Table 5.

Summary of studies in other domains, ordered by condition then year of publication.

Condition	Author (Year)	Study Purpose	Principal Findings	Sensors Used		Sample Size & Type	Study Length (Days)
Addiction	Lee et al. (2014) [48] Korea	Correlate application use with smartphone addiction	Significant positive correlation between smartphone daily use time and the Korean smartphone addiction scale.	• Application usage • GPS		14 General sample	> 7
Addiction	Naughton et al. (2016) [52] UK	Evaluate a just-in-time intervention using location data to send timely messages to smokers	Feasible but some non-compliance in reporting smoking.	• GPS		13 Smokers	34*
Chronic heart failure	Aranki et al. (2016) [31] USA	Sense physical activity among people with chronic heart failure for transmission to doctors	Feasible despite technological and usability challenges.	• Accelerometer • Call logs • GPS • Proximity sensor		15 People with chronic heart failure	< 90
Geriatrics	Vathsangam et al. (2014) [60] USA	Evaluate the detection of physical activity from accelerometer data in order to encourage older adults to exercise	Participants appreciated the utility of the application but would like more feedback	• Accelerometer		8 Older adults	21
Geriatrics	Sanchez et al. (2015) [58] Mexico	Predict loneliness in older adults to send them positive messages	Correct classification of family loneliness and spousal loneliness for > 80% of participants, with the average time spent out of home and total of times out of home found to be the most important attributes	• Call logs • GPS • SMS patterns		12 Older adults	7
Stress	Stutz et al. (2015) [59] Austria	Correlate smartphone data with stress	Significant correlations between perceived stress scores (daily and weekly averages) and the sensed features, with noisiness (positive correlation), number of time device is powered on (positive), and changes in light (positive) among the most significant features		• Accelerometer • Application usage • Call logs • Device activity • Light sensor • Microphone • SMS patterns	15 University students	14
Stress	Garcia-Ceja et al. (2016) [43] Italy	Detect and predict stress from accelerometer data	• Prediction of stress with 95% accuracy using the best similar-user model (decision tree) • Prediction of stress with 95% accuracy using the best similar-user models (decision tree and Naïve Bayes). • Prediction of stress with 87% accuracy using the best general model (decision tree).		• Accelerometer	30 Company employees	40

GPS: Global Positioning System; SMS: Short Message Service;

^*average enrollment length among participants, Older adults: people 60 years old or older.