Correlates of Satisfaction with Community Reintegration Among Stroke Survivors in Kano Metropolis

The aim of stroke rehabilitation is to ensure successful reintegration of stroke survivors (SSV) into their communities to enable them effectively discharge their physical, economic and social roles. This study assessed factors related to satisfaction with community reintegration (CR) of SSV in Kano metropolis. It was a cross sectional survey that recruited 68 consenting SSV using the purposive sampling technique. Assessments of CR, physical function, social support and depression were done with Reintegration to Normal Living Index (RNLI), Functional Independence Measure (FIM), Multidimensional Scale of Perceived Social Support (MPSS) and Patient Health Questionnaire (PHQ-9) respectively. Spearman Rank Order Correlation and Wilcoxon Sign Rank tests were used to analyze the data at a level of statistical significance of 0.05 using SPSS version 16.0. A total of 36(52.9%) males and 32(47.1%) females with mean age of 59.69±13.568 years took part in the study. About 50 (73.6%) are modified dependent and 46 (67.6%) enjoyed moderate social support. The majority 87% (N=59) experienced severe restrictions to CR. There were significant correlations between RNLI and each of MSPSS (rho=0.249, p=0.041) and FIM (rho =0.406, p=0.001) scores. Occupational status (Z=-6.693, p=0.000), income (Z=-3.910, p=0.000) and driving status (Z=-5.292, p=0.000) changed significantly. It was concluded that the level of CR of most SSV in Kano metropolis was not satisfactory with significant loss of employment and earnings and ability to drive post stroke. Increased levels of social support and adequate recovery of physical functions are likely to improve satisfaction with CR.KEY WORDS: stroke, satisfaction, community reintegration, social support, driving, return to wor

Predicting Functional Independence Measure Scores During Rehabilitation with Wearable Inertial Sensors

Evaluating patient progress and making discharge decisions regarding inpatient medical rehabilitation rely upon standard clinical assessments administered by trained clinicians. Wearable inertial sensors can offer more objective measures of patient movement and progress. We undertook a study to investigate the contribution of wearable sensor data to predict discharge functional independence measure (FIM) scores for 20 patients at an inpatient rehabilitation facility. The FIM utilizes a 7-point ordinal scale to measure patient independence while performing several activities of daily living, such as walking, grooming, and bathing. Wearable inertial sensor data were collected from ecological ambulatory tasks at two time points mid-stay during inpatient rehabilitation. Machine learning algorithms were trained with sensor-derived features and clinical information obtained from medical records at admission to the inpatient facility. While models trained only with clinical features predicted discharge scores well, we were able to achieve an even higher level of prediction accuracy when also including the wearable sensor-derived features. Correlations as high as 0.97 for leave-one-out cross validation predicting discharge FIM motor scores are reported

Predicting Functional Independence Measure Scores During Rehabilitation With Wearable Inertial Sensors

Evaluating patient progress and making discharge decisions regarding inpatient medical rehabilitation rely upon standard clinical assessments administered by trained clinicians. Wearable inertial sensors can offer more objective measures of patient movement and progress. We undertook a study to investigate the contribution of wearable sensor data to predict discharge functional independence measure (FIM) scores for 20 patients at an inpatient rehabilitation facility. The FIM utilizes a 7-point ordinal scale to measure patient independence while performing several activities of daily living, such as walking, grooming, and bathing. Wearable inertial sensor data were collected from ecological ambulatory tasks at two time points mid-stay during inpatient rehabilitation. Machine learning algorithms were trained with sensor-derived features and clinical information obtained from medical records at admission to the inpatient facility. While models trained only with clinical features predicted discharge scores well, we were able to achieve an even higher level of prediction accuracy when also including the wearable sensor-derived features. Correlations as high as 0.97 for leave-one-out cross validation predicting discharge FIM motor scores are reported