Group	Number	Share of sample
Women	250	49.80%
Men	248	49.40%
Aged 25 to 29	69	13.75%
Aged 30 to 34	96	19.12%
Aged 35 to 39	90	17.93%
Aged 40 to 44	77	15.34%
Aged 45 to 54	166	33.07%
Working full-time	377	75.10%
Working part-time	121	24.10%
White	340	67.73%
Black	78	15.54%
Mixed	32	6.37%
Asian	28	5.58%

Using AI today is very common, so the more telling question is how far people take it. Among those who use it, 91.86% have at some point used AI in place of a professional because it was cheaper, quicker, or easier to reach, and 76.55% do so at least sometimes. For most of them, AI is no longer a second opinion laid on top of a doctor's advice; it has become the first place they turn, and often the only one.

The national trend points the same way, though less sharply. KFF found in 2025 that about one in three US adults had used an AI chatbot for health, and that 42% of them never followed up with a clinician afterward. These figures sit higher because the panel is working, digitally engaged, and aged 25 to 54, and the answers consistently point in a single direction.

A single headline figure hides a lot of variety, because people bring very different kinds of questions to a chatbot. Some carry almost no risk if the answer is wrong, while others sit much closer to the work of a clinician. Sorting the questions by what is at stake shows how far this reliance actually extends.

Most of what people bring to AI are low-stakes questions, the kind of question where a wrong answer costs very little. Meal and nutrition ideas top the list at 67.10%, followed by workout guidance at 60.91%, and stress or general well-being support at 34.53%. For questions like these, a chatbot is a convenient and largely harmless helper, which goes a long way toward explaining how quickly the habit has spread.

The same list, though, reaches into territory where the stakes are far higher. 28.01% have asked AI about pain, an injury, or a symptom, the kind of question that can carry a real health cost and often calls for a trained eye rather than a generated answer. When a share this large is willing to take clinical questions to a chatbot, it is the first sign of a wider tension. It also sets up the question at the heart of the next section, which is whether people trust AI as much as their behavior suggests they do.

The distance between what people use AI for and what they trust it for defines this section. For low-stakes tasks, the confidence is high, with 78.09% willing to trust AI at least somewhat for meal ideas, and 76.29% for a workout plan. As the stakes of the question rise, trust in AI starts to fade, and by the time the question is a medical symptom, more people distrust AI (55.18%) than trust it (40.24%), the only one of the eight tasks where distrust comes out ahead. 

People have, in effect, worked out where the safe line sits, and it falls close to where a doctor would draw it. That is what makes the findings hard to square, because a real share of users takes AI to exactly these high-stakes questions, the ones they trust it with least, and cross that line anyway.

National polling shows the same wariness, with most Americans still saying they would rather get health advice from a person than AI, and their trust in AI health tools is slipping rather than growing. The puzzle is why people hand AI a doctor's job when they clearly understand the risks, and the reason has less to do with AI than with a habit formed elsewhere.

Their expectations for the technology are measured rather than alarmed. 36.45% think AI will mainly support fitness and wellness professionals over the next five years, 38.45% expect it to take over some routine guidance, and only 5.58% believe it will replace most of them. People are not forecasting the end of the expert, even as many of them already lean on AI in the expert's place.

The caution is genuine, and the expectations are sober, and still the behavior runs ahead of both. Something is quietly pushing people past their own better judgment, and it comes not from the chatbot but from the device on their wrist.

Wearable tech opens the door to AI

The device on the wrist proves to be the clearest link the survey uncovered, and the pattern is less about percentages than about a habit. What matters is whether someone has already learned to let a machine make a call about their own body, because those are the people who go on to let a chatbot do the same.

Consider the people who have changed a workout, a meal, or their bedtime routine because a wearable told them to. 75.47% of them have also used AI in place of a professional at some point. Among those who have never once acted on a device score, that share falls to 49.34%. The nearly 26-point gap is one of the largest and most reliable differences in the study, and the reading is straightforward. People who are used to trusting the verdict of a tracking device are the same people most willing to trust a chatbot's.

Each habit has been studied on its own before, but what this survey adds is watching the two sit together with the same people. The first habit is already widespread, as 42.23% of those surveyed have changed something because a score told them to, and for anyone already in that habit, taking the next step to AI is not very difficult.

Laid out as a funnel, the escalation is easy to follow. 61.16% have used AI for health, 56.18% have used it in place of a professional, 25.10% do so often or very often, and 17.13% have used it for pain, injury, or symptoms.

Each question reveals a category of people who are a smaller and more committed group than the one above it, and together they show how deeply reliance on AI now runs. 

The rest of the report traces the everyday conditions surrounding the shift, starting with something far more basic than any app, which is how much Americans actually move and what gets in their way.

How much Americans exercise, and what holds them back

This reliance on AI and wearables grows out of something much older and simpler - the daily effort of trying to stay active, so it is worth seeing that effort clearly before going further. Most of this sample is active, though only up to a point, because a genuinely daily habit turns out to be the exception rather than the rule.

64.34% of respondents exercise at least three days a week, with 37.25% managing three or four days and 27.09% reaching five to seven. That is a healthier pattern than the country as a whole reports, since the CDC found in 2024 that 47% of US adults met the federal aerobic-activity guidelines, with women (42%) trailing men (52%). By that national benchmark, the people here are doing well, and most of them are moving in some form most weeks of the year.

The more useful question is not whether they exercise but why they do not do more, because very few describe themselves as completely inactive. An answer built on willpower alone does not fit a group that is already this active. It points instead toward the practical obstacles that get in the way of doing more, and those obstacles, rather than any lack of effort, are what the next question brings into focus.

Asked what makes exercising hardest, respondents describe lives that are already full rather than a lack of interest. Lack of time leads at 57.57%, followed closely by lack of energy at 48.61% and lack of motivation at 46.81%, three barriers that tend to travel together in busy working households. Money is a smaller factor here, named by 11.75%, though it connects directly to the cost pressures the report examines shortly.

What stands out is that the leading barriers have little to do with willpower. Time, energy, and motivation are the predictable casualties of demanding jobs and family schedules. When time is the resource in shortest supply, people usually cut something out of their routine. The survey shows exactly what tends to go first, because the same pressure that pushes out exercise also forces a series of harder daily trade-offs between rest, food, and work.

Those trade-offs show how work and time pressure quietly turn into health costs. Asked how often the time crunch forced specific sacrifices over the past three months, 45.62% said they exercised less than they intended, often or very often, and 42.23% said they slept less than they needed, at the same frequency, with skipping meals and working through recovery not far behind.

These sacrifices rarely happen in isolation. Sleeping less and working instead of resting appear in the same people, moving together with a correlation of 0.57, suggesting they are symptoms of a single overloaded schedule rather than separate choices. The pattern is consistent and telling. When the week gets tight, rest and recovery are the first things these adults give up, well before they cut back on work itself. For many households, the time pressure does not stop there, because the next thing to come under strain is the budget, and money shapes health in a completely different way.

What the rising cost of health forces people to cut

Money works much the same way as time. When time runs short, rest is the first thing to go, and when money runs short, it is the cost of staying healthy that gets trimmed. On the basic question of how much room they have to begin with, the sample divides almost exactly down the middle.

The first cost question simply asked how easily people can afford to stay active, and the sample splits almost evenly. 37.65% find it somewhat or very difficult to afford what they need, including 12.35% who find it very difficult, compared with 40.83% who find it easy. For roughly four in ten adults here, staying active is already a financial strain rather than something they can take for granted.

That strain does not stay contained to the gym floor. The harder a respondent finds it to afford staying active, the more often cost shuts them out of professional guidance too, and the two move closely together, with a correlation of -0.60. Money that runs short in one part of a healthy life tends to run short across all of it. When budgets tighten, people rarely trim a little from everything at once. They drop whichever expense is largest and most visible first, and the responses point clearly to fitness: gym memberships, classes, and sports. 

That expense is almost always the gym. Looking across the wider group, the gym stands out as the clear casualty, with 57.56% of them dropping a gym membership, fitness classes, or a sport, far ahead of food, supplements, or workout gear. When something has to go, the recurring monthly membership is first on the list.

The same cost pressure extends beyond memberships into care itself. Across the whole sample, 34.66% say cost blocks them from professional guidance often or very often, which lines up almost exactly with KFF's 2025 finding that 36% of US adults skipped or postponed needed care because of cost.

There is an apparent contradiction in all this, because the fitness industry is booming even as individual households pull back, and the Health and Fitness Association reported a record 77 million US fitness-facility members in 2024. The two facts sit together once you see that they describe different budgets, a healthy national total on one side and, on the other, the family that starts canceling the recurring expenses the moment money is tight. Cost clearly decides what people give up. Whether it also decides who they turn to for advice is a separate question, and the answer is not obvious.

The obvious assumption is that people turn to AI because they cannot afford a professional, but the data does not support that. Respondents who are frequently priced out of professional care turn to AI at a rate of 57.47%, while those who are rarely or never blocked by cost do so slightly more often, at 65.77%. The gap runs in the opposite direction to the cost theory.

What that means is easy to miss but important. Reaching for AI is happening right across the income range, not just among the people shut out of care, so making professional guidance cheaper would not pull many of them back from the chatbot. Money still matters because it decides what people can afford to keep in their lives. It simply does not decide who they listen to for advice. That decision is shaped far more by habit and by what people see every day, which points straight to the feed that fills most of their screens.

How wellness content shapes what people feel and do

For almost everyone in this sample, the social feed is a constant presence, and its effect works both ways at once. Seeing fitness and wellness content is close to universal here, and the same stream that keeps people informed and motivated is, for many, a steady source of pressure.

The starting point for the feed is simply how often people see it, and the answer is almost constantly. 65.94% view fitness or wellness content on social media at least several times a week, including 6.97% who see it multiple times a day, while only 5.98% never see it at all. For a clear majority, this content is not something they go looking for once in a while, but a routine part of daily life.

That level of exposure runs well ahead of the national picture. KFF found in 2025 that 55% of US adults turn to social media for health information at least occasionally, and this employed, tech-savvy sample sits above that, which fits the rest of the survey. The more important point is what a stream of constantly available health and fitness content actually does. It becomes the everyday backdrop against which people measure their own bodies and habits, and the verdict it hands back is far from one-sided. It informs and motivates many of them while quietly unsettling others, sometimes on the same day.

Asked how that content leaves them feeling, the two most common reactions are positive. 45.42% feel better informed, and 44.82% are motivated. The strains follow immediately behind as 32.87% worry they are not doing enough, 29.48% feel guilty about their habits, and 22.71% feel pressured about their bodies or appearance.

The picture is genuinely double-sided rather than simply good or bad. The same feed that informs and motivates a large share of people also leaves nearly a third feeling inadequate or guilty, and in many cases, these are the very same users on different days. That mixed emotional effect is only part of the story, because for many people, the content does something more specific than just shift their mood. It makes them anxious about their health.

Beyond how it makes people feel about their habits, the feed can also make them anxious about their physical health. Among the respondents who regularly see fitness and wellness content, 75.42% say it has, at least occasionally, made them worry that they had or might develop a health problem, while only 23.09% say it never has. 
In other words, roughly three in four viewers have been unsettled about their own health by something they scrolled past.

This is a worry people describe, not a diagnosis any doctor has confirmed, so it says more about the feed's emotional pull than about anyone's actual health. An effect this widespread is worth taking seriously because health anxiety changes what people search for, buy, and act on. But not every kind of content causes this worry equally. A great deal depends on the specific kind of content a person tends to see, and some categories weigh on people far more heavily than others.

The pressure is heaviest wherever the content is about bodies. Weight-loss or body-transformation content is named the single biggest source of pressure at 38.05%, ahead of diet and clean eating at 29.68% and workout challenges at 26.49%, while only 13.15% feel no pressure from any of it. The theme is consistent in its appearance-focused content, not niche wellness advice, which weighs on people most.

This is more than a passing discomfort. A 2025 study in Frontiers in Psychology found that simply seeing fitness posts measurably lowered women's body esteem by prompting them to scrutinize their own bodies more closely, the same appearance-and-guilt strain that runs through this survey, too. But this pressure doesn’t stay private for long, and tends to spill over into behavior, and the feed's real influence becomes clearest in what people do once they close the app.

The feed does not stop at feelings; it visibly changes behavior. The most common response is to start or change an exercise routine (38.45%), followed by buying vitamins or supplements (33.67%) and changing a diet (32.87%). Further down the list, 20.12% started tracking a health or fitness metric, and the same share, 20.12%, searched online for symptoms after consuming any kind of wellness-related content.

Some of this is clearly healthy, and some is a passing trend that people later drop, but the direction is unmistakable. Content routinely moves people to act on their own bodies, from workouts and diets to self-diagnosis. That makes the next question uncomfortable, because if the feed is driving this much behavior, it is fair to ask whether people actually trust the sources they are acting on. The answer, it turns out, is that they mostly do not, and that mismatch is the sharpest tension the survey uncovers.

The gap between who people trust and who they act on

This mismatch between action and trust runs through the whole survey, and it is clearest here. People act readily on what the feed shows them, yet it sits near the bottom of the list when they are asked who they actually trust with their health.

The clearest way to understand it is to line up what people do against what they say they trust. Because of something they saw on social media, 38.45% started or changed an exercise routine, 33.67% bought supplements, 32.87% changed their diet, and 23.90% took a health or fitness question to an AI tool. Only 5.38% went and spoke to a qualified professional about it.

That is roughly seven people acting on the feed for everyone who acts on professional advice, a great deal of influence for a source most people do not rate highly. AI shows the very same split. The 23.90% who have asked a question are far more than the 16.33% who name a general AI assistant among their most-trusted sources, so people are acting on AI far more readily than they claim to trust it. 

Across the board, action runs well ahead of stated trust, and the full trust rankings make that gap starker still, showing just how low these popular and heavily used sources actually sit.

Asked whom they trust most for guidance, 61.75% name a healthcare professional and 46.41% a personal trainer or nutrition professional, far ahead of the 17.13% who name a social media influencer. Read the other way, though: about a quarter of the sample, 25.90% name neither a doctor nor a trainer among their most-trusted sources, leaving a sizable group leaning on less qualified voices.

This wariness toward the feed is both earned and widely shared. KFF found in 2025 that fewer than one in ten social-media users trust most of the health information they see, and that 61% of people who follow health influencers believe those influencers are mostly financially motivated. The result is a strange but consistent habit. People know the feed is compromised, act on it anyway, and reserve their stated trust for professionals that they rarely reach. The devices on their wrists sit in exactly the same double-edged place, relied upon daily and doubted at the same time, and that is where the report turns next.

The upsides and hidden costs of self-tracking

Wearables and tracking apps do more than steer people toward AI. For most people, they have become a constant companion, glanced at throughout the day and frequently acted on, quietly shaping how they judge their own progress. That makes it worth asking what all that measuring actually does to the people who live by it, because the effects run in both directions at once.

Tracking has quietly become a normal part of daily life. Reading the frequency answers together, 25.90% of respondents track their health daily, and another 9.36% check multiple times a day, and once the 22.51% who look several times a week are added in, 57.77% are tracking at least weekly. Only 21.31% do not track their health at all.

That is a striking amount of self-measurement, and it runs well ahead of simple device ownership. A 2025 Rock Health survey found US wearable ownership at nearly 57%. The takeaway is that tracking is now an everyday, mainstream habit rather than something only dedicated athletes do. And once measuring becomes routine, the real question is no longer what people track but what all that data does to them, for better or worse.

For most people who track, the balance comes out positive. Among those who regularly use an app or device, 54.43% say it has left them better informed, 42.28% more motivated, and 41.27% more in control, which is exactly what these products promise and, for a great many users, exactly what they deliver.

The tool that reassures people can also worry them, and many trackers feel the worry very clearly. 27.85% have felt pressure to reach a target, and 23.54% have felt guilty after missing one. Taken together, half of all the people who actively track their health (49.62%) report at least one of these harms, so the strain is far from a fringe experience. It is rarely all bad, since 77.04% of those who report a harm also report a benefit, but it is common enough that it now has a clinical name. Sleep specialists have begun warning about orthosomnia, an anxiety-driven fixation on perfect sleep data that can end up worsening the very sleep it is meant to improve. A tracking device does not simply record how someone is doing. It quietly tells them, day after day, how they ought to feel about it, and that verdict is only ever as trustworthy as the numbers behind it.

That last point matters because a number only reassures when the person understands it. Among the people who track their health, 43.04% have at some point worried about a metric they did not fully understand, while 48.61% have not. For many people, the very data meant to give them clarity has instead handed them a new source of worry.

A reading with no context can unsettle someone just as easily as it can calm them, and that kind of worry rarely sits still. It tends to send people looking for an explanation, and then for something to do in response. What they do next follows the same pattern the report keeps returning to, because more often than not, the action they take is the one the device itself prompts.

The response is common and often repeated. 53.67% of the people who get recommendations from an app or wearable have changed a workout, a meal, or their bedtime because of them, and 32.66% have done so more than once. Acting on a device's prompt is not a rare event but a regular habit for most of the people who track.

This is the same readiness to follow a score that the report identified at the very start as the strongest predictor of turning to AI, only here it is aimed at the device itself. The behavior is now woven into how a large share of working adults run their days. However, it does not weigh equally on everyone. Who feels these pressures most, and who leans hardest on these tools, depends a great deal on gender and stage of life, and that is where the final section of findings turns.

Who feels the pressure most across gender and age?

So far, the report has dealt in averages, and averages hide who is actually carrying the weight. Once the data is broken down by gender and age, a consistent pattern appears. The pressure lands hardest on women, and by age, it lands somewhere most people would not expect.

The clearest divide is between women and men. Women report greater strain across all measures. They are more likely than men to feel pressured by weight loss and body image (26.40% vs. 18.55%), more likely to worry they are not doing enough, more likely to feel guilty about their habits, and more likely to find it hard to afford to stay active.

Where women and men look almost identical is in their use of AI. Women use AI for health at 61.60%, and men at 60.48%; they turn to it in place of a professional at 55.20% and 56.85%, gaps small enough to count as the same. That combination is the real finding. Both groups hand these tools the same share of their decisions, yet women carry far more of the worry and guilt that feeds on it. It is a reminder that the emotional cost of all this falls unevenly, and that a clearer, less pressuring design would help women most. Age divides the sample too, though not in the direction most people assume.

By age, the pattern overturns the usual assumption that young people lead every digital trend. Reliance on AI is heaviest not among the young adults but among midlife adults. The 35 to 44 group uses AI for health (69.46%) and turns to it in place of a professional (64.67%), well above both the 25 to 34s (61.21% and 56.36%) and the 45 to 54s (52.41% and 46.99%). That fits a stage of life short on time and heavy with caregiving, reaching for the quickest answer within reach.

Body-image pressure runs the other way, heaviest among the 25 to 34s (30.91%) and easing steadily with age, to 18.56% of the 35 to 44s and 18.07% of the 45 to 54s. One last group stands out, and it is not the one most people would expect. Part-time workers, rather than full-timers, are the most likely to give up exercise, sleep, meals, and rest all at once, at 19.83% against 9.81%. The likely driver is insecure, unpredictable work rather than long hours, which suggests that precarity, not overwork, is the sharper health risk here. Across gender, age, and employment type, some groups carry far more of this burden than others - women, midlife adults, and part-time workers most of all. A response built only on the average will miss exactly them. 

What it adds up to

Step back from the individual numbers, and a single picture comes into focus. For a large share of working American adults, the everyday business of staying healthy now runs through a screen. They ask an AI tool the questions they once saved for a doctor, they take their cues from a feed they only half-believe, and they let the device on their wrist set the terms of a good day. This pattern does not reflect a lack of awareness. Respondents trust AI least in exactly the tasks where the stakes are highest, they rate the feed as one of their least-trusted sources even while acting on it, and they still name a human professional as the person they trust most. The consistent finding across this survey is a gap between stated trust and actual behavior, with convenience more often determining the outcome than belief. 

What widens that gap is rarely a single dramatic decision. It is the quiet accumulation of small, practical habits. A tracker teaches someone to act on a number, and once acting on a number feels normal, handing the next question to a chatbot feels normal too. Cost plays its part in the background, deciding which gym membership or professional gets cut first, but it is not what tips people toward AI, since that shift is happening evenly across the income range, which means cheaper care alone will not reverse it. The pull toward the machine is about ease and habit far more than money.

The weight of all this does not land evenly, and that is where any response should begin. Women carry more of the emotional weight of the feed while turning to AI at the same rate as men. Reliance on AI is heaviest not among the youngest adults but in midlife, when time and caregiving demands are at their peak. And it is insecure, part-time work, rather than long hours, that most often crowds out exercise, sleep, meals, and rest. Averages hide these groups, and breaking the data down by gender, age, and employment status shows exactly where the pressure falls. 

Looking ahead, the overall trajectory is not really in doubt. AI tools and wearables are only going to become more capable and more woven into daily health, and what this survey shows is likely a preview of where the rest of the country is headed. 

AI did not create the pressure described in this report. It arrived into a working population already short on time, already stretched on money, and already used to letting a device on the wrist make small daily calls for them. Handing the next question to a chatbot was not a leap; it was the same habit extending one step further. What this survey actually measures is capacity; whether there is enough time, money, and attention left over for health once the day has claimed its share of all three. 

Methodology

This study is based on a survey of 502 US adults aged 25 to 54, every one of them in full-time or part-time employment, recruited via the Prolific research panel and fielded in June 2026. It is a convenience sample, directional rather than nationally representative. Two of the 502 respondents withdrew consent for their demographic information only, and because their survey answers are complete, all 502 are included in the question-level findings, appearing as 'not stated' wherever the data is broken down by demographics. The survey covered exercise and its barriers, the affordability of staying active, the effect of social media content, self-tracking, and attitudes toward using AI for health.

• Denominators. Headline figures use the full base of 502. Skip-logic bases vary and are stated inline: AI substitution among the 307 AI users, tracking experiences among the 395 who track, and the wearable gateway among the 364 who answered the recommendation question.
• Significance. Subgroup gaps are tested with two-proportion z-tests. Significance is noted in plain language in the text, and exact p-values, with Cohen's h for the headline mechanism, are shown in the charts.
• Correlational. This is cross-sectional data, so every relationship described is an association and not evidence that one thing causes another.
• Self-report and skew. All results are self-reported. Because the panel is employed, digitally engaged and aged 25 to 54, AI and tracking figures run above national rates and should not be read as population estimates.

Sources

KFF, one in three adults have used AI chatbots for health advice (2025) 
West Health and Gallup, Americans consulting AI instead of a doctor (2026)
CIDRAP, Americans still prefer providers to AI for health advice (2025)
CDC NCHS, aerobic physical activity among US adults (2024)
KFF, Americans' challenges with health care costs (2025)
KFF, employer health benefits survey, family premiums (2025)
Health and Fitness Association, record US fitness membership (2025)
KFF, health information and advice on social media (2025)
Frontiers in Psychology, fitness posts and female body esteem (2025)
Rock Health, US wearable ownership (2025)
Sleep Foundation, orthosomnia (2025)

Prepared from the Sodalemon Fitness Pressure Survey. Significance-tested, source-verified.

Data appendix

Full distributions for every survey question, grouped by theme. Shares are to two decimal places against the base noted for each question.

Theme: activity, barriers and cost

Q1. How often respondents intentionally exercise

Base: 502.

Response	n	Share
5-7 days a week	136	27.09%
3-4 days a week	187	37.25%
1-2 days a week	118	23.51%
1-3 times a month	38	7.57%
Less often	14	2.79%
Never	9	1.79%

 

Q2. What makes it hardest to exercise

Base: 502; more than one answer allowed.

Response	n	Share
Lack of time	289	57.57%
Lack of energy	244	48.61%
Lack of motivation	235	46.81%
Family or caring responsibilities	150	29.88%
Stress or poor mental wellbeing	118	23.51%
Work pressure	97	19.32%
A health condition or physical limitation	63	12.55%
Lack of money	59	11.75%
Feeling self-conscious	49	9.76%
Lack of facilities or support	43	8.57%
Nothing currently prevents me	37	7.37%
Other	5	1.00%
Not sure	1	0.20%

 

Q3. Trade-offs forced by work or time pressure (past three months)

Base: 502.

Exercise less than intended

Response	n	Share
Never	34	6.77%
Rarely	57	11.35%
Sometimes	182	36.25%
Often	133	26.49%
Very often	96	19.12%

Sleep less than needed

Response	n	Share
Never	40	7.97%
Rarely	97	19.32%
Sometimes	153	30.48%
Often	141	28.09%
Very often	71	14.14%

Skip or delay a meal

Response	n	Share
Never	81	16.14%
Rarely	131	26.10%
Sometimes	153	30.48%
Often	91	18.13%
Very often	46	9.16%

Work instead of resting or recovering

Response	n	Share
Never	46	9.16%
Rarely	86	17.13%
Sometimes	175	34.86%
Often	115	22.91%
Very often	80	15.94%

Q4. Reduced or stopped paying because of cost

Base: 502.

Response	n	Share
Yes, more than one	29	5.78%
Yes, one	109	21.71%
I considered it but did not	67	13.35%
No	175	34.86%
I do not pay for these products or services	122	24.30%

 

Q5. What was reduced, stopped or considered

Base: the 205 who cut or considered cutting; more than one answer allowed.

Response	n	Share
Gym membership, fitness classes or sports	118	57.56%
Healthier or higher-cost food	80	39.02%
Vitamins or supplements	80	39.02%
Workout clothing or exercise equipment	63	30.73%
Fitness or wellness apps	57	27.80%
Recovery or relaxation services	26	12.68%
Mental wellbeing support	25	12.20%
Personal training or coaching	21	10.24%
Other	2	0.98%

 

Q6. How often each factor blocked professional guidance

Base: 502.

Cost

Response	n	Share
Never	57	11.35%
Rarely	58	11.55%
Sometimes	107	21.31%
Often	89	17.73%
Very often	85	16.93%
I did not want professional guidance	106	21.12%

Lack of time

Response	n	Share
Never	31	6.18%
Rarely	41	8.17%
Sometimes	136	27.09%
Often	108	21.51%
Very often	83	16.53%
I did not want professional guidance	103	20.52%

 

Q7. How easy or difficult to afford staying active

Base: 502.

Response	n	Share
Very easy	58	11.55%
Somewhat easy	147	29.28%
Neither easy nor difficult	106	21.12%
Somewhat difficult	127	25.30%
Very difficult	62	12.35%
Not sure	2	0.40%

 

Theme: social media and pressure

Q8. Frequency of viewing wellness content

Base: 502.

Response	n	Share
Multiple times a day	35	6.97%
Daily	97	19.32%
Several times a week	199	39.64%
Less often	141	28.09%
Never	30	5.98%

 

Q9. How content made respondents feel

Base: 502; up to three answers.

Response	n	Share
Better informed	228	45.42%
Motivated	225	44.82%
Worried I am not doing enough	165	32.87%
Guilty about my habits	148	29.48%
Pressured about my body or appearance	114	22.71%
Anxious about future health problems	67	13.35%
It has had no meaningful effect	53	10.56%
Distrustful of wellness information	44	8.76%
Confused or overwhelmed	35	6.97%

 

Q10. Frequency of worry about a health problem

Base: respondents who see wellness content.

Response	n	Share
Never	109	23.09%
Rarely	128	27.12%
Sometimes	163	34.53%
Often	50	10.59%
Very often	15	3.18%
Not sure	7	1.48%

 

Q11. Actions taken because of content

Base: 502; more than one answer allowed.

Response	n	Share
Started or changed an exercise routine	193	38.45%
Bought vitamins or supplements	169	33.67%
Changed my diet	165	32.87%
Asked an AI tool a health or fitness question	120	23.90%
Started tracking a health or fitness metric	101	20.12%
Searched online for symptoms	101	20.12%
Bought a fitness or wellness product	88	17.53%
Tried a trend that I later stopped	81	16.14%
None of these	81	16.14%
Unfollowed or muted wellness accounts	58	11.55%
Consulted a qualified professional	27	5.38%

 

Q12. Content types creating the most pressure

Base: 502; more than one answer allowed.

Response	n	Share
Weight loss or body transformations	191	38.05%
Diet or clean eating	149	29.68%
Workout challenges	133	26.49%
Perfect routines or productivity	119	23.71%
Sleep optimisation	74	14.74%
Longevity or anti-ageing	72	14.34%
Mental health or self-improvement	68	13.55%
None of these	66	13.15%
Health warnings or symptoms	63	12.55%
Supplements	41	8.17%
Not sure	4	0.80%

 

Q13. Most trusted sources of guidance

Base: 502; more than one answer allowed.

Response	n	Share
Healthcare professional	310	61.75%
Personal trainer or nutrition professional	233	46.41%
Friends or family	105	20.92%
Fitness or wellness influencer	86	17.13%
General AI assistant	82	16.33%
Specialist fitness or wellness app	82	16.33%
Online publication	81	16.14%
Fitness or wellness brand	55	10.96%
Not sure	10	1.99%
None of these	10	1.99%

 

Theme: self-tracking

Q14. Frequency of tracking with an app or device

Base: 502.

Response	n	Share
Multiple times a day	47	9.36%
Daily	130	25.90%
Several times a week	113	22.51%
Less often	105	20.92%
Never	107	21.31%

 

Q15. Experiences because of tracking

Base: those who track; up to three answers.

Response	n	Share
Felt better informed	215	42.83%
Became more motivated	167	33.27%
Felt more in control	163	32.47%
Improved a health or fitness habit	124	24.70%
Felt pressure to reach a target	110	21.91%
Felt guilty after missing a target	93	18.53%
Checked my data more than felt helpful	42	8.37%
Became anxious about a result	39	7.77%
It had no meaningful effect	20	3.98%

 

Q16. Worried about a metric not fully understood

Base: respondents who receive metrics.

Response	n	Share
Yes, more than once	85	21.52%
Yes, once	85	21.52%
No	192	48.61%
Not sure	20	5.06%
I have not received these metrics	13	3.29%

 

Q17. Changed an activity because of a score

Base: respondents who receive recommendations.

Response	n	Share
Yes, more than once	129	32.66%
Yes, once	83	21.01%
No	152	38.48%
Not sure	6	1.52%
I have not received these recommendations	25	6.33%

 

Theme: AI in health and wellness

Q18. Used an AI tool for health guidance

Base: 502.

Response	n	Share
Often	66	13.15%
Sometimes	148	29.48%
Once or twice	93	18.53%
No, but I have considered it	46	9.16%
No	144	28.69%
Not sure whether a tool used AI	5	1.00%

 

Q19. What AI was used for

Base: AI users; more than one answer allowed.

Response	n	Share
Meal or nutrition ideas	206	41.04%
Workout or exercise guidance	187	37.25%
Stress or mental wellbeing support	106	21.12%
Supplement information	88	17.53%
Pain, injury or symptom guidance	86	17.13%
Weight management	83	16.53%
Habit tracking or motivation	60	11.95%
Sleep or recovery advice	58	11.55%
Interpreting wearable data	43	8.57%
Preparing questions for a professional	31	6.18%
Other	4	0.80%

 

Q20. Used AI instead of a professional

Base: respondents who have used AI for guidance.

Response	n	Share
Very often	57	18.57%
Often	69	22.48%
Sometimes	109	35.50%
Rarely	47	15.31%
Never	22	7.17%
Not applicable	3	0.98%

 

Q21. How far respondents would trust AI, by task

Base: respondents answering the trust grid.

Workout or exercise plans

Response	n	Share
Do not trust at all	45	8.96%
Trust very little	60	11.95%
Trust somewhat	299	59.56%
Trust completely	84	16.73%
Not sure	14	2.79%

Meal or nutrition ideas

Response	n	Share
Do not trust at all	41	8.17%
Trust very little	58	11.55%
Trust somewhat	282	56.18%
Trust completely	110	21.91%
Not sure	11	2.19%

Sleep or recovery advice

Response	n	Share
Do not trust at all	49	9.76%
Trust very little	84	16.73%
Trust somewhat	284	56.57%
Trust completely	64	12.75%
Not sure	21	4.18%

Habit and motivation support

Response	n	Share
Do not trust at all	54	10.76%
Trust very little	92	18.33%
Trust somewhat	240	47.81%
Trust completely	96	19.12%
Not sure	20	3.98%

Wearable-data interpretation

Response	n	Share
Do not trust at all	54	10.76%
Trust very little	86	17.13%
Trust somewhat	225	44.82%
Trust completely	105	20.92%
Not sure	32	6.37%

Supplement or weight-loss guidance

Response	n	Share
Do not trust at all	77	15.34%
Trust very little	136	27.09%
Trust somewhat	207	41.24%
Trust completely	60	11.95%
Not sure	22	4.38%

Pain or injury advice

Response	n	Share
Do not trust at all	91	18.13%
Trust very little	146	29.08%
Trust somewhat	193	38.45%
Trust completely	50	9.96%
Not sure	22	4.38%

Medical symptoms

Response	n	Share
Do not trust at all	111	22.11%
Trust very little	166	33.07%
Trust somewhat	165	32.87%
Trust completely	37	7.37%
Not sure	23	4.58%

 

Q22. Expected effect of AI on professionals

Base: 502.

Response	n	Share
Replace most professionals	28	5.58%
Replace some routine guidance	193	38.45%
Mainly support professionals	183	36.45%
Have limited impact	51	10.16%
Have no meaningful impact	24	4.78%
Not sure	23	4.58%