Evaluating the Energy Efficiency of Popular US Smartphone Health Care Apps: Comparative Analysis Study Toward Sustainable Health and Nutrition Apps Practices.

BACKGROUND: The emergence of smartphones has sparked a transformation across multiple fields, with health care being one of the most notable due to the advent of mobile health (mHealth) apps. As mHealth apps have gained popularity, there is a need to understand their energy consumption patterns as an integral part of the evolving landscape of health care technologies. OBJECTIVE: This study aims to identify the key contributors to elevated energy consumption in mHealth apps and suggest methods for their optimization, addressing a significant void in our comprehension of the energy dynamics at play within mHealth apps. METHODS: Through quantitative comparative analysis of 10 prominent mHealth apps available on Android platforms within the United States, this study examined factors contributing to high energy consumption. The analysis included descriptive statistics, comparative analysis using ANOVA, and regression analysis to examine how certain factors impact energy use and consumption. RESULTS: Observed energy use variances in mHealth apps stemmed from user interactions, features, and underlying technology. Descriptive analysis revealed variability in app energy consumption (150-310 milliwatt-hours), highlighting the influence of user interaction and app complexity. ANOVA verified these findings, indicating the critical role of engagement and functionality. Regression modeling (energy consumption = ß0 + ß1 × notification frequency + ß2 × GPS use + ß3 × app complexity + ε), with statistically significant P values (notification frequency with a P value of .01, GPS use with a P value of .05, and app complexity with a P value of .03), further quantified these bases' effects on energy use. CONCLUSIONS: The observed differences in the energy consumption of dietary apps reaffirm the need for a multidisciplinary approach to bring together app developers, end users, and health care experts to foster improved energy conservation practice while achieving a balance between sustainable practice and user experience. More research is needed to better understand how to scale-up consumer engagement to achieve sustainable development goal 12 on responsible consumption and production.

A study of residential radon in Sweden using multi-level analysis.

A Swedish radon data set, consisting of more than 8,000 measurements of residential radon levels in about 50% of the Swedish municipalities, was analyzed using a multi-level approach. The results were compared with those of a single-level analysis. We found that there was a significant variability between municipalities. The point estimates of the population mean radon levels were similar (geometric mean 60 Bq m-3 and arithmetic mean 106 Bq m-3). The analysis shows the advantages of multi-level modeling compared with a single-level ordinary least squares (OLS) model. A single-level model results in too optimistic standard errors, about 25% of those of the multi-level model, which can lead to erroneous conclusions. In a multi-level model including house type as a fixed effect (single-family house, row house, or apartment in multi-family house), the estimates of the fixed effect of house type were similar for the single-level and the multi-level models.

Was Rodney Ledward a statistical outlier? Retrospective analysis using routine hospital data to identify gynaecologists' performance.

OBJECTIVES: To investigate whether routinely collected data from hospital episode statistics could be used to identify the gynaecologist Rodney Ledward, who was suspended in 1966 and was the subject of the Ritchie inquiry into quality and practice within the NHS. DESIGN: A mixed scanning approach was used to identify seven variables from hospital episode statistics that were likely to be associated with potentially poor performance. A blinded multivariate analysis was undertaken to determine the distance (known as the Mahalanobis distance) in the seven indicator multidimensional space that each consultant was from the average consultant in each year. The change in Mahalanobis distance over time was also investigated by using a mixed effects model. SETTING: NHS hospital trusts in two English regions, in the five years from 1991-2 to 1995-6. Population Gynaecology consultants (n = 143) and their hospital episode statistics data. MAIN OUTCOME MEASURE: Whether Ledward was a statistical outlier at the 95% level. RESULTS: The proportion of consultants who were outliers in any one year (at the 95% significance level) ranged from 9% to 20%. Ledward appeared as an outlier in three of the five years. Our mixed effects (multi-year) model identified nine high outlier consultants, including Ledward. CONCLUSION: It was possible to identify Ledward as an outlier by using hospital episode statistics data. Although our method found other outlier consultants, we strongly caution that these outliers should not be overinterpreted as indicative of "poor" performance. Instead, a scientific search for a credible explanation should be undertaken, but this was outside the remit of our study. The set of indicators used means that cancer specialists, for example, are likely to have high values for several indicators, and the approach needs to be refined to deal with case mix variation. Even after allowing for that, the interpretation of outlier status is still as yet unclear. Further prospective evaluation of our method is warranted, but our overall approach may be potentially useful in other settings, especially where performance entails several indicator variables.