Applying AI to Safely and Effectively Scale Care to Address Chronic MSK Conditions
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Population
2.3. Intervention
2.3.1. Digital Care Program
2.3.2. Workflow-Related AI Tool
2.3.3. Comparison Group (CG)
2.3.4. Intervention Group: PT Portal Powered by AI Tool
2.4. Outcomes
2.5. Safety and Adverse Events
2.6. Sample Size
2.7. Statistical Analysis
3. Results
3.1. Baseline Characteristics
3.2. Outcomes
3.2.1. Clinical Outcomes
3.2.2. Engagement and Satisfaction
3.2.3. Adverse Events
4. Discussion
4.1. Main Findings
4.2. Comparison with Previous Studies
4.3. Strengths and Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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IG (N = 24,083) | CG (N = 12,103) | p-Value | |
---|---|---|---|
Age (years), mean (SD) | 48.5 (11.6) | 50.0 (11.7) | <0.001 |
Age categories (years), N (%): | |||
<25 | 342 (1.4) | 72 (0.6) | <0.001 |
25–40 | 6099 (25.3) | 2795 (23.1) | |
41–60 | 13,615 (56.5) | 6623 (54.7) | |
>60 | 4027 (16.7) | 2613 (21.6) | |
Gender, N (%) a: | |||
Woman | 14,126 (58.7) | 6984 (57.9) | 0.414 |
Man | 9849 (41.0) | 5044 (41.8) | |
Non-binary | 67 (0.3) | 35 (0.3) | |
Other | 4 (0.0) | 1 (0.0) | |
BMI (kg/m2), mean (SD) b | 29.7 (7.0) | 29.3 (6.7) | <0.001 |
BMI categories (kg/m2), N (%) b: | |||
Underweight (<18.5) | 199 (0.8) | 102 (0.8) | 0.001 |
Normal (18.5–25) | 6223 (25.9) | 3349 (27.7) | |
Overweight (≥25–30) | 8100 (33.7) | 4051 (33.5) | |
Obese (≥30) | 9528 (39.6) | 4584 (37.9) | |
Race/ethnicity, N (%) c: | |||
Asian | 2311 (9.8) | 928 (10.2) | 0.017 |
Black | 2297 (9.8) | 982 (10.8) | |
Hispanic | 2268 (9.6) | 890 (9.8) | |
Non-Hispanic White | 15,991(67.9) | 6013 (66.3) | |
Other | 690 (2.9) | 250 (2.8) | |
Education level, N (%) d: | |||
Less than high school diploma | 219 (0.9) | 74 (0.7) | 0.001 |
High school diploma | 2172 (9.1) | 823 (8.1) | |
Some college | 6133 (25.8) | 2528 (25.0) | |
Bachelor’s degree | 9397 (39.5) | 4162 (41.2) | |
Graduate degree | 5846 (24.6) | 2523 (25.0) | |
Geographic location, N (%) e: | |||
Urban | 21,321 (88.8) | 10,767 (89.4) | 0.077 |
Rural | 2693 (11.2) | 1276 (10.6) | |
Employment status, N (%) f: | |||
Full-time job | 20,807 (87.5) | 8987 (75.7) | <0.001 |
Part-time job | 997 (4.2) | 1908 (16.1) | |
Retired | 991 (4.2) | 608 (5.1) | |
Not employed | 990 (4.2) | 376 (3.2) | |
Clinical data, mean (SD) | |||
Analgesic intake, N (%) | 5580 (23.2) | 2850 (23.6) | 0.423 |
Symptomatic anatomical area: | |||
Ankle | 1395 (5.8) | 482 (4.0) | <0.001 |
Elbow | 523 (2.2) | 262 (2.2) | |
Hip | 2592 (10.8) | 1267 (10.5) | |
Knee | 3633 (15.1) | 1832 (15.1) | |
Low back | 8589 (35.7) | 4590 (37.9) | |
Neck | 2601 (10.8) | 1304 (10.8) | |
Shoulder | 3730 (15.5) | 1928 (15.9) | |
Wrist | 1020 (4.2) | 438 (3.6) | |
Pain intensity c, mean (SD) | 4.73 (1.9) | 4.84 (1.9) | <0.001 |
Mental health ≥ 5, mean (SD): | |||
GAD-7 g | 8.72 (3.94) | 8.81 (4.06) | 0.083 |
PHQ-5 h | 9.52 (4.20) | 9.23 (4.27) | 0.688 |
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Areias, A.C.; Janela, D.; Moulder, R.G.; Molinos, M.; Bento, V.; Moreira, C.; Yanamadala, V.; Correia, F.D.; Costa, F. Applying AI to Safely and Effectively Scale Care to Address Chronic MSK Conditions. J. Clin. Med. 2024, 13, 4366. https://doi.org/10.3390/jcm13154366
Areias AC, Janela D, Moulder RG, Molinos M, Bento V, Moreira C, Yanamadala V, Correia FD, Costa F. Applying AI to Safely and Effectively Scale Care to Address Chronic MSK Conditions. Journal of Clinical Medicine. 2024; 13(15):4366. https://doi.org/10.3390/jcm13154366
Chicago/Turabian StyleAreias, Anabela C., Dora Janela, Robert G. Moulder, Maria Molinos, Virgílio Bento, Carolina Moreira, Vijay Yanamadala, Fernando Dias Correia, and Fabíola Costa. 2024. "Applying AI to Safely and Effectively Scale Care to Address Chronic MSK Conditions" Journal of Clinical Medicine 13, no. 15: 4366. https://doi.org/10.3390/jcm13154366