Table 4.
Self-reported reasons for nonuse of digital health technologies by gender, mapped to COM-Ba domains.
| Technology | Nonusers with a codable reason, n (%) | Opportunity, n/N (%)b | Motivation, n/N (%)b | Capability, n/N (%)b | P valuec |
| Telehealth | 30 (96.8) | Md: 4/14 (28.6); Fd: 6/16 (37.5) | M: 8/14 (57.1); F: 10/16 (62.5) | M: 2/14 (14.3); F: 0 | .460 |
| mHealthe app | 82 (88.2) | M: 5/55 (9.1); F: 9/27 (33.3) | M: 22/55 (40.0); F: 12/27 (44.4) | M: 28/55 (50.9); F: 6/27 (22.2) | .009 |
| Wearable device | 78 (89.7) | M: 8/25 (32.0); F: 28/53 (52.8) | M: 12/25 (48.0); F: 22/53 (41.5) | M: 5/25 (20.0); F: 3/53 (5.7) | .079f |
| Chatbot/virtual assistant | 100 (92.6) | M: 7/28 (25.0); F: 16/72 (22.2) | M: 14/28 (50.0); F: 42/72 (58.3) | M: 7/28 (25.0); F: 14/72 (19.4) | .755 |
| e-Pharmacy | 46 (78.0) | M: 5/16 (31.3); F: 6/30 (20.0) | M: 11/16 (68.8); F: 21/30 (70.0) | M: 0; F: 3/30 (10.0) | .464 |
aCOM-B: Capability, Opportunity, Motivation – Behavior.
bPercentages are within-gender column percentages.
cFisher’s exact test.
dM: male; F: female.
emHealth: mobile health.
fP=.05-.10 (trend). P<.05 indicates a statistically significant gender difference. Totals differ from nonuser counts in Table 2 because barrier analyses included only nonusers who provided a codable reason for nonuse.