|Year : 2022 | Volume
| Issue : 3 | Page : 262-268
Use of Mobile Phones in Patients with Stroke
Alvina, Adithya Philip Paul, Atiya R Faruqui
St. John’s Medical College, Bengaluru, India
|Date of Submission||16-May-2021|
|Date of Decision||17-Jul-2022|
|Date of Acceptance||16-Sep-2022|
|Date of Web Publication||07-Dec-2022|
St. John’s Medical College, Medical Graduate, 10/4, 4th Block, Blessing Garden Layout, Byrathi, Bengaluru, Karnataka 560077
Source of Support: None, Conflict of Interest: None
Objectives: There is a paucity of systematic studies conducted to get information on the impact of mobile phone radiation on health. This pilot study was done to explore the use of mobile phones in patients with stroke. Methods: This cross-sectional study used a structured questionnaire among stroke patients in a tertiary hospital in India. We collected demographic details, history, and information on their mobile phone type, usage, and specific absorption rate (SAR) value. We used descriptive statistics to report our findings. Results: This group of 50 stroke patients was predominantly an urban population (78%) with a higher number of male subjects (56%); a higher prevalence of ischemic stroke (76%); 30% were smokers; with comorbidities of hypertension (75%) and diabetes (46%), a significant proportion of whom were not on regular treatment 64% and 74%, respectively. More number of patients used feature phones (82%). The median (SAR) value of all phones was 0.81 W/kg. Hypertension was present in patients who had contact with the phone for >5 hours (78%), subjects who kept their phone closer to the pillow while sleeping (71%), and those carrying a phone in their pockets or on self for >5 hours (80%). Conclusion: This group of patients had predominantly ischemic stroke; a significant proportion having hypertension and diabetes were not on regular treatment and were smokers. Cases of hypertension were noted to be higher in patients with increased exposure to mobile phone radiation. As a pilot study with limitations of a small number of patients, these findings could be only incidental. Further research is required to make any conclusions.
Keywords: Cell phone, diabetes, hypertension, mobile phone, SAR, stroke
|How to cite this article:|
Alvina, Paul AP, Faruqui AR. Use of Mobile Phones in Patients with Stroke. MAMC J Med Sci 2022;8:262-8
| Background and Purpose|| |
Mobile phone use is rapidly growing, but its impact on human health may not be adequately studied. The association between mobile phone use and stroke occurrence is unclear but may be influenced. This study is a pilot study on this topic. Our primary aim was to document the mobile phone use in patients with ischemic and hemorrhagic strokes, to determine the demographic details, document the use of mobile phones, the type of phones, and the duration of use in these patients.
| Introduction|| |
Stroke is an acute heterogeneous syndrome caused by several major and uncommon disorders leading to the occlusion of blood vessels supplying brain tissue. It is the third most common cause of death and the major cause of disability in industrial countries. There are 10 known risk factors for stroke − hypertension, smoking, waist-to-hip ratio, dietary risk factors, diabetes mellitus, physical activity, alcohol intake (binge drinking), psychosocial stress, the ratio of apolipoprotein B to A1, and cardiac causes.
A limited number of studies show a link between mobile phone radiation and brain tumors. However, there is not much literature on mobile phone use and its association with the occurrence of stroke. Approximately 75% of adults worldwide have access to a mobile phone. Mobile phone subscriptions are increasing rapidly, especially in developing countries, including India. Mobile phone devices emit different types of electromagnetic (EM) radiation. Wi-Fi devices also emit EM radiations, the frequency of which ranges from 3 kHz to 300 GHz. The EM absorption by a human is measured in terms of a specific absorption rate (SAR). The SAR values show the radiated power from the mobile phone absorbed by the human corresponding to 1 g of body tissues, and it is measured in watt per kilogram (W/kg). The SAR is affected by factors dependent on the mobile device and body composition. Device-related factors include the network carrier, characteristics of the antenna, the radiated power from the mobile phone,,, and the positioning of the mobile phone. Body tissue with higher water content has greater conductivity, and it thus absorbs more EM waves. The tissue’s degree of conductivity and permittivity also depends on the exposure frequency., Thus, in the human head, an increase in conductivity and a decrease in permittivity cause an increment of SAR.
A safe limit of SAR is chosen to limit exposure to EM radiation. The SAR standards are regulated by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and Federal Communications Commission (FCC). The ICNIRP standard for the safe SAR limit is 2 W/kg for 10 g of body tissue. The FCC standard in India limits SAR of 1.6 W/kg over 1 g of body tissue.
Several human and animal studies have been conducted to assess the impact of EM radiations from mobile phones on human bodies. A study on 12 Wistar albino rats to assess the effect of chronic exposure to EM waves on the auditory system supports that chronic EM field exposure may cause damage by leading to neuronal degeneration of the auditory system.
In a study aimed to assess if neuronal injury and functional impairments were related to high SAR-induced astrogliosis, it was suggested that Radiofrequency Electromagnetic field (RF EMF)-induced astrogliosis had functional consequences on memory but did not demonstrate that it was due to neuronal damage.
It has been suggested that radiofrequency energy might affect glucose metabolism, but two small studies examining brain glucose metabolism after using a cell phone showed inconsistent results., One study showed an increase in glucose metabolism in the brain region close to the antenna compared with tissues on the opposite side of the brain. In contrast, the other study found reduced glucose metabolism on the side of the brain where the phone was used.
Another study investigated whether exposure to the radiofrequency energy from cell phones affects the blood flow in the brain and found no evidence of such an effect. However, short and long-term effects on large arteries due to mobile radiation result in atherosclerotic disorder, which may be possible due to apparent injury to large arteries due to occlusion of the vasa vasorum because the microvasculature is vulnerable to radiation damage.
Thus far, there are no documented human studies on the effect of EM radiation on one of the important circulatory disorders of the brain, stroke. As a pilot study project in this field, we aimed to document the use of mobile phones in patients with stroke.
| Methods|| |
Our study was a cross-sectional study conducted in a tertiary care hospital in South India. Permission from the Institutional Ethics Committee and the departments of general medicine and neurology was obtained. After obtaining informed consent, patients >18 years of age, who were admitted after a stroke and used mobile phones regularly, that is, daily, were included.
The patients were interviewed using a structured questionnaire. We obtained demographic details (age, sex, education status, occupation, income, socioeconomic status, and the primary caregiver), past and present history of hypertension, diabetes, previous history of stroke, heart disease, and cancer, family history (diabetes, hypertension, thyroid disease, stroke, and heart disease), tobacco consumption and alcohol habits of the subject, and also about their belief in complementary and alternative medicine (CAM) treatment.
Information was collected from the subjects about their mobile phone use − the kind of phone they used (smart or feature phone), time spent in contact with the mobile phone for conversation, and time spent in text messaging; if they used earphones or carried their phones in their pockets; the presence of Wi-Fi; signal strength of their phones at the place of residence; and distance of the phone from a pillow while sleeping. We also noted the SAR value of each phone.
In addition, a medical record review for documenting drugs, clinical findings, and comorbidities was also done. We documented the type of stroke (ischemic/hemorrhagic) suffered, the part of the brain affected, and the impaired functions of the subjects through their records.
We used descriptive statistics for data reporting. Free database software Epi Info was used for data entry, and Microsoft Excel (2007) was used for simple analysis. Continuous variables like the duration of mobile phone use and an average daily dose of medications were described using summary measures of mean and standard deviation. Categorical variables like habits, comorbidities, and mobile phone type (based on SAR values) were reported and analyzed using Microsoft Excel.
| Results|| |
Out of a total of 50 participants, 28 were males. The mean age of the subjects was 57.4 (± 12.9) years, and the median age was 60. The mean age at the time of stroke was 55.3 ± 13.7 years in males and 60 ± 11.5 years in females. Males <45 years of age showed a greater number of strokes compared to females in this age group [Table 1]. However, in the higher age group, both genders had similar numbers.
Our population was almost evenly distributed from the three states of Karnataka, Tamil Nadu, and Andhra Pradesh. Most subjects (39, 78%) were from urban areas, 50% (25) were unskilled workers, 34% (15) were semiskilled, 8% (five) skilled, and 8% (five) highly skilled.
Most individuals suffered from ischemic stroke (38, 76%), and 12 (24%) suffered from hemorrhagic stroke.
Among the 50 subjects, 36 (75%) had hypertension, 23 were on regular treatment, and 13 were not. Twenty-three subjects (46%) had diabetes mellitus, out of which 17 (74%) were on regular treatment and six (26%) were not on regular treatment [Figure 1]. Twenty subjects had both hypertension and diabetes mellitus.
Fifteen subjects (30%) gave a history of smoking tobacco, among whom 10 subjects smoked less than one pack a day (six with hypertension and four with diabetes) and five individuals smoked more than one pack a day (four with hypertension and one with diabetes).
Thirteen out of 50 individuals consumed alcohol, among whom six consumed daily. It was noted that three individuals consumed >21 units of alcohol weekly, among whom all three (100%) were hypertensive, and 10 individuals were noted to consume <21 units of alcohol weekly, among whom six had hypertension (60%).
Among the four individuals who had a previous stroke, it is noted that all four (100%) were hypertensive, and two (50%) were diabetic.
Among the 50 subjects, eight individuals have used CAM treatment in the past, among whom five used Ayurveda and three used homeopathy. Ten individuals said that they would seek CAM treatment in the future.
The type of mobile phone in comorbidities and subtypes of stroke are summarized in [Figure 2]. Since each phone has a specific SAR value, we documented it for all users. All the phones used had SAR values in the recommended safe range of <1.6 W/kg. The median SAR value of all phones was 0.81 W/kg. We wanted to see if there was any difference in the occurrence of stroke with the different SAR values, so we did a comparison by taking the median value as a reference, but the numbers were almost similar. Among 22 subjects who used phones with a SAR value < 0.81, five (22.7%) subjects had a hemorrhagic stroke, and 17 (77.3%) had an ischemic stroke. In 23 subjects who used phones with a SAR value > 0.81 W/kg, five (21.7%) subjects had a hemorrhagic stroke, and 18 (78.3%) had an ischemic stroke.
We documented the use of mobile phones by the individuals and noted the number of those with hypertension and diabetes in these groups. We also documented the stroke subtypes in these to see any differences. [Figure 3] shows the phone contact hours in comorbidities and stroke subtypes. [Figure 4] details the duration of carrying a phone in the pocket and comorbidities and stroke subtypes. [Figure 5] gives the distance of the phone from the pillow in comorbidities and stroke subtypes.
|Figure 3 Hours of contact with the phone in comorbidities and stroke subtypes.|
Click here to view
|Figure 4 Details on the duration of carrying a phone in the pocket in comorbidities and stroke subtypes.|
Click here to view
|Figure 5 Distance of phone from the pillow in comorbidities and stroke subtypes.|
Click here to view
We also collected data on whether patients switched off their phones while sleeping; most of them (88%) did not switch off their phones. Text messaging was not used by 91% of patients. Earphones were not used by 93% of patients. Further data analysis for comorbidities and stroke subtypes is not reported here.
| Discussion|| |
The mean age and gender distribution in our study were similar to that reported in earlier studies by Pandian and Sudhan, where the mean age of stroke was 54.5 years, with more males affected.
Most of our subjects were from urban areas, and most of them were unskilled workers. This could be because our hospital generally receives patients from the lower middle to lower class and most of the subjects included here were from the general ward.
The reported percentage of ischemic stroke (76%) and hemorrhagic stroke (24%) from our research were similar to previous studies where the percentage of ischemic stroke was found to be around 80.2% and that of hemorrhagic 17.7%.
An international case–control study (INTERSTROKE) found 10 modifiable risk factors for stroke. The investigators enrolled 3000 patients with stroke and found that hypertension, current smoking, waist-to-hip ratio, diet risk score, regular physical activity, diabetes mellitus, binge alcohol consumption, psychosocial stress and depression, cardiac disease, and the ratio of apolipoprotein B to A1 were all associated with ischemic stroke risk. Hypertension, smoking, waist-to-hip ratio, diet, and heavy alcohol consumption were risk factors for hemorrhagic stroke. In our study, a significant proportion of patients had the abovementioned risk factors. A total of 78% were patients with hypertension, 46% were patients with diabetes, 8% had cardiovascular diseases, 26% consumed large amounts of alcohol, and 30% smoked tobacco regularly. The large proportion having hypertension (78%) is relevant because hypertension is a significant determinant for the risk of both ischemic and hemorrhagic stroke.
Twenty-three (46%) of our subjects had diabetes mellitus. The duration of diabetes is independently associated with the risk of ischemic stroke, which increases by 3% each year, and triples in patients having diabetes for ≥10 years. Other studies have also revealed diabetes as an independent risk factor for stroke, with a two-fold increased risk of stroke in patients with diabetes.
While studies have linked mobile phone use to atherosclerotic changes in large arteries, there have not been any studies exploring the link between mobile phone usage and stroke. Other studies have implicated EM radiation in pathologies of the nervous system, including neuronal degeneration of the auditory system and memory impairment.
On assessing the phone usage amongst our patients, it was noted that hypertension was more commonly seen in feature phone users (82%), which could be because most of our subjects used feature phones. It was also noted that hypertension was seen more in those who used their phone for >5 hours (78%) and those who kept their phones in their pocket for >5 hours (80%). There could be an association with prolonged exposure to the EM radiation of the phone. The number of individuals switching off their phones, using text messaging, and using earphones also had less hypertension, but their proportion was too small to make any inference.
These findings, though in a small number of patients, indicate that mobile phone use could be associated with hypertension and impact stroke. However, since this is a pilot study with a small sample size with no previous data for comparison, definitive conclusions cannot be made. In the presence of multiple confounding factors in this patient group, it is difficult to consider EM radiation as an independent risk factor. Further research is required to prove any association.
Limitations of the Study
This is a pilot study on a small number of patients in a single center, carried out over 2 years. Therefore, inference from findings and generalizability is limited.
| Conclusion|| |
Our study reported that ischemic stroke was seen more than hemorrhagic stroke. Comorbidities of hypertension and diabetes were seen; a significant proportion of whom 36% with hypertension and 26% with diabetes were not on regular treatment. Smokers made up about 30% of the total.
On assessing mobile phone use, it was seen that a greater number of patients used feature phones. It was also noted that cases of hypertension were higher in patients with increased exposure to mobile phone radiation, which could imply concurrence with stroke.
Given the limitations of a small number of patients, these findings could be only incidental. However, considering this as a pilot study, further research is required to make any conclusions.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Kollmar R, Schwab S. Ischaemic stroke: acute management, intensive care, and future perspectives. Br J Anaesth 2007;99:95-101.
Tu JV. Reducing the global burden of stroke: INTERSTROKE. Lancet 2010;376:74-5.
Yang M, Guo W, Yang C et al.
Mobile phone use and glioma risk: a systematic review and meta-analysis. PLoS One 2017;12:e0175136.
Suhag AK, Larik RS, Mangi GZ, Khan M, Abbasi SK, Madiha H. Impact of excessive mobile phone usage on human. J Comput Sci Syst Biol 2016;9:173-7.
World Bank. Information and communications for development 2012: maximizing mobile. Washington, DC: World Bank Publications, 2012. [Report No. 72236]. URL: http://www.worldbank.org/ict/IC4D2012
. [Accessed on March 18, 2018].
Castells M, Fernandez-Ardevol M, Qiu JL, Sey A. The mobile communication society: A cross-cultural analysis of available evidence on the social uses of wireless communication technology. In: Presentation to the International Workshop on Wireless Communication Policies and Prospects: A Global Perspective. 8–9October, 2004; Annenberg School for Communication, University of Southern California, Los Angeles. 2004. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.109.3872&rep=rep1&type=pdf
[Accessed on March 18, 2018].
Wiart J, Dale C, Bosisio AV, Le Cornec A. Analysis of the influence of the power control and discontinuous transmission on RF exposure with GSM mobile phones. IEEE Trans Electromagn Compat 2000;42:376-85.
Chowdhury A, Paul N, Islam SS, Hossain MI. ’’Comparison of Electromagnetic Absorption in Human Head for Dipole and Microstrip Patch Antenna," 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET). 2018; pp. 143-6.
Faruque M, Islam M, Misran N. Effects of dielectric values and substrate materials on electromagnetic (EM) absorption in human head. Frequenz 2012;66:79-83. Available From: De Gruyter https://doi.org/10.1515/freq- 2012-0020
. [Accessed on July 20, 2020].
Beard BB, Kainz W, Onishi T, Iyama T, Watanabe S, Fujiwara O, Wang J, Bit-Babik G, Faraone A, Wiart J, Christ A, Kuster N, Lee AK, Kroeze H, Siegbahn M, Keshvari J, Abrishamkar H, Simon W, Manteuffel D, Nikoloski N et al.
Comparisons of computed mobile phone induced SAR in the SAM phantom to that in anatomically correct models of the human head. IEEE Trans Electromagn Compat 2006;48:397-407.
Keshvari J, Keshvari R, Lang S. The effect of increase in dielectric values on specific absorption rate (SAR) in eye and head tissues following 900, 1800 and 2450 MHz radio frequency (RF) exposure. Phys Med Biol 2006;51:1463-77.
Urriolagoitia-Sosa G, Molina-Ballinas A, Urriolagoitia-Calderón G, Hernandez-Gomez LH, Romero-Ángeles B, Michtchenko A. Numerical and experimental analysis in the manipulation of the mechanical properties for enhancing the mechanical resistance of a material. J Appl Res Technol 2011;9:156-72.
Gajsek P, Hurt WD, Ziriax JM, Mason PA. Parametric dependence of SAR on permittivity values in a man model. IEEE Trans Biomed Eng 2001;48:1169-77.
Varsier N, Wake K, Taki M, Varsier N et al.
Categorization of mobile phones for exposure assessment in epidemiological studies on mobile phone use and brain cancer risk. IEEE Trans Microw Theory Techn 2008;56:2377-84.
Özgür A, Tümkaya L, Terzi S, Kalkan Y, Erdivanlı ÖÇ, Dursun E. Effects of chronic exposure to electromagnetic waves on the auditory system. Acta Otolaryngol 2015;135:765-70.
Barthélémy A, Mouchard A, Bouji M, Blazy K, Puigsegur R, Villégier AS. Glial markers and emotional memory in rats following acute cerebral radiofrequency exposures. Environ Sci Pollut Res Int 2016;23:25343-55.
Volkow ND, Tomasi D, Wang GJ, Vaska P, Fowler JS, Telang F, Alexoff D, Logan J, Wong C et al.
Effects of cell phone radiofrequency signal exposure on brain glucose metabolism. JAMA 2011;305:808-13.
Kwon MS, Vorobyev V, Kännälä S, Laine M, Rinne JO, Toivonen T, Johansson J, Teräs M, Lindholm H, Alanko T, Hämäläinen H et al.
GSM mobile phone radiation suppresses brain glucose metabolism. J Cereb Blood Flow Metab 2011;31:2293-301.
Kwon MS, Vorobyev V, Kännälä S, Laine M, Rinne JO, Toivonen T, Johansson J, Teräs M, Joutsa J, Tuominen L, Lindholm H, Alanko T, Hämäläinen H et al.
No effects of short-term GSM mobile phone radiation on cerebral blood flow measured using positron emission tomography. Bioelectromagnetics 2012;33:247-56.
Murros KE, Toole JF. The effect of radiation on carotid arteries: a review article. Arch Neurol 1989;46:449-55.
Pandian JD, Sudhan P. Stroke epidemiology and stroke care services in India. J Stroke 2013;15:128-34.
Boehme AK, Esenwa C, Elkind MS. Stroke risk factors, genetics, and prevention. Circ Res 2017;120:472-95.
Wajngarten M, Silva GS. Hypertension and stroke: update on treatment. Eur Cardiol 2019;14:111-5.
Banerjee C, Moon YP, Paik MC et al.
Duration of diabetes and risk of ischemic stroke: the Northern Manhattan Study. Stroke 2012;43:1212-7.
Sui X, Lavie CJ, Hooker SP et al.
A prospective study of fasting plasma glucose and risk of stroke in asymptomatic men. Mayo Clin Proc 2011;86:1042-9.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]