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Stefanakis M, Sakellari V, Klenk J, Kampe K, Klimek M, Pfeiffer K, Becker C. Unlocking the black box of geriatric physiotherapy : Quantification of physical activity and walking parameters during inpatient geriatric rehabilitation therapy sessions. Z Gerontol Geriatr 2022; 55:637-643. [PMID: 36326865 DOI: 10.1007/s00391-022-02126-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 09/21/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Physiotherapy and occupational therapy are currently described using the duration of treatment (days or weeks), the frequency of therapy sessions (on a daily or 3-5 days per week basis) and considering the duration of a session (e.g., 30 or 45 min). The content is often poorly defined and the intensity is rarely reported. Using digital technology some of these shortcomings can be overcome. The cumulative parameters of walking and activity sessions, the duration of walking, the time spent in an upright or lying/sitting position and the number of steps can now be analyzed. In this study, we examined the parameters during non-treatment periods and therapy time in patients recovering from fragility fractures. METHODS The study is a secondary data analysis of a trial that examined the improvement of physical activity (PA) and self-efficacy of fragility fracture patients. Changes in mobility parameters were measured using the ActivPal3 sensor during the 1st and 3rd weeks of rehabilitation and 104 patients were analyzed (mean age 82.5 years). Parameters included the time during supervised treatment, the mean number of steps, cumulative time in an upright position and walking duration, the number of walking intervals of > 10 s and sit to stand transfers. RESULTS Patients received 3-4 therapy sessions adding up to 90-120 min per day. More than 50% of the daily walking activities were achieved in these sessions until discharge. With this amount of therapeutic input most parameters meaningfully improved from baseline to the second measurement. The number of steps increased by 30%, the mean time in an upright position increased by 26% and the mean time spent walking increased by 49%. CONCLUSION The sensor-derived measurements describe the amount of walking activity administered during the supervised therapy sessions. This could be used as a starting point for future trials to improve the outcomes or as a standard of process evaluation for clinical services.
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Affiliation(s)
- Marios Stefanakis
- Physiotherapy Department, University of West Attica, 12243, Egaleo, Athens, Greece.
| | - Vasiliki Sakellari
- Physiotherapy Department, University of West Attica, 12243, Egaleo, Athens, Greece
| | - Jochen Klenk
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376, Stuttgart, Germany.,Institute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstr. 22, 89081, Ulm, Germany
| | - Karin Kampe
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376, Stuttgart, Germany
| | - Matthias Klimek
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376, Stuttgart, Germany.,Institute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstr. 22, 89081, Ulm, Germany
| | - Klaus Pfeiffer
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376, Stuttgart, Germany
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376, Stuttgart, Germany. .,Unit Digitale Geriatrie, Universitätsklinikum Heidelberg, Bergheimerstr. 20, 69115, Heidelberg, Germany.
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2
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Breasail MÓ, Biswas B, Smith MD, Mazhar MKA, Tenison E, Cullen A, Lithander FE, Roudaut A, Henderson EJ. Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:8261. [PMID: 34960353 PMCID: PMC8705556 DOI: 10.3390/s21248261] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/26/2021] [Accepted: 12/02/2021] [Indexed: 12/21/2022]
Abstract
Neurodegenerative disorders (NDDs) constitute an increasing global burden and can significantly impair an individual's mobility, physical activity (PA), and independence. Remote monitoring has been difficult without relying on diaries/questionnaires which are more challenging for people with dementia to complete. Wearable global positioning system (GPS) sensors and accelerometers present a cost-effective and noninvasive way to passively monitor mobility and PA. In addition, changes in sensor-derived outcomes (such as walking behaviour, sedentary, and active activity) may serve as potential biomarkers of disease onset, progression, and response to treatment. We performed a systematic search across four databases to identify papers published within the past 5 years, in which wearable GPS or accelerometers were used to monitor mobility or PA in patients with common NDDs (Parkinson's disease, Alzheimer's disease, motor neuron diseases/amyotrophic lateral sclerosis, vascular parkinsonism, and vascular dementia). Disease and technology-specific vocabulary were searched singly, and then in combination, identifying 4985 papers. Following deduplication, we screened 3115 papers and retained 28 studies following a full text review. One study used wearable GPS and accelerometers, while 27 studies used solely accelerometers in NDDs. GPS-derived measures had been validated against current gold standard measures in one Parkinson's cohort, suggesting that the technology may be applicable to other NDDs. In contrast, accelerometers are widely utilised in NDDs and have been operationalised in well-designed clinical trials.
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Affiliation(s)
- Mícheál Ó. Breasail
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
| | - Bijetri Biswas
- Department of Electronic and Electrical Engineering, Computer Science and Mathematics, University of Bristol, Bristol BS8 1TH, UK
| | - Matthew D. Smith
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
- Older Peoples Unit, Royal United Hospital NHS Foundation Trust, Bath BN1 3NG, UK
| | - Md Khadimul A. Mazhar
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
| | - Emma Tenison
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
| | - Anisha Cullen
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
| | - Fiona E. Lithander
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
| | - Anne Roudaut
- Department of Computer Science, University of Bristol, Bristol BS8 1TH, UK;
| | - Emily J. Henderson
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
- Older Peoples Unit, Royal United Hospital NHS Foundation Trust, Bath BN1 3NG, UK
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3
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Polhemus A, Delgado-Ortiz L, Brittain G, Chynkiamis N, Salis F, Gaßner H, Gross M, Kirk C, Rossanigo R, Taraldsen K, Balta D, Breuls S, Buttery S, Cardenas G, Endress C, Gugenhan J, Keogh A, Kluge F, Koch S, Micó-Amigo ME, Nerz C, Sieber C, Williams P, Bergquist R, Bosch de Basea M, Buckley E, Hansen C, Mikolaizak AS, Schwickert L, Scott K, Stallforth S, van Uem J, Vereijken B, Cereatti A, Demeyer H, Hopkinson N, Maetzler W, Troosters T, Vogiatzis I, Yarnall A, Becker C, Garcia-Aymerich J, Leocani L, Mazzà C, Rochester L, Sharrack B, Frei A, Puhan M. Walking on common ground: a cross-disciplinary scoping review on the clinical utility of digital mobility outcomes. NPJ Digit Med 2021; 4:149. [PMID: 34650191 PMCID: PMC8516969 DOI: 10.1038/s41746-021-00513-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/09/2021] [Indexed: 02/08/2023] Open
Abstract
Physical mobility is essential to health, and patients often rate it as a high-priority clinical outcome. Digital mobility outcomes (DMOs), such as real-world gait speed or step count, show promise as clinical measures in many medical conditions. However, current research is nascent and fragmented by discipline. This scoping review maps existing evidence on the clinical utility of DMOs, identifying commonalities across traditional disciplinary divides. In November 2019, 11 databases were searched for records investigating the validity and responsiveness of 34 DMOs in four diverse medical conditions (Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture). Searches yielded 19,672 unique records. After screening, 855 records representing 775 studies were included and charted in systematic maps. Studies frequently investigated gait speed (70.4% of studies), step length (30.7%), cadence (21.4%), and daily step count (20.7%). They studied differences between healthy and pathological gait (36.4%), associations between DMOs and clinical measures (48.8%) or outcomes (4.3%), and responsiveness to interventions (26.8%). Gait speed, step length, cadence, step time and step count exhibited consistent evidence of validity and responsiveness in multiple conditions, although the evidence was inconsistent or lacking for other DMOs. If DMOs are to be adopted as mainstream tools, further work is needed to establish their predictive validity, responsiveness, and ecological validity. Cross-disciplinary efforts to align methodology and validate DMOs may facilitate their adoption into clinical practice.
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Affiliation(s)
- Ashley Polhemus
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
| | - Laura Delgado-Ortiz
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Gavin Brittain
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Michaela Gross
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachele Rossanigo
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Diletta Balta
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Sofie Breuls
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Sara Buttery
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Gabriela Cardenas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Christoph Endress
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Julia Gugenhan
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sarah Koch
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Corinna Nerz
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Chloé Sieber
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Parris Williams
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Ronny Bergquist
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Magda Bosch de Basea
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Ellen Buckley
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | | | - Lars Schwickert
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Kirsty Scott
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Sabine Stallforth
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Janet van Uem
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Heleen Demeyer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | | | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Thierry Troosters
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Letizia Leocani
- Department of Neurology, San Raffaele University, Milan, Italy
| | - Claudia Mazzà
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Anja Frei
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Milo Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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Kampe K, Pfeiffer K, Lindemann U, Schoene D, Taraldsen K, Rapp K, Becker C, Klenk J. Change of physical activity parameters of hip and pelvic fracture patients during inpatient rehabilitation and after discharge: analysis of global and in-depth parameters. Eur Rev Aging Phys Act 2021; 18:9. [PMID: 34118878 PMCID: PMC8199557 DOI: 10.1186/s11556-021-00261-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 05/12/2021] [Indexed: 11/16/2022] Open
Abstract
Background A growing number of older adults suffer hip and pelvic fractures leading to hospital admission. They often result in reduced physical activity (PA) and impaired mobility. PA can be objectively measured with body-worn sensors. Usually, global cumulative PA parameters are analysed, such as walking duration, upright-time and number of steps. These traditional parameters mix different domains of PA, such as physical capacity (PC), behaviour and living environment. We examined the change of global cumulative PA measures during rehabilitation and after discharge in patients with hip or pelvic fracture and whether more ‘in-depth’ PA parameters, such as walking interval length, variability of interval length and sit-to-stand transitions and their changes during rehabilitation and 3 months after discharge might better reflect the above mentioned three clinically relevant domains of PA. Methods This study is a secondary data analysis of a randomised controlled trial to improve PA and fall-related self-efficacy in hip or pelvic fracture patients (≥60 years) with concerns about falling. Changes of accelerometer-measured global cumulative and in-depth PA parameters (activPAL3) were analysed in an observational design before and after discharge combining both groups. For comparison, the same analyses were applied to the traditional PC measures gait speed and 5-chair-rise. Results Seventy-five percent of the 111 study participants were female (mean age: 82.5 (SD = 6.76) years. Daily walking duration, upright time and number of steps as aspects of global PA increased during inpatient rehabilitation as well as afterwards. The in-depth PA parameters showed differing patterns. While the total number of walking bouts increased similarly, the number of longer walking bouts decreased by 50% after discharge. This pattern was also seen for the average walking interval length, which increased by 2.34 s (95% confidence interval (CI): 0.68; 4.00) during inpatient rehabilitation and decreased afterwards below baseline level (− 4.19 s (95% CI: − 5.56; − 2.82)). The traditional PC measures showed similar patterns to the in-depth PA parameters with improvements during rehabilitation, but not at home. Conclusion Our findings suggest that the in-depth PA parameters add further information to the global cumulative PA parameters. Whereas global cumulative PA parameters improved significantly during inpatient rehabilitation and after discharge, in-depth PA parameters as well as PC did not continuously improve at home. In contrast to global cumulative PA parameters the in-depth parameters seem to reflect contextual factors such as the build environment and aspects of PC, which are traditionally assessed by clinical PC measures. These in combination with digital mobility measures can help clinicians to assess the health status of fragility fracture patients, individually tailor therapy measures and monitor the rehabilitation process.
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Affiliation(s)
- Karin Kampe
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376, Stuttgart, Germany
| | - Klaus Pfeiffer
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376, Stuttgart, Germany
| | - Ulrich Lindemann
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376, Stuttgart, Germany
| | - Daniel Schoene
- Institute of Medical Physics, Friedrich-Alexander-University Erlangen-Nürnberg, Henkestr. 91, 91052, Erlangen, Germany
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, N-7491, Trondheim, Norway
| | - Kilian Rapp
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376, Stuttgart, Germany.,Institute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstr. 22, 89081, Ulm, Germany
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376, Stuttgart, Germany
| | - Jochen Klenk
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstr. 110, 70376, Stuttgart, Germany. .,Institute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstr. 22, 89081, Ulm, Germany. .,IB University for Health and Social Sciences, Study Center Stuttgart, Paulinenstraße 45, 70178, Stuttgart, Germany.
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Klenk J, Wekenmann S, Schwickert L, Lindemann U, Becker C, Rapp K. Change of Objectively-Measured Physical Activity during Geriatric Rehabilitation. SENSORS 2019; 19:s19245451. [PMID: 31835673 PMCID: PMC6960991 DOI: 10.3390/s19245451] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/08/2019] [Accepted: 12/09/2019] [Indexed: 02/07/2023]
Abstract
This prospective study investigated feasibility and sensitivity of sensor-based physical activity (PA) measures to monitor changes in PA during geriatric rehabilitation and its relation to clinical parameters at admission. PA was routinely measured at day 2 and day 15 after admission in 647 patients (70.2% women, mean age = 82.0 (SD = 7.19) years) of a German geriatric hospital using a thigh-worn accelerometer. Clinical records were used to include age, Barthel Index, diagnosis, mobility, orientation and cognition. Mean values and 95% confidence intervals (95%-CI) of walking duration, walking bout duration and number of sit-to-stand transfers were calculated to quantify different domains of PA. All observed PA parameters improved during rehabilitation, regardless of age, diagnosis or physical and cognitive function at admission. Walking duration increased by 12.1 (95%-CI: 10.3; 13.8) min, walking bout duration by 2.39 (95%-CI: 1.77; 3.00) s, and number of sit-to-stand transfers by 7 (95%-CI: 5; 8). Floor and ceiling effects were not observed. Walking duration at day 2 as well as day 15 was continuously associated with Barthel Index and statistically significant improved for all levels of Barthel Index. In summary, this study showed that sensor-based PA monitoring is feasible to assess the individual progress in geriatric rehabilitation patients.
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Affiliation(s)
- Jochen Klenk
- Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany; (S.W.); (L.S.); (U.L.); (C.B.); (K.R.)
- Institute of Epidemiology and Medical Biometry, Ulm University, 89081 Ulm, Germany
- IB University of Applied Sciences Berlin, Study Center Stuttgart, 70178 Stuttgart, Germany
- Correspondence: ; Tel.: +49-711-8101-5853
| | - Sebastian Wekenmann
- Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany; (S.W.); (L.S.); (U.L.); (C.B.); (K.R.)
| | - Lars Schwickert
- Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany; (S.W.); (L.S.); (U.L.); (C.B.); (K.R.)
| | - Ulrich Lindemann
- Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany; (S.W.); (L.S.); (U.L.); (C.B.); (K.R.)
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany; (S.W.); (L.S.); (U.L.); (C.B.); (K.R.)
| | - Kilian Rapp
- Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany; (S.W.); (L.S.); (U.L.); (C.B.); (K.R.)
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Leroux A, Di J, Smirnova E, Mcguffey EJ, Cao Q, Bayatmokhtari E, Tabacu L, Zipunnikov V, Urbanek JK, Crainiceanu C. Organizing and analyzing the activity data in NHANES. STATISTICS IN BIOSCIENCES 2019; 11:262-287. [PMID: 32047572 DOI: 10.1007/s12561-018-09229-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
The NHANES study contains objectively measured physical activity data collected using hip-worn accelerometers from multiple cohorts. However, using the accelerometry data has proven daunting because: 1) currently, there are no agreed upon standard protocols for data storage and analysis; 2) data exhibit heterogeneous patterns of missingness due to varying degrees of adherence to wear-time protocols; 3) sampling weights need to be carefully adjusted and accounted for in individual analyses; 4) there is a lack of reproducible software that transforms the data from its published format into analytic form; and 5) the high dimensional nature of accelerometry data complicates analyses. Here, we provide a framework for processing, storing, and analyzing the NHANES accelerometry data for the 2003-2004 and 2005-2006 surveys. We also provide an NHANES data package in R, to help disseminate high quality, processed activity data combined with mortality and demographic information. Thus, we provide the tools to transition from "available data online" to "easily accessible and usable data", which substantially reduces the large upfront costs of initiating studies of association between physical activity and human health outcomes using NHANES. We apply these tools in an analysis showing that accelerometry features have the potential to predict 5-year all cause mortality better than known risk factors such as age, cigarette smoking, and various comorbidities.
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Affiliation(s)
- Andrew Leroux
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
| | - Junrui Di
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
| | - Ekaterina Smirnova
- Department of Biostatistics, Virginia Commonwealth University, Richmond, USA.,Department of Mathematical Sciences, University of Montana, Missoula, USA
| | | | - Quy Cao
- Department of Mathematical Sciences, University of Montana, Missoula, USA
| | | | - Lucia Tabacu
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, USA
| | - Vadim Zipunnikov
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
| | - Jacek K Urbanek
- Division of Geriatric Medicine and Gerontology, Department of Medicine, Center on Aging and Health, School of Medicine, Johns Hopkins University, Baltimore, USA
| | - Ciprian Crainiceanu
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
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7
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Consensus Paper: Neurophysiological Assessments of Ataxias in Daily Practice. THE CEREBELLUM 2018; 17:628-653. [DOI: 10.1007/s12311-018-0937-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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8
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Psychometric properties of outcome measures evaluating decline in gait in cerebellar ataxia: A systematic review. Gait Posture 2018; 61:149-162. [PMID: 29351857 DOI: 10.1016/j.gaitpost.2017.12.031] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 12/14/2017] [Accepted: 12/29/2017] [Indexed: 02/02/2023]
Abstract
Cerebellar ataxia often results in impairment in ambulation secondary to gait pattern dysfunction and compensatory gait adjustments. Pharmaceutical and therapy-based interventions with potential benefit for gait in ataxia are starting to emerge, however evaluation of such interventions is hampered by the lack of outcome measures that are responsive, valid and reliable for measurement of gait decline in cerebellar ataxia. This systematic review aimed for the first time to evaluate the psychometric properties of gait and walking outcomes applicable to individuals with cerebellar ataxia. Only studies evaluating straight walking were included. A comprehensive search of three databases (MEDLINE, CINAHL and EMBASE) identified 53 studies meeting inclusion criteria. Forty-nine were rated as 'poor' as assessed by the COnsensus-based Standards for the selection of health Measurement INstruments checklist. The primary objective of most studies was to explore changes in gait related to ataxia, rather than to examine psychometric properties of outcomes. This resulted in methodologies not specific for psychometric assessment. Thirty-nine studies examined validity, 11 examined responsiveness and 12 measured reliability. Review of the data identified double and single support and swing percentage of the gait cycle, velocity, step length and the Scale for Assessment and Rating of Ataxia (SARA) gait item as the most valid and responsive measures of gait in cerebellar ataxia. However, further evaluation to establish their reliability and applicability for use in clinical trials is clearly warranted. We recommend that inter-session reliability of gait outcomes should be evaluated to ensure changes are reflective of intervention effectiveness in cerebellar ataxia.
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Bhidayasiri R, Martinez-Martin P. Clinical Assessments in Parkinson's Disease: Scales and Monitoring. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2017; 132:129-182. [PMID: 28554406 DOI: 10.1016/bs.irn.2017.01.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Measurement of disease state is essential in both clinical practice and research in order to assess the severity and progression of a patient's disease status, effect of treatment, and alterations in other relevant factors. Parkinson's disease (PD) is a complex disorder expressed through many motor and nonmotor manifestations, which cause disabilities that can vary both gradually over time or come on suddenly. In addition, there is a wide interpatient variability making the appraisal of the many facets of this disease difficult. Two kinds of measure are used for the evaluation of PD. The first is subjective, inferential, based on rater-based interview and examination or patient self-assessment, and consist of rating scales and questionnaires. These evaluations provide estimations of conceptual, nonobservable factors (e.g., symptoms), usually scored on an ordinal scale. The second type of measure is objective, factual, based on technology-based devices capturing physical characteristics of the pathological phenomena (e.g., sensors to measure the frequency and amplitude of tremor). These instrumental evaluations furnish appraisals with real numbers on an interval scale for which a unit exists. In both categories of measures, a broad variety of tools exist. This chapter aims to present an up-to-date summary of the most relevant characteristics of the most widely used scales, questionnaires, and technological resources currently applied to the assessment of PD. The review concludes that, in our opinion: (1) no assessment methods can substitute the clinical judgment and (2) subjective and objective measures in PD complement each other, each method having strengths and weaknesses.
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Affiliation(s)
- Roongroj Bhidayasiri
- Chulalongkorn Center of Excellence for Parkinson's Disease & Related Disorders, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand; Juntendo University, Tokyo, Japan.
| | - Pablo Martinez-Martin
- National Center of Epidemiology and CIBERNED, Carlos III Institute of Health, Madrid, Spain
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