Case Study 20.1: The Wellness Program That Knew Too Much — John Hancock and Behavioral Life Insurance

Background

In September 2018, John Hancock — one of the largest life insurance companies in North America — announced that it would stop selling traditional life insurance and would offer only "interactive" policies that required policyholders to share fitness and health data collected from wearable devices. The announcement was a significant moment in the history of insurance and surveillance: a major insurer had crossed the line from offering optional wellness programs to making behavioral data sharing a condition of coverage.

John Hancock's "Vitality" program, developed in partnership with the South African wellness company Discovery, offered policyholders premium discounts (up to a specified cap) for meeting activity targets tracked through a fitness device, and required policyholders to engage with the wellness platform to maintain their policies. The mandatory data sharing requirement was new; previous Vitality programs had been optional add-ons.

How the Program Works

Under John Hancock's Vitality Life program, policyholders:

• Connect a compatible fitness tracker (Apple Watch, Fitbit, or similar device)
• Earn "Vitality Points" for daily activity, preventive health screenings, and healthy behaviors
• Can receive premium discounts for earning sufficient points
• May face premium increases or coverage impacts for sustained inactivity (the precise terms vary by policy type)

The data the program collects includes:

• Daily step count and activity levels
• Heart rate
• Sleep duration and quality
• Exercise sessions (type, duration, intensity)
• Health screening results (if shared voluntarily for additional points)
• Grocery and food purchase data (through Vitality's retail partnerships — users who purchase healthy foods through partner retailers earn additional points)

The food purchase data element is particularly significant: the program creates an incentive for policyholders to use partner retailers for grocery purchases, generating a record of food buying patterns that is far more intimate than activity data alone.

The Insurance Business Logic

Life insurance pricing is based on actuarial risk assessment: the probability that the insured will die within the policy term, estimated from factors including age, health status, smoking history, and family history. Behavioral data — activity levels, sleep quality, dietary patterns — is relevant to actuarial risk assessment because it correlates with health outcomes.

From the insurer's perspective, behavioral monitoring enables more precise risk assessment (and therefore more precise pricing) and potentially encourages health behaviors that reduce mortality risk. Both effects benefit the insurer.

From the policyholder's perspective, the program offers premium discounts for demonstrated healthy behavior — a financial incentive to engage in behaviors the insurer values. Policyholders who are already active and healthy gain the most; those who are less active or less healthy face higher effective premiums.

This creates a distributional question: who bears the cost of behavioral insurance pricing? The answer, in principle, is people who are less active, less healthy, or less compliant with the monitoring regime — populations that include the elderly, people with disabilities, people with chronic illnesses, people with demanding jobs who lack time for exercise, and people in environments that make healthy food and exercise less accessible.

The Regulatory Landscape

Life insurance in the United States is regulated at the state level. State insurance regulations have traditionally restricted insurers' ability to price based on health status — these "anti-discrimination" provisions aim to prevent the most extreme forms of adverse selection and to ensure that insurance remains accessible to people with health conditions.

The legal question raised by behavioral insurance pricing is whether activity tracking data constitutes "health status" information that state insurance regulators restrict, or whether it constitutes behavioral/lifestyle data that insurers have traditionally been permitted to use in underwriting.

This distinction matters enormously: - If it is health status data, using it for pricing may be restricted or prohibited - If it is behavioral data, it may be permissible under current regulatory frameworks

Advocates for disability rights argued that activity tracking penalizes people with physical disabilities who cannot meet step-count targets regardless of their overall health. John Hancock's Vitality program does include accommodations for disabilities, but critics have argued that the accommodations are inadequate and that the program's fundamental architecture — rewarding measurable physical activity — is inherently disadvantageous for people with mobility limitations.

The Scope of Data Collection

The food purchase tracking element of the Vitality program illustrates how wellness programs can expand far beyond their ostensible health purpose. Grocery purchase data — what you buy, when, where — is among the most intimate commercial data available. It reveals diet, but also household composition (single person vs. family with children), economic constraints (purchase of lower-cost food items), cultural practices (food associated with specific ethnic backgrounds), and health conditions (diabetic-appropriate foods, allergy-specific products).

This data is relevant to actuarial risk assessment — diet is strongly associated with mortality — but it is also commercially valuable far beyond insurance underwriting. It can be sold to data brokers, used to develop advertising profiles, and cross-referenced with other behavioral data to create detailed individual profiles.

Whether Vitality or John Hancock sells or shares grocery purchase data to third parties is governed by their privacy policies. The ability to use this data broadly — in commercial contexts beyond insurance underwriting — is the surveillance capitalism dimension of behavioral insurance: the insurance relationship is the vehicle for data extraction that has uses far beyond insurance.

Analysis

Insurance as a Surveillance Mechanism

The John Hancock case illustrates how commercial relationships outside the typical surveillance contexts can become vehicles for behavioral monitoring. Insurance has always required disclosure of relevant information; traditional life insurance asks about smoking, medical history, and hazardous hobbies. Behavioral life insurance extends this to continuous monitoring of daily activity.

The extension is not merely quantitative (more data). It is qualitative (continuous, behavioral, intimate). The difference between a one-time health questionnaire and continuous wristwatch-based activity monitoring is not just that the latter collects more data; it is that the latter surveil the policyholder's body on a continuous basis, transforms private daily behavior into actuarially relevant commercial data, and creates financial incentives for behavior modification that serve the insurer's interests.

This is the actuarial surveillance version of the employer wellness program examined in the chapter: the surveillance is justified by a legitimate institutional interest (accurate risk assessment), the financial incentives are real, and the "voluntary" character of the monitoring is constrained by the financial stakes of non-participation.

The Disability Rights Critique

The disability rights critique of behavioral insurance pricing is foundational. The Americans with Disabilities Act (ADA) provides some protection against insurance discrimination based on disability status, but its application to activity-tracking insurance programs is contested. The core issue is this: if an insurer offers premium discounts for meeting activity targets that some policyholders cannot achieve due to physical disabilities, the effect is a premium surcharge on people with disabilities — even if the policy design is neutral on its face.

This structural discrimination is not unique to the insurance context. Throughout this textbook, we have examined surveillance systems that are facially neutral but produce disparate outcomes for people with preexisting vulnerabilities. The activity-tracking insurance model produces disparate impact on exactly those populations — elderly, disabled, chronically ill — who are most expensive to insure and most in need of insurance.

The Data Retention Problem

An activity-tracking insurance program generates a longitudinal record of a policyholder's physical activity over potentially decades. This record has significant value beyond current insurance pricing: it could be used by future insurers (if data is retained after policy termination), could be obtained by law enforcement through legal process, could be sold to data brokers, or could be used in ways that future technology enables but that current policyholders cannot anticipate.

The long-term data retention risk is a feature of all forms of continuous surveillance: data collected today may be used in ways that are not foreseeable at collection, in a future regulatory and technological environment that current policyholders cannot evaluate. Consenting to data collection today is not the same as consenting to all future uses of that data.

Discussion Questions

1. John Hancock moved from an optional wellness add-on to a mandatory data-sharing requirement. Analyze this transition using the concept of "voluntary becoming obligatory" from the chapter. What drove the transition, and what does it suggest about the trajectory of behavioral insurance more broadly?

2. The disability rights critique argues that activity-tracking insurance programs structurally disadvantage people with disabilities even if no discriminatory intent exists. Is disparate impact sufficient to make a surveillance-based insurance program unjust? What would a fair adjustment mechanism look like?

3. The program includes grocery purchase data collected through partner retailers. Evaluate whether this data is sufficiently related to insurance risk to justify its collection. What additional commercial uses of this data concern you?

4. Behavioral insurance pricing can be understood as a market efficiency improvement (more accurate risk assessment, premium reductions for healthy behavior) or as a structural disadvantage for people whose health behaviors are constrained by factors beyond their control. How would you weigh these two framings in evaluating the program?

5. Design a regulatory framework for behavioral life insurance that would: allow legitimate risk assessment using behavioral data; protect policyholders from discriminatory outcomes; limit data collection to insurance-relevant purposes; and prevent data sales to third parties.