Has the SARS-CoV-2 outbreak had an impact on uptake of digital cessation support in UK smokers? An interrupted time series analysis (Preprint)

2020 ◽  
Author(s):  
Olga Perski ◽  
Aleksandra Herbec ◽  
Lion Shahab ◽  
Jamie Brown
Keyword(s):  
Time Series ◽  
Smoking Cessation ◽  
Secondary Analysis ◽  
Interrupted Time Series ◽  
Step Change ◽  
Outcome Variable ◽  
Primary Analysis ◽  
Increasing Trend ◽  
The Uk ◽  
Temporary Closure

BACKGROUND The SARS-CoV-2 outbreak may motivate smokers to attempt to stop in greater numbers. However, given the temporary closure of UK stop smoking services and vape shops, smokers attempting to quit may instead seek out digital support, such as websites and smartphone apps. OBJECTIVE We examined, using an interrupted time series approach, whether the SARS-CoV-2 outbreak has been associated with a step change or increasing trend in UK downloads of an otherwise popular smoking cessation app, Smoke Free. METHODS Data were from daily and non-daily adult smokers in the UK who had downloaded the Smoke Free app between 1 January 2020 and 31 March 2020 (primary analysis) and 1 January 2019 and 31 March 2020 (secondary analysis). The outcome variable was the number of downloads aggregated at the 12-hourly (primary analysis) or daily level (secondary analysis). The explanatory variable was the start of the SARS-CoV-2 outbreak, operationalised as 1 March 2020 (primary analysis) and 15 January 2020 (secondary analysis). Generalised Additive Mixed Models adjusted for relevant covariates were fitted. RESULTS Data were collected on 45,105 (primary analysis) and 119,881 (secondary analysis) users. In both analyses, there was no evidence for a step change or increasing trend in downloads attributable to the start of the SARS-CoV-2 outbreak. CONCLUSIONS In the UK, between 1 January 2020 and 31 March 2020, and between 1 January 2019 and 31 March 2020, there was no evidence that the SARS-CoV-2 outbreak has been associated with a surge in downloads of a popular smoking cessation app. CLINICALTRIAL osf.io/zan2s

scholarly journals Influence of the SARS-CoV-2 Outbreak on the Uptake of a Popular Smoking Cessation App in UK Smokers: Interrupted Time Series Analysis

10.2196/19494 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e19494 ◽  
Author(s):  
Olga Perski ◽  
Aleksandra Herbeć ◽  
Lion Shahab ◽  
Jamie Brown
Keyword(s):  
Time Series ◽  
Smoking Cessation ◽  
United Kingdom ◽  
Secondary Analysis ◽  
Interrupted Time Series ◽  
Step Change ◽  
Outcome Variable ◽  
Primary Analysis ◽  
The United Kingdom ◽  
Increasing Trend

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak may motivate smokers to attempt to stop in greater numbers. However, given the temporary closure of UK stop smoking services and vape shops, smokers attempting to quit may instead seek out mobile health support, such as smartphone apps. Objective We examined, using an interrupted time series approach, whether the SARS-CoV-2 outbreak has been associated with a step change or increasing trend in UK downloads of an otherwise popular smoking cessation app, Smoke Free. Methods Data were from daily and nondaily adult smokers in the United Kingdom who had downloaded the Smoke Free app between January 1, 2020, and March 31, 2020 (primary analysis), and January 1, 2019, and March 31, 2020 (secondary analysis). The outcome variable was the number of downloads aggregated at the 12-hourly (primary analysis) or daily level (secondary analysis). The explanatory variable was the start of the SARS-CoV-2 outbreak, operationalized as March 1, 2020 (primary analysis), and January 15, 2020 (secondary analysis). Generalized additive mixed models adjusted for relevant covariates were fitted. Results Data were collected on 45,105 (primary analysis) and 119,881 (secondary analysis) users. In both analyses, there was no evidence for a step change or increasing trend in downloads attributable to the start of the SARS-CoV-2 outbreak. Calculation of Bayes factors (BFs) indicated that the data for the primary analysis favored the null hypothesis compared with large associations (for level, BF=0.25; for slope, BF=0.26) but were insensitive to the detection of small associations (for level, BF=0.78; for slope, BF=1.35). Conclusions In the United Kingdom, between January 1, 2020, and March 31, 2020, and between January 1, 2019, and March 31, 2020, there was no evidence that the SARS-CoV-2 outbreak has been associated with a large step change or increasing trend in downloads of a popular smoking cessation app. Findings on the association of the SARS-CoV-2 outbreak with a small step change or increasing trend were inconclusive.

scholarly journals Influence of the SARS-CoV-2 Outbreak on the Uptake of a Popular Smoking Cessation App in UK Smokers: Interrupted Time Series Analysis (Preprint)

2020 ◽  
Author(s):  
Olga Perski ◽  
Aleksandra Herbeć ◽  
Lion Shahab ◽  
Jamie Brown
Keyword(s):  
Time Series ◽  
Smoking Cessation ◽  
United Kingdom ◽  
Secondary Analysis ◽  
Interrupted Time Series ◽  
Step Change ◽  
Outcome Variable ◽  
Primary Analysis ◽  
The United Kingdom ◽  
Increasing Trend

BACKGROUND The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak may motivate smokers to attempt to stop in greater numbers. However, given the temporary closure of UK stop smoking services and vape shops, smokers attempting to quit may instead seek out mobile health support, such as smartphone apps. OBJECTIVE We examined, using an interrupted time series approach, whether the SARS-CoV-2 outbreak has been associated with a step change or increasing trend in UK downloads of an otherwise popular smoking cessation app, Smoke Free. METHODS Data were from daily and nondaily adult smokers in the United Kingdom who had downloaded the Smoke Free app between January 1, 2020, and March 31, 2020 (primary analysis), and January 1, 2019, and March 31, 2020 (secondary analysis). The outcome variable was the number of downloads aggregated at the 12-hourly (primary analysis) or daily level (secondary analysis). The explanatory variable was the start of the SARS-CoV-2 outbreak, operationalized as March 1, 2020 (primary analysis), and January 15, 2020 (secondary analysis). Generalized additive mixed models adjusted for relevant covariates were fitted. RESULTS Data were collected on 45,105 (primary analysis) and 119,881 (secondary analysis) users. In both analyses, there was no evidence for a step change or increasing trend in downloads attributable to the start of the SARS-CoV-2 outbreak. Calculation of Bayes factors (BFs) indicated that the data for the primary analysis favored the null hypothesis compared with large associations (for level, BF=0.25; for slope, BF=0.26) but were insensitive to the detection of small associations (for level, BF=0.78; for slope, BF=1.35). CONCLUSIONS In the United Kingdom, between January 1, 2020, and March 31, 2020, and between January 1, 2019, and March 31, 2020, there was no evidence that the SARS-CoV-2 outbreak has been associated with a large step change or increasing trend in downloads of a popular smoking cessation app. Findings on the association of the SARS-CoV-2 outbreak with a small step change or increasing trend were inconclusive.

scholarly journals Evaluation of a complex intervention to improve primary care prescribing: a phase IV segmented regression interrupted time series analysis

2017 ◽  
Vol 67 (658) ◽  
pp. e352-e360 ◽  
Author(s):  
Sean MacBride-Stewart ◽  
Charis Marwick ◽  
Neil Houston ◽  
Iain Watt ◽  
Andrea Patton ◽  
...  
Keyword(s):  
Primary Care ◽  
Time Series ◽  
High Risk ◽  
Older People ◽  
Secondary Analysis ◽  
Interrupted Time Series ◽  
Segmented Regression ◽  
Intervention Period ◽  
Interrupted Time Series Analysis ◽  
Primary Analysis

BackgroundIt is uncertain whether improvements in primary care high-risk prescribing seen in research trials can be realised in the real-world setting.AimTo evaluate the impact of a 1-year system-wide phase IV prescribing safety improvement initiative, which included education, feedback, support to identify patients to review, and small financial incentives.Design and settingAn interrupted time series analysis of targeted high-risk prescribing in all 56 general practices in NHS Forth Valley, Scotland, was performed. In 2013–2014, this focused on high-risk non-steroidal anti-inflammatory drugs (NSAIDs) in older people and NSAIDs with oral anticoagulants; in 2014–2015, it focused on antipsychotics in older people.MethodThe primary analysis used segmented regression analysis to estimate impact at the end of the intervention, and 12 months later. The secondary analysis used difference-in-difference methods to compare Forth Valley changes with those in NHS Greater Glasgow and Clyde (GGC).ResultsIn the primary analysis, downward trends for all three NSAID measures that were existent before the intervention statistically significantly steepened following implementation of the intervention. At the end of the intervention period, 1221 fewer patients than expected were prescribed a high-risk NSAID. In contrast, antipsychotic prescribing in older people increased slowly over time, with no intervention-associated change. In the secondary analysis, reductions at the end of the intervention period in all three NSAID measures were statistically significantly greater in NHS Forth Valley than in NHS GGC, but only significantly greater for two of these measures 12 months after the intervention finished.ConclusionThere were substantial and sustained reductions in the high-risk prescribing of NSAIDs, although with some waning of effect 12 months after the intervention ceased. The same intervention had no effect on antipsychotic prescribing in older people.

scholarly journals Seasonal variation and change trends for quit smoking: evidence from Internet search engine query data

2019 ◽  
Author(s):  
Fang Wang ◽  
Dingtao Hu ◽  
Xiaoqi Lou ◽  
Nana Meng ◽  
Qiaomei Xie ◽  
...  
Keyword(s):  
Time Series ◽  
Smoking Cessation ◽  
Seasonal Variation ◽  
New Zealand ◽  
Google Trends ◽  
Internet Search ◽  
Quit Smoking ◽  
Significant Seasonal Variation ◽  
The Usa ◽  
The Uk

Abstract Background: The outcomes of smoking have generated considerable clinical interest in recent years. Although people from different countries are more interested to the topic of quit smoking during the winter, few studies have tested this hypothesis. The current study aimed to quantify public interest in quit smoking via Google.Methods: We use Google Trends to obtain the Internet search query volume for terms relating to quit smoking for major northern and southern hemisphere countries in this research. Normally search volumes for the term “quit smoking + stop smoking + smoking cessation” were retrieved within the USA, the UK, Canada, Ireland, New Zealand and Australia from January 2004 to December 2018. Seasonal effects were investigated using cosinor analysis and seasonal decomposition of time series models.Results: Significant seasonal variation patterns in those search terms were revealed by cosinor analysis and demonstrated by the evidence from Google Trends analysis in the representative countries including the USA (pcos = 2.36×10-7), the UK (pcos < 2.00×10-16), Canada (pcos < 2.00×10-16), Ireland (pcos <2.00×10-16) ,Australia (pcos = 5.13×10-6) and New Zealand (pcos = 4.87×10-7). Time series plots emphasized the consistency of seasonal trends with peaks in winter / late autumn by repeating in nearly all years. The overall trend of search volumes, observed by dynamic series analysis, declined from 2004 to 2018.Conclusions: The preliminary evidence from Google Trends search tool showed a significant seasonal variation and decreasing trend for the RSV of quit smoking. Our novel findings in smoking cessation epidemiology need to be verified with further studies, and the mechanisms underlying these findings must be clarified.

scholarly journals Impact of clinical trial findings on Bell's palsy management in general practice in the UK 2001–2012: interrupted time series regression analysis

BMJ Open ◽  
2013 ◽  
Vol 3 (7) ◽  
pp. e003121 ◽  
Author(s):  
Daniel R Morales ◽  
Peter T Donnan ◽  
Fergus Daly ◽  
Tjeerd Van Staa ◽  
Frank M Sullivan
Keyword(s):  
Clinical Trial ◽  
Time Series ◽  
General Practice ◽  
Regression Analysis ◽  
Interrupted Time Series ◽  
Bell’S Palsy ◽  
Bell's Palsy ◽  
Time Series Regression ◽  
The Uk

scholarly journals Seasonal variation and change trends for quit smoking: evidence from Internet search engine query data

2020 ◽  
Author(s):  
Dingtao Hu ◽  
Xiaoqi Lou ◽  
Nana Meng ◽  
Qiaomei Xie ◽  
Man Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Smoking Cessation ◽  
Seasonal Variation ◽  
New Zealand ◽  
Google Trends ◽  
Quit Smoking ◽  
Significant Seasonal Variation ◽  
Quitting Smoking ◽  
The Usa ◽  
The Uk

Abstract Background: The outcomes of smoking have generated considerable clinical interest in recent years. Although people from different countries are more interested in the topic of quit smoking during the winter, few studies have tested this hypothesis. The current study aimed to quantify public interest in quitting smoking via Google.Methods: We use Google Trends to obtain the Internet search query volume for terms relating to quit smoking in major northern and southern hemisphere countries in this research. Normally search volumes for the term “quit smoking + stop smoking + smoking-cessation” were retrieved within the USA, the UK, Canada, Ireland, New Zealand and Australia from January 2004 to December 2018. Seasonal effects were investigated using cosinor analysis and seasonal decomposition of time series models.Results: Significant seasonal variation patterns in those search terms were revealed by cosinor analysis and demonstrated by the evidence from Google Trends analysis in the representative countries including the USA (pcos = 2.36×10-7), the UK (pcos < 2.00×10-16), Canada (pcos < 2.00×10-16), Ireland (pcos <2.00×10-16) ,Australia (pcos = 5.13×10-6) and New Zealand (pcos = 4.87×10-7). Time series plots emphasized the consistency of seasonal trends with peaks in winter / late autumn by repeating in nearly all years. The overall trend of search volumes for quitting smoking, observed by dynamic series analysis, has declined from 2004 to 2018.Conclusions: The preliminary evidence from Google Trends search tool showed a significant seasonal variation and a decreasing trend for the RSV of quit smoking. Our novel findings in smoking-cessation epidemiology need to be verified with further studies, and the mechanisms underlying these findings must be clarified.

scholarly journals Do changing medical admissions practices in the UK impact on who is admitted? An interrupted time series analysis

BMJ Open ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. e023274 ◽  
Author(s):  
Shona Fielding ◽  
Paul Alexander Tiffin ◽  
Rachel Greatrix ◽  
Amanda J Lee ◽  
Fiona Patterson ◽  
...  
Keyword(s):  
Time Series ◽  
Medical Students ◽  
Medical School ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Target Groups ◽  
Lower Socioeconomic ◽  
The Uk ◽  
The Impact

IntroductionMedical admissions must balance two potentially competing missions: to select those who will be successful medical students and clinicians and to increase the diversity of the medical school population and workforce. Many countries address this dilemma by reducing the heavy reliance on prior educational attainment, complementing this with other selection tools. However, evidence to what extent this shift in practice has actually widened access is conflicting.AimTo examine if changes in medical school selection processes significantly impact on the composition of the student population.Design and settingObservational study of medical students from 18 UK 5-year medical programmes who took the UK Clinical Aptitude Test from 2007 to 2014; detailed analysis on four schools.Primary outcomeProportion of admissions to medical school for four target groups (lower socioeconomic classes, non-selective schooling, non-white and male).Data analysisInterrupted time-series framework with segmented regression was used to identify the impact of changes in selection practices in relation to invitation to interview to medical school. Four case study medical schools were used looking at admissions within for the four target groups.ResultsThere were no obvious changes in the overall proportion of admissions from each target group over the 8-year period, averaging at 3.3% lower socioeconomic group, 51.5% non-selective school, 30.5% non-white and 43.8% male. Each case study school changed their selection practice in decision making for invite to interview during 2007–2014. Yet, this within-school variation made little difference locally, and changes in admission practices did not lead to any discernible change in the demography of those accepted into medical school.ConclusionAlthough our case schools changed their selection procedures, these changes did not lead to any observable differences in their student populations. Increasing the diversity of medical students, and hence the medical profession, may require different, perhaps more radical, approaches to selection.

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