Preliminary analyses
Results
Preliminary analyses
First - Present a table that summarise each of the variables. In the table present means, standard deviations and the range of scores (min and max). This will be for the following variables
WASI
PPVT
Forward Digit Span
Backward Digit Span
SUQ_G
SUQ_A
After you present the table discuss some trends and discuss whether parametric assumptions have been met (i.e., whether the data is normally distributed).
Second Present the two figures that show alpha power on the working memory task. There are two things to discuss here. First present the head plot. This shows the distribution of alpha power across the head during the wait period during the Sternberg task. This figure shows alpha power is strongest at the occipital regions (Oz and PO2).
Data from these two electrodes were submitted to a time-frequency analyses commencing from the time the participants had to wait for three seconds before providing a manual response. of these two electrodes. This part of the task was analysed because it elicited the greatest level of alpha power. The results from the time-frequency analyses are presented in Figure X.
That we selected a region from the time-frequency plot where alpha power weas at its highest. This is shown the black box and then we selected than region for each participants and then averaged all the values. So for each participant we analysed their average alpha power between 500 to 1700 ms from the start of the wait period.
Correlation analyses.
Partial correlations were copmuted between alpha power, digit span (fwd & bwd), and the parts of the smartphone survey. Results from the analyses are presented Table X.
Discuss the correlation alpha power and digit span.
Discussion
Opening paragraph:
re-state aim of the study.
mention whether the data supported the hypothesis.
Positive correlation greater smartphone use was associated with lower levels of alpha power on the Sternberg task and higher scores on the forward digit span. No association on the backward digit. An positive association between short-term memory and alpha power.
Next paragraph: correlation between alpha power and digit span.
Which findings is this consistent with?
Which finding is this not consistent with?
The interpretation: higher working memory is associated with lower power this may occur because people with superior working memory do not need to inhibit as much. In short, lower levels alpha power are associated superior working memory.
Next paragraph: correlation between alpha power and smartphone survey
SUQ_G
SUQ_A
Next paragraph: correlation digit span and smartphone survey
Forward significant
Backward non-significant
Finish this paragraph with what the relationship between smartphone use and working memory.
Limitations
Sample size
Generalisability
Causality
Final conclusion.
Current Sample Size: 34 participants
Participant summary
Participant ages: Mean age: 28.4 years
Standard Dev: 9.89 years
Range: 20 60 years
Gender breakdown:16 female
18 male
0 non-binary
All participants had completed secondary education.
Handedness: 27 participants right handed (score of 80 or more on the handedness inventory)
7 participants left handed (scored -40 or lower)
Results
Subheading: Preliminary analyses
First The first section of the results presents summary data from the standardised tests and Sternberg Task. Present a table that summarise each of the variables. In the table present means, standard deviations and the range of scores (min and max). This will be for the following variables
WASI
PPVT
Forward Digit Span
Backward Digit Span
SUQ_G
SUQ_A
Sternberg Accuracy
Variable M SD Range
Matrix Reasoning Subtest 58.9 6.1 45 - 70
Peabody Picture Vocabulary Test - Forward Digit Span - Backward Digit Span - Sternberg Task Accuracy -
After you present the table discuss some trends and discuss whether parametric assumptions have been met (i.e., whether the data is normally distributed).
Second The next section of the results presents computation of alpha power from each participant. Present the two figures that show alpha power on the working memory task. There are two things to discuss here. First present the head plot. This shows the distribution of alpha power across the head during the wait period during the Sternberg task. This figure shows alpha power is strongest at the occipital regions (Oz and PO2).
Data from these two electrodes were submitted to a time-frequency analyses commencing from the time the participants had to wait for three seconds before providing a manual response. This part of the task was analysed because it elicited the greatest level of alpha power. The results from the time-frequency analyses are presented in Figure X.
That we selected a region from the time-frequency plot where alpha power weas at its highest. This is shown the black box and then we selected than region for each participants and then averaged all the values. So for each participant we analysed their average alpha power between 500 to 1700 ms from the start of the wait period.
Subheading: Correlation between smartphone use working memory and alpha power.
The next set of analyses examined the relationship between alpha power. Partial correlations were compute between alpha power, digit span (fwd & bwd), and the parts of the smartphone survey. Results from the analyses are presented Table X.
Discuss the correlation alpha power and digit span.
Discussion
Opening paragraph:
re-state aim of the study.
mention whether the data supported the hypothesis.
Positive correlation greater smartphone use was associated with lower levels of alpha power on the Sternberg task and higher scores on the forward digit span. No association on the backward digit. An positive association between short-term memory and alpha power.
Next paragraph: correlation between alpha power and digit span.
Which findings is this consistent with?
Which finding is this not consistent with?
The interpretation: higher working memory is associated with lower power this may occur because people with superior working memory do not need to inhibit as much. In short, lower levels alpha power are associated superior working memory.
Next paragraph: correlation between alpha power and smartphone survey
SUQ_G: negative correlation: people who reported using their phone more and lower alpha power.
SUQ_A: alpha power.
Next paragraph: correlation digit span and smartphone survey
Forward significant
Backward non-significant
Finish this paragraph with what the relationship between smartphone use and working memory.
Limitations
Sample size
Generalisability
Causality
Final paragraph.
The Relationship between Smartphones and Working Memory
A Thesis Proposal
Presented to the
Faculty of the
Department of Health
School of Psychology
Deakin University
In Partial Fulfillment
of the Requirements for the Degree
H650
Graduate Diploma of Psychology (Advanced)
By
Lakhmani, Shelica, S.
220475606
November 2022
Thesis Supervisor: Professor Jarrad Lum
TABLE OF CONTENTS
Chapter 1 Introduction
A. Introduction
B. Past Research
C. SynthesisD. FrameworkCE. . Aims and Hypothesis
DF. . Significance of the Study
EG. . Scope and delimitations
Chapter 2 Method
A. Research DesignParticipants
B. Sample and Sampling DesignMaterials
C. Procedure Instruments D. ProcedurElectroencephalographye
E. Data Analysis
F. Methodological Limitations
Results
Discussion
References
Appendix
ABSTRACT
Working Memory helps us hold information and is important for our behavior as it is what we use to make decisions. The objective of the present study was to investigate the relationship of smartphones and how it affects out working memory. A total of ___ participants, ___ females and ___ males, agreed to participate. The instruments that were used for the present study were ________________. The result from analyzing the data set using ______ showed that smartphones appear to be significant predictors of working memory.
CHAPTER IINTRODUCTION
There has been increased use of smartphones (Statistica, 2022). Smartphones were used by 3.67 billion people in 2016, and 6.259 billion people in 2021, and the number is expected to rise to 7.69 billion by 2027. (Cha & Seo, 2018). There has been a debate as to whether prolonged use of smartphones has a positive or negative impact on our cognitive functioning (Akhtar & Ward, 2020). For example, the World Health Organization has advised parents to limit the screentime of children, although, little is known about the correlation between screentime and mental health impacts (WHO, 2018). There is also some debate as to whether the smartphone has a detrimental effect on working memory (Cherry, 2020). This study investigates the relationship between smartphone use and working memory using Electroencephalographic (EEG).
Past research examining the relationship between smartphone use and working memory
Working memory refers to the ability to temporarily store and process or manipulate information (Baddelely, 2003). Initially, it was thought that the frequency of the radio waves has a detrimental impact on brain functioning and working memory (Krause et al., 2000). This proposal has since been thoroughly refuted (Center for Devices and Radiological Health, 2020). More recently, some researchers (Chu, Qaisar, Shah, Jalil, 2021) have suggested excessive smartphone use may reduce the ability to maintain attention, which in turn, has a negative effect on working memory (Olson et al., 2021). Only a small number of studies, however, have been conducted examining the relationship between smartphones and working memory. The results of these studies have been summarisedsummarized in two systematic reviews:
A review by Chein, Wilmer, and Sherman (2017) explored the relationship between smartphone use and a range of cognitive abilities including attention and working memory. In this review, a total of 8eight studies were found that examined the relationship between smartphone use and working memory. These studies measure how much time participants spent on their smartphones. Working memory was measured using a standardized test. In six studies (Abramson et al., 2009; Alloway and Alloway, 2012; Cain et al., 2016; Frein et al., 2013; Uncapher et al., 2015; Sanbonmatsu et al., 2013), greater smartphone use was associated with poorer working memory. . In two other studies (Baumgartner et al., 2014; Sparrow et al., 2011), no association was found. Based on this literature, the authors of the review suggested that available researchesresearch indicate that prolonged smartphone is associated with poorer working memory.
A review by Liebherr, Schubert, Antons, Montag, and Brand (2020) explored whether smartphone use negatively affects attention and working memory. This review concluded smartphone use may have beneficial effects on selective attention, divided attention, and switching attention, despite the growing literature on the adverse effect. A total of 26twenty-six studies were reviewed that examined the relationship between smartphones, attention, and working memory. In these studies, smartphone use was operationalized as participants who spent less than 30 minutes to 12 hours per day on their smartphones. Twenty-one studies examined in this review, studied the association between attention and smartphone use. Attention in these studies were measured using MRI, ambulatory assessmentsassessments, and respective questionnaires to test individual attributes/situational factors. In nineteen of these studies, found smartphone use was associated with poorer attention to their surroundings, for example a participant walking while using a smartphone is not nt aware of what is happening in the roadside and have a decreased auditory attention or using a phone while driving showed in the eye tracking data a reduced attention to the road compared to only driving (Bargh, 1982; Caird et al., 2008; Chang and Tang, 2015; Connor, Egeth, & Yantis, 2004; Corbetta& Shulman, 2002; Geller & Shaver, 1976; Haga et al., 2016; Hadar et al., 2017; Itti & Koch, 2001; Ito and Kawahara, 2017; Katsuki and Constantinidis, 2014; Kushlev et al., 2016; Pielot et al., 2014; Roye et al., 2007; Sahami Shirazi et al., 2014; Shelton, Elliott, Eaves, & Exner, 2009; Strayer and Drews, 2007; Stothart et al., 2015; Ward et al., 2017). One study (Strayer and Johnston, 2001) found that depending on the application that the participant is using that would make them inattentive. . A further study (Marty-Dugas et al., 2018) found no relationship between smartphone use and inattention. Five studies examined in this review studied the association between working memory and smartphone use. Working memory was examined using numerical processing task and ambulatory assessment Inin three studies, greater smartphone use was associated with poorer working memory (Hartanto and Yang, 2016; Kalafatakis, Bekiaridis-Moschou, Gkioka, & Tsolaki, 2017; Ward et al., 2017). In one study (Hadar et al., 2017), no association between smartphone use and working memory was found. Overall, very few studies have examined the relationship between working memory and smartphone use.
The mechanisms through which smartphone use may disrupt working memory has not been extensively examined (Chein, Wilmer, and Sherman, 2017). One idea is that smartphone use may negatively affect working memory via disrupting sleep. He, Tu, Xia, Su, and Tang (2020) examined the relationship between smartphone use before bedtime and working memory. In this study, participants were randomly allocated to two conditions. Participants in the intervention condition were asked not to use their mobile phones 30 minutes before bedtime. Participants in the control condition were not provided with any specific instructions regarding smartphone use. . The smartphone was measured by screentime. In this study sleep quality was also examined. Working memory was examined using 0-,1-,2-back task (n-back task). This task requires both the short-term storage and processing of information. The dependent variables from this task were accuracy and reaction time. The n-back task was presented before and after the intervention. . Participants in the intervention group had faster reaction time in the 0- and 1- back task and higher accuracy in the 1- and 2- back task on the n-back task compared to the control group. The group with restricted mobile phone use also had significant improvement in sleep quality. The results were interpreted to suggest that greater mobile phone use may negatively attentional processes, via disrupting sleep, which in turn decreases working memory capacity.
Another study demonstrated that smartphone use increased inattentiveness in high school students. Abramson, Benke, Dimitiadis, Inyang, Sim, Wolfe, and Croft (2009) examined the relationship between mobile use and cognitive function in young adolescents. They conducted a cross-sectional study among 7thseventh-grade students. The participants filled out an exposure questionnaire based on the Interphone study. The primary objective of this questionnaire is to test RF exposure. In this study to test cognitive ability, they used a computerized cognitive test battery and the Stroop colourcolor-word test. On the cognitive tests, to test working memory 0-,1-,2-back task (n-back task) was used. The dependent variables from the working memory test were accuracy and reaction time. . The results showed that participants who used their mobile phones more had less accuracy and faster response time on the working memory test. Students who received more calls per week had a faster reaction time for learning tasks and less accurately on working memory tests. The authors suggested smartphone use increased inattenstivnessinattentiveness/impulses. This may be related to an impulsive behavior of children who would favor a quicker response time rather than accuracy.
Finally, not all studies show that smartphone use has a negative impact on working memory. A study by Toh, Ng, H.YangH. Yang, S.Yang (2021) examined the relationship between screen time, checking frequency, and problematic use of smartphones on executive function. Participants in this study were 170 undergraduate students. In this study smartphone use was assessed using a self-report measure. Working memory was assessed using operation span tasks that require participants to temporarily store and process information. The results showed that greater smartphone use positively correlated with working memory. That is, participants who used their smartphones more, performed better on the operation span tasks. Switching between applications would need participants to hold the content in their minds which would benefit working memory. Furthermore, the results imply that, for intervention rather than screen time, it is better to aim for checking frequency and problematic use as it can hamper ones cognitive abilities.
The Connection between Working Memory and EEG (Alpha Waves)
It has been suggested that smartphone use may either negatively or positively affect working memory via influences on attention. Attentional-related processes can be measured using electroencephalography (EEG). EEG records the electrical activity of the brain. The waveforms: delta, alpha, beta, and gamma can measure cognitive functions such as attention, excitatory, inhibitory, and distraction. The focuses and oscillation of each EEGs activity are different for example with sleep: Delta waves arise during Stage N3 sleep (1 4 Hz), Theta waves arise through light sleep or deep relaxation (4 8Hz), Alpha waves arise when relaxed and without concentrating on anything (8 12Hz), Beta waves check on being awake (12 -30Hz), and Gamma waves may be involved in higher mental activity (30 - 100Hz). (Medicwiz Editorial Team, 2016; Lum, J. et al., 2022).
This study examines the association between smartphone use and alpha activity. Alpha oscillations play a key role in regulating attentional processes (van Winsun et al., 1984). This is important as inhibition is the ability to stop or suppress from doing certain actions and an increase in alpha power or alpha synchronization reflects inhibition of cortical functioning while a decrease of alpha desynchronize reflects a release from inhibition of cortical functioning (Klimesch, 2012). Alpha power also plays a role in working memory.
During working memory tasks, it is crucial not to get distracted and to stay focused on the task at hand..hand. EEG, specifically alpha oscillations would give us an insight into the brain activity as alpha oscillations stop other parts of the brain from interfering with what we are trying to remember (M. Bonnefond and O. Jensen, 2012). In order for working memory to access the information, lower inhibition is needed as it would notont restrain or surpresssuppress the information. Hence, when a part of the brain needs to do a particular tasktask, the alpha waves desynchronize. For example, the alpha waves in the part of the brain that handles movement will desynchronize if the person is walking (Pfurtscheller, 1992). Different tasks could explain what alpha oscillations do to working memory, an example would be the Sternberg task. Wherein five numbers are flashed on the screen to be memorized then the screen will go blank for three seconds. In those three seconds, there is an increase in the alpha power to block out distracting information as they try to hold the numbers in their working memory after a question is asked about the numbers that were presented (Kilmesch, et al., 2007).
Research by Jang, M.KimM. Kim, and D.Kim (2020) found that patients with ADHD had greater alpha power, meaning they had to suppress more information and required more effort to hold information than the normal study group. Although, the ADHD group had poorer performance on the test. This suggests that poorer working memory is associated with higher alpha power. Regarding the focus of our study, to date, no study has examined the relationship between smartphone use and alpha oscillations during a working memory task.
Aims and Hypotheses
This study aims to examine the relationship between smartphone use and working memory-related alpha power. Based on the assumption that smartphone use leads to greater distractibility (Elgan, 2017), it was first hypothesized that greater smartphone use should be associated with poorer working memory. Second, increased time smartphone use should be associated with higher alpha power.
METHODSParticipantsA total of 34 healthy individuals (female = 16, male = 18) aged 20-60 years old (M=28.40, SD=9.89) were included in the study. All participants had completed secondary education. The sample consists primarily of right-handed individuals. This was done by utilizing the Edinburgh handedness inventory (Oldfield, 1971). There were 27 participants with positive values, indicating a tendency to be right-handed, and 7 participants with negative values, indicating a tendency to be left-handed. Taking part in the study was entirely voluntary, and participants were free to leave at any time if they chose not to. All participants provided written consent before taking part in the study. In accordance with the Ethics Committee of Deakin University, the study was approved. The research protocol followed the Declaration of Helsinki (World Medical Association, 2001). As a reward for participating in this study, participants received a $30 shopping voucher.MaterialsA series of tasks and surveys were presented to participants in this studied to examine their working memory and smartphone used. Additionally, general cognitive testing was presented to all participants. Results from these assessments had been used to compute covariates. Following is a brief description of each of the tasks and surveys used in the studied. Working memory is assessed using two tasks. A version of the digit span task from the Weschler Adult Intelligence Scale 4th Edition (Weschler, 2008) and a version of the Sternberg Working Memory task (Sternberg , 1966).Digit Span TaskWorking Memory TaskWorking memory wasis assessed using two tasks. A version of the digit span task from the Weschler Adult Intelligence Scale 4th Edition (Weschler, 2008) and a version of the Sternberg Working Memory task (Sternberg ,Sternberg, 1966).The forward and backward digit spans arewere given to participants through a computerized version. This pair of tasks assesses each participant's ability to store and manipulate information in working memory temporarily.A computer screen presents an increasing number of digits to participants on both the forward and backward digit span tasks. In the forward digit span task, participants had to recall digits in the same order after being shown a series of digits. The participants pressed matching numbers one by one on a keypad. In the scenario where the numbers 1-7 appeared on the screen, participants had to type in 1 and 7. The digits were displayed for a second. As part of the backward span task, users were asked to recall the digits in reverse order. When the screen displays the number 1 - 7, for instance, participants are required to type "7 - 1." Participants were asked to remember two digits at the beginning of both tasks. After two correct responses, the difficulty of the task increased to require that three digits be remembered. As soon as participants made two errors in the same span, the task was terminated. For example, if the participants could not recall six digits after two trials, the task was abandoned. For this test, accuracy was the dependent variable. On both the forward and backward tasks, a maximum score of 16 can be attained.Sternberg TaskParticipants have to remember a series of four letters in the Sternberg task. On each trial, participants were shown four letters from the alphabet one by one on a computer display. During this task, only consonants were displayed to avoid using real or made-up words as memory aids. A letter was displayed every 1200ms, and after the fourth letter the screen became blank for three seconds. The four letters needed to be retained in short term memory during this part of the task. The computer display, which was presented as a black box, displayed a probe, which was also a letter, after the period of time had elapsed. If the probe had been shown previously, participants were asked to indicate it. In total, forty trials were presented to participants. For half of the trials, the probe was a letter previously seen by the participants. The participants in these trials were asked to press a green button on a response box. On the remainder of the trials, the probe did not match a letter previously shown. The participants in these trials had to press a red button on a response box. This task was used to analyze accuracy and reaction times. To evaluate accuracy, we measured how often participants correctly identified whether the probe was part of the set of letters previously shown. The reaction times are measured by how long the participants take to examine whether it matches the set. Sternberg task measures the reaction times based on the time taken to scan items to memory (Sternberg, 1966). Each participant's alpha power was calculated based on electroencephalography (EEG) while completing this task.Smartphone UseIn the survey developed by Marty-Dugas et al. (2018) that measures smartphone use, participants were asked to rate their frequency of sending and receiving text messages, browsing social media, and using their smartphone for a range of general smartphone-related behaviors, such as When you receive a notification, how often do you check it immediately? and How often do you check social media apps such as Snapchat, Facebook, or Twitter? It is a 10-items survey in which participants are asked questions about their smartphone use and respond on a scale from 1 (never) to 7 (all the time). The reliability of the survey was found to be ______. Appendix B presents a list of all items.General Cognitive FunctioningAs part of the assessment, participants were given the Peabody Picture Vocabulary Test-4th Edition (Dunn & Dunn, 2007) and the Matrix Reasoning Subtests from the Weschler Abbreviated Scale of Intelligence (Wechsler, 2011). They were designed to provide an approximate measure of verbal and non-verbal intelligence. We used tThese tests as covariates in ourthe analysis since working memory correlates with intelligence (Conway, Kane, & Engle, 2003). For example, it is possible that smartphone use is correlated with intelligence instead of working memory. In this section, the Peabody Picture Vocabulary Test-4th Edition (Dunn & Dunn, 2007) and Matrix Reasoning Subtests from the Weschler Abbreviated Scale of Intelligence (Wechsler, 2011) are briefly discussed.As part of the Peabody Picture Vocabulary Test-4th Edition, participants are given an auditory word (e.g., eating) and shown four pictures. Participants must select the picture that corresponds to the word. As a participant progresses through the test, fewer well-known words are used, increasing the difficulty. Based on the age-corrected standard score for this subtest, the mean is 100 and the standard deviation is 15. The mean score for the sample was XX.X (SD = X.X) and the range XX XX. The analyses used standard scores. The following is a sample question from the test.Matrix Reasoning SubtestA series of geometric pictures is presented to the participants in the Matrix Reasoning subtest so they can identify patterns in them. This subtest has high reliability (Split-Half Reliability = .92). A high correlation (r = .87) has also been found between Performance IQ and this subtest. The results of this subtest are described by an age-corrected T-score with a mean of 50 and standard deviation of 10. The mean score for the sample was XX.X (SD = X.X) and the range XX XX. The analyses used T-scores. Below is an example of one of the items.ProceduresA laboratory setting was used at Deakin University to test participants individually.EEG caps were placed on the heads of participants after they signed consent forms. Participants also completed background and handedness surveys. Following this, participants were asked to complete the Peabody Picture Vocabulary Test and the Matrix Reasoning Subtest. The digit span task was completed after the EEG cap was fitted, followed by the Sternberg task. Following the completion of all tasks, participants were asked to complete a Smartphone Use Survey. As a last step, the smartphone survey was administered to avoid items that may affect performance on the working memory tasks. Approximately 70 minutes were spent on each test session.ElectroencephalographyData AnalysisMethodological Limitations ResultsPreliminary analysesFirst The first section of the results presents summary data from the standardised tests and Sternberg Task. Present a table that summarise each of the variables. In the table present means, standard deviations and the range of scores (min and max). This will be for the following variablesWASIPPVTForward Digit SpanBackward Digit SpanSUQ_GSUQ_ASternberg AccuracyVariableMSDRangeMatrix Reasoning Subtest58.96.145-70Peabody Picture Vocabulary Test-Forward Digit Span-Backward Digit Span-Sternberg Task Accuracy-After you present the table discuss some trends and discuss whether parametric assumptions have been met (i.e., whether the data is normally distributed). Second The next section of the results presents computation of alpha power from each participant. Present the two figures that show alpha power on the working memory task. There are two things to discuss here. First present the head plot. This shows the distribution of alpha power across the head during the wait period during the Sternberg task. This figure shows alpha power is strongest at the occipital regions (Oz and PO2). Data from these two electrodes were submitted to a time-frequency analyses commencing from the time the participants had to wait for three seconds before providing a manual response. This part of the task was analysed because it elicited the greatest level of alpha power. The results from the time-frequency analyses are presented in Figure X.That we selected a region from the time-frequency plot where alpha power weas at its highest. This is shown the black box and then we selected than region for each participants and then averaged all the values. So for each participant we analysed their average alpha power between 500 to 1700 ms from the start of the wait period. Subheading: Correlation between smartphone use working memory and alpha power.The next set of analyses examined the relationship between alpha power. Partial correlations were compute between alpha power, digit span (fwd & bwd), and the parts of the smartphone survey. Results from the analyses are presented Table X.Discuss the correlation alpha power and digit span.DiscussionThe purpose of this study is to examine the relationship between smartphone use and working memory-related alpha power. According to Elgan (2017), the use of smartphones leads to a greater level of distractibility. There is a correlation between greater smartphone use and forward digit span, which measures phonological loops (short term memory), but in contradiction to our hypothesis, there is no correlation with backward digit span, which measures working memory (Gathercole et al., 2004). Furthermore, there was a significant positive correlation between greater smartphone use and lower alpha power on the Sternberg task. Alpha power is positively correlated with short-term memory.Next paragraph: correlation between alpha power and digit span.Which findings is this consistent with?Which finding is this not consistent with?The interpretation: higher working memory is associated with lower power this may occur because people with superior working memory do not need to inhibit as much. In short, lower levels alpha power are associated superior working memory.Next paragraph: correlation between alpha power and smartphone surveySUQ_G: negative correlation: people who reported using their phone more and lower alpha power.SUQ_A: alpha power.Next paragraph: correlation digit span and smartphone surveyForward significantBackward non-significantFinish this paragraph with what the relationship between smartphone use and working memory.LimitationsSample sizeGeneralisabilityCausality Final paragraph.AppendixAppendix AAppendix BSmartphone usage questionnaire From Marty-Dugas et al. (2018)752475146685Marty-Dugas, J., Ralph, B. C., Oakman, J. M., & Smilek, D. (2018). The relation between smartphone useuses and everyday inattention. Psychology of Consciousness: Theory, Research, and Practice, 5(1), 46-62.
00Marty-Dugas, J., Ralph, B. C., Oakman, J. M., & Smilek, D. (2018). The relation between smartphone useuses and everyday inattention. Psychology of Consciousness: Theory, Research, and Practice, 5(1), 46-62.
The following statements are about smartphone usage and certain experiences that you may have while using your smartphone. We are interested in how frequently you have these experiences on a typical day.Item1(Never)234567 (All the time)How often do you have your phone on your person?How frequently do you send and receive text messages or e-mails?To what extent do you have push notifications enabled on your phone?How often do you find yourself checking your phone for new events such as text messages or e-mails?How often do you use the phone for reading the news or browsing the web?How often do you use sound notifications on your phone?When you get a notification on your phone, how often do you check it immediately?How often do you use the calendar (or similar productivity apps?)How often do you check social media apps such as Snapchat, Facebook, or Twitter?How often do you use your phone for entertainment purposes (i.e., apps and games)?How often do you open your phone to do one thing and wind up doing something else without realizing it?How often do you check your phone while interacting with other people (i.e., during conversation)?How often do you find yourself checking your phone for no good reason?How often do you automatically check your phone without a purpose?How often do you check your phone out of habit?How often do you find yourself checking your phone without realizing why you did it?How often have you realized you checked your phone only after you have already been using it?How often do you find yourself using your phone absent-mindedly?How often do you wind up using your phone for longer than you intended to?How often do you lose track of time while using your phone?References:
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He JW;Tu ZH;Xiao L;Su T;Tang YX; (n.d.). Effect of restricting bedtime mobile phone use on sleep, arousal, mood, and working memory: A randomized pilot trial. PloS one. Retrieved May 27, 2022, from https://pubmed.ncbi.nlm.nih.gov/32040492/ He, Z., Wu, H., Yu, F., Fu, J., Sun, S., Huang, T., Wang, R., Chen, D., Zhao, G., & Quan, M. (2021). Effects of smartphone-based interventions on physical activity in children and adolescents: Systematic Review and meta-analysis. JMIR MHealth and UHealth, 9(2). https://doi.org/10.2196/22601 Jang, K.-M., Kim, M.-S., & Kim, D.-W. (2020). The dynamic properties of a brain network during spatial working memory tasks in college students with ADHD traits. Frontiers in Human Neuroscience, 14. https://doi.org/10.3389/fnhum.2020.580813 Jung, M. S. J. (n.d.). Effects of smartphone overuse on perceived cognitive function, fatigue, and daytime sleepiness among college students. Journal of the Korean Society of School Health. Retrieved May 27, 2022, from https://www.koreascience.or.kr/article/JAKO201609633505151.page Klimesch, W. (2012). Alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences, 16(12), 606617. https://doi.org/10.1016/j.tics.2012.10.007 Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG alpha oscillations: The inhibitiontiming hypothesis. Brain Research Reviews, 53(1), 6388. https://doi.org/10.1016/j.brainresrev.2006.06.003 Liebherr, M., Schubert, P., Antons, S., Montag, C., & Brand, M. (2020). Smartphones and attention, curse or blessing? - a review on the effects of smartphone usage on attention, inhibition, and working memory. Computers in Human Behavior Reports, 1, 100005. https://doi.org/10.1016/j.chbr.2020.100005 Lum, J. A. G., Clark, G. M., Bigelow, F. J., & Enticott, P. G. (2022). Resting state electroencephalography (EEG) correlates with childrens language skills: Evidence from sentence repetition. Brain and Language, 230, 105137. https://doi.org/10.1016/j.bandl.2022.105137 M. Krause, L. Sillanmki, M. Koivis, C. (2000). Effects of electromagnetic fields emitted by cellular phones on the electroencephalogram during a visual working memory task. International Journal of Radiation Biology, 76(12), 16591667. https://doi.org/10.1080/09553000050201154 Olson, J. A., Sandra, D. A., Chmoulevitch, D., Raz, A., & Veissire, S. P. (2021). A nudge-based intervention to reduce problematic smartphone use: Randomised controlled trial. https://doi.org/10.31234/osf.io/tjynk Published by S. O'Dea, & 23, F. (2022, February 23). Smartphone users 2026. Statista. Retrieved May 27, 2022, from https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/ Pfurtscheller, G. (1992). Event-related synchronization (ERS): An electrophysiological correlate of cortical areas at rest. Electroencephalography and Clinical Neurophysiology, 83(1), 6269. https://doi.org/10.1016/0013-4694(92)90133-3 Robert, M., & Savoie, N. (2006). Are there gender differences in verbal and visuospatial working-memory resources? European Journal of Cognitive Psychology, 18(3), 378397. https://doi.org/10.1080/09541440500234104 Shin, M. S., & Lee, K. L. (2018, April 10). Measuring smartphone usage time is not sufficient to predict smartphone addiction. Journal of Theoretical and Applied Information Technology. Retrieved May 27, 2022, from https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/32899 Team, M. E. (2015, December 8). 9 types of EEG tests - everything about brainwave monitoring. Medicwiz. Retrieved May 27, 2022, from https://www.medicwiz.com/medtech/diagnostics/9-types-of-eeg-tests-everything-about-brainwave-monitoring Toh, W. X., Ng, W. Q., Yang, H., & Yang, S. (2021). Disentangling the effects of smartphone screen time, checking frequency, and problematic use on executive function: A structural equation modelling analysis. Current Psychology. https://doi.org/10.1007/s12144-021-01759-8 van Winsun, W., Sergeant, J., & Geuze, R. (1984). The functional significance of event-related desynchronization of alpha rhythm in attentional and activating tasks. Electroencephalography and Clinical Neurophysiology, 58(6), 519524. https://doi.org/10.1016/0013-4694(84)90042-7 Warsaw, R. E., Jones, A., Rose, A. K., Newton-Fenner, A., Alshukri, S., & Gage, S. H. (2021). Mobile Technology use and its association with executive functioning in Healthy Young Adults: A systematic review. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.643542 WHO. (2018, March 28). Are our smart phones affecting our mental health? World Health Organization. Retrieved May 11, 2022, from https://www.who.int/china/news/detail/28-03-2018-are-our-smart-phones-affecting-our-mental-health-?msclkid=75c0964fd12311ecb5ecf8d145ebf96c Wilmer, H. H., Sherman, L. E., & Chein, J. M. (2017). Smartphones and cognition: A review of research exploring the links between mobile technology habits and cognitive functioning. Frontiers in Psychology, 8.
