Table 4. Summary of behavioral health outcomes from sensors, wearable, and remote monitoring intervention studies.
P- Participants/Study length, CA- Clinical assessment, H/w-Hardware, S/w-Software.
| Study | Period | Population | Method/Outcomes | CA | H/w, S/w | Sensor |
|---|---|---|---|---|---|---|
| Ponzo et al. (2020) | 262/4 weeks | College students | BioBase application was used for 4 weeks to reduce anxiety and promote well-being. | STAI, PHQ, WEMWBS | SP (iOS), Wristband (BioBeam), Biobase app | Accelerometer, actigraph |
| Doryab et al. (2019) | 160/4 weeks | college students | To detect the loniesss, keep an eye on social and sleeping habits. With an accuracy of 80.2%, it can detect loneliness and changes in loneliness levels, and with an accuracy of 88.4%, it can detect changes in loneliness levels. | UCLA | SP, Wristband, AWARE app (freeware data collection app) | Accelerometer, actigraph, Bluetooth, phone usage, GPS, microphone, SMS usage |
| Sano et al. (2018) | 201//4 weeks | college students | Critical items detected using wearable sensors like temperature, barometer such as routine behavior, socializing for stress, depression with 78.3% accuracy for segregating stress level among students. | ASRM, IBS | SP, wristband (Afectiva), Motion Logger (AMI), Funf open-sensing framework | Accelorometer, actigraph, temperature sensor, GPS, light sensor, phone usage |
| Demasi, Aguilera & Recht (2016) | 44/8 weeks | Healthy adults | Change over and abnormality in sleep, length of sleep are used to predict emotional wellbeing. | BDI, PHQ-9 | SP (Android), Funf opensending framework | Accelorometer, actigraph, Bluetooth |
| Gaggioli et al. (2014) | 121/5 weeks | Healthy adults | Participants reported a signifcant increase in the emotional support skill | COPE-NIV, PHQ, SWLS | SP (iPhone), Wireless cardiovascular belt, body worn wireless sensor | Accelorometer, Bluetooth, Camera, ECG, electrodermal sensor |
| Knight & Bidargaddi (2018) | 120/8 months | open | When comparing self-reported data from activity tracker applications to wearables for psychological anguish/moderate level of psychological distress, wearable devices had considerably longer daily activity duration than smartphone apps. | DASS-21 | SP | Accelorometer, actigraph |
| Szydlo & Konieczny (2016) | 25/2 weeks | Outpatient | The smartwatch recognises 75% of archetypal ASD motions after six sessions of use with an electronic photographic activity programme. | None identifed | SP (Android), Smart- watch | Accelorometer, actigraph |
| Garcia-Ceja et al. (2018) | 30/6 weeks | Healthy adults | Stress detection and prediction using accelerometer data with 95% accuracy | None identifed | SP, Wireless Sensor Data Mining (WISDM), chest sensor, wrist sensor | Accelerometer, actigraph, Bluetooth, microphone, Wi-Fi |
| Huang et al. (2016) | 16/10 days | Students | Examine the relationship between university students’ visits to religious sites and their social anxiety. | SIAS | SP | Accelerometer, GPS |
| Wang et al. (2016) | 21/9-36 Weeks | Outpatient | Use random forest regression to correlate smartphone data with schizophrenia symptoms/Significant association between ground truth and anticipated mental health status scores | EMA (measuring sleep, calm, depression, hope, cognition, thoughts of harm, psychotic symptoms) | SP (Android), CrossCheck app, Funf open sensing framework, MobileEMA System | Accelerometer, app usage, GPS, light sensor, microphone, phone usage, SMS usage |