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 Table of Contents    
Year : 2019  |  Volume : 61  |  Issue : 2  |  Page : 221-222
Social media usage-tracking apps as viable alternatives to self-report measures and adoption of technology for mental health research

Clinical Psychologist, PsyClinic, Delhi - 110 063, India

Click here for correspondence address and email

Date of Web Publication11-Mar-2019

How to cite this article:
Verma T. Social media usage-tracking apps as viable alternatives to self-report measures and adoption of technology for mental health research. Indian J Psychiatry 2019;61:221-2

How to cite this URL:
Verma T. Social media usage-tracking apps as viable alternatives to self-report measures and adoption of technology for mental health research. Indian J Psychiatry [serial online] 2019 [cited 2022 Nov 29];61:221-2. Available from:


The research on the use of social networking sites (SNS) has increased tremendously in the last decade. Mental health correlates of social media use are increasingly studied owing to the prevalence of associations among several psychopathological processes and social site usage. In this regard, a recent study by Barman et al.[1] explored the associations of depression and anxiety with social networking usage among medical students. Few concerns must be raised about the methodology of this study.

The authors have used self-report tools in this study which have various drawbacks in providing accurate measurements of variables, so I would like to bring awareness of the readers at the growing need of using better technology to measure social media usage. A recent Indian study[2] utilized a telemetric approach to investigate social media usage among medical students. This study took data of social media usage through Google Play apps – Callistics, App Usage Tracker, and Instant. These apps provide data of real-time usage of participants as compared to self-report retrospective reporting which is subject to bias. Not only that, the type of data provided and the variables measured are also more relevant and useful for research purposes in mental health area. This leads to the availability of better quality of data because every activity is measured by algorithms of computerized apps, and no subjective reporting is required.

For example, Barman et al's. study measured the frequency and duration of use per day as well as whether the SNS were used late night or early morning. These variables were measured by creating arbitrary categories of time intervals under which participants have to indicate their responses based on their memory. On the other hand, Prasad et al's.[2] study measured the variables (call time, messaging, video and music use, browsing, shopping, gaming, etc.) of smartphone use in actual minutes, of which SNS use was one among many. These kinds of data are totally objective and are not subject to any response biases that are commonly observed in self-report tools.

Seabrook et al.[3] conducted a systematic review of studies about SNS use, anxiety, and depression. They found that studies which have utilized real-time monitoring of SNS usage were able to remove biases in reporting, and such methodologies hold larger scope in bringing clarity in associations of mental health status with social media usage. For example, some researchers[4] have begun using experience-sampling methods (ESMs) where participants are instructed to provide their data at several times of the day, and through this, data about various patterns of social interaction, mood, and thoughts can be acquired. This method is similar to State measurement of variables (such as anxiety); however, its application is far superior to State versions of questionnaire as in State-Trait Anxiety Inventory. Monitoring at various time intervals provides patterns of changes across daily activities, compared to one-time resting-state condition of self-reporting.

Affordable and cheap application of ESM is possible through various usage-tracking apps and SNS-driven data (Likes, Comments, Shares, words and language, etc.) that provide more actual data than that of self-reporting. Seabrook et al. observed that realistic mental health status of SNS users is partly derivable from their patterns of use, language expressions, and profile information (WhatsApp status etc.). Such data also have the potential to identify the levels of severity of depressed and anxious moods, along with their sensitive monitoring of symptoms. Hence, they can provide alternatives to depression and anxiety inventories too. This makes them more widely applicable to prevent the distress of mental disorders by early identification.

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Conflicts of interest

There are no conflicts of interest.

   References Top

Barman L, Mukhopadhyay DK, Bandyopadhyay GK. Use of social networking site and mental disorders among medical students in Kolkata, West Bengal. Indian J Psychiatry 2018;60:340-5.  Back to cited text no. 1
[PUBMED]  [Full text]  
Prasad S, Harshe D, Kaur N, Jangannavar S, Srivastava A, Achanta U, et al. A study of magnitude and psychological correlates of smartphone use in medical students: A Pilot study with a novel telemetric approach. Indian J Psychol Med 2018;40:468-75.  Back to cited text no. 2
[PUBMED]  [Full text]  
Seabrook EM, Kern ML, Rickard NS. Social networking sites, depression, and anxiety: A Systematic review. JMIR Ment Health 2016;3:e50.  Back to cited text no. 3
Csikszentmihalyi M, Larson R. Validity and reliability of the experience-sampling method. J Nerv Ment Dis 1987;175:526-36.  Back to cited text no. 4

Correspondence Address:
Tarun Verma
Clinical Psychologist, PsyClinic, Delhi - 110 063
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/psychiatry.IndianJPsychiatry_468_18

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