Table 3.

Field studies of substance use combining EMA with objective measurement of participants’ physiology

Reference	Substance(s)	Measure added to EMA	Participants	Monitoring duration	EMA device; other device(s)	Compliance/Feasibility	Notes
Bodin et al. 2017	tobacco	ECG	35 smokers and 114 non-smokers with high hostility	24 hours	Palm Pilot PDA; Marquette 8500 Holter ECG recorder	EMA and ECG data available for 5067 30-min periods in total, with 259 smoking episodes reported
Chatterjee et al. 2016	tobacco	ECG, respiration, wrist movement and orientation	61 adult smokers making a quit attempt	24 hours pre-quit, 72 hours post-quit	smartphone (model N/A); AutoSense physiologic al monitoring suite + smartwatches (model N/A)	EMA details N/A; 45/61 participants contributed data to model development (2,766 total participant-hours of AutoSense wear)	development of a model to detect cigarette craving based on relationships among craving, stress, and time of day
Hossain et al. 2014	cocaine	ECG, body movement (chest accelerometry)	38 poly-drug users in methadone maintenance treatment + 9 cocaine users in lab-based studies	4 weeks	smartphone (EMA, model N/A); AutoSense physiologic al monitoring suite + Sony Ericsson Xperia X8 smartphone	EMA details N/A; 922 participant-days of field data included 27 instances of cocaine use with acceptable sensor data from 13 participants (out of 142 reports from 20 people in total)	development of a model to detect cocaine use based on heart rate recovery after cocaine use vs. physical activity
Hovsepian et al. 2015	alcohol, tobacco	ECG and respiration (controlling for physical activity)	23 field study participants + 50 participants in lab-based studies	7 days	smartphone (EMA, model N/A); AutoSense physiologic al monitoring suite + Android smartphone (model N/A)	3/23 field participants excluded for poor quality/missing data; good quality physiologic al data available for 1,060 EMA reports in total	development of a model to detect stress; EMA self-reports of smoking and alcohol use mentioned in field study, but use details N/A
Kennedy et al. 2015	opioids, cocaine	heart rate (ECG RR interval)	40 opioid-dependent adults	Up to 4 weeks	smartphone (EMA model N/A); AutoSense physiologic al monitoring suite + Sony Ericsson Xperia X8 smartphone	overall EMA details N/A (heart rate available for 168 EMA drug use reports and 2,329 random prompt responses); 85.7% overall AutoSense data yield (hours acceptable data/hours sensor wear)	some data also used in model development by Hossain et al. 2014, Rahman et al. 2014, and Sarker et al. 2016
Natarajan et al. 2016	cocaine	ECG morphology (6 waveform features/proper ties)	5 cocaine-dependent persons (non-treatment-seekers) + 10 lab-based participants	37 total participant-days	Samsung Galaxy smartphone ; Zephyr BioHarness chest band	Only participant-initiated EMA (no answer %, but incomplete reports made on 8 days); ECG data lost on “some weekend days” due to device power issues	addresses several issues that can affect researchers generalizing lab-based measures to field studies
Rahman et al. 2014	tobacco, illicit drugs (Study 1); alcohol, tobacco (Study 2)	ECG, respiration, accelerometry, temperature (ambient and skin), galvanic skin response	40 “illicit drug users” (Study 1); 30 daily smokers and “social drinkers” (Study 2)	4 weeks (Study 1); 7 days (Study 2)	smartphone (EMA, model N/A); AutoSense physiologic al monitoring suite + Android smartphone (model N/A)	EMA details N/A; 75.3%–85.7% AutoSense data yields (acceptable data hours/sensor wear hours), with up to 65% data availability after excluding physical activity	development of a model to detect stress from multiple physiological parameters
Saleheen et al. 2015	tobacco	respiration, wrist movement and orientation	61 adult smokers making a quit attempt + 6 smokers for field-based training data	1 day with ad lib smoking, 3 days during quit attempt	smartphone (model N/A); AutoSense physiologic al monitoring suite + smartwatches (model N/A)	overall EMA details N/A (9/33 lapsers did not self-report lapse); 33 lapsers/61 participants included in algorithm development (2,766 total participant-hours of Autosense wear)	development of a model to detect smoking puffs from respiration and wrist movement/orientation; some data also used for craving detection by Chatterjee et al. 2016
Sarker et al. 2016	opioids, poly-drug use	ECG and respiration (controlling for physical activity), GPS	38 opioid-dependent poly-drug users receiving opioid agonist maintenance	4 weeks	smartphone (EMA, model N/A); wireless physiologic al sensor suite (ECG, respiration, accelerometry) + Android-based smartphone (model N/A)	88.0% EMA answer rate; 10.54 hours acceptable ECG data from 12.52 hours sensors wear per day; ~70% of physiologic al data available after excluding physical activity; GPS details N/A	stress inferred from physiological monitoring using the model of Hovsepian et al. 2015; GPS then used to assess environmental features associated with stress inference