Research paperRealising the technological promise of smartphones in addiction research and treatment: An ethical review
Introduction
Smartphones are a powerful and ubiquitous technology that combines mobile computing with telecommunication capabilities (Mosa, Yoo, & Sheets, 2012). In 2011, there were over 6 billion phone subscriptions reaching 87% of the world's population (ITU, 2011). A recent survey found that 43% of global respondents have a smartphone (Poushter, 2016). For countries such as Australia or the United States, this figure approaches three-quarters (Poushter, 2016). There is growing interest in the use of smartphones and other mobile technologies for conducting research on drug use and addiction and intervening to reduce drug use and its harmful effects (Kuntsche and Labhart, 2014, Meurk et al., 2014).
The ability of smartphones to run third party software applications (or apps) has generated interest in their use for research in substance abuse and addiction. Smartphones overcome many of the traditional limitations of addiction research that rely upon pen and paper surveys or diaries and retrospective recall. Although gathering retrospective self-report may be cost-efficient and convenient, it has been found to underestimate substance abuse (Kuntsche & Labhart, 2014). Self-reported drug use can be under-reported if participants are unwilling to reveal the true amount consumed. It may also be subject to recall bias when users only remember some of their total drug consumption (Kuntsche & Labhart, 2014). Surveys of drug use generally underrepresent heavy substance abusers in the population (Kuntsche & Labhart, 2014). Less intrusive smartphone technologies can encourage a wider section of the population to participate in surveys. Less time is taken to fill out lengthy questionnaires and diaries, and prompts can be sent throughout the day to collect a greater range of data at more regular intervals (Kuntsche & Labhart, 2014).
Smartphones are also being looked at for use in healthcare settings to improve diagnosis and personalise treatment (Mosa et al., 2012). Smartphones may enable clinicians and other health care professionals to deliver clinically important information in a uniquely timely way. For example, data collected by a smartphone could trigger clinically relevant messages to the user prior to any drug use (Luxton, McCann, Bush, Mishkind, & Reger, 2011). The use of smartphone technologies for this purpose has been termed mHealth (Tamony, Holt, & Barnard, 2015). mHealth falls within the broader field of electronic research or e-research (Kypri and Lee, 2009, Miller and Sønderlund, 2010). E-research is commonly used to study human participants from populations difficult to identify, recruit and retain in research and treatment. Advantages of mHealth and e-research in non-therapeutic research (e.g. epidemiological, social and behavioural, humanities research) (Barratt, 2012, Meurk et al., 2014, Miller et al., 2007, Shearer et al., 2007), include: increased participant comfort and perceived anonymity that encourages more honest disclosure; improved consent processes (Ford Ii et al., 2015, Monney et al., 2015, Patel et al., 2015); reduced research costs; and fewer data errors (Miller et al., 2007, Monney et al., 2015). These approaches have also proven beneficial with human participants in therapeutic research domains (i.e. prevention, treatment and other interventions). Advantages include greater capacity to recruit participants for clinical studies, more efficient intervention delivery, improved monitoring of adherence to treatment protocols (Vahabzadeh & Lin, 2009), and capacity to produce significant intervention effects (Amstadter et al., 2009, Neil et al., 2009).
For both research and treatment of addiction, smartphone monitoring of substance use or treatment is possible through passive data collection or via direct input from patients. Smartphone apps can prompt and record a patient's self-reported drug consumption and cravings, commonly referred to as Ecological Momentary Assessment (EMA) (Serre, Fatseas, Swendsen, & Auriacombe, 2015). Smartphone technologies may passively record patterns of movement within the environment, for example, via global positioning systems (GPS), wireless local area networks (or Wi-Fi), Bluetooth, accelerometers, gyroscopes, pressure-sensors, proximity-sensing magnetometers, barometers, humidity sensors, temperature sensors, and ambient light sensors (Luxton et al., 2011). Microphones and cameras are able to record images and sounds, including personal conversations, in the vicinity of the phone (Pei et al., 2013). From these data it is possible to deduce rich social information about an individual, including their identity, gender, age, marital status, social status, where they live, where their children go to school, health, sex life, religion, mood, and whether they visit a therapist, and if so how often, or how regularly they visit drinking or gambling establishments (Carter et al., 2015a, Carter et al., 2015b, Gasson et al., 2011, King, 2011, Pei et al., 2013, Shilton, 2009).
Physiological information such as heart rate, blood pressure and substance concentration levels may be measured using additional sensors. Remote monitoring devices, for example, are being developed to continuously monitor physiological responses or precursors to cravings or relapse in persons being treated for addiction (Boyer et al., 2010, Yu et al., 2012). Smartphones can also be adapted to directly monitor physiological responses to drug consumption, such as sensor bands that are able to detect electro-dermal activity, body motion and skin temperature (Boyer et al., 2012). This information may be linked to other electronic databases, either commercially available or through agreement with other government agencies (e.g. personal medical records). Algorithms may then be developed to identify behavioural patterns indicative of treatment progress, such as treatment response and triggers for cravings and behaviour that increases the risk of relapse (Ahsan et al., 2013). In order for the technology to provide effective treatments, robust research will need to be conducted. Given the sensitive information being collected and intrusive nature of the equipment, a number of ethical issues arise.
mHealth raises novel ethical issues for research because it differs from traditional means of human participant recruitment, consent, data collection, and analysis (Carter et al., 2015a, Carter et al., 2015b). mHealth methods alter the nature, dynamics and potential consequences of research participation and are evolving rapidly. The potential negative consequences of participation in mHealth research are particularly salient for those with stigmatised disorders or behaviour, such as those with a drug addiction or who use illicit drugs (Meurk et al., 2014).
There are also concerns surrounding the clinical applications of mHealth technology for addiction or substance abuse treatment. Confidentiality and informed consent procedures may need to be revised to consider storage locations and security. Given the wide market available and possibility for corporate interest, evidence of safe and effective treatments may need to be highlighted prior to distribution among potentially vulnerable users. The speed of growth of the smartphone app market appears to have outpaced the medical fraternity's ability to address these ethical challenges (Boyce, 2012).
The pace of development is “forcing researchers and research regulators to rethink and re-evaluate such fundamental research ethics issues as privacy, informed consent, ownership, recruitment, public versus private space, research and scientific integrity itself” (Buchanan & Hvizdak, 2009, p. 37). The World Health Organization has recognised the need for greater consideration of the ethical use of electronic or mobile research and health. Unfortunately, progress in developing ethical guidance has been slow. A recent NHMRC Australian Health Ethics Committee (AHEC) consultation paper on ethical issues in alcohol and drug research acknowledged: “The National Statement was published before the ethical issues raised by these developments became apparent so it currently provides no specific guidance for Internet-based or other forms of online research” (NHMRC, 2011, p. 27). This is particularly the case for mobile technologies. Although recent guidelines have been outlined on the use of digital data in research (Clark et al., 2015), ethical guidelines are still required to clarify best practice in the use of mHealth technology (Carter et al., 2015a, Carter et al., 2015b).
It is important that ethical regulation of the research and clinical use of smartphones keeps pace with the rapid developments in these technologies. Traditional ways of ensuring the confidentiality and privacy of research data collected on drug use and behaviour are not sufficient to deal with the sophisticated array of personal data that are collected via smartphone technologies. Research teams and clinicians must understand these ethical implications if they are to maximise the promise of this technology and minimise any unintended harms. These ethical concerns depend on how the technology is being used, and the sorts of safeguards that are put in place. The use of appropriate technical safeguards during the development of apps can mitigate many of these concerns (e.g. by the use of secure in-boxes, maximising user control over data recorded, transmission of data using secure methods, and providing access to devices for those that do not have them) (Carter et al., 2015a, Carter et al., 2015b). The current lack of ethical guidelines in this area can “result in researchers acting with less consideration, and even behaving unethically towards their study subjects” (Bober, 2004, p. 308).
In order to better understand the ethical issues raised by the use of smartphones in addiction research and treatment, this paper aims to review the ways in which smartphone technologies are currently being employed in the field. From this ethical review, we will conclude with a set of recommendations for the development and use of mHealth apps for researchers and clinicians in the field of substance abuse and addiction.
Section snippets
Methods
A search of three electronic databases (PubMed, PsycInfo and Web of Science) was performed by HC using the following terms: (“substance use” OR “substance abuse” OR “drug dependence” OR addict* OR alcohol* OR smok* OR tobacco OR cannabis OR marijuana OR heroin OR cocaine OR opioid OR opiate) AND (mHealth OR smartphone OR iPhone OR “mobile phone app”) NOT (“smartphone addiction”). Eighty-four articles were downloaded to an Endnote database for further analysis of eligibility. Titles and
Substance investigated
Approximately half of the apps focused on tobacco abuse and one-third involved alcohol use (see Table 1); two examined heroin addiction, and one cocaine abuse. Three studies examined addiction in general, either covering a range of substances or not specifying the substance of addiction.
Study design and aim
The majority (37.1%) of studies analysed were randomised controlled trials (RCT) of clinical smartphone apps. Approximately one-third (31.4%) were observational studies of intervention effects on participants’
Discussion
A range of research methods were observed in the 33 unique studies of smartphone technologies in addiction research and treatment and it was encouraging to find the most common being randomised controlled trials, the ‘gold standard’ research method. Yet, despite some in-depth, potentially identifiable information being collected about the user, many studies may have overlooked the reliability of their security measures. Such oversight has implications on the participant's privacy and informed
Conclusions
Smartphone and mHealth technology provide unique possibilities for collecting valuable information about research and for the treatment of substance abuse and addiction. Given the wide scope of personal information that can be collected, the promise of these technologies also raise a number of ethical issues. Our analysis suggests that there is a lack of awareness of the ethical issues raised by their use, the implications for how the apps are developed, and how both research and clinical
Conflicts of interest
None declared.
References (82)
- et al.
A content analysis of popular smartphone apps for smoking cessation
American Journal of Preventive Medicine
(2013) - et al.
Internet-based interventions for traumatic stress-related mental health problems: A review and suggestion for future research
Clinical Psychology Review
(2009) All watched over by machines of loving grace
The Lancet Technology
(2012)- et al.
Cell phones for ecological momentary assessment with cocaine-addicted homeless patients in treatment
Journal of Substance Abuse Treatment
(2006) - et al.
Development of a personalized bidirectional text messaging tool for HIV adherence assessment and intervention among substance abusers
Journal of Substance Abuse Treatment
(2014) - et al.
Feasibility and validity of computerized ambulatory monitoring in drug-dependent women
Drug and Alcohol Dependence
(2009) - et al.
Geospatial exposure to point-of-sale tobacco: Real-time craving and smoking-cessation outcomes
American Journal of Preventive Medicine
(2013) - et al.
Mobile phone ownership, usage and readiness to use by patients in drug treatment
Drug and Alcohol Dependence
(2015) - et al.
Ecological momentary assessment in the investigation of craving and substance use in daily life: A systematic review
Drug and Alcohol Dependence
(2015) - et al.
Process evaluation of a mHealth program: Lessons learned from Stop My Smoking USA, a text messaging-based smoking cessation program for young adults
Patient Education and Counseling
(2014)
Toward an mHealth intervention for smoking cessation
The efficacy of interviewing young drug users through online chat
Drug and Alcohol Review
Principles of biomedical ethics
Feasibility and user perception of a fully automated push-based multiple-session alcohol intervention for university students: Randomized controlled trial
JMIR Mhealth Uhealth
Assessing the effect of an interactive decision-aid smartphone smoking cessation application (app) on quit rates: A double-blind automated randomised control trial protocol
BMJ Open
Virtual youth research: An exploration of methodologies and ethical dilemmas from a British perspective
Population-level effects of automated smoking cessation help programs: A randomized controlled trial
Addiction
Preliminary efforts directed toward the detection of craving of illicit substances: The iHeal project
Journal of Medical Toxicology
Wireless technologies, ubiquitous computing and mobile health: Application to drug abuse treatment and compliance with HIV therapies
Journal of Medical Toxicology
Randomized, controlled pilot trial of a smartphone app for smoking cessation using acceptance and commitment therapy
Drug and Alcohol Dependence
Online survey tools: Ethical and methodological concerns of human research ethics committees
Journal of Empirical Research on Human Research Ethics
Randomized trial of a smartphone mobile application compared to text messaging to support smoking cessation
Telemedicine and E-Health
Review of iRecovery-iPhone/iPad application
Sexual Addiction & Compulsivity
Mobile phones in research and treatment: Ethical guidelines and future directions
JMIR Mhealth Uhealth
The growing use of smartphones in Parkinson's disease research and treatment: Ethical guidelines and future directions
Neurology
Real-time craving and mood assessments before and after smoking
Nicotine & Tobacco Research
Security concerns in popular cloud storage services
IEEE Pervasive Computing
Guidelines for the ethical use of digital data in human research
New Scientist
Results of a pilot test of a self-administered smartphone-based treatment system for alcohol use disorders: Usability and early outcomes
Substance Abuse
Development of a smartphone-based, self-administered intervention system for alcohol use disorders
Alcoholism Treatment Quarterly
Real-time electronic diary reports of cue exposure and mood in the hours before cocaine and heroin craving and use
Archives of General Psychiatry
Successful organizational strategies to sustain use of A-CHESS: A mobile intervention for individuals with alcohol use disorders
Journal of Medical Internet Research
Mobile phone brief intervention applications for risky alcohol use among university students: A randomized controlled study
Addiction Science & Clinical Practice
Executive functioning in alcoholics following an mHealth cognitive stimulation program: Randomized controlled trial
Journal of Medical Internet Research
Normality mining: Privacy implications of behavioral profiles drawn from GPS enabled mobile phones
IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Examining perceptions of a smartphone-based intervention system for alcohol use disorders
Telemedicine Journal and E-Health
Explicating an evidence-based, theoretically informed, mobile technology-based system to improve outcomes for people in recovery for alcohol dependence
Substance Use & Misuse
A smartphone application to support recovery from alcoholism: A randomized clinical trial
JAMA Psychiatry
HealthCall for the smartphone: Technology enhancement of brief intervention in HIV alcohol dependent patients
Addiction Science & Clinical Practice
Efficacy of a web- and text messaging-based intervention to reduce problem drinking in young people: Study protocol of a cluster-randomised controlled trial
BMC Public Health
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