Smart Mobile Apps Research Articles

Impact of Technological Progress on Energy Consumption in the Hotel Industry in North-East Europe (Latvia)

The impact of technology on energy consumption in the hotel business is significant (Demir & Bahar, 2021). Technological advancements in the hotel industry have led to the development of energy-efficient systems and devices, such as smart thermostats, LED lighting, and occupancy sensors (Toscano et al., 2019). These technologies allow hotels to better monitor and control their energy usage, leading to reduced energy consumption and lower operational costs. By implementing these energy-efficient technologies, hotels can not only reduce their carbon footprint but also improve their overall sustainability and attract environmentally conscious guests. Additionally, technology has also enabled hotels to collect and analyze data on energy usage, allowing them to identify areas of high consumption and implement targeted strategies for energy optimization. Furthermore, technology has also enhanced guest experiences by allowing guests to have better control over their room's energy usage through features like smart room controls and mobile apps. These technological advancements not only benefit the environment but also contribute to cost savings for hotels. The integration of energy-saving technology in hotels has been proven to be beneficial not only for the environment but also for the financial sustainability of hotel businesses. By implementing energy-efficient technologies, hotels can reduce their operational costs and minimize their environmental impact (Wang, 2016). In conclusion, technology plays a crucial role in reducing energy consumption in the hotel business (Toscano et al., 2019). Keywords Energy Consumption, Hotel Business, Hospitality Industry, Gas Energy, Electric Energy, Advanced Technology, Energy Efficient Systems.

Landscape-based nutrient application in wheat and teff mixed farming systems of Ethiopia: farmer and extension agent demand driven approach

IntroductionAdapting fertilizer use is crucial if smallholder agroecosystems are to attain the sustainable development goals of zero hunger and agroecosystem resilience. Poor soil health and nutrient variability characterize the smallholder farming systems. However, the current research at the field scale does not account for nutrient variability across landscape positions, posing significant challenges for targeted nutrient management interventions. The purpose of this research was to create a demand-driven and co-development approach for diagnosing farmer nutrient management practices and determining landscape-specific (hillslope, mid-slope, and foot slope) fertilizer applications for teff and wheat.MethodA landscape segmentation approach was aimed to address gaps in farm-scale nutrient management research as well as the limitations of blanket recommendations to meet local nutrient requirements. This approach incorporates the concept of interconnected socio-technical systems as well as the concepts and procedures of co-development. A smart mobile app was used by extension agents to generate crop-specific decision rules at the landscape scale and forward the specific fertilizer applications to target farmers through SMS messages or print formats.Results and discussionThe findings reveal that farmers apply more fertilizer to hillslopes and less to mid- and foot slopes. However, landscape-specific fertilizer application guided by crop-specific decision rules via mobile applications resulted in much higher yield improvements, 23% and 56% at foot slopes and 21% and 6.5% at mid slopes for wheat and teff, respectively. The optimized net benefit per hectare increase over the current extension recommendation was $176 and $333 at foot slopes and $159 and $64 at mid slopes for wheat and teff (average of $90 and $107 for wheat and teff), respectively. The results of the net benefit-to-cost ratio (BCR) demonstrated that applying landscape-targeted fertilizer resulted in an optimum return on investment ($10.0 net profit per $1.0 investment) while also enhancing nutrient use efficiency across the three landscape positions. Farmers are now cognizant of the need to reduce fertilizer rates on hillslopes while increasing them on parcels at mid- and foot-slope landscapes, which have higher responses and profits. As a result, applying digital advisory to optimize landscape-targeted fertilizer management gives agronomic, economic, and environmental benefits. The outcomes results of the innovation also contribute to overcoming site-specific yield gaps and low nutrient use efficiency, they have the potential to be scaled if complementing innovations and scaling factors are integrated.

Building Smart Mobile Apps with Flutter and OpenAI AI-Powered Text and Images and Chatbots

Abstract: This research paper investigates the use of Flutter and OpenAI for creating intelligent mobile applications that utilize artificial intelligence to enhance text and image generation, as well as Chabot features. By conducting a review of the existing literature and empirical analyses, a prototype application was developed to showcase the potential of these technologies in improving mobile app functionality and user experience. The study delves into the concepts and techniques underlying AIpowered text and image generation, as well as Chabot integration, and evaluates their effectiveness in enhancing user engagement and satisfaction. The research findings reveal that the combination of Flutter and OpenAI can lead to significant improvements in mobile app performance and user experience. The prototype application demonstrates the feasibility of integrating AI-powered text and image generation, as well as Chabot features, in a mobile app. The study also identifies future areas for research and development, such as enhancing the naturalness and accuracy of generated text and images, and improving the intelligence and responsiveness of chatbots.

Мониторинг крупных хищных птиц с применением SMART-системы на юге Костанайской области, Казахстан

There are many effective tools for collecting data on wildlife in world practise, and the SMART system is one of them. It allows us to gather and analyze information. SMART is used in more than 500 protected areas in 48 countries (implemented at the state level in a number of countries). The SMART method is based on the use of mobile application and a computer program; data is collected during field research and by SMART specialist during cameral processing. A smartphone with the SMART mobile app replaces paper forms and GPS navigator. Since 2017, the system has been introduced for wildlife service in “Altybai” and “Saga” hunting services, which are assigned to the Association for the Conservation of Biodiversity of Kazakhstan (ACBK) and are located in the south of the Kostanay region. When raptor is encountered, employees record the following data in the SMART mobile application: species, gender, number, while the smartphone automatically records the track and GPS location, date, and time. It is possible to take photographs that can be linked to a specific observation and coordinated. In addition, the application has a “nest card” section developed by ASBK, which allows to record information about nests and their contents throughout the nesting cycle. Having analyzed the data on encounters (number of registrations) of the most common large raptor species collected over the past five years, such as: Rough-Legged Buzzard (Buteo lagopus), Long-Legged Buzzard (Buteo rufinus), Steppe Eagle (Aquila nipalensis), Eastern Imperial Eagle (Aquila heliaca), Golden Eagle (Aquila chrysaetos), White-Tailed Sea-Eagle (Haliaeetus albicilla), Cinereous Vulture (Aegipius monachus), and Griffon Vulture (Gyps fulvus), we can note that the peak number of observations occurred in 2021, when a decline was observed for most of the listed species, except for the Steppe Eagle and Long-Legged Buzzard. Thus, the SMART system contains good tools for recording observations, with the ability to customize for specific purposes, allows you to prepare reports with maps, tables, and graphs, store a large amount of data, and makes it possible to evaluate the group dynamics and trends for certain species by year.

A Smart Mobile App for the Detection of Diabetic Retinopathy using Artificial Intelligence

Diabetic Retinopathy (DR) is the main cause of blindness for people who have diabetes in the world. This condition occur when high blood sugar levels cause damages to the blood vessel in the retina. These blood vessels can swell and leak thereby cause damage to the normal vision. In this system we are developing an Artificial intelligence-Based smart mobile application for the diagnosis and treatment of diabetic retinopathy. Using Tensor flow mathematical library, the app analyses the eye fund us image through deep learning from Kaggle database. The app would be useful in promoting self evaluation and timely treatment of Diabetic Retinopathy (DR) by physicians. The app would achieve an accuracy about approximately 80-90% and have an overall good performance

A Smart Mobile App to Simplify Medical Documents and Improve Health Literacy: System Design and Feasibility Validation.

BackgroundPeople with low health literacy experience more challenges in understanding instructions given by their health providers, following prescriptions, and understanding their health care system sufficiently to obtain the maximum benefits. People with insufficient health literacy have high risk of making medical mistakes, more chances of experiencing adverse drug effects, and inferior control of chronic diseases.ObjectiveThis study aims to design, develop, and evaluate a mobile health app, MediReader, to help individuals better understand complex medical materials and improve their health literacy.MethodsMediReader is designed and implemented through several steps, which are as follows: measure and understand an individual’s health literacy level; identify medical terminologies that the individual may not understand based on their health literacy; annotate and interpret the identified medical terminologies tailored to the individual’s reading skill levels, with meanings defined in the appropriate external knowledge sources; evaluate MediReader using task-based user study and satisfaction surveys.ResultsOn the basis of the comparison with a control group, user study results demonstrate that MediReader can improve users’ understanding of medical documents. This improvement is particularly significant for users with low health literacy levels. The satisfaction survey showed that users are satisfied with the tool in general.ConclusionsMediReader provides an easy-to-use interface for users to read and understand medical documents. It can effectively identify medical terms that a user may not understand, and then, annotate and interpret them with appropriate meanings using languages that the user can understand. Experimental results demonstrate the feasibility of using this tool to improve an individual’s understanding of medical materials.

Towards an intelligent personal assistant for hearing impaired people

This work has been focused on the part of the population with hearing impairment who owns a dog and that worries about not listening the dog barks, specially when a risky situation is taking place at home. A survey was carried out on people with deafness problems to find out hazard situations which they are exposed at home. A system prototype was developed to be integrated as a component of ambient intelligence (AmI) for ambient assisted living (AAL) that serves to Hearing Impaired People (HIP). The prototype detects dog barks and notifies users through both a smart mobile app and a visual feedback. It consists of a connection between a Raspberry Pi 3 card and a ReSpeaker Mic Array v2.0 microphone array; a communication module with a smartphone was implemented, which displays written messages or vibrations when receiving notifications. The cylinder-shaped device was designed by the authors and sent it to 3D print with a resin material. The prototype recognized the barking efficiently by using a machine learning model based on Support Vector Machine technique. The prototype was tested with deaf people which were satisfied with precision, signal intensity, and activation of lights.

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks.

Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

Can Technology Abate the Experience of Social Isolation for Those Affected by Dementia?

BackgroundThe widespread social isolation measures recently utilized to mitigate the spread of COVID-19 to older adults may have exuded unexpected consequences. Social isolation among older adults is a risk factor for poor health outcomes. Innovative solutions to balancing public safety and health maintenance for those with dementia and their caregivers are needed.MethodsA sample of N = 82 dementia caregivers participated in a web-based survey to investigate their perceptions on (1) changes in personal mental health due to isolation from their loved one, and (2) the perceived need for use of smart mobile device app use in these situations.ResultsThe majority of our sample (87%) reported experiencing negative mental health outcomes beyond those experienced in typical situations. Furthermore, over 70% of caregivers were concerned with the care their loved on received during social isolation. Finally, 67% reported perceived need to use SMD apps in these times of social isolation.ConclusionOur findings provide preliminary insight into troubling consequences occurring when individuals with dementia are socially isolated from their caregivers. An inverse relationship between SMD app use and poor mental health points to the potential for communication technology to lessen the negative impacts of social isolation, when it becomes necessary to public safety.

Qatari Money Reader: A smart mobile app to support persons with visual impairment – Supported by Mada Innovation Program

In 2018, Innovation Factory Limited developed a unique smartphone application called “Qatari Money Reader” fully funded through the direct grant stream under the Mada Innovation Program (MIP, 2021). The app can scan and identify Qatari currency by just using the camera of the smartphone. The solution was effective for people with visual impairment & the elderly while retaining their privacy by identifying the value of the notes when they are in public places or when they need to count their money. In Addition to that, the app includes other features like reading the Qatari Riyal currency in real-time and works entirely offline without the need to have internet connectivity. Once the currency is detected, the app will inform the user about the value of money in Arabic and English. With the update in the Qatari Bills, the Mada Innovation program supported the development of Money Reader App V2 with added features like new bill detection, Counting notes, Exchange notes and Experiment on fake note detection. Mada hosted focus groups and user testing sessions to evaluate the application before the official dissemination.

철도역사에서 시각장애인 이동성 향상을 위한 모바일 앱기반 음성유도기용 스마트 중계기 개발

The voice guide system are installed to make known the location of the visually impaired in major places in railway station, but they are actually less utilized due to various problems.. The main reasons for the low utilization are the need to use a dedicated remote control and the burden of surrounding eyes due to the operation of the voice guide system. To solve these problems, the mobile app.-based smart repeater for voice guide equipment that can be operated through mobile apps was proposed and developed. The developed system was installed at metro Gwangju Songjeong Station, and on-site demonstration tests were conducted for the visually impaired. In addition, a very large increase was confirmed by satisfaction survey for them. Through these field test and satisfaction surveys, the technology and effectiveness of mobile app.-based smart repeaters and mobile apps for the visually impaired were confirmed.

1214. Household Transmission of Febrile Illness Measured by Smartphone-Connected Thermometers, United States, 2016-2021

BackgroundUnderstanding household transmission dynamics of infectious diseases can help develop mitigation strategies. Traditional methods of population-level disease surveillance do not capture household transmission. Data collected from smartphone-connected thermometers that can differentiate among individuals in a household can be used to study these characteristics. Using this technology, we estimated the household secondary attack rate (SAR) of febrile illness, assessed its correlation with CDC-reported influenza-like illness (ILI) and COVID-19 case incidence, and identified risk factors for secondary transmission.MethodsWe conducted a retrospective cohort study among 596,096 febrile illness index cases recorded from August 1, 2016 to January 20, 2021 in households with two or more individuals in all 50 states. Fevers were measured using the Kinsa Smart Thermometer and mobile device app. Secondary cases were defined as household members who recorded a fever 1–10 days after an index case. We calculated SAR prior to and during the COVID-19 pandemic within the study period, and assessed correlation to ILI and COVID-19 case incidence using Spearman’s rank correlation coefficient. Bivariate and multivariable mixed logistic regression models were used to identify risk factors for secondary transmission.ResultsSAR in the pre-COVID-19 period was 5.9% (95% CI: 5.8%–6.0%) during flu season (November to April), and 3.7% (95% CI: 3.6%–3.7%) in flu off-season, and weekly SAR was significantly correlated with ILI reported from CDC (ρ=0.84, p< 0.001). Secondary transmission was 40% more likely to occur in households where the index case’s initial temperature was ≥ 39.1°C. During the COVID-19 period, SAR was 3.3% (95% CI: 3.3%–3.4%), and daily SAR was significantly correlated with national daily COVID-19 incidence rates (ρ=0.86, p< 0.001). Households in census tracts with >50% essential workforce were 50% more likely to experience secondary transmission.ConclusionHousehold SAR was highly correlated with ILI and COVID-19 cases. Capturing household transmission of febrile illness through routine public health surveillance may identify risk factors for infectious disease transmission, allowing for more targeted interventions.Disclosures Danielle Bloch, MPH, Kinsa Health (Employee, Shareholder) John Zicker, MS, Kinsa Health (Employee, Shareholder) Hannah Somhegyi, PhD, Kinsa Health (Employee, Shareholder) Patrick Philips, n/a, Kinsa Health (Employee, Shareholder) Inder Singh, n/a, Kinsa Health (Board Member, Employee, Shareholder) Amy Daitch, PhD, Kinsa Health (Employee, Shareholder)

Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling

The quantity and quality of light captured by a plant’s canopy control many of its growth and development processes. However, light quality-related processes are not very well represented in most traditional and functional–structural crop models, which has been a major barrier to furthering crop model improvement and to better capturing the genetic control and environment modification of plant growth and development. A main challenge is the difficulty in obtaining dynamic data on plant canopy architectural characteristics. Current approaches on the measurement of 3D traits often relies on technologies that are either costly, excessively complicated, or impractical for field use. This study presents a methodology to estimate plant 3D traits using smart mobile app and data modeling. Leaf architecture data on 16 genotypes of rice were collected during two crop seasons using the smart-app PocketPlant3D. Quadratic Bézier curves were fitted to leaf lamina for estimation of insertion angle, elevation angle, and curve height. Leaf azimuth angle distribution, leaf phyllotaxis, canopy leaf angle distribution, and light extinction coefficients were also analyzed. The results could be used for breeding line selection or for parameterizing or evaluating rice 3D architectural models. The methodology opens new opportunities for strengthening the integration of plant 3D architectural traits in crop modeling, better capturing the genetic control and environment modification of plant growth and development, and for improving ideotype-based plant breeding.

Marine Citizen Science: Current State in Europe and New Technological Developments

Marine citizen science is emerging with promising opportunities for science, policy and public but there is still no comprehensive overview of the current state in Europe. Based on 127 projects identified for the North Sea area we estimate there might be as much as 500 marine and coastal citizen science projects running in Europe, i.e., one marine citizen science project per ∼85 km of coastline, with an exponential growth since 1990. Beach-based projects are more accessible and hence most popular (60% of the projects), and the mean duration of the projects is 18–20 years. Current trends, topics, organizers, aims, and types of programme in terms of participation are presented in this overview. Progress in marine citizen science is specially enabled and promoted through technological developments. Recent technological advances and best practise examples are provided here, untapping the potential of smart mobile apps, do-it-yourself (DIY) technologies, drones, and artificial intelligence (AI) web services.

What Makes Consumers Purchase Mobile Apps: Evidence from Jordan

Mobile applications (mobile Apps) have changed the ecosystem of the business world. The rapid progression in the market for smart devices and mobile Apps has brought about a revolution with regard to the mobile Apps’ economy. The major revenue stream of this economy is the sale of mobile Apps, with such sales being expected to increase dramatically every year. However, in spite of this, a considerable number of mobile Apps fail to capture consumers’ attention. Additionally, in developing countries such as Jordan, there is lack of research into understanding and determining the major factors that influence consumers’ decisions to purchase mobile Apps. Accordingly, the main objective of this study is to identify the key factors that Jordanian consumers consider in determining whether or not to purchase mobile Apps for their smartphones. To achieve this objective, a mixed-methods approach is adopted. An exploratory study involving a qualitative methods approach (interviews with consumers) is first conducted in order to determine the factors that influence consumers’ purchase behavior decisions. Then, a confirmatory study that employs a quantitative approach (a survey questionnaire) is undertaken to test the proposed model, the building of which is based on the findings of the exploratory study. The results indicate that seven factors are recognized as being pre-eminent when it comes to decisions to purchase mobile Apps. These factors include the price value of the App, App performance, App enjoyment, App trialability, electronic word-of-mouth (eWOM) about the App, App technical reliability and App usefulness. While this study advances our understanding of the main factors that influence mobile Apps purchase, it also reveals useful implications for mobile Apps publishers and developers, in order to promote and increase the sales of Apps.

Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories.

The commercial availability of many real-life smart sensors, wearables, and mobile apps provides a valuable source of information about a wide range of human behavioral, physiological, and social markers that can be used to infer the user’s mental state and mood. However, there are currently no commercial digital products that integrate these psychosocial metrics with the real-time measurement of neural activity. In particular, electroencephalography (EEG) is a well-validated and highly sensitive neuroimaging method that yields robust markers of mood and affective processing, and has been widely used in mental health research for decades. The integration of wearable neuro-sensors into existing multimodal sensor arrays could hold great promise for deep digital neurophenotyping in the detection and personalized treatment of mood disorders. In this paper, we propose a multi-domain digital neurophenotyping model based on the socioecological model of health. The proposed model presents a holistic approach to digital mental health, leveraging recent neuroscientific advances, and could deliver highly personalized diagnoses and treatments. The technological and ethical challenges of this model are discussed.

Secured smart mobile app for smart home environment

Smart home appliances like smart door, smart AC, smart lights etc., are the facilities which can be utilized because of advancements in Internet of Things. However, security of these smart home appliances is very much important to avoid security threats. Existing smart home appliances are facing problems with security issues such as such as hacking, mishandling of devices etc., due to one stage authentication process. The proposed Secured Smart Mobile App for Smart Home Environment (SSMASHE) provides a two stage authentication process which can secure smart home appliances. It consists of three modules, first one is smart things, next one smart mobile app and the third module is big data platform. In this paper a smart home environment embedding with smart mobile app has been simulated to evaluate the security mechanism through authentication process. In this process the delay has been calculated for the purpose of fast communication and security assigned to big data environment. The proposed method delay time is less when compared to Fast Identity Online (FIDO) method. The proposed method is comfortable to real time heterogeneous smart home IoT devices because this process follows the asynchronous authentication procedure.

Abstract 2033: Reducing cancer caregiver burden: A user-centered design approach for an mHealth app

Abstract Background: Informal cancer caregivers are being asked to provide more extensive home care for cancer patients. It is well documented that caregivers frequently experience psychological and behavioral effects that impact their overall well-being. Digital solutions can support informal caregivers by providing key information on the patient's care and nudging them to focus on their own mental and physical health. Objective: In this paper, we describe a user-centered design approach to build a digital solution by engaging with stakeholders that include clinical staff, health care decision makers, cancer patients and survivors, and informal caregivers. The NIH funded digital solution, TOGETHERCareTM, (Track Outcomes &amp; Guidance, Technology for Health &amp; Effective Resources for Care) is a smart phone mobile app to provide support and resources to informal caregivers while enabling them to monitor the cancer survivor's health for potential adverse events, thereby preventing unnecessary emergency care and reducing daily caregiver worries. Methods: An iterative information gathering process was conducted that included a) key-informant (n=139) and semi-structured interviews (n=11) with stakeholders to assess health care value propositions and corresponding modules of benefit; b) prototype review, evaluation and feedback by a Patient Advisory Council and Clinical Advisory Team and; c) a 28-day beta user-testing with feasibility and acceptability feedback from 15 stakeholders in two geographically diverse academic cancer centers (Duke and Stanford). This study was registered on clinicaltrials.gov (NCT04018677). Results: The interviews conducted prior to developing the mHealth app prototype identified consistency in responses between different stakeholder groups in terms of how the mobile app should work, and areas of difference. Clinical teams focused on app usage efficiency and features that would improve information sharing and follow-up visits with survivors. Survivors and caregivers were more focused on features that would provide assistance with at-home medical tasks and activities of daily living. The interviews and data analysis following the Beta test of the prototype indicated satisfaction with the app's usability, that caregivers preferred to focus primarily on the patient's health and not their own, and that regular surveys on the patient's symptoms educated and reduced anxiety among caregivers. Conclusions: This study describes the user-centered design process and demonstrates the feasibility and acceptability of TOGETHERCareTM, an iOS smart phone app for informal cancer caregivers. Larger studies, in various oncology populations, are needed to further evaluate efficacy of the app across health metrics that matter to the different stakeholders. Citation Format: Ingrid Oakley-Girvan, Vasu Divi, Oxana Palesh, Jena Daniels, Lisa G. Rosas, Dale O'Brien, Sharon W. Davis, Arif H. Kamal, Allison W. Kurian, Michelle R. Longmire. Reducing cancer caregiver burden: A user-centered design approach for an mHealth app [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2033.

Mobile Apps as Personal Assistant Agents: the JaCa-Android Framework for programming Agents-based applications on mobile devices

A relevant application domain for agent-based software is given by mobile and wearable applications. In this context, the impressive progress of technologies in the last decade makes it possible to explore the use of agent-oriented programming languages and frameworks based on cognitive architectures, such as the Belief–Desire–Intention (BDI) one. Accordingly, in this paper we provide a comprehensive description of the JaCa-Android approach, a framework based on the JaCaMo platform that allows for designing and programming smart mobile apps using cognitive agents based on the BDI architecture and the Agents & Artifacts environment conceptual model. In these years, the framework has been applied in real-world projects and application domains, and extended and evolved accordingly. The aim of the paper is to report our experience about designing and programming mobile apps as personal assistant agents, as well as to discuss in detail the architecture of the framework.

It's the Wild, Wild West: Lessons Learned From IRB Members' Risk Perceptions Toward Digital Research Data

Digital technology that is prevalent in people's everyday lives, including smart home devices, mobile apps and social media, increasingly lack regulations for how the user data can be collected, used or disseminated. The CSCW and the larger computing community continue to evaluate and understand the potential negative impacts of research involving digital technologies. As more research involves digital data, Institutional Review Boards (IRBs) take on the difficult task of evaluating and determining risks--likelihood of potential harms--from digital research. Learning more about IRBs' role in concretizing harm and its likelihood will help us critically examine the current approach to regulating digital research, and has implications for how researchers can reflect on their own data practices. We interviewed 22 U.S.-based IRB members and found that, for the interviewees, "being digital" added a risk. Being digital meant increasing possibilities of confidentiality breach, unintended collection of sensitive information, and unauthorized data reuse. Concurrently, interviewees found it difficult to pinpoint the direct harms that come out of those risks. The ambiguous, messy, and situated contexts of digital research data did not fit neatly into current human subjects research protection protocols. We discuss potential solutions for understanding risks and harms of digital technology and implications for the responsibilities of the CSCW and the larger computing community in conducting digital research.