Kennedy Ashley P, Epstein David H, Jobes Michelle L, Agage Daniel, Tyburski Matthew, Phillips Karran A, Ali Amin Ahsan, Bari Rummana, Hossain Syed Monowar, Hovsepian Karen, Rahman Md Mahbubur, Ertin Emre, Kumar Santosh, Preston Kenzie L
Clinical Pharmacology and Therapeutics Research Branch, Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, United States.
Johns Hopkins Bayview Medical Center, Baltimore, MD, United States.
Drug Alcohol Depend. 2015 Jun 1;151:159-66. doi: 10.1016/j.drugalcdep.2015.03.024. Epub 2015 Apr 7.
Ambulatory physiological monitoring could clarify antecedents and consequences of drug use and could contribute to a sensor-triggered mobile intervention that automatically detects behaviorally risky situations. Our goal was to show that such monitoring is feasible and can produce meaningful data.
We assessed heart rate (HR) with AutoSense, a suite of biosensors that wirelessly transmits data to a smartphone, for up to 4 weeks in 40 polydrug users in opioid-agonist maintenance as they went about their daily lives. Participants also self-reported drug use, mood, and activities on electronic diaries. We compared HR with self-report using multilevel modeling (SAS Proc Mixed).
Compliance with AutoSense was good; the data yield from the wireless electrocardiographs was 85.7%. HR was higher when participants reported cocaine use than when they reported heroin use (F(2,9)=250.3, p<.0001) and was also higher as a function of the dose of cocaine reported (F(1,8)=207.7, p<.0001). HR was higher when participants reported craving heroin (F(1,16)=230.9, p<.0001) or cocaine (F(1,14)=157.2, p<.0001) than when they reported of not craving. HR was lower (p<.05) in randomly prompted entries in which participants reported feeling relaxed, feeling happy, or watching TV, and was higher when they reported feeling stressed, being hassled, or walking.
High-yield, high-quality heart-rate data can be obtained from drug users in their natural environment as they go about their daily lives, and the resultant data robustly reflect episodes of cocaine and heroin use and other mental and behavioral events of interest.
动态生理监测能够阐明药物使用的前因后果，并有助于开展由传感器触发的移动干预，自动检测行为风险状况。我们的目标是证明这种监测是可行的，并且能够产生有意义的数据。
我们使用一套名为AutoSense的生物传感器对40名接受阿片类激动剂维持治疗的多药使用者进行了长达4周的心率（HR）评估，该传感器可将数据无线传输至智能手机，受试者在日常生活中使用。参与者还通过电子日记自我报告药物使用情况、情绪和活动。我们使用多水平建模（SAS Proc Mixed）将心率与自我报告进行比较。
对AutoSense的依从性良好；无线心电图的数据产出率为85.7%。参与者报告使用可卡因时的心率高于报告使用海洛因时（F(2,9)=250.3，p<.0001），并且心率也随着报告的可卡因剂量增加而升高（F(1,8)=207.7，p<.0001）。参与者报告渴望使用海洛因（F(1,16)=230.9，p<.0001）或可卡因（F(1,14)=157.2，p<.0001）时的心率高于报告无渴望时。在随机提示的条目中，参与者报告感到放松、快乐或看电视时心率较低（p<.05），而报告感到压力、受困扰或行走时心率较高。
在药物使用者的自然生活环境中，当他们进行日常生活时，可以获得高产、高质量的心率数据，所得数据有力地反映了可卡因和海洛因使用情况以及其他感兴趣的心理和行为事件。
