Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children
Open accessAbstract
Importance: The use of consumer-grade wearable devices for collecting data for biomedical research may be associated with social determinants of health (SDoHs) linked to people’s understanding of and willingness to join and remain engaged in remote health studies. Objective: To examine whether demographic and socioeconomic indicators are associated with willingness to join a wearable device study and adherence to wearable data collection in children. Design, Setting, and Participants: This cohort study used wearable device usage data collected from 10 414 participants (aged 11-13 years) at the year-2 follow-up (2018-2020) of the ongoing Adolescent Brain and Cognitive Development (ABCD) Study, performed at 21 sites across the United States. Data were analyzed from November 2021 to July 2022. Main Outcomes and Measures: The 2 primary outcomes were (1) participant retention in the wearable device substudy and (2) total device wear time during the 21-day observation period. Associations between the primary end points and sociodemographic and economic indicators were examined. Results: The mean (SD) age of the 10 414 participants was 12.00 (0.72) years, with 5444 (52.3%) male participants. Overall, 1424 participants (13.7%) were Black; 2048 (19.7%), Hispanic; and 5615 (53.9%) White. Substantial differences were observed between the cohort that participated and shared wearable device data (wearable device cohort [WDC]; 7424 participants [71.3%]) compared with those who did not participate or share data (no wearable device cohort [NWDC]; 2900 participants [28.7%]). Black children were significantly underrepresented (−59%) in the WDC (847 [11.4%]) compared with the NWDC (577 [19.3%]; P < .001). In contrast, White children were overrepresented (+132%) in the WDC (4301 [57.9%]) vs the NWDC (1314 [43.9%]; P < .001). Children from low-income households (<$24 999) were significantly underrepresented in WDC (638 [8.6%]) compared with NWDC (492 [16.5%]; P < .001). Overall, Black children were retained for a substantially shorter duration (16 days; 95% CI, 14-17 days) compared with White children (21 days; 95% CI, 21-21 days; P < .001) in the wearable device substudy. In addition, total device wear time during the observation was notably different between Black vs White children (β = −43.00 hours; 95% CI, −55.11 to −30.88 hours; P < .001). Conclusions and Relevance: In this cohort study, large-scale wearable device data collected from children showed considerable differences between White and Black children in terms of enrollment and daily wear time. While wearable devices provide an opportunity for real-time, high-frequency contextual monitoring of individuals’ health, future studies should account for and address considerable representational bias in wearable data collection associated with demographic and SDoH factors.
AI snapshot
Written from the paper’s full text.
TL;DR
In a cohort of 10,414 children in the ABCD Study, willingness to enroll in a wearable-device substudy and device wear time differed significantly by race, ethnicity, and socioeconomic indicators. Black children and children from lower-SES households were less likely to participate and wore devices for less time than White children and higher-SES peers.
Key findings
- Of 10,414 children, 7424 (71.3%) had wearable device data and 2990 (28.7%) did not; Black children made up 847 (11.4%) of the wearable cohort vs 577 (19.3%) of the no-device cohort, a −59% relative difference, while White children made up 4301 (57.9%) vs 1314 (43.9%), a +132% relative difference (P < .001).
- At 75% cohort retention, Black children shared data for a shorter duration (16 days; 95% CI, 14-17 days) than White children (21 days; 95% CI, 21-21 days), and children in households earning less than $25,000 had the lowest retention (15 days; 95% CI, 14-17 days).
- Median total device wear time over the 20-day observation period was 400.7 hours (IQR, 286.4-446.0 hours); Black vs White children had significantly less wear time (β = −43.00 hours; 95% CI, −55.11 to −30.88 hours; P < .001), and lower parental education (ISCED 1-3 vs ISCED 8) was associated with −29.55 hours (95% CI, −45.95 to −13.15 hours; P < .001).
- Study site region was also associated with wear time; the Southwest had lower wear time than the Midwest (β = −24.1 hours; 95% CI, −43.77 to −4.43 hours; P = .02).
Methods
- Secondary cohort study using the ABCD Study 4.0 data release, including 10,414 children at the Year 2 follow-up; participants were offered a Fitbit Charge HR 2 and asked to wear it continuously for up to 3 weeks, excluding bathing or water activities.
- Retention was assessed with Kaplan-Meier curves and log-rank tests, using time to retain 75% of the cohort within the 21-day observation period.
- Wear time was approximated from per-minute heart rate data, excluding day 1 and using up to 20 days; associations with demographic and socioeconomic factors were tested with mixed-effects regression models including random intercepts for recruitment site and enrollment quarter.
Limitations
- The paper states that wear time was approximated from per-minute heart rate values, which may be an imperfect proxy affected by motion artifacts; heart rate sensors can also be affected by skin tone, diabetes/obesity, and gender-related cutaneous blood flow differences.
- The paper states that the devices were not certified for children younger than 13 years, and most participants were 11 to 12 years old at data collection.
- The paper states that 28.7% of the Year 2 cohort had no wearable device data and it was unclear how many opted out versus were unable to participate due to site-specific availability or not being mailed a device during COVID-19; evident from the text, the observational design cannot establish causality.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 23 cited sources checked · 0 supported · 0 flagged
- Findingown data
Children from lower socioeconomic status households wore devices for significantly less time than children from higher socioeconomic status households.
“Black children and those from lower socioeconomic status households were less likely to participate and wore devices for significantly less time than White children and those from higher socioeconomic status households, respectively.”
Quote in the paperSubstantial claim- Rests on an assumption: The heart-rate-based proxy for device wear time measures wear time comparably across demographic and socioeconomic groups.
- Findingown data
Children from lower socioeconomic status households were less likely to participate in the wearable device substudy than children from higher socioeconomic status households.
“Black children and those from lower socioeconomic status households were less likely to participate and wore devices for significantly less time than White children and those from higher socioeconomic status households, respectively.”
Quote in the paperSubstantial claim- Rests on an assumption: Not sharing device data (the no wearable device cohort) reflects unwillingness to participate rather than technical or logistical failure.
- Findingown data
In this cohort of 10 414 children, there was a statistically significant association between participants' sociodemographic characteristics and willingness to enroll and engage in a wearable device study.
“In this cohort study of 10 414 children, there was a statistically significant association between participants’ sociodemographic characteristics and willingness to enroll and engage in a wearable device study.”
Quote in the paperSubstantial claim- Rests on an assumption: Not sharing device data (the no wearable device cohort) reflects unwillingness to participate rather than technical or logistical failure.
- Findingown data
Study sites in the western region of the United States had the highest device wear times (West vs Midwest: β = 4.07 hours; 95% CI, −16.46 to 24.59 hours; P < .001).
“whereas sites in the western region of the US had the highest wear times (West vs Midwest: β = 4.07 hours [95% CI, −16.46 to 24.59 hours]; P < .001)”
Quote in the paperNot a substantial claim- Rests on an assumption: The heart-rate-based proxy for device wear time measures wear time comparably across demographic and socioeconomic groups.
- Findingown data
Study sites in the southwestern region of the United States had lower device wear times than the Midwest (β = −24.1 hours; 95% CI, −43.77 to −4.43 hours; P = .02).
“Notably, study sites in the southwestern region of the United States had lower wear times (Southwest vs Midwest: β = −24.1 hours [95% CI: −43.77 to −4.43 hours]; P = .02)”
Quote in the paperNot a substantial claim- Rests on an assumption: The heart-rate-based proxy for device wear time measures wear time comparably across demographic and socioeconomic groups.
- Findingown data
At 75% cohort retention, Black children shared their data for a significantly shorter duration (16 days; 95% CI, 14-17 days; P < .001) compared with White children (21 days; 95% CI, 21-21 days; P < .001).
“At 75% cohort retention, Black children shared their data for a significantly shorter duration (16 days, 95% CI, 14-17 days; P < .001) compared with children from other racial and ethnic groups (White children: 21 days; 95% CI, 21-21 days; P < .001; range across groups, 18-21 days)”
Quote in the paperSubstantial claim- Rests on an assumption: The heart-rate-based proxy for device wear time measures wear time comparably across demographic and socioeconomic groups.
- Findingown data
Children living in households with incomes less than $25 000 had the lowest retention, at 15 days (95% CI, 14-17 days).
“Children living in households with incomes less than $25 000 had the lowest retention, at 15 days (95% CI, 14-17 days)”
Quote in the paperSubstantial claim- Rests on an assumption: The heart-rate-based proxy for device wear time measures wear time comparably across demographic and socioeconomic groups.
- Findingown data
Children whose parents' completed education level was between ISCED 1 to 3 were retained for a significantly shorter period (17 days; 95% CI, 15-18 days; P < .001) compared with children whose parents had completed an ISCED 5 level of education (20 days; 95% CI, 19-20 days).
“Children whose parents completed education level was between ISCED 1 to 3 (at most, upper high school) were retained for a significantly shorter period (17 days; 95% CI, 15-18 days; P < .001) compared with children whose parents had completed an ISCED 5 level of education (associate’s degree equivalent in the United States) (20 days; 95% CI, 19-20 days).”
Quote in the paperSubstantial claim- Rests on an assumption: The heart-rate-based proxy for device wear time measures wear time comparably across demographic and socioeconomic groups.
- Findingown data
The wearable device cohort had a significantly higher relative proportion of White children (4301 [57.9%]) than the no wearable device cohort (1314 [43.9%]; P < .001).
“In contrast, the WDC had a significantly higher (+132%) relative proportion of White children (4301 [57.9%]) than the NWDC (1314 [43.9%]; P < .001).”
Quote in the paperNot a substantial claim- Rests on an assumption: Not sharing device data (the no wearable device cohort) reflects unwillingness to participate rather than technical or logistical failure.
- Findingown data
Household income less than $25 000 was associated with 25.51 fewer hours of device wear time compared with household income of $50 000-$74 999 (95% CI, −40.73 to −10.28 hours; P = .001).
“<25 000 | −25.51 (−40.73 to −10.28) | .001”
Quote in the paperSubstantial claim- Rests on an assumption: The heart-rate-based proxy for device wear time measures wear time comparably across demographic and socioeconomic groups.
- Findingown data
A significantly lower proportion of children in the wearable device cohort (638 [8.6%]) were from households with income less than $24 999 compared with the no wearable device cohort (492 [16.5%]).
“Furthermore, a significantly lower proportion of children in the WDC (638 [8.6%]) were from households with income less than $24 999 compared with the NWDC (492 [16.5%]).”
Quote in the paperNot a substantial claim- Rests on an assumption: Not sharing device data (the no wearable device cohort) reflects unwillingness to participate rather than technical or logistical failure.
- Findingown data
Device wear time was substantially lower for the population recruited during the COVID-19 pandemic than before the pandemic period.
“In addition, device wear time was substantially lower for the population recruited during the COVID-19 pandemic than before the pandemic period (Figure 3C).”
Quote in the paperNot a substantial claim- Rests on an assumption: The heart-rate-based proxy for device wear time measures wear time comparably across demographic and socioeconomic groups.
- Findingown data
Black children wore their devices for significantly less time than White children (β = −43.00 hours; 95% CI, −55.11 to −30.88 hours; P < .001).
“However, participants from racial and ethnic minority groups wore their devices significantly less than their White counterparts (Black vs White children: β = −43.00 hours [95% CI, −55.11 to −30.88 hours]; P < .001)”
Quote in the paperSubstantial claim- Rests on an assumption: The heart-rate-based proxy for device wear time measures wear time comparably across demographic and socioeconomic groups.
- Findingown data
Hispanic or Latino children had a significantly lower relative proportion in the wearable device cohort (1354 [18.2%]) than in the no wearable device cohort (694 [23.2%]; P < .001).
“Similar significant differences in relative proportion for Hispanic or Latino children were observed across the 2 cohorts (WDC, 1354 [18.2%]; NWDC, 694 [23.2%]; P < .001).”
Quote in the paperNot a substantial claim- Rests on an assumption: Not sharing device data (the no wearable device cohort) reflects unwillingness to participate rather than technical or logistical failure.
- Findingown data
Parental education of ISCED 1-3 was associated with 29.55 fewer hours of device wear time compared with ISCED 8 (95% CI, −45.95 to −13.15 hours; P < .001).
“ISCED 1-3 | −29.55 (−45.95 to −13.15) | <.001”
Quote in the paperSubstantial claim- Rests on an assumption: The heart-rate-based proxy for device wear time measures wear time comparably across demographic and socioeconomic groups.
- Findingown data
A notably lower proportion of parents whose children were in the wearable device cohort (1093 [14.7%]) had reached higher than an International Standard Classification of Education level of 3 compared with the no wearable device cohort (705 [23.6%]).
“Similarly, a notably lower proportion of parents whose children were in the WDC (1093 [14.7%]) had reached higher than an International Standard Classification of Education level of 3 (equivalent to grades 10-12 of high school) compared with the NWDC (705 [23.6%]).”
Quote in the paperNot a substantial claim- Rests on an assumption: Not sharing device data (the no wearable device cohort) reflects unwillingness to participate rather than technical or logistical failure.
- Findingown data
Black children were less likely to participate in the wearable device substudy than White children.
“Black children and those from lower socioeconomic status households were less likely to participate and wore devices for significantly less time than White children and those from higher socioeconomic status households, respectively.”
- Also stated in: § Wearable Device Data Collection · plain
“There was a significantly lower (−59%) relative proportion of Black children in the WDC (847 [11.4%]) than in the NWDC (577 [19.3%]; P < .001).”
Quote in the paperSubstantial claim- Rests on an assumption: Not sharing device data (the no wearable device cohort) reflects unwillingness to participate rather than technical or logistical failure.
- Also stated in: § Wearable Device Data Collection · plain
- Findingown data
Wear time was associated with external factors, such as recruitment site.
“(3) wear time was associated with external factors, such as recruitment site;”
Quote in the paperNot a substantial claim - Findingown data
A significantly lower proportion of racial and ethnic minority youths enrolled into the wearable device substudy compared with White youths.
“Specifically, (1) a significantly lower proportion of racial and ethnic minority youths enrolled into the wearable device substudy compared with White youths;”
Quote in the paperSubstantial claim - Findingown data
The study found significant differences in data collection associated with demographic and socioeconomic indicators.
“Our findings using more than 100 000 days of wearable data from one of the largest and most diverse wearable device deployments among more than 10 000 children nationwide show significant differences in data collection association with demographic and socioeconomic indicators.”
Quote in the paperSubstantial claim - Findingnone given
The COVID-19 pandemic was associated with device wear time.
“(4) the COVID-19 pandemic was associated with device wear time.”
Quote in the paperNot a substantial claim - Interpretationown data
Without factoring in the broader social determinants of health that may affect individual and group experiences and participation in research, inequities in data collection using wearable technologies may continue to exist, especially for youths belonging to racial and ethnic minority groups.
“The findings of this study suggest that without factoring in the broader social determinants of health that may affect individual and group experiences and participation in research, inequities in data collection using wearable technologies may continue to exist, especially for youths belonging to racial and ethnic minority groups.”
Quote in the paperSubstantial claim - Premisecitation
There is limited evidence on whether long-term health monitoring using wearable devices will yield reliable and equitable real-world data across the diverse socioeconomic and demographic population spectrum, particularly among racial and ethnic minority groups.
“There is limited evidence on whether long-term health monitoring using wearable devices will yield reliable and equitable real-world data18 across the diverse socioeconomic and demographic19,20 population spectrum,21,22 particularly among racial and ethnic minority groups.23”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [18] US Food and Drug Administration. Real-world evidence. December 12, 2022. Accessed December 14, 2022. https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [19] Huang J, Galal G, Etemadi M, Vaidyanathan M. Evaluation and mitigation of racial bias in clinical machine learning models: scoping review. JMIR Med Inform. 2022; 10(5): e36388. doi: 10.2196/36388 35639450 PMC9198828 source
Not checked in the free run.
- cites [20] Godino JG, Wing D, de Zambotti M,. Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children. PLoS One. 2020; 15(9): e0237719. doi: 10.1371/journal.pone.0237719 32886714 PMC7473549 source
Not checked in the free run.
- Interpretationreasoning
Poorer retention of Black compared with White families likely contributed to lower overall wear time.
“There was also poorer retention of Black compared with White families, which likely contributed to lower overall wear time.”
Quote in the paperSubstantial claim - Premisecitation
Characteristics of the study team may inform whether participants opt in to studies, as racial and ethnic minority communities are more likely to trust those that are like them and understand their experiences.
“Characteristics of the study team may also inform whether participants opt in to studies, as racial and ethnic minority communities are more likely to trust those that are like them and understand their experiences.54,55,56,57”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [54] Benkert R, Peters RM, Clark R, Keves-Foster K. Effects of perceived racism, cultural mistrust and trust in providers on satisfaction with care. J Natl Med Assoc. 2006; 98(9): 1532- 1540. 17019925 PMC2569718
Not checked in the free run.
- cites [55] Pugh M Jr, Perrin PB, Rybarczyk B, Tan J. Racism, mental health, healthcare provider trust, and medication adherence among Black patients in safety-net primary care. J Clin Psychol Med Settings. 2021; 28(1): 181- 190. doi: 10.1007/s10880-020-09702-y 32008136 source
Not checked in the free run.
- cites [56] Schwei RJ, Kadunc K, Nguyen AL, Jacobs EA. Impact of sociodemographic factors and previous interactions with the health care system on institutional trust in three racial/ethnic groups. Patient Educ Couns. 2014; 96(3): 333- 338. doi: 10.1016/j.pec.2014.06.003 24973901 PMC4145031 source
Not checked in the free run.
- Study design and conductcitation
Our approximation of wear time was based on available per-minute HR values, which may be an imperfect proxy because wearable device data are affected by motion artifacts.
“First, our approximation of wear time was based on available per-minute HR values, which have been used in previous studies22; however, wearable device data are affected by motion artifacts.64,65 Thus, our measure of wear time may be an imperfect proxy.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [22] Gorny AW, Liew SJ, Tan CS, Müller-Riemenschneider F. Fitbit Charge HR wireless heart rate monitor: validation study conducted under free-living conditions. JMIR Mhealth Uhealth. 2017; 5(10): e157. doi: 10.2196/mhealth.8233 29055881 PMC5670315 source
Not checked in the free run.
- cites [64] Bent B, Goldstein BA, Kibbe WA, Dunn JP. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit Med. 2020; 3(1): 18. doi: 10.1038/s41746-020-0226-6 32047863 PMC7010823 source
Not checked in the free run.
- cites [65] Lee J, Kim M, Park HK, Kim IY. Motion artifact reduction in wearable photoplethysmography based on multi-channel sensors with multiple wavelengths. Sensors (Basel). 2020; 20(5): 1493. doi: 10.3390/s20051493 32182772 PMC7085621 source
Not checked in the free run.
- Study design and conductown data
No significant differences in sociodemographic characteristics of the participants were observed between those enrolled in the wearable device substudy during the COVID-19 pandemic (694 of 7424 participants [9.4%]) compared with participants enrolling in the study before COVID-19 (6546 participants [88.2%]).
“No significant differences in sociodemographic characteristics of the participants were observed between those enrolled in the wearable device substudy during the COVID-19 pandemic (694 of 7424 participants [9.4%]) compared with participants enrolling in the study before COVID-19 (6546 participants [88.2%]) (eAppendix 2.2 in Supplement 1).”
Quote in the paperNot a substantial claim - Premisecitation
The present wearable technology is known to be less reliable for people with darker skin tones and higher body mass index (e.g., inaccurate HR measurement).
“Finally, the present wearable technology is known to be less reliable for people with darker skin tones and higher body mass index (eg, inaccurate HR measurement).30”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [30] Shcherbina A, Mattsson CM, Waggott D,. Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort. J Pers Med. 2017; 7(2): 3. doi: 10.3390/jpm7020003 28538708 PMC5491979 source
Not checked in the free run.
- Premisecitation
Social determinants of health could potentially affect the acceptability of technology usage among racial and ethnic minority populations.
“Research shows that social determinants of health (SDoHs)24 could potentially affect the acceptability of technology usage among racial and ethnic minority populations.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [24] Sieck CJ, Sheon A, Ancker JS, Castek J, Callahan B, Siefer A. Digital inclusion as a social determinant of health. NPJ Digit Med. 2021; 4(1): 52. doi: 10.1038/s41746-021-00413-8 33731887 PMC7969595 source
Not checked in the free run.
- Study design and conductown data
Sensitivity analysis showed similar differences in retention for participants recruited during the COVID-19 pandemic.
“Sensitivity analysis showed similar differences in retention for participants recruited during the COVID-19 pandemic (eAppendix 2.3 in Supplement 1).”
Quote in the paperNot a substantial claim - Study design and conductown data
Nearly one-third (28.7%) of the Y2 cohort did not have wearable device data.
“Third, nearly one-third (28.7%) of the Y2 cohort did not have wearable device data (eAppendix 2.6.2 in Supplement 1);”
Quote in the paperNot a substantial claim
- May combine two claims. 12
- Classified as probably not a substantial claim. 16
May combine two claims.
Findingown dataThe study used more than 100,000 days of wearable data from more than 10,000 children nationwide, constituting one of the largest and most diverse wearable device deployments among children.
“Our findings using more than 100 000 days of wearable data from one of the largest and most diverse wearable device deployments among more than 10 000 children nationwide show significant differences in data collection association with demographic and socioeconomic indicators.”
- Also stated in: § Conclusions · plain
“In one of the largest deployments of wearable devices in children, we found evidence that children from lower SES backgrounds and Black children were less likely to participate, share data, and engage in wearable device–based health research than those with higher SES backgrounds and White children.”
Quote in the paperNot a substantial claim- Also stated in: § Conclusions · plain
May combine two claims.
Findingown dataRacial and ethnic minority youths, in particular Black children, and those from lower SES households had significantly less total wear time compared with White children or those from higher SES households.
“and had significantly less total wear time compared with White children or those from higher SES households;”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataRacial and ethnic minority youths, in particular Black children, and those from lower SES households shared data for fewer days (lower retention in the 21-day observation period) compared with White children or those from higher SES households.
“(2) racial and ethnic minority youths, in particular Black children, and those from lower SES households, shared data for fewer days (ie, lower retention in the 21-day observation period)”
- Also stated in: § Discussion · hedged
“There was also poorer retention of Black compared with White families, which likely contributed to lower overall wear time.”
Quote in the paperSubstantial claim- Also stated in: § Discussion · hedged
May combine two claims.
Findingown dataChildren from lower SES backgrounds and Black children were less likely to participate, share data, and engage in wearable device-based health research than those with higher SES backgrounds and White children.
“In one of the largest deployments of wearable devices in children, we found evidence that children from lower SES backgrounds and Black children were less likely to participate, share data, and engage in wearable device–based health research than those with higher SES backgrounds and White children.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe relative proportion of total Black children enrolled having wearable device data available was 59% lower in WDC vs NWDC, significantly lower than White children (132% higher in WDC vs NWDC).
“Our analyses reflected this underrepresentation, with the relative proportion of the total Black children (59% lower WDC vs NWDC) enrolled having wearable device data available being significantly lower than that of White children (132% higher in WDC vs NWDC) (Table 1).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe HR sensor can be affected by skin tone, which may bias wearable devices' data against racial and ethnic minority youths who may have darker skin tones.
“The HR sensor can also be affected by skin tone, which may bias wearable devices’ data against racial and ethnic minority youths who may have darker skin tones.64”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [64] Bent B, Goldstein BA, Kibbe WA, Dunn JP. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit Med. 2020; 3(1): 18. doi: 10.1038/s41746-020-0226-6 32047863 PMC7010823
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenIt was unclear how many opted out or were unable to participate in the substudy due to factors such as not being offered a wearable device due to site-specific availability or not being mailed a device for remote assessment due to the COVID-19 pandemic.
“it was unclear how many opted out or were unable to participate in the substudy due to factors, such as participants not being offered a wearable device due to site-specific availability or not being mailed a device for remote assessment due to the COVID-19 pandemic.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductcitationVariation inflation factor was used to assess model collinearity for all model covariates and met acceptable thresholds (<5).
“Variation inflation factor was used to assess model collinearity for all model covariates and met acceptable thresholds (<5).47”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [47] Fox J, Monette G. Generalized collinearity diagnostics. J Am Stat Assoc. 1992; 87(417): 178- 183. doi: 10.1080/01621459.1992.10475190
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenA stratified version of the log-rank test was used to adjust for potential heterogeneity across study sites.
“A stratified version of the log-rank test was used to adjust for potential heterogeneity across study sites.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationRacial and ethnic minority groups are disproportionately affected by diabetes and obesity, which can affect HR measurement.
“In addition, racial and ethnic minority groups are disproportionately affected by diabetes and obesity,66 which can affect HR measurement.67”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [66] Walker RJ, Strom Williams J, Egede LE. Influence of race, ethnicity and social determinants of health on diabetes outcomes. Am J Med Sci. 2016; 351(4): 366- 373. doi: 10.1016/j.amjms.2016.01.008 27079342 PMC4834895
Not checked in the free run.
- cites [67] Ajmal B-AT, Boonya-Ananta T, Rodriguez AJ, Du Le VN, Ramella-Roman JC. Monte Carlo analysis of optical heart rate sensors in commercial wearables: the effect of skin tone and obesity on the photoplethysmography (PPG) signal. Biomed Opt Express. 2021; 12(12): 7445- 7457. doi: 10.1364/BOE.439893 35003845 PMC8713672
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThis secondary cohort study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.
“This secondary cohort study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines and was approved by the research ethics board of the Center of Addiction and Mental Health (Toronto, Canada).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationRacial and ethnic minority participants are underrepresented in clinical trials.
“It is well known that racial and ethnic minority participants are underrepresented in clinical trials.49”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [49] US Food and Drug Administration. FDA issues summary report on global participation in clinical trials: 2015-2019—drug information update. November 9, 2020. Accessed June 19, 2022. https://content.govdelivery.com/accounts/USFDA/bulletins/2aacc22
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationGender affecting cutaneous blood flow may affect data collected from wearable devices.
“Other variables, such as gender affecting cutaneous blood flow,68 may also affect data collected from wearable devices.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [68] Chin LC, Huang TY, Yu CL, Wu CH, Hsu CC, Yu HS. Increased cutaneous blood flow but impaired post-ischemic response of nutritional flow in obese children. Atherosclerosis. 1999; 146(1): 179- 185. doi: 10.1016/S0021-9150(99)00135-5 10487502
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPrevious studies have used heart rate as a proxy for wear time, such that time intervals with no acquired heart rate data were regarded as nonwear periods.
“Previous studies have used HR as a proxy for wear time22 such that time intervals with no acquired HR data were regarded as nonwear periods.44”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [22] Gorny AW, Liew SJ, Tan CS, Müller-Riemenschneider F. Fitbit Charge HR wireless heart rate monitor: validation study conducted under free-living conditions. JMIR Mhealth Uhealth. 2017; 5(10): e157. doi: 10.2196/mhealth.8233 29055881 PMC5670315 source
Not checked in the free run.
- cites [44] Collins JE, Yang HY, Trentadue TP, Gong Y, Losina E. Validation of the Fitbit Charge 2 compared to the ActiGraph GT3X+ in older adults with knee osteoarthritis in free-living conditions. PLoS One. 2019; 14(1): e0211231. doi: 10.1371/journal.pone.0211231 30699159 PMC6353569 source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductcitationThe study recruited children from schools throughout the United States based on stratified probability to match the demographic and socioeconomic diversity of the United States.
“The study recruited children from schools throughout the United States based on stratified probability38 to match the demographic and socioeconomic diversity of the United States.39”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [38] Karcher NR, Barch DM. The ABCD study: understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology. 2021; 46(1): 131- 142. doi: 10.1038/s41386-020-0736-6 32541809 PMC7304245 source
Not checked in the free run.
- cites [39] Thompson WK, Barch DM, Bjork JM,. The structure of cognition in 9 and 10 year-old children and associations with problem behaviors: findings from the ABCD study’s baseline neurocognitive battery. Dev Cogn Neurosci. 2019; 36: 100606. doi: 10.1016/j.dcn.2018.12.004 30595399 PMC6676481 source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
InterpretationreasoningThe latter may have affected wear time, especially for racial and ethnic minority youths who may have been disproportionately affected by psychosocial, economic, and health issues, thus having a diminished capacity to engage with the ABCD Study.
“The latter may have affected wear time, especially for racial and ethnic minority youths who may have been disproportionately affected by psychosocial, economic, and health issues, thus having a diminished capacity to engage with the ABCD Study.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductnone givenThe wearable devices used in this study were not certified to be worn by those younger than 13 years, and most participants were 11 to 12 years of age at the time of data collection.
“Second, the wearable devices used in this study were not certified to be worn by those younger than 13 years, and most participants were 11 to 12 years of age at the time of data collection.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe Adolescent Brain and Cognitive Development (ABCD) Study is the most extensive study of brain development and child health in the United States, with more than 11 000 children aged 9 to 10 years old recruited from 21 sites across the United States.
“The ABCD Study is the most extensive study of brain development and child health in the United States, with more than 11 000 children aged 9 to 10 years old recruited from 21 sites across the United States.36”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [36] Jernigan T, Brown SA, Dale AM,. Adolescent Brain Cognitive Development Study (ABCD)—annual release 4.0 #1299. doi: 10.15154/1523041
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningThe interplay of many sociotechnical factors can lead to nonuniform participant engagement and data collection among racial and ethnic minority populations, which may result in the learning of treatment paradigms that are not appropriate or inadequate for certain underrepresented populations and may further contribute to health disparities in disease and treatment outcomes and access to care.
“The interplay of many such sociotechnical factors can lead to nonuniform participant engagement and data collection among racial and ethnic minority populations, which may result in the learning of treatment paradigms that are not appropriate or inadequate for certain underrepresented populations and may further contribute to health disparities in disease and treatment outcomes and access to care.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningRandom intercepts for recruitment sites and study enrollment quarters were used to account for the potential clustering of participants within the same recruitment site or enrollment patterns during the COVID-19 pandemic.
“We used random intercepts for recruitment sites and study enrollment quarters to account for the potential clustering of participants within the same recruitment site or enrollment patterns during the COVID-19 pandemic.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWearable devices have been found to have adequate performance in younger children.
“While more research is necessary to understand how these devices perform with younger children, they have been found to have adequate performance.20”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [20] Godino JG, Wing D, de Zambotti M,. Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children. PLoS One. 2020; 15(9): e0237719. doi: 10.1371/journal.pone.0237719 32886714 PMC7473549
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataFor sensitivity analysis, the inference drawn from the retention and wear-time data collected from participants recruited before and during the COVID-19 pandemic was compared.
“For sensitivity analysis, we compared the inference drawn from the retention and wear-time data collected from participants recruited before and during the COVID-19 pandemic.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
The heart-rate-based proxy for device wear time measures wear time comparably across demographic and socioeconomic groups.
“Per-minute heart rate (HR) data obtained from devices were used to approximate the total daily wear time (in hours) by calculating the time points for which the device recorded per-minute HR.”
The paper relies on this without support.
Not sharing device data (the no wearable device cohort) reflects unwillingness to participate rather than technical or logistical failure.
“compared with the 2990 (28.7%) who did not participate or share data (no wearable device cohort [NWDC])”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Adopting wearables for health monitoring may inadvertently exacerbate disparities if certain populations are excluded from participation in research studies or clinical trials due to lack of access or technological competencies.
“Furthermore, adopting wearables for health monitoring may inadvertently exacerbate disparities if certain populations are excluded from participation in research studies or clinical trials due to a lack of access or technological competencies (Kim et al., 2023).”
From Sociodemographics and Digital Health Literacy in Using Wearables for Health Promotion and Disease Prevention: Cross-Sectional Nationwide Survey in Germany (2025) · cites this paper as Kim et al., 2023
The Introduction (page not available) identifies unequal access to health care technology as a barrier to representative wearable-device research. The Discussion (page not available) reports lower participation and retention among Black children and warns that uneven engagement could contribute to health disparities. It does not identify a lack of technological competencies as a reason for exclusion from studies or clinical trials. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA recent cohort study in the US indicated that children from lower socioeconomic status households were less likely to participate in a wearable study and wore wearables for significantly shorter durations than those from higher socioeconomic status households.
“Moreover, a recent cohort study in the US indicated that children from lower socioeconomic status households were less likely to participate in a wearable study and wore wearables for significantly shorter durations than those from higher socioeconomic status households (Kim et al., 2023).”
From Sociodemographics and Digital Health Literacy in Using Wearables for Health Promotion and Disease Prevention: Cross-Sectional Nationwide Survey in Germany (2025) · cites this paper as Kim et al., 2023
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
West-region significance conflicts with Table 2
MinorThe results text reports P < .001 for West versus Midwest, while Table 2 reports P = .70 for the same coefficient and confidence interval. The reported statistical significance therefore conflicts with the table.
“West vs Midwest: β = 4.07 hours [95% CI, −16.46 to 24.59 hours]; P < .001”
“| West | 4.07 (−16.46 to 24.59) | .70 |”
Southeast effect is attributed to the Southwest
MinorThe results text assigns the −24.1-hour effect to the Southwest, but Table 2 assigns that estimate to the Southeast. The table gives the Southwest a different estimate and P value.
“Southwest vs Midwest: β = −24.1 hours [95% CI: −43.77 to −4.43 hours]; P = .02”
“| Southeast | −24.1 (−43.77 to −4.43) | .02 |”
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Adolescent Brain Cognitive Development Study (ABCD) - Annual Release 4.0
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Willoughby, A. R., Alikhani, I., Karsikas, M., Chua, X. Y., & Chee, M. W. L. (2023) · Sleep Medicine · doi:10.1016/j.sleep.2023.08.010 · Cited by 134 · see citing works
Patterns of Ownership and Usage of Wearable Devices in the United States, 2020-2022: Survey Study
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“While prior studies have focused on the adoption of wearables within specific populations such as adolescents, older adults, and underserved populations [5-7], large-scale studies examining differences in wearable adoption across sociodemographic characteristics remain limited.”
Chandrasekaran, R., Muhammed Sadiq, T., & Moustakas, E. (2025) · Journal of Medical Internet Research · doi:10.2196/63879 · Cited by 59 · see citing works
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“Third, the lower representation of adolescents identifying as a race other than White who participated in the Fitbit sub-study 81 indicates that our sample is not as diverse as the whole ABCD cohort, and there may be barriers to collecting these data in minorities.”
BackgroundSelf-citationStress recognition from facial images in children during physiotherapy with serious games
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Kiss, O., Shaska, A., Müller‐Oehring, E. M., Hasler, B. P., Franzen, P. L., FitzGerald, D., Clark, D. B., & Baker, F. C. (2025) · SLEEP · doi:10.1093/sleep/zsaf065 · Cited by 16 · see citing works
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Does clinical research account for diversity in deploying digital health technologies?
Coss, N. A., Gaitán, J. M., Adans-Dester, C. P., Carruthers, J., Fanarjian, M., Sassano, C., Manuel, S. P., & Perakslis, E. D. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00928-2 · Cited by 12 · see citing works
“Signi fi cant disparities in participation and device wear time have been demonstrated on the basis of race and income in research involving DHTs 20 .”
BackgroundAngeles, W. W. L.-D. L. (2023) · Academic Pediatrics · doi:10.1016/j.acap.2023.09.014 · Cited by 11 · see citing works
Zhang, R., Schwandt, M. L., Vines, L., & Volkow, N. D. (2024) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2024.11.005 · Cited by 10 · see citing works
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Artese, A., Rawat, R., & Sung, A. D. (2023) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/mco.0000000000000970 · Cited by 10 · see citing works
“This is due to a lack of resources and education as well as issues regarding data privacy and safety [58].”
The All of Us Research Program’s wearables dataset
Patten, T., Preble, E. A., Master, H., Adjemian, J., Ramirez, A. H., McClain, J. J., & Price, A. R. (2026) · Nature Medicine · doi:10.1038/s41591-026-04352-3 · Cited by 11 · see citing works
“However, the potential impact of these technologies remains constrained by substantial demographic biases, with wearables ownership and much of DHT research disproportionately representing white individuals with higher educational and income levels 3,6,7 .”
Wang, Y., Zhao, Z., Zhang, M., Zhang, Y., Yan, J., Jelsma, E. B., Cham, H., Alegria, M. M., & Yip, T. (2025) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2024.12.004 · Cited by 7 · see citing works
Digital Disparities in Healthcare: A Tale of the Haves and Have-Nots?
Chen, A. M. (2025) · Journal of Patient Experience · doi:10.1177/23743735251343585 · Cited by 6 · see citing works
“23 Digital interventions through mobile technology have also been shown to be subjected to disparities.”
Soley, N., Rattsev, I., Speed, T. J., Xie, A., Ferryman, K., & Taylor, C. O. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf053 · Cited by 6 · see citing works
Updating sleep duration benchmarks in surveillance: implications of wearables and objective measures
Chaput, J. (2025) · Sleep Health · doi:10.1016/j.sleh.2025.08.008 · Cited by 5 · see citing works
McKendrick, G., Stull, S. W., Sharma, A., & Dunn, K. E. (2024) · Seminars in Neurology · doi:10.1055/s-0044-1787569 · Cited by 4 · see citing works
“82,83 Inclusion of diverse samples during product development will be a crucial componentofexpand-ing buprenorphine access while reducing care inequities.”
BackgroundNúñez-Gaunaurd, A., & Raya, M. A. (2025) · JMIR mhealth and uhealth · doi:10.2196/80465 · Cited by 4 · see citing works
“Despite the growing popularity of consumer wearables, little is known about long-term adherence to wearable technologies among high school age adolescents from underserved racial and ethnic minority groups [11,28].”
Health System Purchasing Professionals’ Approaches to Considering Equity in Procurement
Hauschildt, K. E., Bernstein, T., Crews, D. C., Drabo, E. F., Ferryman, K., Jackson, J. W., Valley, T. S., Levy, J., & Iwashyna, T. J. (2024) · CHEST Journal · doi:10.1016/j.chest.2024.12.016 · Cited by 3 · see citing works
Kim, K., Jergel, A., Bai, S., Bradley, K., Karim, B., Shah, A. J., Suglia, S. F., Ugboh, N., & Gooding, H. C. (2023) · Pilot and Feasibility Studies · doi:10.1186/s40814-023-01418-8 · Cited by 3 · see citing works
School start times and racial disparities in early adolescent sleep
Yip, T., Yan, J., Zhang, M., Wang, Y., Zhao, Z., Cham, H., & Alegria, M. M. (2025) · Sleep Health · doi:10.1016/j.sleh.2025.07.003 · Cited by 2 · see citing works
“44 This study assessed sleep onset time, offset time, and duration-sleep indicators that are closely linked to SSTs 23,25,26,30 and are critical for youth functioning and health.”
Kwon, S., Gopagani, N. S., Bian, L., & Vu, M. (2026) · Children · doi:10.3390/children13020256 · Cited by 2 · see citing works
“This suboptimal engagement in wear-ables’ data collection in Asian American families has been documented [61].”
Bharadwaj, S. V., Rhoades, E. A., & Perry, H. (2024) · American Journal of Audiology · doi:10.1044/2024_aja-23-00208 · Cited by 2 · see citing works
Gerosa, T., Marzorati, D., Rando, M. G., Baldassari, A., Faraci, F. D., & Cellina, F. (2025) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2025.101761 · Cited by 2 · see citing works
Evaluating AI's Role in Enhancing DE and I: A Bibliometric Approach
Sharma, C., Srivastava, R., Rajput, A., Mukherjee, R., & Chandra, S. (2024) · International Research Journal on Advanced Engineering and Management (IRJAEM) · doi:10.47392/irjaem.2024.0292 · Cited by 2 · see citing works
Neighborhood disadvantage and adolescent sleep health: a longitudinal population-based study
Niu, L., Tan, L., Diaz, A., Xiao, Y., Li, Y., & Wang, Y. (2025) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2025.101320 · Cited by 1 · see citing works
“31 These differences may limit generalizability and likely yield conservative estimates, as neighborhood disadvantage disproportionately affects low-SES and racially minoritized youth.”
Brooke, N., Roberts, K., Heinz, S. A. S., Metzler, E., Peskoe, S., Whitson, H. E., Ginsberg, J. P., & Acker, L. (2026) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-025-10741-9 · Cited by 1 · see citing works
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Digital Simulacra, Bias, and Self-Reinforcing Exclusion Cycles
Bracic, A., & Price, W. N. (2023) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2237460
Núñez-Gaunaurd, A., & Raya, M. A. (2025) · doi:10.2196/preprints.80465
Kaiver, C. M., Thompson, E. L., Hawes, S. W., Lehman, S. M., Adams, A. R., Wing, D., Laird, A. R., & González, R. (2025) · Drug and Alcohol Dependence Reports · doi:10.1016/j.dadr.2025.100373
“Participants who opted to participate in the Fitbit protocol were previously reported to differ demographically from those who did not (Kim et al., 2023).”
Zhang, M., Wang, Y., Zhao, Z., Yan, J., Zhang, Y., Cham, H., Alegria, M. M., & Yip, T. (2025) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2025.08.010
Wearable Activity Trackers: Nurse Practitioners’ Knowledge, Perceptions, and Experiences
Gellasch, P., Kartoz, C., & D’Antonio, G. (2025) · The Journal for Nurse Practitioners · doi:10.1016/j.nurpra.2025.105558
Positive Childhood Experiences, Adverse Childhood Experiences, and Diet in Early Adolescents
Angeles, W. W. L.-D. L., & Logan, N. E. (2025) · Academic Pediatrics · doi:10.1016/j.acap.2025.103176
Hajek, A., König, H., Yon, D. K., Pengpid, S., & Peltzer, K. (2025) · Journal of Public Health · doi:10.1007/s10389-025-02649-0
Chen, N., Hyat, M., Zhu, J., Dey, A. K., & Forsyth, J. K. (2026) · Biological Psychiatry · doi:10.1016/j.biopsych.2026.03.354
Patel, B., Tyrka, A. R., & Angeles, W. W. L.-D. L. (2026) · Journal of Attention Disorders · doi:10.1177/10870547261421667
“…American Community Survey (Heeringa & Berglund, 2020; Lewis-de Los Angeles & Liu, 2021; Raney et al., 2022), this study does not do so due to the missing data and differences in demographics among those missing, compared to those with complete data, as previously reported (E. H. Kim et al., 2023).”
InfluentialImpact of Pubertal Progression on Physical Activity and Sedentary Time in Adolescent Girls
Logan, N. E., Gaudreau, J., & Angeles, W. W. L.-D. L. (2026) · Pediatric Exercise Science · doi:10.1123/pes.2025-0075 · Cited by 1 · see citing works
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Demographic and Socioeconomic Factors Associated with Fitbit Ownership in the NIH All of Us Cohort
Carrier, B., & Navalta, J. W. (2026) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph23070839
“%) in the device cohort than the non-wearable cohort [12].”
InfluentialLetts, E., da Silva, S. M., King‐Dowling, S., Cristofaro, N. A. D., & Obeid, J. (2026) · Pediatric Exercise Science · doi:10.1123/pes.2025-0147
Harnessing Digital Health Innovations in Global Maternal and Child Health
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Logan, N. E., & Angeles, W. W. L.-D. L. (2026) · Restorative Neurology and Neuroscience · doi:10.1177/09226028261476103
Kwon, S., Tandon, P. S., Shah, N. S., & Kandula, N. R. (2026) · Children · doi:10.3390/children13091123
“Studies have reported that Asian Americans have historically been less willing to participate in health research [23,24], and that racial and ethnic minority groups are less likely to enroll in device-based assessment protocols and accumulate less wear time [22,25,26].”
InfluentialTechnology in Diverse Populations
Thames, A. D., & Cavanagh, L. (2026) · Oxford University Press eBooks · doi:10.1093/9780197814116.003.0011
Alexander, J. D., Mills, S., Sumpter, K., Smith, C. J., Das, B., Fan, R., Hunt, E. T., Brown, S. A., Curtis, B. L., Cummins, K. M., et al. (2026) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2026.101826
Self-citationDonnelly, S., Carlin, A., Reilly, J. J., Mackintosh, K. A., Basterfield, L., & O'Kane, M. (2026) · BMJ Public Health · doi:10.1136/bmjph-2026-005513
“Despite these limitations, this review provides an important synthesis of current evidence, high-lighting key methodological and conceptual gaps that should guide future research and policy development.”
Physical Activity and Adolescent Brain Structure, Mental Health, and Academic Outcomes
Li, Q., Paluch, A. E., & Rakesh, D. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.36973
- Authors
- Kim, E. H., Jenness, J. L., Miller, A. B., Halabi, R., de Zambotti, M., Bagot, K. S., Baker, F. C., & Pratap, A.
- Affiliations
- Centre for Addiction and Mental Health (CA)
- University of Washington (US)
- University of North Carolina at Chapel Hill (US)
- Center for Health Sciences, SRI International, Menlo Park, California
- Icahn School of Medicine at Mount Sinai (US)
- SRI International (US)
- Corresponding author
- Pratap, Abhishek
- ORCID
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2023-03-30
- Volume
- 6
- Issue
- 3
- Pages
- e235681
- ISSN
- 2574-3805 (ISSN-L)
- PMC
- PMC10064258
- OpenAlex
- W4361270192
- Cited by
- 53
- References
- 63
- Citations by year
- 2026: 16 · 2025: 19 · 2024: 10 · 2023: 8
- FWCI
- 18.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Krembil Foundation
- National Institute of Mental Health (R34MH128387, K23MH112872, K01MH116325)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- wearable devices, children, social determinants of health, socioeconomic status, racial and ethnic disparities, research participation, participant retention, Adolescent Brain Cognitive Development Study, consumer-grade wearable devices, low-income households, Black children, Hispanic children, data sharing, health equity
- MeSH terms
- Wearable Electronic Devices, Adolescent, Child, Demography, Female, Humans, Longitudinal Studies, Male, Socioeconomic Factors, United States, Cohort Studies
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