Analyze the given Journal article. I already uploaded the article to the files, which named “Cell Phone Use and Stress, Sleep, and Depression Symptoms”. This assignment should put answer into a worksheet, I already uploaded the worksheet. Please directly fill the answer into that worksheet. (no paragraph needed.)Also I uploaded the instruction in PDF, The Rubric form for using. Then, I uploaded some additional help for helping you how to identify journal article components. Learning Objectives: 1) Identify the components and characteristics of primary scientific research. 2) Analyze scholarly, scientific research articles in the field of health and wellness. 3) Describe how the main findings in scientific research apply to personal and public health and wellness.Instructions: For this assignment, you will find, read, and analyze a primary scientific research article pertaining to our current unit of study. Choose a scholarly, scientific article from the options provided, and then type your answers to the questions in the worksheet provided. The boxes in the provided worksheet will expand as necessary. Read with a pen and a highlighter in your hand (or the digital equivalents of these if you prefer to read PDF articles). Please refer to the rubric below for more specific details on assignment expectations. If you have any questions while completing the assignment, please feel free to reach out to your teaching team through Canvas Inbox for additional help. You must use the provided worksheet to complete the assignment. Please do NOT type your answers in a separate document or attempt to make a new worksheet. Fill in the boxes directly into the .docx or .pdf worksheet file provided.Components of Primary Scientific Research: There are five main sections common to all primary research articles. If an article does not have all of these sections, it is not a primary research article. Each journal has its own particular formatting rules, but all primary research articles will contain these sections: • Abstract—a short paragraph at the beginning that gives a summary of the article. • Introduction—it may or may not be labeled “Introduction,” but it is the first section and it describes the problem being researched. • Methods—a description of how the research was conducted. • Results—the data collected during execution of the research study. • Discussion—a description of what the results mean, what contributions to knowledge are made by the research, how the research compares to prior studies, the conclusions derived from the research and, hopefully, how the research’s findings can be applied to “real life.”





Unformatted Attachment Preview

Thomée et al. BMC Public Health 2011, 11:66
Open Access
Mobile phone use and stress, sleep disturbances,
and symptoms of depression among young
adults – a prospective cohort study
Sara Thomée1*, Annika Härenstam2, Mats Hagberg1
Background: Because of the quick development and widespread use of mobile phones, and their vast effect on
communication and interactions, it is important to study possible negative health effects of mobile phone
exposure. The overall aim of this study was to investigate whether there are associations between psychosocial
aspects of mobile phone use and mental health symptoms in a prospective cohort of young adults.
Methods: The study group consisted of young adults 20-24 years old (n = 4156), who responded to a
questionnaire at baseline and 1-year follow-up. Mobile phone exposure variables included frequency of use, but
also more qualitative variables: demands on availability, perceived stressfulness of accessibility, being awakened at
night by the mobile phone, and personal overuse of the mobile phone. Mental health outcomes included current
stress, sleep disorders, and symptoms of depression. Prevalence ratios (PRs) were calculated for cross-sectional and
prospective associations between exposure variables and mental health outcomes for men and women separately.
Results: There were cross-sectional associations between high compared to low mobile phone use and stress, sleep
disturbances, and symptoms of depression for the men and women. When excluding respondents reporting
mental health symptoms at baseline, high mobile phone use was associated with sleep disturbances and symptoms
of depression for the men and symptoms of depression for the women at 1-year follow-up. All qualitative variables
had cross-sectional associations with mental health outcomes. In prospective analysis, overuse was associated with
stress and sleep disturbances for women, and high accessibility stress was associated with stress, sleep disturbances,
and symptoms of depression for both men and women.
Conclusions: High frequency of mobile phone use at baseline was a risk factor for mental health outcomes at
1-year follow-up among the young adults. The risk for reporting mental health symptoms at follow-up was
greatest among those who had perceived accessibility via mobile phones to be stressful. Public health prevention
strategies focusing on attitudes could include information and advice, helping young adults to set limits for their
own and others’ accessibility.
Mental health problems have been increasing among
young people in Sweden and around the world [1,2].
Cultural and social changes in terms of increased materialism and individualism have been discussed in relation to this [3,4], including the possibility of a
decreasing stigma about mental illness, improved
screening for mental illness, and increased help-seeking
* Correspondence:
Occupational and Environmental Medicine, Department of Public Health
and Community Medicine, University of Gothenburg, Gothenburg, Sweden
Full list of author information is available at the end of the article
behaviors [5]. Because of the quick development and
widespread use of mobile phones, and their vast effect
on communication and interactions in work and private
life, it is important to study possible negative health
effects of the exposure. Extensive focus has been on
exposure to electromagnetic fields (EMF). Self-reported
symptoms associated with using mobile phones most
commonly include headaches, earache, and warmth sensations [6,7], and sometimes also perceived concentration difficulties and fatigue [6]. However, EMF exposure
due to mobile phone use is not currently known to have
any major health effects [8]. Another aspect of exposure
© 2011 Thomée et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (, which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Thomée et al. BMC Public Health 2011, 11:66
is ergonomics. Musculoskeletal symptoms due to intensive texting on a mobile phone have been reported [9],
and techniques used for text entering have been studied
in connection with developing musculoskeletal symptoms [10]. However, our perspective is predominantly
In a previous study we found prospective associations
between high information and communications technology (ICT) use, including high frequency of mobile
phone use, and reported mental health symptoms
among young adult college and university students [11],
but concluded that the causal mechanisms are unclear.
The study was followed by a qualitative interview study
with 32 subjects with high computer or mobile phone
use, who had reported mental health symptoms at
1-year follow-up. Based on the young adults’ own perceptions and ideas of associations, a model of possible
paths for associations between ICT use and mental
health symptoms was proposed [12], with pathways to
stress, depression, and sleep disorders via the consequences of high quantitative ICT use, negative quality of
use, and user problems. Central factors appearing to
explain high quantitative use were personal dependency,
and demands for achievement and availability originating from domains of work, study, the social network,
and the individual’s own aspirations. These factors were
also perceived as direct sources of stress and mental
health symptoms. Consequences of high quantitative
mobile phone exposure included mental overload, disturbed sleep, the feeling of never being free, role conflicts, and feelings of guilt due to inability to return all
calls and messages. Furthermore, addiction or dependency was an area of concern, as was worry about possible hazards associated with exposure to electromagnetic
fields. For several participants in the study, however, a
major stressor was to not be available. The study concluded that there are many factors in different domains
that should be taken into consideration in epidemiological studies concerning associations between ICT use
and mental health symptoms [12].
Based on the previous studies, we wanted to focus on
some aspects of mobile phone exposure other than
mere quantity of use. For example, demands on being
available or reachable, regardless of time and space,
could be argued to be a stressor irrespective of actual
frequency of use. Another key determinant may be the
extent to which a person actually perceives his or her
own accessibility as stressful. Furthermore, accessibility
implies the possibility to be disturbed at all hours, even
at nighttime. Having one’s sleep interrupted repeatedly
can have direct effects on recovery and health. In a
study among Finnish adolescents, intensive mobile
phone use was linked to poor perceived health among
girls, both directly and through poor sleep and waking-
Page 2 of 11
time tiredness [13]. Another area of concern could be
addiction to the mobile phone. Intensive mobile phone
use has been associated with dependency on the mobile
phone [14,15], and problematic mobile phone use has
been a focus in the literature concerning psychological
aspects of mobile phone use, where criteria for substance addiction diagnoses or behavioral addictions
[16,17] have been used to define problematic use
[18-24] including compulsive short messaging service
(SMS) use [20]. In this context, heavy or problem
mobile phone use (overuse) has been associated with
somatic complaints, anxiety, and insomnia [21], depression [21,24], psychological distress [22], and an
unhealthy lifestyle [25]. However, possible positive
effects of mobile phone use on mental health can also
be hypothesized, for instance the ease of reaching someone to talk to when in need, implying access to social
support. Social support buffers negative effects of stress
[26], while low social support is a risk factor associated
with mental health symptoms [27].
We have previously studied ICT use in relation to
mental health symptoms among highly selected study
groups (college and university students studying medicine and information technology) [11,12]. Most investigations we have found on mobile phone use and mental
health outcomes have been cross-sectional studies performed among mainly college students (e.g., [15,19-23]).
It is important to examine possible associations between
mobile phone use and mental health outcomes also in a
more general or heterogeneous population of young
adults, using a longitudinal design.
The overall aim of this study was to investigate whether
there are associations between psychosocial aspects of
mobile phone use and mental health symptoms in a
prospective cohort of young adults. Specific aims were
to examine whether the frequency of mobile phone use,
but also more qualitative aspects of mobile phone use
(demands on availability, perceived stressfulness of
accessibility, being awakened at night by the mobile
phone, and perceived personal overuse of the mobile
phone), are associated with reported stress, symptoms of
depression, and sleep disturbances. Furthermore, we
wanted to examine whether frequency of mobile phone
use is associated with perceived social support.
Study population and data collection
The study population consisted of a cohort of young
adults (Figure 1), 20-24 years old (corresponding to the
United Nations’ definition of young adults [28]). Ten
thousand men and 10 000 women, born between 1983
and 1987, were randomly selected from the general
Thomée et al. BMC Public Health 2011, 11:66
Invited to participate
Cohort baseline
Invited to follow-up
Cohort follow-up
Study group
Page 3 of 11
ƃ=10000, Ƃ=10000
Loss at baseline
ƃ -72%, Ƃ -57%
ƃ=2778, Ƃ =4347
Declining further contact
ƃ -24%, Ƃ -16%
ƃ=2100, Ƃ=3634
Loss at follow-up
ƃ -31%, Ƃ -26%
ƃ=1458, Ƃ=2705
Missing on Calls and SMS
ƃ=3, Ƃ=4
ƃ=1455, Ƃ=2701
Figure 1 Participation process. The participation process from study population to study group.
population (from a registry held by the Swedish Tax
Agency), 50% living in the County of Västra Götaland,
Sweden, and 50% in the rest of the country. In October
2007, a questionnaire containing questions about health,
work, and leisure-related exposure factors, background
factors, and psychosocial factors was sent by post to the
selected population [29]. Besides returning the postal
questionnaire it was also possible to respond to the
questionnaire via the web if desired. A lottery ticket
(value approx. 1 Euro) was attached to the cover letter
and could be used whether the recipient participated in
the study or not. Two reminders were sent by post. The
response rate at baseline was 36% (n = 7125). One year
later, those respondents who had indicated that they
would accept to be offered to participate in further studies (n = 5734) were invited to respond to an identical
questionnaire, this time administered via the web. The
data collection process was otherwise similar to that at
baseline, but with the addition of a third reminder offering a paper version of the questionnaire and two cinema
tickets to respondents. The response rate at follow-up
was 73% (n = 4163). After excluding those who failed to
respond to both questions concerning frequency of
mobile phone and SMS use at baseline, 4156 remained
in the study group. All in all, non-participation and
dropout from the study was 79% (Figure 1).
Mobile phone exposure variables
Information about mobile phone exposure was collected
from the baseline questionnaire. This included the average
number of mobile phone calls made and received, and of
SMS messages sent and received, per day, but also more
qualitative aspects of mobile phone use, including how
often the respondent was awakened at night by the mobile
phone, how he or she perceived demands on availability,
and whether he or she perceived the accessibility via mobile
phones to be stressful, as well as perceptions regarding personal overuse of the mobile phone. Responses were divided
into high, medium, and low categories, based on the frequency distribution of responses, except for overuse which
was categorized according to number of items confirmed.
A combined quantitative mobile phone use variable was
constructed by merging the variables frequency of calls and
frequency of SMS messages (Spearman correlation r = 0.35,
p < 0.0001). The mobile phone use variable correlated well with the original calls and SMS variables (r = 0.73, p <.0001, and r = 0.84, p <. 0001, respectively). Mobile phone variables, questionnaire items, response categories, and response classifications are presented in Table 1. Mental health outcome variables Information about mental health symptoms was collected from the cohort study questionnaire at baseline and at follow-up. The outcome variable Current stress was constituted by a validated single-item stress-indicator [30]: Stress means a situation when a person feels tense, restless, nervous, or anxious or is unable to sleep at night because his/her mind is troubled all the time. Are you currently experiencing this kind of stress? Response categories were: a = not at all, b = just a little, c = to some extent, Thomée et al. BMC Public Health 2011, 11:66 Page 4 of 11 Table 1 Mobile phone variables at baseline Category Mobile phone variables Freq Men Women n = 1455 n = 2701 % Freq % Frequency of calls Low 0 per day 69 5 51 2 Low 1-5 per day 952 65 1946 72 20 Med 6-10 per day 301 21 543 High 11-20 per day 97 7 108 4 High More than 20 per day Frequency of SMS messages 36 2 47 2 Low 0 per day 126 9 58 2 Low 1-5 per day 906 62 1609 60 Med 6-10 per day 262 18 634 23 High 11-20 per day 98 7 259 10 High More than 20 per day 60 4 140 5 Mobile phone use Low Med Low Calls + Low SMS Low Calls + Med SMS or vice versa 804 326 55 22 1433 616 53 23 High High Calls and/or High SMS, or Med Calls + Med SMS 323 22 645 24 Awakened at night Low Never 600 41 989 37 Med Only occasionally 657 45 1248 46 14 High A few times per month 164 11 386 High A few times per week 27 2 68 3 High Almost every day Availability demands 6 0 9 0 Low Never 23 2 12 0 Low Now and then, but not daily 82 6 86 3 Low Daily, but not all day 278 19 828 31 Med All day 680 47 1127 42 Around the clock 388 27 642 24 Not at all stressful A little bit stressful 892 418 61 29 1229 1083 46 40 High Rather stressful 115 8 311 12 High Very stressful 28 2 75 3 Item 1: Use too much 184 13 587 22 Item 2: Tried to cut down unsuccessfully 87 6 371 14 High Accessibility stress Low Med Overuse Low No item 1199 84 1898 71 Med High One item Both items 183 41 13 3 579 187 22 7 Frequencies (Freq) and percentages (%) in mobile phone variables for the men and women, including categorizations into Low, Medium (Med), and High. Questionnaire items are presented in footnote1. Missing values (non-responses to items) are not accounted for, which means that the n varies for the variables. 1 Questionnaire items; Frequency of calls: How many mobile phone calls on average have you made and received per day (the past 30 days)?, Frequency of SMS messages: How many SMS messages on average have you sent and received per day (the past 30 days)?, Awakened at night: How often have you been awakened by the mobile phone at night (the past 30 days)?, Availability demands: To what extent are you expected by those around you to be accessible via the mobile phone?, Accessibility stress: To what extent do you perceive accessibility via mobile phones as stressful?, Overuse: 1. Do you or someone close to you think that you use the mobile phone too much?, 2. Have you tried, but failed, to cut down on your use of the mobile phone? d = rather much, e = very much. The responses were divided into Yes (responses d-e) and No (responses a-c), based on frequency distribution while yet taking content of response categories into account. The Sleep disturbances variable was constructed by including the most common sleep disorders (insomnia, fragmented sleep and premature awakening) into a single-item, adapted from the The Karolinska Sleep Thomée et al. BMC Public Health 2011, 11:66 Questionnaire [31]: How often have you had problems with your sleep these past 30 days (e.g., difficulties falling asleep, repeated awakenings, waking up too early)? Response categories were: a = never, b = a few times per month, c = several times per week, and d = every day. The responses were divided into Yes (responses c-d) and No (responses a-b), based on clinical significance. Symptoms of depression (one item) and symptoms of depression (two items) were made up by the two depressive items from the Prime-MD screening form [32]: During the past month, have you often been bothered by: (a) little interest or pleasure in doing things? (b) feeling down, depressed, or hopeless? Response categories were Yes and No. It is proposed that it is sufficient if one of the two items is confirmed in screening to go forward with clinical assessment of mood disorder. This procedure has high sensitivity for major depression diagnosis in primary care populations [32,33]. In our cohort study group, approximately 50% of the men and almost 65% of the women confirmed at least one of the two depressive items, which indicates that the instrument is probably very sensitive but has low specificity in our study group. Therefore, we constructed two outcomes: Symptoms of depression (one item), in which the Yes category contained those who confirmed only one of the depressive items, and Symptoms of depression (two items), in which the Yes category contained those who confirmed both depressive items. The No category in both outcomes contained those who disclaimed the two depressive items. Background factors and social support Background factors were collected to describe the study group and to adjust for in the multivariate analysis, including: relationship status: single or in a relationship; highest completed educational level: elementary school (basic schooling for 6-16-year-olds), upper secondary school, or college or university studies; and occupation: working, studying, or other (other included being on longterm sick leave, or on parental or other leave, or being unemployed). The variable social support was based on the item: When I have problems in my private life I have access to support and help, a one-item adaptation of the social support scale in the Karasek-Theorell job content questionnaire [34], here relating to private life (rather than work life). Response categories were: a = applies very poorly; b = applies rather poorly; c = applies rather well; d = applies very well. The responses were categorized as low (response categories a and b), medium (response category c), and high (response category d). Analysis All analyses were performed using the statistical software package SAS, version 9.2 (SAS Institute, Cary, NC, USA). Page 5 of 11 Spearman correlation analysis was used to examine associations between the mobile phone exposure variables, and between mobile phone use and social support. The Cox proportional hazard model (PHREG proc with time set to 1) was used to calculate prevalence ratios (PRs) with a 95% confidence interval (CI) for multivariate analysis of cross-sectional and prospective associations between exposure variables and mental health outcomes. The robust variance option ( ... Purchase answer to see full attachment