A method of performing a structured collection protocol by utilizing a collection device comprising a processor, wherein the collection device is a self-monitoring blood glucose meter, and wherein the method comprises: initializing a first sampling instance using the collection device and collecting at least one sample for diabetes management using the collection device, wherein the at least one sample is associated with biomarker data; associating a classification with the at least one sample via the processor, wherein the classification is based upon an intended use of the at least one sample as a primary or a secondary sample; wherein primary samples are samples of a sampling instance intended to be utilized in calculations performed by the processor that yield adjusted insulin dosage amounts for a diabetic person, and secondary samples are non-critical sampling instances not intended to be utilized in calculations performed by the processor that yield adjusted insulin dosage amounts for the diabetic person unless promoted to primary samples; and determining compliance with an adherence criteria for the at least one sample via the processor, wherein adherence criteria are the procedural requirements that the diabetic person must follow when conducting the structured collection protocol, wherein when the at least one sample is not compliant with the adherence criteria: an adherence event is associated with the at least one sample; and the processor performs at least one additional task that is based upon the classification associated with the at least one sample, characterized in that if a sample is recorded with a corresponding recorded adherence event and the sample is classified as a primary sample, if there are not a sufficient number of primary samples without recorded adherence events in the sampling instance and there are secondary samples from the same sampling instance which comply with the adherence criteria, the at least one additional task comprises replacing the primary sample with a secondary sample, wherein the primary sample and the secondary sample are from the same sampling instance and the secondary sample complies with the adherence criteria, thereby promoting the secondary sample to a primary sample. initializing a first sampling instance using the collection device and collecting at least one sample for diabetes management using the collection device, wherein the at least one sample is associated with biomarker data; associating a classification with the at least one sample via the processor, wherein the classification is based upon an intended use of the at least one sample as a primary or a secondary sample; wherein primary samples are samples of a sampling instance intended to be utilized in calculations performed by the processor that yield adjusted insulin dosage amounts for a diabetic person, and secondary samples are non-critical sampling instances not intended to be utilized in calculations performed by the processor that yield adjusted insulin dosage amounts for the diabetic person unless promoted to primary samples; and primary samples are samples of a sampling instance intended to be utilized in calculations performed by the processor that yield adjusted insulin dosage amounts for a diabetic person, and secondary samples are non-critical sampling instances not intended to be utilized in calculations performed by the processor that yield adjusted insulin dosage amounts for the diabetic person unless promoted to primary samples; and determining compliance with an adherence criteria for the at least one sample via the processor, wherein adherence criteria are the procedural requirements that the diabetic person must follow when conducting the structured collection protocol, wherein when the at least one sample is not compliant with the adherence criteria: an adherence event is associated with the at least one sample; and the processor performs at least one additional task that is based upon the classification associated with the at least one sample, an adherence event is associated with the at least one sample; and the processor performs at least one additional task that is based upon the classification associated with the at least one sample, characterized in that if a sample is recorded with a corresponding recorded adherence event and the sample is classified as a primary sample, if there are not a sufficient number of primary samples without recorded adherence events in the sampling instance and there are secondary samples from the same sampling instance which comply with the adherence criteria, the at least one additional task comprises replacing the primary sample with a secondary sample, wherein the primary sample and the secondary sample are from the same sampling instance and the secondary sample complies with the adherence criteria, thereby promoting the secondary sample to a primary sample.