Lessons learned from unsuccessful use of personal carbon monoxide monitors to remotely assess abstinence in a pragmatic trial of a smartphone stop smoking app – A secondary analysis ☆
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Abstract
Introduction
Verifying abstinence remotely in trials of digital cessation interventions remains
a major challenge. This study reports on using personal carbon monoxide (CO) monitors
to assess abstinence in a pragmatic trial of a standalone cessation app involving
automated recruitment with no researcher contact.
Methods
The study involved secondary data analysis of remote CO testing in a randomized trial
(ISRCTN10548241) comparing two versions of a cessation app (BupaQuit). Trial participants
were adult UK-based smokers interested in quitting, who were recruited online (02/2015–03/2016).
Participants were followed-up through the app, email or phone at 4 weeks. Fifty-nine
participants reporting not smoking were posted a personal CO monitor with instructions,
and emailed two reminders. The monitors required installing software on a Windows
PC. Participants were not reimbursed but retained the device. We recorded the proportion
of CO tests returned, test results, self-reported ease of use, correct use, acceptability,
and reasons for missing results.
Results
Fifteen (25.4%) CO results were returned, of which 86.6% were <10 ppm and 53.3% were
<5 ppm, indicating abstinence (corresponding to 20.9% and 12.9% of all trial participants
self-reporting abstinence, respectively). These 15 participants found the test easy,
acceptable and believed they conducted it correctly. Eight (18.2%) of the missing
results were accounted for, including no access to a Windows PC, barriers to receiving
packages, and unwillingness to share results.
Conclusion
Remote validation using personal CO monitors may not yet be feasible in pragmatic
studies of cessation apps in which participants are recruited with no reimbursement
or direct contact with researchers.
Highlights
•
Conducting remote verification of smoking abstinence remains a challenge in trials.
•
Personal carbon monoxide (CO) monitors could be posted to trial participants.
•
Only 25% of participants reporting abstinence returned their CO test results.
•
Remote testing with personal PC-connected CO monitors emerged as infeasible.
•
Lack of reimbursement and contact with researchers could contribute to the findings.
Tobacco use is estimated to kill 7 million people a year. Nicotine is highly addictive, but surveys indicate that almost 70% of US and UK smokers would like to stop smoking. Although many smokers attempt to give up on their own, advice from a health professional increases the chances of quitting. As of 2016 there were 3.5 billion Internet users worldwide, making the Internet a potential platform to help people quit smoking. To determine the effectiveness of Internet‐based interventions for smoking cessation, whether intervention effectiveness is altered by tailoring or interactive features, and if there is a difference in effectiveness between adolescents, young adults, and adults. We searched the Cochrane Tobacco Addiction Group Specialised Register, which included searches of MEDLINE, Embase and PsycINFO (through OVID). There were no restrictions placed on language, publication status or publication date. The most recent search was conducted in August 2016. We included randomised controlled trials (RCTs). Participants were people who smoked, with no exclusions based on age, gender, ethnicity, language or health status. Any type of Internet intervention was eligible. The comparison condition could be a no‐intervention control, a different Internet intervention, or a non‐Internet intervention. To be included, studies must have measured smoking cessation at four weeks or longer. Two review authors independently assessed and extracted data. We extracted and, where appropriate, pooled smoking cessation outcomes of six‐month follow‐up or more, reporting short‐term outcomes narratively where longer‐term outcomes were not available. We reported study effects as a risk ratio (RR) with a 95% confidence interval (CI). We grouped studies according to whether they (1) compared an Internet intervention with a non‐active control arm (e.g. printed self‐help guides), (2) compared an Internet intervention with an active control arm (e.g. face‐to‐face counselling), (3) evaluated the addition of behavioural support to an Internet programme, or (4) compared one Internet intervention with another. Where appropriate we grouped studies by age. We identified 67 RCTs, including data from over 110,000 participants. We pooled data from 35,969 participants. There were only four RCTs conducted in adolescence or young adults that were eligible for meta‐analysis. Results for trials in adults: Eight trials compared a tailored and interactive Internet intervention to a non‐active control. Pooled results demonstrated an effect in favour of the intervention (RR 1.15, 95% CI 1.01 to 1.30, n = 6786). However, statistical heterogeneity was high (I 2 = 58%) and was unexplained, and the overall quality of evidence was low according to GRADE. Five trials compared an Internet intervention to an active control. The pooled effect estimate favoured the control group, but crossed the null (RR 0.92, 95% CI 0.78 to 1.09, n = 3806, I 2 = 0%); GRADE quality rating was moderate. Five studies evaluated an Internet programme plus behavioural support compared to a non‐active control (n = 2334). Pooled, these studies indicated a positive effect of the intervention (RR 1.69, 95% CI 1.30 to 2.18). Although statistical heterogeneity was substantial (I 2 = 60%) and was unexplained, the GRADE rating was moderate. Four studies evaluated the Internet plus behavioural support compared to active control. None of the studies detected a difference between trial arms (RR 1.00, 95% CI 0.84 to 1.18, n = 2769, I 2 = 0%); GRADE rating was moderate. Seven studies compared an interactive or tailored Internet intervention, or both, to an Internet intervention that was not tailored/interactive. Pooled results favoured the interactive or tailored programme, but the estimate crossed the null (RR 1.10, 95% CI 0.99 to 1.22, n = 14,623, I 2 = 0%); GRADE rating was moderate. Three studies compared tailored with non‐tailored Internet‐based messages, compared to non‐tailored messages. The tailored messages produced higher cessation rates compared to control, but the estimate was not precise (RR 1.17, 95% CI 0.97 to 1.41, n = 4040), and there was evidence of unexplained substantial statistical heterogeneity (I 2 = 57%); GRADE rating was low. Results should be interpreted with caution as we judged some of the included studies to be at high risk of bias. The evidence from trials in adults suggests that interactive and tailored Internet‐based interventions with or without additional behavioural support are moderately more effective than non‐active controls at six months or longer, but there was no evidence that these interventions were better than other active smoking treatments. However some of the studies were at high risk of bias, and there was evidence of substantial statistical heterogeneity. Treatment effectiveness in younger people is unknown. Can Internet‐based interventions help people to stop smoking? Background Tobacco use is estimated to kill 7 million people a year. Nicotine is highly addictive, but surveys indicate that almost 70% of US and UK smokers would like to stop smoking. Although many smokers attempt to give up on their own, advice from a health professional increases the chances of quitting. As of 2016 there were 3.5 billion Internet users worldwide. The Internet is an attractive platform to help people quit smoking because of low costs per user, and it has potential to reach smokers who might not access support because of limited health care availability or stigmatisation. Internet‐based interventions could also be used to target young people who smoke, or others who may not seek traditional methods of smoking treatment. Study Characteristics Up to August 2016, this review found 67 trials, including data from over 110,000 participants. Smoking cessation data after six months or more were available for 35,969 participants. We examined a range of Internet interventions, from a low intensity intervention, for example providing participants with a list of websites for smoking cessation, to intensive interventions consisting of Internet‐, email‐ and mobile phone‐delivered components. We classed interventions as tailored or interactive, or both. Tailored Internet interventions differed in the amount of tailoring, from multimedia components to personalised message sources. Some interventions also included Internet‐based counselling or support from nurses, peer coaches or tobacco treatment specialists. Recent trials incorporated online social networks, such as Facebook, Twitter, and other online forums. Key results In combined results, Internet programmes that were interactive and tailored to individual responses led to higher quit rates than usual care or written self‐help at six months or longer. Quality of evidence There were not many trials conducted in younger people. More trials are needed to determine the effect on Internet‐based methods to aid quitting in youth and young adults. Results should be interpreted with caution, as we rated some of the included studies at high risk of bias, and for most outcomes the quality of evidence was moderate or low.
National smoking prevalence estimates are the primary basis for assessing progress in tobacco control across the world. They are based on surveys of self-reported cigarette smoking. It has been assumed that this is sufficiently accurate for policy purposes, but this assumption has not been adequately tested. We report data from the 2003 Health Survey for England, the U.S. National Health and Nutrition Examination Survey for 2001-2002, and the 2004 national smoking behaviors survey in Poland as examples of countries at different stages in the "tobacco epidemic." Self-reported cigarette and total tobacco smoking prevalence were assessed by means of the standard questions used in each country. In subsamples, specimens were collected for analysis of cotinine (saliva, N = 1,613 in England; serum, N = 4,687 in the United States; and saliva, N = 388 in Poland) providing an objective means of determining active smoking. A cut point of 15 ng/mL was used to discriminate active smoking from passive smoke exposure. Self-reported cigarette smoking prevalence using the standard methods underestimated true tobacco smoking prevalence by an estimated 2.8% in England, 0.6% in the United States, and 4.4% in Poland. Cotinine concentrations in those misclassified as nonsmokers were indicative of high levels of smoke intake. Underestimation of smoking prevalence was minimal in the United States but significant in England and Poland. A review of methodologies for assessing tobacco smoking prevalence worldwide is urgently needed.
Heart rate variability (HRV) is a useful clinical tool for autonomic function assessment and cardiovascular diseases diagnosis. It is traditionally calculated from a dedicated medical electrocardiograph (ECG). In this paper, we demonstrate that HRV can also be extracted from photoplethysmograms (PPG) obtained by the camera of a smartphone. Sixteen HRV parameters, including time-domain, frequency-domain, and nonlinear parameters, were calculated from PPG captured by a smartphone for 30 healthy subjects and were compared with those derived from ECG. The statistical results showed that 14 parameters (AVNN, SDNN, CV, RMSSD, SDSD, TP, VLF, LF, HF, LF/HF, nLF, nHF, SD1, and SD2) from PPG were highly correlated (r > 0.7, P < 0.001) with those from ECG, and 7 parameters (AVNN, TP, VLF, LF, HF, nLF, and nHF) from PPG were in good agreement with those from ECG within the acceptable limits. In addition, five different algorithms to detect the characteristic points of PPG wave were also investigated: peak point (PP), valley point (VP), maximum first derivative (M1D), maximum second derivative (M2D), and tangent intersection (TI). The results showed that M2D and TI algorithms had the best performance. These results suggest that the smartphone might be used for HRV measurement.
[a
]Department of Behavioural Science and Health, University College London, London, UK
[b
]UCL Tobacco and Alcohol Research Group (UTARG), UK
Author notes
[*
]Corresponding author at: Department of Behavioural Science and Health, University
College London, 1-19 Torrington Place, London WC1E 6BT, UK.
a.herbec@
123456ucl.ac.uk
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