TY - JOUR
T1 - Using novel mobile sensors to assess stress and smoking lapse
AU - Nakajima, Motohiro
AU - Lemieux, Andrine M.
AU - Fiecas, Mark
AU - Chatterjee, Soujanya
AU - Sarker, Hillol
AU - Saleheen, Nazir
AU - Ertin, Emre
AU - Kumar, Santosh
AU - al'Absi, Mustafa
N1 - Publisher Copyright:
© 2020
PY - 2020/12
Y1 - 2020/12
N2 - Mobile sensors can now provide unobtrusive measurement of both stress and cigarette smoking behavior. We describe, here, the first field tests of two such methods, cStress and puffMarker, that were used to examine relationships between stress and smoking behavior and lapse from a sample of 76 smokers motivated to quit smoking. Participants wore a mobile sensors suite, called AutoSense, which collected continuous physiological data for 4 days (24-hours pre-quit and 72-hours post-quit) in the field. Algorithms were applied to the physiological data to create indices of stress (cStress) and first lapse smoking episodes (puffMarker). We used mixed effects interrupted autoregressive time series models to assess changes in heart rate (HR), cStress, and nicotine craving across the 4-day period. Self-report assessments using ecological momentary assessment (EMA) of mood, withdrawal symptoms, and smoking behavior were also used. Results indicated that HR and cStress, respectively, predicted smoking lapse. These results suggest that measures of traditional psychophysiology, such as HR, are not redundant with cStress; both provide important information. Results are consistent with existing literature and provide clear support for cStress and puffMarker in ambulatory clinical research. This research lays groundwork for sensor-based markers in developing and delivering sensor-triggered, just-in-time interventions that are sensitive to stress-related lapser risk factors.
AB - Mobile sensors can now provide unobtrusive measurement of both stress and cigarette smoking behavior. We describe, here, the first field tests of two such methods, cStress and puffMarker, that were used to examine relationships between stress and smoking behavior and lapse from a sample of 76 smokers motivated to quit smoking. Participants wore a mobile sensors suite, called AutoSense, which collected continuous physiological data for 4 days (24-hours pre-quit and 72-hours post-quit) in the field. Algorithms were applied to the physiological data to create indices of stress (cStress) and first lapse smoking episodes (puffMarker). We used mixed effects interrupted autoregressive time series models to assess changes in heart rate (HR), cStress, and nicotine craving across the 4-day period. Self-report assessments using ecological momentary assessment (EMA) of mood, withdrawal symptoms, and smoking behavior were also used. Results indicated that HR and cStress, respectively, predicted smoking lapse. These results suggest that measures of traditional psychophysiology, such as HR, are not redundant with cStress; both provide important information. Results are consistent with existing literature and provide clear support for cStress and puffMarker in ambulatory clinical research. This research lays groundwork for sensor-based markers in developing and delivering sensor-triggered, just-in-time interventions that are sensitive to stress-related lapser risk factors.
KW - Abstinence
KW - Lapse
KW - Smoking
KW - mHealth
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U2 - 10.1016/j.ijpsycho.2020.11.005
DO - 10.1016/j.ijpsycho.2020.11.005
M3 - Article
C2 - 33189770
AN - SCOPUS:85097395569
SN - 0167-8760
VL - 158
SP - 411
EP - 418
JO - International Journal of Psychophysiology
JF - International Journal of Psychophysiology
ER -