Reducing Sedentary Time After Knee Replacement Using a Multicomponent mHealth Intervention: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND Total knee replacement (TKR) is a common surgery for end-stage knee osteoarthritis. Although reductions in pain and improvements in mobility occur after surgery, physical activity levels often do not change. Given the challenges of increasing physical activity in this population, targeting reductions in sedentary behavior may be a first step; however, no prior studies have examined the feasibility and effects of a sedentary reduction intervention after TKR. OBJECTIVE This study examined the effects of a 2-month multicomponent mobile health sedentary reduction intervention (NEAT!2) on sedentary time in adults with TKR. METHODS Adults (N=83; mean age 65.3, SD 9.4 years; mean BMI 32.7, SD 6.9 kg/m2; 62/83, 74.7% female; 64/83, 77.1% White) with a TKR ≤1 year ago were randomized to the NEAT!2 group (n=42, 50.6%) or the attention-matched control group (n=41, 49.4%). The NEAT!2 intervention focused on reducing sedentary time via a smartphone app designed to interrupt prolonged bouts (≥30 minutes) of sedentary behavior and through 5 coaching calls emphasizing goal setting and problem solving. The control group focused on surgery recovery via an app or website and 5 educational calls. Sedentary time, total physical activity, physical function, and pain were measured at 2 and 5 months. Linear mixed-effects models examined intervention effects and differences between groups at each time point. RESULTS Retention was 96% and 95% at 2 and 5 months, respectively, with no differences between groups. Participants in the NEAT!2 group completed an average of 4.95 (SD 0.2) calls, used the app on an average of 40.3 (SD 13.8) days (out of 56 days), and received an average of 9.6 (SD 6.0) notifications per day. At 5 months, there was a significant increase in sit-to-stand transitions in the NEAT!2 group and a substantial decrease in the control group, resulting in a significant difference between groups (mean difference 4.06, 95% CI 0.13-7.99; P=.04); however, the NEAT!2 intervention did not result in significant effects on any of the other study outcomes at 2 or 5 months. Additionally, more days of app use were associated with greater increases in moderate-to-vigorous intensity physical activity (r=0.335; 95% CI 0.017-0.585; P=.04). CONCLUSIONS This study highlights the challenges of reducing sitting time in adults with TKR. Future studies should explore alternative behavior change techniques across different levels of influence (eg, environmental and social) to support interventions implemented within the first year after TKR. CLINICALTRIAL ClinicalTrials.gov NCT04482400; https://clinicaltrials.gov/ct2/show/NCT04482400
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".