Health and Technology

In recent times, researchers showed huge interest in machine learning approaches that attempts to develop the information representations via computational modules. Past decade gained momentum by deep learning approaches and their potential of enhancing the performance for numerous automation operations with superior future research applications. The novelties in medical image processing initialized the unique perspective to diagnose the human body with superior resolution and enhanced accuracy. This paper offers a comprehensive work on existing methodologies that attain optimum results in their respective domains. There exist various Magnetic Resonance Imaging (MRI) brain scan classifiers to obtain efficient features extraction images. The fundamental step in these methods includes several actions to be performed by using different approaches in order to characterize the anomalous developments in MRI scans of brain. Mostly, current techniques are utilizing deep learning feature extraction algorithm from MRI brain scans to obtain their relevant features. Currently, deep learning algorithms associated with medical imaging results in achieving remarkable performance enhancement in diagnosis as well as characterization of complex pathologies in case of brain tumors. This paper provides existing research gaps in identification, segmentation and feature extraction among current approaches. This paper also suggests the future directions to increase the efficiency of current models.

The use of mobile phones may avail early detection of adverse drug reactions (ADRs) in pharmacovigilance activities. The study aimed to explore the feasibility of outpatients to self-report ADRs using mobile phone technology and to picture such ADRs and medications received. Outpatients taking medications at the Gitwe hospital pharmacy during the study period were eligible, provided they were using a mobile phone and could talk about side effects. Participants were requested to call the investigator after one week of treatment or to accept a call from the investigator and answer some questions. The investigator collected the phone number and the prescribed medications of every participant. The data collected were analyzed to list all types of ADRs reported and match them with the actual side effects documented on each medication received. Of 80 patients enrolled, we called 79(98.75%) but one (1.25%) who called himself the investigator. Ten (12.5%) persons did not respond to the call. Of 70 respondents who picked calls, 36(51.43%) said not having experienced any side effects, and 34(48.57%) participants did. Dry cough, headache, dizziness, and swelling were dominant with14-21%. Two cases of heartbeat and dizziness pushed patients to return to the hospital. All respondents carried a functional mobile phone and demonstrated interest in self-reporting ADRs. Facilitated toll-free- call service may be an effective means of extending the scope of ADRs tracking in addition to the Yellow Card Scheme, and enhance the involvement of pharmacists and consumers in the pharmacovigilance programs.

In the study of detection of an epileptic seizure using Electroencephalogram (EEG), pattern recognition has been recognised as a valued tool. In this pattern recognition study, the first time the authors have attempted to use time domain (TD) features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) derived directly from filtered EEG data and from discrete wavelet transform (DWT) of filtered EEG data for the detection of an epileptic seizure. Further, the authors attempted to study the performance of other time domain features such as mean absolute value (MAV), standard deviation (SD), average power (AVP) which had been attempted by other researchers. The performance of the TD features is studied using naïve Bayes (NB) and support vector machines (SVM) classifiers for University of Bonn database with fourteen different combinations of set E with set A to D and clinically inferred with Christian Medical College, Vellore database. The proposed scheme was also compared with other existing scheme in the literature. The implementation results showed that the proposed scheme could attain the highest accuracy of 100% for normal eyes open and epileptic data set with direct as well as DWT based TD features. For other data sets, the highest accuracy are obtained with DWT based TD features using SVM.

Children need to be provided with sufficient facts regarding the hazardous effects that smoking can have on their health. To attain this, persuasive technology such as inducement and social pressure, can be employed to transform the mind-sets and personal conduct of the users. This research is aimed at providing a new approach for campaigning against smoking through the use of an interactive mobile game called Smoke Shooter. In this research, the principles of persuasive technology will be applied in Smoke Shooter to influence children to reject the smoking habit from an early age. The development and analysis of Smoke Shooter are presented. The results indicate that Smoke Shooter has gained positive feedback from the users.

With patients depending on ‘at-home’ devices to measure or monitor body composition changes to determine health- risks, there is the need for critical evaluation of these instruments. The purpose of this study was to determine the validity (i.e., accuracy) and reliability (i.e., consistency) of body fat percentage (BF%) estimates using the consumer Fitbit Aria™ foot-to-foot bioelectrical impedance analysis (BIA) Wi-Fi smart scale. Forty-three healthy volunteers [male (n = 22), female (n = 21); mean ± SD, age: 27.9 ± 5.6y; BMI: 23.7 ± 3.3 kg/m2] underwent measures of residual lung volume, hydration status, and BF% via the Aria™ smart scale [‘Regular’ (AR) and ‘Lean’ (AL) modes] vs. [hydrostatic weighing (HW)] on three separate days. Aria™ validity was assessed using Bland-Altman plots identifying mean biases and limits of agreement [mean difference (Aria–HW) ± 1.96SD] and between-day and -week reliability using two-way mixed, average measures absolute agreement intraclass correlation coefficients (ICC). Standard error of estimate (SEE) could not exceed ±3.5% for acceptable non-research agreement between methods. There were no significant differences between HW compared with AR for all participants (−0.3 ± 9.7%), females (1.1 ± 11.3%), and males (−1.6 ± 7.1%). AL also agreed with HW for females (−1.9 ± 8.6%), but significantly underestimated BF% for all participants and males when analyzed separately (p ≤ 0.05). However, in each measure of validity the SEE fell outside ±3.5%, suggesting BF% measurements from HW and this smart scale cannot be used interchangeably. While not accurate for all individuals, the Aria™ Wi-Fi smart scale is a reliable device to measure BF% over time. Health care professionals may consider recommending this technology to empower patients who want to monitor their body composition at home.

Figure 22 should not have been placed in Section “9 Conclusions” on Page 312. Fig. 22 should be correctly situated in Section “8.3 Righting the perspectives on modern workforces”.

Healthcare is increasingly permeated with digital platforms supporting cooperative care involving both caregivers (i.e. nurses and physicians) and also patients. New mobile technologies allow for patients to continuously monitor and document their symptoms and gather data that can increase self-care and support the nurse’s decision-making process. A design process of such platforms calls for new design approaches involving heterogeneous conditions and goals. Our research is conducted at a clinic that supports cancer patients in their struggles with treatment induced illnesses. The methodological approach is design ethnography that draws from two years of following a design process that resulted in a digital platform to support the care provided by the clinic. The aim of the paper is to analyze how the boundary objects are engaged in the design phases, both concerning what type of boundary objects as well as how they play a role in the different stages of design and we show how boundary objects in design can be used as a mediator for different users’ needs and conditions. The research question that this paper explores is: what type of boundary objects can be used, and how are those boundary objects engaged in different design phases during healthcare platform design? We show how different boundary objects come into play during different design phases, from rich narratives, to conceptual formulations and finally into concrete prototypes of the platform. We argue that using boundary objects actively as design tools can inform and forward the design of healthcare platforms and that the approach can guide future design processes, where co-designing with boundary objects can be especially useful as a design approach when doing design with heterogenous user groups in complex settings, such as healthcare settings.

On February 7, 2013, the Associated Press reported [1] that “more than half of California’s population relies on a drinking water supply contaminated by arsenic, nitrates and other contaminants, though most communities blend or treat their water to make it safe, according to a new report by the State Water Resources Control Board released earlier this week. . . Arsenic was the most-detected naturally occurring contaminant, while nitrate was the human-caused contaminant detected the most. Chemical fertilizers and livestock manure are the main source of nitrate contamination in groundwater. . . Some communities cannot afford water treatment or other alternatives.” The report further stated 265 community water systems have delivered water from wells that have exceeded the nitrate, arsenic, or other standards. Most of these communities are located in rural Kern, Tulare, and Madera counties and serve contaminated water to about 2 million Californians [1]. The access and the quality of our drinking water is and should be a major concern to guarantee the global citizen’s safety and security regardless where people live. The original intention of this paper was to further investigate and better understand the risks and the consequences of the February 2013 water report from the State of California where the drinking water in many locations was found to have high levels of arsenic. In the literature, we found many products in the food chain that contain arsenic, and that led us to look deeper into the nexus between water, agriculture, food (i.e., cattle, poultry, fish/aquaculture, etc.) and even the energy sector, because of the current practices of fracking and the derived concerns of contamination to our environment, the water, the soil, and all the derived products. We realized very quickly that this is not a California issue but rather a Global Health one. The Issue of Arsenic—Concerns to ponder include: • Because agriculture is the number one industry in California, how has arsenic contamination impacted agriculture, cattle, poultry, water supplies, and our food security and what are the repercussions we face as society and individuals with our public health related risks? • Can part of this problem be explained by the public’s perception and the policy about the water critical infrastructure alone? • Are the current techniques of fracking used for energy-related purposes too risky in releasing arsenic and other contaminants that ends up affecting our water and our food supply?

Racial and ethnic healthcare disparities remain after differences in income, access, and insurance status have been considered, partly because of the healthcare delivery system’s failure to respond to cultural differences. The Institute of Medicine has called for the development and deployment of culturally appropriate healthcare services to mitigate these disparities. This complex problem of determining how to address the cultural components of racial and ethnic healthcare disparities is an example of what Russell Ackoff terms “messes.” Given consumer health information technology (IT)’s increasing role in patients’ self-care and self-management, one potential solution lies in designing consumer health IT that is culturally-informed. Although both the healthcare informatics community and the engineering design community have begun to seriously consider the role of culture in design to enhance usability, much work remains. Unfortunately, creating culturally-informed consumer health IT can seem daunting, limiting designers’ efforts. We propose the Culturally-Informed Design Framework as a guide for designers of consumer health IT. Designers may use this framework to conceptualize four dimensions of a consumer health IT – technology platform, functionality, content, user interface—in which design choices should be informed by a deep understanding of the users’ culture.