Share and intensity of work current AI systems can materially affect.
SOC 29-1128
Exercise Physiologists AI displacement risk
Exercise physiologists prescribe and supervise exercise programs for patients with cardiac, pulmonary, and metabolic conditions. Wearables now stream heart-rate and activity data continuously, sharpening the prescription — while clinical oversight of at-risk patients during exercise stays in the room.
Likely potential for exposed tasks to move to software after workflow integration.
This is clinical work, not fitness training: emergency response duty and individualized prescription for diseased populations require credentialed judgment. Wearable data expands what one physiologist can monitor between sessions.
Distribution
Where Exercise Physiologists sits across 620 tracked roles
Displacement pressure 20 — higher than 23% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
25 O*NET task statements matched to SOC 29-1128. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $59,460 (May 2025, US national). The latest BLS row matched SOC 29-1128.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Exercise Physiologists
SOC 29-1128 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 20/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Exercise Physiologists
The current evidence import matched 25 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks25
SOC29-1128
- Core task / ID 19194
Explain exercise program or physiological testing procedures to participants.
- Core task / ID 19193
Develop exercise programs to improve participant strength, flexibility, endurance, or circulatory functioning, in accordance with exercise science standards, regulatory requirements, and credentialing requirements.
- Core task / ID 19200
Provide clinical oversight of exercise for participants at all risk levels.
- Core task / ID 19212
Provide emergency or other appropriate medical care to participants with symptoms or signs of physical distress.
- Core task / ID 19207
Interview participants to obtain medical history or assess participant goals.
- Core task / ID 19192
Demonstrate correct use of exercise equipment or performance of exercise routines.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Develop individualized exercise programs
Exposure 40, automation 19%, augmentation 58%.
O*NET evidence: Prescribe individualized exercise programs, specifying equipment, such as treadmill, ex... (ID 19199)
Provide clinical oversight of exercise at all risk levels
Exposure 20, automation 7%, augmentation 44%.
O*NET evidence: Provide clinical oversight of exercise for participants at all risk levels. (ID 19200)
Interpret participant data to evaluate progress
Exposure 48, automation 25%, augmentation 62%.
O*NET evidence: Interpret exercise program participant data to evaluate progress or identify needed pro... (ID 19195)
Demonstrate correct use of equipment and routines
Exposure 20, automation 8%, augmentation 42%.
O*NET evidence: Demonstrate correct use of exercise equipment or performance of exercise routines. (ID 19192)
TaskExposureAutomationAugmentation
Develop individualized exercise programs4019%58%
Provide clinical oversight of exercise at all risk levels207%44%
Interpret participant data to evaluate progress4825%62%
Demonstrate correct use of equipment and routines208%42%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowCardiac Rehabilitation Lead
Training horizon: 6-12 months. Skill overlap 66. Wage preservation signal 116.
- Lead rehab programs
- Own telemetry protocols
- Coordinate with cardiology
credentialed transition
LowClinical Exercise Director
Training horizon: 12-24 months. Skill overlap 60. Wage preservation signal 122.
- Complete a master degree
- Earn ACSM clinical certification
- Manage program outcomes
Comparison guides
Compare the next move before you commit
6-12 months
Exercise Physiologists to Cardiac Rehabilitation Lead
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Exercise Physiologists into Cardiac Rehabilitation Lead.
12-24 months
Exercise Physiologists to Clinical Exercise Director
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Exercise Physiologists into Clinical Exercise Director.
What the AI risk score means for Exercise Physiologists
The displacement pressure score for Exercise Physiologists is 20. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Interpret participant data to evaluate progress carries 25% automation pressure, while Interpret participant data to evaluate progress carries 62% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $59,460 (May 2025, US national). Employment context: Clinical exercise role with wearable-data augmentation. Typical education: Bachelor degree; certification common.
Wage vulnerability is 44, while transition feasibility is 64. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Low displacement pressure
- Wearables extend monitoring between sessions
- At-risk supervision stays in person
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Exercise Physiologists, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Exercise prescription
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Clinical supervision
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Wearable data analysis
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Emergency response
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Cardiac Rehabilitation Lead, such as lead rehab programs.
- By 90 days, compare internal openings and external postings for Cardiac Rehabilitation Lead or Clinical Exercise Director and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Exercise Physiologists
Will AI replace Exercise Physiologists?
Exercise physiologists prescribe and supervise exercise programs for patients with cardiac, pulmonary, and metabolic conditions. Wearables now stream heart-rate and activity data continuously, sharpening the prescription — while clinical oversight of at-risk patients during exercise stays in the room. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Exercise Physiologists work are most exposed to AI?
Interpret participant data to evaluate progress and Develop individualized exercise programs show the strongest automation pressure in this model. Interpret participant data to evaluate progress and Develop individualized exercise programs are better treated as AI-augmented work.
What should Exercise Physiologists learn next?
Start with Exercise prescription, Clinical supervision, Wearable data analysis. The most practical adjacent paths in this model are Cardiac Rehabilitation Lead and Clinical Exercise Director.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
OECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics