Alcohol, clinical & experimental research · 2026
Validating a New-Generation Wrist-Worn Alcohol Biosensor Among Persons With and Without HIV in a Laboratory Setting.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Richards Veronica L, Barrow Amadou, Ahn Seungjun, Chavez Alvarez Andy, Leeman Robert F, Barnett Nancy P, Porges Eric C, Cook Robert L, Wang Yan
Аннотация
BACKGROUND: Wrist-worn transdermal alcohol concentration (TAC) sensors enable continuous, objective measurement of alcohol use. This study validated a newer generation alcohol sensor (BACtrack Skyn) in a laboratory setting among persons with and without HIV. We examined: (1) correspondence between TAC and breath alcohol concentration (BrAC) overall and by HIV status and (2) factors associated with TAC (peak and area under the curve [AUC]) controlling for peak BrAC.
METHODS: Twenty-four participants (32% persons with HIV [PWH], 55% male, Mage = 34.4) completed 40 laboratory alcohol administration sessions while wearing the sensor and providing BrACs. All participants underwent the same dosing procedures, consuming three 4.5% beers. Correlations, multilevel models, and generalized estimating equations models examined relationships between TAC, BrAC, and individual factors.
RESULTS: In this single-dose study, peak TAC and TAC AUC significantly correlated with peak BrAC (rs = 0.36-0.39). Correlations between peak TAC and BrAC were similar by HIV status (rs = 0.5) but varied between TAC AUC and peak BrAC (persons without HIV r = 0.57, PWH r = 0.33), with nonsignificant associations among PWH. Full time-series analyses showed stronger TAC-BrAC correlations (r = 0.64) with no HIV differences. Age (b = 0.01, 95% CI: 0.01, 0.02), race/ethnicity other than non-Hispanic White (b = -0.44, 95% CI: -0.79, -0.08), positive HIV status (b = 0.67, 95% CI: 0.24, 1.09), and AUDIT score (b = 0.09, 95% CI: 0.04, 0.14) were associated with peak TAC. Positive HIV status (b = 1.33, 95% CI: 0.48, 2.18), AUDIT score (b = 0.11, 95% CI: 0.02, 0.19), and BMI (b = -0.06, 95% CI: -0.11, -0.00) were associated with TAC AUC.
CONCLUSION: In light of the fixed dosing procedure, peak-based correlations reflect comparisons that limit BrAC variability to a narrow band, thereby constraining TAC-BrAC correlations. TAC and BrAC were significantly correlated, with the strongest concordance observed using full time-series data. Findings suggest similarities and differences in TAC-BrAC relationships by HIV status depending on metric. Individual factors including age, race/ethnicity, HIV, and AUDIT score were associated with TAC. Future research should confirm these findings in larger, diverse samples.
METHODS: Twenty-four participants (32% persons with HIV [PWH], 55% male, Mage = 34.4) completed 40 laboratory alcohol administration sessions while wearing the sensor and providing BrACs. All participants underwent the same dosing procedures, consuming three 4.5% beers. Correlations, multilevel models, and generalized estimating equations models examined relationships between TAC, BrAC, and individual factors.
RESULTS: In this single-dose study, peak TAC and TAC AUC significantly correlated with peak BrAC (rs = 0.36-0.39). Correlations between peak TAC and BrAC were similar by HIV status (rs = 0.5) but varied between TAC AUC and peak BrAC (persons without HIV r = 0.57, PWH r = 0.33), with nonsignificant associations among PWH. Full time-series analyses showed stronger TAC-BrAC correlations (r = 0.64) with no HIV differences. Age (b = 0.01, 95% CI: 0.01, 0.02), race/ethnicity other than non-Hispanic White (b = -0.44, 95% CI: -0.79, -0.08), positive HIV status (b = 0.67, 95% CI: 0.24, 1.09), and AUDIT score (b = 0.09, 95% CI: 0.04, 0.14) were associated with peak TAC. Positive HIV status (b = 1.33, 95% CI: 0.48, 2.18), AUDIT score (b = 0.11, 95% CI: 0.02, 0.19), and BMI (b = -0.06, 95% CI: -0.11, -0.00) were associated with TAC AUC.
CONCLUSION: In light of the fixed dosing procedure, peak-based correlations reflect comparisons that limit BrAC variability to a narrow band, thereby constraining TAC-BrAC correlations. TAC and BrAC were significantly correlated, with the strongest concordance observed using full time-series data. Findings suggest similarities and differences in TAC-BrAC relationships by HIV status depending on metric. Individual factors including age, race/ethnicity, HIV, and AUDIT score were associated with TAC. Future research should confirm these findings in larger, diverse samples.