In-patient step count predicts re-hospitalization after cardiac surgery

Takahashi, Toshio; Kumamaru, Megumi; Jenkins, Sue; Saitoh, Masakazu; Morisawa, Tomoyuki; Matsuda, Hikaru

doi:10.1016/j.jjcc.2015.01.006

Cited by 59 publications

(39 citation statements)

References 30 publications

(27 reference statements)

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“…Low Fitbit step counts during in-patient recovery could help providers to identify patients in need of additional monitoring or intervention prior to discharge [12]. Results are consistent with previous research in general medicine and cardiac patients indicating associations between accelerometer-assessed steps and readmission risk (e.g., [9,10]). To our knowledge, this is the first study to link Fitbit wristband-assessed step counts to readmission in any clinical sample as well as the first to examine associations between steps and readmission risk after cancer surgery.…”

Section: Discussionsupporting

confidence: 78%

“…Covariates were selected on the basis of previously published associations with readmission risk and/or postoperative ambulation (i.e., age, body mass index, length of stay, comorbidities; [3, 9, 10]). In exploratory analyses, we also considered the role of preoperative activity level, which was assessed prior to surgery using the following question from the National Health Interview Survey: “Outside of your normal work or daily responsibilities, how often do you engage in exercise that at least moderately increases your breathing and heart rate, and makes you sweat, for at least 20 min (such as brisk walking, cycling, swimming, jogging, aerobic dance, stair climbing, rowing, basketball, racquetball, vigorous yard work (gardening), etc.…”

Section: Methodsmentioning

confidence: 99%

“…Previous research with a cardiac surgery population suggests that higher numbers of Fitbit-assessed steps during postoperative hospitalization are associated with shorter length of inpatient stay [8]. Lower accelerometer-assessed step counts have also been associated with higher patient-reported 1 year readmission risk after cardiac surgery [9] and higher 30-day readmission in older hospitalized medical patients [10]. …”

Section: Introductionmentioning

confidence: 99%

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Fitbit step counts during inpatient recovery from cancer surgery as a predictor of readmission

Low

Bovbjerg

Ahrendt

et al. 2017

Annals of Behavioral Medicine

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Background Postoperative ambulation is encouraged to promote timely recovery but is rarely monitored objectively or examined as a predictor of clinical outcomes, despite growing availability of wearable devices that allow passive quantification and remote real-time monitoring of the number of steps taken during recovery. Purpose To determine whether the number of steps taken during inpatient recovery predicts 30- and 60-day readmission risk after metastatic cancer surgery. Methods Patients diagnosed with metastatic peritoneal cancer and scheduled for surgical resection were enrolled in this observational cohort study at their preoperative clinic visit. Fitbits were placed on patients’ wrists upon transfer from the ICU following surgery and worn for the duration of their inpatient stay. Information about hospital readmission was extracted from electronic medical records. Results Seventy-one patients participated in the study (mean age = 57.14, range = 31–80 years; 42% female; 51% diagnosed with appendiceal cancer). Mean steps per day were calculated for each participant over the entire inpatient recovery period (mean stay = 12.12 days, 4–37 days). Readmission within 30 and 60 days was medically indicated for 34% and 39% of patients, respectively. After statistically adjusting for age, body mass index, comorbidity, and length of postoperative stay, higher mean steps per day predicted lower 30-day and 60-day readmission risk. Conclusions Higher Fitbit step counts during inpatient recovery predicted lower risk of 30- and 60-day readmission after surgery for metastatic peritoneal cancer. Results suggest that passively monitoring peri-operative ambulation may identify patients at risk for readmission and highlight opportunities for behavioral intervention.

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Section: Discussionsupporting

confidence: 78%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fitbit step counts during inpatient recovery from cancer surgery as a predictor of readmission

Low

Bovbjerg

Ahrendt

et al. 2017

Annals of Behavioral Medicine

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“…Clinicians traditionally relied on scores derived from inpatient data [10,11,13,22,26,31,35] or patient mobility [16,30]. In particular, LACE index has been widely adopted to predict readmission.…”

Section: Related Workmentioning

confidence: 99%

Feasibility Study of Monitoring Deterioration of Outpatients Using Multimodal Data Collected by Wearables

Vaidya

Wang

et al. 2020

ACM Trans. Comput. Healthcare

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Hospital readmission rate is high for heart failure patients. Early detection of deterioration will help doctors prevent readmissions, thus reducing health care cost and providing patients with just-in-time intervention. Wearable devices (e.g., wristbands and smart watches) provide a convenient technology for continuous outpatient monitoring. In the paper, we explore the feasibility of monitoring outpatients using Fitbit Charge HR wristbands and the potential of machine learning models to predicting clinical deterioration (readmissions and death) among outpatients discharged from the hospital. We developed and piloted a data collection system in a clinical study which involved 25 heart failure patients recently discharged from a hospital. The results from the clinical study demonstrated the feasibility of continuously monitoring outpatients using wristbands. We observed high levels of patient compliance in wearing the wristbands regularly and satisfactory yield, latency and reliability of data collection from the wristbands to a cloud-based database. Finally, we explored a set of machine learning models to predict deterioration based on the Fitbit data. Through 5-fold cross validation, K nearest neighbor achieved the highest accuracy of 0.8800 for identifying patients at risk of deterioration using the health data from the beginning of the monitoring. Machine learning models based on multimodal data (step, sleep and heart rate) significantly outperformed the traditional clinical approach based on LACE index. Moreover, our proposed weighted samples one class SVM model can reach high accuracy (0.9635) for predicting the deterioration happening in the future using data collected by a sliding window, which indicates the potential for allowing timely intervention.

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“…The validation of the pedometer step count indicating the value of physical activity was verified in several studies. 11,12) Furthermore, the pedometer step count can predict exercise tolerance and correlate with several vascular or metabolic markers. 13) Although the importance of the pedometer step count in evaluating the effectiveness of CR or monitoring the state of exercise tolerance should be addressed in future research, this simple parameter may be useful for predicting the future risk of atherosclerotic disease.…”

Section: Article P597mentioning

confidence: 99%

Physical Activity

2015

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In-patient step count predicts re-hospitalization after cardiac surgery

Abstract: In-patient step count appears to be a risk factor for cardiac re-hospitalization within the first year following cardiac surgery. Further studies are needed to clarify the clinical significance of step count both preoperatively and following discharge.

Cited by 59 publications

References 30 publications

Fitbit step counts during inpatient recovery from cancer surgery as a predictor of readmission

Fitbit step counts during inpatient recovery from cancer surgery as a predictor of readmission

Feasibility Study of Monitoring Deterioration of Outpatients Using Multimodal Data Collected by Wearables

Physical Activity

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