Is the time spent on associated with an increased risk of high blood pressure? A Review

J. Andrés Delgado-Ron.1

[1] School of Population and Public Health, University of British Columbia. Address: 2206 East Mall. Vancouver, BC Canada V6T 1Z3. E-mail: [email protected]; [email protected] ORCID: https://orcid.org/0000-0001-7051-6481 Abstract

Smartphone use is associated with poor quality, rumination, , sedentarism, forward posture, and social isolation. In turn, these health outcomes had also been linked to either increased or decreased levels of blood pressure. We aimed to review the extant literature that studied the acute and chronic effects of use on blood pressure to determine whether a direct link has found either a positive or negative correlation between smartphone use and hypertension. We searched Medline, Embase, PsycINFO, CINAHL,

Cochrane Central Register of Controlled Trials (CENTRAL), OpenGrey.eu, and reference lists of included studies. We also used Google Scholar for article chasing. Because this is the first review that we are aware of, our search included any study design. We found five studies: four cross-sectional in adolescents (12-18 years) and a case-crossover study in university students (18-30 years). None of the studies reported the exposure as a protective factor for hypertension, two of them reported no difference between compared groups, and three reported higher levels of blood pressure associated with increased smartphone use. The pooled evidence is generally of low‐ quality and not able to adequately answer the question as to what the effect of smartphone use in blood pressure is. Good‐ quality trials addressing this question are needed.

Keywords: blood pressure, hypertension, review, smartphone. Background

In 2019, the number of active mobile-broadband subscribers in the world increased more than any other previous year, resulting in an all-time high of 83 subscribers worldwide per 100 inhabitants.1 Most people under 34 own a smartphone, access the internet, and use social media. Moreover, older age groups are steadily adopting new technologies, too, as demonstrated by a recent survey conducted among 30,133 people in 27 countries.2

The wide adoption of mobile technologies has implications for public health. Most people researching hypertension have focused on using these technologies to track blood pressure levels or increase compliance.3 The relationship between smartphone use and blood pressure levels has received less attention. Past research has documented linkages between the use of this technology and several potential mediators: reduced sleep quality,4 forward posture,5 rumination,6 depression, anxiety, and daytime dysfunction are a few examples.7 In turn, most of these conditions and either higher6,8,9 or lower10 levels of arterial pressure.

Furthermore, similar technologies like television, computer, and videogames had been associated with an increased likelihood of metabolic syndrome “in a dose-dependent manner independent of physical activity,”11 increased insulin levels, insulin resistance, blood pressure,12 obesity, and inadequate levels of physical activity.13

Researchers have tried to identify direct effects of smartphone use and blood pressure (BP).

However, there is a need to systematize the current findings in order to provide clarity about such relationship. The objective of this literature review is to assess the acute or chronic effects of smartphone use on blood pressure in either experimental or observational studies. Methods

Criteria for selecting studies for this review

We included randomized and non-randomized trials, supplemented by cohort, cross-sectional, case-control, and other observational studies in men and women of all ages and ethnicities.

Smartphone use was defined as the use of a cellular phone with advanced computing and connectivity capability built on an operating system (following the U.S. National Library of

Medicine's controlled vocabulary thesaurus). We included studies that reported usage as a unit of time or as a composite measure (e.g. addiction score). We excluded studies that reported only smartphone ownership or past use without a specific definition. We also excluded studies that reported exclusively health-related internet use. "Internet use" as a proxy was assessed for individual studies considering the age distribution of the population, socioeconomic characteristics, and year of the study. We did not include studies that looked at specific Apps or sites within our review.

Search methods for identification of studies

A search strategy with natural and structured language was formulated using the PECO structure. We searched Medline (Ovid), Embase (Ovid), CENTRAL (Ovid), PsycINFO

(Ebscohost), and CINAHL (Ebscohost). Searches were not limited by language or publishing date. The date of the last search was December 1, 2019. Citation via Google Scholar and related article chasing was used; see Supplementary Text 1 for full search strategies.

A similar search was run in OpenGrey.eu to identify articles from the grey literature. Finally,

Google search for online news articles that referenced scientific studies published in peer- reviewed journals was used (“smartphone use high blood pressure study," unquoted during search). Outcome measure

The primary outcome of interest was change in arterial BP reported in millimetres of mercury, be it systolic, diastolic, mean, or any combination of those. Studies that included self-reported or medical diagnoses of hypertension or hypotension, without direct measurement, were included too. We did not include any other secondary outcome.

Study selection, data collection and analysis

The files containing the exported results of the searches were uploaded in Mendeley (version

1.19.4). Subsequently, we uploaded to the files to Rayyan, an online tool that provides procedural support in the selection and deduplication of articles for systematic reviews.14,15

The reviewer screened titles and abstracts of all identified studies. Where there appeared to meet the inclusion criteria, full text was obtained and screened. If the study met the selection criteria, the information was extracted into predefined spreadsheets elaborated for such purpose. The risk of bias for observational studies was conducted following guidelines from the ROBINS-E draft tool, which was considered the most appropriate tool despite its current limitations.16

The ROBINS-E draft tool recommends the identification of predefined domains and assumptions before assessing the risk of bias. We identified two main confounders: age, mental health disorders (mainly anxiety and depression), gender, and smoking. Younger people have a higher whereas the risk of hypertension increases with age.17

Smartphone use also associates with higher levels of anxiety/mood disorder.7 Mental health conditions, in turn, are associated with lower levels of blood pressure. Paradoxically, antidepressants are associated with higher levels of BP.10 Gender is an ancestor of mental health because females are at higher risk of mental health conditions. But men are at a higher risk of hypertension, especially before the age of fifty.18 Finally, anxiety could also be associated with smoking, which in turn increases the risk of high blood pressure. We also identified potential co-exposures that could differ among exposed and unexposed groups, mainly other forms of media consumption.

Results

Results of the search

We searched the literature for this review in December 2019. The database search yielded

564 records. The Google Scholar search, grey literature search, reference lists and information from authors about studies yielded 4 additional records. We excluded 474 titles and abstracts and reviewed 15 full‐ text papers.19,20,29–33,21–28 Figure 1 in provides further information on the identification and screening of relevant records and studies.

Characteristics of the included studies

Five studies met our eligibility criteria. Four of them investigated the effect of smartphone use on the BP of adolescents using a cross-sectional design.29–32 The last study investigated the effect of internet exposure in university students (aged 18-30) using a case-crossover experimental design.34 These studies occurred in the United States, Turkey, China, Japan, and

Great Britain, respectively. No studies were found in adults over the age of 30.

Most studies (4 out of 5) used self-reported measures of hourly smartphone use, and two of them used addiction scores. Not a single study measured smartphone screen-time habits objectively (e.g. screen time reports provided by the smartphone), and only one of them simulated a short 15 min session to gather data about browsing history. Smartphone usage was assessed with different parameters like "usually," "in the last 30 days", "in the past year,"

"during the evening," and "over the last two months."

All studies measured BP directly through validated instruments appropriate for the age of the participants. All observational studies used an averaged measure to analyze the relationship between smartphone use and BP, whereas the experimental study used two averaged measures (before and after) to estimate the effect. All studies compared heavy smartphone/internet use (exposed group) with light smartphone/internet use (control group).

Table 1 shows a summary of the main characteristics of the included studies.

Risk of bias analysis All the studies included in this review used self-reported measures of internet use, which had not been previously validated and are subject to recall bias.

The study by Cassidy-Bushrow et al.29 examined the association between time spent on the

Internet and elevated BP through logistic regression. The authors controlled for the confounders identified in our Methods section (See Table 1). While they did not control for anxiety, or addiction, the authors mention in the discussion their participants were "healthy," making unclear whether this could have caused bias. The study is unlikely to be underpowered as the authors used a low “prevalence” of internet use (10%). The participants volunteered (self-selection bias) and knew about the objective of the study, which might have influenced their answers. The reported week mean usage (15.1+12.1 hr) is consistent with objectively measured screen time of more recent reports.35

The authors divided usage in heavy, moderate, and light based on the previous study, this seems arbitrary as the original study36 using this distribution (as cited by Cassidy-Bushrow et al.) does not explain its reasoning. Moreover, this division dates to 2009, when smartphone and internet consumption patterns were significantly different.37 Given the top 20% of smartphone users have daily screen time above 4.5 hours, 35 this division seems outdated.

However, the comparison per se is not invalid.

Overall, the study has a low-to-moderate risk of bias, considering the type of study and the objective. The authors did acknowledge most of the limitations stated above, including the study design's inability to infer causality, other co-exposures and confounders that were not measured, and the generalizability of their findings.

Çakır & Çetinkaya30 tried to replicate the design reported by Cassidy-Bushrow et al.

However, there were significant limitations compared to the original study. Unlike the previous study, the authors did not control for any confounders, introducing a serious risk of bias in their study. While the authors used randomization to sample districts, participation ultimately was voluntary, introducing the risk of self-selection bias. Furthermore, 40 participants were excluded because they were "preparing for university examination" or

"nonvoluntary." The authors did not provide an explanation about this potential selection bias. The authors do not make clear in BP was measured by trained personnel. For the categorization of their sample (hypertension, prehypertension, and normal), the authors used the charts designed for a different population, inducing to measurement error. Overall, we considered this study has a serious-to-critical risk of bias.

Zou et al.31 recruited over 2.6 thousand healthy patients without a family history of hypertension, who did not use antidepressants, and controlled for BMI, gender, sleep quality, and age. The studied districts were selected randomly, and the participants' schools were selected randomly too. They used a validated instrument, checked for internal consistency after translating the scale performed cross-cultural adaptation. The equipment and personnel were adequate, and the categorization was done using charts designed for the sample population. The study reported both the bivariate analysis and the multivariate analysis.

Overall, the study has a low-to-moderate risk of bias, considering they did not report about different measures of BP that were obtained as reported in the methods section. While the overall quality of the study is good, the authors misrepresent their findings claiming causal inferences.

Nose et al.32 performed an unconventional study in a very small sample (it is likely underpowered). They compared healthy subjects matched by gender, age, and exercise level using an unpaired Student’s t-test. The self-reported measure “from sunset to bedtime” does not consider the total hours of daily/weekly use. The timeframe of the exposure was not specified either (could be days or months). We considered the study had a serious-to-critical risk of bias due to a questionable design, small sample size, and potential measurement errors for both exposure and outcome.

Reed et al. explored the impact of internet cessation on the physiological functioning of those who reported higher- or lower-problematic internet use over two months.33 The authors did not report on the general characteristics of their sample, nor they assessed if they were comparable outside of the realm of mental health. The authors did not control for potential variables like the previous history of high BP, race, age, or physical activity. However, their case-crossover design was meant to detect acute changes in BP and as such we considered the study to have a low-to-moderate risk of bias. Effects of exposure We provide a summary of the findings for each study in Table 2. We include the results of the exposed compared to unexposed group only. It is worth noting that sometimes the authors controlled for some variables that might be considered mediators (like body mass index) in other causality models. As such, the results on the table should be interpreted with caution.

Overall, we see that the studies with low-to-moderate risk of bias show a positive association between smartphone use and blood pressure.

Characteristics of the excluded studies

We read the full text of ten studies before excluding them. Older studies (2004-2012) looked at the effect of either non-smart cell phone use on BP or internet use in fixed devices and BP.

Some studies had enough data to run the analysis but did not. Finally, one study from the

Framingham cohort reported ownership of smartphones but did not assess use in a significant manner. Detailed reasons for study exclusion can be found in Supplementary Text 2

Discussion This systematic reviewed aimed to analyze the literature looking at the relationship between smartphone use and blood pressure. We found five studies that fulfilled the inclusion criteria.

Three studies with low-to-moderate risk of bias reported a positive association between smartphone use and high blood pressure: one found changes in systolic BP, one in diastolic

BP, and one in overall blood pressure. Two studies with serious-to-critical risk of bias found no associations.

While there are no studies correlating self-reported screen time and objectively measured screen time, we considered the former for our review as the data reported coincided with usage levels reported at population level.35 We found no studies conducted in adults over the age of 30, signaling a potential area of research given the increasing adoption of smartphones in young and older adults. Most studies were published in the last couple of years, highlighting this is an emerging issue in need of research that uses strict standards, and a robust study design.

The included studies presented several limitations. We found no clinical trials nor longitudinal studies. Out of the studies we found, four were cross-sectional and one was experimental (case-crossover design). For these studies, there was no consensus on the exposure time, ranging from “usually” and “the past week” to “during the last year.”

Moreover, not even the most recent studies (2019) included objectively measured screen time

(currently, most smartphones provide these data to their users as a measure of hours used per week, and hourly use by App). The studies also presented significant differences when reporting outcomes; some considered exclusively raw blood pressure values while others used categorical definitions. However, the categories were not homogeneous either: some decided to look at the risk of prehypertension and hypertension and others only at the latter.

None of the studies reported their causal model using directed acyclic graphs or similar tools.

As a result, it is unclear whether their statistical models were biased or to what extent. Future studies should establish causal diagrams a priori. Because the evidence is not conclusive, we also suggest that future exploratory analyses use indicators of long-term risk, such as arteriolar narrowing.38 While such an indicator can be used in young populations, future research would also benefit from including people of older age.

To the best of our knowledge, this is the first review looking at the effect of smartphones on blood pressure. Our study is strengthened using a systematic search of the literature in several databases without limits of language or publication date (this has shown to reduce overall bias). In addition, we used Google Scholar, which has proven to be highly sensitive to most scientific publications in the biomedical sciences.38 This review has several limitations.

Mainly, all processes of the review were carried out by one reviewer, which might increase the risk bias when selecting or analyzing articles. In order to allow others to weight in such potential outcome, we also provide the search strategy used as well as a list of included and excluded studies (after full-text analysis). Besides, due to the very small number of studies, it is possible for people interested in the field to review the articles themselves in a short amount of time. We also used a risk of bias tool that has not been fully developed yet and as such it was not used systematically but as a guidance in the process. While this is a strong limitation, it is also true that currently there is no systematic tool that has shown to be reliable enough at measuring bias in observational studies.

Currently, there is not enough evidence to establish a definitive effect of smartphone use on blood pressure. There is a need for well-designed longitudinal studies looking at this relationship. Ideally, with objectively measures of screen time and a more current classification of internet use. Few studies have looked at this relationship among adolescents and adults and not a single study has researched people over thirty. There is an urgent need to address this gap in our understanding of smartphone use and its impact in blood pressure of the general population.

Conflict of interest None to declare. References

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