Quick look: A human factors sampling strategy for platform devices
Finola Austin - human factors engineering manager, Owen Mumford Pharmaceutical Services outlines a sampling strategy which can be used as a framework for manufacturers to carry out user evaluation without identification of a specified user group.
Drug delivery devices for self-administration must go through compulsory human factors (HF) studies before reaching the market, to help identify and eliminate any risks the device may present to the end user. These tests are carried out on a sample population reflecting the device’s intended users to best predict problem areas. With platform devices, however, the intended patient profile is unknown, given the device’s versatility across a range of therapy areas. In this case, the device must be suitable for use across a wide demographic, covering varying physical and cognitive abilities as well as a range of ages. An inclusive strategy is therefore required to achieve thorough testing. The purpose of this article is to outline a useful sampling strategy which can be used as a framework for manufacturers to carry out user evaluation without identification of a specified user group.
Selecting an appropriate sample size is the first step towards building a representative test group. In the early stages of development, a sample size of five to eight participants per distinct user group is considered good practice, while at the validation stage, it is recommended by US and UK regulators to have 15 to 20 participants per user group. However, the number of user groups can vary and is likely to be higher for platform devices given the wide range of potential users. At Owen Mumford, we suggest dividing subjects into seven user groups to best cover the range of characteristics which could affect how end users operate a device (see table below).
The first four user groups represent people who may handle the device as a patient or to assist the patient. The remaining three user groups cover different perception, cognition and action (PCA) levels. Ideally, each use-impairment group must be mutually exclusive to get the best results. However, some overlap may prove helpful, for instance including people with both biomechanical as well as neurological impairments within the ‘Action ability’ group (see row 7). To get the best representation, each group can be further divided into sub-sections such as gender, ethnicity and hand dominance. Breaking down each group in this way may help identify the root cause when difficulties arise, allowing potential corrective action to be taken with the device design or instructions for use.
The sampling strategy outlined above can act as a valuable framework for manufacturers carrying out Human Factors (HF) studies for platform devices. An effective sampling strategy is critical to assessing risk across the range of possible users, and anticipating the needs of future customers in the most cost-effective manner. Comprehensive testing not only enables product designers to make informed decisions about a device but also assures business partners that any usability-related risk factors have been identified and dealt with during development.