Answering the missed call: Initial exploration of cognitive and electrophysiological changes associated with smartphone use and abuse

<div><p>Background</p><p>Smartphone usage is now integral to human behavior. Recent studies associate extensive usage with a range of debilitating effects. We sought to determine whether excessive usage is accompanied by measurable neural, cognitive and behavioral changes.</p><p>Method</p><p>Subjects lacking previous experience with smartphones (n = 35) were compared to a matched group of heavy smartphone users (n = 16) on numerous behavioral and electrophysiological measures recorded using electroencephalogram (EEG) combined with transcranial magnetic stimulation (TMS) over the right prefrontal cortex (rPFC). In a second longitudinal intervention, a randomly selected sample of the original non-users received smartphones for 3 months while the others served as controls. All measurements were repeated following this intervention.</p><p>Results</p><p>Heavy users showed increased impulsivity, hyperactivity and negative social concern. We also found reduced early TMS evoked potentials in the rPFC of this group, which correlated with severity of self-reported inattention problems. Heavy users also obtained lower accuracy rates than nonusers in a numerical processing. Critically, the second part of the experiment revealed that both the numerical processing and social cognition domains are causally linked to smartphone usage.</p><p>Conclusion</p><p>Heavy usage was found to be associated with impaired attention, reduced numerical processing capacity, changes in social cognition, and reduced right prefrontal cortex (rPFC) excitability. Memory impairments were not detected. Novel usage over short period induced a significant reduction in numerical processing capacity and changes in social cognition.</p></div

Bar charts show mean ± SEM values of main behavioural tasks.

<p>(a) Mean K coefficient values were significantly higher in SU than in NU (t (49) = 2.14, p = 0.04, d = 0.5) and the resulting discounting curve demonstrated a markedly steeper discounting rate. This effect was not observed in the second phase (F(1,23) = 0.7, p = 0.4). (b) Mean memory accuracy did not differ between SU and NU groups (t(45) = 0.6, p = 0.5). (c) No significant effect on memory was observed (p = 0.6) (d) SU showed significantly poorer arithmetic accuracy as compared to NU (t(42) = 2, p = 0.05, d = 0.6; 6 participants scored zero on accuracy indicating less than 3 accurate and timely trials and were discarded from the analysis). (e) A significant Manipulation X Time interaction effect was found [F (1,22) = 5.3, p = 0.03, η²_p = 0.19)]. Posthoc analysis highlighted a significant (NUsp group; p = 0.025) decline from baseline in accuracy levels of smartphone users while such pattern was not found in the control group (NUco group; n.s).</p

Electrophysiological responses to single TMS pulses in the rPFC.

<p>(a) Grand average rectified ERP plots of early TEP taken from all electrodes under the stimulation coil (FC4, F4, FC6, F6) in the Smartphone users (SU) and nonusers (NU) groups. On the top right, bar charts represent mean ± SEM mean area under curve (AUC) of average early TEP taken from (t (45) = 2.4, p = 0.03, d = 0.6). For a longer time window see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180094#pone.0180094.s004" target="_blank">S1 Fig</a>. (b) Two dimensional topographical plots of EEG recorded activity at 20 ms, 25 ms and 30 ms after pulse onset for SU and NU groups. (c) Three-dimensional topographical plot of group TEP difference (Δ plot) representing the difference in average voltage between SU and NU participants over the time-period 15–40 ms. (d) Early TEP was showed significant negative correlation with hyperactivity subscale as measured in the CAARS questionnaire (R (45) = -0.49, p = 0.006, Bonferroni corrected) (e) The magnitude of the early TEP was negatively correlated with total ADHD symptoms phase 1 (R (45) = -0.39, p = 0.013).</p

Questionnaire results and their correlation with real usage frequency.

<p>Bar charts show mean ± SEM values. (a) CAARS score from the first phase. Total ADHD index (t (49) = 2.8, p = 0.008, d = 0.8), impulsivity (t (49) = 2.5, p = 0.01; p_corrected = 0.07, d = 0.75) and hyperactivity (t (49 = 2.9, p = 0.006, p_corrected = 0.035, d = 0.7) scores were significantly higher in the SU group as compared to the NU group. ADHD symptoms (t (49) = 2.2, p = 0.03, p_corrected = 0.21, d = 0.61) and hyperactivity symptoms (t (49) = 2.3, p = 0.02, p_corrected = 0.14, d = 0.68) also showed a non-significant trend in the same direction (b) Changes in CAARS scores between end and start of intervention in the second study phase is shown. No significant differences between groups were observed (p>0.2 for all domains). (c) The scatterplot shows a marginally significant positive correlation between the total frequency of app usage and CAARS inattention subscales of both SU and NUsp participants (R (24) = 0.5, p = 0.013, p_corrected = 0.08). (d) SU obtained higher CAS (social concern) score than NU (t (49) = -2, p = 0.052, d = 0.61). (e) A significant interaction effect of the manipulation on CAS scores was found (F (1,24) = 6.4, p = 0.018, η²_p = 0.21). Posthoc analysis revealed a significant increase (p = 0.034) from baseline in CAS score in the NUsp group while NUco showed a mild non- significant decrease in CAS (p = 0.15). p_corrected term refers to instances of multiple comparisons where the p value was Bonferroni corrected.</p