Wearable Technology to Quantify the Nutritional Intake of Adults: Validation Study
Open accessPublished version of a preprint
Abstract
Background: Wearable and mobile sensor technologies can be useful tools in precision nutrition research and practice, but few are reliable for obtaining accurate and precise measurements of diet and nutrition. Objective: This study aimed to assess the ability of wearable technology to monitor the nutritional intake of adult participants. This paper describes the development of a reference method to validate the wristband’s estimation of daily nutritional intake of 25 free-living study participants and to evaluate the accuracy (kcal/day) and practical utility of the technology. Methods: Participants were asked to use a nutrition tracking wristband and an accompanying mobile app consistently for two 14-day test periods. A reference method was developed to validate the estimation of daily nutritional intake of participants by the wristband. The research team collaborated with a university dining facility to prepare and serve calibrated study meals and record the energy and macronutrient intake of each participant. A continuous glucose monitoring system was used to measure adherence with dietary reporting protocols, but these findings are not reported. Bland-Altman tests were used to compare the reference and test method outputs (kcal/day). Results: A total of 304 input cases were collected of daily dietary intake of participants (kcal/day) measured by both reference and test methods. The Bland-Altman analysis had a mean bias of −105 kcal/day (SD 660), with 95% limits of agreement between −1400 and 1189. The regression equation of the plot was Y=−0.3401X+1963, which was significant (P<.001), indicating a tendency for the wristband to overestimate for lower calorie intake and underestimate for higher intake. Researchers observed transient signal loss from the sensor technology of the wristband to be a major source of error in computing dietary intake among participants. Conclusions: This study documents high variability in the accuracy and utility of a wristband sensor to track nutritional intake, highlighting the need for reliable, effective measurement tools to facilitate accurate, precision-based technologies for personal dietary guidance and intervention.
AI snapshot
Written from the paper’s full text.
TL;DR
This validation study tested a wristband (GoBe2) against a calibrated dining-facility reference method for estimating daily nutritional intake in free-living adults. It found high variability and wide limits of agreement, with the wristband tending to overestimate lower calorie intake and underestimate higher intake.
Key findings
- Across 304 daily intake measurements from 24 participants, Bland-Altman analysis showed a mean bias of −105 kcal/day (SD 660), with 95% limits of agreement from −1400 to 1189 kcal/day.
- The regression equation on the Bland-Altman plot was Y=−0.3401X+1963 (P<.001), indicating systematic overestimation at lower calorie intake and underestimation at higher intake; Pearson correlation between methods was r=−0.496 (95% CI −0.576 to −0.406; P<.001).
- Transient signal loss from the wristband sensor was reported as a major source of error in computing dietary intake.
- Age, sex, and BMI classification did not significantly affect bias in multiple regression or ANOVA tests (P=.15, .18, and .12, respectively).
Methods
- Prospective validation study in free-living adults aged 18–50 years; intended sample N=25, with 304 kcal/day measurements retained from 24 participants during phase 2 after data cleaning.
- Participants used the GoBe2 wristband and app for two 14-day test periods; all meals were prepared, calibrated, served, and weighed at a UC Davis dining facility, with offsite foods self-reported and packaged foods provided when possible.
- Daily energy intake was compared between the reference method and wristband using Bland-Altman analysis; continuous glucose monitoring was used to assess adherence to dietary reporting protocols but was not incorporated into the present dataset.
Limitations
- The paper states that exact nutrient quantities in each portion could not be consistently and routinely ensured in the high-throughput dining facility, and protocol adherence was lower than anticipated: only 56% of scheduled meals were attended (565/1008).
- The paper reports form-factor limitations including required daily charging, bulky size, and poor skin contact, which caused data loss; CGM devices also became dislodged (13/72, 18%), with 2 of 24 participants (8%) having repeated displacement.
- The text makes evident additional constraints: the analyzed sample was 24 participants, two participants completed only 10 of 14 days in phase 2, offsite food consumption was discouraged but not prohibited, and CGM adherence data were not included in the validation dataset.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
7 of 17 cited sources checked · 4 supported · 0 flagged · accuracy not yet measured
- Premisecitation
The FSL's mean absolute relative difference compared with measured capillary blood glucose levels was 13.2% (95% CI 12.0% to 14.4%).
“One study showed that the FSL’s mean absolute relative difference compared with measured capillary blood glucose levels was 13.2% (95% CI 12.0% to 14.4%) [29].”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [29] source
- Findingown data
The Bland-Altman analysis comparing reference and test methods had a mean bias of −105 kcal/day (SD 660).
“The Bland-Altman analysis had a mean bias of −105 kcal/day (SD 660), with 95% limits of agreement between −1400 and 1189.”
Quote in the paperSubstantial claim - Findingown data
The 95% limits of agreement between reference and test methods were between −1400 and 1189 kcal/day.
“The Bland-Altman analysis had a mean bias of −105 kcal/day (SD 660), with 95% limits of agreement between −1400 and 1189.”
Quote in the paperSubstantial claim - Findingnone given
Transient signal loss from the sensor technology of the wristband was a major source of error in computing dietary intake among participants.
“Researchers observed transient signal loss from the sensor technology of the wristband to be a major source of error in computing dietary intake among participants.”
Quote in the paperNot a substantial claim - Findingown data
Of those with rapid glucose increases inconsistent with meal time, 68.3% (41/60) were attributed to reported bouts of exercise or other physical activity.
“Of those, 68.3% (41/60) were attributed to reported bouts of exercise or other physical activity.”
Quote in the paperNot a substantial claim
- Findingreasoning
Signal interruption occurred often and for a variety of reasons in this study, including periodic loss of contact with the skin likely depending on the user's wrist size and shape.
“Unfortunately, signal interruption occurred often and for a variety of reasons in this study; for example, periodic loss of contact with the skin was likely depending on the user’s wrist size and shape.”
Quote in the paperSubstantial claim - Findingnone given
This study documented high variability in both the utility and accuracy of a wristband sensor to track nutritional intake (kcal/day).
“This study documented high variability in both the utility and accuracy of a wristband sensor to track nutritional intake (kcal/day).”
Quote in the paperSubstantial claim - Findingown data
Repeated CGM sensor displacement was more likely to occur during physical activity (biking, gym workout, and weight lifting) and/or excessive sweating.
“which was more likely to occur during physical activity (biking, gym workout, and weight lifting) and/or excessive sweating.”
Quote in the paperNot a substantial claim - Findingown data
Pearson correlation coefficient between the 2 methods was r=−0.496 (95% CI −0.576 to −0.406; P<.001).
“Pearson correlation coefficient between the 2 methods was r=−0.496 (95% CI −0.576 to −0.406; P<.001).”
Quote in the paperSubstantial claim- Rests on an assumption: The reference method accurately measures true daily energy intake.
- Findingown data
A multiple regression analysis with participants' age, sex, and BMI classification as independent confounding variables showed no significant effects on the bias.
“A multiple regression analysis was conducted with the participants’ age, sex, and BMI classification as independent confounding variables, but no significant effects were seen on the bias.”
Quote in the paperNot a substantial claim- Rests on an assumption: The reference method accurately measures true daily energy intake.
- Findingown data
Examination of CGM data confirmed that a few participants (n=2) were likely nonadherent with dietary intake reporting protocols.
“Examination of CGM data confirmed that although a few participants (n=2) were likely nonadherent with dietary intake reporting protocols, aberrant increases in blood glucose levels could be attributed to multiple factors including exercise or other bouts of physical activity.”
Quote in the paperNot a substantial claim - Findingnone given
This study validated participants' calorie intake as recorded by the wearable device, in comparison with a reference diet.
“This study validated participants’ calorie intake as recorded by the wearable device, in comparison with a reference diet.”
Quote in the paperSubstantial claim- Rests on an assumption: The reference diet used in this study accurately represents participants' actual calorie intake.
- Findingown data
The wristband's bulky size, dimensions, and/or appearance were challenges for some users to maintain comfort and position on the arm.
“the wristband’s bulky size, dimensions, and/or appearance were challenges for some users to maintain comfort and position on the arm”
Quote in the paperSubstantial claim - Findingown data
Aberrant increases in blood glucose levels could be attributed to multiple factors including exercise or other bouts of physical activity.
“Examination of CGM data confirmed that although a few participants (n=2) were likely nonadherent with dietary intake reporting protocols, aberrant increases in blood glucose levels could be attributed to multiple factors including exercise or other bouts of physical activity.”
Quote in the paperNot a substantial claim - Findingown data
Of the remaining cases, 22.1% (60/271) had at least one event per day of rapid blood glucose increase that was inconsistent with the recorded meal time.
“Of the remaining cases, 22.1% (60/271) had at least one event per day of rapid blood glucose increase that was inconsistent with the recorded meal time.”
Quote in the paperNot a substantial claim - Findingown data
The form factor of the wristband technology was the main barrier to collecting consistent, uninterrupted data during the postprandial digestion period lasting several hours beyond each meal.
“During these pilot trials, it was observed that the form factor of the technology was the main barrier to collecting consistent, uninterrupted data during the postprandial digestion period that lasts several hours beyond each meal.”
Quote in the paperSubstantial claim - Findingown data
Some CGM devices became dislodged during the 14-day study period (13/72, 18% CGMs attached), causing complete or partial data loss.
“some devices became dislodged during the 14-day study period (13/72, 18% CGMs attached), causing complete or partial data loss.”
Quote in the paperNot a substantial claim - Findingown data
Of the 24 participants, 2 (8%) had repeated CGM sensor displacement.
“Of the 24 participants, 2 (8%) had repeated CGM sensor displacement, which was more likely to occur during physical activity (biking, gym workout, and weight lifting) and/or excessive sweating.”
Quote in the paperNot a substantial claim - Findingown data
Some challenges to using the CGM devices to collect data over continuous 14 days were related to form factor limitations.
“Some challenges to using the CGM devices to collect data over continuous 14 days were also related to form factor limitations.”
Quote in the paperNot a substantial claim - Premisecitation
Memory-based dietary assessment methods have demonstrated poor validity because of human under- or overestimation of intake and intentional or unintentional alteration of intake patterns.
“These memory-based assessment methods have demonstrated poor validity because of human under- or overestimation of intake and intentional or unintentional alteration of intake patterns [23].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] source
- Premisecitation
The rate of breakdown and net usable energy vary depending on macronutrient composition.
“The rate of breakdown and net usable energy vary depending on macronutrient composition (ie, a mixed meal high in fiber, protein, and fat will digest much more slowly than a meal high in simple carbohydrates) [25].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [25] source
- Premisenone given
Standard approaches for recording dietary intake do not account for inherent nutrient losses in absorption and metabolism.
“Standard approaches for recording dietary intake do not account for inherent nutrient losses in absorption and metabolism, the transformative processes by which food becomes usable energy for the body.”
Quote in the paperSubstantial claim - Premisereasoning
Each traditional dietary assessment method reflects the individual's perceived intake rather than an accurate measure of true intake.
“Each traditional assessment method is a reflection of the individual’s perceived intake rather than an accurate measure of true intake.”
Quote in the paperSubstantial claim - Premisereasoning
Traditional dietary assessment methods are nonfalsifiable because participant reports must be accepted as truth despite likely incongruence.
“Furthermore, such assessment methods are nonfalsifiable, as what the participant reports must be accepted as truth, despite knowledge of likely incongruence.”
Quote in the paperNot a substantial claim - Premisereasoning
Nutrient and energy quantification by nutrition facts label analysis is error prone because the FDA allows margins of error in nutrient reporting on packaged food labels.
“Even nutrient and energy quantification by way of nutrition facts label analysis is error prone, as the Food and Drug Administration allows for certain margins of error in nutrient reporting on packaged food labels.”
Quote in the paperNot a substantial claim - Interpretationown data
This study documents high variability in the accuracy and utility of a wristband sensor to track nutritional intake.
“This study documents high variability in the accuracy and utility of a wristband sensor to track nutritional intake, highlighting the need for reliable, effective measurement tools to facilitate accurate, precision-based technologies for personal dietary guidance and intervention.”
Quote in the paperSubstantial claim - Premisenone given
Interindividual differences in metabolic rate, gastrointestinal health, and previous meals consumed contribute to discrepancies between measured intake and bioavailable energy.
“Furthermore, interindividual differences in metabolic rate, gastrointestinal health, and previous meals consumed all contribute to discrepancies between measured intake and bioavailable energy.”
Quote in the paperSubstantial claim - Study design and conductnone given
In the food facility where dishes were prepared for high throughput mass consumption, the exact quantity of nutrients in each portion could not be consistently and routinely ensured using these methods alone.
“In the food facility where dishes were prepared for high throughput mass consumption, the exact quantity of nutrients in each portion could not be consistently and routinely ensured using these methods alone.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Small or tapered wrists were likely to result in inconsistent sensor contact.
“small or tapered wrists were likely to result in inconsistent sensor contact”
Quote in the paperSubstantial claim - Study design and conductown data
During the second 14-day testing period, 56% of scheduled meals (565 of 1008) were attended by the 24 participants.
“Of the 42 total study meals offered to each of the 24 participants during the second 14-day testing period (1008 total meals), 56% of the scheduled meals were attended (565 meals).”
Quote in the paperSubstantial claim - Interpretationnone given
To achieve precise detection and accurate estimation of dietary intake, the unit's sensor required adequate skin contact be maintained at all times.
“However, to achieve precise detection and accurate estimation of dietary intake, the unit’s sensor required adequate skin contact be maintained at all times.”
Quote in the paperSubstantial claim - Interpretationown data
Negative bias in the Bland-Altman analysis indicated a general underestimation of daily calorie intake by the wristband compared with the reference method.
“Negative bias in the Bland-Altman analysis indicated a general underestimation of daily calorie intake by the wristband compared with the reference method.”
Quote in the paperSubstantial claim- Rests on an assumption: The reference method accurately measures true daily energy intake.
- Interpretationreasoning
The feasibility challenges were largely attributed to limitations in the technology's form factor.
“These challenges were largely attributed to limitations in the technology’s form factor.”
Quote in the paperSubstantial claim - Interpretationown data
Data loss from poor sensor contact was a significant barrier to the technology's ability to reliably detect calorie intake.
“However, data loss from poor sensor contact was a significant barrier to the technology’s ability to reliably detect calorie intake.”
Quote in the paperSubstantial claim - Study design and conductown data
Of the 35 participants originally enrolled in phase 1, 304 measurements (kcal/day) collected from 24 participants were retained from phase 2 after data cleaning to remove missing or aberrant values.
“Of the 35 participants who were originally enrolled in phase 1 of the study, 304 measurements (kcal/day) collected from 24 participants were retained from phase 2 after data cleaning to remove missing or aberrant values.”
Quote in the paperSubstantial claim - Study design and conductown data
Protocol adherence was less than anticipated, particularly with regard to meal attendance, among students on a university campus.
“In addition, considering that the project targeted students on a university campus, protocol adherence was less than anticipated, particularly with regard to meal attendance.”
Quote in the paperSubstantial claim - Premisereasoning
CGM measurement of the body's relative physiological response to food intake can serve as a proxy to identify inconsistencies in reported intake data and blood glucose activity.
“Although CGM data do not provide a direct measure of dietary intake, its measurement of the body’s relative physiological response to food intake can serve as a proxy to identify inconsistencies in reported intake data and blood glucose activity.”
Quote in the paperSubstantial claim - Interpretationreasoning
Notable feasibility challenges were observed for free-living study participants to reliably use the GoBe2 technology to achieve accurate and precise measurements.
“notable feasibility challenges were observed for free-living study participants to reliably use the technology to achieve accurate and precise measurements.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 41
- May combine two claims. 19
Classified as probably not a substantial claim. May combine two claims.
PremisecitationDifferent people respond differently to foods and nutrients, warranting personalized approaches to nutrition interventions and services.
“It is now understood that different people respond differently to foods and nutrients, warranting personalized approaches to nutrition interventions and services [3,17-21].”
Quote in the paperSubstantial claim3 of 6 sources checked- cites [3] source
- cites [17] source
The Conclusions report substantial individual variation in lipidomic responses to omega-3 fatty acids, and the Discussion links that variation to the goal of a more personalized approach. The study concerns one nutrient intervention in 12 healthy subjects; it does not establish differing responses to foods and nutrients generally or address personalized nutrition services. (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites [18] source
- cites [19] source
- cites [20] source
- cites [21] source
Classified as probably not a substantial claim.
PremisecitationWearable and mobile sensor technologies can be useful tools in precision nutrition research and practice.
“Wearable and mobile sensor technologies can be useful tools in precision nutrition research and practice”
- Also stated in: § Introduction · plain
“mobile and wearable body sensors that enable more spatially and temporally specific measures of a broader range of phenotypic factors [4,13-15].”
Quote in the paperSubstantial claim3 of 4 sources checked- cites [4] source
- cites [13] source
- cites [14] source
The Electrochemical Biosensors section describes a wireless mouth-guard biosensor and its potential extension to wearable health and fitness monitoring. The Current Research Trends section also discusses wearable sensors that screen metabolites as indicators of health. Neither passage addresses their use in precision nutrition research or practice, and the paper does not establish the claimed role for mobile sensors. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [15] source
The Introduction describes wearable sensors as potentially useful for health monitoring, including measuring energy consumption. However, it does not discuss their use in precision nutrition research or practice, or establish the usefulness of mobile sensor technologies for that purpose. The Results (Calorie Accuracy for Different Devices) also report poor accuracy for energy-consumption measurements. (LLM, conf=0.82)
Experimental — accuracy not yet measured
- Also stated in: § Introduction · plain
Classified as probably not a substantial claim.
Findingown dataA total of 304 input cases of daily dietary intake (kcal/day) measured by both reference and test methods were collected.
“A total of 304 input cases were collected of daily dietary intake of participants (kcal/day) measured by both reference and test methods.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe regression equation of the Bland-Altman plot was Y=−0.3401X+1963, which was significant (P<.001).
“The regression equation of the plot was Y=−0.3401X+1963, which was significant (P<.001)”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataLinear regression analysis on the Bland-Altman plot revealed a regression equation of Y=−0.3401X+1963 that was significant (P<.001).
“Linear regression analysis on the Bland-Altman plot revealed a regression equation of Y=−0.3401X+1963 that was significant (P<.001).”
Quote in the paperNot a substantial claim- Rests on an assumption: The reference method accurately measures true daily energy intake.
May combine two claims.
Findingown dataAnalysis of variance tests assessing the effects of participants' age, sex, and BMI classification on bias were not significant (P=.15, .18, and .12, respectively).
“Analysis of variance tests were conducted to assess the effects of the participants’ age, sex, and BMI classification on bias, and the effects were not significant (P=.15, .18, and .12, respectively).”
Quote in the paperNot a substantial claim- Rests on an assumption: The reference method accurately measures true daily energy intake.
Classified as probably not a substantial claim. May combine two claims.
Findingnone givenThe wristband required an hour each day to obtain a full charge, and any loss of charge would disable data collection.
“In addition, the wristband required an hour each day to obtain a full charge; any loss of charge would disable data collection accordingly.”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningThe unit required an hour of charging each day, which required removal of the device, preventing the detection of calories ingested within several hours before removal.
“battery life, as the unit required an hour of charging each day, which required removal of the device, preventing the detection of calories ingested within several hours before removal”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingreasoningA skin adhesive (Skin-Tac) was useful in reinforcing the CGM attachment.
“In these cases, a skin adhesive (Skin-Tac) was useful in reinforcing the CGM attachment.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataBland-Altman analysis showed a mean bias of −105 (SD 660) kcal/day, with 95% limits of agreement between −1400 and 1189.
“As depicted in Figure 2, a Bland-Altman analysis showed a mean bias of −105 (SD 660) kcal/day, with 95% limits of agreement (LoA) between −1400 and 1189.”
Quote in the paperSubstantial claim- Rests on an assumption: The reference method accurately measures true daily energy intake.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationContinuous glucose monitors are designed to provide more precise tracking of glucose levels for diabetic patients compared with standard blood sampling methods, to more precisely guide disease management.
“For example, continuous glucose monitors are designed to provide more precise tracking of glucose levels for diabetic patients compared with standard blood sampling methods, the goal being to more precisely guide disease management [26,27].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [26] source
- cites [27] Dungan, K, 2000, Monitoring Technologies – Continuous Glucose Monitoring, Mobile Technology, Biomarkers of Glycemic Control
Not checked in the free run.
Classified as probably not a substantial claim.
Premisenone givenThe USDA Food Composition Database cannot account for inherent variability in climate, soil quality, geographic location, item ripeness, and cooking method, which may alter the nutrient composition of food.
“The United States Department of Agriculture (USDA) Food Composition Database is the gold standard for nutrient analysis; albeit comprehensive in scope, this tool cannot possibly account for inherent variability in climate, soil quality, geographic location, item ripeness, and cooking method, all of which may significantly alter the nutrient composition of food.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe wristband tends to overestimate for lower calorie intake and underestimate for higher calorie intake.
“indicating a tendency for the wristband to overestimate for lower calorie intake and underestimate for higher intake.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataBetween August 2018 and September 2018, 76 adults were screened, and 35 met the inclusion criteria for enrollment in phase 1.
“Between August 2018 and September 2018, 76 adults were screened, and 35 met the inclusion criteria for enrollment in phase 1 of the study that would take place from September 25 to October 9, 2018.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenCGM data were analyzed to assess the adherence of individual participants to reference dietary intake reporting protocols.
“CGM data were analyzed to assess the adherence of individual participants to reference dietary intake reporting protocols.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningParticipants were blinded to their personal glucose data because the FSL Pro System did not include individual readers with each unit.
“As the FSL Pro System did not include individual readers with each unit, participants were blinded to their personal glucose data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAll project staff were trained by the RD in appropriate food handling and safety, food weighing, and meal recording duties.
“All project staff were trained by the RD in appropriate food handling and safety, food weighing, and meal recording duties.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenThe plate waste from each participant was deconstructed by ingredient and individually weighed at the end of each meal period.
“The plate waste from each participant was deconstructed by ingredient and individually weighed at the end of each meal period.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataDespite a relatively small bias, the limits of agreement were wide, making the results of the comparison ambiguous.
“Despite a relatively small bias, the LoAs were wide, making the results of the comparison ambiguous.”
Quote in the paperSubstantial claim- Rests on an assumption: The reference method accurately measures true daily energy intake.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAlthough CGM was used to measure nonadherence to dietary intake reporting protocols, these data were not incorporated in the present dataset.
“Although CGM was used to measure nonadherence to dietary intake reporting protocols, these data were not incorporated in the present dataset.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningGiven numerous factors involved with intermittent data loss from the wristband technology, 2 weeks was defined as the minimum period required to gather continuous data from 25 free-living participants for validation purposes.
“Given that numerous factors were involved with intermittent data loss from the wristband technology, 2 weeks was defined as the minimum period required to gather continuous data from 25 free-living participants for validation purposes.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataThe initial sample included 20 women and 15 men, with an ethnic distribution of 38% white, 41% Asian, and 21% Hispanic, an average age of 25.3 (SD 6.4) years, and a mean BMI of 24.2 (SD 5.1) kg/m2.
“The initial sample included 20 women and 15 men, with an ethnic distribution of 38% white, 41% Asian, and 21% Hispanic, an average age of 25.3 (SD 6.4) years, and a mean BMI of 24.2 (SD 5.1) kg/m2.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThree participants dropped out during the first week of phase 1 because of time constraints that prohibited multiple visits to the campus dining facility each day.
“Three participants dropped out during the first week of phase 1 because of time constraints that prohibited multiple visits to the campus dining facility each day.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenTwo research staff were trained and designated to analyze each project menu item for energy and macronutrient content.
“Overall, 2 research staff were trained and designated to analyze each project menu item for energy (kcal) and macronutrient content.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenSeveral strategies were included in the study design to prevent data loss, such as targeting problematic cases early, checking in with participants, and monitoring sensor position daily.
“As described previously, several strategies were included in the study design to prevent data loss, such as targeting problematic cases early, checking in with participants, and monitoring sensor position daily.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenBlood samples were analyzed according to the manufacturer's instructions and quality controls by UC Davis Health System Medical Diagnostics.
“Tests were performed according to the manufacturer’s instructions and quality controls by UC Davis Health System Medical Diagnostics.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataPhase 1 was completed by 32 participants, of which 24 enrolled in phase 2.
“Phase 1 was completed by 32 participants, of which 24 enrolled in phase 2 (October 30 to November 13, 2018).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenSignificant glucose increases (>20 mg/dL per 30 min) occurring outside of specified study mealtimes or not reported in food intake diaries were flagged for further examination.
“Significant glucose increases (>20 mg/dL per 30 min) occurring outside of specified study mealtimes or not reported in food intake diaries were flagged for further examination.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataResearch staff used a third-party site (Dietitian's Cabinet) to access participants' deidentified data up to the minute to assess the frequency of contact interruptions.
“Research staff used a third-party site (Dietitian’s Cabinet) to access participants’ deidentified data up to the minute from which the frequency of contact interruptions could be assessed.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningMixed dishes were avoided to reduce error in calculating calories and macronutrients served at each meal.
“Mixed dishes were avoided to reduce error in calculating calories and macronutrients that were served at each meal.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenDuring the 14-day test period, CGM units would occasionally become detached.
“During the 14-day test period, units would occasionally become detached.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationAnthropometric measurements were conducted by the principal investigator using methods defined in the anthropometric standardization reference manual.
“All anthropometric measurements were conducted by the principal investigator (PI) using methods defined in the anthropometric standardization reference manual [28].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [28] Lohan, T, 1988, Anthropometric Standardization Reference Manual
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductreasoningMenu items were selected to serve at breakfast, lunch, and dinner using criteria of balanced macronutrients at each meal per USDA MyPlate guidelines and minimal multi-ingredient mixed dishes.
“Menu items were selected to serve to study participants at breakfast, lunch, and dinner, using the following criteria: balanced macronutrients at each meal per USDA MyPlate guidelines and minimal multi-ingredient mixed dishes (ie, no casseroles, lasagna, pizza, etc).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenContinuous blood glucose was monitored as a strategy to measure and account for nonadherence to the study's dietary intake reporting protocols.
“Continuous blood glucose was monitored as a strategy to measure and account for nonadherence to the study’s dietary intake reporting protocols.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataDuring phase 2, 2 participants completed 10 of 14 days because of scheduling conflicts and were included in the analyses.
“During phase 2, 2 participants completed 10 of 14 days because of scheduling conflicts and were included in the analyses.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataThe reference method measured dietary intake by preparing, calibrating, and serving all meals at a campus dining facility and consuming them under direct observation of a trained research team.
“A reference method was designed to measure dietary intake; all meals were prepared, calibrated, and served at a campus dining facility and consumed under the direct observation of a trained research team.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataRegression analyses indicated a tendency for the wristband to systematically overestimate for lower calorie intake and underestimate for higher intake.
“Regression analyses indicated a tendency for the wristband to systematically overestimate for lower calorie intake and underestimate for higher intake.”
Quote in the paperSubstantial claim- Rests on an assumption: The reference method accurately measures true daily energy intake.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningIt was acceptable to charge the wristband at any point during the day as long as no food had been consumed for 3 hours prior.
“It was acceptable to charge the wristband at any point during the day as long as no food had been consumed for 3 hours prior.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Interpretationown dataLonger study periods could affect adherence issues without stricter guidelines around participant meal attendance.
“Longer study periods could affect adherence issues without stricter guidelines around participant meal attendance.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningParticipants were instructed to charge the wristband fully before any meal to avoid missing food intake and its subsequent digestion.
“Participants were instructed to charge the wristband fully before any meal to avoid missing food intake and its subsequent digestion (ie, charging band in the morning before consuming food for the day).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningSeveral strategies were used to mitigate form factor challenges with the wristband.
“Several strategies were used to mitigate these challenges with the form factor.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningResearch staff downloaded data files from the participants' sensors every 2 days to minimize any data loss.
“The research staff downloaded data files from the participants’ sensors every 2 days to minimize any data loss.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningSeparating ingredients allowed staff to weigh foods more precisely and calculate energy and macronutrient profiles accordingly.
“Separating ingredients allowed the staff to weigh foods more precisely and calculate energy and macronutrient profiles accordingly.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningThe deviations in and between methods could be explained by any combination of factors including form factor limitations, nonadherence to dietary protocol, interindividual differences in measured intake versus actual nutrient absorption and metabolism, human error in calculating food intake using the USDA Database, potential deviations from the standardized recipe, inability of the USDA Databaseto
“The deviations in and between methods could be explained by any combination of the following factors:”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationEstablishing reliable adherence or compliance protocols is a widespread goal in measuring the dietary intake of human subjects.
“Establishing reliable adherence or compliance protocols is a widespread goal in measuring the dietary intake of human subjects [23].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] source
Classified as probably not a substantial claim.
InterpretationreasoningAchieving precision of reference methods for dietary intake measurement is a notable challenge.
“The researchers acknowledge that because dietary intake measurement of individuals has inherent challenges related to accuracy and variability, achieving precision of reference methods is a notable challenge.”
Quote in the paperSubstantial claim- Rests on an assumption: Dietary intake measurement of individuals has inherent challenges related to accuracy and variability.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenParticipants who had significant interruptions were targeted for individual solutions to improve sensor contact with the wrist, for example, tightening the wristband to achieve optimal sensor positioning.
“Those who had significant interruptions were targeted for individual solutions to improve sensor contact with the wrist, for example, tightening the wristband to achieve optimal sensor positioning.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenIndividual menu items were weighed and recorded using calibrated food scales, portioned using standardized tools, and served at each onsite meal.
“Individual menu items were weighed and recorded (0.0 g) using calibrated food scales, portioned using standardized tools, and served at each onsite meal.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataOf the total cases, 10.9% (33/304) were excluded because they lacked an accompanying set of complete CGM data for the 24-hour period.
“Of the total cases, 10.9% (33/304) were excluded because they lacked an accompanying set of complete CGM data for the 24-hour period.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningAny signal interruption during a meal or in the hours following it results in loss of data and underestimation of calorie intake by the wristband technology.
“Practically, any signal interruption during the meal or in the hours following it would result in loss of data and underestimation of calorie intake by the technology.”
Quote in the paperSubstantial claim
The paper relies on this without support.
Few wearable/mobile sensor technologies are reliable for obtaining accurate and precise measurements of diet and nutrition.
“Wearable and mobile sensor technologies can be useful tools in precision nutrition research and practice, but few are reliable for obtaining accurate and precise measurements of diet and nutrition.”
The paper relies on this without support.
No technologies are available that can effectively assess dietary intake directly.
“No technologies are available that can effectively assess dietary intake directly, although some methods are claiming the ability to estimate dietary intake by assessing the physiological response of the body to food intake and bioavailable energy.”
The paper relies on this without support.
The 24-hour in-patient study is the gold standard of dietary assessment.
“Currently, the gold standard of dietary assessment is the 24-hour in-patient study, yet major limitations include cost, reduced physical activity, boredom, depression, and weight loss because of reduced dietary freedom and food options deviating from one’s personal routine.”
The paper relies on this without support.
The USDA Food Composition Database is the gold standard for nutrient analysis.
“The United States Department of Agriculture (USDA) Food Composition Database is the gold standard for nutrient analysis; albeit comprehensive in scope, this tool cannot possibly account for inherent variability in climate, soil quality, geographic location, item ripeness, and cooking method, all of which may significantly alter the nutrient composition of food.”
The paper relies on this without support.
The GoBe2 wristband technology uses computational algorithms to convert bioimpedance signals into measured patterns of extracellular and intracellular fluids associated with the influx of glucose and essential nutrients into the body.
“The technology uses computational algorithms to convert bioimpedance signals into measured patterns of extracellular and intracellular fluids associated with the influx of glucose and essential nutrients into the body. From changes in fluid concentration, the technology estimates calories congruent with glucose absorption into the bloodstream.”
The paper relies on this without support.
The reference method accurately measures true daily energy intake.
“The Bland-Altman analysis was conducted to compare daily energy intake (kcal/day) estimated by both the reference method and the wristband technology.”
The paper relies on this without support.
The dietary intake reports used as reference are accurate enough despite nonadherence, because CGM nonadherence data were not incorporated.
“Although CGM was used to measure nonadherence to dietary intake reporting protocols, these data were not incorporated in the present dataset.”
The paper relies on this without support.
The reference diet used in this study accurately represents participants' actual calorie intake.
“This study validated participants’ calorie intake as recorded by the wearable device, in comparison with a reference diet.”
The paper relies on this without support.
Dietary intake measurement of individuals has inherent challenges related to accuracy and variability.
“dietary intake measurement of individuals has inherent challenges related to accuracy and variability”
The paper relies on this without support.
Accurate and precise measurements of diet and nutrition are needed.
“Tools are urgently needed to obtain accurate and precise measurements of diet and nutrition.”
The paper relies on this without support.
The app market does not offer reliable solutions for automating the quantification of dietary intake that would significantly impact individualized quality of life decisions.
“However, to date, the app market does not offer reliable solutions for automating the quantification of dietary intake that would significantly impact individualized quality of life decisions.”
The paper relies on this without support.
Validation and effectiveness testing of candidate devices are essential steps to be taken for the use of precision technologies to inform personalized diet and lifestyle guidance.
“Validation and effectiveness testing of candidate devices are essential steps to be taken for the use of precision technologies to inform personalized diet and lifestyle guidance.”
The paper relies on this without support.
Enhancing knowledge about individual phenotypes allows for more precise and predictive dietary guidance and intervention.
“Enhancing knowledge about individual phenotypes allows for more precise and predictive dietary guidance and intervention”
Cites
Personalized nutrition by prediction of glycemic responses
Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., Ben-Yacov, O., Lador, D., Avnit-Sagi, T., Lotan-Pompan, M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.001 · Cited by 2,929 · see citing works
Anthropometric Standardization Reference Manual
Lohman, T. J., Roache, A. F., & Martorell, R. (1992) · Medicine & Science in Sports & Exercise · doi:10.1249/00005768-199208000-00020 · Cited by 2,764 · cited 1× · see citing works
“All anthropometric measurements were conducted by the principal investigator (PI) using methods defined in the anthropometric standardization reference manual [28].”
MethodBiosensors and their applications – A review
Mehrotra, P. (2016) · Journal of Oral Biology and Craniofacial Research · doi:10.1016/j.jobcr.2015.12.002 · Cited by 1,526 · see citing works
Personalized medicine: Time for one-person trials
Schork, N. J. (2015) · Nature · doi:10.1038/520609a · Cited by 1,229 · see citing works
Mobile-health: A review of current state in 2015
Silva, B. M. C., Rodrigues, J. J. P. C., Díez, I. D. L. T., López-Coronado, M., & Saleem, K. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.06.003 · Cited by 876 · see citing works
Recent Advances in Biosensor Technology for Potential Applications – An Overview
Vigneshvar, S., Sudhakumari, C. C., Senthilkumaran, B., & Prakash, H. (2016) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2016.00011 · Cited by 674 · cited 1× · see citing works
“Emerging commercial and medical technologies designed to detect a person’s physiological fluctuations claim to capture more dynamic aspects of cardiometabolic health [14].”
Personalised nutrition and health
Ordovás, J. M., Ferguson, L. R., Tai, E. S., & Mathers, J. C. (2018) · BMJ · doi:10.1136/bmj.k2173 · Cited by 634 · see citing works
Xie, J., Wen, D., Liang, L., Jia, Y., Gao, L., & Lei, J. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.9754 · Cited by 310 · see citing works
Martin, C. K., Correa, J. B., Han, H., Allen, H. R., Rood, J. C., Champagne, C. M., Gunturk, B. K., & Bray, G. A. (2011) · Obesity · doi:10.1038/oby.2011.344 · Cited by 270 · see citing works
Dietary Guidelines in the 21st Century—A Time for Food
Mozaffarian, D., & Ludwig, D. S. (2010) · JAMA · doi:10.1001/jama.2010.1116 · Cited by 262 · see citing works
Bakker, G. C., van Erk, M. J., Pellis, L., Wopereis, S., Rubingh, C. M., Cnubben, N. H., Kooistra, T., van Ommen, B., & Hendriks, H. F. (2010) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.2009.28822 · Cited by 252 · see citing works
Ólafsdóttir, A. F., Attvall, S., Sandgren, U., Dahlqvist, S., Pivodic, A., Skrtic, S., Theodorsson, E., & Lind, M. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2016.0392 · Cited by 173 · see citing works
Personalising nutritional guidance for more effective behaviour change
Celis-Morales, C., Lara, J., & Mathers, J. C. (2015) · Proceedings of the Nutrition Society · doi:10.1017/s0029665114001633 · Cited by 130 · see citing works
Archer, E., Marlow, M. L., & Lavie, C. J. (2018) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2018.08.003 · Cited by 115 · see citing works
Individual variation in lipidomic profiles of healthy subjects in response to omega-3 Fatty acids
Nording, M. L., Yang, J., Georgi, K., Hegedus Karbowski, C., German, J. B., Weiss, R. H., Hogg, R. J., Trygg, J., Hammock, B. D., & Zivkovic, A. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076575 · Cited by 100 · see citing works
Self-citationNutrigenomics and Personalized Diets: What Will They Mean for Food?
German, J. B., Zivkovic, A. M., Dallas, D. C., & Smilowitz, J. T. (2011) · Annual Review of Food Science and Technology · doi:10.1146/annurev.food.102308.124147 · Cited by 90 · see citing works
Self-citationIdentification of Differential Responses to an Oral Glucose Tolerance Test in Healthy Adults
Morris, C., O’Grada, C., Ryan, M., Roche, H. M., Gibney, M. J., Gibney, E. R., & Brennan, L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072890 · Cited by 88 · see citing works
Wearing, Thinking, and Moving: Testing the Feasibility of Fitness Tracking with Urban Youth
Schaefer, S. E., Ching, C. C., Breen, H., & German, J. B. (2016) · American Journal of Health Education · doi:10.1080/19325037.2015.1111174 · Cited by 82 · see citing works
Self-citationParr, E. B., CAMERA, D. M., Burke, L. M., Phillips, S. M., Coffey, V. G., & Hawley, J. A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152545 · Cited by 82 · see citing works
Personalised nutrition: What makes you so special?
Betts, J. A., & Gonzalez, J. T. (2016) · Nutrition Bulletin · doi:10.1111/nbu.12238 · Cited by 78 · see citing works
Reddy, N., Verma, N., & Dungan, K. M. (2020) · MDText.com, Inc. eBooks · Cited by 68 · see citing works
Zivkovic, A. M., Wiest, M. M., Nguyen, U., Nording, M. L., Watkins, S. M., & German, J. B. (2009) · Metabolomics · doi:10.1007/s11306-008-0136-0 · Cited by 63 · see citing works
Self-citationGlasgow, R. E., Kwan, B. M., & Matlock, D. D. (2018) · Journal of Clinical and Translational Science · doi:10.1017/cts.2018.31 · Cited by 59 · see citing works
Piccolo, B. D., Keim, N. L., Fiehn, O., Adams, S. H., Loan, M. D. V., & Newman, J. W. I. (2015) · Journal of Nutrition · doi:10.3945/jn.114.201574 · Cited by 38 · see citing works
21st century toolkit for optimizing population health through precision nutrition
O'Sullivan, A., Henrick, B., Dixon, B., Barile, D., Zivkovic, A., Smilowitz, J., Lemay, D., Martin, W., German, J. B., & Schaefer, S. E. (2018) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2017.1348335 · Cited by 36 · see citing works
Self-citationWoolf, S. H., & Purnell, J. Q. (2016) · JAMA · doi:10.1001/jama.2016.4263 · Cited by 34 · see citing works
Bonn, S. E., Löf, M., Östenson, C., & Lagerros, Y. T. (2019) · BMC Public Health · doi:10.1186/s12889-019-6595-6 · Cited by 29 · see citing works
Dorn, D., Gorzelitz, J., Gangnon, R. E., Bell, D. R., Koltyn, K. F., & Cadmus‐Bertram, L. (2019) · JMIR mhealth and uhealth · doi:10.2196/13547 · Cited by 28 · see citing works
The curiously long absence of cooking in evolutionary thought
Wrangham, R. (2016) · Learning & Behavior · doi:10.3758/s13420-016-0223-4 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wearable and Mobile Sensors for Personalized Nutrition
Sempionatto, J. R., Montiel, V. R.-V., Vargas, E., Teymourian, H., & Wang, J. (2021) · ACS Sensors · doi:10.1021/acssensors.1c00553 · Cited by 237 · see citing works
- Authors
- Dimitratos, S. M., German, J. B., & Schaefer, S. E.
- Affiliations
- University of California, Davis (US)
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2020-07-22
- Volume
- 8
- Issue
- 7
- Pages
- e16405
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC7407252
- OpenAlex
- W3015373359
- Semantic Scholar
- ae3b090a7d86b117aef7149081ea4e67b983f6e8
- Cited by
- 27
- References
- 29
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 3 · 2021: 7
- FWCI
- 1.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of California, Davis
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Keywords
- wearable devices, nutritional intake, dietary assessment, wrist-worn sensors, personalized nutrition, energy intake, macronutrient intake, measurement accuracy, reference method, mobile application, continuous glucose monitoring, dietary adherence
- MeSH terms
- Wearable Electronic Devices, Adult, Eating, Humans, Reproducibility of Results
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
