Table 1.

Key aspects and limitations of studies included in the final analysis

Study (year), # of patients	Patient population and technology used	Key findings and study quality	Limitations
Pyrkov et al. (2018), n = 745411	–Participants in the NHANES cohort –ActiGraph AM-7164 single-axis piezoelectric accelerometer	–Machine learning algorithms are able to predict biological age from activity counts as recorded by wearable technology –Derived biological age is a significant predictor of all-cause mortality –High quality	–Limited number of deaths could make prediction models inaccurate –Complex analysis that may not be widely generalizable –Wearable technology data were not used prospectively to intervene and prevent clinical deterioration
Low et al (2018), n = 7115	–Metastatic peritoneal cancer –Fitbit Flex or Charge	–Mean steps/inpatient day was significantly associated with 30-day and 60-day readmissions (OR 0.83 and 0.82, respectively) –Moderate quality	–Inpatient data only –Very specific patient population/limited generalizability –Did not incorporate pain severity in predictive models, which correlates with mobility –Wearable technology data were not used prospectively to intervene and prevent clinical deterioration
Joseph et al (2017), n = 10114	–Elderly patients admitted to a trauma service –triaxial wearable gyroscope sensor	–Upper extremity function (derived from wearable sensor data) used as a surrogate for frailty was significantly associated with readmissions in multivariate model –High quality	–Data collected by the wearable sensor were not passively collected and require protocolized instruction to patient –Derived frailty index using wearable sensor requires additional steps of data analysis –Not all patients may be able to perform required motions for data capture –Data not used prospectively to prevent unfavorable clinical outcomes –Questionable generalizability
Bae et al. (2016), n = 2512,13	–Metastatic peritoneal cancer –Fitbit Flex	–Extracted 89 features from Fitbit data for model building –Readmitted patients had significantly longer sedentary bouts, fewer daily steps –Using Fitbit step counts and behavioral data, model predicted readmission with 88.3% accuracy –Using only Fitbit step counts predicted readmission 67.1% of the time –High quality	–Inpatient data only –Very specific patient population/limited generalizability –Small sample size –Did not incorporate pain severity in predictive models, which correlates with mobility –Wearable technology data were not used prospectively to intervene and prevent clinical deterioration –Data capture rate was not reported
Takahashi et al. (2015), n = 13317	–Post cardiac surgery patients –Active Style Pro HJA-350IT	–Mean number of steps walked during the last three inpatient days was significantly lower in patients who were re-hospitalized in the year after cardiac surgery –Moderate quality	–Used only step counts –Dropout rate of 17% –Inpatient data only –Wearable technology data were not used prospectively to intervene and prevent clinical deterioration
Yates et al. (2014), n = 93069,10	–Cardiovascular disease or cardiovascular disease risk factors –Pedometers	–For each 2000 step/day increase in baseline steps, risk of a cardiovascular event decreased 10% –For each 2000 step/day increase in steps over time, risk of a cardiovascular event decreased by 8% –Moderate quality	–Only used step counts –Only tracked step counts for two 1-week periods at 0 and 12 months –Primary goal of the original study was not to model clinical outcomes with wearable technology data –Conducted in 2002-2004, since which time wearable technology has advanced –Relied on patients to record step counts from the pedometer –Dichotomized or categorized step counts rather than using full breadth of data for modeling (eg average number of steps/day, change in activity from baseline at 12 months) –Cox proportional hazards rather than machine learning –25% of the cohort had missing pedometer data at baseline –45% of the cohort had missing pedometer data at 12 months –Wearable technology data were not used prospectively to intervene and prevent clinical deterioration
Fisher et al. (2013) n = 11116	–Elderly medicine patients –waterproof dual-axis accelerometer	–in unadjusted models, mean daily step count was associated with 30-day readmission –in multivariate logistic regression, mean daily step count was retained in the final model, but not a statistically significant predictor of 30-day readmission –Moderate quality	–Included only mean daily step count in multivariable model –Wearable technology data was not used prospectively to intervene and prevent clinical deterioration
Walsh et al. (1997), n = 848	–Heart failure –Pedometers	–Patients who took >25, 000 steps/week had relative risk of death of 0.2236 –Moderate quality	–Published in 1997, since which time wearable technology has advanced –Only used step counts –Small sample size –Step counts were dichotomized –Primary goal of the original study was not to model clinical outcomes with wearable technology data –Wearable technology data were not used prospectively to intervene and prevent clinical deterioration –Data capture rate was not reported