Barriers and facilitators to the uptake of electronic collection and use of patient-reported measures in routine care of older adults: a systematic review with qualitative evidence synthesis.

Objective: The aims of this systematic review were to (1) synthesize the available qualitative evidence on the barriers and facilitators influencing implementation of the electronic collection and use of patient-reported measures (PRMs) in older adults' care from various stakeholder perspectives and (2) map these factors to the digital technology implementation framework Non-adoption, Abandonment, challenges to the Scale-up, Spread, Sustainability (NASSS) and behavior change framework Capability, Opportunity, Motivation, Behaviour (COM-B). Materials and Methods: A search of MEDLINE, CINAHL Plus, and Web of Science databases from 1 January 2001 to 27 October 2021 was conducted and included English language qualitative studies exploring stakeholder perspectives on the electronic collection and use of PRMs in older adults' care. Two authors independently screened studies, conducted data extraction, quality appraisal using the Critical Appraisal Skills Programme (CASP), data coding, assessed confidence in review findings using Grading of Recommendations Assessment, Development, and Evaluation Confidence in the Evidence from Reviews of Qualitative Research (GRADE CERQual), and mapped the findings to NASSS and COM-B. An inductive approach was used to synthesize findings describing the stakeholder perspectives of barriers and facilitators. Results: Twenty-two studies were included from the 3368 records identified. Studies explored older adult, caregiver, healthcare professional, and administrative staff perspectives. Twenty nine of 34 review findings (85%) were graded as having high or moderate confidence. Key factors salient to older adults related to clinical conditions and socio-cultural factors, digital literacy, access to digital technology, and user interface. Factors salient to healthcare professionals related to resource availability to collect and use PRMs, and value of PRMs collection and use. Conclusion: Future efforts to implement electronic collection and use of PRMs in older adults' care should consider addressing the barriers, facilitators, and key theoretical domains identified in this review. Older adults are more likely to adopt electronic completion of PRMs when barriers associated with digital technology access, digital literacy, and user interface are addressed. Future research should explore the perspectives of other stakeholders, including those of organizational leaders, digital technology developers and implementation specialists, in various healthcare settings and explore factors influencing implementation of PREMs. PROSPERO registration number: CRD42022295894.

Utilization of Emergency Medical Services by Culturally and Linguistically Diverse Patients: A Population-Based Retrospective Study.

OBJECTIVES: To compare emergency medical services (EMS) utilization between culturally and linguistically diverse (CALD) and non-CALD patients in Victoria, Australia. METHODS: A retrospective study of EMS attendances and transports in Victoria from January 2015 to June 2019, utilizing linked EMS, hospital emergency and admissions data. The CALD and non-CALD patients who received EMS care and transport to a Victorian public emergency department were included. The incidence of EMS use for CALD and non-CALD patients based on the 2016 Census population and expressed per 100,000 person-years. RESULTS: In 1,261,167 included patients, there were 272,100 (21.6%) CALD and 989,067 (78.4%) non-CALD patients. Before adjustment for age and sex, EMS utilization for CALD patients was 13% lower than non-CALD patients (incidence rate ratio [IRR] 0.87, 95% CI: 0.87-0.87). When stratified by age groups, CALD patients aged under 70 years had significantly lower rates of EMS utilization than non-CALD patients, while CALD patients aged 75 years or older were more likely than non-CALD patients to use EMS (IRR 1.08, 95% CI: 1.07-1.09). The CALD patients were less likely to utilize EMS for trauma/external injury (IRR = 0.67, 95% CI: 0.66-0.68) and mental health/alcohol/drug problems (IRR = 0.39, 95% CI: 0.38-0.40). After adjustment for differences in the age and sex distribution of CALD and non-CALD populations, CALD patients were 51% less likely to utilize EMS than non-CALD patients (IRR 0.49, 95% CI: 0.42-0.56). CONCLUSIONS: The CALD patients used EMS less frequently than non-CALD patients with significant variation observed across age groups, countries of birth, and clinical presentation. Further research is needed to understand the factors that may be contributing to these disparities.

Advances in E-Health and Mobile Health Monitoring.

E-health as a new industrial phenomenon and a field of research integrates medical informatics, public health and healthcare business, aiming to facilitate the provision of more accessible healthcare services, such as remote health monitoring, reducing healthcare costs and enhancing patient experience [...].

Smartphone-Based Ecological Momentary Assessment for Collecting Pain and Function Data for Those with Low Back Pain.

Smartphone-based ecological momentary assessment (EMA) methods are widely used for data collection and monitoring in healthcare but their uptake clinically has been limited. Low back pain, a condition with limited effective treatments, has the potential to benefit from EMA. This study aimed to (i) determine the feasibility of collecting pain and function data using smartphone-based EMA, (ii) examine pain data collected using EMA compared to traditional methods, (iii) characterize individuals' progress in relation to pain and function, and (iv) investigate the appropriation of the method. Our results showed that an individual's 'pain intensity index' provided a measure of the burden of their low back pain, which differed from but complemented traditional 'change in pain intensity' measures. We found significant variations in the pain and function over the course of an individual's back pain that was not captured by the cohort's mean scores, the approach currently used as the gold standard in clinical trials. The EMA method was highly acceptable to the participants, and the Model of Technology Appropriation provided information on technology adoption. This study highlights the potential of the smartphone-based EMA method for enhancing the collection of outcome data and providing a personalized approach to the management of low back pain.

Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification Study.

BACKGROUND: Traditional monitoring for adverse events following immunization (AEFI) relies on various established reporting systems, where there is inevitable lag between an AEFI occurring and its potential reporting and subsequent processing of reports. AEFI safety signal detection strives to detect AEFI as early as possible, ideally close to real time. Monitoring social media data holds promise as a resource for this. OBJECTIVE: The primary aim of this study is to investigate the utility of monitoring social media for gaining early insights into vaccine safety issues, by extracting vaccine adverse event mentions (VAEMs) from Twitter, using natural language processing techniques. The secondary aims are to document the natural language processing techniques used and identify the most effective of them for identifying tweets that contain VAEM, with a view to define an approach that might be applicable to other similar social media surveillance tasks. METHODS: A VAEM-Mine method was developed that combines topic modeling with classification techniques to extract maximal VAEM posts from a vaccine-related Twitter stream, with high degree of confidence. The approach does not require a targeted search for specific vaccine reaction-indicative words, but instead, identifies VAEM posts according to their language structure. RESULTS: The VAEM-Mine method isolated 8992 VAEMs from 811,010 vaccine-related Twitter posts and achieved an F1 score of 0.91 in the classification phase. CONCLUSIONS: Social media can assist with the detection of vaccine safety signals as a valuable complementary source for monitoring mentions of vaccine adverse events. A social media-based VAEM data stream can be assessed for changes to detect possible emerging vaccine safety signals, helping to address the well-recognized limitations of passive reporting systems, including lack of timeliness and underreporting.

Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms.

With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world example from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper.

Investigating Individuals' Perceptions Regarding the Context Around the Low Back Pain Experience: Topic Modeling Analysis of Twitter Data.

BACKGROUND: Low back pain (LBP) remains the leading cause of disability worldwide. A better understanding of the beliefs regarding LBP and impact of LBP on the individual is important in order to improve outcomes. Although personal experiences of LBP have traditionally been explored through qualitative studies, social media allows access to data from a large, heterogonous, and geographically distributed population, which is not possible using traditional qualitative or quantitative methods. As data on social media sites are collected in an unsolicited manner, individuals are more likely to express their views and emotions freely and in an unconstrained manner as compared to traditional data collection methods. Thus, content analysis of social media provides a novel approach to understanding how problems such as LBP are perceived by those who experience it and its impact. OBJECTIVE: The objective of this study was to identify contextual variables of the LBP experience from a first-person perspective to provide insights into individuals' beliefs and perceptions. METHODS: We analyzed 896,867 cleaned tweets about LBP between January 1, 2014, and December 31, 2018. We tested and compared latent Dirichlet allocation (LDA), Dirichlet multinomial mixture (DMM), GPU-DMM, biterm topic model, and nonnegative matrix factorization for identifying topics associated with tweets. A coherence score was determined to identify the best model. Two domain experts independently performed qualitative content analysis of the topics with the strongest coherence score and grouped them into contextual categories. The experts met and reconciled any differences and developed the final labels. RESULTS: LDA outperformed all other algorithms, resulting in the highest coherence score. The best model was LDA with 60 topics, with a coherence score of 0.562. The 60 topics were grouped into 19 contextual categories. "Emotion and beliefs" had the largest proportion of total tweets (157,563/896,867, 17.6%), followed by "physical activity" (124,251/896,867, 13.85%) and "daily life" (80,730/896,867, 9%), while "food and drink," "weather," and "not being understood" had the smallest proportions (11,551/896,867, 1.29%; 10,109/896,867, 1.13%; and 9180/896,867, 1.02%, respectively). Of the 11 topics within "emotion and beliefs," 113,562/157,563 (72%) had negative sentiment. CONCLUSIONS: The content analysis of tweets in the area of LBP identified common themes that are consistent with findings from conventional qualitative studies but provide a more granular view of individuals' perspectives related to LBP. This understanding has the potential to assist with developing more effective and personalized models of care to improve outcomes in those with LBP.

Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications.

As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments.

Context Definition and Query Language: Conceptual Specification, Implementation, and Evaluation.

As IoT grows at a staggering pace, the need for contextual intelligence is a fundamental and critical factor for IoT intelligence, efficiency, effectiveness, performance, and sustainability. As the standardisation efforts for IoT are fast progressing, efforts in standardising context management platforms led by the European Telecommunications Standards Institute (ETSI) are gaining more attention from both academic and industrial research organizations. These standardisation endeavours will enable intelligent interactions between 'things', where things could be devices, software components, web-services, or sensing/actuating systems. Therefore, having a generic platform to describe and query context is crucial for the future of IoT applications. In this paper, we propose Context Definition and Query Language (CDQL), an advanced approach that enables things to exchange, reuse and share context between each other. CDQL consists of two main parts, namely: context definition model, which is designed to describe situations and high-level context; and Context Query Language (CQL), which is a powerful and flexible query language to express contextual information requirements without considering details of the underlying data structures. An important feature of the proposed query language is its ability to query entities in IoT environments based on their situation in a fully dynamic manner where users can define situations and context entities as part of the query. We exemplify the usage of CDQL on three different smart city use cases to highlight how CDQL can be utilised to deliver contextual information to IoT applications. Performance evaluation has demonstrated scalability and efficiency of CDQL in handling a fairly large number of concurrent context queries.

Investigating Subjective Experience and the Influence of Weather Among Individuals With Fibromyalgia: A Content Analysis of Twitter.

BACKGROUND: Little is understood about the determinants of symptom expression in individuals with fibromyalgia syndrome (FMS). While individuals with FMS often report environmental influences, including weather events, on their symptom severity, a consistent effect of specific weather conditions on FMS symptoms has yet to be demonstrated. Content analysis of a large number of messages by individuals with FMS on Twitter can provide valuable insights into variation in the fibromyalgia experience from a first-person perspective. OBJECTIVE: The objective of our study was to use content analysis of tweets to investigate the association between weather conditions and fibromyalgia symptoms among individuals who tweet about fibromyalgia. Our second objective was to gain insight into how Twitter is used as a form of communication and expression by individuals with fibromyalgia and to explore and uncover thematic clusters and communities related to weather. METHODS: Computerized sentiment analysis was performed to measure the association between negative sentiment scores (indicative of severe symptoms such as pain) and coincident environmental variables. Date, time, and location data for each individual tweet were used to identify corresponding climate data (such as temperature). We used graph analysis to investigate the frequency and distribution of domain-related terms exchanged in Twitter and their association strengths. A community detection algorithm was applied to partition the graph and detect different communities. RESULTS: We analyzed 140,432 tweets related to fibromyalgia from 2008 to 2014. There was a very weak positive correlation between humidity and negative sentiment scores (r=.009, P=.001). There was no significant correlation between other environmental variables and negative sentiment scores. The graph analysis showed that "pain" and "chronicpain" were the most frequently used terms. The Louvain method identified 6 communities. Community 1 was related to feelings and symptoms at the time (subjective experience). It also included a list of weather-related terms such as "weather," "cold," and "rain." CONCLUSIONS: According to our results, a uniform causal effect of weather variation on fibromyalgia symptoms at the group level remains unlikely. Any impact of weather on fibromyalgia symptoms may vary geographically or at an individual level. Future work will further explore geographic variation and interactions focusing on individual pain trajectories over time.