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During the COVID-19 pandemic, new digital solutions have been developed for infection control. In particular, contact tracing mobile apps provide a means for governments to manage both health and economic concerns. However, public reception of these apps is paramount to their success, and global uptake rates have been low.
In this study, we sought to identify the characteristics of individuals or factors potentially associated with voluntary downloads of a contact tracing mobile app in Singapore.
A cohort of 505 adults from the general community completed an online survey. As the primary outcome measure, participants were asked to indicate whether they had downloaded the contact tracing app TraceTogether introduced at the national level. The following were assessed as predictor variables: (1) participant demographics, (2) behavioral modifications on account of the pandemic, and (3) pandemic severity (the number of cases and lockdown status).
Within our data set, the strongest predictor of the uptake of TraceTogether was the extent to which individuals had already adjusted their lifestyles because of the pandemic (z=13.56;
Efforts to introduce contact tracing apps could capitalize on pandemic-related behavioral adjustments among individuals. Given that a large number of individuals is required to download contact tracing apps for contact tracing to be effective, further studies are required to understand how citizens respond to contact tracing apps.
ClinicalTrials.gov NCT04468581, https://clinicaltrials.gov/ct2/show/NCT04468581
In May 2020, Google and Apple released the Exposure Notification System, which is an application programming interface that logs the following: who a phone user has been in contact with, for how long, and at what distance [
Less than a year after the first reported cases, over 33 million individuals have tested positive for COVID-19 worldwide and more than 1 million have died [
To address both infection control and economic concerns, several countries have turned to contact tracing to keep the economy running [
Early during the pandemic (and in previous infectious disease outbreaks), contact tracing was manually performed [
Considering these limitations of manual contact tracing, several mobile apps have been developed to facilitate automated contact tracing [
Despite the potential of digital contact tracing, a recent meta-analysis concluded that owing to implementation barriers, manual contact tracing should remain the order of the day [
To increase uptake, Qatar made it mandatory for residents to use the official contact tracing app [
Given the urgent need to boost contact tracing apps, this study is the first to identify sociodemographic factors predicting voluntary uptake. Our study was conducted in Singapore, where the world’s first nationwide contact tracing app TraceTogether was launched in March 2020 [
As Singapore was the forerunner of this technology, the app has accrued 2.3 million users within 6 months, including approximately 40% of Singapore’s resident population or 50% of all smartphone users (considering a smartphone penetration rate of 82%) [
Between April 3 and July 17, 2020, we recruited 505 adults who met the following eligibility criteria: (1) at least 21 years of age and (2) had lived in Singapore for a minimum of 2 years. All participants responded to online advertisements. Within the constraints of online sampling owing to the pandemic, we strove to obtain a representative sample by placing advertisements in a wide range of online community groups (eg, Facebook or WhatsApp groups among individuals in residential estates, universities, and workplaces) and by using paid online advertisements targeting the broad spectrum of Singapore residents.
Prior to study enrolment, participants provided informed consent in accordance with a protocol approved by the Yale-NUS College Ethics Review Committee (#2020-CERC-001; ClinicalTrials.gov ID NCT04468581). They then completed a 10-min online survey hosted on the platform Qualtrics [
As the primary outcome variable, participants were asked to indicate whether they had downloaded the government’s contact tracing app TraceTogether (binary variables: 1=they had, 0=they had not).
As predictors of TraceTogether usage, participants then reported the following demographic data: age, gender, citizenship, ethnicity, marital status, education level, house type (a proxy of socioeconomic status in Singapore), and household size. Based on the survey timestamp, we also included the following as predictors: (1) the total number of cases in Singapore to date, (2) whether the nation was in a lockdown at the time of participation (0=no, 1=yes), and (3) a self-reported measure of confidence the government could control COVID-19 spread (4-point scale: 1=“not confident at all,” 4=“very confident”).
As a basis of comparison, participants were also asked to identify which of 18 other behavioral modifications they had made as a result of the pandemic (apart from downloading TraceTogether). Specifically, participants were asked whether they had (1) washed their hands more frequently, (2) used hand sanitizers, (3) worn a mask in public voluntarily (before a law was passed), (4) avoided taking public transport, (5) stayed home more than usual, (6) avoided crowded places, (7) chosen outdoor over indoor venues, (8) missed or postponed social events, (9) changed their travel plans voluntarily, (10) reduced physical contact with others (eg, by not shaking hands), (11) avoided visiting hospitals or other health care settings, (12) avoided visiting places where COVID-19 cases had been reported, (13) maintained distance from people suspected of recent contact with a COVID-19–positive individual, (14) maintained distance from people who might have recently traveled to countries with an outbreak, (15) maintained distance from people with flu-like symptoms, (16) relied more on online shopping (eg, for groceries), (17) stocked up on more household supplies and groceries than usual, or (18) taken their children out of school (for each item, 0=the measure was not taken, 1=the measure was taken). These values were then summed to compute an aggregated measure of behavioral change (out of 18), and were included as a predictor to assess whether contact tracing usage was associated with conventional behavioral modifications one undertakes during an epidemic [
As part of the survey, participants were also asked to specify any other behavioral modifications (n=9, 1.8%) or no other behavioral modification (n=2, 0.4%). However, these data were excluded from the statistical analyses owing to the low base rate of affirmative responses.
For primary analysis, binary logistic regression was used to identify predictors TraceTogether uptake. In the first model (model 1), participants' demographics were included as predictors (age, citizenship, gender, marital status, education level, ethnicity, household type, and household size). Citizenship (base=others), gender (base=female), marital status (base=single), and ethnicity (base=Chinese) were coded as dummy variables. In the second model (model 2), we repeated the first model with the inclusion of situational variables (log-transformed total number of COVID-19 cases to date and lockdown status). Finally, in the third model (model 3), we repeated the second model with the inclusion of the total number of behavioral modifications as a predictor. All data were analyzed using SPSS (version 23, IBM Corp) and R (version 3.6.0, The R Foundation), with the type 1 familywise error rate controlled at α=.05 via Bonferroni correction (Bonferroni-adjusted α=.003 [.05/17 predictors]).
Baseline characteristics of survey respondents (N=505).
Variable | Value | |
Age (years), mean (SD) | 37.82 (11.31) | |
Number of behavioral modifications, mean (SD) | 9.81 (3.82) | |
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Female | 313 (62.0) |
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Male | 192 (38.0) |
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Singaporean | 456 (90.3) |
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Others | 49 (9.7) |
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No formal education | 2 (0.4) |
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Primary school | 2 (0.4) |
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Secondary school | 23 (4.6) |
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Junior college | 26 (5.1) |
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Institution of Technical Education | 12 (2.4) |
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Polytechnic (diploma) | 88 (17.4) |
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University (degree) | 265 (52.5) |
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Postgraduate (masters/PhD) | 87 (17.2) |
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Chinese | 412 (81.6) |
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Malay | 38 (7.5) |
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Indian | 32 (6.3) |
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Eurasian | 15 (3.0) |
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Others | 8 (1.6) |
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Single | 170 (33.7) |
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Dating | 64 (12.7) |
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Married | 241 (47.7) |
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Widowed/separated/divorced | 30 (5.9) |
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HDBa flat: 1-2 rooms | 14 (2.8) |
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HDB flat: 3 rooms | 50 (9.9) |
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HDB flat: 4 rooms | 132 (26.1) |
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HDB flat: 5 rooms or executive flats | 149 (29.5) |
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Condominium or private apartments | 122 (24.2) |
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Landed property | 38 (7.5) |
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1 | 26 (5.1) |
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2 | 88 (17.4) |
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3 | 119 (23.6) |
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4 | 133 (26.3) |
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5+ | 139 (27.5) |
aHDB: Housing & Development Board.
Of the 505 participants, 274 (54.3%; 95% CI 49.8%-58.7%) reported having downloaded TraceTogether. The download rate in this sample matches that of smartphone users in the resident population [
Characteristics of the users of TraceTogether (N=505).
Variable | TraceTogether usage | ||
|
Users (n=274) | Nonusers (n=231) | |
Age (years), mean (SD) | 38.57 (11.57) | 36.95 (10.96) | |
Household type, mean (SD) | 3.92 (1.18) | 3.76 (1.21) | |
Household size, mean (SD) | 3.54 (1.26) | 3.53 (1.15) | |
Number of behavioral modifications, mean (SD) | 10.33 (3.83) | 8.96 (3.65) | |
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Female | 177 (64.6) | 136 (58.9) |
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Male | 97 (35.4) | 95 (41.1) |
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Singaporean | 240 (87.6) | 216 (93.5) |
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Others | 34 (12.4) | 15 (6.5) |
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No formal education | 1 (0.4) | 1 (0.4) |
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Primary school | 1 (0.4) | 1 (0.4) |
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Secondary school | 15 (5.5) | 8 (3.5) |
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Junior college | 14 (5.1) | 12 (5.2) |
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Institution of Technical Education | 8 (2.9) | 4 (1.7) |
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Polytechnic (diploma) | 41 (15.0) | 47 (20.3) |
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University (degree) | 136 (49.6) | 129 (55.8) |
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Postgraduate (masters/PhD) | 58 (21.2) | 29 (12.6) |
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Chinese | 218 (79.6) | 194 (84.0) |
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Malay | 20 (7.3) | 18 (7.8) |
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Indian | 19 (6.9) | 13 (5.6) |
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Eurasian | 13 (4.7) | 2 (0.9) |
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Others | 4 (1.5) | 4 (1.7) |
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Single | 85 (31.0) | 85 (36.8) |
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Dating | 38 (13.9) | 26 (11.3) |
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Married | 133 (48.5) | 108 (46.8) |
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Widowed/separated/divorced | 18 (6.6) | 12 (5.2) |
Logistic regression models of predictors of the uptake of TraceTogether (dependent variable=downloaded TraceTogether).
Variable | Model 1: demographicsa | Model 2: demographics and situational variables | Model 3: demographics, situational variables, and behavioral modifications | ||||
|
Odds ratio (95% CI) | Odds ratio (95% CI) | Odds ratio (95% CI) | ||||
Age (years) | 1.018 (1.00-1.04) | .09 | 1.020 (1.00-1.04) | .06 | 1.021 (1.00-1.04) | .05 | |
Gender (base=female) | 0.771 (0.53-1.12) | .17 | 0.799 (0.55-1.17) | .25 | 0.904 (0.61-1.34) | .61 | |
Citizenship (base=others) | 0.546 (0.26-1.14) | .11 | 0.597 (0.28-1.13) | .17 | 0.651 (0.30-1.39) | .27 | |
Household type | 1.082 (0.92-1.27) | .76 | 1.056 (0.90-1.25) | .52 | 1.011 (0.85-1.20) | .90 | |
Household size | 1.042 (0.89-1.23) | .62 | 1.028 (0.87-1.21) | .74 | 1.036 (0.88-1.22) | .67 | |
Highest education | 1.032 (0.89-1.19) | .67 | 1.029 (0.89-1.25) | .70 | 0.993 (0.85-1.16) | .92 | |
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Malay | 1.057 (0.53-2.11) | .88 | 1.050 (0.52-2.14) | .89 | 0.980 (0.48-2.02) | .96 |
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Indian | 1.112 (0.52-2.37) | .78 | 0.984 (0.45-2.13) | .97 | 0.928 (0.43-2.03) | .85 |
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Eurasian | 3.454 (0.70-17.02) | .13 | 3.475 (0.70-17.37) | .13 | 3.402 (0.66-17.42) | .14 |
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Others | 0.720 (0.16-3.17) | .66 | 0.724 (0.16-3.29) | .68 | 0.851 (0.18-4.037) | .84 |
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Dating | 1.505 (0.82-2.76) | .19 | 1.555 (0.84-2.90) | .16 | 1.392 (0.74-2.63) | .31 |
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Married | 0.968 (0.62-1.52) | .89 | 0.974 (0.61-1.55) | .91 | 0.900 (0.56-1.44) | .66 |
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Widowed/separated/divorced | 1.146 (0.47-2.79) | .76 | 1.155 (0.47-2.84) | .75 | 1.034 (0.41-2.59) | .94 |
Local COVID-19 cases to date (log) | N/Ab | N/A | 0.774 (0.50-1.21) | .26 | 0.752 (0.48-1.18) | .22 | |
Lockdown (base=no lockdown) | N/A | N/A | 0.561 (0.30-1.04) | .07 | 0.599 (0.32-1.12) | .11 | |
Confidence in the government | N/A | N/A | 1.372 (1.05-1.79) | .02 | 1.363 (1.04-1.79) | .03 | |
Number of behavioral modifications | N/A | N/A | N/A | N/A | 1.102 (1.05-1.16) | <.001c |
aModel 1: Overall percentage of users correctly classified-56.6%, Nagelkerke R2-0.048; Model 2: Overall percentage of users correctly classified-58.2%, Nagelkerke R2-0.068; Model 3: Overall percentage of users correctly classified-60.2%, Nagelkerke R2-0.103.
bN/A: not applicable.
c
In the logistic regression analyses, TraceTogether downloads were predicted from the number of behavioral modifications because of the pandemic. To understand this association better, we conducted further exploratory analyses.
As shown in
Self-reported behavioral modifications , other than downloading TraceTogether, among the study participants undertaken in response to the COVID-19 outbreak in Singapore. Error bars=95% CI. Numbers in brackets represent the total number of respondents who reported the behavioral change.
A corollary question is how TraceTogether usage is associated with other health protective behaviors; that is, how likely were people to download TraceTogether if they had modified their behavior in other ways? To address this question, we conducted network analyses by estimating a mixed graphical model (MGM) with the R package
As shown in
For sensitivity analysis, we performed logistic regression analysis using TraceTogether downloads as the dependent variable, and 18 other behavioral modifications (see Methods) as the predictors. Our conclusions did not change, as indicated in
A model depicting how TraceTogether usage relates to other pandemic-related behavioral changes. Line thickness represents the strength of an association.
As lockdowns owing to COVID-19 ease globally, digital contact tracing will play an increasingly critical role in managing the epidemic curve. However, this requires the public to actively download a contact tracing app—a step that has proven elusive among public health agencies worldwide [
As our primary outcome, we observed that the number of behavioral modifications significantly predicted the use of TraceTogether. In other words, a person who had already changed his/her lifestyle on account of the pandemic was also likely to download a contact tracing app. Network analyses revealed that downloads clustered with (1) using hand sanitizers, (2) avoiding public transport, and (3) preferring outdoor to indoor venues. This finding may suggest that public health campaigns could capitalize on other behavioral modifications when seeking to promote app downloads, for example, by printing information regarding a contact tracing app on the packaging of hand sanitizers or by framing the use of digital contact tracing as a preventive behavior. Policy makers might also expect app download rates to track behavioral modifications, anticipating, for example, higher download rates when the public fears an increase in COVID-19 cases (leading to more behavioral modifications) [
Theoretically, our findings further corroborate those of previous studies on how individuals change their behaviors during a pandemic. Based on prior outbreaks, a taxonomy of modifications had been identified whereby (1) “avoidant behaviors” are measures taken to avoid contact with potential carriers (eg, avoiding crowded places), while (2) “prevention behaviors” are those associated with maintaining hygiene (eg, regular hand washing) [
Apart from behavioral modifications, it is notable that no demographic (eg, age, gender, etc) or situational variable (eg, number of COVID-19 cases and lockdown status) significantly predicted TraceTogether uptake. Prior to our study, it would have been conceivable that only a subset of the population would download a contact tracing app (eg, demographic groups based on gender, educational level, or age) [
While the lack of significant associations may be counterintuitive, a recent study reported similar results when predicting COVID-19–related behavioral modifications [
As public health agencies develop strategies to promote downloads for contact tracing apps, the pattern of our findings may in turn suggest that demographic-specific messages are not needed. This is encouraging because the behavioral sciences offer widespread measures to “nudge” the general population [
Our study has several limitations of note. As the first study of its kind, we made several choices at the exclusion of others. First, we opted for a cross-sectional design that precludes strong conclusions regarding causality. Second, we included an online sample to minimize person-to-person contact during the pandemic. Although we sampled individuals from a wide array of demographic groups, respondents were not representative of the general nationwide population; this may have deterred the establishment of potential associations among variables (eg, by including a high proportion of educated participants). Third, our survey relied on participants’ self-reported use of a contact tracing app. Although our download rate is similar to that of the general population, further studies may seek to verify actual usage (eg, by incorporating survey questions in a contact tracing app). Fourth, our survey was not intended to measure every aspect of TraceTogether usage, and there were several notable omissions (eg, reasons why individuals chose to use or not use the app, phone ownership, and usage-related questions). Indeed, our model metrics (eg, small Nagelkerke R2) indicate small effect sizes, highlighting the need for further studies to include a more comprehensive set of variables that may account for app downloads. Finally, we examined TraceTogether—an app with a centralized contact tracing protocol. Future studies are required to assess whether our findings extend to apps with decentralized protocols or to other forms of digital contact tracing that do not rely on mobile apps (eg, public acceptance of cloud-based contact in South Korea).
In conclusion, the potential contribution of digital technology to pandemic management is receiving increasing attention. What remains unclear, however, is how this technology is received and how best to promote its uptake. Focusing on contact tracing, this study shows that downloads of a mobile app was best predicted from the adoption of other infection control measures such as increased hand hygiene. In other words, the introduction of digital contact tracing is not merely a call to “trace together” but rather to “modify together,” to use contact tracing apps as part of the broader spectrum of behavioral modifications during a pandemic.
Adjacency matrix.
Sensitivity analysis.
mixed graphical model
reproduction number
This research was funded by a grant awarded to JCJL from the JY Pillay Global Asia Programme (grant number: IG20-SG002).
None declared.