The Quality and Accuracy of Mobile Apps to Prevent Driving After Drinking Alcohol

Background Driving after the consumption of alcohol represents a significant problem globally. Individual prevention countermeasures such as personalized mobile apps aimed at preventing such behavior are widespread, but there is little research on their accuracy and evidence base. There has been no known assessment investigating the quality of such apps. Objective This study aimed to determine the quality and accuracy of apps for drink driving prevention by conducting a review and evaluation of relevant mobile apps. Methods A systematic app search was conducted following PRISMA guidelines. App quality was assessed using the Mobile App Rating Scale (MARS). Apps providing blood alcohol calculators (hereafter “calculators”) were reviewed against current alcohol advice for accuracy. Results A total of 58 apps (30 iOS and 28 Android) met inclusion criteria and were included in the final analysis. Drink driving prevention apps had significantly lower engagement and overall quality scores than alcohol management apps. Most calculators provided conservative blood alcohol content (BAC) time until sober calculations. None of the apps had been evaluated to determine their efficacy in changing either drinking or driving behaviors. Conclusions This novel study demonstrates that most drink driving prevention apps are not engaging and lack accuracy. They could be improved by increasing engagement features, such as gamification. Further research should examine the context and motivations for using apps to prevent driving after drinking in at-risk populations. Development of drink driving prevention apps should incorporate evidence-based information and guidance, lacking in current apps.

[1]  Ruth M. Masterson Creber,et al.  Review and Analysis of Existing Mobile Phone Apps to Support Heart Failure Symptom Monitoring and Self-Care Management Using the Mobile Application Rating Scale (MARS). , 2016 .

[2]  H. Moskowitz,et al.  A review of the literature on the effects of low doses of alcohol on driving-related skills , 2000 .

[3]  Robert West,et al.  Identification of Behavior Change Techniques and Engagement Strategies to Design a Smartphone App to Reduce Alcohol Consumption Using a Formal Consensus Method , 2015, JMIR mHealth and uHealth.

[4]  K Papafotiou Owens,et al.  Evaluating the deterrent effect of random breath testing (RBT) and random drug testing (RDT): the driver's perspective: research findings , 2011 .

[5]  Angela Watson,et al.  Profile of women detected drink driving via Roadside Breath Testing (RBT) in Queensland, Australia, between 2000 and 2011. , 2014, Accident; analysis and prevention.

[6]  J. Pekar,et al.  Alcohol Intoxication Effects on Simulated Driving: Exploring Alcohol-Dose Effects on Brain Activation Using Functional MRI , 2004, Neuropsychopharmacology.

[7]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[8]  I G Brouwer The Widmark formula for alcohol quantification. , 2004, SADJ : journal of the South African Dental Association = tydskrif van die Suid-Afrikaanse Tandheelkundige Vereniging.

[9]  Richard D. Blomberg,et al.  CRASH RISK OF ALCOHOL IMPAIRED DRIVING , 2002 .

[10]  Lorien C Abroms,et al.  A content analysis of popular smartphone apps for smoking cessation. , 2013, American journal of preventive medicine.

[11]  R G Gullberg,et al.  Guidelines for estimating the amount of alcohol consumed from a single measurement of blood alcohol concentration: re-evaluation of Widmark's equation. , 1994, Forensic science international.

[12]  Rod G Gullberg,et al.  Estimating the uncertainty associated with Widmark's equation as commonly applied in forensic toxicology. , 2007, Forensic science international.

[13]  B K Logan,et al.  An evaluation of the reliability of Widmark calculations based on breath alcohol measurements. , 1995, Journal of forensic sciences.

[14]  Nick Wilson,et al.  Smartphone apps for weight loss and smoking cessation: Quality ranking of 120 apps. , 2015, The New Zealand medical journal.

[15]  S. Michie,et al.  Behavior Change Techniques in Popular Alcohol Reduction Apps: Content Analysis , 2015, Journal of medical Internet research.

[16]  Adam Graycar Australian Institute of Criminology in an era of change , 1996 .

[17]  Oksana Zelenko,et al.  Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps , 2015, JMIR mHealth and uHealth.

[18]  Francisco Alonso,et al.  Driving under the influence of alcohol: frequency, reasons, perceived risk and punishment , 2015, Substance Abuse Treatment, Prevention, and Policy.

[19]  Kevin A Hallgren,et al.  Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial. , 2012, Tutorials in quantitative methods for psychology.

[20]  GirouxDanielle,et al.  Examining perceptions of a smartphone-based intervention system for alcohol use disorders. , 2014 .

[21]  Gary G Bennett,et al.  How behavioral science can advance digital health , 2013, Translational behavioral medicine.

[22]  Mark Petticrew,et al.  Population-level interventions to reduce alcohol-related harm: an overview of systematic reviews. , 2013, Preventive medicine.

[23]  Rebecca Jenkinson,et al.  “Let’s get Wasted!” and Other Apps: Characteristics, Acceptability, and Use of Alcohol-Related Smartphone Applications , 2013, JMIR mHealth and uHealth.

[24]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[25]  Jürgen Rehm,et al.  The relationship between alcohol consumption and fatal motor vehicle injury: high risk at low alcohol levels. , 2012, Alcoholism, clinical and experimental research.