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. Author manuscript; available in PMC: 2024 Dec 10.
Published in final edited form as: Soc Sci Med. 2024 Jun 28;354:117075. doi: 10.1016/j.socscimed.2024.117075

Table 1.

Study characteristics (N = 47).

Citation Participants’ Characteristics Geographic Method Ecological Momentary Assessment Other Passively Sensor-Based Data Research Objectives
Population Sample size Technology Device Purpose Sampling method Frequency/Interval Monitoring duration Device Mobile APP Outcome measures
1 Ben-Zeev et al., 2015; Ben-Zeev et al., 2015) College students: A cohort of undergraduate and graduate students recruited through class announcements 47 persons GPS Mobile phone Deriving activity’s space/space time characteristics; Quantifying participants’ mobility Random-time NR 10 weeks Mobile phone Unspecified Stress Physical activity pattern, smartphone communication pattern Examine the relationship between Daily Stress (outcome) and several covariates derived from smartphone sensing- Geospatial Activity, Kinesthetic Activity, Speech Duration, and Sleep Duration.
2 Beres et al., 2022; Beres et al. (2022) General population aged 18+: A cohort of adult participants from the Rakai Community Cohort Study (RCCS) 48 persons GPS Mobile phone Recording location coordinates without further analysis Fixed-time, Random-time, User-initiated 2/day, 1/week 90 days Mobile phone Unspecified Food intake, Alcohol use, Smoking, Sexual activity, Condom use None Examine the feasibility and acceptability of EMA and the feasibility of geospatial data collection.
3 Bollenbach et al., 2022; Bollenbach et al. (2022) General population aged 18+: A cohort of population living in a (sub)-urban residential areas in Germany 46 persons GPS, GIS Mobile phone Deriving environmental exposure; Triggering EMA surveys Event-contingent, location-change 5/day 9 days Mobile phone movisensXS Affective states, Social interaction Physical activity pattern Examine associations between social- and physical environmental factors and affective states during walking episodes.
4 Bolte et al., 2019; Bolte et al. (2019) General population: A cohort of participants who were self-declared electrosensitive all over the Netherlands 57 persons GPS GPS logger Interpreting and checking the quality of the sensor data Random-time A 2- or 3-h interval 5 days Mobile phone Unspecified Physical symptoms, Stress, Cognitive deficit in attention Environmental condition Examine association between the measured exposure to radiofrequency electromagnetic fields and nonspecific physical symptoms.
5 Byrnes et al., 2017; Byrnes et al., 2017 Teens aged 14–16: A cohort of teens in a longitudinal study (the Healthy Communities for Teens study) and from 10 cities in the San Francisco, CA area in U.S. 170 persons GPS, GIS Mobile phone Deriving environmental exposure Random-time 2/day 1 month Mobile phone NR Alcohol use, other problem behaviors None Examine the relationships between observed and objective indicators of contextual risks, and the relations of indicators of contextual risks with teen alcohol use and problem behavior.
6 Cornwell and Cagney (2020); Cornwell and Goldman (2020) General population aged 55+: A cohort of older adults in the Realtime Neighborhoods and Social Life Study (RNSLS) 61 persons GPS, GIS Mobile phone Deriving environmental exposure Random-time 4/day 1 week Mobile phone Survey Swipe Pain, Fatigue, Affect, Stress, Sense of safety, Social interaction None Examine the relationships between observed and objective indicators of contextual risks, and examine the relationships between observed and objective indicators of contextual risks with teen alcohol use and problem behavior.
7 Crochiere et al. (2021); Crochiere et al. (2021) Adults with obesity aged 18–70: A cohort of adults reporting BMI 25 50 kg/m2 15 persons GPS Mobile phone Creating a geographic location pair by linking the GPS coordinates with spatial data reported by EMA Semirandom-time, User-initiated 6/day 6 weeks Mobile phone Paco Dietary lapse Physical activity pattern, Sleep pattern Compare the burden and accuracy of commercially available sensors (i. e., GPS, (accelerometer) versus established EMA in dietary lapse prediction.
8 Doherty et al., 2014; Doherty et al., 2014) General population: A group of visitors to the Pinery Provincial Park in Canada 72 persons GPS Mobile phone Mapping participants’ spatial activity Random-time, User-initiated An interval of 35 min plus a random number 1 day Mobile phone Unspecified Mood/Emotion None Demonstrate how passive tracking of human activity using GPS/accelerometers can be combined with ESM to explore the perceived health and well-being impacts of contact with nature.
9 Elliston et al., 2020; Elliston et al., (2020) General population aged 18+: A cohort of participants recruited by looking at everyday food choices through social media advertising and a university staff newsletter in Tasmania 79 persons GPS, GIS Mobile phone Deriving environmental exposure Random-time 5/day 2 weeks Mobile phone Unspecified Mood, Food and drink intake, Food craving None Compare the subjective and GIS assessments of the momentary food environment and assess the feasibility of using GIS data to predict eating behavior and inform geofenced interventions.
10 Epstein et al., 2014; Epstein et al., 2014 Outpatients aged 18–65: A cohort of outpatients admitted for methadone maintenance at a research clinic in Baltimore, MD 27 persons GPS, GIS GPS logger Deriving environmental exposure Random-time 3/day 16 weeks PalmPilot Unspecified Mood, Stress, Drug craving None Examine the relationship between the neighborhood surroundings and mood and behavior in drug misusers.
11 Glasgow et al., 2019; Glasgow et al. (2019) General population aged 18+: A cohort of population living in three metropolitan areas in U.S 229 persons GPS, GIS Mobile phone Deriving environmental exposure User-initiated NR 1 week Mobile phone Daynamica Mood, Physical activity None Explore the relationship between mood during travel and transport modes, activity, and the built and natural environments.
12 Jacobson and Bhattacharya, 2022; Jacobson and Bhattacharya (2022) College students with clinical anxiety disorder symptoms: A cohort of students from a psychology subject pool 32 persons GPS Mobile phone Deriving environmental exposure NR A 1-h interval 1 week Mobile phone Mood Triggers Anxiety, Depression, Behavioral avoidance Physiological outcomes, smartphone communication pattern, Environmental condition Predict future anxiety symptoms among a sample reporting clinical anxiety disorder symptoms by using smartphone sensor-based data and personalized deep learning models.
13 Kamalyan et al., 2021; Kamalyan et al. (2021) Patients aged 50+: A cohort of patients from a participant pool at the University of California San Diego (UCSD) HIV Neurobehavioral Research Program (HNRP) or through the community (HIV clinics, flyers, and community centers) 88 persons GPS Mobile phone Quantifying participants’ mobility Unspecified 4/day 2 weeks Mobile phone NR Mood, Fatigue, Pain, Social interaction None Examine real-time relationships between life-space, mood, fatigue, and pain, and assess the moderating effect of social interactions on the effect of life-space on mood.
14 Kirchner et al., 2013; Kirchner et al., 2013 Smokers aged 18+: A cohort of smokers who lived in Washington DC (DC) and contacted the DC Tobacco Quitline (DCQL) 475 persons GPS, GIS Mobile phone Deriving environmental exposure Random-time, User-initiated 3–4/day 1 month Mobile phone mEX system Craving to smoke, smoking status None Examine the association between the real-time geospatial exposure to point-of-sale tobacco (POST) and subjective craving to smoke.
15 Koch et al., 2018; Koch et al., 2018 General population aged 12–17: A cohort of Adolescents from the URGENCY study (Impact of Urbanicity on Genetics, Cerebral Functioning and Structure and Condition in Young People) in Germany. 113 persons GPS Mobile phone Triggering EMA surveys Fixed-time, location-change 4–7/day, 8–17/day 1 week Mobile phone movisensXS Mood Physical activity pattern Investigate the association of mood with non-exercise activity in adolescents.
16 Koch et al., 2020; Koch et al., (2020) General population aged 12–17: A cohort of Adolescents from the URGENCY study (Impact of Urbanicity on Genetics, Cerebral Functioning and Structure and Condition in Young People) in Germany. 134 persons GPS Mobile phone Triggering EMA surveys Random-time, location-change 4–7/day 1 week Mobile phone movisensXS Mood Physical activity pattern Investigate the association of mood incidental activity, exercise activity, and sports in adolescents.
17 Kondo et al., 2020; Kondo et al. (2020) General population aged 18–75: A cohort of participants from the PHENOTYPE (The Positive Health Effects on the Natural Outdoor environment in Typical Populations) project 368 persons GPS, GIS Mobile phone Deriving environmental exposure Random-time NR 1 week Mobile phone CalFit Mood Physical activity pattern Examine the association between mood and exposure to green space.
18 Kou et al., 2020; Kou et al. (2020) General population aged 18–60: A cohort of population residing Meiheyuan community for over 1 year in Beijing, China 101 persons GPS Mobile phone Deriving activity space/space time characteristics; Validating and correcting the data of participants’ activity-travel dairies Fixed-time 4/day 2 days Mobile phone NR Stress, Environmental perception None Examine the relationships among contextual effects, momentary measured noise, perceived noise, and psychological stress.
19 Kowitt et al., 2021; Kowitt et al. (2021) General population aged 16–20: A cohort of youth in California city areas 83 persons GPS, GIS Mobile phone Deriving environmental exposure Fixed-time 1/day 2 weeks Mobile phone Unspecified Cigar use, Environmental perception None Examine associations between perceived and objective exposure to tobacco marketing and cigar use.
20 Labhart et al., 2020; Labhart et al. (2020) General population aged 16–25: A cohort of population in the nightlife districts of the two major nightlife hubs in Switzerland, Lausanne, and Zurich 241 persons GPS Mobile phone Recording location coordinates without further analysis Fixed-time, Random-time 2/day 1 week Mobile phone Youth@Night Drinking behaviors, Environmental perception Physical activity pattern, Smartphone communication pattern, Smartphone usage Describe a smartphone application developed to document young adults’ nightlife and drinking behaviors and investigate the impact of this application on participants’ lives.
21 Lipperman-Kreda et al., 2020; Lipperman-Kreda et al. (2020) General population aged 16–20: A cohort of youth in California city areas 100 persons GPS, GIS Mobile phone Deriving environmental exposure Fixed-time 1/day 2 weeks Mobile phone Unspecified Smoking None Examine whether daily exposure to tobacco outlets within activity spaces is associated with cigarette smoking and with the number of cigarettes smoked by youth that day.
22 Lipperman-Kreda et al., 2022; Lipperman-Kreda et al. (2022) General population aged 16–20: A cohort of youth in California city areas 100 persons GPS, GIS Mobile phone Deriving environmental exposure Fixed-time 1/day 2 weeks Mobile phone Unspecified Tobacco and cannabis use and co-use, Environmental perception None Investigated the association of tobacco and cannabis use and co-use with youth daily activity spaces, travel patterns, and exposure to tobacco retail marketing.
23 MacKerron and Mourato, 2013; MacKerron and Mourato, 2013 General population: A cohort of participants recruited by coverage in traditional and social media 21947 persons GPS, GIS Mobile phone Deriving environmental exposure Random-time 2/day 6 months Mobile phone Mappiness Happiness, Physical activity, Social interaction None Explore the relationship between happiness and individuals’ immediate environment.
24 Mardini et al., 2021; Mardini et al. (2021) Patients aged 65+: A cohort of older adults with knee osteoarthritis 19 persons GPS Smartwatch Quantifying participants’ mobility Random-time 3/day NR Smartwatch ROAMM Pain None Examine the temporal association between ecological momentary assessments of pain and GPS metrics in older adults with symptomatic knee osteoarthritis.
25 McIntyre et al., 2021; McIntyre et al. (2021) Patients aged 18–65: A cohort of adults diagnosed with Major Depressive Disorder by a healthcare provider 200 persons GPS Mobile phone Quantifying participants’ mobility NR NR 90 days Mobile phone mind.me Depression None Validate the accuracy of the mind.me application for the assessment of depressive symptoms in adults.
26 McQuoid et al., 2018; McQuoid et al., 2018 Young adults aged 18–26: A cohort of young adult bisexual smokers in a larger GEMA study 17 persons GPS, GIS Mobile phone Mapping participants’ spatial activity User-initiated, Random-time 3/day 30 days Mobile phone PiLR Health Smoking, Cigarette craving, Mood, Environmental perception None Investigate participants’ spatial and temporal patterns of smoking and cravings, situational factors and place-based practices driving patterns of smoking and cravings, and how bisexual identity interplays with situational factors and place-practices of smoking and cravings.
27 McQuoid et al., 2019; McQuoid et al. (2019) Young adults aged 18–26: A cohort of young adult bisexual smokers in a larger GEMA study 17 persons GPS, GIS Mobile phone Mapping participants’ spatial activity User-initiated, Random-time 3/day 30 days Mobile phone PiLR Health Smoking, Cigarette craving, Mood, Environmental perception None Investigate participants’ spatial and temporal patterns of smoking and cravings, situational factors and place-based practices driving patterns of smoking and cravings, and how bisexual identity interplays with situational factors and place-practices of smoking and cravings.
28 Mennis et al., 2016; Mennis et al., 2016 General populations aged 13–14: A cohort of participants enrolled in a study of urban adolescent substance use 139 persons GPS, GIS Mobile phone Recording location coordinates without further analysis NR 3–6/day 1 year Mobile phone NR Stress None Investigates the association of activity space-based exposure to neighborhood disadvantage with momentary perceived stress and safety, and the moderation of substance use on those association.
29 Meyerhoff et al., 2021; Meyerhoff et al. (2021) General population aged 18+: A cohort of population recruited through ResearchMatch and the Center for Behavioral Intervention Technologies research registry. 282 persons GPS Mobile phone Quantifying participants’ movement/mobility NR NR 16 weeks Mobile phone Passive Data Kit Depression None Evaluate the association of changes in phone sensor-derived behavioral features with the subsequent changes in mental health symptoms (i. e., anxiety and social Anxiety).
30 Mitchell et al., 2014; Mitchell et al., 2014 Patients aged 18–50: A cohort of adults with attention deficit hyperactivity disorder enrolled in a larger EMA study on smoking and psychiatric symptoms in U.S. 10 persons GPS GPS logger Recording location coordinates without further analysis User-initiated 5/day 1 week Handheld computer Entryware Designer Smoking Smartphone communication pattern, Smartphone usage Assess the acceptability and feasibility of acquiring and combining EMA and GPS data from adult smokers with attention deficit hyperactivity disorder.
31 Parrish et al., 2022; Parrish et al. (2020) Patients aged 18–65: A cohort of adults with schizophrenia or schizoaffective disorder 105 persons GPS Mobile phone Quantifying participants’ movement/mobility Random-time 7/day 1 week Mobile phone Samplex Emotion None Evaluated the associations between emotional experiences in relation to life-space among people with schizophrenia compared to healthy controls
32 Pellegrini et al., 2022 (Pellegrini et al., 2022) Patients versus Health control aged 18+: A cohort of outpatients from Massachusetts General Hospital with major depressive disorder, bipolar I or II disorder, schizophrenia or schizoaffective disorder vs. A cohort of population with no axis I psychiatric disorder 45 persons GPS Mobile phone Quantifying participants’ movement/mobility Unspecified At least 5/week 8 weeks Mobile phone Beiwe Mood, Sleep quality, Physical activity, Social interaction Physical activity, Smartphone communication pattern Predict depression severity based on phone- based PHQ-8 and passive measures.
33 Raugh et al., 2020; Raugh et al. (2020) Patients versus Health control: A cohort of patients with psychiatric diagnoses from local community outpatient mental health centers vs. A cohort of population without psychiatric diagnoses from local community 105 persons GPS Mobile phone Quantifying participants’ movement/mobility Quasi-random time 8/day 6 days Mobile phone mEMA Avolition, Asociality, Anhedonia, Physical activity, Social interaction None Evaluated the psychometric properties of a novel “passive” digital phenotyping method: Geolocation.
34 Raugh et al., 2021; Raugh et al. (2021) Patients versus Health control: A cohort of patients with psychiatric diagnoses from local community outpatient mental health centers vs. A cohort of population without psychiatric diagnoses from local community 109 persons GPS Mobile phone Recording location coordinates without further analysis Quasi-random time, user-initiated 8/day 6 days Mobile phone mEMA Anhedonia Physiological outcomes, Physical activity pattern, smartphone communication pattern Evaluated levels of adherence, feasibility, and tolerability for active and passive digital phenotyping methods recorded from smartphone and smartband devices.
35 Reichert et al., 2017; Reichert et al., 2017 General population aged 18–28: A cohort of Adolescents from the URGENCY study (Impact of Urbanicity on Genetics, Cerebral Functioning and Structure and Condition in Young People) in Germany. 93 persons GPS Mobile phone Triggering EMA surveys Fixed-time, location-change 9–22/day 1 week Mobile phone movisensXS Mood Physical activity Assess the association of exercise and non-exercise with mood and investigate differential effects of exercise and non-exercise on mood.
36 Rhew et al., 2022; Rhew et al. (2022) General population aged 21–27: A cohort of young adults participating in two separate research projects related to substance use in U.S. 14 persons GPS, GIS GPS logger Deriving environmental exposure Fixed-time, Random-time 4/day 2 weeks Mobile phone NR Marijuana use, Craving to marijuana None Examine spatio-temporal exposures associated with marijuana use among young adults.
37 Roy et al., 2019; Roy et al. (2019) General population aged 25–65: A cohort of population enrolled in the African American Women’s Daily Life Study 79 persons GPS GPS logger Deriving environmental exposure Random-time 5/day 1 week Mobile phone NR Snack and sweetened beverage intake, Physical activity, Social interaction, Environmental perception None Examined relationships between contextual factors and within-person variations in snack food and sweetened beverage intake in African American women.
38 Seto et al., 2016; Seto et al., 2016 College students: A cohort of students at the Kunming Medical University in China 12 persons GPS, GIS Mobile phone Deriving environmental exposure NR 5/day 2 weeks Mobile phone CalFit Chi and Dong Emotion, Meal and snack intake Physical activity pattern Demonstrate individual-based modeling methods relevant to a person’s eating behavior and compare such approach to typical regression models.
39 Shoval et al., 2018; Shoval et al., 2018 General population: A group of tourists visiting Jerusalem and residing at a centrally located youth hostel in Israel 144 persons GPS Mobile phone Triggering EMA surveys; Mapping participants’ emotional characteristics of urban environments Geofencing, Random-time NR 1 day Mobile phone Sensometer Emotion Physiological outcomes Map the emotional characteristics of a large-scale urban environment using aggregative measures of emotion.
40 Su et al., 2022; Su et al. (2022) General population: A cohort of population residing in Tangxia Street in Tianhe District in Guangzhou, China 144 persons GPS, GIS Mobile phone Deriving environmental exposure Fixed-time 4/day 2 days Mobile phone NR Emotion Environmental condition Examine the association of momentary happiness with immediate urban environments.
41 Sukei et al., 2021; Sukei et al. (2021) Outpatients aged 18+: A cohort of outpatients recruited from community clinics 943 persons GPS Mobile phone Quantifying participants’ movement/mobility NR NR At least 30 days Mobile phone eB2 MindCare Emotion, Sleep Sleep pattern, Smartphone usage Present a machine learning-based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions.
42 Tao et al., 2021; Tao et al. (2021) General population: A cohort of population residing in Meiheyuan Community in Beijing, China 120 persons GPS Mobile phone Recording the precise activity and travel location to correct the detailed spatiotemporal information of activity-travel dairies Fixed-time 4/day 2 days Mobile phone NR Stress environmental condition Assess the associations of co-exposures to air pollution and noise with psychological stress.
43 Tornros et al., 2016; Tornros et al., 2016 General population: A cohort of Adolescents from the URGENCY study (Impact of Urbanicity on Genetics, Cerebral Functioning and Structure and Condition in Young People) in Germany. 143 persons GPS, GIS mobile phone Deriving environmental exposure; Triggering EMA surveys Fixed-time, location-change NR 1 week Mobile phone movisensXS Mood None Compare temporal and location-based sampling strategies for global positioning system-triggered electronic diaries.
44 Wray et al., 2019; Wray et al. (2019) General population aged 18+: A cohort of population using gay-oriented smartphone dating applications 76 persons GPS mobile phone Triggering EMA surveys Geofencing, User-initiated NR 30 days Mobile phone MetricWire Alcohol use, Sexual activity, Social interaction, Environmental perception None Examine the feasibility of using geofencing to examine social/environmental factors related to alcohol use and sexual perceptions in a sample of gay and bisexual men.
45 Xia et al., 2022; Xia et al. (2022) Adolescent patients: A cohort of adolescents and young adults with affective instability from the Penn/CHOP Lifespan Brain Institute or through the Outpatient Psychiatry Clinic at the University of Pennsylvania. 41 persons GPS Mobile phone Quantifying participants’ movement/mobility Fixed-time NR 3 months Mobile phone Beiwe Mood, Sleep Physical activity pattern Examined whether individuals have person-specific mobility pattern by linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity.
46 Yerushalmi et al., 2021; Yerushalmi et al. (2021) Patients and their partners: A cohort of population with bipolar disorder and their partners living together 8 persons GPS Mobile phone Deriving environmental exposure Random-time, User-initiated 2/day An average of 123 days Mobile phone BADAS Depression, Mania, Sleep, Medication adherence None Assess the association of BD symptoms (both depression and hypo/mania) with partner mood (positive and negative affect.
47 Zhang et al., 2020; Zhang et al., 2020) General population aged 18+: A cohort of population residing in Tangxia Street in Tianhe District in Guangzhou, China GPS, GIS Mobile phone Linking participants with their respective partners; Determining whether participants were with their partners during EMA surveys Fixed-time 4/day 2 days Mobile phone NR Annoyance, Environmental perception Environmental condition Examine the influence of the geographic context of the activity places and daily acoustic environment on participants’ real-time annoyance.