lifestyle changes, such as increasing physical activity and eating a balanced macronutrient diet. Lumen is a small handheld device that measures metabolic fuel usage through exhaled carbon dioxide (CO2), which allows individuals to monitor their metabolic flexibility and make lifestyle changes to enhance it.

    Objective: This retrospective study aims to examine the postprandial CO2 response to meals logged by Lumen users and its relationship with macronutrient intake and BMI.

    Methods: We analyzed deidentified data from 2607 Lumen users who logged their meals and measured their exhaled CO2 before and after those meals between May 1, 2023, and October 18, 2023. A linear mixed model was fitted to test the association between macronutrient consumption, BMI, age, and gender to the postprandial CO2 response, followed by a 2-way ANOVA.

    Results: The model demonstrated significant associations (P<.001) between CO2 response after meals and both BMI and carbohydrate intake (BMI: β=–0.112, 95% CI –0.156 to –0.069; carbohydrates: β=0.046, 95% CI 0.034-0.058). In addition, a 2-way ANOVA revealed that higher carbohydrate intake resulted in a higher CO2 response compared to low carbohydrate intake (F2,2569=24.23; P<.001), and users with high BMI showed modest responses to meals compared with low BMI (F2,2569=5.88; P=.003).

    Conclusions: In this study, we show that Lumen’s CO2 response is influenced both by macronutrient consumption and BMI. The results of this study highlight a distinct pattern of reduced metabolic flexibility in users with obesity, indicating the value of Lumen for assessing postprandial metabolic flexibility.

    JMIR Mhealth Uhealth 2024;12:e56083

    doi:10.2196/56083

    Keywords

    app; applications; association; BMI; body mass index; carbohydrate; carbon dioxide; consumption; correlate; correlation; diet; dietary; exhalation; exhale; food; Lumen; macronutrient; meal; metabolic flexibility; metabolic; metabolism; mHealth; mobile health; nutrient; nutrition; nutritional; obese; obesity; postprandial; prandial; retrospective; weight 


    Background

    The presence of obesity is the leading risk factor for metabolic disorders such as type 2 diabetes (T2D) and cardiovascular diseases (CVDs), and it has been linked to a reduced life expectancy [1,2]. Metabolic syndrome represents a collection of metabolic abnormalities that includes obesity as well as insulin resistance, hypertension, and dyslipidemia [3]. Changing the macronutrient distribution, such as low-fat or low-carbohydrate diets, has been proposed for the treatment and management of metabolic syndrome [4], and they have been shown to improve several clinical features of metabolic syndrome, including weight loss and cardiovascular risk [5,6].

    Metabolic flexibility is the ability to switch between fuel sources (such as carbohydrates and fats) in response to their availability and metabolic demands, which is crucial for maintaining overall health and well-being [7,8]. Lifestyle modifications, such as exercise and a balanced diet, can therefore enhance metabolic flexibility, and high metabolic flexibility is associated with improved health outcomes, including a reduced risk of metabolic syndrome [7]. In addition, it was found to be impaired in individuals with obesity and T2D compared to lean and healthy individuals [9,10]. Assessment of metabolic flexibility can be conducted through postprandial changes (following a meal or any other insulin stimulation) in the respiratory exchange ratio (RER) from the metabolic cart [11,12], which estimates the body’s preference for macronutrient oxidation. Accordingly, several studies showed different RER responses between participants with high metabolic flexibility compared with participants with low flexibility for fasting, a high-carbohydrate diet, and a high-fat diet [13]. A recent study has shown that RER is greater after high-carbohydrate overfeeding than high-fat overfeeding, and impaired metabolic flexibility, as measured by ΔRER, is associated with greater weight gain over the following 6- and 12-month periods [14]. Furthermore, ΔRER is significantly elevated after high-carbohydrate intake compared with high-fat intake, indicating that metabolic flexibility is influenced by macronutrient composition [15]. These studies show that metabolic flexibility can be assessed based on RER from the metabolic cart.

    However, metabolic carts are expensive, time-consuming, and only available in health care laboratories [16]. Thus, a small handheld device was developed to measure metabolic fuel usage by analyzing exhaled carbon dioxide (CO2). The Lumen device is a portable breath-analyzer that measures metabolic fuel use through exhaled CO2 and was found to be in agreement with the RER measured by the metabolic cart [17]. Furthermore, Lumen was found to be able to detect different metabolic responses to low- or high-carb lifestyles through CO2 changes [18].

    A variety of mobile health apps, including Lumen, which are designed for mobility and ubiquity, have demonstrated promising results in enhancing metabolism and facilitating weight management [19]. Using real-time data tracking, these tools empower individuals to make more informed health decisions [20]. As such, mobile apps such as Lumen have the potential to make a positive difference in people’s health and well-being [21].

    Objective

    This study examined data collected from Lumen users’ exhaled CO2 measurements taken before and after logging a meal. The objective of this analysis was to investigate the postprandial response of Lumen’s CO2 measurements to meals logged by Lumen users and to understand the association of this response with macronutrient consumption as well as users’ age, gender, and BMI.


    Ethical Considerations

    This study was determined to be exempt from institutional board review (IRB) under category 2, as detailed in 45 CFR 46.104(d) and the standard operating procedure of the Biomedical Research Alliance of New York (BRANY), by the BRANY Social, Behavioral, and Educational Research (SBER) IRB on May 9, 2023 (BRANY IRB File 23-119-1476).

    Study Design

    This is a retrospective observational study based on deidentified data collected from users of the Lumen device and app.

    Participants

    Participants’ data were collected retrospectively between May 1, 2023, and October 18, 2023. All users in the analysis were aged 18 years or older. Since only 12 users who were underweight (BMI≤18.5) were found in the database, they were removed from the analysis.

    Data Sources

    As part of the onboarding process, users were required to specify their gender, date of birth, height, and weight. The use of the Lumen app includes an optional morning fasted measurement with the Lumen device, as well as recommended pre- and postmeal measurements throughout the day and a bedtime measurement before sleeping. The app provides nutritional recommendations based on the user’s personal preferences as well as their morning CO2 measurement regarding the amount of macronutrients one should consume during the day. The app also allows users to log their meals—whether they are breakfast, lunch, dinner, or snacks. The macronutrient composition of these meals is determined by the nutritional database of Nutritionix [22], which includes all nutritional data available from the United States Department of Agriculture’s (USDA) Food Composition Database, restaurant chain data, and foods added by Nutritionix’s dietitians. An example of the food log feature in the app is shown in Figure 1.

    Exhaled CO2 measurements were obtained using the Lumen device (Metaflow Ltd). The Lumen mobile app guides the user through each phase of the Lumen maneuver (inhale, breath hold, and exhale), as previously described [17].

    In this study, premeal and fasting measurements were defined as “premeal %CO2,” while postmeal and bedtime measurements were considered to be the “postmeal %CO2” measure, with a maximum of 210 minutes between pre- and postmeal measurements selected for the analysis. The relative change in %CO2 from premeal to postmeal was calculated for the final analysis. In addition, meals that were tagged as either breakfast, lunch, or dinner were selected for the analysis, while snacks were removed. Further data were excluded from the analysis using the Tukey method for outlier removal with an IQR of 1.5 for all macronutrients.

    ‎
    Figure 1. Lumen mobile app screenshots detailing the logged meals and their macronutrient distribution.

    Statistical Analysis

    Data were analyzed using Python JupyterLab (version 3.6.3; Project Jupyter), and all statistical analyses were conducted with the Python programming language, using custom scripts and the statsmodels package [23]. Figures were made with GraphPad Prism (version 10.1.0 for Windows, GraphPad Software) [24].

    A linear mixed model (LMM) was fitted in order to test the relationship between the quantity of macronutrients (carbohydrates, fats, and proteins) and personal information (gender, age, and BMI) to the outcome variable of percentage of change in %CO2 from premeal to postmeal, where users’ unique ID was adjusted for as a random effect [25]. A 2-way ANOVA was used to test the differences between different groups of statistically significant variables in the LMM. Among all the analyses performed, a 2-sided P≤.05 was considered statistically significant.


    Participants

    Overall, a total of 2607 users who completed 6671 coupled pre- and postmeal sessions from 6207 logged meals were used in the final analysis, with most users contributing only 1 session into the analysis (median 1, IQR 1-2 sessions per user).

    Descriptive

    A total of 81.97% (2137/2607) of the participants were women, and men and women did not differ in their ages or BMIs. A summary of the characteristics of the participants in the study is presented in Table 1.

    Additionally, the macronutrient composition of the meals consumed by the participants was primarily carbohydrates, followed by proteins and fats, as detailed in Table 2.

    Table 1. Sample characteristics of users (N=2607).
    CharacteristicsValues
    Gender (female), n (%)2137 (81.97)
    Age (years), mean (SD)47.6 (9.4)
    BMI (kg/m2), mean (SD)28.8 (6.0)
    Table 2. Macronutrient composition of meals (N=6207).
    ParametersValues, mean (SD)
    Carbohydrates (grams)32.5 (20.5)
    Proteins (grams)29.7 (14.7)
    Fats (grams)22.5 (13.7)

    Evaluation Outcomes

    The overall CO2 response after meal consumption at various time points is shown in Figure 2. As expected, %CO2 measured by Lumen 30 to 210 minutes following meal intake increased significantly compared to premeal measurement.

    The relationship between postprandial CO2 response and macronutrient intake, as recorded in the Lumen app and users' personal information, was examined using Linear Mixed Modeling (LMM). The users' unique ID was incorporated as a random effect in the analysis. The model revealed a statistically significant relationship between the CO2 response and the BMI of the users (P<.001), as well as their carbohydrate intake (P<.001). Lower BMI predicted a more significant increase in postprandial CO2 response, and increased carbohydrate intake predicted a greater postprandial CO2 response (Table 3). In contrast, their postprandial CO2 response was not significantly influenced by their age, gender, fat intake, or protein intake (all P>.05; Table 3).

    As the LMM analysis demonstrated significant associations between BMI, carbohydrate intake, and postprandial CO2 response, these parameters were then divided into 3 categories each. BMI was classified as healthy (18.5-25 kg/m2), overweight (25-30 kg/m2), and obese (≥30 kg/m2), while carbohydrate intake was classified as low (0-30 grams), medium (30-60 grams), and high (≥60 grams).

    To examine how the CO2 response differed between each BMI category for each carbohydrate intake class, we conducted a 2-way ANOVA. In this analysis, only the last session of each user was used to eliminate the effect within the same user’s sessions. In agreement with the results from the LMM, this analysis revealed a significant effect of users’ BMI category (F2,2569=5.88; P=.003) and level of carbohydrate intake (F2,2569=24.23; P<.001), where users with obesity tended to have a lower postprandial CO2 response than those with healthy BMI, and high consumption of carbohydrates resulted in a higher CO2 response (Figure 3). Nevertheless, users with high BMI had similar elevated CO2 response as overweight and healthy users when consuming meals high in carbohydrates.

    ‎
    Figure 2. Exhaled carbon dioxide (CO2) response to meal at different time intervals (N=6671). Results represent mean (95% CI).
    Table 3. Determinants of CO2 response to food intake (N=6671).
    Variablesβ (95% CI)z statisticsP value
    Intercept5.221 (3.242 to 7.199)5.172<.001
    BMI–0.112 (–0.156 to –0.069)–5.026<.001
    Age–0.021 (–0.049 to 0.007)–1.483.14
    Gender–0.612 (–1.324 to 0.100)–1.683.09
    Carbohydrates0.046 (0.034 to 0.058)7.496<.001
    Fat0.018 (–0.001 to 0.038)1.816.07
    Protein0.016 (–0.002 to 0.035)1.755.08
    ‎
    Figure 3. Carbon dioxide (CO2) response to different carbohydrates intake in different BMI categories (N=2607). Results represent mean (95% CI).

    Principal Findings

    This study showed how the Lumen device can detect the %CO2 response to meals with a different macronutrient composition (mixed meals). Notably, this response is influenced by the carbohydrate consumption in each meal as well as the BMI of the user. Lumen’s measured exhaled %CO2 was higher when carbohydrate intake was higher, in accordance with previous studies describing the association between RER and carbohydrates [11,14,15,18]. In addition, it reveals metabolic inflexibility in individuals with obesity as they show a reduced %CO2 response compared with healthy individuals, which again is in accordance with the literature on metabolic flexibility [8,9,26].

    Comparison With Previous Work

    A recent prospective study has shown that a high-carbohydrate diet results in an elevation of Lumen’s %CO2 compared with a low-carbohydrate diet [18]. In this retrospective analysis of Lumen users, we show similar results, as %CO2 is elevated when carbohydrate consumption is increased, which highlights Lumen’s ability to detect changes in carbohydrate intake. Although previous studies that included high-fat and high-protein consumption resulted in elevated RER [14,27,28], we did not observe this in this study. Further study with a larger sample size is warranted to check for the effect of dietary fats and proteins on exhaled %CO2.

    The duration of %CO2 elevation has shown a consistent plateau, spanning from 45 minutes postmeal to the last checkup at 210 minutes, where a subsequent decline was observed. Lumen users often analyze their postmeal breath between 60 and 120 minutes, so understanding how %CO2 responds over longer periods is challenging. Woerle et al [29] showed carbohydrate oxidation up to 360 minutes after meal consumption, long after glucose and insulin levels in the plasma peaked. Future prospective studies should address when %CO2 decreases after a mixed macronutrient meal and how it changes compared to other metabolites.

    The impact of postprandial measurement of glucose levels on metabolic disease prevention has been discussed in previous studies [30]. Novel technological advancements have made it available to consumers, mostly with recent developments in continuous glucose monitoring (CGM) [31]. Other metabolic indicators, such as insulin, lipids, metabolomics, and indirect calorimetry, have also been used to assess metabolic flexibility and were found to be predictive of body weight, metabolic syndrome, T2D, and CVDs [11,25,32,33]. As of today, CGM devices need replacing every 2 weeks and are somewhat invasive, whereas most of the other aforementioned measurements can only be performed in a laboratory setting. In contrast, the Lumen device can be used at home and measures an individual’s metabolic state with a simple breath maneuver, which was found to be in agreement with the RER measured in the metabolic cart [17]. Moreover, compared to the metabolic cart, the portability of the Lumen device enables the collection of CO2 measurements from a large number of participants after consuming mixed macronutrient meals, which provides a higher level of external validity.

    In addition to the effect of carbohydrate consumption, BMI was also found to be a key parameter in the postprandial %CO2 response, as a higher BMI resulted in a reduced response. These results are in accordance with the literature from the metabolic cart, where participants with obesity and T2D showed low ΔRER after insulin stimulation [9,10].

    In this analysis, the majority of participants were women (2137/2607, 81.97%), in line with similar nutrition and weight management studies finding that women participate 3 times more often than men [34,35]. Age and gender were both statistically insignificant in their effect on the postprandial %CO2 response in the LMM, while some studies mention that metabolic flexibility is reduced with age [36]. Although progesterone is directly associated with CO2 [37,38], our analysis did not reveal any influence of gender on metabolic flexibility, possibly since women may have been in different phases of their menstrual cycle or at menopause. Future studies should investigate the effects of gender and the menstrual cycle on Lumen’s %CO2.

    This study shows the potential of the Lumen device and mobile app to improve outcomes for people with metabolic disorders. There is increasing evidence showing that mobile health technologies can improve metabolic outcomes, in particular for lifestyle modifications in T2D and weight loss [39-41]. While most of these technologies incorporate a mobile app only, many studies have shown the benefits of an app accompanied by a device capable of providing real-time feedback on how those lifestyle modifications affect their measurements, which can improve engagement with them [42-45]. In a recent pilot clinical study, Lumen device and app usage improved several metabolic parameters in prediabetic patients after 3 months [46]. In light of these findings, the potential public health implications of mobile and ubiquitous health tools such as Lumen are noteworthy, showing great promise for addressing the epidemic of metabolic dysfunction across the globe in the near future.

    Limitations

    Despite the large data set of real-world evidence that was used in this analysis, several limitations need to be mentioned. First, due to its retrospective and observational nature, we cannot identify causal relationships between any of the variables in this study. Second, since this analysis is based on real-world data, the macronutrient consumption of each meal may be miscalculated or incomplete (recall bias), and a misuse of the device might occur as well. Moreover, the heterogeneous composition of the meals makes it difficult to determine if other factors in the meal might influence the %CO2 as well, in particular the type of carbohydrate and the fiber composition. In addition, Lumen users’ macronutrient intake might not be representative of most typical diets, as the Lumen app guides them toward a specific diet, primarily low in carbohydrate, and with most of them on a weight loss journey. As this study has specific user characteristics, caution should be exercised in interpreting these conclusions for a broader audience. Furthermore, this analysis did not consider the fasted %CO2 levels, which, together with the postprandial levels, could also be useful in the metabolic flexibility assessment [47]. Nonetheless, we believe that due to the nature of the outcome variable, which is the percentage of change from premeal to postmeal, it might have limited to no effect on the current results.

    Lastly, postprandial %CO2 might be affected by other confounding variables, including preexisting conditions; medication use; dietary restrictions; the menstrual cycle; and lifestyle factors such as stress, sleep, and physical activity, which were not controlled for in this study. Future studies, both prospective and retrospective, should aim to address these limitations, particularly through controlled prospective investigations involving diverse populations and retrospective analyses that comprehensively consider potential covariates.

    Conclusions

    In conclusion, Lumen’s ability to evaluate metabolic flexibility following mixed meals was demonstrated through this retrospective analysis of postprandial exhaled %CO2, in which increased %CO2 was specific to carbohydrate consumption but not to fat consumption. Furthermore, a moderate %CO2 response was also observed among users with a high BMI, which suggests metabolic inflexibility among this group. As such, we propose Lumen as an accessible tool allowing individuals to make informed dietary choices conducive to metabolic health.

    Acknowledgments

    This study was funded by Metaflow Ltd. The authors would like to express their thanks to all app users who contributed their data to this study and to the Lumen team for their support. We would also like to thank all the reviewers for their timely and constructive inputs to this manuscript.

    Authors' Contributions

    SY, TC, and MM led the design and conceptualization of the study. MM was the principal supervisor of the study. SY led the curation of the data and conducted data interpretation and formal analysis. SY wrote the first draft of the manuscript. TC and DS contributed to the data analysis. All authors critically revised the manuscript and gave their final approval.

    Conflicts of Interest

    MM is the chief scientific officer and the cofounder of MetaFlow Ltd. DS is a consultant at MetaFlow. SY and TC are paid employees at MetaFlow.

    1. Powell-Wiley TM, Poirier P, Burke LE, Després JP, Gordon-Larsen P, Lavie CJ, et al. Obesity and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2021;143(21):e984-e1010. [FREE Full text] [CrossRef] [Medline]
    2. Cercato C, Fonseca FA. Cardiovascular risk and obesity. Diabetol Metab Syndr. 2019;11:74. [FREE Full text] [CrossRef] [Medline]
    3. Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640-1645. [FREE Full text] [CrossRef] [Medline]
    4. Castro-Barquero S, Ruiz-León AM, Sierra-Pérez M, Estruch R, Casas R. Dietary strategies for metabolic syndrome: a comprehensive review. Nutrients. 2020;12(10):2983. [FREE Full text] [CrossRef] [Medline]
    5. Ge L, Sadeghirad B, Ball GDC, da Costa BR, Hitchcock CL, Svendrovski A, et al. Comparison of dietary macronutrient patterns of 14 popular named dietary programmes for weight and cardiovascular risk factor reduction in adults: systematic review and network meta-analysis of randomised trials. BMJ. 2020;369:m696. [FREE Full text] [CrossRef] [Medline]
    6. Danesi F, Mengucci C, Vita S, Bub A, Seifert S, Malpuech-Brugère C, et al. Unveiling the correlation between inadequate energy/macronutrient intake and clinical alterations in volunteers at risk of metabolic syndrome by a predictive model. Nutrients. 2021;13(4):1377. [FREE Full text] [CrossRef] [Medline]
    7. Smith RL, Soeters MR, Wüst RCI, Houtkooper RH. Metabolic flexibility as an adaptation to energy resources and requirements in health and disease. Endocr Rev. 2018;39(4):489-517. [FREE Full text] [CrossRef] [Medline]
    8. Goodpaster BH, Sparks LM. Metabolic flexibility in health and disease. Cell Metab. 2017;25(5):1027-1036. [FREE Full text] [CrossRef] [Medline]
    9. Kelley DE, Goodpaster B, Wing RR, Simoneau JA. Skeletal muscle fatty acid metabolism in association with insulin resistance, obesity, and weight loss. Am J Physiol. 1999;277(6):E1130-E1141. [FREE Full text] [CrossRef] [Medline]
    10. Kelley DE, Mandarino LJ. Fuel selection in human skeletal muscle in insulin resistance: a reexamination. Diabetes. 2000;49(5):677-683. [FREE Full text] [CrossRef] [Medline]
    11. Yu EA, Le NA, Stein AD. Measuring postprandial metabolic flexibility to assess metabolic health and disease. J Nutr. 2021;151(11):3284-3291. [FREE Full text] [CrossRef] [Medline]
    12. Glaves A, Díaz-Castro F, Farías J, Ramírez-Romero R, Galgani JE, Fernández-Verdejo R. Association between adipose tissue characteristics and metabolic flexibility in humans: a systematic review. Front Nutr. 2021;8:744187. [FREE Full text] [CrossRef] [Medline]
    13. Galgani JE, Fernández-Verdejo R. Pathophysiological role of metabolic flexibility on metabolic health. Obes Rev. 2021;22(2):e13131. [CrossRef] [Medline]
    14. Begaye B, Vinales KL, Hollstein T, Ando T, Walter M, Bogardus C, et al. Impaired metabolic flexibility to high-fat overfeeding predicts future weight gain in healthy adults. Diabetes. 2020;69(2):181-192. [FREE Full text] [CrossRef] [Medline]
    15. Gribok A, Leger JL, Stevens M, Hoyt R, Buller M, Rumpler W. Measuring the short-term substrate utilization response to high-carbohydrate and high-fat meals in the whole-body indirect calorimeter. Physiol Rep. 2016;4(12):e12835. [FREE Full text] [CrossRef] [Medline]
    16. Delsoglio M, Achamrah N, Berger MM, Pichard C. Indirect calorimetry in clinical practice. J Clin Med. 2019;8(9):1387. [FREE Full text] [CrossRef] [Medline]
    17. Lorenz KA, Yeshurun S, Aziz R, Ortiz-Delatorre J, Bagley JR, Mor M, et al. A handheld metabolic device (Lumen) to measure fuel utilization in healthy young adults: device validation study. Interact J Med Res. 2021;10(2):e25371. [FREE Full text] [CrossRef] [Medline]
    18. Roberts J, Dugdale-Duwell D, Lillis J, Pinto JM, Willmott A, Yeshurun S, et al. The efficacy of a home-use metabolic device (Lumen) in response to a short-term low and high carbohydrate diet in healthy volunteers. J Int Soc Sports Nutr. 2023;20(1):2185537. [FREE Full text] [CrossRef] [Medline]
    19. Ghelani DP, Moran LJ, Johnson C, Mousa A, Naderpoor N. Mobile apps for weight management: a review of the latest evidence to inform practice. Front Endocrinol (Lausanne). 2020;11:412. [FREE Full text] [CrossRef] [Medline]
    20. Kang HS, Exworthy M. Wearing the future-wearables to empower users to take greater responsibility for their health and care: scoping review. JMIR Mhealth Uhealth. 2022;10(7):e35684. [FREE Full text] [CrossRef] [Medline]
    21. Han M, Lee E. Effectiveness of mobile health application use to improve health behavior changes: a systematic review of randomized controlled trials. Healthc Inform Res. 2018;24(3):207-226. [FREE Full text] [CrossRef] [Medline]
    22. Nutrition API. Nutritionix. URL: https://www.nutritionix.com/business/api [accessed 2024-02-08]
    23. Seabold S, Perktold J. Statsmodels: econometric and statistical modeling with Python. 2010. Presented at: Proceedings of the 9th Python in Science Conference; 2010;92-96; Austin. [CrossRef]
    24. PRISM: analyze, graph and present your scientific work. GraphPad. URL: https://www.graphpad.com/ [accessed 2024-03-12]
    25. Lépine G, Tremblay-Franco M, Bouder S, Dimina L, Fouillet H, Mariotti F, et al. Investigating the postprandial metabolome after challenge tests to assess metabolic flexibility and dysregulations associated with cardiometabolic diseases. Nutrients. 2022;14(3):472. [FREE Full text] [CrossRef] [Medline]
    26. Galgani JE, Moro C, Ravussin E. Metabolic flexibility and insulin resistance. Am J Physiol Endocrinol Metab. 2008;295(5):E1009-e1017. [FREE Full text] [CrossRef] [Medline]
    27. Hollstein T, Basolo A, Ando T, Krakoff J, Piaggi P. Reduced adaptive thermogenesis during acute protein-imbalanced overfeeding is a metabolic hallmark of the human thrifty phenotype. Am J Clin Nutr. 2021;114(4):1396-1407. [FREE Full text] [CrossRef] [Medline]
    28. Flint A, Helt B, Raben A, Toubro S, Astrup A. Effects of different dietary fat types on postprandial appetite and energy expenditure. Obes Res. 2003;11(12):1449-1455. [FREE Full text] [CrossRef] [Medline]
    29. Woerle HJ, Meyer C, Dostou JM, Gosmanov NR, Islam N, Popa E, et al. Pathways for glucose disposal after meal ingestion in humans. Am J Physiol Endocrinol Metab. 2003;284(4):E716-e725. [FREE Full text] [CrossRef] [Medline]
    30. Blaak EE, Antoine JM, Benton D, Björck I, Bozzetto L, Brouns F, et al. Impact of postprandial glycaemia on health and prevention of disease. Obes Rev. 2012;13(10):923-984. [FREE Full text] [CrossRef] [Medline]
    31. Cappon G, Vettoretti M, Sparacino G, Facchinetti A. Continuous glucose monitoring sensors for diabetes management: a review of technologies and applications. Diabetes Metab J. 2019;43(4):383-397. [FREE Full text] [CrossRef] [Medline]
    32. Bermingham KM, Mazidi M, Franks PW, Maher T, Valdes AM, Linenberg I, et al. Characterisation of fasting and postprandial NMR metabolites: insights from the ZOE PREDICT 1 study. Nutrients. 2023;15(11):2638. [FREE Full text] [CrossRef] [Medline]
    33. Newman JW, Krishnan S, Borkowski K, Adams SH, Stephensen CB, Keim NL. Assessing insulin sensitivity and postprandial triglyceridemic response phenotypes with a mixed macronutrient tolerance test. Front Nutr. 2022;9:877696. [FREE Full text] [CrossRef] [Medline]
    34. Crane MM, Jeffery RW, Sherwood NE. Exploring gender differences in a randomized trial of weight loss maintenance. Am J Mens Health. 2017;11(2):369-375. [FREE Full text] [CrossRef] [Medline]
    35. Robertson C, Avenell A, Boachie C, Stewart F, Archibald D, Douglas F, et al. Should weight loss and maintenance programmes be designed differently for men? a systematic review of long-term randomised controlled trials presenting data for men and women: the ROMEO project. Obes Res Clin Pract. 2016;10(1):70-84. [FREE Full text] [CrossRef] [Medline]
    36. Houtkooper RH, Williams RW, Auwerx J. Metabolic networks of longevity. Cell. 2010;142(1):9-14. [FREE Full text] [CrossRef] [Medline]
    37. Dutton K, Blanksby BA, Morton AR. CO2 sensitivity changes during the menstrual cycle. J Appl Physiol (1985). 1989;67(2):517-522. [CrossRef] [Medline]
    38. Hadziomerović D, Moeller KT, Licht P, Hein A, Veitenhansel S, Kusmitsch M, et al. The biphasic pattern of end-expiratory carbon dioxide pressure: a method for identification of the fertile phase of the menstrual cycle. Fertil Steril. 2008;90(3):731-736. [FREE Full text] [CrossRef] [Medline]
    39. Wu X, Guo X, Zhang Z. The efficacy of mobile phone apps for lifestyle modification in diabetes: systematic review and meta-analysis. JMIR Mhealth Uhealth. 2019;7(1):e12297. [FREE Full text] [CrossRef] [Medline]
    40. Islam MM, Poly TN, Walther BA, Li YCJ. Use of mobile phone app interventions to promote weight loss: meta-analysis. JMIR Mhealth Uhealth. 2020;8(7):e17039. [FREE Full text] [CrossRef] [Medline]
    41. Alharbi NS, Alsubki N, Jones S, Khunti K, Munro N, de Lusignan S. Impact of information technology-based interventions for type 2 diabetes mellitus on glycemic control: a systematic review and meta-analysis. J Med Internet Res. 2016;18(11):e310. [FREE Full text] [CrossRef] [Medline]
    42. Kumbara AB, Iyer AK, Green CR, Jepson LH, Leone K, Layne JE, et al. Impact of a combined continuous glucose monitoring-digital health solution on glucose metrics and self-management behavior for adults with type 2 diabetes: real-world, observational study. JMIR Diabetes. 2023;8:e47638. [FREE Full text] [CrossRef] [Medline]
    43. Yost O, DeJonckheere M, Stonebraker S, Ling G, Buis L, Pop-Busui R, et al. Continuous glucose monitoring with low-carbohydrate diet coaching in adults with prediabetes: mixed methods pilot study. JMIR Diabetes. 2020;5(4):e21551. [FREE Full text] [CrossRef] [Medline]
    44. Fundoiano-Hershcovitz Y, Bacher D, Ritholz MD, Horwitz DL, Manejwala O, Goldstein P. Blood pressure monitoring as a digital health tool for improving diabetes clinical outcomes: retrospective real-world study. J Med Internet Res. 2022;24(2):e32923. [FREE Full text] [CrossRef] [Medline]
    45. Bian RR, Piatt GA, Sen A, Plegue MA, De Michele ML, Hafez D, et al. The effect of technology-mediated diabetes prevention interventions on weight: a meta-analysis. J Med Internet Res. 2017;19(3):e76. [FREE Full text] [CrossRef] [Medline]
    46. Buch A, Yeshurun S, Cramer T, Baumann A, Sencelsky Y, Sagi SZ, et al. The effects of metabolism tracker device (Lumen) usage on metabolic control in adults with prediabetes: pilot clinical trial. Obes Facts. 2023;16(1):53-61. [FREE Full text] [CrossRef] [Medline]
    47. Li-Gao R, de Mutsert R, Rensen PCN, van Klinken JB, Prehn C, Adamski J, et al. Postprandial metabolite profiles associated with type 2 diabetes clearly stratify individuals with impaired fasting glucose. Metabolomics. 2018;14(1):13. [FREE Full text] [CrossRef] [Medline]

    ‎
    BRANY: Biomedical Research Alliance of New York
    CGM: continuous glucose monitoring
    CO2: carbon dioxide
    CVD: cardiovascular disease
    IRB: institutional board review
    LMM: linear mixed model
    RER: respiratory exchange ratio
    SBER: Social, Behavioral, and Educational Research
    T2D: type 2 diabetes
    USDA: United States Department of Agriculture

    Edited by L Buis; submitted 09.01.24; peer-reviewed by A Scarry, D Kirk, P Gnaganrella; comments to author 31.01.24; revised version received 21.02.24; accepted 29.02.24; published 01.04.24.

    Copyright

    ©Shlomo Yeshurun, Tomer Cramer, Daniel Souroujon, Merav Mor. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 01.04.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.