Intention to use Medical Apps Among Older Adults in the Netherlands: Cross-Sectional Study

Factors Associated With the Intention to Use mHealth Among Thai Middle-Aged Adults and Older Adults: Cross-Sectional Study

Background

Mobile health care (mHealth) apps are emerging worldwide as a vital component of internet health care, but there are issues, especially among older adults.

Objective

We aim to investigate the factors influencing the intention to use (ITU) mHealth apps, focusing on those with and without prior mHealth experience.

Methods

A cross-sectional study conducted from August 2022 to July 2023 included Thai citizens aged 45 years or older. Self-reported questionnaires collected data on sociodemographic information, health conditions, smartphone or tablet ownership, and mHealth usage experience. The Thai mHealth Senior Technology Acceptance Model questionnaires with a 10-point Likert scale evaluated mHealth acceptance. A multivariable logistic regression analysis, adjusted for age, gender, education, income, and living area, was performed for 2 subgroups: those who used ITU mHealth apps and those who did not.

Results

Of 1100 participants, 537 (48.8%) intended to use mHealth apps, while 563 (51.2%) did not. The ITU group had a younger average age, higher education levels, higher income, and fewer underlying diseases compared to those who did not intend to use mHealth apps. For those who had never used mHealth apps, having a smartphone was strongly associated with higher odds of ITU (adjusted odds ratio 2.81, 95% CI 1.6 to 4.93; P<.001), while having any underlying disease was associated with lower odds of ITU (adjusted odds ratio 0.63, 95% CI 0.42 to 0.97; P=.034). Higher acceptance levels, characterized by a positive attitude toward mHealth and lower fear of making mistakes, were also associated with higher ITU. For those with prior mHealth experience, acceptance in areas such as perceived ease of use, gerontechnology anxiety, and facilitating conditions was significantly associated with ITU.

Conclusions

Among inexperienced users, a positive attitude toward mHealth significantly enhanced ITU. Conversely, having an underlying disease decreased ITU, indicating a need for tailored mHealth apps. For experienced users, acceptance levels in areas such as ease of use and gerontechnology anxiety were crucial. Future research should explore specific mHealth apps for more targeted insights.

Support in digital health skill development for vulnerable groups in a public library setting: perspectives of trainers

Introduction

The digitalization of healthcare poses a risk of exacerbating health inequalities. Dutch public libraries offer freely accessible e-health courses given by trainers. However, there is limited knowledge on whether these libraries successfully reach and support those in need. This study aimed to explore trainers' perspectives on the challenges, successes, and potential improvements in digital health skill education in a library setting.

Materials and methods

Trainers of the e-health course were interviewed. Topics included: the role of the library in digital health skills education, the successes and challenges in reaching groups with a low socioeconomic position, the perceived impact of the digital health skills education, and strategies for future improvement in digital health skills education. A deductive analysis based upon the interview guide topics was performed. A second inductive analysis was applied to identify underlying patterns. Coding was done independently and cross-checked. Codebooks and themes were determined in discussion with authors.

Results

Three themes emerged. 1) Trainers' services, skills and expertise: Trainers identified older adults, youth, people with low (digital) literacy, the unemployed, and people from non-native cultural backgrounds as the groups most in need of support. Trainers felt equipped to address these groups' needs. 2) The libraries' reach: improving engagement, perceived accessibility, and clients' barriers: Despite trainers' efforts to adjust the course to the target groups' level of commitment, digital and literacy levels, and logistics, the digital health course predominantly engages older adults. Experienced barriers in reach: limited perceived accessibility of the public library and clients' personal barriers. 3) Collaborations with healthcare, welfare and community organizations: Trainers emphasized that collaborations could enhance the diversity and number of participants. Current partnerships provided: reach to target groups, teaching locations, and referral of clients.

Discussion

Trainers in public libraries recognize a various target groups that need support in digital health skill development. The study identified three challenges: accessibility of the digital health course, reach of the public library, and clients' personal barriers. Public libraries have potential to support their target groups but need strategies to improve their engagement and reach. Collaborations with healthcare, welfare, and community organizations are essential to improve their reach to those most in need of support.

Association Between Intergenerational Support, Technology Perception and Trust, and Intention to Seek Medical Care on the Internet Among Chinese Older Adults: Cross-Sectional Questionnaire Study

BACKGROUND

Avoiding technological innovation does not simplify life. In fact, using internet-based medical services can enhance the quality of life for older adults. In the context of an aging population and the growing integration of information technology, the demand for internet-based medical services among older adults is gaining increased attention. While scholars have highlighted the important role of intergenerational support in promoting digital inclusion for older adults, research on the relationship between intergenerational support and older adults' intentions to seek online care remains limited.

OBJECTIVE

This study aims (1) to explore the association between intergenerational support, online medical information, and older adults' intention to seek medical care online, and (2) to examine the mediating role of technology perception and trust, as well as the moderating role of eHealth literacy.

METHODS

A cross-sectional survey was conducted in China, collecting 958 valid responses from older adults aged 60 years and above. A vast majority of participants were between the ages of 60 and 75 years (771/958, 80.5%). Of the 958 participants, 559 (58.4%) resided in rural areas, while 399 (41.6%) lived in urban areas. The survey included questions on intergenerational support, perceived usefulness, perceived ease of use, trust, online medical information, eHealth literacy, and the intention to seek medical care online. Structural equation modeling and linear regression analysis were applied to explore the relationship between intergenerational support and the intention to seek medical care on the internet.

RESULTS

Intergenerational support was positively associated with perceived ease of use (β=.292, P<.001), perceived usefulness (β=.437, P<.001), trust (β=.322, P<.001), and the intention to seek medical care online (β=.354, P<.001). Online medical information also positively affected the intention to seek medical care online among older adults (β=.109, P<.001). Perceived ease of use (β=.029, 95% CI 0.009-0.054), perceived usefulness (β=.089, 95% CI 0.050-0.130), and trust (β=.063, 95% CI 0.036-0.099) partially mediated the association between intergenerational support and the intention to seek medical care online. Further analysis found that perceived ease of use, perceived usefulness, and trust played a chain mediating role between intergenerational support and the intention to seek medical care online (β=.015, 95% CI 0.008-0.027; β=.022, 95% CI 0.012-0.036). Additionally, eHealth literacy played a moderating role in the relationship between intergenerational support and perceived ease of use (β=.177, P<.001), perceived usefulness (β=.073, P<.05), trust (β=.090, P<.01), and the intention to seek medical care online (β=.124, P<.001).

CONCLUSIONS

An integrated model of health communication effects was constructed and validated, providing empirical support for the intention to seek medical care online and for the impact of health communication. This model also helps promote the role of technology in empowering the lives of seniors.

[Acceptance and intention to use of a physiological sensor shirt in health field in older patients]

OBJECTIVES

To assess the acceptance and intention to use physiological sensor shirt among older patients in healthcare, as well as to identify the factors that encourage a positive attitude towards its adoption.

METHODS

Cross-sectional study using questionnaires addressed to patients between 60 and 85years of age, administered via face-to-face interviews to determine intention to use a shirt with physiological sensors. The extended version of the Technology Acceptance Model (TAM) questionnaire was used in conjunction with administration of the quality of life and perceived stress scales.

RESULTS

Forty-eight patients participated in the study, 54.2% were male with a mean age of 70.5years. The mean scores for attitude towards use and intention to use were 3.9 (SD: 0.8) and 3.3 (SD: 0.8), respectively, out of a maximum of 5. The willingness to use the device was positive in 72.9% of patients The patients intending to use wearable technology (n=35) scored significantly higher than those unwilling to wear the shirt (n=13) on perceived ubiquity (P=.031), perceived ease of use (P=.002), and perceived utility (P=.007).

CONCLUSIONS

In a sample of independent older adults from the healthcare sector, a high willingness to use a shirt with physiological sensors was found. Through the TAM questionnaire, ubiquity, perceived ease of use, and perceived usefulness were identified as key factors predisposing its adoption.

Pathways to usage intention of mobile health apps among hypertensive patients: A fuzzy-set qualitative comparative analysis

Background: The efficacy of mHealth apps in managing hypertension has been proven; however, low usage intention remains a significant challenge, warranting an in-depth exploration of the influencing factors. Objectives: This study aimed to examine the factors influencing hypertensive new users' intention to use mobile health applications through a cross-sectional survey. Methods: Fuzzy-set qualitative comparative analysis (fsQCA) was employed to investigate the combinations of various determinants, including technology acceptance, adoption factors, compliance behavior initiation factors, and time motivation factors for decision making. Results: A total of 100 middle-aged and elderly hypertensive individuals participated in the survey, with 98 responses included in the final statistical analysis. The analysis identified four distinct configurations that contribute to high usage intentions, with solution consistency and coverage values of 0.93 and 0.36, respectively. Conclusion: The findings suggest that intervention strategies should account for the various pathways leading to usage intentions.

Descriptive Norms and eHealth Use Among Older Adults: A Cross-Country Comparative Study

eHealth use enables older adults to access and manage healthcare resources, and benefits their health; however, older adults' uptake of eHealth remains low across societies. Social influences such as descriptive norms may be of critical importance in promoting the elderly's usage of eHealth. Based on the Integrative Model of Behavioral Prediction, this study investigates how descriptive norms relate to eHealth use among the elderly in China and the United States. Analysis of the combined sample (N = 1,070) showed that descriptive norms were positively related to eHealth use. Also, descriptive norms were indirectly associated with eHealth use via injunctive norms, attitudes and self-efficacy. Moderated mediation analysis indicated that these direct and indirect relationships differed across the two countries. This study highlights the important role of descriptive norms in promoting older adults' eHealth use behavior and addresses potential country differences in how the elderly respond to descriptive norms. Several important theoretical and practical implications are also discussed.

The SElf-Care After REnal Transplantation Study: A Retrospective Evaluation of a Home-Monitoring Program Implemented as Standard Care

After transplantation self-management is essential for graft survival and optimal quality of life. To address the need for home-based support in self-management, we implemented the "SelfCare after Renal Transplantation" (SeCReT) box, containing home-monitoring equipment combined with a smartphone application that was linked to the electronic patient records. This study investigated the uptake and continuation, protocol adherence, and subjective evaluation of this home-monitoring program. All "de novo" kidney recipients who received the SeCReT-box in the study period (Aug 2021-Dec 2022) were eligible for inclusion. Protocol adherence was defined as ≥75%. Subjective evaluation was assessed with a 5-item questionnaire. Of the 297 recipients transplanted, 178 participants (60%) were included in the analysis. Protocol adherence was 83%, 73%, 66%, and 57% respectively at 5, 10, 20, and 40 weeks of the protocol. With regard to continuation, 135 participants were still in the program at the end of the study period (75% retention rate). Regarding subjective evaluations, 82% evaluated the program positively, and 52% reported lower care needs due to home-monitoring. Results are positive among those who entered and continued the program. Qualitative research is needed on barriers to entering the program and facilitators of use in order to promote optimal implementation.

Telemedicine for Kidney Transplant Recipients: Current State, Advantages, and Barriers

Telemedicine is defined as the use of electronic information and communication technologies to provide and support healthcare at a distance. In kidney transplantation, telemedicine is limited but is expected to grow markedly in the coming y. Current experience shows that it is possible to provide transplant care at a distance, with benefits for patients like reduced travel time and costs, better adherence to medication and appointment visits, more self-sufficiency, and more reliable blood pressure values. However, multiple barriers in different areas need to be overcome for successful implementation, such as recipients' preferences, willingness, skills, and digital literacy. Moreover, in many countries, limited digital infrastructure, legislation, local policy, costs, and reimbursement issues could be barriers to the implementation of telemedicine. Finally, telemedicine changes the way transplant professionals provide care, and this transition needs time, training, willingness, and acceptance. This review discusses the current state and benefits of telemedicine in kidney transplantation, with the aforementioned barriers, and provides an overview of future directions on telemedicine in kidney transplantation.

Mobile health application (WeFit) among community-dwelling older adults: Development, validation, acceptance, and usability study

Background

Technology advancement along with the increase in the older adults' population leads to the creation of health applications. The combination of exercise, nutrition, and cognition should be studied carefully in improving older adults' health.

Objective

The purpose of this study is to develop a health application, WeFit contains these three components and to determine its content validity, acceptability, and usability.

Methodology

This study is a design and development study involving three phases. The first phase is the need analysis involving a review on 16 mobile applications available in Google play and iTunes App store as well as a review of six articles for identifying the perception of older adults in using mobile applications. Second phase is mobile application development and content validity. The content validity was determined using the Content Validity Index for Individual Items (I-CVI). Phase 3 evaluated the acceptance of the WeFit mobile application among older adults and health practitioners.

Results

Phase 1 indicated that half of the applications reviewed (50.0%) had physical activity component and the other half (50.0%) had a cognitive component, and none on nutrition. No application is reported to have all three components. In Phase 2, WeFit health application containing the three components was developed where users can view exercise and food recommendations and play cognitive games. WeFit had an I-CVI value of 0.98. With respect to acceptability, majority of the study participants (93.3%) understood the WeFit's content and the graphics used were appropriate. The usability study found that the majority of the older adults were satisfied with the interface and content. All health practitioners (100%) agreed WeFit is easy to use and agreed that it can guide them in giving medical advices.

Conclusion

WeFit mobile app has been successfully developed, validated, and tested for acceptance among the older adults and health practitioners.

Information-Motivation-Behavioral Skills Model Supplemented With the Moderated-Mediation Path: A Framework for Interpreting Patients' Online Medical Services Utilization

PURPOSE

This study aimed to explore the internal determinants affecting patients' utilization of online medical services (OMS) based on the information-motivation-behavioral skills model from a behavioral perspective.

DESIGN

A cross-sectional study.

SETTING

This study was conducted in three medical institutions in Jiangsu Province, China.

SUBJECTS

470 internet users were enrolled from patients who came to the outpatient clinics.

MEASURES

A self-administered questionnaire with feasible reliability and validity was used to investigate the demographic characteristics and OMS utilization-related information, motivation, behavioral skills, intention, and behavior.

ANALYSIS

According to the constructed framework, structural equation modeling was used to test the relationships between those factors and OMS utilization behaviors.

RESULTS

All direct paths are established except the path between information and intention. Information and motivation positively affected OMS utilization behavior through behavioral skills and intention (P < .001). Motivation and behavioral skills could positively influence OMS utilization behavior through intention (P < .01). Motivation was found to be the largest predictor of OMS utilization behavior. Moreover, gender played a moderating role in the interpretation of the behavior.

CONCLUSIONS

Interventions should be conducted regarding information, motivation, and behavioral skills to promote patients' use of OMS. At the same time, the impact of gender on intervention effectiveness should also be considered.

A Sensor-Based Feedback Device Stimulating Daily Life Upper Extremity Activity in Stroke Patients: A Feasibility Study

This study aims to evaluate the feasibility and explore the efficacy of the Arm Activity Tracker (AAT). The AAT is a device based on wrist-worn accelerometers that provides visual and tactile feedback to stimulate daily life upper extremity (UE) activity in stroke patients.

METHODS

A randomised, crossover within-subject study was conducted in sub-acute stroke patients admitted to a rehabilitation centre. Feasibility encompassed (1) adherence: the dropout rate and the number of participants with insufficient AAT data collection; (2) acceptance: the technology acceptance model (range: 7-112) and (3) usability: the system usability scale (range: 0-100). A two-way ANOVA was used to estimate the difference between the baseline, intervention and control conditions for (1) paretic UE activity and (2) UE activity ratio.

RESULTS

Seventeen stroke patients were included. A 29% dropout rate was observed, and two participants had insufficient data collection. Participants who adhered to the study reported good acceptance (median (IQR): 94 (77-111)) and usability (median (IQR): 77.5 (75-78.5)-). We found small to medium effect sizes favouring the intervention condition for paretic UE activity (η²G = 0.07, p = 0.04) and ratio (η²G = 0.11, p = 0.22).

CONCLUSION

Participants who adhered to the study showed good acceptance and usability of the AAT and increased paretic UE activity. Dropouts should be further evaluated, and a sufficiently powered trial should be performed to analyse efficacy.

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.

Will they or won't they? Understanding New Zealand adults' attitudes towards using digital interventions

Background

Digital interventions deliver healthcare via the internet or smartphone application to support people's well-being and health. Yet uptake is relatively poor. Furthermore, several studies exploring attitudes towards digital interventions have found inconsistent attitudes. In addition to this, regional and cultural nuances may further influence attitudes to digital interventions.

Objective

This study aimed to understand New Zealand adults' attitudes towards digital interventions and their influences.

Results

A mixed-method design consisting of a cross-sectional survey and semi-structured interviews found that New Zealand adults hold varied and complex attitudes towards digital interventions. Attitudes were found to be influenced by group membership and the scenarios in which digital interventions are made available. In addition, beliefs about the benefits and concerns surrounding digital interventions, knowledge, perceived views of others, and previous experience and confidence influenced these attitudes.

Conclusions

Findings indicated that digital interventions would be acceptable if offered as part of the healthcare service rather than a standalone intervention. Key modifiable factors that could positively influence attitudes were identified and could be leveraged to increase the perceived acceptability of digital interventions.

Predicting Older Adults' Mobile Payment Adoption: An Extended TAM Model

This study adopted an advanced model, combining the technology acceptance model, the theory of reasoned action, the diffusion of innovations, trust, and five aspects of perceived risk, to measure the factors that influence the behavioral intentions of older adults to use mobile payments. A total of 365 questionnaires were collected from older adults aged 55 years or older from 20 community care sites in central Taiwan. Partial least-squares structural equation modeling was used to test our research model. The results showed that attitude was the main determinant of M-payment in older adults. Moreover, increasing the usefulness, ease of use, and observability of M-payment helped older adults improve their attitudes toward M-payment, thereby increasing their intention to use it. Trust had a significant effect on the usefulness and ease of use of M-payment, while the main factors affecting trust were only performance and financial risks.

Older people's intention to use medical apps during the COVID-19 pandemic in China: an application of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Technology of Acceptance Model (TAM)

Previous studies on older adults' intention to adopt medical apps during irregular circumstances like the COVID-19 outbreak are still in its infancy. In order to fill this knowledge gap, we developed a theoretical framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Technology of Acceptance Model (TAM) to explain Chinese older people's willingness to use medical apps during the COVID-19 pandemic. We collected 1,318 online questionnaires during the first wave of the pandemic in China in early 2020. We employed structural equation modelling to analyse the data, and the results show that (a) attitudes towards using apps influence older people's intention to use apps significantly; (b) only two factors, perceived usefulness and facilitating conditions, which were proposed in the UTAUT model, significantly predicted the older adults' intention to use apps, but not others; and (c) perceived usefulness, perceived ease of use, subjective norm and facilitating conditions all significantly impact attitudes towards using apps. Further mediation analysis found that attitudes towards using apps significantly mediated the paths suggested in the original UTAUT model. Due to the online survey method we used, older people who do not use the internet were excluded from our sampling process. However, our timely research contributes to the existing literature by showcasing older people's usage of eHealth technology in public health emergencies. It also builds on the broader discussions on technology use by combining the TAM and the UTAUT model, highlighting the vital role of people's attitude towards using technology in shaping their intention to use it.

The Acceptance Behavior of Smart Home Health Care Services in South Korea: An Integrated Model of UTAUT and TTF

With the COVID-19 pandemic, the importance of home health care to manage and monitor one's health status in a home environment became more crucial than ever. This change raised the need for smart home health care services (SHHSs) and their extension to everyday life. However, the factors influencing the acceptance behavior of SHHSs have been inadequately investigated and failed to address why users have the intention to use and adopt the services. This study aimed to analyze the influential factors and measure the behavioral acceptance of SHHSs in South Korea. This study adopted the integrated model of the unified theory of acceptance and use of technology (UTAUT) and task-technology fit (TTF) to understand the behavioral acceptance of SHHSs from users' perceptions and task-technology fit. Multiple-item scales were established based on validated previous measurement scales and adjusted in accordance with SHHS context. Data from 487 valid samples were analyzed statistically, applying partial least square structural equation modeling. The results indicated that the integrated acceptance model explained 55.2% of the variance in behavioral intention, 44.9% of adoption, and 62.5% of the continuous intention to use SHHSs, supporting 11 of the 13 proposed hypotheses. Behavioral intention was positively influenced by users' perceptions on performance expectancy, effort expectancy, social influence, and functional conditions. Task-technology fit significantly influenced performance expectancy and behavioral intention, validating the linkage between the two models. Meanwhile, task characteristics were insignificant to determine task-technology fit, which might stem from complex home health care needs due to the COVID-19 pandemic, but were not sufficiently resolved by current service technologies. The findings implied that the acceptance of SHHSs needs to be evaluated according to both the user perceptions of technologies and the matching fit of task and technology. Theoretically, this study supports the applicability of the integrated model of UTAUT and TTF to the domain of SHHS, and newly proposed the measurement items of TTF reflecting the domain specificity of SHHS, providing empirical evidence during the pandemic era in South Korea. Practically, the results could suggest to the planners and strategists of home health care services how to promote SHHS in one's health management.

Agreement Between Clinically Measured Weight and Self-reported Weight Among Patients With Type 2 Diabetes Through an mHealth Lifestyle Coaching Program in Denmark: Secondary Analysis of a Randomized Controlled Trial

Background

Digital health interventions are increasingly used to handle and promote positive health behaviors. Clinical measures are often used, and a certain precision is essential for digital health interventions to have an effect. Only few studies have compared clinically measured weights with self-reported weights. No study has examined the validity of self-reported weight from a mobile app used in a tailored weight loss intervention.

Objective

The aim of this study was to analyze the agreement between clinically measured weight and self-reported weight collected from a mobile health lifestyle coaching program during a 12-month weight loss intervention for obese patients with and without type 2 diabetes. The secondary aim was to investigate the determinants for possible discrepancies between clinically measured and self-reported weights of these patients with different demographic and lifestyle characteristics and achievements of weight loss goals.

Methods

Weight registrations were collected from participants (N=104) in a Danish randomized controlled trial examining the effect of a digital lifestyle intervention on weight loss among obese patients with and without type 2 diabetes. Data were collected at baseline and after 6 and 12 months. Self-reported weight was measured at home and registered in the app.

Results

Self-reported body weight was lower than the weight measured in the clinic after 6 months by 1.03 kg (95% CI 1.01-1.05; P<.001) and after 12 months also by 1.03 kg (95% CI 0.99-1.04; P<.001). After 6 months, baseline weight and BMI were associated with a discrepancy of 0.03 kg (95% CI 0.01-0.04; P=.01) and 0.09 kg (95% CI 0.02-0.17; P=.02) per increment of 1 kg and 1 kg/m2, respectively, between clinically measured weight and self-reported weight. Weight change during the first 6 months was also associated with a difference of 0.1 kg (95% CI 0.04-0.01; P<.001) per kilogram of difference in weight between clinically measured weight and self-reported weight. Participants who did not achieve the 5% weight loss goal underestimated their weight by 0.79 kg (95% CI 0.34-1.23) at 6 months. After 12 months, only baseline weight was associated with a discrepancy of 0.03 kg (95% CI 0.01-0.05; P=.02) per increment of kilogram between clinically measured weight and self-reported weight. None of the other factors showed any significant discrepancy after 12 months.

Conclusions

Self-reported weight obtained from mobile health is a valid method for collecting anthropometric measurements.

Trial Registration

ClinicalTrials.gov NCT03788915; https://clinicaltrials.gov/ct2/show/NCT03788915

Gender differences regarding intention to use mHealth applications in the Dutch elderly population: a cross-sectional study

BACKGROUND

In light of the increasing demands in health care, a call has been made for the development of new strategies. One of these strategies is placing a higher emphasis on individuals, who are expected to better manage their own health and illness. mHealth applications could increase this self-management behaviour among older adults. However, it is crucial to know the intention to use mHealth of older adults before implementing these services. Even less is known regarding differences between genders on factors influencing this intention to use mHealth applications.

OBJECTIVE

The aim of this study was to study the gender differences regarding the relationship between technology acceptance factors and the intention to use mHealth applications in the Dutch elderly population.

METHODS

We conducted a quantitative cross-sectional study using questionnaires. The participants were 65 years or older, lived independently or in a senior living facility, without cognitive impairment. Logistic regression with interaction terms was done to determine gender differences in the relationship between the intention to use mHealth applications and technology acceptance factors.

RESULTS

While we found that half of the studied population had intention to use medical applications (50.3%) a notable difference was observed within gender groups which showed more men had intention to use medical applications rather than women (59.4% vs. 43.4% respectively). Adjusted logistic regression analysis per factor on the male and female part of the study population respectively showed that the factors Perceived usefulness (OR 21,69 and 2,39, resp.), Perceived ease of use (OR 7,21 and 2,74), Attitude toward use (OR 24,61 and 4,94), Sense of control (OR 4,12 and 2,67), Personal innovativeness (OR 2,54 and 1,58), Self-perceived effectiveness (OR 3,21 and 2,34), Service availability (OR 4,38 and 2,51) and Facilitating circumstances (OR 3,04 and 2,18) had a statistically significant influence on intention to use in both models. Logistic regression with interaction terms showed that two of the technology acceptance factors differed statistically significant in their relationship with intention to use when comparing females to males, namely Perceived usefulness (OR 0,11) and Attitude toward use (OR 0.24). Both factors were more strongly associated with intention to use for men compared to women.

CONCLUSION

Policymakers and interventions aiming to stimulate the uptake of mHealth applications should acknowledge gender differences. Interventions based on improving the Perceived usefulness and Attitude toward use among female users could be a means to stimulate the full potential of medical applications and improve the uptake.

Lean ICU Layout Re-Design: A Simulation-Based Approach

Healthcare facilities require flexible layouts that can adapt quickly in the face of various disruptions. COVID-19 confirmed this need for both healthcare and manufacturing systems. Starting with the transfer of decision support systems from manufacturing, this paper generalizes layout re-design activities for complex systems by presenting a simulation framework. Through a real case study concerning the proliferation of nosocomial cross-infection in an intensive care unit (ICU), the model developed in systems dynamics, based on a zero order immediate logic, allows reproducing the evolution of the different agencies (e.g., physicians, nurses, ancillary workers, patients), as well as of the cyber-technical side of the ICU, in its general but also local aspects. The entire global workflow is theoretically founded on lean principles, with the goal of balancing the need for minimal patient throughput time and maximum efficiency by optimizing the resources used during the process. The proposed framework might be transferred to other wards with minimal adjustments; hence, it has the potential to represent the initial step for a modular depiction of an entire healthcare facility.

Assessing initial validity of the PortionSize app to estimate dietary intake among adults: A pilot and feasibility study (Preprint)

Background

Accurately assessing dietary intake can promote improved nutrition. The PortionSize app (Pennington Biomedical Research Center) was designed to quantify and provide real-time feedback on the intake of energy, food groups, saturated fat, and added sugar.

Objective

This study aimed to assess the preliminary feasibility and validity of estimating food intake via the PortionSize app among adults.

Methods

A total of 15 adults (aged 18-65 years) were recruited and trained to quantify the food intake from a simulated meal by using PortionSize. Trained personnel prepared 15 simulated meals and covertly weighed (weigh back) the amount of food provided to participants as well as food waste. Equivalence tests (±25% bounds) were performed to compare PortionSize to the weigh back method.

Results

Participants were aged a mean of 28 (SD 12) years, and 11 were female. The mean energy intake estimated with PortionSize was 742.9 (SD 328.2) kcal, and that estimated via weigh back was 659.3 (SD 190.7) kcal (energy intake difference: mean 83.5, SD 287.5 kcal). The methods were not equivalent in estimating energy intake (P=.18), and PortionSize overestimated energy intake by 83.5 kcal (12.7%) at the meal level. Estimates of portion sizes (gram weight; P=.01), total sugar (P=.049), fruit servings (P=.01), and dairy servings (P=.047) from PortionSize were equivalent to those estimated via weigh back. PortionSize was not equivalent to weigh back with regard to estimates for carbohydrate (P=.10), fat (P=.32), vegetable (P=.37), grain (P=.31), and protein servings (P=.87).

Conclusions

Due to power limitations, the equivalence tests had large equivalence bounds. Though preliminary, the results of this small pilot study warrant the further adaptation, development, and validation of PortionSize as a means to estimate energy intake and provide users with real-time and actionable dietary feedback.

An Integrated, Multimodal, Digital Health Solution for Chronic Obstructive Pulmonary Disease: Prospective Observational Pilot Study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) affects millions of Americans and has a high economic impact partially due to frequent emergency room visits and hospitalizations. Advances in digital health have made it possible to collect data remotely from multiple devices to assist in managing chronic diseases such as COPD.

OBJECTIVE

In this pilot study, we evaluated the ability of patients with COPD to use the Wellinks mHealth platform to collect information from multiple modalities important to the management of COPD. We also assessed patient satisfaction and engagement with the platform.

METHODS

A single-site, observational, prospective pilot study (N=19) was conducted using the Wellinks platform in adults with COPD. All patients were aged over 30 years at screening, owned an iPhone, and were currently undergoing a treatment regimen that included nebulized therapy. Enrolled patients received a study kit consisting of the Flyp nebulizer, Smart One spirometer, the Nonin pulse oximeter, plus the Wellinks mHealth app, and training for all devices. For 8 weeks, participants were to enter daily symptoms and medication use manually; spirometry, nebulizer, and pulse oximeter data were automatically recorded. Data were sent to the attending physician in a monthly report. Patient satisfaction was measured via a 5-point scale and the Net Promoter Score (NPS) captured in interviews at the end of the observation period.

RESULTS

Average age of the patients was 79.6 (range 65-95) years. Participants (10 female; 9 male) had an average FEV1% (forced expiratory volume in 1 second as % of predicted for the patient) of 56.2% of predicted (range 23%-113%) and FEV1/forced vital capacity of 65%. COPD severity, as assessed by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, was mild in 2 patients, moderate in 6, and severe/very severe in 11; 9 patients were on home oxygen. During this 8-week study, average use of the spirometer was 2.5 times/week, and the pulse oximeter 4.2 times/week. Medication use was manually documented 9.0 times/week, nebulizer use 1.9 times/week, and symptoms recorded 1.2 times/week on average. The correlation coefficients of home to office measurements for peak flow and FEV1 were high (r=0.94 and 0.96, respectively). Patients found the app valuable (13/16, 81%) and easy to use (15/16, 94%). The NPS was 59.

CONCLUSIONS

This study demonstrates that our cohort of patients with COPD engaged with the Wellinks mHealth platform avidly and consistently over the 8-week period, and that patient satisfaction was high, as indicated by the satisfaction survey and the NPS of 59. In this small, selected sample, patients were both willing to use the technology and capable of doing so successfully regardless of disease severity, age, or gender. The Wellinks mHealth platform was considered useful and valuable by patients, and can assist clinicians in improved, timely decision making for better COPD management.

Willingness, perceived barriers and motivators in adopting mobile applications for health-related interventions among older adults: a scoping review

OBJECTIVES

This scoping review aims to identify the level of willingness, the existing barriers, and motivators among older adults in using mobile applications to monitor and manage their health conditions. The secondary aim of this paper is to categorise these willingness, barriers and motivators using the Theoretical Domains Framework (TDF).

DESIGN

Scoping review.

DATA SOURCE

PubMed, Embase, CINAHL, Cochrane Library, Google Scholar and Science Direct (January 2009-December 2020).

STUDY SELECTION

Studies that describe older adults' perspectives with regard to their willingness, barriers or motivators towards the use of mobile applications in monitoring and managing their health condition were included.

DATA EXTRACTION

Titles and abstracts were initially screened by two reviewers. Articles agreed by both reviewers were proceeded to full-text screening. One reviewer extracted the data, which were verified by a second reviewer. Findings were further classified according to the 14 TDF domains by two researchers.

RESULTS

Six studies were included in the final scoping review. Barriers to adopting mobile applications for health-related interventions among older adults were the most common topic identified in the included studies. Barriers included being unaware of the existence of mobile health applications, lack of technological skills, lack of perceived ability and time, absence of professional involvements, and violation of trust and privacy. With regard to willingness, older adults are willing to use mobile applications if the apps incorporated features from a trusted source and have valid credentials. Motivators included continuous improvements of mobile applications' design interface and personalised features tailored to older adults' needs.

CONCLUSIONS

With the constant research for more diversified technology, the development of mobile applications to help older adults to manage and monitor health is seen as feasible, but barriers have to be addressed. The most prominent barriers linked to TDF domains were: (1) technological skills, (2) belief about consequences, and (3) memory, attention and decision process. Future interventions should use behaviour change techniques that target these three TDF domains in order to improve the ability to engage older adults with mobile technology.

Understanding Patients' Intention to Use Digital Health Apps That Support Postdischarge Symptom Monitoring by Providers Among Patients With Acute Coronary Syndrome: Survey Study

Background

After hospital discharge, patients with acute coronary syndrome (ACS) often experience symptoms that prompt them to seek acute medical attention. Early evaluation of postdischarge symptoms by health care providers may reduce unnecessary acute care utilization. However, hospital-initiated follow-up encounters are insufficient for timely detection and assessment of symptoms. While digital health tools can help address this issue, little is known about the intention to use such tools in ACS patients.

Objective

This study aimed to assess ACS patients’ intention to use digital health apps that support postdischarge symptom monitoring by health care providers and identify patient-perceived facilitators and barriers to app use.

Methods

Using email invitations or phone calls, we recruited ACS patients discharged from a central Massachusetts health care system between December 2020 and April 2021, to participate in the study. Surveys were delivered online or via phone to individual participants. Demographics and access to technology were assessed. The intention to use a symptom monitoring app was assessed using 5-point Likert-type (from strongly agree to strongly disagree) items, such as “If this app were available to me, I would use it.” Responses were compared across demographic subgroups and survey delivery methods. Two open-ended questions assessed perceived facilitators and barriers to app use, with responses analyzed using qualitative content analysis.

Results

Among 100 respondents (response rate 8.1%), 45 (45%) completed the survey by phone. The respondents were on average 68 years old (SD 13 years), with 90% (90/100) White, 39% (39/100) women, and 88% (88/100) having access to the internet or a mobile phone. Most participants (65/100, 65%) agreed or strongly agreed that they would use the app, among which 53 (82%) would use the app as often as possible. The percentage of participants with the intention to use the app was 75% among those aged 65-74 years and dropped to 44% among those older than 75 years. The intention to use was higher in online survey respondents (vs phone survey respondents; odds ratio 3.07, 95% CI 1.20-7.88) after adjusting for age and access to technology. The analysis of open-ended questions identified the following 4 main facilitators (motivations): (1) easily reaching providers, (2) accessing or providing information, (3) quickly reaching providers, and (4) consulting providers for symptoms, and the following 4 main barriers: (1) privacy/security concerns, (2) uncomfortable using technology, (3) user-unfriendly app interface, and (4) preference for in-person/phone care.

Conclusions

There was a strong intention to use a symptom monitoring app postdischarge among ACS patients. However, this intent decreased in patients older than 75 years. The survey identified barriers related to technology use, privacy/security, and the care delivery mode. Further research is warranted to determine if such intent translates into app use, and better symptom management and health care quality.

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

The coronavirus disease 2019 (COVID-19) has changed the way we use and perceive online services. This study examined the influence of service quality factors during COVID-19 on individuals' intention to continue use mHealth services. A decision-making trial and evaluation laboratory (DEMATEL) approach was used to identify and analyse the relationships between service quality and individuals' intention to continue use mHealth during the COVID-19 pandemic. Individuals' direct, indirect, and interdependent behaviours in relation to service quality and continues use of mHealth were studied. A total of 126 respondents were involved in this study. The results identified several associations between service quality factors and individuals' continuous use of mHealth. The most important factor found to influence users' decision to continuously use mHealth was assurance, followed by hedonic benefits, efficiency, reliability, and content quality. The relevant cause-and-effect relationships were identified and the direction for quality improvement was discussed. The outcomes from this study can support healthcare policy makers to swiftly and widely respond to COVID-19 challenges. The findings provide fundamental insights for healthcare organisations to promote continuous use of mHealth among people by prioritising service improvements.

Design of an Integrated Acceptance Framework for Older Users and eHealth: Influential Factor Analysis

Background

eHealth and telehealth play a crucial role in assisting older adults who visit hospitals frequently or who live in nursing homes and can benefit from staying at home while being cared for. Adapting to new technologies can be difficult for older people. Thus, to better apply these technologies to older adults’ lives, many studies have analyzed the acceptance factors for this particular population. However, there is not yet a consensual framework that can be used in further development and to search for solutions.

Objective

This paper aims to present an integrated acceptance framework (IAF) for older users’ acceptance of eHealth based on 43 studies selected through a systematic review.

Methods

We conducted a 4-step study. First, through a systematic review in the field of eHealth from 2010 to 2020, the acceptance factors and basic data for analysis were extracted. Second, we conducted a thematic analysis to group the factors into themes to propose an integrated framework for acceptance. Third, we defined a metric to evaluate the impact of the factors addressed in the studies. Finally, the differences among the important IAF factors were analyzed according to the participants’ health conditions, verification time, and year.

Results

Through a systematic review, 731 studies were found in 5 major databases, resulting in 43 (5.9%) selected studies using the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) methodology. First, the research methods and acceptance factors for eHealth were compared and analyzed, extracting a total of 105 acceptance factors, which were grouped later, resulting in an IAF. A total of 5 dimensions (ie, personal, user–technology relational, technological, service-related, and environmental) emerged, with a total of 23 factors. In addition, we assessed the quality of evidence and then conducted a stratification analysis to reveal the more appropriate factors depending on the health condition and assessment time. Finally, we assessed the factors and dimensions that have recently become more important.

Conclusions

The result of this investigation is a framework for conducting research on eHealth acceptance. To elaborately analyze the impact of the factors of the proposed framework, the criteria for evaluating the evidence from the studies that have the extracted factors are presented. Through this process, the impact of each factor in the IAF has been presented, in addition to the framework proposal. Moreover, a meta-analysis of the current status of research is presented, highlighting the areas where specific measures are needed to facilitate eHealth acceptance.

User Engagement and Abandonment of mHealth: A Cross-Sectional Survey

Mobile health (mHealth) apps have great potential to improve health outcomes. Given that mHealth apps have become ubiquitous, there is limited focus on their abandonment. Data concerning crucial metrics, including reasons for adoption and discontinued use, are limited. This study aims to gain broad insights into utilization of mHealth and game-like features promoting user engagement. We conducted a cross-sectional survey of 209 mHealth users worldwide. The 17-item survey assessed sociodemographics, as well as the key motivators for mHealth uptake and discontinued use. Our findings show that sports and fitness activity tracking were the most common categories of health apps, with most users engaging with them at least several times a week. Interestingly, the most downloaded mHealth apps among younger adults include MyFitnessPal, Fitbit, Nike Run Club, and Samsung Health. Critical drivers of abandonment of mHealth apps were amotivation, loss of interest, and experimenting with different apps to identify the most suitable tool. Additionally, the financial cost of mHealth apps is crucial, with most participants advocating for free or more affordable apps. The study findings suggest that while many individuals utilize mHealth, several factors drive their abandonment. Moreover, data indicate that mHealth developers need to consider gamification strategies to sustain user commitment, as well as psychological variables, such as intrinsic motivation.

Factors Associated With the Experience of Cognitive Training Apps for the Prevention of Dementia: Cross-sectional Study Using an Extended Health Belief Model

BACKGROUND

The prevalence and economic burden of dementia are increasing dramatically. Using information communication technology to improve cognitive functions is proven to be effective and holds the potential to serve as a new and efficient method for the prevention of dementia.

OBJECTIVE

The aim of this study was to identify factors associated with the experience of mobile apps for cognitive training in middle-aged adults. We evaluated the relationships between the experience of cognitive training apps and structural variables using an extended health belief model.

METHODS

An online survey was conducted on South Korean participants aged 40 to 64 years (N=320). General characteristics and dementia knowledge were measured along with the health belief model constructs. Statistical analysis and logistic regression analysis were performed.

RESULTS

Higher dementia knowledge (odds ratio [OR] 1.164, P=.02), higher perceived benefit (OR 1.373, P<.001), female gender (OR 0.499, P=.04), and family history of dementia (OR 1.933, P=.04) were significantly associated with the experience of cognitive training apps for the prevention of dementia.

CONCLUSIONS

This study may serve as a theoretical basis for the development of intervention strategies to increase the use of cognitive training apps for the prevention of dementia.

Predictors of the Acceptance of an eCoach targeting Self-management of Type 2 Diabetes Patients: A Web-Based Survey (Preprint)

Background

Type 2 diabetes (T2D) is a lifestyle-related disease whose prevalence increases with age. Diabetes self-management through mobile health (mHealth) apps enables patients with T2D to improve their health. According to the Technology Acceptance Model (TAM), technology acceptance (ie, intended use) is necessary to ensure mHealth can be implemented successfully. Therefore, the specific acceptance requirements of patients with T2D should be considered.

Objective

This cross-sectional study aims to examine the extent to which different TAM predictors are associated with the acceptance of a diabetes app including an electronic coach (eCoach; Iris app) among patients with T2D.

Methods

Using a web-based survey, data on 92 patients with T2D (mean age 62.76 years, SD 8.29 years) were collected. Acceptance of the Iris app with the TAM predictors (ie, perceived usefulness, perceived ease of use, social influence, perceived self-efficacy, perceived security, prior usage experience, perceived health, and propensity of data/information sharing) was assessed. Further, control variables (ie, gender, age, education, ethnicity, household, BMI, amount of years with diabetes, diabetes-related complaints, and medication use) were assessed.

Results

Multiple linear regression analyses showed that acceptance of the Iris app was positively associated with perceived usefulness (β=.57, P<.001), social influence (subjective norm; β=.20, P=.004), and willingness to share data (β=.25, P<.001). In addition, acceptance regarding the Iris app was higher among patients with T2D with overweight (β=.23, P=.01) or obese BMI (β=.21, P=.01). The model explained 75.8% of the variance in the acceptance of the Iris app by patients with T2D. In addition, perceived usefulness of the Iris app was positively related to perceived ease of use (β=.32, P<.001), subjective norm (β=.26, P=.004), perceived control (β=.19, P=.03), willingness to share data (β=.20, P=.01) regarding the Iris app, and perceived security regarding general use of apps/smartphone/internet (β=.15, P=.04). The model explained 58.2% of the variance in patients’ perceived usefulness about the Iris app.

Conclusions

Among patients with T2D, the belief that the use of the Iris app is helpful/beneficial, the willingness to share their Iris app data, and others’ approval of using this app can stimulate the acceptance of this app. In addition, the belief that the use of (health) apps is reliable and secure, the belief that the use of the Iris app is easy to use, a higher perceived capability and personal control with using this app, the willingness to share their Iris app data, and others’ approval of using this app can stimulate the perceived usefulness of such an app. These TAM predictors explained a high variance in acceptance and perceived usefulness of the Iris app. Implications for practice are addressed.

Study on the Types of Elderly Intelligent Health Management Technology and the Influencing Factors of Its Adoption

[Background]: In recent years, aging has become a global social problem. Intelligent health management technology (IHMT) provides solutions for the elderly to deal with various health risks. However, the elderly are facing many difficulties in using IHMT. Studying the application types of IHMT and the influencing factors of the elderly’s acceptance of it will help to improve the use behavior of the elderly. [Methods]: This paper summarizes the application types of IHMT, identifies the influencing factors of the elderly’s adaption of IHMT, and makes a systematic comment on the influencing factors. [Results]: We divide the different functions of IHMT for the elderly into four types: self-monitoring, medical care, remote monitoring, and health education. The influencing factors are divided into three types: individual, social, and technology. [Conclusions]: This study finds that IHMT’s application covers all aspects of the health services of the elderly. Among these applications, self-monitoring is the most used. We divided the influencing factors of the elderly’s acceptance of IHMT into three categories and nine subcategories, having 25 variables.

Investigating the effective factors of using mHealth apps for monitoring COVID-19 symptoms and contact tracing: A survey among Iranian citizens

BACKGROUND AND OBJECTIVES

The use of mHealth applications depends on cognitive and social factors of individuals in different nations. This study aimed to identify the factors influencing the use of mHealth applications for both "contact-tracing" and "symptom-monitoring" of COVID-19 among Iranian citizens.

METHODS

A cross-sectional study with an online survey was conducted among Iranian citizens. Correlation calculation and multiple linear regression analysis were performed on the studied variables to find the effective factors.

RESULTS

A total of 1031 Iranian citizens over the age of 18 participated in this survey. A large percentage of the participants wanted to use the mHealth app to trace contacts of COVID-19 (74.5%) and the mHealth app to identify and monitor COVID-19 symptoms (74.0%). Gender, age, level of education, attitude towards technology, and fear of COVID-19 were among the factors that influenced the intention to use these two apps. The top reasons for using these apps were: "to keep myself and my family safe", "to control the spread of the coronavirus in general", and "to cooperate with healthcare professionals". The reasons given for not using these two apps were related to the issues of "security and privacy" and "doubt in efficiency and usefulness" of them.

CONCLUSIONS

The study showed that many participants in this survey were interested in using the COVID-19 apps. Policies, regulations and procedures are needed to protect the privacy of individuals by ensuring data governance. Further investigation with a larger sample is suggested to generalize these results.

Preference, Expected Burden, and Willingness to Use Digital and Traditional Methods to Assess Food and Alcohol Intake

We conducted an online survey to examine the preference, expected burden, and willingness of people to use four different methods of assessing food and alcohol intake such as food/drink record, 24-h recall, Remote Food Photography Method© (RFPM, via SmartIntake^® app), and a novel app (PortionSize^®) that allows the in-app portion size estimation of foods/drinks by the user. For food (N = 1959) and alcohol (N = 466) intake assessment, 67.3% and 63.3%, respectively, preferred the RFPM/SmartIntake^®, 51.9% and 53.4% preferred PortionSize^®, 48.0% and 49.3% the food records, and 32.9% and 33.9% the 24-h recalls (difference in preference across all methods was p < 0.001 for food and alcohol intake). Ratings of burden and preference of methods were virtually superimposable, and we found strong correlations between high preference and low expected burden for all methods (all ρ ≥ 0.82; all p < 0.001). Willingness (mean (SD)) to use the RFPM/SmartIntake^® (food: 6.6 (2.0); alcohol: 6.4 (2.4)) was greater than PortionSize^® (food: 6.0 (2.2); alcohol: 6.0 (2.4); all p < 0.001) and 24-h recalls (food: 6.1 (2.2); alcohol: 5.7 (2.7); p < 0.001), but not different from food records (food: 6.6 (2.0); alcohol: 6.5 (2.3); all p ≥ 0.33). Our results can be used in conjunction with existing data on the reliability and validity of these methods in order to inform the selection of methods for the assessment of food and alcohol intake.

Factors Associated With Intention to Adopt mHealth Apps Among Dementia Caregivers With a Chronic Condition: Cross-sectional, Correlational Study

Background

In the United States, nearly 80% of family caregivers of people with dementia have at least one chronic condition. Dementia caregivers experience high stress and burden that adversely affect their health and self-management. mHealth apps can improve health and self-management among dementia caregivers with a chronic condition. However, mHealth app adoption by dementia caregivers is low, and reasons for this are not well understood.

Objective

The purpose of this study is to explore factors associated with dementia caregivers’ intention to adopt mHealth apps for chronic disease self-management.

Methods

We conducted a cross-sectional, correlational study and recruited a convenience sample of dementia caregivers. We created a survey using validated instruments and collected data through computer-assisted telephone interviews and web-based surveys. Before the COVID-19 pandemic, we recruited dementia caregivers through community-based strategies, such as attending community events. After nationwide closures due to the pandemic, the team focused on web-based recruitment. Multiple logistic regression analyses were used to test the relationships between the independent and dependent variables.

Results

Our sample of 117 caregivers had an average age of 53 (SD 17.4) years, 16 (SD 3.3) years of education, and 4 (SD 2.5) chronic conditions. The caregivers were predominantly women (92/117, 78.6%) and minorities (63/117, 53.8%), experienced some to extreme income difficulties (64/117, 54.7%), and were the child or child-in-law (53/117, 45.3%) of the person with dementia. In logistic regression models adjusting for the control variables, caregiver burden (odds ratio [OR] 1.3, 95% CI 0.57-2.8; P=.57), time spent caregiving per week (OR 1.7, 95% CI 0.77-3.9; P=.18), and burden of chronic disease and treatment (OR 2.3, 95% CI 0.91-5.7; P=.08) were not significantly associated with the intention to adopt mHealth apps. In the final multiple logistic regression model, only perceived usefulness (OR 23, 95% CI 5.6-97; P<.001) and the interaction term for caregivers’ education and burden of chronic disease and treatment (OR 31, 95% CI 2.2-430; P=.01) were significantly associated with their intention to adopt mHealth apps. Perceived ease of use (OR 2.4, 95% CI 0.67-8.7; P=.18) and social influence (OR 1.8, 95% CI 0.58-5.7; P=.31) were not significantly associated with the intention to adopt mHealth apps.

Conclusions

When designing mHealth app interventions for dementia caregivers with a chronic condition, it is important to consider caregivers’ perceptions about how well mHealth apps can help their self-management and which app features would be most useful for self-management. Caregiving factors may not be relevant to caregivers’ intention to adopt mHealth apps. This is promising because mHealth strategies may overcome barriers to caregivers’ self-management. Future research should investigate reasons why caregivers with a low education level and low burden of chronic disease and treatment have significantly lower intention to adopt mHealth apps for self-management.

Relationship Between Perceived Risks of Using mHealth Applications and the Intention to Use Them Among Older Adults in the Netherlands: Cross-sectional Study

BACKGROUND

Considering the increasing demand for health services by older people and the ongoing COVID-19 pandemic, digital health is commonly viewed to offer a pathway to provide safe and affordable health services for older adults, thus enabling self-management of their health while health care systems are struggling. However, several factors cause older people to be particularly reluctant to adopt digital health technologies such as mobile health (mHealth) tools. In addition to previously studied technology acceptance factors, those related to perceived risks of mHealth use (eg, leakage of sensitive information or receiving incorrect health recommendations) may further diminish mHealth adoption by older adults.

OBJECTIVE

The aim of this study was to explore the relationship between perceived risks of using mHealth applications and the intention to use these applications among older adults.

METHODS

We designed a cross-sectional study wherein a questionnaire was used to collect data from participants aged 65 years and older in the Netherlands. Perceived risk was divided into four constructs: privacy risk, performance risk, legal concern, and trust. Linear regression analyses were performed to determine the associations between these perceived risk constructs and the intention to use mHealth applications.

RESULTS

Linear regression per perceived risk factor showed that each of the four constructs is significantly associated with the intention to use mobile medical applications among older adults (adjusted for age, sex, education, and health status). Performance risk (β=-.266; P=<.001), legal concern (β=-.125; P=.007), and privacy risk (β=-.100; P=.03) were found to be negatively correlated to intention to use mHealth applications, whereas trust (β=.352; P=<.001) was found to be positively correlated to the intention to use mHealth applications.

CONCLUSIONS

Performance risk, legal concern, and privacy risk as perceived by older adults may substantially and significantly decrease their intention to use mHealth applications. Trust may significantly and positively affect this intention. Health care professionals, designers of mHealth applications, and policy makers can use these findings to diminish performance risks, and tailor campaigns and applications to address legal and privacy concerns and promote mHealth uptake and health care access for older adults, especially during the COVID-19 pandemic.

Factors Affecting Portal Usage Among Chronically Ill Patients During the COVID-19 Pandemic in the Netherlands: Cross-sectional Study

Background

The COVID-19 pandemic has impacted the capacity of the regular health care system, which is reflected in limited access to nonurgent care for patients who are chronically ill in the Dutch health care system. Nevertheless, many of them still depend on health care assistance to manage their illnesses. Patient portals are used to provide continued health care (remotely) and offer self-management tools during COVID-19 and potentially after. However, little is known about the factors influencing portal use and users’ satisfaction among patients who are chronically ill during the COVID-19 pandemic.

Objective

This study aims to examine predictors of patient portal use among patients who are chronically ill, the willingness to recommend the portal to others, and the likelihood of future use among portal nonusers.

Methods

An online self-administered questionnaire was distributed among patients who are chronically ill via social media in May 2020. The questionnaire consisted of four parts: (1) demographics including age and hours of daily internet use; (2) physical health status including COVID-19 infection, perceived level of control, and hospital visits; (3) mental health status including depression and life satisfaction; and (4) portal use including response waiting time and awareness. Descriptive, correlation, univariate, and multivariate analyses were conducted to identify factors that affect portal use, users’ willingness to recommend, and nonusers’ likelihood of future portal use.

Results

A total of 652 patients responded, and 461 valid questionnaires were included. Among the 461 patients, 67% (n=307) were identified as patient portal users. Of the nonusers, 55% (85/154) reported not being aware of the existence of a patient portal at their hospital. Significant predictors of portal use include level of control (P=.04), hospital visit time (P=.03), depression scale (P=.03), and status of life satisfaction (P=.02). Among portal users, waiting time to get a response via the portal (P<.001) and maximum acceptable waiting time (P<.001) were the strongest predictors for willingness to recommend the portal; among nonusers, the model predicted that those who were not aware of patient portals (P<.001) and were willing to wait moderately long (P<.001) were most likely to use the portal in the future.

Conclusions

This study provides insights into factors that influence portal use and willingness to recommend, based on which health care providers can improve the adoption of patient portals and their services. It suggests that health care providers should leverage efficient operations management to improve responsiveness and reduce waiting time to enhance user satisfaction and willingness to recommend use. Health care organizations need to increase portal awareness among nonusers and train their patients to increase both use and longer adoption of patient portals. Factors including depression and life satisfaction can influence portal use; therefore, future studies on determinants of portal use and nonuse in this specific population are needed.

eHealth Applications to Support Independent Living of Older Persons: Scoping Review of Costs and Benefits Identified in Economic Evaluations

BACKGROUND

eHealth applications are constantly increasing and are frequently considered to constitute a promising strategy for cost containment in health care, particularly if the applications aim to support older persons. Older persons are, however, not the only major eHealth stakeholder. eHealth suppliers, caregivers, funding bodies, and health authorities are also likely to attribute value to eHealth applications, but they can differ in their value attribution because they are affected differently by eHealth costs and benefits. Therefore, any assessment of the value of eHealth applications requires the consideration of multiple stakeholders in a holistic and integrated manner. Such a holistic and reliable value assessment requires a profound understanding of the application's costs and benefits. The first step in measuring costs and benefits is identifying the relevant costs and benefit categories that the eHealth application affects.

OBJECTIVE

The aim of this study is to support the conceptual phase of an economic evaluation by providing an overview of the relevant direct and indirect costs and benefits incorporated in economic evaluations so far.

METHODS

We conducted a systematic literature search covering papers published until December 2019 by using the Embase, Medline Ovid, Web of Science, and CINAHL EBSCOhost databases. We included papers on eHealth applications with web-based contact possibilities between clients and health care providers (mobile health apps) and applications for self-management, telehomecare, telemedicine, telemonitoring, telerehabilitation, and active healthy aging technologies for older persons. We included studies that focused on any type of economic evaluation, including costs and benefit measures.

RESULTS

We identified 55 papers with economic evaluations. These studies considered a range of different types of costs and benefits. Costs pertained to implementation activities and operational activities related to eHealth applications. Benefits (or consequences) could be categorized according to stakeholder groups, that is, older persons, caregivers, and health care providers. These benefits can further be divided into stakeholder-specific outcomes and resource usage. Some cost and benefit types have received more attention than others. For instance, patient outcomes have been predominantly captured via quality-of-life considerations and various types of physical health status indicators. From the perspective of resource usage, a strong emphasis has been placed on home care visits and hospital usage.

CONCLUSIONS

Economic evaluations of eHealth applications are gaining momentum, and studies have shown considerable variation regarding the costs and benefits that they include. We contribute to the body of literature by providing a detailed and up-to-date framework of cost and benefit categories that any interested stakeholder can use as a starting point to conduct an economic evaluation in the context of independent living of older persons.