Mobile data collection has the potential to be better, easier, faster, and cheaper than paper-based collection. In more specific terms, it can affect the quality, reliability, usability. time, and cost of data collection, all of which may be interrelated. These advantages and challenges are divided into measured outcomes showing study results, and lessons learned showing more general observations. These examples emphasize peer-reviewed studies in developing countries, which do not represent all current forms of mobile data collection.

When planning adoption, numerous qualities of mobile data collection should be considered below.

DATA QUALITY

Measured Outcomes:

• Equivalent or improved accuracy of mobile data collection versus paper, in a clinical study, geo-referenced survey and laboratory results
• Reduced protocol errors and errors of omission
• Accuracy of data entry can vary between text and voice formats
• Increase or decrease in social desirability bias due to device.

Lessons Learned:

• Data quality is improved by auto skips, option menus, compulsory fields and range-checks at the time of data entry.
• Difficulty of using devices for open-ended responses
• Advantages of GPS-based data collection over traditional cluster survey procedure
• Audit trails can be included to track changes
• Ease of mobile data entry may facilitate “garbage” data entry
• Use of data records to reconstruct interviewer activities

RELIABILITY FOR DATA COLLECTION

Measured Outcomes:

• Little loss of data in scaled use, after correcting initial problems
• Lower return rates on data due to technical difficulties and stolen devices

Lessons Learned:

• Use for multiple languages and a variety of survey lengths
• Challenges of incompatible data files and inadequate back up
• Need to ensure adequate backup system to guard against data loss
• Limitations of battery life

USABILITY OF MOBILE DEVICES

Measured Outcomes:

• Interviewers’ and respondents’ preference for device over paper, for field survey and laboratory data

Lessons Learned:

• Graphical elements may be difficult to include

TIME NEEDED FOR DATA COLLECTION

Measured Outcomes:

• Faster or slower preparation of data collection instrument
• Decreased time and perceived time to complete questionnaire.
• Faster data entry, validation and cleaning
• Reduced work hours to process laboratory results

COSTS OF MOBILE DATA COLLECTION

Measured Outcomes:

• Decreased costs of mobile data collection compared to paper, including preparation, printing, equipment, collection, entry and cleaning, assuming no costs for software, software training, or expert questionnaire development

Lessons Learned:

• Compared to paper-based surveys, mobile data collection has higher upfront cost offset by economies of scale when reused in multiple studies

BROADER IMPACT AND OPPORTUNITIES

In addition to the immediate impact of mobile technology on data collection, it can conceivably change how data is used in health programs.

• Faster decision-making and reallocation of resources
• Decentralization of decision making due to faster dissemination of data analysis
• Changes to health service delivery models based on faster access to data