Fitness App Agrees to Pay $56 Million to Settle Class Action Alleging Dark Pattern Practices
On February 14, 2022, Noom Inc., a popular weight loss and fitness app, agreed to pay $56 million, and provide an additional $6 million in subscription credits to settle a putative class action in New York federal court. The class is seeking conditional certification and has urged the court to preliminarily approve the settlement.
The suit was filed in May 2020 when a group of Noom users alleged that Noom “actively misrepresents and/or fails to accurately disclose the true characteristics of its trial period, its automatic enrollment policy, and the actual steps customer need to follow in attempting to cancel a 14-day trial and avoid automatic enrollment.” More specifically, users alleged that Noom engaged in an unlawful auto-renewal subscription business model by luring customers in with the opportunity to “try” its programs, then imposing significant barriers to the cancellation process (e.g., only allowing customers to cancel their subscriptions through their virtual coach), resulting in the customers paying a nonrefundable advance lump-sum payment for up to eight (8) months at a time. According to the proposed settlement, Noom will have to substantially enhance its auto-renewal disclosures, as well as require customers to take a separate action (e.g., check box or digital signature) to accept auto-renewal, and provide customers a button on the customer’s account page for easier cancellation.
Regulators at the federal and state level have recently made clear their focus on enforcement actions against “dark patterns.” We previously summarized the FTC’s enforcement policy statement from October 2021 warning companies against using dark patterns that trick consumers into subscription services. More recently, several state attorneys general (e.g., in Indiana, Texas, the District of Columbia, and Washington State) made announcements regarding their commitment to ramp up enforcement work on “dark patterns” that are used to ascertain consumers’ location data.