| Title |
Development of SmokeFree Baby: a smoking cessation smartphone app for pregnant smokers
|
|---|---|
| Published in |
Translational Behavioral Medicine, October 2016
|
| DOI | 10.1007/s13142-016-0438-0 |
| Pubmed ID | |
| Authors |
Ildiko Tombor, Lion Shahab, Jamie Brown, David Crane, Susan Michie, Robert West |
| Abstract |
Pregnant smokers may benefit from digital smoking cessation interventions, but few have been designed for this population. The aim was to transparently report the development of a smartphone app designed to aid smoking cessation during pregnancy. The development of a smartphone app ('SmokeFree Baby') to help pregnant women stop smoking was guided by frameworks for developing complex interventions, including the Medical Research Council (MRC), Multiphase Optimization Strategy (MOST) and Behaviour Change Wheel (BCW). Two integrative behaviour change theories provided the theoretical base. Evidence from the scientific literature and behaviour change techniques (BCTs) from the BCT Taxonomy v1 informed the intervention content. The app was developed around five core modules, each with a distinct intervention target (identity change, stress management, health information, promoting use of face-to-face support and behavioural substitution) and available in a 'control' or 'full' version. SmokeFree Baby has been developed as part of a multiphase intervention optimization to identify the optimum combination of intervention components to include in smartphone apps to help pregnant smokers stop smoking. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 144 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 144 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 29 | 20% |
| Student > Bachelor | 21 | 15% |
| Student > Ph. D. Student | 20 | 14% |
| Student > Doctoral Student | 10 | 7% |
| Researcher | 9 | 6% |
| Other | 21 | 15% |
| Unknown | 34 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 35 | 24% |
| Medicine and Dentistry | 21 | 15% |
| Nursing and Health Professions | 20 | 14% |
| Computer Science | 9 | 6% |
| Social Sciences | 6 | 4% |
| Other | 13 | 9% |
| Unknown | 40 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 October 2016.
All research outputs
#13,990,855
of 22,890,496 outputs
Outputs from Translational Behavioral Medicine
#647
of 991 outputs
Outputs of similar age
#177,741
of 321,456 outputs
Outputs of similar age from Translational Behavioral Medicine
#11
of 18 outputs
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We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.