ELISA reader does not interfere by mobile phone radiofrequency radiation

Digital cellular telephone interaction with implantable cardioverter-defibrillators.

This paper presents design and development of an Automated Home Control System (AHCS) using mobile phone. A cell (mobile) phone acts as a modem for the control of electrical home appliances. This is achieved when the mobile phone number is dialed and an appropriate command button is pressed. The paper demonstrates how to develop a system that aids the control of remote devices using mobile phones to enable devices without infrared though connected to power sources to be controlled and considering the possibility of users to monitor the status and usage of these devices. It makes use of a programmable interface controller (PIC) to control the switching of the output. This design is customized as a central device for four pieces of home appliances using relay to activate each of the respective electronic gadgets.

Electromagnetic interference (EMI) can cause malfunctions on electro-medical devices, and it is a difficult task to determine it. EMI can be caused by RF mobile transceivers like cell phones. Clinical engineers can perform ad hoc EMI test protocols to determine EMI caused by cell phones on electro-medical devices. In this paper, four ad hoc EMI test protocols are introduced (Mayo Clinic, ANSI, MDA and JHH) and their common characteristics are compared. Particular characteristics are discussed too. The comparison can help clinical engineers to choose the ad hoc EMI test protocol that better fills his hospital needs.

Electromagnetic interference hazards in flight and the 5G mobile phone: Review of critical issues in aviation security

<p dir="ltr"><b>Background</b></p><p dir="ltr">A credible public health survey or surveillance program is judged by its ability to measure intended outcomes accurately and cost effectively. However, in the pursuit of operational efficiency, such programs risk introducing bias in their estimates. This thesis examines several key potential sources of bias in public health surveys, focusing on three main domains: the characteristics and selection of respondents paying attention to the hard-to-reach late respondents, the use of mobile phone technology for data collection, and the adaptation of standardized measurement tools-specifically the Framingham Risk Score (FRS) and the Finnish Diabetes Risk Score (FINDRISC). The tools are internationally recognized for estimating the 10-year risk of cardiovascular disease and type 2 diabetes, respectively. By exploring these domains, this work aims to highlight critical methodological considerations that survey designers and implementers must consider minimizing measurement bias and enhance data validity.</p><p dir="ltr"><b>Methods</b></p><p dir="ltr">The study was conducted within the Rakai Community Cohort Study (RCCS), a long-standing surveillance program in south-central Uganda. Covering 34 diverse communities, the RCCS focuses on HIV and other health priorities. In each round, 70-80% respond on time, while late respondents require extra effort from the researchers to track them. Study I examined whether late respondents differ from early ones and if their (late respondents) exclusion could bias results. We conducted descriptive analyses and compared these groups using Chi-square tests. Multivariable logistic regression was used to adjust for confounders, reporting adjusted odds ratios (AORs) with 95% confidence intervals. We also modelled interactions with age to estimate marginal predictive means (MPMs), illustrating how the likelihood of late response varies across age groups. The results are reported in constituent manuscript I. The use of mobile phones for population-based surveys is an ongoing global discussion, particularly in resource-limited settings. We leveraged RCCS Rounds 19 and 18 data, collected around the COVID-19 period, and data from a concurrent mobile phone-based surveillance in the same population to compare participation rates by survey modality (face-to-face vs. mobile phone surveys), we assessed phone access, and characterized the differences between respondents reached and missed by each method. The primary independent variable was interviewing modality, with adjustments made for sex, age, community type, HIV status, and other covariates. Robust standard errors accounted for clustering at the individual level. We also examined longitudinal trends in mobile phone ownership, including the frequency of mobile phone number change, the time to change of numbers and multiple SIM ownership. Phone number change was defined as the complete replacement of previously recorded numbers between survey rounds, excluding partial changes. Rates and associated factors were estimated using Poisson regression with generalized estimating equations (GEE), while time to number change was assessed using the Andersen-Gill Cox model to account for repeated measurements. The results are reported in studies II (face-to-face vs mobile phone surveys) and study III (mobile phone ownership, numbers change and time to change analyses) We conducted qualitative exit interviews to assess participants' experiences with the FRS and FINDRISC risk score tools. Interviews were held on the same day, immediately after participants completed the questionnaires and measurement procedures. The analysis, based on 15 interviews, followed an iterative approach. Thematic coding was used to identify and summarize key themes that we report in constituent study IV.</p><p dir="ltr"><b>Results</b></p><p dir="ltr">Overall, 68,263 participants were included in the late respondent analysis. Men were 22% times more likely to be late respondents than women (23% vs. 19%, respectively). This was true for younger participants 15-19 years old relative to 40+years old participants. Compared to the unemployed, participants engaged in formal employment followed by those engaged in fishing were more likely to be late respondents (28% and 27% respectively). When comparing response times between people living with HIV and those who are HIV-negative, no significant difference was observed (AOR=1.06; 95% C.I .: 0.99, 1.13). In women, HIV status had no effect on the marginal predictive mean (MPM) of being late, which declined with age in both groups. Among young men, MPM of being a late respondent was significantly higher in those with HIV but decreased with age. Conversely, in HIV- negative men, the MPM increased with age. In study II, we observed 90% access to mobile phones, with lower access among older people, and people living with HIV. When including only individuals who participated in the previous RCCS survey round, participation in the face-to-face survey (82%) was higher than participation in the mobile phone survey (75%, p < . 001). Survey participation was higher among people living with HIV compared to HIV-negative individuals (84% vs 82%, p< . 001) in the face-to-face survey. In study III we assessed mobile phone ownership. A total of 97,034 participants were included in this analysis. Overall, 61.8% reported owning a phone. Mobile phone ownership increased by 33% over the study period, from 51% in 2010 to 68% in 2020 (p<0.001). Ownership was higher among men. There was no difference in phone ownership by HIV status (adjPR=1.01, 95% CI 0.97-1.05). About 75% of participants who reported more than one sexual partner in the past 12 months owned a mobile phone, compared to 58% who reported zero or one partner in the same period (p<0.001). Phone number change is as high as 15-19/100 person years and consistent across the ten years of observation.</p><p dir="ltr">When we examined participants' experiences with FRS and FINDRISC, we found that overall, the tools were clear and acceptable in this setting. Participants were comfortable with having their weights, waists, heights and other measurements taken if it was by a health worker. There were a few challenges with interpretation and measurement of physical activity, and dietary practice questions given the seasonal variations in their availability in the community.</p><p dir="ltr"><b>Conclusion</b></p><p dir="ltr">The studies reveal that late respondents are different and cannot be ignored therefore means to speed up their participation should be devised. The use of mobile phones is feasible, there are some differentials in those who are missed by phone surveys (especially men). Meanwhile, mobile phone ownership is on a rapid rise across both sexes with men slightly higher and women rapidly catching up (by 2020). However, the rate of phone number change is quite high and this needs to be considered. The FRS and the FINDRISC scores are generally acceptable, however, some adaptation to match local practices and the seasonal dietary practices need to be addressed during survey planning, data analysis and interpretation.</p><p dir="ltr">Key terms: Late respondents, Mobile phones, Bias, Framingham, Finnish Diabetes Risk Score.</p><h3>List of scientific papers</h3><p dir="ltr">Paper I: Characterizing late respondents in the Rakai Community Cohort Study, 2012-2020, Uganda<b>. </b><b>Robert Ssekubugu</b>, Zangin Zeebari, Hadija Nakawooya, Ping Teresa Yeh, Anthony Ndyanabo, Fredrick Edward Makumbi, Anna Mia Ekström, Grace Nalwoga Kigozi, Eli. M Binder, Kate Grabowski, David Serwadda, Victor Ssempijja, Larry W Chang, Godfrey Kigozi, Helena Nordenstedt [Manuscript]</p><p dir="ltr">Paper II: Use of Mobile Phone to collect data on COVID-19; phone access and participation rates in Rakai, Uganda. <b>Robert Ssekubugu</b>, Anthony Ndyanabo, Fredrick Edward Makumbi, Anna Mia Ekström, Laura Bere, Grace Nalwoga Kigozi, Hadijja Nakawooya, Joseph Ssekasanvu, Maria J. Wawer, Fred Nalugoda, Nelson Sewankambo, Victor Ssempijja, Betty Nantume, David Serwadda, Godfrey Kigozi, Ronald H. Gray, Larry W Chang, M. Kate Grabowski, Helena Nordenstedt & Joseph Kagaayi Glob Health Action. 2024 Dec 31;17(1):2419160. <a href="https://doi.org/10.1080/16549716.2024.2419160" rel="noreferrer" target="_blank">https://doi.org/10.1080/16549716.2024.2419160</a></p><p dir="ltr">Paper III: Mobile phone ownership, number change, time to change and the implications to public health services delivery in Uganda, 2010-2020<b>. </b><b>Robert Ssekubugu</b>, Ping Teresa Yeh, Hadija Nakawooya, Victor Ssempijja, Godfrey Kigozi, Joseph Kagaayi, Fred Nalugoda, Anna Mia Ekström, Betty Nantume, David Serwadda, Philip Kreniske, Zangin Zeebari, Michelle A. Moffa, Larry W. Chang, Kate M. Grabowski, Fredrick Makumbi and Helena Nordenstedt [Manuscript]</p><p dir="ltr">Paper IV: Cardiovascular (Framingham) and type II diabetes (Finnish Diabetes) risk scores: a qualitative study of local knowledge of diet, physical activity and body measurements in rural Rakai, Uganda<b>. </b><b>Robert Ssekubugu</b>, Fredrick Makumbi1, Rocio Enriquez, Susanne Rautiainen Lagerstrom, Ping Teresa Yeh, Caitlin E. Kennedy, Ronald H. Gray, Lilian Negesa, David M. Serwadda, Godfrey Kigozi, Anna Mia Ekström and Helena Nordenstedt BMC Public Health. 2022 Nov 29;22(1):2214. <a href="https://doi.org/10.1186/s12889-022-14620-9" rel="noreferrer" target="_blank">https://doi.org/10.1186/s12889-022-14620-9</a></p>

In this paper the main motive is to study the Electromagnetic Interference which is very prominent field in today's life. Electromagnetic interference also called radio frequency interference (RFI) is a disturbance that affects an electrical circuit due to either electromagnetic conduction or electromagnetic radiation emitted from an external source. EMI problems have been increasing with the proliferation of mobile electronic systems, wireless communication systems, and computer networks. The electromagnetic spectrum is becoming increasingly crowded. After this the reasons are elaborated; why electromagnetic interference takes place and how it affects the system. The presence of electrical field contributes to the surrounding levels of electrical and electromagnetic noise and interference. The increasing demand and dependence on the use of electromagnetic transmission systems for information technology applications has enabled revolutionary communications capabilities to include cordless communication devices, wireless networking, and satellite communication systems. As the number of these devices increases, there is an increase in electromagnetic radiation within the segments of the electromagnetic spectrum in which these systems operate. These emissions have the potential to interfere with the normal operation of electronic communication links and systems. The electromagnetic energy disrupting the normal operation or function of electronic devices is known as environmental interference (El). The source of El can be categorized as environmental (naturally occurring), incidental or intentional. The effects of El can be significant regardless of their source, however, engineering design and other considerations that can be used to limit the impact and disruption of El on electronic, communication and information systems. As we are aware of the bad impacts of Electromagnetic Interference so main concentration will be on Negative Effects of EMI. In the end of this paper different methods for reducing the EMI and selecting the best one will be discussed.

This paper emphasizes the need for a risk-based EMC approach in the hospital environment. A modern hospital with various kinds of wireless medical electronic equipment especially in the intensive care, operation theatre, neonatology, etc., pollutes the environment by creating electromagnetic interference with other equipment in the vicinity or even implanted equipment inside the patient. Even following the IEC 60601-1-2 product standard for medical equipment which is based on a rule-based approach, suppressing electromagnetic interference effectively is an arduous task. In the upcoming years, the number of wireless devices in a modern hospital will significantly increase and might cause EMC instability in this highly complex system. Hence, an urgent requirement for an advanced and smarter approach leads to the use of a risk-based EMC approach. The detrimental effect of intentional sources causing electromagnetic interference in the hospital environment is briefly discussed. The risk-based EMC approach has been applied in the Medisch Spectrum Twente hospital, where it was discovered that wireless devices such as digital mobile radio, cell phone, and radio frequency identification equipment critically affect equipment pumps and patient monitors.

The aim of the present study is to evaluate whether or not the electromagnetic field generated by mobile phones interferes with the function of a SPECT γ-camera during data acquisition. We tested the effects of 7 models of mobile phones on 1 SPECT γ-camera. The mobile phones were tested when making a call, in ringing mode, and in standby mode. The γ-camera function was assessed during data acquisition from a planar source and a point source of Tc with activities of 10 mCi and 3 mCi, respectively. A significant visual decrease in count number was considered to be electromagnetic interference (EMI). The percentage of induced EMI with the γ-camera per mobile phone was in the range of 0% to 100%. The incidence of EMI was mainly observed in the first seconds of ringing and then mitigated in the following frames. Mobile phones are portable sources of electromagnetic radiation, and there is interference potential with the function of SPECT γ-cameras leading to adverse effects on the quality of the acquired images.

Protecting COVID-19 Vaccine Transportation and Storage from Analog Cybersecurity Threats

Over the last few years, the number of mobile phones in the world has increased at an exponential rate with many developed countries reaching 60% ownership rates. The reasons for this are numerous, however low prices and the availability of new technology mean that even children now own and regularly use mobile phones. Furthermore, the number of mobile phones in the world has already passed the number of fixed land lines and the revenue from mobiles phones will soon exceed that of fixed land lines (ITU 2001). This paper explores the relationships between a number of key input factors and customers' overall satisfaction with their mobile phone, and develops a neural network model to predict the overall level of customer satisfaction derived from mobile phones in the UK. The final model uses eleven input factors, the most important of which are experience of product quality, level of service charges, level of call charges, and level of satisfaction with the service provider. The estimated neural network model predicted very well and appears to be very robust. Finally, the implications of these results for marketers are discussed.

End-of-life (EoL) mobile phone management in Hong Kong households

Privacy in India: Attitudes and Awareness V 2.0

This paper describes ceramic feedthrough capacitor electromagnetic interference (EMI) broadband filter design and installation techniques that generally make cardiac pacemakers and implantable cardioverter defibrillators (ICDs) immune to EMI from handheld wireless communication devices (including cellular phones) and other RF emitters. A typical cardiac pacemaker with four platinum lead wires which egress through a titanium housing (can) via a gold brazed alumina hermetic terminal is studied. The titanium can is seam welded to act both as an electromagnetic shield and a hermetic barrier to protect the internal components from body fluids. These lead wires, which normally carry the pacing pulses and sense cardiac activity, can also act as an antenna and conduct undesirable RF carrier signals (such as EMI induced by cellular phones) to sensitive internal electronic circuits. The EMI filter design described herein effectively de-couples and shields such signals over a broad frequency range before they can enter into the titanium can and disrupt normal pacemaker or ICD functions.

In this column, BI&T features questions and answers between AAMI members and George Mills, director of the Department of Engineering at The Joint Commission. Become part of the discussion and ask questions about those subjects that matter to you the most. You can submit your Joint Commission question at this link: www.aami.org/tmcconnect/askgeorge.html. Or, to join AAMI's Joint Commission e-forum, which is open to all AAMI members, visit www.aami.org/tmcconnect/discussiongroups.html.Some of the questions and answers may have been edited for space, style, and clarity.Our equipment inventories and preventive maintenance/repair histories have been stored electronically for years. In my experience, some Joint Commission surveyors still want to see our policies and procedures in print. Is there any reason why we can't present these electronically instead of sharing “the book”?There is absolutely no requirement for the information to be in hard copy such as “the book.” It may be that some surveyors have asked to review the management plans or other policies in the work room provided during survey, and simply assumed it would be a hard copy. When the documentation is online, you will need to provide access for its review.Part of our equipment inspection process includes checking the area in which the equipment is located for safety hazards—or example, loose monitor mounts or frayed power cords. Is there any regulatory requirement to perform safety inspections in each hospital area for medical equipment problems, separate from the general area inspections?EC.04.01.01 EPs 12 – 14 require environmental tours “to evaluate the effectiveness of previously implemented activities intended to minimize the risks in the environment.” Effective environmental tours include representatives from various departments, and would benefit from your participation.What is the biggest problem or most-often cited issue that surveyors face in terms of clinical engineering responsibilities?“Information is not available at this time.”Can you describe how your surveyors are trained to ensure that they conduct surveys in a consistent manner?The Joint Commission trains the surveyors using the actual Elements of Performance language and provides examples of compliance. Once they are trained by central office engineers, the surveyors are trained in actual surveys by experienced surveyors.I have been challenged to provide evidence as to why mobile phones should be banned from the operating theater at my hospital. I believe there are two reasons: 1) bacteria, the threat posed by introducing a nonsterile item into a sterile environment, and 2) the danger of electromagnetic interference with medical equipment from mobile phones. What are your thoughts on my position?The Joint Commission standards do not provide specific guidance related to mobile phones in the healthcare environment. Therefore, we expect the organization to determine if mobile phones present a risk in these environments by completing a risk assessment. You provide solid reasons why you believe mobile phones may be restricted, but the final decision is solely up to the organization, and The Joint Commission will survey accordingly.It is interesting to note that concerns over electro-magnetic interference have been mitigated by low power-cell technology, and now infection control has been identified as a more significant concern. It may be that “distraction” will eclipse both in some settings. This underscores the need to constantly monitor and re-evaluate risk (see EC.02.01.01 and EC.04.01.01).

This study assessed the relationships among cognitive risk, phone use behaviors, and sleep quality. We used a questionnaire, which included the Pittsburgh Sleep Quality Index (PSQI), mobile phone use behaviours, and questionnaires on mobile phone use cognitive risk to gather information from 1204 college students. T-test, chi-square test, and Wilcoxon signed rank test were applied to test differences in measurement data. Generalized linear regression analysis was used to analyze the factors influencing sleep quality. The percentage of people with poor sleep quality significantly increased during COVID-19, with a significant increase in sleep latency and diurnal dysfunction and a significant decrease in sleep persistence, sleep disturbance, and sleep efficiency. The PSQI scores significantly decreased during the COVID-19 pandemic (0.27 and 0.34 for overall and females in the ‘Often take a break’ subgroup), only females in the ‘Often increase the distance between the screen and the eyes’ subgroup had a significant decrease in the PSQI score (0.37). Bedtime use and bedtime light use in females significantly increased during the epidemic. Regression analysis revealed that rest or increased screen distance significantly improved sleep quality compared to rarely rest or rarely increased screen distance and had a more significant effect on sleep quality during the COVID-19 pandemic compared to before. The time spent using a phone with lights off before bedtime was significantly associated with poor sleep quality. Often resting, increasing the distance between the screen and the eyes, reducing phone use time, and reducing the time of lights off when using mobile phones are all protective factors for sleep quality. Females were more often affected by poor sleep quality.