Valuing Mobile Health: An Open-Ended Contingent Valuation Survey of a National Digital Health Program

Consumers’ Willingness to Pay for eHealth and Its Influencing Factors: Systematic Review and Meta-analysis

Background

Despite the great potential of eHealth, substantial costs are involved in its implementation, and it is essential to know whether these costs can be justified by its benefits. Such needs have led to an increased interest in measuring the benefits of eHealth, especially using the willingness to pay (WTP) metric as an accurate proxy for consumers’ perceived benefits of eHealth. This offered us an opportunity to systematically review and synthesize evidence from the literature to better understand WTP for eHealth and its influencing factors.

Objective

This study aimed to provide a systematic review of WTP for eHealth and its influencing factors.

Methods

This study was performed and reported as per the Cochrane Collaboration and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, CINAHL Plus, Cochrane Library, EconLit, and PsycINFO databases were searched from their inception to April 19, 2022. We conducted random-effects meta-analyses to calculate WTP values for eHealth (at 2021 US dollar rates) and meta-regression analyses to examine the factors affecting WTP.

Results

A total of 30 articles representing 35 studies were included in the review. We found that WTP for eHealth varied across studies; when expressed as a 1-time payment, it ranged from US $0.88 to US $191.84, and when expressed as a monthly payment, it ranged from US $5.25 to US $45.64. Meta-regression analyses showed that WTP for eHealth was negatively associated with the percentages of women (β=−.76; P<.001) and positively associated with the percentages of college-educated respondents (β=.63; P<.001) and a country’s gross domestic product per capita (multiples of US $1000; β=.03; P<.001). Compared with eHealth provided through websites, people reported a lower WTP for eHealth provided through asynchronous communication (β=−1.43; P<.001) and a higher WTP for eHealth provided through medical devices (β=.66; P<.001), health apps (β=.25; P=.01), and synchronous communication (β=.58; P<.001). As for the methods used to measure WTP, single-bounded dichotomous choice (β=2.13; P<.001), double-bounded dichotomous choice (β=2.20; P<.001), and payment scale (β=1.11; P<.001) were shown to obtain higher WTP values than the open-ended format. Compared with ex ante evaluations, ex post evaluations were shown to obtain lower WTP values (β=−.37; P<.001).

Conclusions

WTP for eHealth varied significantly depending on the study population, modality used to provide eHealth, and methods used to measure it. WTP for eHealth was lower among certain population segments, suggesting that these segments may be at a disadvantage in terms of accessing and benefiting from eHealth. We also identified the modalities of eHealth that were highly valued by consumers and offered suggestions for the design of eHealth interventions. In addition, we found that different methods of measuring WTP led to significantly different WTP estimates, highlighting the need to undertake further methodological explorations of approaches to elicit WTP values.

Recommendations for developing a lifecycle, multidimensional assessment framework for mobile medical apps

Digital health and mobile medical apps (MMAs) have shown great promise in transforming health care, but their adoption in clinical care has been unsatisfactory, and regulatory guidance and coverage decisions have been lacking or incomplete. A multidimensional assessment framework for regulatory, policymaking, health technology assessment, and coverage purposes based on the MMA lifecycle is needed. A targeted review of relevant policy documents from international sources was conducted to map current MMA assessment frameworks, to formulate 10 recommendations, subsequently shared amongst an expert panel of key stakeholders. Recommendations go beyond economic dimensions such as cost and economic evaluation and also include MMA development and update, classification and evidentiary requirements, performance and maintenance monitoring, usability testing, clinical evidence requirements, safety and security, equity considerations, organizational assessment, and additional outcome domains (patient empowerment and environmental impact). The COVID-19 pandemic greatly expanded the use of MMAs, but temporary policies governing their use and oversight need consolidation through well-developed frameworks to support decision-makers, producers and introduction into clinical care processes, especially in light of the strong international, cross-border character of MMAs, the new EU medical device and health technology assessment regulations, and the Next Generation EU funding earmarked for health digitalization.

Willingness to Use and Pay for Digital Healthcare Services According to Four Scenarios: Results from a National Survey (Preprint)

BACKGROUND

Smartphones and their associated technology have evolved to an extent where these devices can be used to provide digital health interventions. However, few studies have been conducted on the willingness to use (WTU) and willingness to pay (WTP) for digital health interventions.

OBJECTIVE

The purpose of this study was to investigate how previous service experience, the content of the services, and individuals' health status affect WTU and WTP.

METHODS

We conducted a nationwide web-based survey in 3 groups: nonusers (n=506), public service users (n=368), and private service users (n=266). Participants read scenarios about an imagined health status (such as having a chronic illness) and the use of digital health intervention models (self-management, expert management, and medical management). They were then asked to respond to questions on WTU and WTP.

RESULTS

Public service users had a greater intention to use digital health intervention services than nonusers and private service users: scenario A (health-risk situation and self-management), nonusers=odd ratio [OR] .239 (SE .076; P<.001) and private service users=OR .138 (SE .044; P<.001); scenario B (health-risk situation and expert management), nonusers=OR .175 (SE .040; P<.001) and private service users=OR .219 (SE .053; P<.001); scenario C (chronic disease situation and expert management), nonusers=OR .413 (SE .094; P<.001) and private service users=OR .401 (SE .098; P<.001); and scenario D (chronic disease situation and medical management), nonusers=OR .480 (SE .120; P=.003) and private service users=OR .345 (SE .089; P<.001). In terms of WTP, in scenarios A and B, those who used the public and private services had a higher WTP than those who did not (scenario A: β=-.397, SE .091; P<.001; scenario B: β=-.486, SE .098; P<.001). In scenario C, private service users had greater WTP than public service users (β=.264, SE .114; P=.02), whereas public service users had greater WTP than nonusers (β=-.336, SE .096; P<.001). In scenario D, private service users were more WTP for the service than nonusers (β=-.286, SE .092; P=.002).

CONCLUSIONS

We confirmed that the WTU and WTP for digital health interventions differed based on individuals' prior experience with health care services, health status, and demographics. Recently, many discussions have been made to expand digital health care beyond the early adapters and fully into people's daily lives. Thus, more understanding of people's awareness and acceptance of digital health care is needed.

Adopting wearables to customize health insurance contributions: a ranking-type Delphi

Background

Wearables, as small portable computer systems worn on the body, can track user fitness and health data, which can be used to customize health insurance contributions individually. In particular, insured individuals with a healthy lifestyle can receive a reduction of their contributions to be paid. However, this potential is hardly used in practice.

Objective

This study aims to identify which barrier factors impede the usage of wearables for assessing individual risk scores for health insurances, despite its technological feasibility, and to rank these barriers according to their relevance.

Methods

To reach these goals, we conduct a ranking-type Delphi study with the following three stages. First, we collected possible barrier factors from a panel of 16 experts and consolidated them to a list of 11 barrier categories. Second, the panel was asked to rank them regarding their relevance. Third, to enhance the panel consensus, the ranking was revealed to the experts, who were then asked to re-rank the barriers.

Results

The results suggest that regulation is the most important barrier. Other relevant barriers are false or inaccurate measurements and application errors caused by the users. Additionally, insurers could lack the required technological competence to use the wearable data appropriately.

Conclusion

A wider use of wearables and health apps could be achieved through regulatory modifications, especially regarding privacy issues. Even after assuring stricter regulations, users’ privacy concerns could partly remain, if the data exchange between wearables manufacturers, health app providers, and health insurers does not become more transparent.

Willingness to pay for an mHealth anti-retroviral therapy adherence and information tool: Transitioning to sustainability, Call for life randomised study experience in Uganda

Introduction

Evidence shows benefit of digital technology for people living with human immunodeficiency virus on antiretroviral therapy adherence and retention in care, however, scalability and sustainability have scarcely been evaluated. We assessed participants’ willingness to pay a fee for mHealth “Call for life Uganda” support, a mobile-phone based tool with the objective to assess sustainability and scalability.

Methods

“Call for Life study”, approved by Makerere University, School of Public Health research & ethics committee, at 2 sites in Uganda, evaluated a MoTech based software “CONNECT FOR LIFE™” mHealth tool termed “Call for life Uganda”. It provides short messages service or Interactive Voice Response functionalities, with a web-based interface, allows a computer to interact with humans through use of voice and tones input via keypad. Participants were randomized at 1:1 ratio to Standard of Care or standard of care plus Call for life Uganda. This sends pill reminders, visit reminders, voice messages and self-reported symptom support. At study visits 18 and 24 months, through mixed method approach we assessed mHealth sustainability and scalability. Participants were interviewed on desire to have or continue adherence support and willingness to pay a nominal fee for tool. We computed proportions willing to pay (± 95% confidence interval), stratified by study arm and predictors of willingness to continue and to pay using multivariate logistic regression model backed up by themes from qualitative interviews.

Results

95% of participants were willing to continue using C4LU with 77.8% willing to pay for the service. Persons receiving care at the peri-urban clinic (OR 3.12, 95% CI 1.43–9.11.86) and those with exposure to the C4LU intervention (OR 4.2, 95% CI 1.55–11.84) were more likely to continue and pay for the service. Qualitative interviews revealed mixed feelings regarding amounts to pay, those willing to pay, argued that since they have been paying for personal phone calls/messages, they should not fail to pay for Call for life.

Conclusions

Payment for the service offers opportunities to scale up and sustain mHealth interventions which may not be priorities for government funding. A co-pay model could be acceptable to PLHIV to access mHealth services in low resource settings.

Clinical Trial Number NCT 02953080.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01782-0.

Are caregivers ready for digital? Caregiver preferences for health technology tools to monitor medication adherence among patients with serious mental illness

Background

Adherence to antipsychotic medication is critical for bipolar disorder (BPD), major depression (MDD) and schizophrenia (SCZ) patients. Digital tools have emerged to monitor medication adherence along with tracking general health. Evidence on physician or patient preferences for such tools exists but is limited among caregivers. The study objective was to assess preferences and willingness-to-pay (WTP) for medication adherence monitoring tools among caregivers of SMI patients.

Methods

A web-based survey was administered to caregivers of adult SMI patients. Twelve discrete choice questions comparing adherence monitoring tools that varied across two attribute bundles: (1) tool attributes including source of medication adherence information, frequency of information updates, access to adherence information, and physical activity, mood, and rest tracking, and (2) caregiver monthly out-of-pocket cost attribute were administered to caregiver respondents. Attributes were parameterized for both digital and non-digital tools. Random utility models were used to estimate caregivers’ preferences and WTP.

Results

Among 184 study-eligible caregivers, 57, 61 and 66 participants cared for BPD, MDD, and SCZ patients, respectively. Caregivers highly preferred (odds ratio (OR): 7.34, 95% confidence interval (CI): 5.00–10.79) a tool that tracked medication ingestion using a pill embedded with an ingestible event market (IEM) sensor and tracked patients’ physical activity, mood, and rest than a non-digital pill organizer. Additionally, caregivers were willing to pay $255 per month (95% CI: $123–$387) more for this tool compared to a pill organizer.

Conclusion

Caregivers of SMI patients highly preferred and were willing to pay more for digital tools that not only measures medication ingestion but also tracks general health.

A multi-method analytical approach to predicting young adults' intention to invest in mHealth during the COVID-19 pandemic

Mobile-based health (mHealth) systems are proving to be a popular alternative to the traditional visits to healthcare providers. They can also be useful and effective in fighting the spread of infectious diseases, such as the COVID-19 pandemic. Even though young adults are the most prevalent mHealth user group, the relevant literature has overlooked their intention to invest in and use mHealth services. This study aims to investigate the predictors that influence young adults' intention to invest in mHealth (IINmH), particularly during the COVID-19 crisis, by designing a research methodology that incorporates both the health belief model (HBM) and the expectation-confirmation model (ECM). As an expansion of the integrated HBM-ECM model, this study proposes two additional predictors: mobile Internet speed and mobile Internet cost. A multi-method analytical approach, including partial least squares structural equation modelling (PLS-SEM), fuzzy-set qualitative comparative analysis (fsQCA), and machine learning (ML), was utilised together with a sample dataset of 558 respondents. The dataset-about young adults in Bangladesh with an experience of using mHealth-was obtained through a structured questionnaire to examine the complex causal relationships of the integrated model. The findings from PLS-SEM indicate that value-for-money, mobile Internet cost, health motivation, and confirmation of services all have a substantial impact on young adults' IINmH during the COVID-19 pandemic. At the same time, the fsQCA results indicate that a combination of predictors, instead of any individual predictor, had a significant impact on predicting IINmH. Among ML methods, the XGBoost classifier outperformed other classifiers in predicting the IINmH, which was then used to perform sensitivity analysis to determine the relevance of features. We expect this multi-method analytical approach to make a significant contribution to the mHealth domain as well as the broad information systems literature.

Health Technology Assessment for Cardiovascular Digital Health Technologies and Artificial Intelligence: Why Is It Different?

Innovations in health care are growing exponentially, resulting in improved quality of and access to care, as well as rising societal costs of care and variable reimbursement. In recent years, digital health technologies and artificial intelligence have become of increasing interest in cardiovascular medicine owing to their unique ability to empower patients and to use increasing quantities of data for moving toward personalised and precision medicine. Health technology assessment agencies evaluate the money spent on a health care intervention or technology to attain a given clinical impact and make recommendations for reimbursement considerations. However, there is a scarcity of economic evaluations of cardiovascular digital health technologies and artificial intelligence. The current health technology assessment framework is not equipped to address the unique, dynamic, and unpredictable value considerations of these technologies and highlight the need to better approach the digital health technologies and artificial intelligence health technology assessment process. In this review, we compare digital health technologies and artificial intelligence with traditional health care technologies, review existing health technology assessment frameworks, and discuss challenges and opportunities related to cardiovascular digital health technologies and artificial intelligence health technology assessment. Specifically, we argue that health technology assessments for digital health technologies and artificial intelligence applications must allow for a much shorter device life cycle, given the rapid and even potentially continuously iterative nature of this technology, and thus an evidence base that maybe less mature, compared with traditional health technologies and interventions.

Mobile Apps for Dental Caries Prevention: Systematic Search and Quality Evaluation

Background

Dental caries is the most common multifactorial oral disease; it affects 60% to 90% of the global population. Dental caries is highly preventable through prevention behaviors aimed at improving oral hygiene, adequate fluoride usage, and dietary intake. Mobile apps have the potential to support patients with dental caries; however, little is known about the availability, target audience, quality, and features of these apps.

Objective

This review aims to systematically examine dental caries prevention apps; to describe their content, availability, target audience, and features; and to assess their quality.

Methods

We systematically identified and evaluated apps in a process paralleling a systematic review. This included a search strategy using search terms; an eligibility assessment using inclusion and exclusion criteria focused on accessibility and dental caries self-management behaviors, including oral hygiene, dietary intake, and fluoride usage; data extraction on app characteristics, including app store metrics; prevention behavior categorization; feature identification and description; a quality appraisal of all apps using the validated Mobile App Rating Scale (MARS) assessment tool; and data comparison and analysis.

Results

Using our search strategy, we retrieved 562 apps from the Google Play Store and iTunes available in Australia. Of these, 7.1% (40/562) of the apps fit our eligibility criteria, of which 55% (22/40) targeted adults, 93% (37/40) were free to download, and 65% (26/40) were recently updated. Oral hygiene was the most common dental caries prevention behavior domain, addressed in 93% (37/40) of the apps, while dietary intake was addressed in 45% (18/40) of the apps and fluoride usage was addressed in 42% (17/40) of the apps. Overall, 50% (20/40) of the apps addressed only 1 behavior, and 38% (15/40) of the apps addressed all 3 behaviors. The mean MARS score was 2.9 (SD 0.7; range 1.8-4.4), with 45% (18/40) of the apps categorized as high quality, with a rating above 3.0 out of 5.0. We identified 21 distinctive features across all dental caries prevention behaviors; however, the top 5 most common features focused on oral hygiene. The highest-ranking app was the Brush DJ app, with an overall MARS score of 4.4 and with the highest number of features (n=13). We did not find any apps that adequately addressed dental caries prevention behaviors in very young children.

Conclusions

Apps addressing dental caries prevention commonly focus on oral hygiene and target young adults; however, many are not of high quality. These apps use a range of features to support consumer engagement, and some of these features may be helpful for specific patient populations. However, it remains unclear how effective these apps are in improving dental caries outcomes, and further evaluation is required before they are widely recommended.

Potential Benefits and Risks Resulting From the Introduction of Health Apps and Wearables Into the German Statutory Health Care System: Scoping Review

Background

Germany is the first country worldwide that has introduced a digital care act as an incentive system to enhance the use of digital health devices, namely health apps and wearables, among its population. The act allows physicians to prescribe statutory financed and previously certified health apps and wearables to patients. This initiative has the potential to improve treatment quality through better disease management and monitoring.

Objective

The aim of this paper was to outline the key concepts related to the potential risks and benefits discussed in the current literature about health apps and wearables. Furthermore, this study aimed to answer the research question: Which risks and benefits may result from the implementation of the digital care act in Germany?

Methods

We conducted the scoping study by searching the databases PubMed, Google Scholar, and JMIR using the keywords health apps and wearables. We discussed 55 of 136 identified articles published in the English language from 2015 to March 2019 in this paper using a qualitative thematic analysis approach.

Results

We identified four key themes within the articles: Effectivity of health apps and wearables to improve health; users of health apps and wearables; the potential of bring-your-own, self-tracked data; and concerns and data privacy risks. Within these themes, we identified three main stages of benefits for the German health care system: Usage of health apps and wearables; continuing to use health apps and wearables; and sharing bring-your-own; self-tracked data with different agents in the health care sector.

Conclusions

The digital care act could lead to an improvement in treatment quality through better patient monitoring, disease management, personalized therapy, and better health education. However, physicians should play an active role in recommending and supervising health app use to reach digital-illiterate or health-illiterate people. Age must not be an exclusion criterion. Yet, concerns about data privacy and security are very strong in Germany. Transparency about data processing should be provided at all times for continuing success of the digital care act in Germany.

Who is Tracking Health on Mobile Devices: Behavioral Logfile Analysis in Hong Kong

Background

Health apps on mobile devices provide an unprecedented opportunity for ordinary people to develop social connections revolving around health issues. With increasing penetration of mobile devices and well-recorded behavioral data on such devices, it is desirable to employ digital traces on mobile devices rather than self-reported measures to capture the behavioral patterns underlying the use of mobile health (mHealth) apps in a more direct and valid way.

Objective

The objectives of this study were to (1) assess the demographic predictors of the adoption of mHealth apps; (2) investigate the temporal pattern underlying the use of mHealth apps; and (3) explore the impacts of demographic variables, temporal features, and app genres on the use of mHealth apps.

Methods

Logfile data of mobile devices were collected from a representative panel of about 2500 users in Hong Kong. Users’ mHealth app activities were analyzed. We first conducted a binary logistic regression analysis to uncover demographic predictors of users’ adoption status. Then we utilized a multilevel negative binomial regression to examine the impacts of demographic characteristics, temporal features, and app genres on mHealth app use.

Results

It was found that 27.5% of mobile device users in Hong Kong adopt at least one genre of mHealth app. Adopters of mHealth apps tend to be female and better educated. However, demographic characteristics did not showcase the predictive powers on the use of mHealth apps, except for the gender effect (B_female vs B_male=–0.18; P=.006). The use of mHealth apps demonstrates a significant temporal pattern, which is found to be moderately active during daytime and intensifying at weekends and at night. Such temporal patterns in mHealth apps use are moderated by individuals’ demographic characteristics. Finally, demographic characteristics were also found to condition the use of different genres of mHealth apps.

Conclusions

Our findings suggest the importance of dynamic perspective in understanding users’ mHealth app activities. mHealth app developers should consider more the demographic differences in temporal patterns of mHealth apps in the development of mHealth apps. Furthermore, our research also contributes to the promotion of mHealth apps by emphasizing the differences of usage needs for various groups of users.