Strategies to increase downloads of COVID-19 exposure notification apps: A discrete choice experiment.
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ABSTRACT:
SUBMITTER: Frimpong JA
PROVIDER: S-EPMC8559927 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
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Strategies to increase downloads of COVID-19 exposure notification apps: A discrete choice experiment.
Frimpong Jemima A JA Helleringer Stéphane S
PloS one 20211101 11
Exposure notification apps have been developed to assist in notifying individuals of recent exposures to SARS-CoV-2. However, in several countries, such apps have had limited uptake. We assessed whether strategies to increase downloads of exposure notification apps should emphasize improving the accuracy of the apps in recording contacts and exposures, strengthening privacy protections and/or offering financial incentives to potential users. In a discrete choice experiment with potential app use ...[more]
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| S-EPMC8703239 | biostudies-literature
Project description:IntroductionDigital contact-tracing smartphone apps have the potential to slow the spread of disease but are not widely used. We tested whether messages describing how a COVID-19 digital contact-tracing app protects users' privacy led to increased or decreased intentions to download the app by either calming privacy concerns or increasing their saliency.DesignRandomized controlled trial.Setting/participantsWe recruited adult smartphone owners in the U.S. (oversampled for younger adults aged 18-34 years) in November 2020 through an online panel.InterventionSurvey software randomly assigned 860 participants to 1 of 2 parallel messaging conditions (n=430 privacy assured, n=430 no privacy described).Main outcome measures4-point scale of intention to use the app "if public health officials released a COVID Exposure Notification app in their state" that averaged likelihood to (1) download and install the app on their phone; (2) keep the app active on their phone; and (3) keep Bluetooth active on their phone (needed for the app to work).ResultsAfter removing incompletes, those who failed the manipulation checks, or those who had already downloaded a COVID-19 digital contact-tracing app, we analyzed 671 participants (n=330 privacy, n=341 no privacy) in 2021. There was no relationship between privacy condition and download intention (meanprivacy=2.69, meannoprivacy=2.69, b=0.01, 95% CI= -0.13, 0.15, p=0.922) but also no evidence that describing the app's security increased context-dependent privacy concerns (measured 3 ways). Instead, we found increased endorsement of data security in the privacy condition using a scale of beliefs about the app keeping privacy secure (meanprivacy=2.74, meannoprivacy=2.58, b=0.16, 95% CI=0.04, 0.28, p=0.009, small effect ω2=0.009).ConclusionsThis study provides some evidence that people developing contact-tracing messaging campaigns do not need to worry that describing a digital contact-tracing app's privacy protections will backfire. Future mixed methods testing of messages about who has access to information-and for how long-may uncover new communication strategies to increase public trust in contact-tracing apps.Trial registrationThis study is registered with AsPredicted#51826.
| S-EPMC9771837 | biostudies-literature
Project description:BackgroundSmartphone-based contact tracing apps can contribute to reducing COVID-19 transmission rates and thereby support countries emerging from lockdowns as restrictions are gradually eased.ObjectiveThe primary objective of our study is to determine the potential uptake of a contact tracing app in the Dutch population, depending on the characteristics of the app.MethodsA discrete choice experiment was conducted in a nationally representative sample of 900 Dutch respondents. Simulated maximum likelihood methods were used to estimate population average and individual-level preferences using a mixed logit model specification. Individual-level uptake probabilities were calculated based on the individual-level preference estimates and subsequently aggregated into the sample as well as subgroup-specific contact tracing app adoption rates.ResultsThe predicted app adoption rates ranged from 59.3% to 65.7% for the worst and best possible contact tracing app, respectively. The most realistic contact tracing app had a predicted adoption of 64.1%. The predicted adoption rates strongly varied by age group. For example, the adoption rates of the most realistic app ranged from 45.6% to 79.4% for people in the oldest and youngest age groups (ie, ≥75 years vs 15-34 years), respectively. Educational attainment, the presence of serious underlying health conditions, and the respondents' stance on COVID-19 infection risks were also correlated with the predicted adoption rates but to a lesser extent.ConclusionsA secure and privacy-respecting contact tracing app with the most realistic characteristics can obtain an adoption rate as high as 64% in the Netherlands. This exceeds the target uptake of 60% that has been formulated by the Dutch government. The main challenge will be to increase the uptake among older adults, who are least inclined to install and use a COVID-19 contact tracing app.
| S-EPMC7584977 | biostudies-literature
Project description:BackgroundIf the most evidence-based and effective smoking cessation apps are not selected by smokers wanting to quit, their potential to support cessation is limited.ObjectiveThis study sought to determine the attributes that influence smoking cessation app uptake and understand their relative importance to support future efforts to present evidence-based apps more effectively to maximize uptake.MethodsAdult smokers from the United Kingdom were invited to participate in a discrete choice experiment. Participants made 12 choices between two hypothetical smoking cessation app alternatives, with five predefined attributes reflecting domains from the theoretical domains framework: (1) monthly price of the app (environmental resources), (2) credible source as app developer (social influence), (3) social proof as star rating (social influence), (4) app description type (beliefs about consequences), and (5) images shown (beliefs about consequences); or opting out (choosing neither app). Preferences and the relative importance of attributes were estimated using mixed logit modeling. Willingness to pay and predicted uptake of the most and least preferred app were also calculated.ResultsA total of 337 adult smokers completed the survey (n=168, 49.8% female; mean age 35, SD 11 years). Participants selected a smoking cessation app rather than opting out for 90% of the choices. Relative to other attributes, a 4.8-star user rating, representing social proof, was the strongest driver of app selection (mean preference parameter 2.27, SD 1.55; 95% CI 1.95-2.59). Participants preferred an app developed by health care-orientated trusted organization (credible source) over a hypothetical company (mean preference parameter 0.93, SD 1.23; 95% CI 0.72-1.15), with a logo and screenshots over logo only (mean preference parameter 0.39, SD 0.96; 95% CI 0.19-0.59), and with a lower monthly cost (mean preference parameter -0.38, SD 0.33; 95% CI -0.44 to -0.32). App description did not influence preferences. The uptake estimate for the best hypothetical app was 93% and for the worst, 3%. Participants were willing to pay a single payment of up to an additional US $6.96 (UK £5.49) for 4.8-star ratings, US $3.58 (UK £2.82) for 4-star ratings, and US $2.61(UK £2.06) for an app developed by a trusted organization.ConclusionsOn average, social proof appeared to be the most influential factor in app uptake, followed by credible source, one perceived as most likely to provide evidence-based apps. These attributes may support the selection of evidence-based apps.
| S-EPMC11775483 | biostudies-literature
Project description:BACKGROUND: Guidelines underline the role of individual preferences in the selection of a screening test, as insufficient evidence is available to recommend one screening test over another. We conducted a study to determine the preferences of individuals and to predict uptake for colorectal cancer (CRC) screening programmes using various screening tests. METHODS: A discrete choice experiment (DCE) questionnaire was distributed among naive subjects, yet to be screened, and previously screened subjects, aged 50-75 years. Subjects were asked to choose between scenarios on the basis of faecal occult blood test (FOBT), flexible sigmoidoscopy (FS), total colonoscopy (TC) with various test-specific screening intervals and mortality reductions, and no screening (opt-out). RESULTS: In total, 489 out of 1498 (33%) screening-naïve subjects (52% male; mean age+/-s.d. 61+/-7 years) and 545 out of 769 (71%) previously screened subjects (52% male; mean age+/-s.d. 61+/-6 years) returned the questionnaire. The type of screening test, screening interval, and risk reduction of CRC-related mortality influenced subjects' preferences (all P<0.05). Screening-naive and previously screened subjects equally preferred 5-yearly FS and 10-yearly TC (P=0.24; P=0.11), but favoured both strategies to annual FOBT screening (all P-values <0.001) if, based on the literature, realistic risk reduction of CRC-related mortality was applied. Screening-naive and previously screened subjects were willing to undergo a 10-yearly TC instead of a 5-yearly FS to obtain an additional risk reduction of CRC-related mortality of 45% (P<0.001). CONCLUSION: These data provide insight into the extent by which interval and risk reduction of CRC-related mortality affect preferences for CRC screening tests. Assuming realistic test characteristics, subjects in the target population preferred endoscopic screening over FOBT screening, partly, due to the more favourable risk reduction of CRC-related mortality by endoscopy screening. Increasing the knowledge of potential screenees regarding risk reduction by different screening strategies is, therefore, warranted to prevent unrealistic expectations and to optimise informed choice.
| S-EPMC2844026 | biostudies-other
Project description:Objectives: To decrease the rapid growth of SARS-CoV-2 in Germany, a stepped lockdown was conducted. Acceptance and compliance regarding entering and exiting lockdown measures are key for their success. The aim of the present study was to analyse the population's preferences for exiting lockdown measures. Methods: To evaluate population's preferences and identify trade-offs between different exit strategies, a discrete choice experiment was conducted on 28-29 April (n = 1,020). Overall, six attributes and 16 choice sets (fractional-factorial design) without an opt-out were chosen. Conditional logit and latent class models were conducted. Results: Most attributes proved to be significant. Two attributes dominated all others: Avoiding a mandatory tracing app, and providing sufficient intensive care capacities. Preventing a high long-term unemployment rate and avoiding the isolation of persons aged 70+, were relevant, though utilities were comparatively lower. We identified subgroups (elderly persons and persons with school children) with different utilities, which indicates specific attributes affecting them dissimilarly. Conclusions: The population prefers cautious re-opening strategies and is at least sceptical about the adoption of severe protection measures. Government should balance interests between subgroups.
| S-EPMC8565260 | biostudies-literature
Project description:BackgroundThe benefits of health apps can only be realized when consumers purchase them for use. Thus, it is important to understand what factors influence consumers' willingness to purchase health apps. Therefore, this study aimed to examine the influence of health app attributes and sociodemographic characteristics on consumers' willingness to purchase health apps, and how the value of the health app attributes varies for individuals with different sociodemographic characteristics.MethodsA questionnaire-based discrete choice experiment (DCE) was conducted with a random sample of 561 adults. A standard logit regression was applied to assess the influence of health app attributes and sociodemographic characteristics on consumers' willingness to purchase health apps, and marginal willingness to pay (MWTP) was calculated for each factor using regression coefficients. Interaction effects were also examined to determine how the value of health app attributes varies by sociodemographic characteristics.ResultsUsefulness, ease of use, security and privacy, and attitudes of healthcare professionals toward consumers' use of health apps were the attributes of health apps that positively influenced consumers' willingness to purchase them. Conversely, smartphone storage consumption, mobile Internet data consumption, and app price negatively influenced consumers' willingness to purchase the apps. For sociodemographic characteristics, being male, having a household size greater than three, having a monthly household income of HK$30,000 or more, having a lower education level (below diploma), having previously used health apps, and having previously purchased health apps were associated with a higher willingness to purchase health apps.ConclusionsHealth app attributes that influenced consumers' willingness to purchase the apps and populations that were less willing to purchase health apps were identified. Efforts should be made to improve health app attributes and enhance the promotion of health apps among these underserved populations.
| S-EPMC10364011 | biostudies-literature
Project description:ObjectivesThe objective of this paper is to study under which circumstances wearable and health app users would accept a compensation payment, namely a digital dividend, to share their self-tracked health data.MethodsWe conducted a discrete choice experiment alternative, a separated adaptive dual response. We chose this approach to reduce extreme response behavior, considering the emotionally-charged topic of health data sales, and to measure willingness to accept. Previous experiments in lab settings led to demands for high monetary compensation. After a first online survey and two pre-studies, we validated four attributes for the final online study: monthly bonus payment, stakeholder handling the data (e.g., health insurer, pharmaceutical or medical device companies, universities), type of data, and data sales to third parties. We used a random utility framework to evaluate individual choice preferences. To test the expected prices of the main study for robustness, we assigned respondents randomly to one of two identical questionnaires with varying price ranges.ResultsOver a period of three weeks, 842 respondents participated in the main survey, and 272 respondents participated in the second survey. The participants considered transparency about data processing and no further data sales to third parties as very important to the decision to share data with different stakeholders, as well as adequate monetary compensation. Price expectations resulting from the experiment were high; pharmaceutical and medical device companies would have to pay an average digital dividend of 237.30€/month for patient generated health data of all types. We also observed an anchor effect, which means that people formed price expectations during the process and not ex ante. We found a bimodal distribution between relatively low price expectations and relatively high price expectations, which shows that personal data selling is a divisive societal issue. However, the results indicate that a digital dividend could be an accepted economic incentive system to gather large-scale, self-tracked data for research and development purposes. After the COVID-19 crisis, price expectations might change due to public sensitization to the need for big data research on patient generated health data.ConclusionA continuing success of existing data donation models is highly unlikely. The health care sector needs to develop transparency and trust in data processing. An adequate digital dividend could be an effective long-term measure to convince a diverse and large group of people to share high-quality, continuous data for research purposes.
| S-EPMC8312968 | biostudies-literature
Project description:BackgroundPrior to COVID-19, little was known about how risks associated with such a pandemic would compete with and influence patient decision making regarding cancer risk reducing medical decision making. We investigated how the pandemic affected preferences for medical risk-reducing strategies among women at elevated risk of breast or ovarian cancer.MethodsWe conducted a discrete choice experiment. Women about to undergo genetic testing and counseling at 2 medical centers participated. Enrollment occurred between 2019 and 2022, allowing us to investigate changes in preferences from before the pandemic to after the pandemic. Women chose from permuted scenarios that specified type of surgery, age of menopause, quality of menopausal symptoms, and risk of ovarian cancer, heart disease, or osteoporosis.ResultsA total of 355 women, with a median age of 36 y, participated. In 2019, women were less likely to choose prevention scenarios with higher ovarian cancer risk (odds ratio [OR] = 0.42 per 10-point increase in risk, 95% confidence interval [CI] 0.22-0.61). In June 2020, the effect of higher ovarian cancer risk scenarios on choice was attenuated (OR = 0.86, 95% CI 0.68-1.04), with the effect becoming more salient again by July 2021 (OR = 0.59, 95% CI 0.52-0.67) (P = 0.039 for test of temporal interaction). No other attribute demonstrated a temporal trend.ConclusionThe risks associated with the COVID-19 pandemic may have attenuated the impact of risk of ovarian cancer on choice of risk-reducing prevention strategies for ovarian cancer. The maximum attenuation occurred at the beginning of the pandemic when access to risk-reducing surgery was most restricted. Our findings highlight how individuals evaluate competing health risks and adjust their uptake of cancer prevention strategies when faced with a future pandemic or similar global crisis.HighlightsIn this discrete choice experiment, women were much less likely to choose prevention scenarios that had higher ovarian cancer risk prior to the COVID-19 pandemic than after the pandemic.The attenuation of preferences may have persisted through 2022.COVID-19 may have altered the relative importance of factors that motivate women to undergo risk-reducing surgeries.
| S-EPMC11881031 | biostudies-literature
Project description:ObjectivesThe global coronavirus disease 2019 (COVID-19) pandemic has not been well controlled, and vaccination could be an effective way to prevent this pandemic. By accommodating attribute nonattendance (ANA) in a discrete choice experiment (DCE), this paper aimed to examine Chinese public preferences and willingness to pay (WTP) for COVID-19 vaccine attributes, especially the influence of ANA on the estimated results.MethodsA DCE was designed with four attributes: effectiveness, protection period, adverse reactions and price. A random parameter logit model with an error component (RPL-EC) was used to analyse the heterogeneity of respondents' preferences for COVID-19 vaccine attributes. Two equality constraint latent class (ECLC) models were used to consider the influence of ANA on the estimated results in which the ECLC-homogeneity model considered only ANA and the ECLC-heterogeneity model considered both ANA and preference heterogeneity.ResultsData from 1,576 samples were included in the analyses. Effectiveness had the highest relative importance, followed by adverse reactions and protection period, which were determined by the attributes and levels presented in this study. The ECLC-heterogeneity model improved the goodness of fit of the model and obtained a lower probability of ANA. In the ECLC-heterogeneity model, only a small number of respondents (29.09%) considered all attributes, and price was the most easily ignored attribute (64.23%). Compared with the RPL-EC model, the ECLC-homogeneity model obtained lower WTPs for COVID-19 vaccine attributes, and the ECLC-heterogeneity model obtained mixed WTP results. In the ECLC-heterogeneity model, preference group 1 obtained higher WTPs, and preference groups 2 and 3 obtained lower WTPs.ConclusionsThe RPL-EC, ECLC-homogeneity and ECLC-heterogeneity models obtained inconsistent WTPs for COVID-19 vaccine attributes. The study found that the results of the ECLC-heterogeneity model considering both ANA and preference heterogeneity may be more plausible because ANA and low preference may be confused in the ECLC-homogeneity model and the RPL-EC model. The results showed that the probability of ANA was still high in the ECLC-heterogeneity model, although it was lower than that in the ECLC-homogeneity model. Therefore, in future research on DCE (such as the field of vaccines), ANA should be considered as an essential issue.Public contributionChinese adults from 31 provinces in mainland China participated in the study. All participants completed the COVID-19 vaccine choice questions generated through the DCE design.
| S-EPMC9122444 | biostudies-literature