Literature DB >> 19199025

Psychophysiological patterns during cell phone text messaging: a preliminary study.

I-Mei Lin1, Erik Peper.   

Abstract

This study investigated the psychophysiological patterns associated with cell phone text messaging (texting). Twelve college students who were very familiar with texting were monitored with surface electromyography (SEMG) from the shoulder (upper trapezius) and thumb (abductor pollicis brevis/opponens pollicis); blood volume pulse (BVP) from the middle finger, temperature from the index finger, and skin conductance (SC) from the palm of the non-texting hand; and respiration from the thorax and abdomen. The counter-balanced procedure consisted of a 2 min pre-baseline, 1 min receiving text messages, 2 min middle baseline, 1 min sending text messages and 2 min post-baseline. The results indicated that all subjects showed significant increases in respiration rate, heart rate, SC, and shoulder and thumb SEMG as compared to baseline measures. Eighty-three percentage of the participants reported hand and neck pain during texting, and held their breath and experienced arousal when receiving text messages. Subjectively, most subjects were unaware of their physiological changes. The study suggests that frequent triggering of these physiological patterns (freezing for stability and shallow breathing) may increase muscle discomfort symptoms. Thus, participants should be trained to inhibit these responses to prevent illness and discomfort.

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Year:  2009        PMID: 19199025     DOI: 10.1007/s10484-009-9078-1

Source DB:  PubMed          Journal:  Appl Psychophysiol Biofeedback        ISSN: 1090-0586


  3 in total

1.  The BlackBerry Project: The Hidden World of Adolescents' Text Messaging and Relations With Internalizing Symptoms.

Authors:  Marion K Underwood; Samuel E Ehrenreich; David More; Jerome S Solis; Dawn Y Brinkley
Journal:  J Res Adolesc       Date:  2015-03-01

2.  Effect of Brief Biofeedback via a Smartphone App on Stress Recovery: Randomized Experimental Study.

Authors:  John F Hunter; Meryl S Olah; Allison L Williams; Acacia C Parks; Sarah D Pressman
Journal:  JMIR Serious Games       Date:  2019-11-26       Impact factor: 4.143

3.  Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia.

Authors:  Vincent W-S Tseng; Akane Sano; Dror Ben-Zeev; Rachel Brian; Andrew T Campbell; Marta Hauser; John M Kane; Emily A Scherer; Rui Wang; Weichen Wang; Hongyi Wen; Tanzeem Choudhury
Journal:  Sci Rep       Date:  2020-09-15       Impact factor: 4.379

  3 in total

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