Adherence to Accelerometry Measurement of Community Ambulation Poststroke
Open accessAbstract
Background: The step activity monitor (SAM) quantifies steps taken in the home and community by patient populations. Although the SAM has been used to study individuals after stroke, adherence to the use of SAMs has not been addressed. Participants' adherence to wearing the monitor is critical for obtaining accurate assessments. Objective: The purpose of this study was to determine the rate of and predictors for inferred adherence to the use of the SAM after stroke. Design: A cross-sectional design was used. Methods: Community-dwelling individuals (n=408) 2 months after stroke with moderate-to-severe gait impairment (gait speed of ≤0.8 m/s) were studied. Step activity was assessed for 2 days with the SAM. Inferred adherence was established in 3 periods: 6:00 am to 12:00 pm, 12:01 pm to 6:00 pm, and 6:01 pm to 12:00 am. Adherence was defined as activity recorded in all 3 periods. The percentage of participant adherence for the first day, second day, both days, and either day was calculated. Demographic and clinical characteristics of adherers and nonadherers were compared. Independent adherence predictors were identified by means of stepwise logistic regression. Results: Inferred adherence rates for the first day, second day, both days, and either day were 68%, 61%, 53%, and 76%, respectively. Upper and lower extremity impairment, balance control, and endurance were significantly different between adherers and nonadherers. On the other hand, older age, greater balance self-efficacy, and better walking endurance were significant predictors of adherence. Limitations: Participants were individuals with subacute stroke. Therefore, the findings of this study may not be generalized to individuals during the acute and chronic phases of stroke recovery. Conclusions: Strategies to improve adherence are needed when collecting data for more than 1 day and in samples with younger individuals and those with low levels of balance self-efficacy and walking endurance.
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A large study (n = 408) investigating adherence to a step activity monitor over 2 days reported adherence rates between 61 and 68% for separate days.
“A large study (n = 408) that investigating adherence to the use of step activity monitor over 2-day reported adherence rates between 61 and 68% for separate days, but only 53% of participants wore the sensors for two consecutive days [63].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [63]
Older individuals and those with better balance self-efficacy and walking endurance showed better adherence.
“Older individuals and those with better balance self-efficacy and walking endurance showed better adherence [63].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [63]
Only 53% of participants in that study wore the sensors for two consecutive days.
“A large study (n = 408) that investigating adherence to the use of step activity monitor over 2-day reported adherence rates between 61 and 68% for separate days, but only 53% of participants wore the sensors for two consecutive days [63].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [63]
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Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · see citing works
DiMatteo, M. R., Lepper, H. S., & Croghan, T. W. (2000) · Archives of Internal Medicine · doi:10.1001/archinte.160.14.2101 · Cited by 4,177 · see citing works
Measuring balance in the elderly: validation of an instrument.
Berg, K., Wood-Dauphinée, S., Williams, J. I., & Maki, B. E. (1992) · PubMed · Cited by 2,961 · see citing works
Gladstone, D. J., Danells, C. J., & Black, S. E. (2002) · Neurorehabilitation and neural repair · doi:10.1177/154596802401105171 · Cited by 2,042 · see citing works
Conducting accelerometer-based activity assessments in field-based research
TROST, S. G., MCIVER, K. L., & PATE, R. R. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185657.86065.98 · Cited by 2,033 · see citing works
Classification of Walking Handicap in the Stroke Population
Perry, J., Garrett, M., Gronley, J. K., & Mulroy, S. J. (1995) · Stroke · doi:10.1161/01.str.26.6.982 · Cited by 1,560 · see citing works
Arbuthnott, K., & Frank, J. (2000) · Journal of Clinical and Experimental Neuropsychology · doi:10.1076/1380-3395(200008)22:4;1-0;ft518 · Cited by 1,087 · see citing works
Mehrholz, J., Wagner, K., Rutte, K., Meiβner, D., & Pohl, M. (2007) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2007.06.764 · Cited by 831 · see citing works
Enright, P., McBurnie, M. A., Bittner, V., Tracy, R. P., McNamara, R. L., Arnold, A. M., & Newman, A. B. (2003) · CHEST Journal · doi:10.1378/chest.123.2.387 · Cited by 787 · see citing works
Accelerometer Use in Physical Activity: Best Practices and Research Recommendations
WARD, D. S., EVENSON, K. R., VAUGHN, A., RODGERS, A. B., & TROIANO, R. P. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185292.71933.91 · Cited by 779 · see citing works
Physical Activity Assessments for Health-Related Research
Welk, G. J. (2002) · Medical Entomology and Zoology · Cited by 694 · see citing works
Accelerometer Data Reduction: A Comparison of Four Reduction Algorithms on Select Outcome Variables
Mâsse, L. C., Fuemmeler, B. F., Anderson, C. B., Matthews, C. E., Trost, S. G., Catellier, D., & Treuth, M. S. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185674.09066.8a · Cited by 676 · see citing works
Improvements in Speed-Based Gait Classifications Are Meaningful
Schmid, A. A., Duncan, P. W., Studenski, S. A., Lai, S. M., Richards, L. G., Perera, S., & Wu, S. S. (2007) · Stroke · doi:10.1161/strokeaha.106.475921 · Cited by 655 · see citing works
Self-citationDiscriminative and Evaluative Properties of the Activities-specific Balance Confidence (ABC) Scale
Myers, A. M., Fletcher, P. C., Myers, A., & Sherk, W. (1998) · The Journals of Gerontology Series A · doi:10.1093/gerona/53a.4.m287 · Cited by 550 · see citing works
Reduced Ambulatory Activity After Stroke: The Role of Balance, Gait, and Cardiovascular Fitness
Michael, K. M., Allen, J. K., & Macko, R. F. (2005) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2004.12.026 · Cited by 506 · see citing works
Performance of the PHQ-9 as a screening tool for depression after stroke
Williams, L. S., Brizendine, E. J., Plue, L., Bakas, T., Tu, W., Hendrie, H., & Kroenke, K. (2005) · Stroke · doi:10.1161/01.str.0000155688.18207.33 · Cited by 397 · see citing works
Huybrechts, K. F., Ishak, K. J., & Jaime, J. (2005) · Bone · doi:10.1016/j.bone.2005.10.022 · Cited by 344 · see citing works
The Effects of Multidimensional Home-Based Exercise on Functional Performance in Elderly People
Nelson, M. E., Layne, J. E., Bernstein, M. J., Nuernberger, A., Castañeda, C., Kaliton, D., Hausdorff, J. M., Judge, J. O., Buchner, D. M., Roubenoff, R., et al. (2004) · The Journals of Gerontology Series A · doi:10.1093/gerona/59.2.m154 · Cited by 309 · see citing works
Uswatte, G., Giuliani, C., Winstein, C., Zeringue, A., Hobbs, L., & Wolf, S. L. (2006) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2006.06.006 · Cited by 241 · see citing works
The LEAPS Investigative Team, Duncan, P. W., Sullivan, K. J., Behrman, A. L., Azen, S. P., Wu, S. S., Nadeau, S. E., Dobkin, B. H., Rose, D. K., & Tilson, J. K. (2007) · BMC Neurology · doi:10.1186/1471-2377-7-39 · Cited by 205 · see citing works
Self-citationMicroprocessor-based ambulatory activity monitoring in stroke patients
Macko, R. F., Haeuber, E., Shaughnessy, M., Coleman, K. L., Boone, D. A., Smith, G. V., & Silver, K. H. C. (2002) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-200203000-00002 · Cited by 185 · see citing works
Haeuber, E., Shaughnessy, M., Forrester, L. W., Coleman, K. L., & Macko, R. F. (2004) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2003.11.035 · Cited by 182 · see citing works
Cooper, A. R., Page, A. S., Fox, K. R., & Misson, J. (2000) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1601116 · Cited by 172 · see citing works
Ambulatory Activity Intensity Profiles, Fitness, and Fatigue in Chronic Stroke
Michael, K., & Macko, R. F. (2007) · Topics in Stroke Rehabilitation · doi:10.1310/tsr1402-5 · Cited by 167 · see citing works
Cavanaugh, J. T., Coleman, K. L., Gaines, J. M., Laing, L. S., & Morey, M. C. (2006) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2006.00997.x · Cited by 141 · see citing works
Shaughnessy, M., Michael, K. M., Sorkin, J. D., & Macko, R. F. (2005) · Stroke · doi:10.1161/01.str.0000166202.00669.d2 · Cited by 133 · see citing works
The stroke rehabilitation outcome study--Part I: General description.
Granger, C. V., Hamilton, B. B., & Gresham, G. E. (1988) · PubMed · Cited by 109 · see citing works
Feasibility of Using Accelerometers to Measure Physical Activity in Young Adolescents
Coevering, P. V., Harnack, L., Schmitz, K. H., Fulton, J. E., Galuska, D. A., & Gao, S. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000162694.66799.fe · Cited by 88 · see citing works
Factors affecting drug treatment compliance in patients with rheumatoid arthritis
Tuncay, R., Ekşioğlu, E., Cakir, B., Gürçay, E., & Çakçı, A. (2007) · Rheumatology International · doi:10.1007/s00296-006-0299-9 · Cited by 83 · see citing works
Adherence to Accelerometer Protocols Among Women From Economically Disadvantaged Neighborhoods
Sharpe, P. A., Wilcox, S., Rooney, L., Strong, D., Hopkins-Campbell, R., Butel, J., Ainsworth, B. E., & Parra-Medina, D. M. (2011) · Journal of Physical Activity and Health · doi:10.1123/jpah.8.5.699 · Cited by 25 · see citing works
Guidelin, P.-S. R., & Public Helath Service Agency for He (1996) · Journal of Pharmaceutical Care in Pain & Symptom Control · doi:10.1300/j088v04n04_06 · Cited by 21 · see citing works
Flagged first, then by citations
1 of the 32 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [3]3. Haeuber E, Shaughnessy M, Forrester LW, et al. Accelerometer monitoring of home- and community-based ambulatory activity after stroke. Arch Phys Med Rehabil. 2004;85:1997–2001 [DOI] [PubMed] [Google Scholar]
Cited by
Sorted by citations
Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review.
Block, V. A. J., Pitsch, E., Tahir, P., Cree, B. A. C., Allen, D. D., & Gelfand, J. M. (2016) · PLOS ONE · doi:10.1371/journal.pone.0154335 · Cited by 198 · see citing works
“Danks et al, 2014 [91] Undefined > 3 months StepWatch Activity Monitor (b*) Walking /gait/ LE physical activity Open- label >50 16 N/A 2–6 days None listed”
BackgroundJohansson, D., Malmgren, K., & Alt Murphy, M. (2018) · Journal of Neurology · doi:10.1007/s00415-018-8786-y · Cited by 188 · see citing works
Exploring the Role of Accelerometers in the Measurement of Real World Upper-Limb Use After Stroke
Hayward, K. S., Eng, J. J., Boyd, L. A., Lakhani, B., Bernhardt, J. A., & Lang, C. E. (2015) · Brain Impairment · doi:10.1017/brimp.2015.21 · Cited by 126 · see citing works
Frost, R., Levati, S., McClurg, D., Brady, M., & Williams, B. (2016) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2016.08.482 · Cited by 127 · see citing works
A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training
Dobkin, B. H. (2016) · Neurorehabilitation and neural repair · doi:10.1177/1545968316680490 · Cited by 130 · see citing works
Doman, C., Waddell, K. J., Bailey, R. R., Moore, J. L., & Lang, C. E. (2016) · American Journal of Occupational Therapy · doi:10.5014/ajot.2016.020891 · Cited by 91 · see citing works
Wearable Sensors to Monitor, Enable Feedback, and Measure Outcomes of Activity and Practice
Dobkin, B. H., & Martinez, C. A. (2018) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-018-0896-5 · Cited by 76 · see citing works
Rose, D. K., Nadeau, S. E., Wu, S. S., Tilson, J. K., Dobkin, B. H., Pei, Q., & Duncan, P. W. (2017) · Physical Therapy · doi:10.1093/ptj/pzx079 · Cited by 71 · see citing works
Self-citationMeasuring Habitual Physical Activity in Neuromuscular Disorders: A Systematic Review
Jimenez‐Moreno, C., Newman, J., Charman, S. J., Catt, M., Trenell, M. I., Gorman, G. S., Hogrel, J., & Lochmüller, H. (2017) · Journal of Neuromuscular Diseases · doi:10.3233/jnd-160195 · Cited by 35 · see citing works
Katzan, I. L., Schuster, A. T., & Kinzy, T. G. (2020) · JMIR mhealth and uhealth · doi:10.2196/14494 · Cited by 34 · see citing works
Predictors of Daily Steps at 1-Year Poststroke
Handlery, R., Regan, E. W., Stewart, J. C., Pellegrini, C. A., Monroe, C. M., Hainline, G., Handlery, K., & Fritz, S. L. (2021) · Stroke · doi:10.1161/strokeaha.121.034249 · Cited by 29 · see citing works
Wearables for Neurologic Conditions
Minen, M. T., & Stieglitz, E. J. (2020) · Neurology Clinical Practice · doi:10.1212/cpj.0000000000000971 · Cited by 28 · see citing works
Handlery, R., Fulk, G. D., Pellegrini, C. A., Stewart, J. C., Monroe, C. M., & Fritz, S. L. (2020) · Physical Therapy · doi:10.1093/ptj/pzaa020 · Cited by 24 · see citing works
Chaparro, D., Daviet, J., Borel, B., Kammoun, B., Salle, J., Tchalla, A. E., & Mandigout, S. (2018) · Trials · doi:10.1186/s13063-017-2410-9 · Cited by 19 · see citing works
Kelly, R., Jones, S., Price, B. A., Katz, D., McCormick, C., & Pearce, O. (2020) · JMIR mhealth and uhealth · doi:10.2196/22846 · Cited by 18 · see citing works
Veerubhotla, A. L., Krantz, A., Ibironke, O., & Pilkar, R. B. (2021) · PM&R · doi:10.1002/pmrj.12725 · Cited by 15 · see citing works
Wearable Sensors for Stroke Rehabilitation
Adans-Dester, C. P., Lang, C. E., Reinkensmeyer, D. J., & Bonato, P. (2022) · Springer eBooks · doi:10.1007/978-3-031-08995-4_21 · Cited by 14 · see citing works
Klassen, T. D., Dukelow, S. P., Bayley, M. T., Benavente, O., Hill, M. D., Krassioukov, A. V., Liu-Ambrose, T. Y. L., Pooyania, S., Poulin, M. J., Yao, J. K., et al. (2018) · International Journal of Stroke · doi:10.1177/1747493018785064 · Cited by 15 · see citing works
Bland, M. D., Barco, P. P., Lang, C. E., Lenard, E. M., Kallmi, S., Pennock, S., & Lenze, E. J. (2020) · Journal of Geriatric Physical Therapy · doi:10.1519/jpt.0000000000000259 · Cited by 13 · see citing works
Morgan-Jones, P., Jones, A. E., Busse, M. E., Mills, L., Pallmann, P., Drew, C. J. G., Arnesen, A., & Wood, F. (2022) · JMIR Formative Research · doi:10.2196/36870 · Cited by 9 · see citing works
Macpherson, C. E., Bland, M. D., Gordon, C. M., Miller, A. E., Newman, C., Holleran, C. L., Dy, C. J., Peterson, L. L., Lohse, K. R., & Lang, C. E. (2025) · Sensors · doi:10.3390/s25154618 · Cited by 7 · see citing works
Heino, M. T. J., Knittle, K. P., Haukkala, A., Vasankari, T. J., & Hankonen, N. E. (2018) · BMC Public Health · doi:10.1186/s12889-018-6121-2 · Cited by 5 · see citing works
Bansal, K., Clark, D. J., Fox, E. J., & Rose, D. K. (2023) · Scientific Reports · doi:10.1038/s41598-023-47365-5 · Cited by 5 · see citing works
Zawada, S. J., Ganjizadeh, A., Conte, G. M., Demaerschalk, B. M., & Erickson, B. J. (2024) · JMIR Neurotechnology · doi:10.2196/56679 · Cited by 2 · see citing works
Exploring outcome measures for adults with myotonic dystrophy type 1
Moreno, J., & Cecilia, A. (2017)
Wang, Y., Li, J., Hu, W., Xiu, Q., Hao, Y., Chen, X., & Xie, P. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3474175
Choi, S. I., Lee, W. H., Yang, H. W., Choi, W. J., Kim, Y., & Kim, N. Y. (2026) · doi:10.2196/preprints.99667
- Authors
- Barak, S., Wu, S. S., Dai, Y., Duncan, P. W., & Behrman, A. L.
- Affiliations
- Kaye Academic College of Education (IL)
- University of Florida (US)
- Wake Forest University (US)
- University of Louisville (US)
- Corresponding author
- Barak, Sharon
- Venue
- Physical Therapy
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-01-01
- Volume
- 94
- Issue
- 1
- Pages
- 101-110
- ISSN
- 0031-9023 (ISSN-L)
- 1538-6724
- PMC
- PMC3892677
- OpenAlex
- W2154316997
- Cited by
- 27
- References
- 32
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 1 · 2022: 2 · 2021: 2 · 2020: 5 · 2018: 5 · 2017: 3 · 2016: 4 · 2015: 1
- FWCI
- 1.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Physical Therapy (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute on Aging (P30AG028740)
- National Institute of Neurological Disorders and Stroke (R01NS050506)
- Topics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- accelerometry, adherence, community-dwelling stroke survivors, gait disturbance, balance self-efficacy, walking endurance, balance control, predictors of adherence
- MeSH terms
- Stroke Rehabilitation, Adolescent, Adult, Aged, Aged, 80 and over, Cross-Sectional Studies, Postural Balance, Female, Humans, Male, Middle Aged, Patient Compliance, Physical Endurance, Surveys and Questionnaires, Recovery of Function, Gait Disorders, Neurologic, Stroke, Accelerometry
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