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Participants in an EMD intervention pilot study described the importance of past experiences for current health beliefs and a sense of responsibility for achieving and maintaining symptom control. Participants described an increased awareness of their condition, which was in part due to the EMD data. In general, they were open to integrating EMD data with environmental, physiological, and activity data and saw ways in which such technologies could improve asthma care.
Similar to the findings in this study, previous researchers have noted heterogeneous responses to EMD use. Some adolescents described feeling an increased sense of control and responsibility for their condition34. Others saw the observation as a sign that their clinicians did not trust them35. Similar to previous studies in which adults and adolescents described behavioral and/or attitudinal change from EMD use (including “habit forming”), with some adults associating this with better asthma control34,36. Perceptions of how permanent these changes would be were mixed35,36. These changes appear to be somewhat related to baseline attitudes and beliefs and existing adherence to or dislike of routines36.
Other studies also describe data facilitating conversations with healthcare teams34,36desire to have data access restricted34 and concern that data will replace seeing them as a person35. Although reminders were not formally used in this study, some participants reported finding and using this feature. The literature suggests variable acceptance of reminder features with potential implications for their use34,35,36.
The relationship between the self-regulation perspective (including its specific application, the need-consideration framework) and adherence in asthma has already been outlined. Participants discussed beliefs about asthma and its treatment, influenced by their different experiences, which appeared to motivate or demotivate inhaler use according to what was already known15,36,37. As also noted earlier, there were elements in addition to the necessity-consideration framework that appeared to play a role16.
Participants in this study described being motivated to avoid a recurrence of the type of worsening that led to their most recent exacerbations, highlighting the target population that may benefit from the intervention. In the protection-motivation model of behavior change,threat assessment susceptibility, severity, and fear are central to motivating the intention to engage in adaptive behavior to address the threat38.
Importantly, defense motivation theory notes the risk of maladaptive responses, including avoidance, denial, and hopelessness, when accurate threat assessment has been done but has low confidence in treatment efficacy, high concern about treatment costs, and low self-efficacy38. This may in part explain why some intervention participants demonstrate persistent poor adherence and why other narratives surrounding poor ICS efficacy and low self-efficacy seem to blunt intentions to adhere to treatment.
Previous work has similarly identified a subgroup of individuals with poor adherence that is resistant to intervention39,40,41. EMD-based interventions can help identify this group, facilitating sensitive exploration of underlying beliefs and tailoring of interventions. This may prove key for some in tipping the balance in favor of adherence-consistent beliefs and adaptive behavior38,42,43.
Participants also emphasized the importance of cues that were visual, auditory, and event-based, a finding also observed by Foster et al. in their study36. Participants felt that factors that disrupted routines (eg, shift work) had a negative impact on their adherence. Habit theory suggests that while beliefs inform the initial motivation to initiate a new behavior, habit formation involves changing behavior by making the new behavior automatic. This allows new behaviors to be maintained long after the motivation and awareness have ended. For such automaticity to occur, a new behavior must be learned in a favorable environment, a critical cue for action must be identified, and a plan put in place to perform the desired action when given44.
Overall, data from this study suggest that in a selected post-exacerbation population, EMD interventions involving clinician input have the potential to influence beliefs and increase motivation to adhere to ICS. They may also help identify individuals who need more complex engagement around treatment efficacy and concerns, as well as self-efficacy. Finally, they can provide an opportunity to target habit formation as a means of embedding behavior change.
Research on EMD in asthma has focused primarily on its potential to reduce adverse risk through improved adherence. However, participants in this study were curious about the potential of an integrated technology platform to inform lifestyle choices such as exercise and trigger avoidance. They wanted to integrate data with environmental data, physiological and activity markers, and validated symptoms to self-monitor and provide better information for collaborative decision-making with their clinical teams, a finding supported by previous work19.
Self-monitoring is central to chronic disease care where clinician involvement is limited by time. The US Institute of Medicine suggests that it includes “confidence to deal with medical management, role management and emotional management” of condition45. In asthma, sustained self-control has been shown to be effective in improving outcomes46,47. Evidence suggests that self-monitoring behaviors are most effectively influenced when clinicians spend time interacting with individuals37.
By providing clinicians with the same personalized information available to consumers, integrated with markers of modifiable factors and outcomes relevant to consumers, EMD-based interventions can provide a common language to increase consumer and clinician engagement in their self-management with their patients. Personalization has the potential to enable personalized self-management interventions, including personalized data-informed asthma action plans, automated advice and access to mental health support using validated apps.
Ethical use of data is key, with an expectation of data security, transparency about what the data is being used for, and some level of control over data access. Without this, there is a risk of a breakdown in trust. This is particularly key when considering the implications of platform technologies that enable the integration of commercial sensor data with potentially sensitive health data and where the development of automated interpretation is likely to involve algorithms that require training using existing data. Recent controversies highlight the importance of transparency48,49 under such circumstances.
Participants in this study were largely interested in EMD-based interventions being delivered in primary care, enhancing rather than replacing their routine examinations. Given that the key role of EMD is likely to be in supported self-management, this seems a natural choice. However, primary care services in many health systems are already under pressure, meaning that their implementation needs careful consideration. This study suggests that without training or allocated time for interpretation, clinicians generally do not find EMD data useful in informing management.
For surveillance interventions to be successful, people need help processing and effectively using the data they receive50. This is something that participants in this study actively anticipated, but will require training of clinicians and time to be able to interpret and use the data. Therefore, baseline data will need to be presented in ways that are interpretable for consumers with asthma, as well as standardized and clinically useful for their clinicians. If the data is to be from a platform source, automated integration and interpretation will likely be required. This will need to add value and reduce clinician burden/time, for example by enabling remote monitoring and passive collection of inputs that form a core part of the asthma review.
By spotlighting the expert perspectives of end users at high risk of adverse events, this study places those most likely to benefit from digital interventions at its center. Timing the interview at the last visit maximized participation by limiting inconvenience while also aiming to reduce recall bias. However, this may have reduced the opportunity for participants to process and contextualize their experiences. Other limitations include the predominantly female and Caucasian samples. Participants volunteered to participate in the pilot study, potentially self-selected as a group more likely to be engaged in their self-management. This is likely reflected in the relatively high adherence rates observed. The findings are also the perceptions and experiences of this unique group of individuals who offer their own valuable insights and perspectives.
In conclusion, data from participants in a pilot intervention study support a model integrating beliefs and habit formation to achieve behavior change. Participants expressed readiness for a more integrated, platform-based approach to digital self-governance, but were clear that they expect their data to be used ethically. This study establishes general optimism about the potential of inhaler technology to have both personal and broader impacts on self-control and shared decision-making.
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