Don't judge a book or health app by its cover: User ratings and downloads are not linked to quality

OBJECTIVE

To analyse the relationship between health app quality with user ratings and the number of downloads of corresponding health apps.

MATERIALS AND METHODS

Utilising a dataset of 881 Android-based health apps, assessed via the 300-point objective Organisation for the Review of Care and Health Applications (ORCHA) assessment tool, we explored whether subjective user-level indicators of quality (user ratings and downloads) correlate with objective quality scores in the domains of user experience, data privacy and professional/clinical assurance. For this purpose, we applied spearman correlation and multiple linear regression models.

RESULTS

For user experience, professional/clinical assurance and data privacy scores, all models had very low adjusted R squared values (< .02). Suggesting that there is no meaningful link between subjective user ratings or the number of health app downloads and objective quality measures. Spearman correlations suggested that prior downloads only had a very weak positive correlation with user experience scores (Spearman = .084, p = .012) and data privacy scores (Spearman = .088, p = .009). There was a very weak negative correlation between downloads and professional/clinical assurance score (Spearman = -.081, p = .016). Additionally, user ratings demonstrated a very weak correlation with no statistically significant correlations observed between user ratings and the scores (all p > 0.05). For ORCHA scores multiple linear regression had adjusted R-squared = -.002.

CONCLUSION

This study highlights that widely available proxies which users may perceive to signify the quality of health apps, namely user ratings and downloads, are inaccurate predictors for estimating quality. This indicates the need for wider use of quality assurance methodologies which can accurately determine the quality, safety, and compliance of health apps. Findings suggest more should be done to enable users to recognise high-quality health apps, including digital health literacy training and the provision of nationally endorsed "libraries".

The impact of the COVID-19 pandemic on grocery shopper behaviour: Analysis of shopper behaviour change using store transaction data

The systemic shock of coronavirus (COVID-19) and its impact on the global economy has been unprecedented with grocery shopper behaviour changing dramatically through various stages of the pandemic. COVID-19 has caused unusual market conditions, with significant changes to grocery shopper behaviour that need to be understood to allow for appreciation of shopper behaviour change and retail planning implications during future systemic shocks. The aim of this study was therefore to understand grocery-shopping behaviour during COVID-19. Specific objectives were to investigate changes to grocery sale patterns by basket size, composition and category, as well as during specific time periods of the pandemic. The use of transaction data using a range of market basket indicators (e.g., value, size, product mix), revealed profound changes that indicate the challenge shoppers faced navigating a new 'normal grocery shop' and the pressure on retailers to analyse consumption changes in order to prioritise demand planning. While the use of this data and analysis approach is an important contribution to consumer behaviour research, our focus was on the bigger patterns observed through the data pertaining to changes in shopper behaviour during systemic shocks. A key contribution of this paper is how the use of transaction data from grocery retail provides a nuanced understanding of how grocery shoppers responded leading up to and during the pandemic. For example, we found that grocery shoppers purchased more than just 'daily staples' to stock-up during the pandemic, with increased awareness of health and wellbeing an important aspect.

Towards Secure and Privacy-Preserving IoT Enabled Smart Home: Architecture and Experimental Study

Internet of Things (IoT) technology is increasingly pervasive in all aspects of our life and its usage is anticipated to significantly increase in future Smart Cities to support their myriad of revolutionary applications. This paper introduces a new architecture that can support several IoT-enabled smart home use cases, with a specified level of security and privacy preservation. The security threats that may target such an architecture are highlighted along with the cryptographic algorithms that can prevent them. An experimental study is performed to provide more insights about the suitability of several lightweight cryptographic algorithms for use in securing the constrained IoT devices used in the proposed architecture. The obtained results showed that many modern lightweight symmetric cryptography algorithms, as CLEFIA and TRIVIUM, are optimized for hardware implementations and can consume up to 10 times more energy than the legacy techniques when they are implemented in software. Moreover, the experiments results highlight that CLEFIA significantly outperforms TRIVIUM under all of the investigated test cases, and the latter performs 100 times worse than the legacy cryptographic algorithms tested.