Loneliness by the Numbers: Why the Surveys Disagree

Cigna’s 2020 workplace report put the figure at 61% of U.S. adults reporting they sometimes or always feel lonely, a seven-point rise on the year before. AARP Foundation’s 2018 survey of adults 45 and older put the figure at one in three. Both are self-report surveys of loneliness in the United States, conducted within two years of each other. The gap between them is not a sign that loneliness is spreading fast or that one organization got it wrong. It is a sign that “loneliness” is being measured with two different instruments, on two different populations, and the resulting numbers are not the same quantity.

This matters because both figures circulate as though they answer the same question: how lonely is the country. They do not. Getting clear on why is a useful case study in what a prevalence number in this field actually is.

Two instruments, two questions

AARP’s survey used the UCLA Loneliness Scale, a 20-item instrument developed and validated over decades of academic use. It does not ask “are you lonely” directly. It asks a battery of indirect items — how often a respondent feels left out, how often they feel their relationships are not meaningful, how often they feel part of a group of friends — and combines the answers into a score. Because the UCLA scale is the standard instrument in the peer-reviewed literature, AARP’s number is directly comparable to the loneliness measures used in the meta-analyses that link loneliness to mortality risk, including Julianne Holt-Lunstad’s 2015 review, which reports loneliness as one of three distinct risk factors alongside social isolation and living alone.

Cigna’s index is not built the same way. It is a proprietary instrument developed for a corporate wellness report, and it asks respondents directly whether they sometimes or always feel lonely — a single, blunter question rather than an aggregated behavioral scale. A direct question about a stigmatized feeling state can produce either an inflated or a deflated answer relative to an indirect scale, depending on how comfortable respondents are admitting to it, and there is no way to know from the published report which direction the Cigna number is biased in, because the item wording and validation data are not public in the same way the UCLA scale’s are.

The populations differ too. AARP surveyed 3,020 adults 45 and older, a demographic that skews toward retirement, widowhood, and smaller households. Cigna’s report is framed around the workplace, and it reports loneliness by generation among employed people — 73% of workers aged 18 to 22, and over 80% of employed Gen Z respondents overall. A survey of the employed, weighted toward younger workers, is measuring a different slice of the population than a survey of Americans 45 and up. The two numbers were never going to converge, because they were never measuring the same group with the same tool.

None of this means either survey is wrong. It means that headline prevalence figures for loneliness cannot be stacked next to each other as though they are readings on the same thermometer. They are readings on different thermometers, in different rooms.

Self-report versus something closer to observed

Set the loneliness numbers aside and look at isolation, which is a different construct — Cacioppo’s work on loneliness treats it as a subjective, aversive signal, closer to hunger than to a fact about someone’s social network. Isolation, by contrast, is structural: how many people someone sees, how often, and how varied that network is. It is not fully observable in the way a step count is, since most isolation research still relies on people reporting their own contact frequency and network size rather than on direct observation. But it sits closer to the observed end of the spectrum than a feeling state does, because contact frequency and household composition are things a researcher can, in principle, verify independently of how the respondent feels about them.

The 2020 National Academies consensus report on social isolation and loneliness in older adults uses this distinction explicitly, and its headline figure — roughly one quarter of adults 65 and older are considered socially isolated — is built from structural indicators: network size, frequency of contact, and living arrangement, rather than a felt-loneliness scale. Holt-Lunstad’s 2015 meta-analysis breaks the same territory into three separate measures with three separate effect sizes: social isolation (OR = 1.29), loneliness (OR = 1.26), and living alone (OR = 1.32), each independently associated with early mortality after adjusting for health status. Living alone is the closest thing in this literature to an administrative measure — it is a household fact that shows up in census records and could, in principle, be counted without asking anyone how they feel about it. That it carries an effect size on par with subjective loneliness, and that all three remain predictive after adjustment for baseline health, is one of the stronger pieces of evidence that isolation and loneliness are not the same phenomenon wearing two names. A person can live alone and not be lonely. A person can be surrounded by others and be lonely regardless.

AARP’s data illustrate the same point from another angle, using a behavioral proxy rather than a household fact. Among respondents who reported having spoken to their neighbors, 33% were classified as lonely on the UCLA scale; among those who reported never having spoken to a neighbor, the figure was 61%. Reported neighbor contact is still self-report, not a direct observation, but it is a report of a behavior rather than a report of a feeling, and behavioral self-report tends to be more reliable than affective self-report because there is less social desirability bias in answering “have you spoken to your neighbor” than in answering “are you lonely.” The gap between 33% and 61% is one of the largest predictor splits in the AARP survey, and it points toward the same conclusion as the mortality meta-analyses: structural facts about contact track loneliness closely but not perfectly, which is exactly what would be expected if they were correlated but distinct constructs rather than the same thing measured twice.

What a genuine administrative measure would look like

Nothing in this literature is a true administrative record in the sense that, say, hospital admissions or unemployment claims are. There is no register anyone is entered into for being lonely. The closest approximations are household composition data of the kind national censuses already collect, which is why living-alone rates are usable as a rough proxy for one component of isolation risk even though they say nothing about loneliness directly. The United Kingdom’s 2018 loneliness strategy took a step in this direction by embedding loneliness measurement into the Office for National Statistics rather than leaving it to ad hoc surveys commissioned by individual organizations — a move toward a standardized, government-run measurement infrastructure rather than a patchwork of incompatible instruments, though even the ONS approach still depends on people answering survey questions about how they feel, not on any independently verifiable record.

A stronger evidence base would pair self-reported loneliness scales with something genuinely observed: contact diaries, call and message logs, or administrative household records, matched against the same respondents’ UCLA scores over time. That kind of linkage exists in small clinical samples but not at the scale of a national survey. Until it does, every prevalence figure in this field — 61%, one in three, one in four — describes what a particular instrument recorded in a particular population, and the honest response to seeing two such figures side by side is not to average them but to ask what each one was actually built to measure.