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Published on in Vol 8, No 4 (2022): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39271, first published .
Using Wearable Inertial Sensors to Assess Mobility of Patients With Hematologic Cancer and Associations With Chemotherapy-Related Symptoms Before Autologous Hematopoietic Stem Cell Transplant: Cross-sectional Study

Using Wearable Inertial Sensors to Assess Mobility of Patients With Hematologic Cancer and Associations With Chemotherapy-Related Symptoms Before Autologous Hematopoietic Stem Cell Transplant: Cross-sectional Study

Using Wearable Inertial Sensors to Assess Mobility of Patients With Hematologic Cancer and Associations With Chemotherapy-Related Symptoms Before Autologous Hematopoietic Stem Cell Transplant: Cross-sectional Study

Authors of this article:

Meghan B Skiba1, 2 Author Orcid Image ;   Graham Harker3 Author Orcid Image ;   Carolyn Guidarelli4 Author Orcid Image ;   Mahmoud El-Gohary5 Author Orcid Image ;   Fay Horak3, 5 Author Orcid Image ;   Eric J Roeland4, 6 Author Orcid Image ;   Rebecca Silbermann4, 6 Author Orcid Image ;   Brandon Hayes-Lattin4, 6 Author Orcid Image ;   Kerri Winters-Stone4 Author Orcid Image
Article Authors Cited by (4) Tweetations (4) Metrics

    Original Paper

    1Biobehavioral Health Science Division, College of Nursing, University of Arizona, Tucson, AZ, United States

    2The University of Arizona Cancer Center, University of Arizona, Tucson, AZ, United States

    3Department of Neurology, School of Medicine, Oregon Health & Science University, Portland, OR, United States

    4Division of Oncological Sciences, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States

    5APDM, Inc, a division of Clario International, Portland, OR, United States

    6Division of Hematology and Medical Oncology, School of Medicine, Oregon Health & Science University, Portland, OR, United States

    Corresponding Author:

    Kerri Winters-Stone, PhD

    Division of Oncological Sciences

    Knight Cancer Institute

    Oregon Health & Science University

    3181 SW Sam Jackson Park Rd

    KCRB-CPC

    Portland, OR, 97239

    United States

    Phone: 1 503 494 0813

    Fax:1 503 346 8296

    Email: wintersk@ohsu.edu


    Background: Wearable sensors could be a simple way to quantify and characterize mobility in patients with hematologic cancer scheduled to receive autologous hematopoietic stem cell transplant (autoHSCT) and how they may be related to common treatment-related symptoms and side effects of induction chemotherapy.

    Objective: We aimed to conduct a cross-sectional study comparing mobility in patients scheduled to receive autoHSCT with that in healthy, age-matched adult controls and determine the relationships between patient mobility and chemotherapy-related symptoms.

    Methods: Patients scheduled to receive autoHSCT (78/156, 50%) and controls (78/156, 50%) completed the prescribed performance tests using wearable inertial sensors to quantify mobility including turning (turn duration and number of steps), gait (gait speed, stride time, stride time variability, double support time, coronal trunk range of motion, heel strike angle, and distance traveled), and balance (coronal sway, coronal range, coronal velocity, coronal centroidal frequency, sagittal sway, sagittal range, sagittal velocity, and sagittal centroidal frequency). Patients completed the validated patient-reported questionnaires to assess symptoms common to chemotherapy: chemotherapy-induced peripheral neuropathy (Functional Assessment of Cancer Therapy/Gynecologic Oncology Group–Neurotoxicity subscale), nausea and pain (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire), fatigue (Patient-Reported Outcomes Measurement Information System Fatigue Short Form 8a), vertigo (Vertigo Symptom Scale–short form), and depression (Center for Epidemiological Studies–Depression). Paired, 2-sided t tests were used to compare mobility between patients and controls. Stepwise multivariable linear regression models were used to evaluate associations between patient mobility and symptoms.

    Results: Patients aged 60.3 (SD 10.3) years had significantly worse turning (turn duration; P<.001), gait (gait speed, stride time, stride time variability, double support time, heel strike angle, stride length, and distance traveled; all P<.001), and balance (coronal sway; P<.001, range; P<.001, velocity; P=.02, and frequency; P=.02; and sagittal range; P=.008) than controls. In patients, high nausea was associated with worse stride time variability (ß=.001; P=.005) and heel strike angle (ß=−.088; P=.02). Pain was associated with worse gait speed (ß=−.003; P=.003), stride time variability (ß=.012; P=.02), stride length (ß=−.002; P=.004), and distance traveled (ß=−.786; P=.005). Nausea and pain explained 17% to 33% and 14% to 36% of gait variance measured in patients, respectively.

    Conclusions: Patients scheduled to receive autoHSCT demonstrated worse mobility in multiple turning, gait, and balance domains compared with controls, potentially related in part to nausea and pain. Wearable inertial sensors used in the clinic setting could provide granular information about mobility before further treatment, which may in turn benefit from rehabilitation or symptom management. Future longitudinal studies are needed to better understand temporal changes in mobility and symptoms across the treatment trajectory to optimally time, design, and implement strategies, to preserve functioning in patients with hematologic cancer in the long term.

    JMIR Cancer 2022;8(4):e39271

    doi:10.2196/39271

    Keywords

    wearable inertial sensor; mobility; gait; induction chemotherapy; autologous hematopoietic stem cell transplant; autoHSCT; chemotherapy-related symptoms 


    Background

    The increasing frequency of autologous hematopoietic stem cell transplant (autoHSCT) to treat hematologic malignancies, especially among older adults, has contributed to increased survival [1,2]. AutoHSCT is preceded by myeloablative induction chemotherapy [3], which often leads to deconditioning and worsening of symptoms before transplant [4,5]. These pretransplant treatment-related impacts could predispose patients to altered mobility (ie, altered gait and balance) that could worsen after transplant and threaten patient’s functioning and quality of life [6,7]. Mobility declines have broad health implications, as patients undergoing transplants who report low physical functioning are at high risk for morbidity and mortality following transplant [8,9]. Over the past few years, a few studies have evaluated the feasibility and potential clinical utility of wearable sensors in the oncology setting [10-12]. Wearable sensors could describe specific patterns of mobility impairment and their potential attribution to treatment-related symptoms and potentially identify patient risk at discrete intervals along the treatment trajectory. In turn, this information could be used to inform timing and design of rehabilitation and symptom management strategies to positively affect clinical outcomes for the patient with hematologic cancer [13].

    Before transplant, patients undergo conditioning therapy, which can include any combination of radiation therapy, immunotherapy, or induction chemotherapy [3], all of which cause treatment-related symptoms and side effects that may linger into transplant [14]. Induction chemotherapy, in particular, can cause symptoms known to affect mobility including fatigue, neuropathy, vestibular dysfunction, dizziness, and pain [15]. Symptom clusters in patients undergoing transplant include fatigue, weakness, and anorexia; anxiety and depression; and nausea and vomiting [16]. These symptom clusters are associated with decreased self-reported physical functioning during autoHSCT and increased fall risk [17,18]. Current knowledge has relied on patient-reported measures of physical functioning, which can be less sensitive and informative and more prone to bias than objective measures of mobility and functioning [19,20]. It is also possible that using self-report may underestimate the degree of functional limitation among patients before autoHSCT. In addition, identifying the potential influence of treatment-related symptoms that are present at the time of transplant on mobility could identify patients at high risk for further decline after autoHSCT and who could benefit from appropriately timed rehabilitation and symptom management.

    Objective mobility measurements can assess turning, gait, and balance during prescribed tasks, such as walking at a usual pace, walking while turning, and standing in place. Using technology to capture mobility measures can provide greater precision, sensitivity, and granularity of information than clinical or field tests [21-23]. Characterizing the mobility characteristics of turning, gait, and balance using indices of support, stance, swing, spatial temporal patterns, stability, and range of motion typically requires advanced laboratory techniques (ie, motion cameras) that limit their application in nonresearch settings. Advancements in wearable sensors to quantify the same laboratory-based assessments in a clinic or home setting widen the scope of objective mobility assessment to include clinical populations undergoing intensive treatment and requiring hospitalization, such as patients undergoing autoHSCT. So far, a single study using insole sensors to measure gait patterns in patients after receiving allogeneic hematopoietic stem cell transplant (HSCT) reported slower walking speeds and shorter stride times than healthy matched controls, suggesting that treatment may have altered gait [24]. However, as gait was measured after treatment, it remains unknown whether patients already experienced some mobility limitations from treatments before transplant and whether and which persistent symptoms may be associated with mobility in patients receiving autoHSCT.

    Objectives

    We conducted a cross-sectional study using wearable inertial sensors to measure mobility in patients with hematologic cancer after induction chemotherapy and before autoHSCT to identify (1) differences in mobility between patients and age-matched controls and (2) whether and which symptoms typically related to chemotherapy may be associated with pretransplant mobility in patients.


    Study Design

    We used a case-control design to compare the mobility of 78 patients with hematologic cancer before transplant with that of healthy age-matched controls and a cross-sectional design to identify chemotherapy-related symptoms associated with mobility in patients.

    Participants and Setting

    Eligible patients were recruited through the Oregon Health & Science University Knight Cancer Institute Center for Hematologic Malignancies HSCT unit. Eligible patients were those who were scheduled to receive autoHSCT for a hematopoietic or lymphatic malignancy, were aged ≥21 years at the time of enrollment, had no cognitive difficulties that precluded completing surveys, were participating in performance testing, provided informed consent, and had no preexisting medical conditions that significantly affect mobility (ie, severe dystrophy, severe spasticity, epilepsy, seizures, Alzheimer disease, dementia, severe balance disorder, and inability to ambulate independently). Patients completed assessments after the completion of initial induction chemotherapy and within 2 weeks before hospitalization for autoHSCT.

    Age-Matched Controls

    Age-matched controls were selected from a preexisting sample of healthy adults recruited from the local community for 2 study protocols [25,26]. Eligible controls had no history of falls, chronic diseases including cancer, significant neurological or musculoskeletal impairment, or medication use that affects mobility or limits their ability to follow instructions or provide informed consent. Controls were age-matched to participants according to age at the time of assessment within 1 year.

    Ethics Approval

    The Oregon Health & Science University institutional review board approved the study (16760), and informed consent was obtained from all participants before data collection. Participant data were deidentified using individual code numbers assigned upon enrollment. Participants were not compensated for participating in the study. The survey and mobility assessment took approximately 30 to 45 minutes to complete and thus was not considered to pose a significant burden to participants.

    Demographic Measurements

    Patient demographics (age, sex, ethnicity or race, education, marital status, employment, and history of falls in the previous year) were self-reported. Comorbidities were determined using the Functional Comorbidity Index, a self-administered 18-item checklist of chronic conditions that affect physical functioning [27]. Self-reported cancer diagnosis and treatment history were adjudicated by the research staff. Height and weight were measured in the clinic, and BMI was calculated as kg/m2. The control group’s self-reported demographic data included age, sex, height, weight, health history, and education.

    Objective Mobility Assessment

    Objective mobility measures were assessed using Mobility Lab (APDM, Inc), a portable system of unobtrusive, body-worn, wireless, inertial sensors that quickly and automatically provide objective mobility measures, including turning, gait, and balance [28-30]. Patients’ Mobility Lab assessments were collected in the clinic using available space (eg, hallways) during a single appointment. Participants wore inertial sensors (Opal; APDM, Inc), placed at the sternum, lumbar spine, wrists, and ankles (Figure 1), and performed 2 standard physical functioning assessments—a 6-minute walk test (6MWT) and a 30-second quiet stance [19]. The 6MWT assesses distance walked over 6 minutes and is one of the most established outcome measures of functional mobility in clinical trials [31,32]. Participants walked at their usual pace for 6 minutes on a 20-meter course. Each full lap provided gait and turns averaged together, considerably reducing variability and performance bias compared with a single walk [33,34]. For controls, if a 6MWT was not performed owing to differences in protocol at the time of consent, a 400-meter walk was completed [25,26], which provides similar estimates of turning and gait [35]. Balance was measured using a 30-second quiet stance test, where participants stood as still as possible for 30 seconds with eyes open, feet together, and hands on their hips. Measures specific to turning, gait, and balance selected for these analyses (Table 1) have been previously used to assess fall risk, including dynamics during turning, postural adjustments associated with step initiation, spatial and temporal components of gait, and postural sway during standing balance [25,36-38]. Data processing was performed using Mobility Lab (version 2; APDM, Inc) and established algorithms [28,39]. The algorithms account for difference in physical stature (eg, height) of participants and in physical functioning assessment protocols for samples recruited at different times, allowing for a large sample of community-dwelling healthy adults with valid mobility data to select age-matched controls.

    ‎
    Figure 1. Inertial sensor placement (Mobility Lab Opal; APDM, Inc). In total, 6 sensors are placed—sternum (1 sensor; centered just below the collar bones, on the flat part of the chest), lumbar spine (1 sensor; centered at the base of the spine), wrist (2 sensors; on the wrist, similar to a watch), and ankles (2 sensors; centered on the front of the ankle). The figure was reproduced with permission from APDM, Inc.
    View this figure
    Table 1. Definitions of selected Mobility Lab mobility measures of turning, gait, and balance.
    MeasureDefinition
    Turning (6MWTa)

    		Turn duration (s)Duration of 180° turn

    		Number of stepsNumber of steps during 180° turn
    Gait (6MWT)

    		Gait speed (m/s)Forward speed of the individual, measured as the forward distance traveled during the gait cycle divided by the gait cycle duration

    		Stride time (s)Duration of a full gait cycle, measured from the left foot’s initial contact to the next initial contact of the left foot

    		Stride time variability (%)Coefficient of variation stride length (SD/mean)

    		Double support time (%)Rate of gait cycle while both feet are on the ground

    		Coronal trunk ROMb (°)Angular range of the lumbar spine in the coronal plane

    		Heel strike angle (°)Angle of the foot at the point of initial contact; the pitch of the foot is 0 when flat and positive when the heel contacts first

    		Stride length (m)Forward distance traveled by a foot during a gait cycle

    		Distance (m)Total distance traveled during the timed test, at usual walking speed
    Balance (30-second quiet stance)

    		Coronal sway RMSc (m/s2)Amplitude of lateral sway

    		Coronal range (m/s2)Angular range of the lateral thoracic spine (roll)

    		Coronal velocity (m/s)Mean velocity of lateral sway

    		Coronal centroidal frequency (Hz)Frequency of centroidal lateral sway

    		Sagittal sway RMS (m/s2)Amplitude of anterior-posterior sway

    		Sagittal range (m/s2)Angular range of the anterior-posterior thoracic spine (pitch)

    		Sagittal velocity (m/s)Mean velocity of anterior-posterior sway

    		Sagittal centroidal frequency (Hz)Frequency of centroidal anterior-posterior sway

    a6MWT: 6-minute walk test.

    bROM: range of motion.

    cRMS: root mean square.

    Patient-Reported Symptoms

    Overview

    For patients scheduled for autoHSCT, validated, patient-reported outcomes on symptoms typically associated with chemotherapy were collected using the following instruments and administered electronically in REDCap (Research Electronic Data Capture; Vanderbilt University) [40]. High scores indicate high level of symptoms for all questionnaires, unless otherwise described. Symptoms were not assessed for controls.

    Chemotherapy-Induced Peripheral Neuropathy

    Numbness, tingling, or uncomfortable sensations in hands and feet over the previous 7 days were measured using the 4-item Functional Assessment of Cancer Therapy/Gynecologic Oncology Group–Neurotoxicity subscale, a reliable and valid measure of chemotherapy-induced peripheral neuropathy (score range 0-16, where high scores indicate less-severe chemotherapy-induced peripheral neuropathy; minimally clinically important difference [MCID] 1.38-3.68) [41].

    Nausea and Pain

    Symptoms during the previous week were assessed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire–nausea or vomiting and pain symptom subscales (score range 0-100; MCID 2.4-15.5 [nausea] and 14.4-28.5 [pain]) [42,43]. This questionnaire is an acceptable measure of chronic pain [44].

    Fatigue

    Fatigue over the previous week was determined using the Patient-Reported Outcomes Measurement Information System Fatigue Short Form 8a (score range 0-100; MCID 3-5) [45,46].

    Vertigo

    The Vertigo Symptom Scale–short form was used to measure vertigo, dizziness, and somatic anxiety over the past month (score range 0-60; MCID 3) [47,48].

    Depression

    Depressive symptoms over the past week were assessed using the Center for Epidemiological Studies–Depression scale (score range 0-16; MCID 9-11) [49,50].

    Statistical Analysis

    Distributions were inspected for normality; balance measures were log transformed to improve normality, but model results were consistent; therefore, nontransformed variables and parametric tests were used for all analyses. Demographic characteristics were assessed using descriptive statistics, and paired, 2-sided t tests were used to determine the differences in mobility between patients and matched controls.

    Linear regression models were used to determine the association between symptoms and mobility. Univariate linear regression models with α≤.05 were used to determine the model demographic control variables. The final models were adjusted for age, sex, and BMI. Symptom selection criteria for linear regression models were determined using Pearson correlations to mobility characteristics, with cutoff points ρ≥0.3 and α≤.10 [51]. Final stepwise multivariable linear regression models were built using α≤.05 with any mobility characteristic to symptoms and were externally validated with 1000 bootstrap replications. Variability of symptoms in mobility characteristics was estimated using standardized ß coefficients. Post hoc Benjamini-Hochberg false discovery rate adjustment [52] with α=.05 was completed for paired t tests and linear regression models. Analyses were completed using STATA (version 16.1; StataCorp, LLC), with α≤.05 for statistical significance.


    Participants

    Between August 2017 and May 2019, 78 patients completed the Mobility Lab assessments before autoHSCT. The average age of patients before transplant was 60.3 (SD 10.3; range 31-76) years, and the most common cancer diagnosis was multiple myeloma (Table 2). The mean time since diagnosis to the scheduled autoHSCT was 9.9 (SD 11) months. All patients (78/78, 100) received induction chemotherapy before autoHSCT, with the average induction chemotherapy regimen lasting 4.7 (SD 3.2) months. In the year before the transplant, 17% (13/78) of the patients experienced a fall. The average age of matched controls (78/156, 50%) was 60.2 (SD 10.4) years. Most patients were men (50/78, 64%), whereas most controls were women (57/78, 73%). Controls had lower BMI and attained higher level of education than patients, and the control group had a high proportion of women compared with the patient group.

    Table 2. Demographics and clinical characteristics of patients with hematologic cancer scheduled for autoHSCTa compared with that of healthy, age-matched controls (N=156).
    CharacteristicsPatients scheduled for autoHSCT (n=78, 50%)Healthy controls (n=78, 50%)
    Age (years), mean (SD)60.3 (10.3)60.2 (10.4)
    Sex, n (%)

    		Female28 (36)57 (73)

    		Male50 (64)21 (27)
    Ethnicity, n (%)

    		Non-Hispanic69 (88)N/Ab

    		Declined to answer9 (12)N/A
    Race, n (%)

    		White63 (81)N/A

    		Non-Whitec5 (6)N/A

    		Declined to answer10 (13)N/A
    Education, n (%d)

    		High school diploma or equivalent20 (26)2 (3)

    		Some college or associate degree18 (23)8 (10)

    		Bachelor’s degree or higher31 (40)68 (87)

    		Declined to answer9 (12)N/A
    Marital status, n (%)

    		Married or living with partner53 (68)N/A

    		Divorced or separated7 (9)N/A

    		Single11 (14)N/A

    		Declined to answer7 (9)N/A
    Employment, n (%)

    		Full time23 (29)N/A

    		Part time6 (8)N/A

    		Not workinge39 (50)N/A

    		Declined to answer10 (13)N/A
    BMI (kg/m2), mean (SD)29.8 (5.7)25.2 (3.5)
    Height (m), mean (SD)1.7 (0.1)1.6 (0.1)
    Weight (kg), mean (SD)88.9 (21.2)66.3 (15.1)
    Cancer diagnosis, n (%)

    		Multiple myeloma53 (68)N/A

    		Hodgkin lymphoma6 (8)N/A

    		Non-Hodgkin lymphoma19 (24)N/A
    Cancer stagef, n (%)

    		I11 (14)N/A

    		II21 (27)N/A

    		III21 (27)N/A

    		IV12 (15)N/A

    		Missing or unknown13 (17)N/A
    Time since diagnosis (months), mean (SD)9.9 (11)N/A
    Received induction chemotherapy, n (%)78 (100)N/A
    Duration of induction chemotherapy (months), mean (SD)4.7 (3.2)N/A
    Time since last induction chemotherapy (days), mean (SD)20.2 (91.9)N/A
    Received radiation treatment, n (%)14 (18)N/A
    Functional Comorbidity Index scoreg, mean (SD)1.3 (1.3)N/A
    History of fall in past year, n (%)13 (17)N/A

    aautoHSCT: autologous hematopoietic stem cell transplant.

    bN/A: not available; data were not collected for controls.

    cCollapsed category including individuals who self-reported as being Asian, Black, or American Indian or Alaska Native or having >1 race.

    dPercentages may not add up to 100% owing to rounding.

    eCollapsed category including individuals who self-reported as being retired, unemployed, or homemaker. Disability status was not captured.

    fStaging for multiple myeloma included International Staging System, Revised International Staging System, and Durie-Salmon staging classifications.

    gMissing data <10%.

    Objective Mobility

    Mobility was significantly worse across most measures among patients than among controls (Table 3). In the 6MWT, turn duration was 0.28 (SD 0.54) seconds longer for patients than for controls (P<.001). Patients demonstrated an altered gait pattern, as exhibited by significantly slower gait speed (mean −0.32, SD 0.25 seconds), longer stride time (mean 0.13, SD 0.13 seconds), higher stride time variability (mean 1.07%, SD 1.42%), longer double support time (mean 5.91%, SD 4.23%), shallower heel strike angle (mean 0.81°, SD 3.56°), shorter stride length (mean −0.18, SD 0.19 m), and shorter distance traveled (mean −60.01, SD 93.49 m) than controls (P<.001). During standing balance, patients had significantly larger coronal sway (mean 0.02, SD 0.03 m/s2; P<.001), longer coronal range (mean 0.10, SD 0.16 m/s2; P<.001), higher coronal velocity (mean 0.03, SD 0.10 m/s; P=.02), lower coronal centroidal frequency (mean −0.11, SD 0.39 Hz; P=.02), and longer sagittal range (mean 0.08, SD 0.27 m/s2; P=.008) than controls. Sensitivity analyses restricting the analytical sample to controls with gait data from the 6MWT (31/78, 40%) or who were both age-matched and sex-matched (44/78, 56%) yielded results consistent with those obtained using the full sample of controls.

    Table 3. Comparison of mobility measures of turning, gait, and balance between patients with hematologic cancer scheduled for autoHSCTa and age-matched healthy controls.
    MeasuresPatients scheduled for autoHSCT, mean (SD)Healthy controls, mean (SD)Difference, mean (SD)P valueb
    Turning

    		Turn duration (s)2.43 (0.37)2.15 (0.40)0.28 (0.54)<.001

    		Number of steps4.04 (0.68)4.07 (0.78)−0.03 (0.97).82
    Gait

    		Gait speed (m/s)1.11 (0.19)1.43 (0.15)−0.32 (0.25)<.001

    		Stride time (s)1.14 (0.11)1 (0.08)0.13 (0.13)<.001

    		Stride time variability (%)3.55 (1.25)2.48 (0.71)1.07 (1.42)<.001

    		Double support time (%)23.24 (3.62)17.33 (2.99)5.91 (4.23)<.001

    		Coronal trunk ROMc (°)7.14 (2.68)6.33 (2.66)0.81 (3.56).06

    		Heel strike angle (°)23 (5.57)26.32 (4.23)−3.32 (6.47)<.001

    		Stride length (m)1.25 (0.16)1.43 (0.13)−0.18 (0.19)<.001

    		Distance (m)375.76 (64)435.78 (55.91)−60.01 (93.49)<.001
    Balance

    		Coronal sway RMSd (m/s2)0.06 (0.02)0.04 (0.02)0.02 (0.03)<.001

    		Coronal range (m/s2)0.33 (0.13)0.23 (0.09)0.10 (0.16)<.001

    		Coronal velocity (m/s)0.10 (0.06)0.07 (0.07)0.03 (0.10).02

    		Coronal centroidal frequency (Hz)1.05 (0.32)1.16 (0.26)−0.11 (0.39).02

    		Sagittal sway RMS (m/s2)0.08 (0.04)0.07 (0.04)0.01 (0.05).06

    		Sagittal range (m/s2)0.43 (0.22)0.34 (0.16)0.08 (0.27).008

    		Sagittal velocity (m/s)0.15 (0.08)0.13 (0.14)0.02 (0.15).26

    		Sagittal centroidal frequency (Hz)0.95 (0.22)0.96 (0.24)−0.01 (0.32).83

    aautoHSCT: autologous hematopoietic stem cell transplant.

    bPaired, 2-sided t test, with Benjamini-Hochberg false discovery rate adjustment set at α=.05, and all significant P values remained significant.

    cROM: range of motion.

    dRMS: root mean square.

    Mobility and Chemotherapy-Related Symptoms

    Of the 78 patients with mobility data, 69 (88%) completed the patient-reported chemotherapy-related symptom questionnaires (Table 4). Reasons for missing questionnaires included incomplete responses, refusal, or acute illness. Patients with missing symptom data did not significantly differ from those with complete data on age (P=.73), BMI (P=.97), sex (P=.57), or Functional Comorbidity Index (P=.91); therefore, complete case analysis was conducted. Models were built for symptoms associations with gait only, because prespecified criteria for building regression models were met for symptoms and gait but not for turning or balance measurements. Symptoms that remained significantly associated with any gait metric were nausea and pain (Table 5). High nausea was associated with great stride time variability (ß=.023, 95% CI −0.007 to 0.039) and shallow heel strike angle (ß=−.088, 95% CI −0.160 to −0.017). High pain was associated with slow gait speed (ß=−.003, 95% CI −0.004 to –0.001), short stride length (ß=−.002, 95% CI −0.003 to −0.001), short distance (ß=−.786, 95% CI −1.321 to −0.252), and great stride time variability (ß=.012, 95% CI −0.002 to −0.023). Nausea better explained the variance in stride time variability (33%) and heel strike angle (31%), whereas pain better explained the variance in gait speed (36%), stride length (35%), and distance (34%).

    Table 4. Chemotherapy-related symptom intensity among patients with hematologic cancer scheduled for autoHSCTa (n=69).
    Chemotherapy-related symptomMeasure possible score, rangeMeasure MCIDb, rangeSample score, mean (SD)Sample score, range
    CIPNc0-161.38-3.6813.01 (3.63)0-16
    Nausea0-1002.4-15.511.35 (18.63)0-100
    Pain0-10014.4-28.527.05 (29.02)0-100
    Fatigue0-1003-553 (7.84)33.1-69.8
    Vertigo0-6034.81 (5.74)0-31
    Depression0-609-119.19 (7.81)0-39

    aautoHSCT: autologous hematopoietic stem cell transplant.

    bMCID: minimally clinically important difference.

    cCIPN: chemotherapy-induced peripheral neuropathy.

    Table 5. Associations between chemotherapy-related symptoms and gait characteristics among patients with hematologic cancer scheduled for autoHSCTa (n=69).
    Gait characteristicsNauseaPain

    		ß coefficient (95% CI)Standardized ß coefficientP valuebß coefficient (95% CI)Standardized ß coefficientP valueb
    Gait speed (m/s)−.002 (−0.005 to 0.0003)−.189.09−.003 (−0.004 to −0.001)−.355.003
    Stride time (s).001 (−0.001 to 0.002).127.28.001 (−0.0001 to 0.002).221.07
    Stride time variability (%).023 (−0.007 to 0.039).331.005.012 (−0.002 to 0.023).275.02
    Double support time (%).018 (−0.028 to 0.064).09.43.014 (−0.017 to 0.044).104.38
    Coronal trunk ROMc (°).027 (−0.011 to 0.066).165.16−.033 (−0.058 to 0.007)−.310.16
    Heel strike angle (°)−.088 (−0.160 to −0.017)−.305.02−.026 (−0.074 to 0.021)−.141.28
    Stride length (m)−.001 (−0.003 to −0.0005).174.13−.002 (−0.003 to −0.001)−.349.004
    Distance (m)−.662 (−1.464 to 0.140)−.184.10−.786 (−1.321 to −0.252)−.340.005

    aautoHSCT: autologous hematopoietic stem cell transplant.

    bLinear regression models adjusted for age, sex, and BMI, with Benjamini-Hochberg false discovery rate adjustment set at α=.05, and all significant P values remained significant.

    cROM: range of motion.


    Principal Findings

    To the best of our knowledge, this study is the first to measure pretransplant mobility in patients with hematologic cancer using an innovative system of wearable inertial sensors to characterize patients’ mobility compared with that of healthy adults and determine whether symptoms may identify patients with altered mobility characteristics. Mobility was significantly worse for patients than for controls, indicating that chemotherapy may directly or indirectly alter systems that control turning, gait, and balance. Among patients, those with high levels of nausea and pain before transplant had worse gait characteristics, demonstrating a conservative gait pattern of slow shuffled walking associated with functional limitations and fall risk [53-55].

    Comparison With Previous Studies

    Wearable inertial sensors that measure multiple characteristics of turning, gait, and balance could better describe the mobility patterns affected by induction chemotherapy than self-report or field tests. Although other studies have only assessed gait, typically using a timed single walk test, our wearable sensor detected an aggregate of gait alterations in patients, along with differences in turning and balance. Gait parameters in our sample of patients were similar to those in a previous analysis using insole-worn sensors in a small sample of patients several months after allogenic HSCT [24]; however, our study provided great sensitivity by including additional gait metrics. These findings are consistent with slow and conservative gait patterns comparable with adults who are 20 years older [24,56], suggesting that patients may experience accelerated aging from induction chemotherapy [57]. The slow gait speed observed in our sample, consistent with previous findings in patients undergoing transplant [24], is concerning, given that slow gait speed at diagnosis is associated with subsequent hospitalizations and worse survival in older patients with hematologic malignancies [58]. Patients had a longer turn duration than controls, a measure associated with increased fall risk [59]; however, patients and controls took, on average, the same number of steps per turn. Increased double limb support time associated with falls [60] is a compensatory mechanism to make walking more secure with less time spent in single limb support. Gait may compensate for impaired balance [61,62], and thus, increased variability of gait characteristics could be owing to both compensatory mechanisms for balance deficits and multijoint incoordination. Control of balance while walking involves adjusting foot placement. Both variability in foot placement and double support time while walking also reflect impaired balance.

    Postural sway during normal, quiet standing has long been shown to be a sensitive measure of balance control, with large, fast sway being associated with increased fall risk [63]. Consistent with other studies, primarily in survivors of breast cancer, patients exhibited worse balance, likely exacerbated by chemotherapy [26,64,65]. Sagittal and coronal sway values observed in our sample were worse than those previously associated with falls in elderly populations [66]. Chemotherapy can have neurologic and musculoskeletal impacts affecting mobility including distal sensory loss, ototoxicity, myelopathy, weakness, atrophy, and sarcopenia [67,68]. In addition, glucocorticoids coadministered during chemotherapy and deconditioning from hospitalization for cancer treatment lead to muscle loss that could also underpin decline in mobility [69,70]. These findings suggest that patients planning to receive autoHSCT may undergo a pretransplant mobility risk assessment to identify patients at the highest risk for falls and functional decline throughout their treatment trajectory. Moreover, pretransplant mobility assessment may allow clinical teams to prioritize limited rehabilitation expertise and resources for patients at the highest risk of functional decline and more extended hospital stays.

    Symptoms may contribute to and co-occur with changes in mobility. Therefore, poorly controlled symptoms may help to identify patients at risk and those who may benefit from optimal symptom management and early palliative care integration [71]. We assessed multiple treatment-related symptoms previously associated with mobility and physical function in survivors of hematologic cancer [15-18]. In our sample, high nausea was significantly associated with great stride time variability and shallow heel strike angle. The pattern was also similar for pain, where high pain was significantly associated with slow gait speed, short stride length, great stride time variability, and less distance traveled. Persistent and severe nausea and pain clustering have been associated with poor performance status and limited physical function after cancer treatment [72]. Central nervous system disturbances owing to certain chemotherapies can affect cognition and movement, causing a sequela of symptoms comprising nausea and pain [73]. Chemotherapy can cause vestibular toxicity, resulting in nausea that intensifies over the transplant phase [74,75], which could directly or indirectly affect gait and balance [76]. Chronic pain is prevalent among survivors of hematologic cancer [77], has been associated with gait deficits in older adults [78], and is a significant risk factor for falls in survivors of cancer [79]. Persistent control of symptoms, including nausea and pain management, may be important for preserving physical functioning throughout the full treatment trajectory.

    Integration of Wearable Inertial Sensors in Clinical Care

    Providers subjectively assess a patient’s functional status before autoHSCT using the Karnofsky Performance Status assessment—a tool with good reliability and validity, but which is subjective and prone to clinician bias [80-82]. Until recently, characterizing mobility was only possible with complex and expensive laboratory-based systems, making it difficult to assess patients at the point of care. Introduction of wearable inertial sensors to assess mobility in the clinic setting widens the scope of what can be learned and implemented in clinical practice [83]. Mobility Lab is an affordable (comparable with other mobile gait assessment platforms) and time-effective approach to assess patients for aspects associated with risk for functional decline including postural sway, spatial and temporal components of gait, and dynamic balance during common movement such as turning [36]. The average time for an in-clinic assessment is 15 minutes, and it provides clinically relevant and accurate mobility evaluation that could be integrated into patient care and inform clinical decision-making.

    Detecting dynamic and potentially reversible gait changes during pretransplant appointments may minimize future health care use by directing resources to patients at high risk of treatment-associated disability or falls. Interventions to promote physical activity and exercise before or during treatment would improve physical function and mobility [84]. Exercise is feasible and can safely be initiated after induction chemotherapy [85]. Exercise interventions before autoHSCT have shown to improve quality of life and functional capacity, as measured by the 6MWT [86]. Symptom management itself may also lead to increased activity level in patients, and exercise has also been used to manage chemotherapy-related symptoms [87-89].

    Strengths and Limitations

    A significant strength of our study was the use of wearable inertial sensors to obtain objective measures of mobility characteristics before autoHSCT. We were able to collect high-quality mobility data in the domains of turning, gait, and balance using wearable inertial sensors in a clinic setting, which may have future utility in patient care. Wearable inertial sensors have additional benefits including low cost and portability for assessment outside laboratory settings [90]. This study also has limitations. Our case-control analysis used a previously collected set of data on controls, causing the patient and control samples to be unbalanced on some characteristics such as sex and body composition (eg, height, weight, and BMI), which may influence mobility measures; thus, future studies should prospectively enroll a matched control cohort. Our sample size was modest for linear regression; thus, findings should be interpreted accordingly. We did not have access to data from previous induction chemotherapy (eg, chemotherapy drug or classification, dose, number of cycles, and weight change) and concurrent medication use (eg, antiemetics and pain medications); thus, we are limited in what can be inferred about symptoms assessed before transplant. Similarly, we did not collect data on physical activity levels, but it is possible that there may be interactions between symptoms, mobility, and physical activity. For example, patients with low symptom severity may be more physically active and therefore demonstrate better mobility. In contrast, patients experiencing nausea or pain may require increased need for rest, which negatively affects their mobility. Similarly, we did not have information about symptom management interventions that may have similar interactions. In addition, our cross-sectional analysis could not establish causality between gait characteristics and patient-reported chemotherapy-related symptoms. Thus, they may be co-occurring problems. However, it is possible that symptoms could serve as a surrogate indicator of developing mobility deficits. As the average time from induction chemotherapy to enrollment was approximately 3 weeks, patient-reported pretransplant symptoms may be related to chemotherapy or comorbidities. Future studies could better establish the temporality of symptom onset and progression regarding mobility using longitudinal serial assessment.

    Conclusions

    Patients with hematologic cancer who have completed induction chemotherapy experience multiple alterations in mobility, as detected by a system of wearable inertial sensors. These altered gait patterns, which may have resulted from cancer treatment, place older patients with hematologic cancer at an elevated fall risk [91,92], which could ultimately increase morbidity and mortality risk [93,94]. Patients experiencing great nausea and pain at the time of autoHSCT may be at high risk of experiencing mobility limitations during and after transplant. Although this study could not infer whether chemotherapy-related symptoms directly alter gait, the findings highlight distinct mobility deficits in patients, which could not have been easily identified using standard mobility tests alone. Patients experiencing symptoms may warrant a more thorough assessment of their mobility using wearable sensors by the clinical team, including rehabilitation specialists, during routine appointments before hospitalization. Understanding these relationships could improve preventive care, symptom management, and rehabilitation efforts by identifying patients scheduled for autoHSCT who are at risk for further functional decline or falls after induction chemotherapy.

    Acknowledgments

    The authors would like to acknowledge the time and effort of the study participants; the assistance of Oregon Health & Science University Knight Cancer Institute’s registered nurse coordinators; in particular, Malinda Burt, the statistical support of Sydnee Stoyles; and the contributions of Maddy Dunn and Ashley Lyons for participant recruitment and data collection. This study was supported by the National Institutes of Health National Cancer Institute Small Business Innovation Research Grant (HHSN261201600067C SBIR) and National Institutes of Health National Cancer Institute Center Core Grant (3 P30 CA069533-22S6) awarded to the Knight Cancer Institute.

    Data Availability

    The data that support the findings of this study are not publicly available owing to privacy or ethical reasons but may be provided from the corresponding author upon reasonable request. Restrictions apply to the availability of some data, which were used with permission of APDM, Inc for this study.

    Authors' Contributions

    KWS, FH, and MEG contributed to the study design. KWS, MEG, and FH were involved in protocol development. CG was involved in participant recruitment and data collection. MBS and GH contributed to data analysis plan and execution. MBS, GH, FH, MEG, and KWS were involved in interpretation of results. MBS and KWS wrote the manuscript. GH, FH, MEG, CG, BHL, RS, and EJR edited the manuscript. MEG, FH, and KWS were involved in obtaining the funding. All the authors approved the final version of the manuscript.

    Conflicts of Interest

    MEG is an employee of APDM, Inc, and is required to complete training and disclosure regarding financial conflicts of interest before engaging in research conducted at Oregon Health & Science University. FH is a part-time employee and has significant financial interest in APDM, Inc. APDM, Inc may have commercial interest in the application of the results of this study. This potential conflict of interest has been reviewed and managed by Oregon Health & Science University.

    1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin 2022 Jan;72(1):7-33 [FREE Full text] [CrossRef] [Medline]
    2. Kennedy VE, Olin RL. Haematopoietic stem-cell transplantation in older adults: geriatric assessment, donor considerations, and optimisation of care. Lancet Haematol 2021 Nov;8(11):e853-e861. [CrossRef] [Medline]
    3. Deshantri AK, Varela Moreira A, Ecker V, Mandhane SN, Schiffelers RM, Buchner M, et al. Nanomedicines for the treatment of hematological malignancies. J Control Release 2018 Oct 10;287:194-215. [CrossRef] [Medline]
    4. Murao M, Hamada R, Kondo T, Miyasaka J, Yoshida M, Yonezawa H, et al. Analysis of factors associated with patient-reported physical functioning scores at discharge of allogeneic hematopoietic stem cell transplantation patients: a cross-sectional study. Support Care Cancer 2021 Dec;29(12):7569-7576 [FREE Full text] [CrossRef] [Medline]
    5. Lemieux C, Ahmad I, Bambace NM, Bernard L, Cohen S, Delisle J, et al. Evaluation of the impact of autologous hematopoietic stem cell transplantation on the quality of life of older patients with lymphoma. Biol Blood Marrow Transplant 2020 Jan;26(1):157-161 [FREE Full text] [CrossRef] [Medline]
    6. Nawas MT, Sheng Y, Huang C, Andreadis C, Martin TG, Wolf JL, et al. Serial comprehensive geriatric and quality of life assessments in adults age ≥ 50 years undergoing autologous hematopoietic cell transplantation. J Geriatr Oncol 2021 May;12(4):531-539. [CrossRef] [Medline]
    7. Morishita S, Kaida K, Aoki O, Yamauchi S, Wakasugi T, Ikegame K, et al. Balance function in patients who had undergone allogeneic hematopoietic stem cell transplantation. Gait Posture 2015 Sep;42(3):406-408. [CrossRef] [Medline]
    8. Rosko AE, Huang Y, Benson DM, Efebera YA, Hofmeister C, Jaglowski S, et al. Use of a comprehensive frailty assessment to predict morbidity in patients with multiple myeloma undergoing transplant. J Geriatr Oncol 2019 May;10(3):479-485 [FREE Full text] [CrossRef] [Medline]
    9. Nawas MT, Andreadis C, Martin TG, Wolf JL, Ai WZ, Kaplan LD, et al. Limitation in patient-reported function is associated with inferior survival in older adults undergoing autologous hematopoietic cell transplantation. Biol Blood Marrow Transplant 2019 Jun;25(6):1218-1224 [FREE Full text] [CrossRef] [Medline]
    10. Beg MS, Gupta A, Stewart T, Rethorst CD. Promise of wearable physical activity monitors in oncology practice. J Oncol Pract 2017 Feb;13(2):82-89 [FREE Full text] [CrossRef] [Medline]
    11. Gresham G, Schrack J, Gresham LM, Shinde AM, Hendifar AE, Tuli R, et al. Wearable activity monitors in oncology trials: current use of an emerging technology. Contemp Clin Trials 2018 Jan;64:13-21 [FREE Full text] [CrossRef] [Medline]
    12. Beauchamp UL, Pappot H, Holländer-Mieritz C. The use of wearables in clinical trials during cancer treatment: systematic review. JMIR Mhealth Uhealth 2020 Nov 11;8(11):e22006 [FREE Full text] [CrossRef] [Medline]
    13. Muhsen IN, Rasheed OW, Habib EA, Alsaad RK, Maghrabi MK, Rahman MA, et al. Current status and future perspectives on the Internet of Things in oncology. Hematol Oncol Stem Cell Ther (forthcoming) 2021 Oct 18 [FREE Full text] [CrossRef] [Medline]
    14. Zulu S, Kenyon M. Principles of conditioning therapy and cell infusion. In: Kenyon M, Babic A, editors. The European Blood and Marrow Transplantation Textbook for Nurses: Under the Auspices of EBMT. Cham, Switzerland: Springer; 2018:89-96.
    15. Magge RS, DeAngelis LM. The double-edged sword: neurotoxicity of chemotherapy. Blood Rev 2015 Mar;29(2):93-100 [FREE Full text] [CrossRef] [Medline]
    16. Esser P, Kuba K, Scherwath A, Johansen C, Schwinn A, Schirmer L, et al. Stability and priority of symptoms and symptom clusters among allogeneic HSCT patients within a 5-year longitudinal study. J Pain Symptom Manage 2017 Oct;54(4):493-500 [FREE Full text] [CrossRef] [Medline]
    17. Morishita S, Kaida K, Ikegame K, Yoshihara S, Taniguchi K, Okada M, et al. Impaired physiological function and health-related QOL in patients before hematopoietic stem-cell transplantation. Support Care Cancer 2012 Apr;20(4):821-829. [CrossRef] [Medline]
    18. Wildes TM, Fiala MA. Falls in older adults with multiple myeloma. Eur J Haematol 2018 Mar;100(3):273-278 [FREE Full text] [CrossRef] [Medline]
    19. Bennett JA, Winters-Stone K, Nail L. Conceptualizing and measuring physical functioning in cancer survivorship studies. Oncol Nurs Forum 2006 Jan 01;33(1):41-49. [CrossRef] [Medline]
    20. Winters-Stone KM, Medysky ME, Savin MA. Patient-reported and objectively measured physical function in older breast cancer survivors and cancer-free controls. J Geriatr Oncol 2019 Mar;10(2):311-316 [FREE Full text] [CrossRef] [Medline]
    21. Horak F, King L, Mancini M. Role of body-worn movement monitor technology for balance and gait rehabilitation. Phys Ther 2015 Mar;95(3):461-470 [FREE Full text] [CrossRef] [Medline]
    22. Coulthard JT, Treen TT, Oates AR, Lanovaz JL. Evaluation of an inertial sensor system for analysis of timed-up-and-go under dual-task demands. Gait Posture 2015 May;41(4):882-887. [CrossRef] [Medline]
    23. Ghislieri M, Gastaldi L, Pastorelli S, Tadano S, Agostini V. Wearable inertial sensors to assess standing balance: a systematic review. Sensors (Basel) 2019 Sep 20;19(19):4075 [FREE Full text] [CrossRef] [Medline]
    24. Kneis S, Straub E, Walz ID, von Olshausen P, Wehrle A, Gollhofer A, et al. Gait analysis of patients after allogeneic hematopoietic cell transplantation reveals impairments of functional performance. Integr Cancer Ther 2020;19:1534735420915782. [CrossRef] [Medline]
    25. Park JH, Mancini M, Carlson-Kuhta P, Nutt JG, Horak FB. Quantifying effects of age on balance and gait with inertial sensors in community-dwelling healthy adults. Exp Gerontol 2016 Dec 01;85:48-58 [FREE Full text] [CrossRef] [Medline]
    26. Fino PC, Horak FB, El-Gohary M, Guidarelli C, Medysky ME, Nagle SJ, et al. Postural sway, falls, and self-reported neuropathy in aging female cancer survivors. Gait Posture 2019 Mar;69:136-142 [FREE Full text] [CrossRef] [Medline]
    27. Groll DL, To T, Bombardier C, Wright JG. The development of a comorbidity index with physical function as the outcome. J Clin Epidemiol 2005 Jun;58(6):595-602. [CrossRef] [Medline]
    28. Mancini M, King L, Salarian A, Holmstrom L, McNames J, Horak FB. Mobility lab to assess balance and gait with synchronized body-worn sensors. J Bioeng Biomed Sci 2011 Dec 12;Suppl 1:007 [FREE Full text] [CrossRef] [Medline]
    29. Salarian A, Horak FB, Zampieri C, Carlson-Kuhta P, Nutt JG, Aminian K. iTUG, a sensitive and reliable measure of mobility. IEEE Trans Neural Syst Rehabil Eng 2010 Jun;18(3):303-310 [FREE Full text] [CrossRef] [Medline]
    30. Mancini M, Salarian A, Carlson-Kuhta P, Zampieri C, King L, Chiari L, et al. ISway: a sensitive, valid and reliable measure of postural control. J Neuroeng Rehabil 2012 Aug 22;9:59 [FREE Full text] [CrossRef] [Medline]
    31. Olsson LG, Swedberg K, Clark AL, Witte KK, Cleland JG. Six minute corridor walk test as an outcome measure for the assessment of treatment in randomized, blinded intervention trials of chronic heart failure: a systematic review. Eur Heart J 2005 Apr;26(8):778-793. [CrossRef] [Medline]
    32. Burr JF, Bredin SS, Faktor MD, Warburton DE. The 6-minute walk test as a predictor of objectively measured aerobic fitness in healthy working-aged adults. Phys Sportsmed 2011 May;39(2):133-139. [CrossRef] [Medline]
    33. Nordanstig J, Broeren M, Hensäter M, Perlander A, Osterberg K, Jivegård L. Six-minute walk test closely correlates to "real-life" outdoor walking capacity and quality of life in patients with intermittent claudication. J Vasc Surg 2014 Aug;60(2):404-409 [FREE Full text] [CrossRef] [Medline]
    34. Steffen TM, Hacker TA, Mollinger L. Age- and gender-related test performance in community-dwelling elderly people: six-minute walk test, berg balance scale, timed up and go test, and gait speeds. Phys Ther 2002 Feb;82(2):128-137. [CrossRef] [Medline]
    35. Simonsick EM, Montgomery PS, Newman AB, Bauer DC, Harris T. Measuring fitness in healthy older adults: the Health ABC Long Distance Corridor Walk. J Am Geriatr Soc 2001 Nov;49(11):1544-1548. [CrossRef] [Medline]
    36. Mancini M, Chiari L, Holmstrom L, Salarian A, Horak FB. Validity and reliability of an IMU-based method to detect APAs prior to gait initiation. Gait Posture 2016 Jan;43:125-131 [FREE Full text] [CrossRef] [Medline]
    37. Cabral KD, Brech GC, Alonso AC, Soares AT, Opaleye DC, Greve JM, et al. Posturographic measures did not improve the predictive power to identify recurrent falls in community-dwelling elderly fallers. Clinics (Sao Paulo) 2020 Apr 3;75:e1409 [FREE Full text] [CrossRef] [Medline]
    38. Wang K, Lovell NH, Del Rosario MB, Liu Y, Wang J, Narayanan MR, et al. Inertial measurements of free-living activities: assessing mobility to predict falls. Annu Int Conf IEEE Eng Med Biol Soc 2014;2014:6892-6895. [CrossRef] [Medline]
    39. Morris R, Stuart S, McBarron G, Fino PC, Mancini M, Curtze C. Validity of Mobility Lab (version 2) for gait assessment in young adults, older adults and Parkinson's disease. Physiol Meas 2019 Sep 30;40(9):095003 [FREE Full text] [CrossRef] [Medline]
    40. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009 Apr;42(2):377-381 [FREE Full text] [CrossRef] [Medline]
    41. Cheng HL, Lopez V, Lam SC, Leung AK, Li YC, Wong KH, et al. Psychometric testing of the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity (FACT/GOG-Ntx) subscale in a longitudinal study of cancer patients treated with chemotherapy. Health Qual Life Outcomes 2020 Jul 23;18(1):246 [FREE Full text] [CrossRef] [Medline]
    42. Fayers PM, Aaronson NK, Bjordal K, Sullivan M, Curran D, Bottomley A, EORTC Quality of Life Group. The EORTC QLQ-C30 Scoring Manual. 3rd edition. European Organisation for Research and Treatment of Cancer. 2001.   URL: https://www.eortc.org/app/uploads/sites/2/2018/02/SCmanual.pdf [accessed 2021-11-30]
    43. Raman S, Ding K, Chow E, Meyer RM, van der Linden YM, Roos D, et al. Minimal clinically important differences in the EORTC QLQ-C30 and brief pain inventory in patients undergoing re-irradiation for painful bone metastases. Qual Life Res 2018 Apr;27(4):1089-1098. [CrossRef] [Medline]
    44. Fredheim OM, Borchgrevink PC, Saltnes T, Kaasa S. Validation and comparison of the health-related quality-of-life instruments EORTC QLQ-C30 and SF-36 in assessment of patients with chronic nonmalignant pain. J Pain Symptom Manage 2007 Dec;34(6):657-665 [FREE Full text] [CrossRef] [Medline]
    45. Ameringer S, Elswick Jr RK, Menzies V, Robins JL, Starkweather A, Walter J, et al. Psychometric evaluation of the patient-reported outcomes measurement information system fatigue-short form across diverse populations. Nurs Res 2016;65(4):279-289 [FREE Full text] [CrossRef] [Medline]
    46. Yost KJ, Eton DT, Garcia SF, Cella D. Minimally important differences were estimated for six Patient-Reported Outcomes Measurement Information System-Cancer scales in advanced-stage cancer patients. J Clin Epidemiol 2011 May;64(5):507-516 [FREE Full text] [CrossRef] [Medline]
    47. Kondo M, Kiyomizu K, Goto F, Kitahara T, Imai T, Hashimoto M, et al. Analysis of vestibular-balance symptoms according to symptom duration: dimensionality of the Vertigo Symptom Scale-short form. Health Qual Life Outcomes 2015 Jan 22;13:4 [FREE Full text] [CrossRef] [Medline]
    48. Yardley L, Donovan-Hall M, Smith HE, Walsh BM, Mullee M, Bronstein AM. Effectiveness of primary care-based vestibular rehabilitation for chronic dizziness. Ann Intern Med 2004 Oct 19;141(8):598-605. [CrossRef] [Medline]
    49. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1977 Jun;1(3):385-401. [CrossRef]
    50. Haase I, Winkeler M, Imgart H. Ascertaining minimal clinically meaningful changes in symptoms of depression rated by the 15-item Centre for Epidemiologic Studies Depression Scale. J Eval Clin Pract 2022 Jun;28(3):500-506. [CrossRef] [Medline]
    51. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd edition. Cambridge, MA, USA: Academic press; 1988.
    52. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 1995;57(1):289-300. [CrossRef]
    53. Winters-Stone KM, Horak F, Jacobs PG, Trubowitz P, Dieckmann NF, Stoyles S, et al. Falls, functioning, and disability among women with persistent symptoms of chemotherapy-induced peripheral neuropathy. J Clin Oncol 2017 Aug 10;35(23):2604-2612 [FREE Full text] [CrossRef] [Medline]
    54. Campbell G, Wolfe RA, Klem ML. Risk factors for falls in adult cancer survivors: an integrative review. Rehabil Nurs 2018;43(4):201-213 [FREE Full text] [CrossRef] [Medline]
    55. Middleton A, Fritz SL, Lusardi M. Walking speed: the functional vital sign. J Aging Phys Act 2015 Apr;23(2):314-322 [FREE Full text] [CrossRef] [Medline]
    56. Fang X, Liu C, Jiang Z. Reference values of gait using APDM movement monitoring inertial sensor system. R Soc Open Sci 2018 Jan;5(1):170818 [FREE Full text] [CrossRef] [Medline]
    57. Guida JL, Ahles TA, Belsky D, Campisi J, Cohen HJ, DeGregori J, et al. Measuring aging and identifying aging phenotypes in cancer survivors. J Natl Cancer Inst 2019 Dec 01;111(12):1245-1254 [FREE Full text] [CrossRef] [Medline]
    58. Liu MA, DuMontier C, Murillo A, Hshieh TT, Bean JF, Soiffer RJ, et al. Gait speed, grip strength, and clinical outcomes in older patients with hematologic malignancies. Blood 2019 Jul 25;134(4):374-382 [FREE Full text] [CrossRef] [Medline]
    59. Leach JM, Mellone S, Palumbo P, Bandinelli S, Chiari L. Natural turn measures predict recurrent falls in community-dwelling older adults: a longitudinal cohort study. Sci Rep 2018 Mar 12;8(1):4316 [FREE Full text] [CrossRef] [Medline]
    60. Verghese J, Holtzer R, Lipton RB, Wang C. Quantitative gait markers and incident fall risk in older adults. J Gerontol A Biol Sci Med Sci 2009 Aug;64(8):896-901 [FREE Full text] [CrossRef] [Medline]
    61. Dulaney CR, McDonald AM, Wallace AS, Fiveash J. Gait speed and survival in patients with brain metastases. J Pain Symptom Manage 2017 Jul;54(1):105-109 [FREE Full text] [CrossRef] [Medline]
    62. Monfort SM, Pan X, Patrick R, Ramaswamy B, Wesolowski R, Naughton MJ, et al. Gait, balance, and patient-reported outcomes during taxane-based chemotherapy in early-stage breast cancer patients. Breast Cancer Res Treat 2017 Jul;164(1):69-77. [CrossRef] [Medline]
    63. Peterka RJ. Sensory integration for human balance control. Handb Clin Neurol 2018;159:27-42. [CrossRef] [Medline]
    64. Wampler MA, Topp KS, Miaskowski C, Byl NN, Rugo HS, Hamel K. Quantitative and clinical description of postural instability in women with breast cancer treated with taxane chemotherapy. Arch Phys Med Rehabil 2007 Aug;88(8):1002-1008. [CrossRef] [Medline]
    65. Schmitt AC, Repka CP, Heise GD, Challis JH, Smith JD. Comparison of posture and balance in cancer survivors and age-matched controls. Clin Biomech (Bristol, Avon) 2017 Dec;50:1-6. [CrossRef] [Medline]
    66. Maki BE, Holliday PJ, Topper AK. A prospective study of postural balance and risk of falling in an ambulatory and independent elderly population. J Gerontol 1994 Mar;49(2):M72-M84. [CrossRef] [Medline]
    67. Diaz M, Schiff D. Neurological complications of chemotherapy. In: Ahluwalia M, Metellus P, Soffietti R, editors. Central Nervous System Metastases. Cham, Switzerland: Springer; 2020:329-340.
    68. Knobf MT, Winters-Stone K. Exercise and cancer. Annu Rev Nurs Res 2013;31:327-365. [CrossRef] [Medline]
    69. Murnane A, Keogh J, Magat F, Imbesi S, Coulombe M, Patchell S, et al. The impact of an inpatient hospital admission on patients’ physical functioning and quality of life in the oncology setting. J Nurs Educ Pract 2015 Apr 28;5(7):75-82. [CrossRef]
    70. Wang DX, Yao J, Zirek Y, Reijnierse EM, Maier AB. Muscle mass, strength, and physical performance predicting activities of daily living: a meta-analysis. J Cachexia Sarcopenia Muscle 2020 Feb;11(1):3-25 [FREE Full text] [CrossRef] [Medline]
    71. El-Jawahri A, LeBlanc T, VanDusen H, Traeger L, Greer JA, Pirl WF, et al. Effect of inpatient palliative care on quality of life 2 weeks after hematopoietic stem cell transplantation: a randomized clinical trial. JAMA 2016 Nov 22;316(20):2094-2103 [FREE Full text] [CrossRef] [Medline]
    72. Ferreira KA, Kimura M, Teixeira MJ, Mendoza TR, da Nóbrega JC, Graziani SR, et al. Impact of cancer-related symptom synergisms on health-related quality of life and performance status. J Pain Symptom Manage 2008 Jun;35(6):604-616 [FREE Full text] [CrossRef] [Medline]
    73. Stone JB, DeAngelis LM. Cancer-treatment-induced neurotoxicity--focus on newer treatments. Nat Rev Clin Oncol 2016 Feb;13(2):92-105 [FREE Full text] [CrossRef] [Medline]
    74. Prayuenyong P, Taylor JA, Pearson SE, Gomez R, Patel PM, Hall DA, et al. Vestibulotoxicity associated with platinum-based chemotherapy in survivors of cancer: a scoping review. Front Oncol 2018 Sep 25;8:363 [FREE Full text] [CrossRef] [Medline]
    75. Anderson LJ, Yin C, Burciaga R, Lee J, Crabtree S, Migula D, et al. Assessing cachexia acutely after autologous stem cell transplant. Cancers (Basel) 2019 Sep 04;11(9):1300 [FREE Full text] [CrossRef] [Medline]
    76. Winters-Stone KM, Torgrimson B, Horak F, Eisner A, Nail L, Leo MC, et al. Identifying factors associated with falls in postmenopausal breast cancer survivors: a multi-disciplinary approach. Arch Phys Med Rehabil 2011 Apr;92(4):646-652 [FREE Full text] [CrossRef] [Medline]
    77. Ma JD, El-Jawahri AR, LeBlanc TW, Roeland EJ. Pain syndromes and management in adult hematopoietic stem cell transplantation. Hematol Oncol Clin North Am 2018 Jun;32(3):551-567. [CrossRef] [Medline]
    78. Ogawa EF, Shi L, Bean JF, Hausdorff JM, Dong Z, Manor B, et al. Chronic pain characteristics and gait in older adults: the MOBILIZE Boston study II. Arch Phys Med Rehabil 2020 Mar;101(3):418-425 [FREE Full text] [CrossRef] [Medline]
    79. Spoelstra S, Given B, von Eye A, Given C. Falls in the community-dwelling elderly with a history of cancer. Cancer Nurs 2010;33(2):149-155. [CrossRef] [Medline]
    80. Schag CC, Heinrich RL, Ganz PA. Karnofsky performance status revisited: reliability, validity, and guidelines. J Clin Oncol 1984 Mar;2(3):187-193. [CrossRef] [Medline]
    81. Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 1982 Dec;5(6):649-655. [Medline]
    82. Kelly CM, Shahrokni A. Moving beyond Karnofsky and ECOG performance status assessments with new technologies. J Oncol 2016;2016:6186543 [FREE Full text] [CrossRef] [Medline]
    83. Gresham G, Hendifar AE, Spiegel B, Neeman E, Tuli R, Rimel BJ, et al. Wearable activity monitors to assess performance status and predict clinical outcomes in advanced cancer patients. NPJ Digit Med 2018 Jul 5;1:27 [FREE Full text] [CrossRef] [Medline]
    84. Stout NL, Fu JB, Silver JK. Prehabilitation is the gateway to better functional outcomes for individuals with cancer. J Cancer Rehabil 2021;4:283-286 [FREE Full text] [Medline]
    85. Paul KL. Rehabilitation and exercise considerations in hematologic malignancies. Am J Phys Med Rehabil 2011 May;90(5 Suppl 1):S88-S94. [CrossRef] [Medline]
    86. Prins MC, van Hinte G, Koenders N, Rondel AL, Blijlevens NM, van den Berg MG. The effect of exercise and nutrition interventions on physical functioning in patients undergoing haematopoietic stem cell transplantation: a systematic review and meta-analysis. Support Care Cancer 2021 Nov;29(11):7111-7126 [FREE Full text] [CrossRef] [Medline]
    87. Hunter EG, Gibson RW, Arbesman M, D'Amico M. Systematic review of occupational therapy and adult cancer rehabilitation: Part 1. Impact of physical activity and symptom management interventions. Am J Occup Ther 2017;71(2):7102100030p1-710210003011. [CrossRef] [Medline]
    88. Campbell KL, Winters-Stone KM, Wiskemann J, May AM, Schwartz AL, Courneya KS, et al. Exercise guidelines for cancer survivors: consensus statement from international multidisciplinary roundtable. Med Sci Sports Exerc 2019 Nov;51(11):2375-2390 [FREE Full text] [CrossRef] [Medline]
    89. Winters-Stone KM, Moe EL, Perry CK, Medysky M, Pommier R, Vetto J, et al. Enhancing an oncologist's recommendation to exercise to manage fatigue levels in breast cancer patients: a randomized controlled trial. Support Care Cancer 2018 Mar;26(3):905-912. [CrossRef] [Medline]
    90. Najafi B, Khan T, Wrobel J. Laboratory in a box: wearable sensors and its advantages for gait analysis. Annu Int Conf IEEE Eng Med Biol Soc 2011;2011:6507-6510. [CrossRef] [Medline]
    91. Harrington CB, Hansen JA, Moskowitz M, Todd BL, Feuerstein M. It's not over when it's over: long-term symptoms in cancer survivors--a systematic review. Int J Psychiatry Med 2010;40(2):163-181. [CrossRef] [Medline]
    92. Ness KK, Wogksch MD. Frailty and aging in cancer survivors. Transl Res 2020 Jul;221:65-82 [FREE Full text] [CrossRef] [Medline]
    93. Sattar S, Haase K, Kuster S, Puts M, Spoelstra S, Bradley C, et al. Falls in older adults with cancer: an updated systematic review of prevalence, injurious falls, and impact on cancer treatment. Support Care Cancer 2021 Jan;29(1):21-33. [CrossRef] [Medline]
    94. Toomey A, Friedman L. Mortality in cancer patients after a fall-related injury: the impact of cancer spread and type. Injury 2014 Nov;45(11):1710-1716. [CrossRef] [Medline]

    ‎
    6MWT: 6-minute walk test
    autoHSCT: autologous hematopoietic stem cell transplant
    HSCT: hematopoietic stem cell transplant
    MCID: minimally clinically important difference
    REDCap: Research Electronic Data Capture

    Edited by A Mavragani; submitted 04.05.22; peer-reviewed by I Walz, ER Khalilian, S Choi, C Park; comments to author 22.09.22; revised version received 29.10.22; accepted 14.11.22; published 08.12.22

    Copyright

    ©Meghan B Skiba, Graham Harker, Carolyn Guidarelli, Mahmoud El-Gohary, Fay Horak, Eric J Roeland, Rebecca Silbermann, Brandon Hayes-Lattin, Kerri Winters-Stone. Originally published in JMIR Cancer (https://cancer.jmir.org), 08.12.2022.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on https://cancer.jmir.org/, as well as this copyright and license information must be included.