Searching for medical advice can become complex and misleading when bad information ranks at the top of the search results query page, but a recent study suggests that sending the correct message directly to the person interested in the information can promote better health and understanding long-term.
The joint study from a researcher at Microsoft, two directors at the ad firm J. Walter Thompson, and a professor of health policy at Columbia University found that people exposed to public health ads online were more likely to search for health-related information in the future.
The team developed and ran ads that promote exercise and healthy eating.
About 2,996 people took place in the study of a controlled group of more than 500,000. The study appears in npj Digital Medicine.
To participate, people were required to have been users of Microsoft’s search engine Bing while logged in to their account and searches for keywords related to weight during the month-long analysis because searching for these types of keywords triggered the experiment.
The searches for health-promoting goods or services were recorded for one month. The results showed that 48% of people who were exposed to the ads made future searches for keywords related to weight loss, compared with 32% of those in the control group.
Effectiveness of the ads was improved by targeting individuals based on their lifestyle preferences and/or socio-demographic characteristics.
While the researchers didn’t find that any specific message in an ad campaign was more effective in provoking future weight loss-related searches, exposure to multiple targeted ads increased the chance of a search for weight loss information by 11%.
The study concludes that it is technically possible to launch an online campaign that effectively improves health behaviors and that corporations promoting an unhealthy diet or a sedentary lifestyle can potentially be outbid when it comes to search ads.
Researchers also used supplementary materials from another study released in February 2018, as well as propensity score matching of users meeting inclusion characteristics, who were matched to unexposed users based on age, gender, and zip code, and analyzed using the above characteristics.