Evidence generation plan for digital technologies to support self-management of asthma: early-use assessment
2 Evidence gaps
This section describes the evidence gaps, why they need to be addressed and their relative importance for future committee decision making.
The committee will not be able to make a positive recommendation without the essential evidence gaps (see section 2.1) being addressed. The companies can strengthen the evidence base by addressing as many other evidence gaps (see section 2.2) as possible. This will help the committee to make a recommendation by ensuring it has a better understanding of the patient or healthcare system benefits of the technology.
2.1 Essential evidence for future committee decision making
Clinical outcomes
Data on clinical outcomes related to the condition are important to help understand whether changes in management or self-management lead to meaningful improvements in asthma control. Current evidence on exacerbations, medicine use and lung function is limited and mostly non-comparative, so it is difficult to assess the clinical effect of the technologies. It would be useful to collect information on the impact of the technologies on these outcomes and long-term quality of life, as this is an important component of the overall effectiveness of asthma care.
Clinical effectiveness will need to be evaluated in the following subgroups:
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adults (aged 17 and over), including families or carers
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young people (aged 12 to 16) and children (aged 5 to 11), supported by families or carers
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families or carers of children under 5 years
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people with severe asthma
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people with newly diagnosed asthma.
Uptake and attrition rates
Quantitative evidence on uptake and attrition is lacking across the technologies. This limits the understanding of how people engage with the technologies, specifically across people with different:
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levels of asthma control
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demographic characteristics
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types of inhaler
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socioeconomic factors
This data is essential for the economic modelling, which is sensitive to the technologies' use. Information on how much people engage and sustain engagement with the technologies is key for future assessments.
Impact on condition management
Qualitative evidence suggests that it is possible to improve symptom awareness and understanding of the condition, but the evidence is still limited. More research is essential to determine whether the technologies can help improve people's knowledge, self-management, appropriate use of their personalised asthma control regimens and correct use of different inhaler types.
Healthcare resource use
Evidence about the impact of the technologies on healthcare resource use is limited. Few studies reported outcomes such as GP consultations, specialist reviews, emergency department visits, hospital admissions or changes in service use related to asthma control. More information on healthcare use is needed to help understand the potential system benefits and inform the economic modelling.
Also, evidence on NHS staff time associated with the implementation of the technology and its use is currently lacking. This includes data about the time needed to train staff and patients on the technology, provide support and do clinical reviews. Generating data on this aspect of the technology can ensure that the potential workforce burden can be calculated and fully accounted for in the economic model.
2.2 Evidence that further supports committee decision making
Generalisability to NHS practice
A substantial level of evidence was generated outside the NHS or lacked details about the clinical pathway, medicines and baseline asthma severity. This limits the confidence in how these findings can be generalised to NHS practice and NICE-recommended management of asthma. Studies that can collect data on how the technologies fit into UK practice would improve clarity in their applicability.
Barriers and facilitators to using apps
Existing qualitative evidence highlights the potential benefits and practical challenges, including reduced engagement over time, limited digital confidence and mixed user experience with the technologies. A clearer understanding of barriers and facilitators across different demographic and clinical groups would support more realistic implementation planning and reduce the uncertainty in their adoption.
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