Table 1.
Eligibility criteria [34].
Table 2.
Description of measures.
Fig 1.
Abbreviations: ASPD- Average Steps/Day, LR- Logistic Regression, SVM- Support Vector Machine, RF- Random Forest, CI- Confidence Interval.
Table 3.
Characteristics of study sample (n = 268)a.
Fig 2.
Drop column feature importance for home vs. community threshold (2500 steps/day).
Red markers show mean feature importance with 95% bootstrapped confidence interval. 6MWT was the only feature found to be important across all three algorithms. Abbreviations: ADI_N- Area Deprivation Index (national percentile), PHQ-9- Patient Health Questionnaire-9, Readiness_Relapse- Readiness to change relapse score, SSWS- self-selected walking speed, 6MWT- 6-Minute Walk Test, LR- Logistic regression, SVM- Support vector machine, RF- Random forest.
Fig 3.
Model performance for the home vs. community threshold (2500 steps/day; A- upper figure) and aerobic threshold (5500 steps/day; B- bottom figure). Model performance for each algorithm is displayed with all features included (AF) and with feature selection (FS) that occurred as a result of the regularization step. Circles represent individual accuracy results for model performance during the 100 different train-tests splits of the data. Diamonds represent outliers. A higher accuracy score reflects better model performance.
Fig 4.
Drop column feature importance for aerobic threshold (5500 steps/day).
Red markers show mean feature importance with 95% bootstrapped confidence interval. Speed modulation was the only feature found to be important across all three algorithms. Abbreviations: ABC- Activities Specific Balance Confidence Scale, ADI_N- Area Deprivation Index (national percentile), BMI- body mass index, CCI- Charlson Comorbidity Index (age-adjusted), PHQ-9 (Patient Health Questionnaire-9), Readiness_Stage- Readiness to change stage score, 6MWT- 6-Minute Walk Test, TSIS- time since initial stroke, LR- Logistic regression, SVM- Support vector machine, RF- Random forest.