Table 3 Summary of the findings from the expert opinion elicitation
Component | Hyp. | Result | Comment/remark |
|---|---|---|---|
Study type, randomization, and control | H1.1 | Supported | The correlation between the study design and the DTx development stage is in line with personal experiences of development and assessment of DTx products |
H1.2 | Partly supported | It is reasonable that product features do not influence the study types, however, personal experiences suggest not to exclude that different disease clusters (e.g., chronic vs. acute) might affect the study design | |
H2 | Supported | The essential component of any trial before market approval should be the control, whose outcomes get more reliable in case of randomization | |
Patients and study duration | H3.1 | Supported | Both healthy volunteers and target patients during the initial stages should be considered; later, efforts should be devoted to target patients for clinical purposes |
H3.2 | Supported | The number of patients ultimately depends on the target disease characteristics (e.g., spread, incidence, presence of comorbidities), as not all the patients are suitable for the same treatment | |
H3.3 | Supported | The training effort is fundamental, as well as the presence of backup support provided by the familiar context or caregivers | |
H4 | Supported | The factors mainly affecting the study duration include the rarity of the disease and the timespan of outcome realization | |
Comparators and study arms | H5.1 | Supported | Comparators are, by definition, dependent on product features and are often related to the target disease, as the standard of care is disease-specific |
H5.2 | Supported | Using a digital placebo would be correct from a methodological standpoint, as it would allow to produce more reliable and generalizable results | |
H5.3 | Not supported | It would be optimal to isolate the effects of multiple mechanisms of action, but this is rarely done due to economic and time constraints. What matters, in the end, is the evaluation of the overall effect of the DTx on the patient | |
H5.4 | Partly supported | Even though not fundamental, an arm comparing the DTx with the standard of care could prove that digitalization improves both the efficacy and efficiency of the patient’s care pathway | |
H5.5 | Supported | The number of arms/cohorts is directly dependent on the patients and comparators chosen | |
Outcomes and scales | H6.1 | Supported | Clinical evidence can be gathered using patterns analysis, relying on machine learning technology to save economic and time resources |
H6.2 | Supported | There is the need to collect since the very early stages data supporting subsequent economic study phases, which might accelerate later HTA activities | |
H6.3 | Partly Supported | In addition, cultural change should be considered, especially by considering the patient’s willingness to pay and be responsible for their care. | |
H6.4 | Supported | Economic analyses depend on organizational ones and are carried out in later stages | |
H6.5 | Supported | Dropout and non-responders’ analyses should be included in any study protocol, and statistical plans should inform about how to address them adequately, as collecting this kind of information may help to manage product development and training initiatives better | |
H6.6 | Partly supported | Collecting extra data is a good practice; however, resource availability might limit this practice | |
Sources of evidence | H7 | Supported | Study design and need of evidence guide the selection of sources of evidence. There is a need to strategically plan data collection and statistical analysis of RWE and use patterns |