mHealth Services Research Articles

Perception and Initial Adoption of Mobile Health Services of Older Adults in London: Mixed Methods Investigation.

BackgroundAdvances in mobile technology and public needs have resulted in the emergence of mobile health (mHealth) services. Despite the potential benefits of mHealth apps, older adults face challenges and barriers in adopting them.ObjectiveThe aims of this study are to understand older adults’ perception of mHealth services and to discover the barriers that older adults face in the initial adoption of mHealth apps.MethodsThis paper systematically analyzed main determinants related to mHealth services and investigated them through questionnaires, interviews, and a workshop. Two studies were carried out in London. In study 1, the questionnaires with follow-up interviews were conducted based on the literature review to uncover older adults’ perception (including perceived usefulness, perceived ease of use, and perceived behavioral control) of mHealth services. Study 2 was a workshop helping older adults to trial selected mHealth apps. The workshop was conducted by the first author (JP) with assistance from 5 research students. The barriers that older adults faced in the initial adoption period were observed. The interviews and workshop were audiotaped and transcribed. Descriptive statistics and the thematic analysis technique were used for data analysis.ResultsIn total, 30 older adults in London completed the questionnaires and interviews in study 1. The results of study 1 show that the lack of obvious advantage, low reliability, scary information, and the risk of privacy leakage would decrease older adults’ perceived usefulness of mHealth services; the design of app interface would directly affect the perceived ease of use; and aging factors, especially the generation gap, would create barriers for older users. In total, 12 participants took part in the workshop of study 2, including 8 who took part in study 1. The results of study 2 identified that access to technology, the way of interaction, the risk of money loss, heavy workload of using an mHealth app, and different lifestyle are influential factors to older adults’ adoption of mHealth services.ConclusionsThe perceptions of mHealth services of older adults were investigated; the barriers that older adults may face in the initial adoption stage were identified. On the basis of the synthesis of these results, design suggestions were proposed, including technical improvement, free trial, information clarification, and participatory design. They will help inform the design of mHealth services to benefit older adults.

Digital health and artificial intelligence in kidney research: a report from the 2020 Kidney Disease Clinical Trialists (KDCT) meeting.

The exponential growth in digital technology coupled with the global coronavirus disease 2019 pandemic is driving a profound change in the delivery of medical care and research conduct. The growing availability of electronic monitoring, electronic health records, smartphones and other devices and access to ever greater computational power provides not only new opportunities, but also new challenges. Artificial intelligence (AI) exemplifies the potential of this digital revolution, which also includes other tools such as mobile health (mHealth) services and wearables. Despite digital technology becoming commonplace, its use in medicine and medical research is still in its infancy, with many clinicians and researchers having limited experience with such tools in their usual practice. This article, derived from the 'Digital Health and Artificial Intelligence' session of the Kidney Disease Clinical Trialists virtual workshop held in September 2020, aims to illustrate the breadth of applications to which digital tools and AI can be applied in clinical medicine and research. It highlights several innovative projects incorporating digital technology that range from streamlining medical care of those with acute kidney injury to the use of AI to navigate the vast genomic and proteomic data gathered in kidney disease. Important considerations relating to any new digital health project are presented, with a view to encouraging the further evolution and refinement of these new tools in a manner that fosters collaboration and the generation of robust evidence.

An extension of technology acceptance model for mHealth user adoption

Providing health care services has become a challenge for the government, especially for emerging economies, which face huge resource problems. mHealth (mobile health) has the potential to reduce health-related problems significantly in the long run. It can be used as a preventive healthcare tool also. Despite the potential, minimal studies exist on the technology adoption of mHealth. There have been inadequate studies in the context of India. To find out the domains where studies can be conducted and after a thorough literature review, the study employed the Technology Acceptance Model (TAM) for the smooth running of mobile health services.As a theoretical contribution, this research is an extended version of the TAM, suggested by Davis, through considering six additional variables such as social influence, technology anxiety, trust, perceived risk, perceived physical condition, Resistance to change. To validate the linkages, a close-ended questionnaire was developed after a thorough literature survey. The current study collected 289 valid responses from different mHealth services users. The constructs of the model have been tested in Indian settings by conducting exploratory and confirmatory factor analysis. Further, Structural equation modelling is ably employed to validate the model to suit the Indian requirements. The adoption of mHealth was found to have had an enormous impact on social influence, behavioural intention and trust. The empirical examination showed high predictive power for adoption intention of mHealth services and the influential role of these important constructs. The implications for academics and policymakers have been discussed in this study. Finally, the future scope and limitations of the study have been discussed.

Playing alone: can game design elements satisfy user needs in gamified mHealth services?

Chronic health conditions have necessitated the need for behavioral interventions (such as exercise programs) outside of clinical contexts, increasingly managed through technology such as mobile health (mHealth) services. Gamification has emerged as a promising tool to facilitate greater engagement in these services; however, no studies investigate the links between specific game design elements (GDEs) and psychological or behavioral outcomes within a health context. This domain is motivationally complex and has shown resistance to the satisfaction of social (relatedness) needs, presenting a challenge to the design of gamification products for health promotion. Drawing on self-determination theory, this research demonstrates the strengths of a taxonomy based upon structural features of GDEs (such as social, narrative or reward elements) rather than the design intent definitions of these elements used in previous studies. This taxonomy is then used to assess the relationship between GDEs and psychological needs satisfaction in a survey (N = 236) of gamified exercise/fitness application users. Further qualitative interviews (N = 20) were conducted to clarify survey findings. This research demonstrates the positive association between control and presentation elements and autonomy satisfaction, and control and reward elements and competency satisfaction. However, it also suggests that player type and context may limit the ability for GDEs alone to support relatedness satisfaction in mHealth services. Implications for managers and researchers are discussed, particularly the strengths and weaknesses of using structural taxonomies in gamification assessment.

Requirements and Architecture of a Cloud Based Insomnia Therapy and Diagnosis Platform: A Smart Cities Approach

Insomnia is the most common sleep disorder worldwide. Its effects generate economic costs in the millions but could be effectively reduced using digitally provisioned cognitive behavioural therapy. However, traditional acquisition and maintenance of the necessary technical infrastructure requires high financial and personnel expenses. Sleep analysis is still mostly done in artificial settings in clinical environments. Nevertheless, innovative IT infrastructure, such as mHealth and cloud service solutions for home monitoring, are available and allow context-aware service provision following the Smart Cities paradigm. This paper aims to conceptualise a digital, cloud-based platform with context-aware data storage that supports diagnosis and therapy of non-organic insomnia. In a first step, requirements needed for a remote diagnosis, therapy, and monitoring system are identified. Then, the software architecture is drafted based on the above mentioned requirements. Lastly, an implementation concept of the software architecture is proposed through selecting and combining eleven cloud computing services. This paper shows how treatment and diagnosis of a common medical issue could be supported effectively and cost-efficiently by utilising state-of-the-art technology. The paper demonstrates the relevance of context-aware data collection and disease understanding as well as the requirements regarding health service provision in a Smart Cities context. In contrast to existing systems, we provide a cloud-based and requirement-driven reference architecture. The applied methodology can be used for the development, design, and evaluation of other remote and context-aware diagnosis and therapy systems. Considerations of additional aspects regarding cost, methods for data analytics as well as general data security and safety are discussed.

Brain+ AlcoRecover: A Randomized Controlled Pilot-Study and Feasibility Study of Multiple-Domain Cognitive Training Using a Serious Gaming App for Treating Alcohol Use Disorders.

Background: Patients with alcohol use disorder (AUD) exhibit deficits in various cognitive domains, including executive functioning, working memory, and learning and memory, which impede the effectiveness of conventional AUD treatment and enhance relapse. Mobile health (mHealth) services are promising in terms of delivering cognitive training in gamified versions. So far, studies examining the effects of mHealth-based cognitive training in AUD patients have, however, focused on specific rather than multiple cognitive domains and overlooked the importance of clinical outcomes. Furthermore, research has yet to investigate the acceptability and feasibility of this type of cognitive training.Aims: The aims of this pilot study are to examine (1) whether using smartphone-based, multi-domain cognitive training with gamified elements as part of conventional treatment for AUD indicate effect, and (2) whether the intervention is acceptable and feasible as a part of conventional treatment for AUD.Methods: Patients from the alcohol outpatient clinic, Odense Municipality, Denmark will be invited to participate in the study on a consecutive basis until a total of 60 patients have been recruited. The study will be performed as a combined parallel randomized controlled trial (RCT) and qualitative feasibility study. The patients will be randomly assigned to one of two groups. The intervention group (n = 30) will receive smartphone-based, multi-domain cognitive training with gamified elements together with treatment as usual (TAU). The active control group (n = 30) will receive a sham version of the same cognitive training together with TAU. Cognitive outcomes will be assessed via the training application at baseline and post-treatment. Clinical outcomes will be assessed at baseline, post-treatment, and at 6-month follow-up using the Addiction Severity Index. Furthermore, the 30 patients randomized to the intervention group will be invited to participate in the second phase, that is the feasibility study, at post-treatment. A questionnaire inquiring about the use of mHealth treatment in general will be administered. Further, feedback regarding functionality and meaningfulness of the application in addition to other qualitative aspects relating to the use of the application will be collected. The patients will also be asked to provide suggestions about how to improve and potentially implement the tool.Implications: It is anticipated that this pilot study will provide tentative evidence for the effectiveness of smartphone-based, multi-domain cognitive training as well as information about the usability and feasibility of this type of training, including acceptability and compliance. The study will also contribute with feedback derived from the patients about how to improve and implement the tool. If promising, the findings will be used to plan a large-scale RCT. Since cognitive deficits are not addressed in current treatments for AUD, gamified cognitive training delivered through smartphones may increase the effectiveness of current treatment for AUD as well as introduce more mHealth-based treatment that is both accessible and cost-effective.

Impact of Mobile Technologies on Cervical Cancer Screening Practices in Lagos, Nigeria (mHealth-Cervix): A Randomized Controlled Trial.

PURPOSEWe assessed the impact of mHealth on Pap test screening uptake and also determined the factors that affect screening uptake among women in Lagos, Nigeria.MATERIALS AND METHODSA randomized controlled trial was carried out among women in two tertiary health institutions in Lagos, Nigeria, between July 2020 and March 2021. Participants were randomly assigned to either a text message (mHealth) intervention or usual care arm. The main study outcome was the uptake of Pap smear testing within 6 months of enrollment in the study. We tested the associations between two groups of continuous variables using the unpooled independent-sample t-test (normal distribution) and that of two groups of categorical variables with the chi-square (χ2) test. Using a multinomial logistic regression model, we adjusted for relevant sociodemographic and clinical predictors of uptake of Pap smear screening. Statistical significance was defined as P < .05.RESULTSThere was a significantly higher rate of uptake of Pap smear screening among women in the mHealth arm compared with those in the usual care arm (51.0% v 35.7%, P = .031). Following adjustment in the final multivariate model, level of income (odds ratio [OR] = 5.13, 95% CI, 1.55 to 16.95), awareness of Pap smear (OR = 16.26; 95% CI, 2.49 to 76.64), General Outpatient clinic attendance, and introduction of mHealth intervention during follow-up (OR = 4.36; 95% CI, 1.44 to 13.22) were the independent predictors of Pap smear uptake.CONCLUSIONThe use of mHealth technologies intervention via short-text message services is a feasible solution for cervical cancer prevention in low- and middle-income countries, and thus, the widespread use of mHealth services by health care providers and policymakers could contribute to the implementation of cervical cancer prevention services in Nigeria and in the settings of other low- and middle-income countries.

Service Quality Analysis of Mhealth Services Using Text Mining Method : Alodokter and Halodoc

The digital transformation of health services causes an increasing number of digital health service providers in Indonesia. The user shares their experiences and reviews each other on the online platform. This study aims to understand user perceptions of m-health services in Indonesia based on m-health service quality with a big data approach. Research using text mining is derived from the results of the reviews of the application Alodokter and Halodoc. User-generated content was gathered from the platform Google Play Store in the period April to December 2020. Based on the sentiment analysis, Alodokter performs well with 73% positive and 27% negative, while Halodoc also dominated with 86% positive and 14% negative. User reviews are categorized based on three dimensions of health service quality with a multiclass classification. It is possible to identify the word networks that often appear in user reviews through text network analysis. The dimension that reviews chiefly on Alodokter and Halodoc is perceived outcome quality. The result of this study could help or use as guidance to be a reference for evaluations to improve Indonesia's quality of m-health services.

A Clinician-Led, Experience-Based Co-Design Approach for Developing mHealth Services to Support the Patient Self-management of Chronic Conditions: Development Study and Design Case.

BackgroundDespite the increasing use of mobile health (mHealth) services, such as mHealth apps or SMS text messaging services, that support the patient self-management of chronic conditions, many existing mHealth services lack theoretical guidance. In addition, although often the target audience for requirement acquisition at the initial mHealth app design stage, it is a common challenge for them to fully conceptualize their needs for mHealth services that help self-manage chronic conditions.ObjectiveThis study proposes a novel co-design approach with the initial requirements for mHealth services proposed by clinicians based on their experiences in guiding patients to self-manage chronic conditions. A design case is presented to illustrate our innovative approach to designing an mHealth app that supports the self-management of patients with obesity in their preparation for elective surgery.MethodsWe adopted a clinician-led co-design approach. The co-design approach consisted of the following four cyclic phases: understanding user needs, identifying an applicable underlying theory, integrating the theory into the prototype design, and evaluating and refining the prototype mHealth services with patients. Expert panel discussions, a literature review, intervention mapping, and patient focus group discussions were conducted in these four phases.ResultsIn stage 1, the expert panel proposed the following three common user needs: motivational, educational, and supportive needs. In stage 2, the team selected the Social Cognitive Theory to guide the app design. In stage 3, the team designed and developed the key functions of the mHealth app, including automatic push notifications; web-based resources; goal setting and monitoring; and interactive health-related exchanges that encourage physical activity, healthy eating, psychological preparation, and a positive outlook for elective surgery. Push notifications were designed in response to a patient’s risk level, as informed by the person’s response to a baseline health survey. In stage 4, the prototype mHealth app was used to capture further requirements from patients in the two focus group discussions. Focus group participants affirmed the potential benefits of the app and suggested more requirements for the function, presentation, and personalization needs. The app was improved based on these suggestions.ConclusionsThis study reports an innovative co-design approach that was used to leverage the clinical experiences of clinicians to produce the initial prototype app and the approach taken to allow patients to effectively voice their needs and expectations for the mHealth app in a focus group discussion. This approach can be generalized to the design of any mHealth service that aims to support the patient self-management of chronic conditions.

Anaphylaxis and digital medicine.

Digital medicine (mHealth) aims to help patients and healthcare providers (HCPs) improve and facilitate the provision of patient care. It encompasses equipment/connected medical devices, mHealth services and mHealth apps (apps). An updated review on digital health in anaphylaxis is proposed. In anaphylaxis, mHealth is used in electronic health records and registries.It will greatly benefit from the new International Classification of Diseases-11 rules and artificial intelligence. Telehealth has been revolutionised by the coronavirus disease 2019 pandemic, and lessons learnt should be extended to shared decision making in anaphylaxis. Very few nonvalidated apps exist and there is an urgent need to develop and validate such tools. Although digital health appears to be of great importance in anaphylaxis, it is still insufficiently used.

A mixed-methods study to explore opportunities and challenges with using a mHealth approach to engage men who have sex with men in HIV prevention, treatment and care in Lomé, Togo.

In Togo, men who have sex with men (MSM) experience disproportionately high rates of human immuno-deficiency virus (HIV), with prevalence at 13% compared to the countrywide general HIV prevalence of 2.5%. Mobile phone adoption has grown rapidly in West Africa in recent years and mobile health (mHealth) provides an opportunity to engage high-risk populations in HIV prevention, treatment and care (PTC). This study focuses on the text messaging component of a mHealth program and resulting linkages to PTC in Lomé, Togo. We used a mixed-methods approach to collect information directly from a purposive sample of MSM to estimate the potential impact of the mHealth intervention on specific outcomes. A structured survey captured 503 MSM experiences with program activities to increase access to PTC through short message system (SMS), including data on socio-demographics, HIV knowledge and testing history, program awareness, use of services and referrals, use of mHealth, and experiences with and preferences for receiving HIV SMS messages. Twelve in-depth interviews captured qualitative data on MSM experiences and opinions related to the mHealth intervention, as well as barriers and facilitators to linkages between mHealth and clinic services. Predicted probabilities of MSM being reached by the program and linked to PTC is higher for those exposed to the program and significantly different (P<0.001). The probability of being linked to HIV prevention emerges as the highest among MSM exposed to SMS messages with 92% probability. MSM exposed to SMS had a 63% probability to be linked to treatment compared to only 40% among those not exposed to SMS. Program barriers and facilitators were identified through the thematic analysis of the qualitative data. Several facilitators to PTC were reported including: increased knowledge and awareness; convenient location of HIV testing coupled with distribution of products; ensuring individual confidentiality; and perceived care of providers. The main barriers to the program were lack of program awareness, program functionality and affordability for referral services, and peer educator training. The probability of being reached, and adhering to PTC is significantly higher among MSM exposed to HIV related SMS messages; however, the usefulness of SMS in HIV programs targeting MSM needs to be better understood.

A Comprehensive 6A Framework for Improving Patient Self-Management of Hypertension Using mHealth Services: Qualitative Thematic Analysis.

BackgroundHypertension affects over 15% of the world’s population and is a significant global public health and socioeconomic challenge. Mobile health (mHealth) services have been increasingly introduced to support hypertensive patients to improve their self-management behaviors, such as adherence to pharmacotherapy and lifestyle modifications.ObjectiveThis study aims to explore patients’ perceptions of mHealth services and the mechanisms by which the services support them to self-manage their hypertension.MethodsA semistructured, in-depth interview study was conducted with 22 outpatients of the General Hospital of Ningxia Medical University from March to May 2019. In 2015, the hospital introduced an mHealth service to support community-dwelling outpatients with self-management of hypertension. Content analysis was conducted by following a grounded theory approach for inductive thematic extraction. Constant comparison and categorization classified the first-level codes with similar meanings into higher-level themes.ResultsThe patient-perceived mechanisms by which the mHealth service supported their self-management of hypertension were summarized as 6A: access, assessment, assistance, awareness, ability, and activation. With the portability of mobile phones and digitization of information, the mHealth service provided outpatients with easy access to assess their vital signs and self-management behaviors. The assessment results gave the patients real-time awareness of their health conditions and self-management performance, which activated their self-management behaviors. The mHealth service also gave outpatients access to assistance, which included health education and self-management reminders. Both types of assistance could also be activated by abnormal assessment results, that is, uncontrolled or deteriorating blood pressure values, discomfort symptoms, or not using the service for a long period. With its scalable use to handle any possible information and services, the mHealth service provided outpatients with educational materials to learn at their own pace. This led to an improvement in self-management awareness and ability, again activating their self-management behaviors. The patients would like to see further improvements in the service to provide more useful, personalized information and reliable services.ConclusionsThe mHealth service extended the traditional hypertension care model beyond the hospital and clinician’s office. It provided outpatients with easy access to otherwise inaccessible hypertension management services. This led to process improvement for outpatients to access health assessment and health care assistance and improved their awareness and self-management ability, which activated their hypertension self-management behaviors. Future studies can apply the 6A framework to guide the design, implementation, and evaluation of mHealth services for outpatients to self-manage chronic conditions.

Human Factors and Requirements of People with Cognitive Impairment, Their Caregivers, and Healthcare Professionals for mHealth Apps Including Reminders, Games, and Geolocation Tracking: A Survey-Questionnaire Study.

Background:Mobile Health (mHealth) apps can delay the cognitive decline of people with dementia (PwD), by providing both objective assessment and cognitive enhancement.Objective:This patient involvement survey aims to explore human factors, needs and requirements of PwD, their caregivers, and Healthcare Professionals (HCPs) with respect to supportive and interactive mHealth apps, such as brain games, medication reminders, and geolocation trackers through a constructive questionnaire.Methods:Following the principles of user-centered design to involve end-users in design we constructed a questionnaire, containing both open-ended and closed-ended questions as well as multiple choice and Likert scale, in order to investigate the specific requirements and preferences for mHealth apps. We recruited 48 participants including people with cognitive impairment (n = 15), caregivers (n = 16), and HCPs (n = 17) and administered the questionnaire.Results:All participants are likely to use mHealth apps, with the primary desired features being the improvement of memory and cognition, assistance on medication treatment, and perceived ease to use. HCPs, caregivers, and PwD consider brain games as an important technology-based, non-pharmaceutical intervention. Both caregivers and patients are willing to use a medication reminder app frequently. Finally, caregivers are worried about the patient wandering. Therefore, global positioning system tracking would be particularly important to them. On the other hand, patients are concerned about their privacy, but are still willing to use a geolocation app for cases of emergency.Conclusion:This research contributes to mHealth app design and potential adoption. All three groups agree that mHealth services could facilitate care and ameliorate behavioral and cognitive disturbances of patients.

M-health services for COVID-19 afflicted and infected victims

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Development of a Mobile Edge Device and an Information System to Analyze Particulate Matter Distribution as a mHealth Service

Particulate matter is an air pollutant consistent of very small particles that are suspended in the air. Shortterm exposure may result in respiratory symptoms such as shortness of breath, throat and nose irritation, chest tightness, coughing, in addition to eye irritation. The research aimes at creating a prototype of a mobile sensor system that can be used to analyze the particulate matter pollution on a location and on a time scale to measure the degree of pollution in the city. The engineering requirement to construct the edge device includes temperature (humidity) sensor, particulate matter sensor, GPS module, and an LCD for displaying the current sensor values. The health data of the mobile edge device can be analyzed through a developed analytics system, which allows the user to identify and avoid pollution sources. For the implementation of the web service the framework ReactJS, NodeJS with Express.js, and the database MongoDB are being used. The mHelath service is evaluated through field trials: New Year’s Eve, various source identifications, and a demonstration through a journey from the subway station to the university. The paper outlines a mHealth service, which can collect data records of the surroundings and analyze the particulate matter in an information system to visualize risk locations of a user.

Exploring the human factors for mindfulness in mHealth service usage: An Elaboration Likelihood Model

MHealth service is widely accepted as a good path towards healthcare promotion. However, patients’ low-level usage restricted its effectiveness. This work draws upon the elaboration likelihood model and mindfulness theory to investigate the mindfulness mechanism in mHealth service usage, which highlights: First, both perceived information quality and mindfulness positively affect mHealth service usage. Also, perceived information quality is observed to affect the establishment of mindfulness positively. These findings stress that both perceived information quality and mindfulness are essential for mHealth service usage. Second, peer patient influence and physicians’ recommendations positively affect the establishment of mindfulness. These findings stress that human factors are essential for consumers’ establishment of mindfulness. Third, mindfulness mediates the relationship between human factors and mHealth service usage. This phenomenon indicates that when consumers adopt a mHealth service, both peer patient influence and physicians’ recommendations have a positive impact on mHealth service usage indirectly. Fourth, the effects of both peer patient influence and physicians’ recommendations on mindfulness depend on patients’ perceived eHealth literacy levels. This finding illustrates that while peer patient influence is especially crucial for improving mindfulness in a cohort with a high-level perceived eHealth literacy, physicians’ recommendations are especially crucial for improving mindfulness in a cohort with low-level perceived eHealth literacy.

Authentication based on electrocardiography signals and machine learning

Among the information security problems involved in Telemedicine Information Systems (TMIS), the authentication area of the various entities involved has been extensively discussed in recent years and shown a wide range of possibilities. The problems caused by the application of inadequate authentication processes may lead to the death of patients who depend on Mobile Healthcare (M-Health) services. User authentication can be based on several physiological traits (e.g., iris, retina, and fingerprint) for biometric recognition, including electrocardiography (ECG) signals. Some ECG patterns are relatively robust to daily changes associated with normal heart rate variability. In fact, the relative lengths of PQ, QR, and RS intervals, as well as Q, R, and S relative amplitudes constitute individual traits. A few studies have succeeded in using ECG signals as an accurate authentication input and offered some advantages in comparison to biometrics traditional approaches. ECG-based user authentication can be built on Machine Learning (ML) models, used for classification purposes and reductions in distortions caused by misinterpretation of ECG data. Among the ML models adopted for ECG signals classification, ensembles have shown a good research opportunity. Random Under-Sampling Boosting (RUSBoost), a boosting algorithm not yet explored (to the best of our knowledge) for such a problem, can achieve comparatively high performance after a supervised training stage, even from relatively few training examples. This manuscript reports on a comparison of RUSBoost with Nearest Neighbour Search (NNS) regarding the classification of ECG signals for biometric authentication applications. The two ML techniques were compared by a random subsampling technique that considers four analysis metrics, namely accuracy, precision, sensitivity, and F1-score. The experimental results showed the better performance of RUSBoost regarding accuracy (97.4%), sensitivity (96.1%) and F1-score (97.4%). On the other hand, NNS provided better precision (99.5%).

Elderly and middle-aged intention to use m-health services: an empirical evidence from a developing country

Purposem-health services for different age groups are becoming an emerging field in the health-care industry, especially in low-resource environments such as developing countries such as Bangladesh. Hence, this study’s primary aim is to identify the factors that influence the middle-aged and elderly’s intention to use m-health services.Design/methodology/approachThis study applied the extended version of the unified theory of acceptance and use of technology to explore middle-aged and elderly’s intention to use m-health services. There were 235 respondents, of which 123 (52.34%) were in the middle-aged group, whereas 112 (47.66%) were in the older group. Both groups were found to have more male participants than female participants. The partial least square (PLS) method was used to analyze data.FindingsThe study found that performance expectancy, effort expectancy, facilitating condition, technological anxiety and resistance to change (p < 0.05) had a significant influence on middle-aged intention to use m-health services. Social influence and perceived physical condition (p > 0.05) had no significant effect on middle-aged intention to use m-health services. On the other hand, performance expectancy, effort expectancy, facilitating condition and resistance to change (p <* 0.05) significantly influenced the elderly’s intention to use m-health services. However, the social impact of perceived physical condition and technological anxiety (p > 0.05) had no significant effect on the elderly’s intention to use m-health services.Originality/valueA good number of studies are available in the current literature, examining the factors adoption of m-health services in both developed and developing economy context. However, very few studies examine the factors that influence behavioral intention to use m-health services concerning the two different age groups, such as middle-aged and elderly. Moreover, to the best of the authors’ knowledge, there is a shortage of literature on this topic built on the comparative analysis between the two age groups.

Determinants Impacting User Behavior towards Emergency Use Intentions of m-Health Services in Taiwan.

Emergency usage intention and behavior are crucial to business service success for m-Health providers and patient healthcare service. This study aimed to identify the factors that influence m-Health acceptance and the effect of emergency use intentions on usage behavior among Taiwanese m-Health users by adopting and extending the Unified Theory of Acceptance and Use of Technology (UTAUT). This study also examines the moderating role of gender and age in the effects of the independent variables on satisfaction with m-Health services. An online questionnaire was used to collect data from 371 participants. The results revealed that performance expectancy, facilitating conditions, and trust had positive effects on user satisfaction. Additionally, m-Health knowledge and user satisfaction had positive effects on emergency use intentions. However, social influence and effort expectancy did not have a significant effect on satisfaction. Moreover, age and gender significantly moderated the effects of some predictors.

Determinants of mHealth success: An empirical investigation of the user perspective

Mobile health (mHealth) applications have become an important tool to support public health, especially in times of increased health awareness in the midst of the COVID-19 pandemic. However, there is still uncertainty about what factors determine successful mHealth services from the users’ perspective. Based on the results of a systematic literature review, a qualitative content analysis of available apps and semi-structured user and expert interviews, we derive a structural model with antecedents on user attitudes towards mHealth and user satisfaction with the mHealth application. These variables determine users’ intention to continue using the application and their intention to recommend it to others. For verification, we tested the model with a sample of 249 German mHealth users from the “MyFitnessPal” community using structural equation modelling and found that all derived path relations have significant coefficients.