Using passively collected sedentary behavior to predict hospital readmission

Bae, Sangwon; Dey, Anind K.; Low, Carissa A.

doi:10.1145/2971648.2971750

Cited by 32 publications

(25 citation statements)

References 16 publications

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“…Collecting data on reasons for lack of ambulation may also inform the design and delivery of interventions, as reasons could include symptom burden such as uncontrolled pain or fatigue, disruption of typical routines and environment while in the hospital, anxiety about falling or irritating surgical incisions, or beliefs about whether activity will help or hinder healing. Passively sensed data on sedentary behavior may account for additional variance over and above the predictive utility of step counts [17]. Testing whether behavioral interventions aimed at increasing perioperative ambulation influence readmission and other clinical outcomes would shed light on whether activity has a causal influence on readmission.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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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“…Other features were extracted from bouts, where a bout is a continuous period of time during which a certain characteristic is exhibited. Examples of such features included the total number of active or sedentary bouts [26], and the maximum, minimum, and average length of active or sedentary bouts. We also calculated minimum, maximum, and average number of steps over all active bouts.…”

Section: Methodsmentioning

confidence: 99%

Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

Doryab¹,

Villalba²,

Chikersal³

et al. 2019

JMIR Mhealth Uhealth

130

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Background Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. Objective The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns. Methods Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner. Results The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]). Our machine learning pipeline achieved an accuracy of 80.2% in detecting the binary level of loneliness and an 88.4% accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17% and confidence=92%). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31% and confidence=92%). Conclusions Passive sensing has the potential for detecting loneliness in college stu...

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“…The daily chair rise transition analysis included sit-to-stand transition duration, velocity, peak power,maximum jerk, maximum acceleration and frequency [31] [32] [33]. Moreover, active and sedentarybout lengths at multiple activity threshold levels were calculated [34]. Non-wearing periods were detected as the absence of movement for at least 15 minutes, based on sensor values, and excluded from the analysis.…”

Section: Remote Tug Evaluation Model Developmentmentioning

confidence: 99%

Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

et al. 2019

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Background. Mobility impairment is common in older adults and negatively influences the quality of life. Mobility level may change rapidly following surgery or hospitalization in the elderly. The Timed Up and Go (TUG) is a simple, frequently used clinical test for functional mobility; however, TUG requires supervision from a trained clinician, resulting in infrequent assessments. Additionally, assessment by TUG in clinic settings may not be completely representative of the individual's mobility in their home environment. Objective. In this paper, we introduce a method to estimate TUG from activities detected in free-living, enabling continuous remote mobility monitoring without expert supervision. The method is used to monitor changes in mobility following Total Hip Arthroplasty (THA). Methods. Community-living elderly (n=239, 65-91 years) performed a standardized TUG in a laboratory and wore a wearable pendant device that recorded accelerometer and barometric sensor data for at least 3-days. Activities of daily living, including walks and sit-to-stand transitions, and their related mobility features were extracted and used to develop a regularized linear model for remote TUG test estimation. Changes in the remote TUG were evaluated in orthopaedic patients (n=15, 55-75 years), during 12-weeks following THA. Results. In leave-one-out-cross-validation, a strong correlation (ρ=0.70) was observed between the new remote TUG and standardized TUG times. Test-retest reliability of 3-day estimates was high (ICC=0.94). Compared to 2-weeks post-THA, remote TUG was significantly improved at 6-weeks (11.7±3.9 s vs 8.0±1.8 s, p<0.001), with no further change at 12-weeks (8.1±3.9 s, p=0.37). Conclusions. Remote TUG can be estimated in older adults using 3-days of ADLs data recorded using a wearable pendant. Remote TUG has discriminatory potential for identifying frail elderly and may provide a convenient way to monitor changes in mobility in unsupervised settings.

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Using passively collected sedentary behavior to predict hospital readmission

Cited by 32 publications

References 16 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

Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

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