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
; 40(5): 468-475, 2018.
Article
em En
| MEDLINE
| ID: mdl-30275623
CONTEXT: Smartphone use is being investigated as a potential behavioral addiction. Most of the studies opt for a subjective questionnaire-based method. This study evaluates the psychological correlates of excessive smartphone use. It uses a telemetric approach to quantitatively and objectively measure participants' smartphone use. METHODOLOGY: One hundred forty consenting undergraduate and postgraduate students using an Android smartphone at a tertiary care teaching hospital were recruited by serial sampling. They were pre-tested with the Smartphone Addiction Scale-Short Version, Big five inventory, Levenson's Locus of Control Scale, Ego Resiliency Scale, Perceived Stress Scale, and Materialism Values Scale. Participants' smartphones were installed with tracker apps, which kept track of total smartphone usage and time spent on individual apps, number of lock-unlock cycles, and total screen time. Data from tracker apps were recorded after 7 days. RESULTS: About 36 % of participants fulfilled smartphone addiction criteria. Smartphone Addiction Scale score significantly predicted time spent on a smartphone in the 7-day period (ß = 0.234, t = 2.086, P = 0.039). Predictors for time spent on social networking sites were ego resiliency (ß = 0.256, t = 2.278, P = 0.008), conscientiousness (ß = -0.220, t = -2.307, P = 0.023), neuroticism (ß = -0.196, t = -2.037, P = 0.044), and openness (ß = -0.225, t = -2.349, P = 0.020). Time spent gaming was predicted by success domain of materialism (ß =0.265, t = 2.723, P = 0.007) and shopping by ego resiliency and happiness domain of materialism. CONCLUSIONS: Telemetric approach is a sound, objective method for evaluating smartphone use. Psychological factors predict overall smartphone usage as well as usage of individual apps. Smartphone Addiction Scale scores correlate with and predict overall smartphone usage.
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2018
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Article