Users' intention to continue using mHealth services: A DEMATEL approach during the COVID-19 pandemic

Determining information system end-user satisfaction and continuance intension with a unified modeling approach

With the different characters of datatypes and large amount of data going to be managed in open-source database, localization to the specific linguistics is the major concern in Ethiopia, as the nation used different datatypes compared to the Gregorian systems. In this regard Amharic localization in open-source database can handle the difficulties in managing data for governmental and non-governmental organizations. Amharic Extension Module was introduced to governmental organizations for the data management capabilities. But, there is no research that can explore the system's quality, the users' satisfaction and intension of continuance of Amharic Extension Module from the perspective of both computer literates and illiterates. Therefore, this research work attempt or try to empirically examine and analyze the system quality, the users' satisfaction and intension of continuance of Amharic Extension Module from the perspective of all users in POESSA The major purpose/aim of this study/research is to brand or make up the research break/gap in the area of localization specific to the Amharic locals, and to show the implication of the practical and theoretical way based on the results of the research. For this purpose, questionnaires were used for the collection of the research data. A total of 395 copies of the questionnaires were distributed and 385 of them are collected without any problem from the organization indicated herewith. The statistical analysis tools such as SPSS and AMOS, and methods such as Structural equation model were used for the analysis of the research data. The results of the research recommended and suggested that system quality can significantly influence confirmation. Meanwhile, confirmation can directly and significantly influence perceived usefulness, performance expectations, and satisfaction. Additionally, performance expectation, perceived usefulness and confirmation can significantly impact/influence satisfaction. The satisfaction directly and most importantly and significantly influences the continuance intension. Finally, the research delivers/provides a concert indication for the legitimacy and validity of the integrated and combined models of UTUAT, ECTM, and D&M ISS in the field of localizations which can be a hypothetical and theoretical foundation for Amharic Extension Module-AEM users' and services of it.

Applying the Participatory Slow Design Approach to a mHealth Application for Family Caregivers in Pediatric Ear, Nose, and Throat Surgery

Pediatric ear, nose, and throat (ENT) surgery is very common, and its outcomes may improve with family education. In this regard, mobile health (mHealth) applications (apps), which are on the rise due to digital transformation, can be beneficial in healthcare. This study outlines the user-centered design and development of a mHealth app (version 5.15.0) to support family caregivers during the perioperative process of pediatric ENT surgery. Conducted over two years in an Italian maternal and child health hospital (January 2020-May 2022), the study employed a participatory design method based on the Information System Research (ISR) framework and guided by the principles of Slow Medicine. Utilizing the Relevance, Rigor, and Design cycles of the ISR framework, the mHealth app's content, functionalities, and technical features were defined and developed. A committee of fifteen experts guided the process with input from 25 family caregivers and 24 healthcare providers enrolled in the study. The mHealth app content was structured around five crucial educational moments characterizing the ENT perioperative period, providing evidence-based information on surgical procedures, strategies for preparing children for hospitalization and surgery, pain management, and post-discharge care. The mHealth app featured a function that sends customized notifications to guide caregivers at specific perioperative stages. The development of mHealth apps by implementing a rigorous, participatory, and Slow design process can foster accessible and family-centered information and care in the field of maternal and child health and beyond.

The intention to use mHealth applications among Dutch older adults prior and during the COVID pandemic

Background

Mobile health (mHealth) applications are widely valued for their potential to increase self-management among older adults and reduce their healthcare demands. However, the intention to use mHealth of Dutch older adults before the COVID-19 pandemic was modest. Healthcare access was considerably reduced during the pandemic and mHealth services substituted for in person health services. As older adults utilize health services more frequently and have been particularly vulnerable to the pandemic, they can be viewed to have especially benefitted from the transition toward mHealth services. Furthermore, one might expect their intention to use these services and reap the potential benefits has increased, especially during the pandemic.

Objective

The aim of this study was to examine whether the intention of Dutch older adults to use medical applications increased during the COVID pandemic and how the explanatory power of the extended Technology Acceptance Model (TAM) developed for this purpose was affected by the onset of the pandemic.

Methods

We conducted a cross-sectional survey using two samples collected before (n = 315) and after (n = 501) the onset of the pandemic. Data was collected using questionnaires which were distributed digitally and on paper, by convenience sampling and snowballing. Participants were 65 years or older, lived independently or in a senior living facility, without cognitive impairment. A controlled analysis was performed to test for significant differences in the intention to use mHealth. The before and after differences in extended TAM variables and their relationship with intention to use (ITU) were analyzed using controlled (multivariate) logistic and linear regression models. These models were also used to explore whether the onset of the pandemic had an effect on ITU not captured by the extended TAM model.

Results

While the two samples differed in ITU (p = 0.017; uncontrolled) there was no statistically significant difference in ITU in the controlled logistic regression analysis (p = 0.107). The scores of the extended TAM variables explaining intention to use were all significantly higher, except for Subjective norm and Feelings of Anxiety. The relationships of these variables with intention to use before and after the onset of the pandemic were similar, except for Social relationships which lost its significance. We found no indications of effects of the pandemic on intention to use not captured by our instrument.

Conclusion

The intention to use mHealth applications of Dutch older adults has not changed since the onset of the pandemic. The extended TAM model has robustly explained intention to use, with only minor differences after the first months of the pandemic. Interventions targeting facilitation and support are likely to promote the uptake of mHealth. Follow-up studies are needed to investigate whether the pandemic has had long term effects on the ITU of the older adult.

A fuzzy-set qualitative comparative analysis exploration of multiple paths to users' continuous use behavior of diabetes self-management apps

BACKGROUND

Despite the obvious potential benefits of diabetes self-management apps, users' continuous use of diabetes self-management apps is still not widespread. Influential factors coexisted in information ecologies are likely to have a synthetic effect on users' continuous use behavior. However, it is less clear how factors in information ecologies combine to influence users' continuous use behavior.

OBJECTIVE

The objectives of this study are to explore combinations of factors (perceived severity, information quality, service quality, system quality, and social influence) in information ecologies that lead to users' continuous use behavior of diabetes self-management apps and which combination is the most important.

METHODS

Purpose sampling was used to recruit diabetes self-management app users from July 1, 2021 to January 31, 2022. Fuzzy-set qualitative comparative analysis (fsQCA) was then employed by conducting necessity and sufficiency analysis.

RESULTS

In total 280 diabetes self-management app users participated. The necessity analysis indicated that no single factor was necessary to cause users' continuous use behavior, and the sufficiency analysis identified five different combinations of factors that lead to users' continuous use behavior. Of these five, the combination of high information quality, high service quality, and high social influence was found to be the most important path.

CONCLUSIONS

Users' continuous use behavior of diabetes self-management apps results from the synergistic effects of factors in information ecologies. The five paths that directly contribute to users' continuous use, as well as the four user types preliminarily identified in this study may provide a reference for healthcare providers and app developers.

Counseling for Health: How Psychological Distance Influences Continuance Intention towards Mobile Medical Consultation

As mobile healthcare services entered the public sight with high frequency during the COVID-19 pandemic, patients are increasingly recognizing the effectiveness of mobile medical consultation (MMC). Earlier studies have investigated what influences continuance intention (CI) towards MMC, but few studies have scrutinized it from the perspective of patients' psychological distance. We formulated a framework to examine the psychological factors influencing CI towards MMC by integrating the information systems continuance model and psychological distance theory. The framework was validated using the partial least squares structural equation modeling (PLS-SEM) approach and data from 475 MMC users in China. The empirical results revealed that immediacy, telepresence, intimacy, and substitutability were significant predictors of CI, while satisfaction mediated these pathways. Pandemic-induced anxiety positively moderated the effect of immediacy on satisfaction and the effect of satisfaction on CI. Practical implementations for MMC healthcare practitioners, designers, and marketers are drawn.

The Impact of Fitness Influencers on a Social Media Platform on Exercise Intention during the COVID-19 Pandemic: The Role of Parasocial Relationships

In the context of COVID-19 pandemic lockdowns, fitness influencers on social media are greatly involved in people's home fitness processes, but there is limited research examining the role of fitness influencers on social media in exercise intention. This study aimed to explore whether people's perceptions of the personal attributes and content quality of fitness influencers can promote a strong emotional connection between the two, thereby influencing people's exercise intentions. Based on the theory of the parasocial relationship, we investigated the influence of social attractiveness (SA), physical attractiveness (PA), task attractiveness (TA), and content quality (CQ) of fitness influencers on the parasocial relationships (PSRs) between viewers and fitness influencers on social media and whether PSRs positively contribute to viewers' exercise intentions (EI). The results revealed that SA, PA, TA, and CQ have positive influences on PSRs and CQ; PSRs directly contribute to EI; and PSRs have a mediating effect between EI and CQ. This study provides new insights into understanding the relationship between fitness influencers and people's online fitness behaviors.

Predicting factors affecting the intention to use a 3PL during the COVID-19 pandemic: A machine learning ensemble approach

The COVID-19 pandemic had brought changes to individuals, especially in consumer behavior. As the government of different countries has been implementing safety protocols to mitigate the spread of the virus, people became apprehensive about traveling and going out. This paved way for the emergence of third-party logistics (3PL). Statistics have proven the rapid escalation regarding the use of 3PL in various countries. This study utilized Artificial Neural Network and Random Forest Classifier to validate and justify the factors that affect consumer intention in selecting a 3PL service provider during the COVID-19 pandemic integrating the Service Quality Dimensions and Pro-Environmental Theory of Planned Behavior. The findings of this study revealed that attitude is the most significant factor that affects the consumers' behavioral intention. Other factors such as customer satisfaction, customer perceived value, perceived environmental concern, assurance, responsiveness, empathy, reliability, tangibility, perceived behavioral control, subjective norm, and perceived authority support, are all contributing factors that affect behavioral intention. Machine learning algorithms, specifically ANN and RFC, resulted to be reliable in predicting factors as they obtained accuracy rates of 98.56% and 93%. Results presented that consumers’ attitude, satisfaction, perceived value, assurance by the 3PL, and perceived environmental concerns were highly influential in choosing a 3PL package carrier. It was seen that people would be encouraged to use 3PL service providers if they demonstrate availability and environmental concerns in catering to the customers' needs. Subsequently, 3PL providers must assure safety and convenience before, during, and after providing the service to ensure continuous patronage of consumers. This is considered to be the first study that utilized a machine learning ensemble to measure behavioral intention for the logistic sector. The framework, analysis tools, and findings of this study could be extended and applied among other behavioral intentions regarding transportation worldwide. Managerial insights among service providers are discussed.

Feasibility of a Theory-Based, Online Tailored Message Program to Motivate Healthier Behaviors in College Women

We aimed to test the feasibility of an online survey and tailored message program in young women. Recruited from college campuses, women (n = 189) completed an online survey assessing preference for and behaviors toward diet and physical activity as well as theory-based influencers of these behaviors (knowledge/information, motivation, and confidence). Health messages were tailored to the participant’s survey responses and learning style to address misconceptions and motivate or reinforce healthy physical activity and dietary behaviors. Most women reported the survey as relevant (92%) and useful for reflecting on their health (83%), with survey responses variable in level of nutrition and physical activity knowledge, motivation, and confidence. Each woman received four tailored messages—most reported the messages as relevant (80%) and learning new information (60%). Across all messages, nearly half of the participants (~48%) reported willingness to try or maintain healthier behaviors and confidence in their ability. Body size discrepancy and dietary restraint had small effects message responses of information learned, and the motivation and confidence in trying healthier behaviors. In summary, these data support the feasibility of this online tailored message program. The college women found the tailored message program acceptable and useful to motivate healthier behaviors. The findings provide direction for behaviorally focused interventions to improve dietary and physical activity behaviors.

An intuitionistic fuzzy decision support system for COVID-19 lockdown relaxation protocols in India

In January 2020, the World Health Organization (WHO) identified a world-threatening virus, SARS-CoV-2. To diminish the virus spread rate, India implemented a six-month-long lockdown. During this period, the Indian government lifted certain restrictions. Therefore, this study investigates the efficacy of India's lockdown relaxation protocols using fuzzy decision-making. The decision-making trial and evaluation laboratory (DEMATEL) is one of the fuzzy MCDM methods. When it is associated with intuitionistic fuzzy circumstances, it is known as the intuitionistic fuzzy DEMATEL (IF-DEMATEL) method. Moreover, converting intuitionistic fuzzy into a crisp score (CIFCS) algorithm is an aggregation technique utilized for the intuitionistic fuzzy set. By using IF-DEMATEL and CIFCS, the most efficient lockdown relaxation protocols for COVID-19 are determined. It also provides the cause and effect relationship of the lockdown relaxation protocols. Additionally, the comparative study is carried out through various DEMATEL methods to see the effectiveness of the result. The findings would be helpful to the government's decision-making process in the fight against the pandemic.

Determinants of the Mobile Health Continuance Intention of Elders with Chronic Diseases: An Integrated Framework of ECM-ISC and UTAUT

With the deepening of population aging in China, chronic diseases are a major public health concern that threatens the life and health of nationals. Mobile health or mHealth can effectively monitor chronic diseases, which holds vital significance to the alleviation of social pressure caused by aging. To patients with chronic diseases, mHealth cannot give full play to its value, only when it is used in the long term. However, there is not yet research exploring mHealth continuance intention from the perspective of elders with chronic diseases. So, this research represents the first attempt to empirically analyze mHealth continuance intention from the perspective of elders with chronic diseases. The purpose of this research is to make up the research gap of the mHealth field and to put forward theoretical and practical implications based on research results. To obtain research data, a questionnaire was conducted. A total of 926 copies were collected online and 527 copies were collected offline. The structural equation model (SEM) was used for data analysis. Research results suggest that confirmation can significantly influence satisfaction, performance expectancy and effort expectancy. Meanwhile, confirmation and performance expectancy can significantly influence satisfaction. Additionally, effort expectancy, performance expectancy, social influence and facilitating conditions can directly and significantly influence continuance intention. Among them, performance expectancy can directly influence continuance intention in the most significant way. This research provides solid evidence for the validity of the integrated model of ECM-ISC and UTAUT in the mHealth field, which can be a theoretical basis for mHealth operators' product R&D.

Knowledge-based normative safety measure approach: systematic assessment of capabilities to conquer COVID-19

In this research article, we have introduced a knowledge-based approach to regional/national security measures. Proposed Knowledge-based Normative Safety Measure algorithm for safety measures helps to take practical actions to conquer COVID-19. We analyzed based on five dimensions: the correlation between detected cases and confirmed cases, social distance, the speed of detected cases, the correlation between imported cases and inbound cases, and the proportion of masks worn. It prompts actions based on the security level of the region. Through the use of our proposed algorithm, the government has accelerated the implementation of social distancing, accelerated test cases, and policies, etc., to prevent people from contracting COVID-19. This idea can be a very effective way to realize the impending danger and take action in advance. Help speed up the process of controlling the COVID-19. In pandemic times, it can be helpful to understand better. Holding the normative safety measure at a high level leads nations to perform excellently on triple T's (testing, tracking, and treatment) policy and other safety acts. The proposed NSM approach facilitates for improve the governance of cities and communities.

Health Services and Patient Satisfaction in IRAN during the COVID-19 Pandemic: A Methodology Based on Analytic Hierarchy Process and Artificial Neural Network

The aim of this study is to identify and classify the most important factors affecting patient satisfaction in the COVID-19 pandemic crisis considering economic effects. This is an analytical study using the analytic hierarchy process (AHP) method and ANN-MLP (Artificial neural network based on multilayer perceptron model as a supervised learning algorithm) as an innovative methodology. The questionnaire was completed by 72 healthcare experts (N = 72). The inter-class correlation (ICC) coefficient value was confirmed in terms of consistency to determine sampling reliability. The findings show that interpersonal care and organizational characteristics have the greatest and least influence, respectively. Furthermore, the observations confirm that the highest and lowest effective sub-criteria, respectively, are patient safety climate and accessibility. Based on the study’s objective and general context, it can be claimed that private hospitals outperformed public hospitals in terms of patient satisfaction during the COVID-19 pandemic. Focusing on performance sensitivity analysis shows that, among the proposed criteria to achieve the study objective, the physical environment criterion had the highest difference in private and public hospitals, followed by the interpersonal care criterion. Furthermore, we used a multilayer perceptron algorithm to assess the accuracy of the model and distinguish private and public hospitals as a novelty approach. Overfitting results in finding an MLP model which is reliable, and the accuracy of the model is acceptable.