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Record W2604509270 · doi:10.1093/pch/20.7.351

Using mobile technologies for immunization: Predictors of uptake of a pan-Canadian immunization app (ImmunizeCA)

2015· article· en· W2604509270 on OpenAlexaffabout
Katherine Atkinson, Jacqueline Westeinde, Steven Hawken, Robin Ducharme, Kim Barnhardt, Kumanan Wilson

Bibliographic record

VenuePaediatrics & Child Health · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsCanadian Medical AssociationOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsImmunizationMobile appsMedicineComputer scienceImmunologyWorld Wide WebAntibody

Abstract

fetched live from OpenAlex

The authors have no conflicts of interest to declare. Substandard vaccination compliance in many areas of North America has resulted in outbreaks of vaccine-preventable diseases such as measles (1). Mobile technologies offer an opportunity for public health to improve vaccination rates by providing accurate information and reminding individuals of immunization appointments (2). However, mobile apps are limited by the willingness of the public to download them. ImmunizeCA is a pan-Canadian immunization app, which provides users with a mechanism to store their family’s immunization records, access accurate immunization information, schedule appointments and be notified of vaccine-preventable diseases in their vicinity. We sought to measure uptake and use of ImmunizeCA over a six-month period, and to assess the effectiveness of various promotional strategies in driving uptake and use of the app (Table 1). Strategies examined included: government and hospital press releases, Apple (Apple Inc, USA) App Store placement in “Best New Apps” section at launch, direct-to-household flyers enclosed in federal universal child benefit cheques (flyers), as well as online and print news articles and mentions in social media (Twitter [Twitter Inc, USA] and Facebook [Facebook Inc, USA]). Period of investigation and critical events during the study period March 20 to September 20, 2014 Period of investigation and critical events during the study period March 20 to September 20, 2014 Downloads, website visits and media mentions for ImmunizeCA for six months following launch, March 20 to September 20, 2014 Data presented as n Downloads, website visits and media mentions for ImmunizeCA for six months following launch, March 20 to September 20, 2014 Data presented as n Media monitoring was conducted using MediaMiser SNAP (MediaMiser Ltd, Canada), a media monitoring and analysis platform, which collects search result data from digital news, blogs, Twitter, YouTube (Google, USA), Facebook and reddit (reddit Inc, USA) (3). Articles were retrieved according to the following inclusion criteria: #immunizecanada, OHRI, immunization, Immunize Ontario, ImmunizeON, Ottawa Hospital Research Institute, Kumanan Wilson, ImmunizeCanada app, ImmunizeON, ImmunizeCA, yellow card app, vaccine app. A research assistant manually reviewed all content collected to confirm that the article mentioned or referred to the “ImmunizeCA app”. All content that did not mention or refer to the app was manually removed from results before analysis. Data regarding downloads were collected through iTunes Connect (Apple Inc, USA), Google Play (Google, USA) and BlackBerry World (BlackBerry, Canada) (4–6). Google Analytics (Google, USA) provided a variety of aggregate use metrics for iOS (Apple Inc, USA) and Android (Google, USA) platforms, as well as website traffic in both English and French languages (immunize. ca/app). Daily total download data were available from March 20 to September 20, 2014. An autoregressive integrated moving average time-series model was initially considered to analyze daily download frequency, which revealed no evidence of autocorrelation (correlation between serial observations) or temporal trends. Therefore, a simple ANOVA model was used, with total daily downloads as the response variable and week of the year as the classification variable; mean number of downloads for each week of the study were compared with the overall mean of the total daily downloads. Changes in frequency of downloads due to events of interest (ie, media mentions/App Store Feature) were evaluated by comparing the mean daily downloads in the week in which the event occurred with the overall mean. In the first six months following release, ImmunizeCA was downloaded a total of 54,610 times. Of these, 41,066 (75.2%) were for iOS, 12,560 (22.9%) for Android and 984 (1.8%) for BlackBerry (BB). The app was mentioned 781 times in the media (48 print articles, 68 online news articles, 152 Facebook posts and 494 tweets) and the website had 55,656 visits (Table 2). The most downloads occurred in March (launch) and July (government mailouts), with 20,835 (38.1%) and 11,945 (21.9%), respectively (Figure 1). March downloads included 85.0% for iOS, 14.5% for Android and 0.5% for BB. In July, downloads included 61.6% for iOS, 35.5% for Android and 2.9% for BB. The app received 359 (46.0%) media mentions in March and, 31 (3.8%) media mentions, two news articles, 23 tweets and six Facebook posts in July. Total downloads, records created and information accesses for ImmunizeCA March 20 to September 20, 2014. NIAW National Immunization Awareness Week During the first six months, ImmunizeCA had a total of 174,038 sessions, producing 909,257 screen views, an average of 5.22 screens per session. The average session duration was >3 min (03:02 min). Information was accessed 82,126 times, and the most views occurred in March (23,242 views) (Figure 1). Across 54,610 users, 45,157 individual records were created. The most were created in March (12,347 records), followed by July (12,215 records). Of the records created, the minimum age was zero years and the maximum was 90 years of age. Fifty-five percent (24,836) of records were created for children ≤5 years of age. Of those, 52% (13,003) were zero to one year of age. The “add to calendar” feature for vaccination encounters was activated 7691 times by 5548 unique users. The ANOVA model demonstrated that there were two instances of a statistically significant increase in total daily downloads: during the four weeks following launch of the app and while it was featured in the App Store; and during the two weeks following mail-out flyers. These periods were highly significant in the model (P<0.0001), confirming that app downloads sharply increased during these times, whereas no other factors (social media, press releases, etc) had any significant influence. An analysis of log(total_downloads), used to reduce the impact of extreme outliers, yielded similar findings. The log transformation pulled extremely high daily downloads closer to the remainder of the data, reducing the impact of these observations on the overall variance, which could obscure important findings. The two major drivers of app downloads were activities surrounding the app launch at the end of March, and the distribution of government flyers in July (the exact date of flyer delivery by post is not known). However, there was a higher proportion of iOS downloads in March compared with July (85% versus 61.6%, respectively), which may be secondary to placement in the iOS App Store’s “Best New Apps” section or capturing early adopters. Despite there being almost twice as many downloads in March compared with July, both months reported approximately the same number of app records created (12,347 versus 12,215). This may indicate that how the product is marketed can influence how it will be used, with direct-to-consumer marketing exhibiting a higher use/download ratio. While app use analytics are valuable, proper interpretation requires a knowledge and understanding of the analytic tag structure. We recommend working closely with your software development team when implementing in-app analytics for health applications. Social media strategies did not have a detectable independent effect on uptake. Further adoption of the app may be encouraged by having the app serve as both an official record, and a mechanism by which public health can communicate accurate immunization information to individuals. Based on our experience, successful uptake of public health apps focused on child health would benefit from app store endorsements and using direct-to-consumer marketing strategies. The authors thank their funders, the Canadian Association for Immunization Research and Evaluation (CAIRE) and the Public Health Agency of Canada (PHAC). CAIRE provided research funding to the authors to conduct this work. PHAC supported the development of ImmunizeCA through a grants and contribution agreement. They also thank the ImmunizeCA team; developers Cameron Bell, Julien Guerinet and Yulric Sequeira, their partners at CPHA, Greg Penney, Chandni Sondagar and at ImmunizeCanada, a coalition of CPHA, Lucie Marisa Bucci.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.312
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations5
Published2015
Admission routes2
Has abstractyes

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