Harnessing physical activity monitoring and digital biomarkers of frailty from pendant based wearables to predict chemotherapy resilience in veterans with cancer
This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle...
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| Published in | Scientific reports Vol. 14; no. 1; pp. 2612 - 11 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
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London
Nature Publishing Group UK
31.01.2024
Nature Publishing Group Nature Portfolio |
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| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-024-53025-z |
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| Abstract | This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle, participants wore a water-resistant pendant sensor for 14 days. This device tracked frailty markers like cadence (slowness), daily steps (inactivity), postural transitions (weakness), and metrics such as longest walk duration and energy expenditure (exhaustion). Participants were divided into resilient and non-resilient groups based on adverse events within 6 months post-chemotherapy, including dose reduction, treatment discontinuation, unplanned hospitalization, or death. A Chemotherapy-Resilience-Index (CRI) ranging from 0 to 1, where higher values indicate poorer resilience, was developed using regression analysis. It combined physical activity data with baseline Eastern Cooperative Oncology Group (ECOG) assessments. The protocol showed a 97% feasibility rate, with sensor metrics effectively differentiating between groups as early as day 6 post-therapy. The CRI, calculated using data up to day 6 and baseline ECOG, significantly distinguished resilient (CRI = 0.2 ± 0.27) from non-resilient (CRI = 0.7 ± 0.26) groups (p < 0.001, Cohen’s d = 1.67). This confirms the potential of remote monitoring systems in tracking post-chemotherapy functional capacity changes and aiding early non-resilience detection, subject to validation in larger studies. |
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| AbstractList | This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle, participants wore a water-resistant pendant sensor for 14 days. This device tracked frailty markers like cadence (slowness), daily steps (inactivity), postural transitions (weakness), and metrics such as longest walk duration and energy expenditure (exhaustion). Participants were divided into resilient and non-resilient groups based on adverse events within 6 months post-chemotherapy, including dose reduction, treatment discontinuation, unplanned hospitalization, or death. A Chemotherapy-Resilience-Index (CRI) ranging from 0 to 1, where higher values indicate poorer resilience, was developed using regression analysis. It combined physical activity data with baseline Eastern Cooperative Oncology Group (ECOG) assessments. The protocol showed a 97% feasibility rate, with sensor metrics effectively differentiating between groups as early as day 6 post-therapy. The CRI, calculated using data up to day 6 and baseline ECOG, significantly distinguished resilient (CRI = 0.2 ± 0.27) from non-resilient (CRI = 0.7 ± 0.26) groups (p < 0.001, Cohen’s d = 1.67). This confirms the potential of remote monitoring systems in tracking post-chemotherapy functional capacity changes and aiding early non-resilience detection, subject to validation in larger studies. This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle, participants wore a water-resistant pendant sensor for 14 days. This device tracked frailty markers like cadence (slowness), daily steps (inactivity), postural transitions (weakness), and metrics such as longest walk duration and energy expenditure (exhaustion). Participants were divided into resilient and non-resilient groups based on adverse events within 6 months post-chemotherapy, including dose reduction, treatment discontinuation, unplanned hospitalization, or death. A Chemotherapy-Resilience-Index (CRI) ranging from 0 to 1, where higher values indicate poorer resilience, was developed using regression analysis. It combined physical activity data with baseline Eastern Cooperative Oncology Group (ECOG) assessments. The protocol showed a 97% feasibility rate, with sensor metrics effectively differentiating between groups as early as day 6 post-therapy. The CRI, calculated using data up to day 6 and baseline ECOG, significantly distinguished resilient (CRI = 0.2 ± 0.27) from non-resilient (CRI = 0.7 ± 0.26) groups (p < 0.001, Cohen's d = 1.67). This confirms the potential of remote monitoring systems in tracking post-chemotherapy functional capacity changes and aiding early non-resilience detection, subject to validation in larger studies.This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle, participants wore a water-resistant pendant sensor for 14 days. This device tracked frailty markers like cadence (slowness), daily steps (inactivity), postural transitions (weakness), and metrics such as longest walk duration and energy expenditure (exhaustion). Participants were divided into resilient and non-resilient groups based on adverse events within 6 months post-chemotherapy, including dose reduction, treatment discontinuation, unplanned hospitalization, or death. A Chemotherapy-Resilience-Index (CRI) ranging from 0 to 1, where higher values indicate poorer resilience, was developed using regression analysis. It combined physical activity data with baseline Eastern Cooperative Oncology Group (ECOG) assessments. The protocol showed a 97% feasibility rate, with sensor metrics effectively differentiating between groups as early as day 6 post-therapy. The CRI, calculated using data up to day 6 and baseline ECOG, significantly distinguished resilient (CRI = 0.2 ± 0.27) from non-resilient (CRI = 0.7 ± 0.26) groups (p < 0.001, Cohen's d = 1.67). This confirms the potential of remote monitoring systems in tracking post-chemotherapy functional capacity changes and aiding early non-resilience detection, subject to validation in larger studies. Abstract This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle, participants wore a water-resistant pendant sensor for 14 days. This device tracked frailty markers like cadence (slowness), daily steps (inactivity), postural transitions (weakness), and metrics such as longest walk duration and energy expenditure (exhaustion). Participants were divided into resilient and non-resilient groups based on adverse events within 6 months post-chemotherapy, including dose reduction, treatment discontinuation, unplanned hospitalization, or death. A Chemotherapy-Resilience-Index (CRI) ranging from 0 to 1, where higher values indicate poorer resilience, was developed using regression analysis. It combined physical activity data with baseline Eastern Cooperative Oncology Group (ECOG) assessments. The protocol showed a 97% feasibility rate, with sensor metrics effectively differentiating between groups as early as day 6 post-therapy. The CRI, calculated using data up to day 6 and baseline ECOG, significantly distinguished resilient (CRI = 0.2 ± 0.27) from non-resilient (CRI = 0.7 ± 0.26) groups (p < 0.001, Cohen’s d = 1.67). This confirms the potential of remote monitoring systems in tracking post-chemotherapy functional capacity changes and aiding early non-resilience detection, subject to validation in larger studies. This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle, participants wore a water-resistant pendant sensor for 14 days. This device tracked frailty markers like cadence (slowness), daily steps (inactivity), postural transitions (weakness), and metrics such as longest walk duration and energy expenditure (exhaustion). Participants were divided into resilient and non-resilient groups based on adverse events within 6 months post-chemotherapy, including dose reduction, treatment discontinuation, unplanned hospitalization, or death. A Chemotherapy-Resilience-Index (CRI) ranging from 0 to 1, where higher values indicate poorer resilience, was developed using regression analysis. It combined physical activity data with baseline Eastern Cooperative Oncology Group (ECOG) assessments. The protocol showed a 97% feasibility rate, with sensor metrics effectively differentiating between groups as early as day 6 post-therapy. The CRI, calculated using data up to day 6 and baseline ECOG, significantly distinguished resilient (CRI = 0.2 ± 0.27) from non-resilient (CRI = 0.7 ± 0.26) groups (p < 0.001, Cohen’s d = 1.67). This confirms the potential of remote monitoring systems in tracking post-chemotherapy functional capacity changes and aiding early non-resilience detection, subject to validation in larger studies. |
| ArticleNumber | 2612 |
| Author | Finco, M. G. Yellapragada, Sarvari Uddin Atique, Md Moin Azarian, Mehrnaz Sada, Yvonne H. Cay, Gozde Rodriguez, Naima Dehghan Rouzi, Mohammad Najafi, Bijan |
| Author_xml | – sequence: 1 givenname: Gozde surname: Cay fullname: Cay, Gozde organization: Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine – sequence: 2 givenname: Yvonne H. surname: Sada fullname: Sada, Yvonne H. organization: Michael E. DeBakey Department of Veterans Affairs Medical Center – sequence: 3 givenname: Mohammad surname: Dehghan Rouzi fullname: Dehghan Rouzi, Mohammad organization: Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine – sequence: 4 givenname: Md Moin surname: Uddin Atique fullname: Uddin Atique, Md Moin organization: Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine – sequence: 5 givenname: Naima surname: Rodriguez fullname: Rodriguez, Naima organization: Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine – sequence: 6 givenname: Mehrnaz surname: Azarian fullname: Azarian, Mehrnaz organization: Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine – sequence: 7 givenname: M. G. surname: Finco fullname: Finco, M. G. organization: Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine – sequence: 8 givenname: Sarvari surname: Yellapragada fullname: Yellapragada, Sarvari organization: Michael E. DeBakey Department of Veterans Affairs Medical Center – sequence: 9 givenname: Bijan surname: Najafi fullname: Najafi, Bijan email: bijan.najafi@bcm.edu organization: Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38297103$$D View this record in MEDLINE/PubMed |
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AroraSDifferences in pretreatment frailty across gastrointesti J Razjouyan (53025_CR25) 2020; 20 SM Nguyen (53025_CR57) 2022; 29 F Pamoukdjian (53025_CR62) 2018; 37 S Arora (53025_CR46) 2022; 18 ED de Bruin (53025_CR33) 2007; 44 53025_CR61 GE Kang (53025_CR30) 2020; 20 J Carroll (53025_CR58) 2012; 136 R Mishra (53025_CR20) 2021; 21 P Macía (53025_CR10) 2020; 15 Y Shen (53025_CR47) 2021; 2 HD Klepin (53025_CR6) 2014; 10 JE Abraham (53025_CR56) 2015; 13 V Santilli (53025_CR60) 2014; 11 GH Lyman (53025_CR4) 2003; 21 J Cohen (53025_CR41) 2013 B Najafi (53025_CR36) 2013; 7 TCF Polo (53025_CR63) 2020; 19 EB Moth (53025_CR53) 2019; 10 J Ruiz (53025_CR45) 2019; 10 M Zahiri (53025_CR28) 2019; 10 J Freytag (53025_CR22) 2022; 22 ED De Bruin (53025_CR64) 2008; 22 S Keesara (53025_CR18) 2020; 382 M Hale (53025_CR50) 2019; 19 LP Fried (53025_CR51) 2001; 56 RM Goldberg (53025_CR12) 2002; 20 JH Schiller (53025_CR16) 2002; 346 S Laxminarayan (53025_CR43) 2020; 11 MM Oken (53025_CR14) 1982; 5 AD Vandyk (53025_CR5) 2012; 20 J Razjouyan (53025_CR26) 2018; 18 A Seiler (53025_CR9) 2019; 10 M Pavic (53025_CR66) 2020; 98 J Overcash (53025_CR8) 2023; 27 DR Barpe (53025_CR59) 2010; 41 J Razjouyan (53025_CR27) 2017; 43 C Park (53025_CR23) 2021; 21 YH Sada (53025_CR29) 2021; 16 W Zijlstra (53025_CR65) 2007; 4 DA Karnofsky (53025_CR15) 1964; 14 B Najafi (53025_CR35) 2002; 49 J Razjouyan (53025_CR40) 2017; 13 A Pearce (53025_CR2) 2017; 12 W Misiąg (53025_CR54) 2022; 14 G Gresham (53025_CR32) 2018; 1 S Parvaneh (53025_CR39) 2017; 63 MT Puts (53025_CR52) 2011; 78 G Bonadonna (53025_CR7) 1995; 332 53025_CR1 B Najafi (53025_CR34) 2003; 50 S Yarlagadda (53025_CR31) 2020; 38 53025_CR55 53025_CR13 IJ Myung (53025_CR42) 2003; 47 Y Wang (53025_CR48) 2019; 3 S Dai (53025_CR49) 2021; 8 M Schwenk (53025_CR38) 2015; 61 C Park (53025_CR24) 2021; 21 L Festerling (53025_CR11) 2023; 149 B Najafi (53025_CR19) 2020; 14 DH Henry (53025_CR3) 2008; 16 M Lee (53025_CR21) 2022; 22 A Paraschiv-Ionescu (53025_CR37) 2004; 20 RJ Wong (53025_CR44) 2021; 21 R Chow (53025_CR17) 2016; 5 |
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| SubjectTerms | 631/67 639/166/985 692/53/2423 Biomarkers Chemotherapy Energy expenditure Exercise Frailty Humanities and Social Sciences multidisciplinary Physical activity Regression analysis Science Science (multidisciplinary) |
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| Title | Harnessing physical activity monitoring and digital biomarkers of frailty from pendant based wearables to predict chemotherapy resilience in veterans with cancer |
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