Step‐rate threshold for physical activity intensity in Parkinson’s disease

Jeng, Brenda; Cederberg, Katie L.J.; Lai, Byron; Sasaki, Jeffer Eidi; Bamman, Marcas M.; Motl, Robert W.

doi:10.1111/ane.13250

Cited by 8 publications

(5 citation statements)

References 16 publications

(33 reference statements)

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“…Indeed, the median cadence of 107.3 (SD 10.9) spm across LONG walks included in this study is comparable to cadences at preferred walking speeds reported by others [54,57]. Importantly, this cadence aligns with the 100 spm threshold that has been shown to represent a moderate intensity in adults aged 21-85 years [10] and exceeds the 80 spm threshold that has been shown to represent a moderate intensity in people living with an NDD, specifically PD [28].…”

Section: Principal Findingssupporting

confidence: 80%

“…Walking bouts had a maximum resting period of 5 seconds [ 49 ], a median cadence above 80 steps per minute (spm), and a minimum duration of 60 seconds (referred to as LONG walks) [ 50 ]. These criteria were selected to identify bouts of purposeful, continuous walking assumed to reflect moderate-intensity activity [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

“…These guidelines include recommendations for time spent in MVPA, light activity, and sedentary behavior, based on evidence that each intensity has independent impacts on morbidity and mortality [17,18,53]. Walking at one's preferred pace represents moderate-intensity activity throughout the adult lifespan and for various health conditions [11,28,54,55]. As the energy cost and expenditure at a given speed increase with age, preferred gait speed slows to maintain a constant energy expenditure [54][55][56].…”

Section: Principal Findingsmentioning

confidence: 99%

“…Specifically, isolated arm movement (eg, gesticulation), absence of arm movement (eg, using a mobility aid such as a walker), or activities of daily living (ADLs; eg, pushing a shopping cart) may result in over-or underestimation of activity intensity [24]. Disease-or age-related changes in arm movement, including variable arm swing, limb asymmetry or impaired limb coordination, and reduced arm swing during slow gait [25][26][27], can also impact the relationship between arm motion and walking, leading to a dissociation between wrist-based estimates of intensity and the actual energy requirements of the activity [28]. Given the potential susceptibility to error and the ubiquitous use of wrist-worn accelerometry for PA intensity classification in research and clinical applications, there is an important need to confront the limitations of wrist-derived PA intensity estimates during periods of unconstrained, free-living walking.…”

Section: Introductionmentioning

confidence: 99%

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Wrist Accelerometer Estimates of Physical Activity Intensity During Walking in Older Adults and People Living With Complex Health Conditions: Retrospective Observational Data Analysis Study

Weber¹,

Godkin²,

Cornish³

et al. 2023

JMIR Form Res

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Background Accurate measurement of daily physical activity (PA) is important as PA is linked to health outcomes in older adults and people living with complex health conditions. Wrist-worn accelerometers are widely used to estimate PA intensity, including walking, which composes much of daily PA. However, there is concern that wrist-derived PA data in these cohorts is unreliable due to slow gait speed, mobility aid use, disease-related symptoms that impact arm movement, and transient activities of daily living. Despite the potential for error in wrist-derived PA intensity estimates, their use has become ubiquitous in research and clinical application. Objective The goals of this work were to (1) determine the accuracy of wrist-based estimates of PA intensity during known walking periods in older adults and people living with cerebrovascular disease (CVD) or neurodegenerative disease (NDD) and (2) explore factors that influence wrist-derived intensity estimates. Methods A total of 35 older adults (n=23 with CVD or NDD) wore an accelerometer on the dominant wrist and ankle for 7 to 10 days of continuous monitoring. Stepping was detected using the ankle accelerometer. Analyses were restricted to gait bouts ≥60 seconds long with a cadence ≥80 steps per minute (LONG walks) to identify periods of purposeful, continuous walking likely to reflect moderate-intensity activity. Wrist accelerometer data were analyzed within LONG walks using 15-second epochs, and published intensity thresholds were applied to classify epochs as sedentary, light, or moderate-to-vigorous physical activity (MVPA). Participants were stratified into quartiles based on the percent of walking epochs classified as sedentary, and the data were examined for differences in behavioral or demographic traits between the top and bottom quartiles. A case series was performed to illustrate factors and behaviors that can affect wrist-derived intensity estimates during walking. Results Participants averaged 107.7 (SD 55.8) LONG walks with a median cadence of 107.3 (SD 10.8) steps per minute. Across participants, wrist-derived intensity classification was 22.9% (SD 15.8) sedentary, 27.7% (SD 14.6) light, and 49.3% (SD 25.5) MVPA during LONG walks. All participants measured a statistically lower proportion of wrist-derived activity during LONG walks than expected (all P<.001), and 80% (n=28) of participants had at least 20 minutes of LONG walking time misclassified as sedentary based on wrist-derived intensity estimates. Participants in the highest quartile of wrist-derived sedentary classification during LONG walks were significantly older (t16=4.24, P<.001) and had more variable wrist movement (t16=2.13, P=.049) compared to those in the lowest quartile. Conclusions The current best practice wrist accelerometer method is prone to misclassifying activity intensity during walking in older adults and people living with complex health conditions. A multidevice approach may be warranted to advance methods for accurately assessing PA in these groups.

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Section: Principal Findingssupporting

confidence: 80%

Section: Methodsmentioning

confidence: 99%

Section: Principal Findingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wrist Accelerometer Estimates of Physical Activity Intensity During Walking in Older Adults and People Living With Complex Health Conditions: Retrospective Observational Data Analysis Study

Weber¹,

Godkin²,

Cornish³

et al. 2023

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…For example, despite the ubiquitous use of wrist worn accelerometers to derive physical activity intensity estimates in free-living settings, 62 this method may be unreliable in older adults or persons living with NDD due to age- and impairment-related changes in movement. 36 Instead, an approach that uses multiple body-worn sensors to resolve uncertainty, 43 as well as age- or disease-appropriate thresholds for activity intensity classification, 63 – 65 can help to improve real-world activity estimates in these groups. Even where known features of a particular cohort (e.g.…”

Section: Discussionmentioning

confidence: 99%

User-centered design of feedback regarding health-related behaviors derived from wearables: An approach targeting older adults and persons living with neurodegenerative disease

Van Ooteghem,

Godkin,

Thai

et al. 2023

DIGITAL HEALTH

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Objective There has been tremendous growth in wearable technologies for health monitoring but limited efforts to optimize methods for sharing wearables-derived information with older adults and clinical cohorts. This study aimed to co-develop, design and evaluate a personalized approach for information-sharing regarding daily health-related behaviors captured with wearables. Methods A participatory research approach was adopted with: (a) iterative stakeholder, and evidence-led development of feedback reporting; and (b) evaluation in a sample of older adults (n = 15) and persons living with neurodegenerative disease (NDD) (n = 25). Stakeholders included persons with lived experience, healthcare providers, health charity representatives and individuals involved in aging/NDD research. Feedback report information was custom-derived from two limb-mounted inertial measurement units and a mobile electrocardiography device worn by participants for 7–10 days. Mixed methods were used to evaluate reporting 2 weeks following delivery. Data were summarized using descriptive statistics for the group and stratified by cohort and cognitive status. Results Participants (n = 40) were 60% female (median 72 (60–87) years). A total of 82.5% found the report easy to read or understand, 80% reported the right amount of information was shared, 90% found the information helpful, 92% shared the information with a family member or friend and 57.5% made a behavior change. Differences emerged in sub-group comparisons. A range of participant profiles existed in terms of interest, uptake and utility. Conclusions The reporting approach was generally well-received with perceived value that translated into enhanced self-awareness and self-management of daily health-related behaviors. Future work should examine potential for scale, and the capacity for wearables-derived feedback to influence longer-term behavior change.

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