Table 2.

Summary of prevention, detection, and regulatory recommendations in My Health Story 2022.

Prevention

Use a geofence surrounding data collection software to prohibit IP addresses from outside the project catchment area from accessing the survey.

Protect staff time and budget resources for a data integrity forensic efforts and potential programming of more complex software programming protections, such as “honey pot” questions.

Plan ahead to create and test logic or attention-check questions within your target population prior to initiating study recruitment.

Do not mention the presence of incentives on recruitment materials and do not automate the study payment process upon completion of the project survey.

Detection

Create a data integrity review team to mitigate potential unintended exclusion of underrepresented populations.

Use multi-modal recruitment methods and track data collected via in-person and online recruitment tools, separately.

Create separate survey data collection databases for records stemming from online recruitment methods and for those stemming from in-person, or more trusted, recruitment methods.

Create process and content-based flag definitions and repeatedly test them in records that are trusted verses suspicious before deciding if they are effective and reliable.

Review any flag definitions with target population members to check for biases and potential unintended, overly exclusionary consequences.

Regulatory

Include language in the general information section, or informed consent, indicating the potential risk that unintended, inauthentic record infiltration could compromise the integrity of the dataset.

Include language indicating that study incentives may be delayed or cancelled if a participant record is flagged as inauthentic.