| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | AA | AB | AC | AD | AE | AF | AG | AH | AI | |
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1 | Social | Feature | Measurement | Hypothesis | Do Other Frameworks offer this? (or pretend to?) | Feasibility | Available in Public SDK | Sampling Rate? | Privacy Invasiveness | Privacy Considerations | Behavioral Noise | Technical Noise | Technical Effort | API to use | Previous Research | Notes | |||||||||||||||||||
2 | Features describing social behavior, including activity related to phone calls, texting, social network size, and other people in the user’s context. | Call duration (incoming or outgoing)-Call log | The social category had the lowest percentage of statistically significant correlations, by vote counting, across studies (10/38, 26%). Social included features such as call duration and number of conversations, which can be accessed on Android phones, contrary to iPhones [77]. | Ben-Zeev D, Schueller S, Begale M, Duffecy J, Kane J, Mohr D. Strategies for mHealth research: lessons from 3 mobile intervention studies. Adm Policy Ment Health 2015 Mar;42(2):157-167 [FREE Full text] [doi: 10.1007/s10488-014-0556-2] [Medline: 24824311] | Yes | No | all calls? | Low | https://www.apple.com/ca/privacy/ | 2 | 1 | 3 | https://dl.acm.org/doi/pdf/10.1145/1864349.1864394. | ||||||||||||||||||||||
3 | Call frequency (incoming or outgoing)-Call log | Yes | No | All calls? | Low | 2 | 1 | 3 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6200557 | ||||||||||||||||||||||||||
4 | Maximum call duration-Call log | Yes | No | all calls? | Low | 2 | 1 | 3 | https://mental.jmir.org/2018/3/e56/ | ||||||||||||||||||||||||||
5 | SMS text messages (sent or received)-SMS text message log | Yes | No | all texts? (group texts, sms, MMS, messaging apps?) | High | 1 | 1 | 2 | |||||||||||||||||||||||||||
6 | Characters in SMS text message (sent or received)-SMS text message log | Yes | Only as initiated by an app | all texts? (group texts, sms, MMS, messaging apps?) | High | 1 | 1 | 2 | |||||||||||||||||||||||||||
7 | SMS text messages received (characters)-SMS text message log | Yes | Only as initiated by an app | all texts? (group texts, sms, MMS, messaging apps?) | High | 1 | 1 | 2 | |||||||||||||||||||||||||||
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9 | Physical Movement | ||||||||||||||||||||||||||||||||||
10 | Features describing physical activity, including movement and step count. | Activity (afternoon, day, evening, morning, night)-Accelerometer | Compare Acc movement in x,y,z planes during daytime and nighttime | Pattern of lower daytime activity but higher nighttime activity in depression | Burton C, McKinstry B, Szentagotai TA, Serrano-Blanco A, Pagliari C, Wolters M. Activity monitoring in patients with depression: a systematic review. J Affect Disord 2013 Feb 15;145(1):21-28. [doi: 10.1016/j.jad.2012.07.001] [Medline: 22868056] | Yes | Apps can read their own usage and background usage if given permission | What are the cut offs for this and how many samples per hour? | Low | https://www.apple.com/legal/privacy/apple-health-studies/en-ww/ | 1 | 2 | 2 | https://docs.expo.io/versions/v38.0.0/sdk/gyroscope/ | |||||||||||||||||||||
11 | Autocorrelation-Accelerometer. | Autocorrelation is the average of the product of a data sample with a version of itself advanced by a lag. The autocorrelation plot can provide answers to the following questions: (1) Is an observation related to adjacent observation? (2) Is the observed time series white noise? (3) Is the observed time series autoregressive? The autocorrelation function is described by the equation below, | Lower autocorrelation = irregular movement patterns = more depression | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3859053/ | Yes | Yes | What are the cut offs for this and how many samples per hour? | Low | 3 | 2 | 1 | https://dl.acm.org/doi/abs/10.1145/3089341.3089342?casa_token=JuazzHXzK-8AAAAA:7KYsz2_vBzYHC9BgYtgj6Sd-LC1RkB72-dWHYmL29uuTLy9_0w9NHlN5YQ3_WOB_3s6vQ9FHC7hB6g | |||||||||||||||||||||||
12 | Vigorous activity-Accelerometer | In order for physical activity to be classified as MVPA, mean acceleration in the 5s-epoch (ENMO) needed to be at or above 100 mg | A majority of these features indicates that enhanced physical activity and more movement outside of the house are observed when participants score lower on the depression scale. | Yes | Yes | What are the cut offs for this and how many samples per hour? | Low | 3 | 2 | 1 | https://link.springer.com/article/10.1007%2Fs00779-011-0465-2 | ||||||||||||||||||||||||
13 | Distance-Accelerometer, GPS | Accelerometer x, y planes. Access iPhone health app (pedometer). Or GPS distance traveled | Abdullah S, Matthews M, Frank E, Doherty G, Gay G, Choudhury T. Automatic detection of social rhythms in bipolar disorder. J Am Med Inform Assoc 2016 May;23(3):538-543. [doi: 10.1093/jamia/ocv200] [Medline: 26977102] | Yes | Yes | What are the cut offs for this and how many samples per hour? | High | 3 | 2 | 1 | https://www.jmir.org/2015/7/e175/?ncid=txtlnkusaolp00000618 | ||||||||||||||||||||||||
14 | Fourier analysis-Accelerometer | https://www.npmjs.com/package/fourier-transform | Hauge ER, Berle J, Oedegaard KJ, Holsten F, Fasmer OB. Nonlinear analysis of motor activity shows differences between schizophrenia and depression: a study using Fourier analysis and sample entropy. PLoS One 2011 Jan 28;6(1):e16291 [FREE Full text] [doi: 10.1371/journal.pone.0016291] [Medline: 21297977] | Yes | Yes | What are the cut offs for this and how many samples per hour? | Low | 3 | 2 | 1 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6200557 | ||||||||||||||||||||||||
15 | Inactivity duration-Accelerometer | No movement in x,y,z planes. | Faurholt-Jepsen M, Brage S, Vinberg M, Christensen EM, Knorr U, Jensen HM, et al. Differences in psychomotor activity in patients suffering from unipolar and bipolar affective disorder in the remitted or mild/moderate | Yes | Apps can read their own usage and background usage if given permission | What are the cut offs for this and how many samples per hour? | Low | 3 | 2 | 1 | |||||||||||||||||||||||||
16 | Sample Entropy-Accelerometer | Yes | Apps can read their own usage and background usage if given permission | What are the cut offs for this and how many samples per hour? | Low | 3 | 2 | 1 | |||||||||||||||||||||||||||
17 | Movement duration-GPS | Time between start of movement and end of movement | Yes | Apps can read their own usage and background usage if given permission | If I read this in morning and once at night I'd get a distance travelled of 0, probably... so you have to sample throughout day | High | 3 | 2 | 1 | ||||||||||||||||||||||||||
18 | Movement speed-Accelerometer, GPS | GPS: newLocation.speed | Yes | Apps can read their own usage and background usage if given permission | What are the cut offs for this and how many samples per hour? | High | 3 | 2 | 1 | ||||||||||||||||||||||||||
19 | Movement speed variance-GPS | GPS: var(newLoc.speed) (newLoc.speed - averageSpeed) ** 2 for every recorded speed | Yes | Apps can read their own usage and background usage if given permission | What are the cut offs for this and how many samples per hour? | Low | 3 | 2 | 1 | ||||||||||||||||||||||||||
20 | Steps-Accelerometer, Pedometer | Time-sampled total number of steps via pedometer | Yes | Apps can read their own usage and background usage if given permission | What are the cut offs for this and how many samples per hour? | Low | 3 | 2 | 1 | ||||||||||||||||||||||||||
21 | 0 | ||||||||||||||||||||||||||||||||||
22 | Location | ||||||||||||||||||||||||||||||||||
23 | Features describing mobility, including GPS tracking, clustering of location (eg, home stay), and transition time. | Home stay-GPS | Within 100m around their specified address (or the location where they sleep at night) | Longer durations of staying at home correlated with depression | Yes | Yes | How to detect "at home" | High | https://www.apple.com/privacy/docs/Location_Services_White_Paper_Nov_2019.pdf | 1 | 1 | 1 | https://docs.expo.io/versions/v38.0.0/sdk/location/ | Will need to have a way for user to define "home" | |||||||||||||||||||||
24 | Location clusters-GPS | Locations where person spends time | Yes | Yes | Low | 1 | 1 | 1 | https://psycnet.apa.org/doiLanding?doi=10.1037%2Fprj0000130 | Needs more definition | |||||||||||||||||||||||||
25 | Circadian rhythm-GPS/acc | No more location changes on GPS + no more movement in x,y,z planes for 2 hours+ | More regular sleep/wake cycles in healthy participants. Sleeping late and waking up late or at random hours in depression | Yes | Yes | Sleep cycles? | Low | 3 | 2 | 1 | https://dl.acm.org/doi/abs/10.1145/2750858.2805845?casa_token=6DZx7khAVxUAAAAA:NYYCSZnWSVs7mSm4Rqch0VYI2BZkRiBt-uDiWWuryHeg1Kb0ho8wHM3ovpMKnNZ2vf4yviapazRuBQ | Needs more definition | |||||||||||||||||||||||
26 | Entropy-GPS | Time spent at each location cluster | Entropy is a measure that captures the distribution of time spent at the different location clusters registered. Thus, a high entropy would indicate that the participant spends time more uniformly across different location clusters. Because all studies consistently showed a negative correlation, this implies that a higher entropy correlates with a better mood. | Yes | Yes | Low | 2 | 2 | 1 | https://dl.acm.org/doi/abs/10.1145/2462456.2464449?casa_token=8JttqeRVHkQAAAAA:mDJPjaDOme8drRFN9Ht1wU-rPj26T88Kj_5nOUCsVwpZ9znh-6Xy-T9L-wkMixMPkHxjM5jPiEcz-A | |||||||||||||||||||||||||
27 | Home to location cluster-GPS | Average distance between home GPS location and GPS locations where participant spends time | Geographical distance between home and other locations where the participant spends time. The hypothesis is that more depressed participants will remain in closer vicinity to their homes and thus have a smaller home to location cluster. | Yes | Yes | Low | 2 | 2 | 1 | Needs more definition | |||||||||||||||||||||||||
28 | Maximum distance between clusters-GPS | GPS coordinates of farthest two location clusters | Yes | Yes | Low | 3 | 1 | 1 | |||||||||||||||||||||||||||
29 | Transition time-GPS | Time traveling between locations. | Transition time has been currently only investigated by the research group of Saeb et al [7,39], who conducted a study to replicate previous findings of the same features. The first study showed a positive correlation (r=0.21, P=.40), while a second study showed negative correlation (r=−0.32, nonsignificant). The feature then yields a low negative wD due to the latter including more participants and placed more centrally due to the contradictive results. | Yes | Yes | Low | 2 | 2 | 1 | ||||||||||||||||||||||||||
30 | Location variance-GPS | GPS mobility independent of location | Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015). Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. Journal of medical Internet research, 17(7), e175. | Yes | Yes | Low | 2 | 2 | 1 | ||||||||||||||||||||||||||
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32 | Device | ||||||||||||||||||||||||||||||||||
33 | Features describing device (mobile phone or wearable) usage, including app usage, lock or unlock events, and classification of app usage. | Communication or social app usage | The low variability with device-based features could indicate that there is a general tendency for participants to use their phones more, but at the same time, withdraw from the social context by lowering the communication app usage. | Mestry M, Mehta J, Mishra A, Gawande K. Identifying associations between smartphone usage and mental health during depression, anxiety and stress. 2015 Presented at: Proceedings - 2015 International Conference on Communication, Information and Computing Technology, ICCICT 2015; January 16-17, 2015; Mumbai, India. [doi: 10.1109/ICCICT.2015.7045656] | Yes | No | High | https://www.apple.com/privacy/control/ | 3 | 3 | 3 | manually screenshotting their screen time screen every day | https://www.annualreviews.org/doi/pdf/10.1146/annurev-clinpsy-032816-044949 | ||||||||||||||||||||||
34 | Duration of app usage | Yes | Yes | High | 3 | 2 | 2 | ||||||||||||||||||||||||||||
35 | Browser app usage | Yes | No | High | https://www.apple.com/safari/docs/Safari_White_Paper_Nov_2019.pdf | 3 | 3 | 2 | |||||||||||||||||||||||||||
36 | Images taken-Camera | Yes | Yes | High | https://www.apple.com/ios/photos/pdf/Photos_Tech_Brief_Sept_2019.pdf | 1 | 1 | 1 | |||||||||||||||||||||||||||
37 | Number of running apps | Yes | No | Low | https://www.apple.com/privacy/control/ | 3 | 2 | 3 | |||||||||||||||||||||||||||
38 | Response time for Notifications | Yes | App Can Measure this for its own | Low | https://developer.apple.com/library/archive/documentation/NetworkingInternet/Conceptual/RemoteNotificationsPG/APNSOverview.html#//apple_ref/doc/uid/TP40008194-CH8-SW1 | 3 | 3 | 3 | |||||||||||||||||||||||||||
39 | Screen active duration | Thomée S, Härenstam A, Hagberg M. Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults--a prospective cohort study. BMC Public Health 2011 Jan 31;11:66 [FREE Full text] [doi: 10.1186/1471-2458-11-66] [Medline: 21281471] | Yes | No | High | https://www.apple.com/privacy/control/ | 2 | 2 | 2 | ||||||||||||||||||||||||||
40 | Screen Active Frequency | Yes | No | High | 2 | 2 | 2 | ||||||||||||||||||||||||||||
41 | Screen clicks | Yes | Yes, via Mic | High | 3 | 3 | 3 | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6076371/ | |||||||||||||||||||||||||||
42 | Data transmitted via Wi-Fi | Yes | App Can Measure this for its own | High | 2 | 1 | 2 | ||||||||||||||||||||||||||||
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45 | Deviation of F0-Microphone | Muaremi A, Gravenhorst F, Grünerbl A, Arnrich B, Tröster G. Assessing bipolar episodes using speech cues derived from phone calls. In: Lect Notes Inst Comput Sci Soc Telecommun Eng LNICST. 2014 Presented at: MindCare: Pervasive Computing Paradigms for Mental Health; May 8-9, 2014; Tokyo, Japan p. 103-114. [doi: 10.1007/978-3-319-11564-1_11] | |||||||||||||||||||||||||||||||||
46 | Fundamental frequency-Microphone • Harmonics-to-noise ratio-Microphone • Pauses in recording-Microphone • Short turns during conversation-Microphone • Sleep (duration, efficiency, onset latency)-Accelerometer • SD pitch frequency-Microphone • Laying down-Camera • SD sleep-Accelerometer | ||||||||||||||||||||||||||||||||||
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