Figures
Abstract
Objective
To perform a systematic review of studies using remote physical activity monitoring in neurological diseases, highlighting advances and determining gaps.
Methods
Studies were systematically identified in PubMed/MEDLINE, CINAHL and SCOPUS from January 2004 to December 2014 that monitored physical activity for ≥24 hours in adults with neurological diseases. Studies that measured only involuntary motor activity (tremor, seizures), energy expenditure or sleep were excluded. Feasibility, findings, and protocols were examined.
Results
137 studies met inclusion criteria in multiple sclerosis (MS) (61 studies); stroke (41); Parkinson's Disease (PD) (20); dementia (11); traumatic brain injury (2) and ataxia (1). Physical activity levels measured by remote monitoring are consistently low in people with MS, stroke and dementia, and patterns of physical activity are altered in PD. In MS, decreased ambulatory activity assessed via remote monitoring is associated with greater disability and lower quality of life. In stroke, remote measures of upper limb function and ambulation are associated with functional recovery following rehabilitation and goal-directed interventions. In PD, remote monitoring may help to predict falls. In dementia, remote physical activity measures correlate with disease severity and can detect wandering.
Conclusions
These studies show that remote physical activity monitoring is feasible in neurological diseases, including in people with moderate to severe neurological disability. Remote monitoring can be a psychometrically sound and responsive way to assess physical activity in neurological disease. Further research is needed to ensure these tools provide meaningful information in the context of specific neurological disorders and patterns of neurological disability.
Citation: Block VAJ, Pitsch E, Tahir P, Cree BAC, Allen DD, Gelfand JM (2016) Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review. PLoS ONE 11(4): e0154335. https://doi.org/10.1371/journal.pone.0154335
Editor: Peipeng Liang, Xuanwu Hospital, Capital Medical Universty, CHINA
Received: December 23, 2015; Accepted: April 11, 2016; Published: April 28, 2016
Copyright: © 2016 Block et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work was supported by the National Center for Advancing Translational Sciences of National Institute of Health - under award KL2TR000143 (JMG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have read the journal's policy and have the following competing interests: VAJB and PT have no disclosures. EP has received compensation for teaching continuing education for Summit Professional Education. BACC has received personal compensation for consulting from Abbvie, Biogen, EMD Serono, MedImmune, Novartis, Genzyme/sanofi aventis, Teva and has received contracted research support (including clinical trials) from Acorda, Biogen, EMD Serono, Hoffman La Roche, MedImmune, Novartis and Teva. DDA has received compensation as an instructor for the Neurologic Physical Therapy Residency Program at Kaiser Redwood City. She has also received compensation for co-developing an online continuing education course in rehabilitation for people with multiple sclerosis for Western Schools. JMG has served as a consultant on a scientific advisory board for MedImmune; has received research support from Quest Diagnostics through UCSF on a dementia care pathway; and has received compensation for medical legal consulting related to CNS inflammatory disorders. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.
Introduction
Research over the last decade has examined accelerometer-based remote monitoring of physical activity in health and disease.[1–6] Wearable physical activity monitors have also become increasingly commonplace as consumer products, primarily marketed for fitness. When considering whether remote physical activity monitoring can inform decision-making for use in clinical populations, questions about validity, reliability, feasibility and responsiveness arise.[7–10]
Physical activity is typically defined as voluntary bodily movement using skeletal muscle that requires energy beyond resting levels.[11] Measurement of physical activity is important because of established links between physical inactivity and various morbidities.[5, 12, 13] Neurological disease can also increase the risk of physical inactivity secondary to associated disability.[14–17] Physical activity monitoring using accelerometers, pedometers, and gyroscopes has gained traction in healthcare, wellness and medical research.[5, 18–20] Monitoring can focus on gait, upper or lower limb function or other patterns of body movement or behavior. Potential variables that can be used to measure physical activity include step count, activity count, activity bouts, active minutes and energy expenditure. Remote physical activity monitoring provides a convenient way of assessing movement outside of the clinic setting and may correlate with disease-specific predictors, outcomes, or interventions.
However, remote measurement of physical activity in people with neurological disease has the potential to be complicated by neurological impairments such as gait abnormalities, weakness, spasticity or tremor that could confound remote measurement in these populations. While disease-specific examination and validation of remote physical activity is needed, systematically reviewing the literature across neurological disorders may reveal lessons about feasibility, implementation and interpretation that apply across neurological indications.
This systematic review summarizes research on remote physical activity monitoring in neurological diseases, including multiple sclerosis (MS), stroke, Parkinson’s disease (PD), dementia, traumatic brain injury (TBI), ataxia, epilepsy and migraine. To focus primarily on physical activity outside of the immediate clinical setting, studies were included that monitored physical activity for at least 24 hours.
Methods
Data Sources
Original research studies were identified from the PubMed/MEDLINE, CINAHL and SCOPUS databases. Once relevant articles were identified, they were located individually in the Web of Science database and in Google Scholar to examine citing and cited-by articles. The search strategy used a combination of MeSH (Medical Subject Headings) terms and keywords. The search terms used alone and in combination were categorized according to PICO: Population: “multiple sclerosis,” “parkinson*,” “stroke,” “cerebrovascular accident,” “brain injury,” “ataxia,” “headache,” “migraine,” and “epilepsy”. Intervention/ indicator: “acceleromet*,” “activity monitor*,” “free living physical activity,” “pedometer,” “wearable sensor*”.
Comparator/ Control: Not using the device. Inclusion criteria did not require studies to be intervention trials. Outcome: physical activity (measured heterogeneously e.g. step or activity count, movement count, bouts of activity)
We also examined articles that reported physical activity monitoring in samples with “heart disease” or “diabetes” to identify if sub-populations of neurological conditions were evaluated. A medical librarian (P.T.) advised on search strategy, search terms, and methodology.
Study Selection.
Studies were included if they 1) recorded human physical activity, defined as voluntary (skeletal) muscle movement during daily functioning requiring energy expenditure [3]; 2) monitored subjects for ≥24 hours; 3) used remote monitoring via devices that employ accelerometers, gyroscopes and/or pedometers to measure physical activity and capture data remotely for subsequent analysis; 4) enrolled adults 18 years of age or older with a diagnosis of MS, stroke, PD, dementia, TBI, epilepsy, migraine, headache or ataxia; 5) and were published between January 2004 and December 2014. Studies were excluded that recorded involuntary motor activity such as seizures or tremor; focused on movement during sleep or examined sleep as the primary outcome; extrapolated measures for average step counts from shorter monitoring periods; measured total daily energy expenditure (such as daily calorie consumption or diet interventions) without physical activity monitoring; or measured global positioning satellite (GPS) data exclusively rather than more direct measurement or corroboration of physical activity. We also excluded case reports and case studies.
Two authors (V.B., E.P.) searched independently. Titles and abstracts were screened for relevance and supplementary review. One author (V.B.) manually searched the reference sections of complete manuscripts for additional articles. Consensus for meeting the eligibility criteria was achieved by comparing search results (V.B., E.P.).
Data extraction and Analysis.
Data were extracted (V.B.) and checked (E.P., D.D.A., J.M.G), with final adjudication by consensus from two senior authors (D.D.A., J.M.G.). Variables included population studied; disease-specific severity levels; device name, placement and intent (i.e. patient behavior change or healthcare monitoring); intervention (if any); setting; demographic data; and study details, including design, funding sources and motivational factors (i.e. subject imbursement, visual display of data). Studies were graded for risk of bias based on methodology proposed by the Cochrane Collaborations [21] (see S1a and S1e Table). Conclusions and lessons learned across studies were summarized.
Results
The systematic review identified 745 studies through the databases and an additional 25 articles through recursive and manual reference searches. Once eligibility criteria were applied, 137 studies remained (Fig 1 and S1 Fig) [22]. Individual studies are summarized in Tables 1–5. Table 6 (sections a-e) documents the sample characteristics. The risk of bias with level of evidence for interventional studies is reported in S1 Table. A description of the most common devices used in the included studies appears in S2 Table.
Notes: * 1 Article includes multiple groups of neurological diagnosis—MS, Parkinson’s and neuromuscular disease—(Busse et al, 2004) α 1 Article includes TBI and Stroke (Fulk et al, 2014)
Multiple Sclerosis
The majority of the 61 studies (60/61, 98.4%) that remotely monitored activity in MS [23–82] (Tables 1 and 6 section a) measured physical activity by walking; one study focused on upper extremity movement.[83] The length of continuous monitoring ranged from 3 to 7 days for each discrete measurement period [33, 66] with 7 days being the measurement paradigm for the majority (41/61, 67.2%) of studies. Most of the studies (44/61, 72.2%) [23, 27, 28, 31, 32, 34, 37, 39, 41, 44, 46–56, 58–74, 76–79, 82, 84] included both relapsing and progressive MS phenotypes; >78% of participants had relapsing MS. Although MS disease duration varied, studies primarily included persons with disease duration of less than 20 years. Fifty-two studies focused on people having mild to moderate disability (able to walk without a cane or support) [23, 25–27, 29–31, 34–37, 40, 42–44, 47–50, 52, 55–61, 64, 65, 67–74, 76, 78, 79], and only two studies reported inclusion of people with greater levels of disability (requiring a walker or wheelchair for mobility).[24, 83] One research group (Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, Illinois) authored 49/61 studies (81.7%)[23, 25–28, 32, 34, 37, 39–69, 72–74, 78–82]; results from studies conducted by other groups generally corroborated this group’s results. No studies reported direct research funding by monitoring device manufacturers.
In two studies focused on people with MS, average daily activity and step count measured via wearable accelerometers correlated with performance-based and self-reported walking mobility and physical activity.[78, 81] A third study observed that accelerometers correlate only with performance-based measures of walking (6-minute walk; [6MW,] and the Timed-Up and Go, test; [TUG]) and not self-reported walking activity.[82]
People with MS record lower levels of physical activity than the general population and unaffected controls.[23, 27, 28, 32, 34, 37, 38, 52, 63, 66, 69, 73, 75, 77, 80] People with MS also frequently fail to reach daily levels of intensity and duration recommended for the general population.[85] Lower physical activity levels in MS are associated with higher levels of disability and lower scores in a range of clinical and self-reported outcomes such as walking speed and endurance (Timed 25-Foot Walk [23, 24, 29, 30, 37, 38, 62, 68, 76], 2-minute walk and 6MW [23–25, 31, 37, 38, 44, 49, 60, 67, 80, 82]), fatigue (i.e. Fatigue Severity Scale)[40, 48, 57], depression (i.e. Hospital Anxiety and Depression Scale [48, 57, 68]), self-efficacy, [39, 40, 62] and balance (Berg Balance Scale [24, 31, 38], TUG [23, 31, 80, 82]). Higher levels of physical activity correlate significantly with better performance on mobility measures in the clinic, self-reported disability questionnaires and cognitive processing speed.[41, 45, 53] Lower physical activity in MS correlates with age, [64] disease duration, [34] progressive forms of MS, [27] spasticity, [23] and unemployment, [27] but not race.[34] However, rate of disability accumulation over 6 months was similar in an active versus a more sedentary group in one study.[50] Of the 4 studies in MS that tested interventions, internet-based interventions appear to be beneficial in promoting objective and self-reported physical activity and are associated with decreased disability.[26, 30, 42, 68]
Stroke
More than half of the studies (24/41 studies, 58.5%)[15, 86–125] that reported on activity monitoring post-stroke (Tables 2 and 6 section b) measured walking or gait; 14 studies (34.1%)[94, 98, 99, 103, 104, 109, 115, 118, 119, 121–125] assessed upper extremity or arm movement; and 3 (7.3%)[100, 112, 120] measured both arm movement and walking. One study included participants with either a diagnosis of stroke (n = 30) or TBI (n = 20). This study is listed under both diagnostic headings and results are analyzed by diagnosis group.[126] Monitoring duration was usually between 2 and 6 days (28 studies, 68.3%)[86–93, 98–102, 104, 105, 110–114, 117–120, 122–125], although one study monitored step count for 4 weeks, reporting change in daily average steps between the 5 days prior and post intervention.[106] Monitoring usually commenced between 3 and 6 months after the stroke (28, 68.3%)[15, 86–88, 92–97, 101, 102, 104, 106–111, 113, 114, 116, 117, 119, 122–125]. Fewer than 40% of studies reported details about the type of stroke (i.e. ischemic or hemorrhagic and/or neuroanatomical localization). The presence and side of paralysis or paresis was reported in 92.7% (38/41)[15, 86–101, 103, 104, 106–125] of the articles; one article reported on the presence or absence of tremor as a potential confounder.[125] During monitoring, participants were in the “home/ community” or “hospital—acute care” settings; none of these studies specifically monitored patient activity in acute rehabilitation or at skilled nursing facilities.
Post-stroke, people tend to have a lower frequency of moderate to vigorous bouts of physical activity and are less likely to reach generally recommended minimum levels of physical activity than healthy controls.[15, 86] However, one study found that the “time participants spent on their feet” was similar to healthy controls.[86] Lower physical activity level post-stroke is associated with poor balance and greater depression scores.[88] Four intervention studies were identified: 3 aimed at improving arm function, [104, 122, 123] and 1 successfully increased daily step counts using a goal-directed step activity-monitoring program.[93] An observational study showed little change in daily limb use with accelerometer results, despite significant improvements in clinical measures.[112] Measuring both upper extremities post-stroke facilitated differentiation of uni- vs. bi-manual tasks, distribution of arm usage, and comparison of impaired vs. unimpaired arm function.[104] Spontaneous early arm movement activity was associated with greater neurological recovery post stroke, [118] although results varied regarding prediction of upper extremity recovery. [100, 119–121, 124, 125]
Parkinson’s Disease
All 20 studies [127–146] that reported on activity monitoring in PD (Tables 3 and 6 section c) measured physical activity through walking. Durations of monitoring were mostly for 2–6 days (6, 35.0%)[127, 134–136, 140, 144] or 7 days (8, 40.0%).[129–133, 137, 142, 143] Thirty-five per cent of studies (7/20) reported on the presence or absence of tremor as a potential confounder.[127, 134–136, 139–141]
One activity-monitoring device (DynaPort Hybrid) was able to differentiate between ON/OFF phases and detect “missteps/ near falls” in people with PD in the clinic and home environments.[136] Participants wore the device in the clinic while missteps were induced, an algorithm was developed to detect deviations from their gait patterns, and the algorithms were validated during an additional three days of device wear-time outside the clinic. Abnormal gait patterns, such as lower amplitude and greater step-to-step variability, were associated with fall risk in people with PD whereas total walking amount was not.[144]
People with PD tend to take fewer steps and do shorter bouts of physical activity than the general population.[130, 137, 147] A reduction in total number of steps per day correlates with PD progression, [128] and milder severity of PD is associated with higher physical activity levels.[135] People with PD tend to have a smaller number of longer sedentary periods than healthy controls, although total sedentary time is similar.[129] An intervention study aimed at increasing physical activity in people with PD resulted in increased muscle strength and flexibility, self-directed exercise frequency and duration, reduced fear of falls, but no overall change in the total amount of physical activity.[135]
Dementia
Nine [148–156] of the 11 [148–158] studies (81.8%) that reported on activity monitoring in dementia (Tables 4 and 6 section d) measured physical activity as walking. Two studies focused on upper extremity or arm movement in addition to walking or gait.[157, 158] Monitoring typically lasted 2–6 days (6/11 studies, 54.5%).[149, 153, 155–158] Most studies involved people with a presumed Alzheimer's dementia or a combination of Alzheimer’s dementia and frontotemporal or Lewy Body dementias (8/11 studies, 72.7%).[148–150, 152, 153, 155, 157, 158] Severity of cognitive dysfunction was usually mild to moderate (9/11 studies, 81.8%).[148, 149, 151–157, 159] Only 2 studies involved people with severe cognitive disability.[150, 158]
Physical activity level in people with dementia depended on stage of disease. People with mild Alzheimer’s dementia have lower mean physical activity (associated with apathy and more daytime napping)[148] and lower step count per day [149] compared to people with mild cognitive impairment (MCI) or healthy controls. Monitoring was feasible in people with cognitive impairment [149, 155] and accelerometry was able to distinguish partners with and without early Alzheimer’s disease even before deficits were clinically visible.[155] Monitoring in people with dementia distinguished “intensive wandering behavior,” which, when assessed along with estimations of energy expenditure, facilitated accurate calculation of nutritional requirement.[152]
Traumatic Brain Injury
The single study in TBI concluded that 7 days of accelerometry was feasible in 30 people more than 3 months post-TBI (adherence >86%). Physical activity was below recommended levels.[160] Data were more reliable than a self-reported physical activity questionnaire to determine amount, but not type of, moderate to vigorous physical activity.[160]
Ataxia
In a single study of physical activity monitoring in ataxia, 19 participants with spinocerebellar ataxia wore a step activity monitor for 7 days; greater physical activity was associated with shorter disease duration and lower disability scores.[161]
The remaining studies that reported physical activity monitoring in mixed populations [33, 126, 147] measured walking activity or gait (Tables 5 and 6 section e). One study observed 50 people with either TBI or stroke over the age of 50 and greater than 3 months post injury assessing various activity monitoring systems.[126] Another study evaluated a tri-axial accelerometer (TriTrac RT3) over 7 days in a study sample of patients with stroke (> 6 months in duration) (20), PD (7), or MS (11), and sedentary healthy controls (9).[33] Mobility was more accurately assessed using 7-day activity monitoring than with a patient reported measure. A third study measured step count in participants with PD (10), MS (10), primary muscle disorder (10) and healthy controls (30) over 7 days in free-living conditions.[147] Neurological patients were observed to have a lower level of physical activity than healthy controls.
Reliability and Validity
Many studies provided evidence of the reliability of various devices. For the StepWatch Activity Monitor post-stroke, the test-retest interclass correlation coefficient (ICC) values were 0.93–0.99 over a minimum of 3 days.[110] Other studies documented similar ICC values for Actical accelerometer activity counts (ICC >0.94; 95% CI 0.91–0.97) in people post-stroke with no differences between workdays and weekend days.[114] In MS, test-retest ICC values were 0.91 and 0.88 for steps per day and activity counts per day (ActiGraph GT3X), respectively, over 6 months, although the ICC was smaller for people with greater disability (ICC = 0.672 for activity counts/day and ICC = 0.774 for steps/day).[37] In a direct comparison in MS, seven days of monitoring (ActiGraph 7164) produced an ICC of 0.93 whereas three days yielded an ICC of 0.80, with no difference noted between days of the week (weekdays or weekend days) when measuring walking activity or gait.[66] A 7-day period (using a TriTrac RT3 accelerometer) was most reliable in patients with stroke, MS or PD.[33] In PD, 24 hours of monitoring was found to be reliable to record a participants’ functional activity (average step count, inactive vs. active minutes using an activity monitor).[145] In spinocerebellar ataxia, internal consistency was highest with 7-days of monitoring, but 3 days of monitoring using a step activity monitor still correlated strongly with 7-day measures.[161]
Evidence of validity primarily comes from comparison of activity data collected remotely with established performance-based and self-report measures. In MS, number of steps per day correlates with the Expanded Disability Status Scale (EDSS), the Patient Determined Disease Steps (PDDS) scale, performance-based ambulatory measures in the clinic and patient-reported outcomes.[24, 31, 37, 38] Post-stroke, the ICC was high when comparing activity counts for the paretic and non-paretic hip (0.96), [114] but correlation was moderate when comparing activity with patient-reported activity questionnaires.[90] Post-stroke, activity counts for the upper extremity had high predictive value for good arm recovery; [98–100] both arms are used less than by healthy controls, and less arm activity correlates with increased impairment and reduced muscle activity measured by EMG.[98, 99, 103, 118–125] In TBI, activity counts were more accurate than questionnaires in characterizing levels of moderate to vigorous physical activity.[160] In spinocerebellar ataxia, average step count across 7 days correlated strongly with disability scores and moderately with walking speed.[161]
Discussion
This systematic review examines a decade of literature on remote monitoring of physical activity in people with neurological diseases. Physical activity monitoring is feasible in these populations, including in those with impaired cognition. Some of the evidence was sparse: very few of the eligible studies used remote activity monitoring as an outcome for an intervention (9/134), [26, 30, 42, 68, 93, 104, 122, 123, 135] indicating that use of these tools in neurological populations is still primarily in an observational or validation phase. Nevertheless, the data in some diagnostic groups indicate that remote monitoring of physical activity can be a clinically useful way to assess activity status over time.
A wide array of variables can be used to measure physical activity. The most common are permutations of activity count or step count. However, other activity variables may provide better prognostic value in disease-specific situations. For example, length and number of moderate to vigorous activity bouts [86, 105] reflected differences better than total step count in some studies following stroke, [86, 116] whereas total step count, highest step rate in 1 minute, highest step rate in 5 minutes, and peak activity index appeared most reliable in others.[110] Detection of upper limb recovery via accelerometer measures of arm/upper extremity movement was also favored post-stroke, [98–100, 103, 104, 109, 112, 118–125] and may prove helpful in other populations, such as upper limb function in MS. In PD, average number of steps per day correlated with activity level and disease progression in many studies.[128, 132, 133] However, in a minority there was no correlation between activity count and patient-reported assessments of symptom severity.[140] Physical activity monitoring using specialized devices may also be used to predict fall-risk and measure missteps in PD, [129] functionality that, if replicated and validated, could be very be useful in other neurological populations, including MS and stroke.
Across diagnoses, physical activity is consistently lower in neurological populations than in those without neurological disease.[34–36, 83, 129, 148, 149] The total amount of activity or step counts measured via accelerometers is lower in MS (e.g.[63, 69]), dementia (e.g.[151, 153]), and stroke [118] than in controls. In people with moderate to severe PD, pattern of activity was different (sedentary bouts were longer) but total volume of sedentary time was similar to controls.[129] In those with mild to moderate PD, speed of turns was slower than in healthy controls, and reductions in daily ambulatory activity (volume of moderate to vigorous physical activity) were detected over a year, even without evident changes in clinical measures of gait or disease severity.[128]
Remote physical activity monitoring for durations of >24 hours was feasible in the neurological populations studied; [76] however, adherence was a potential concern. Post-stroke, the placement of sensors in pockets (confounding clothing movement with activity and increasing risk of leaving the device behind when changing clothes), impaired mental status, depression, and device discomfort (leading to withdrawal of 25% of patients from one study) all reduced adherence.[89, 91, 96] In PD, patients concerned with appearance also had reduced adherence (affecting over a quarter of participants in one study).[127] Physical activity monitoring for extended periods of time was well tolerated in people with Alzheimer's Disease, although adherence was lower (83%) compared to healthy controls (100%).[149] Tolerability was not recorded as a significant problem in studies involving people with MS, although adherence and loss of data from attrition was noted in several studies (S1a Table).
Intervention studies in stroke are heterogeneous with regards to adherence and walking performance. A circuit-based rehabilitation study aimed at increasing stroke patients’ amount and rate of walking in their home environment, found high adherence rates to the program.[108] Specific mention of device adherence was not recorded.[108] A separate intervention study recording steps per day during 4 weeks, reported ~25% attrition due to non-compliance.[106]
Interventional studies testing physical activity monitoring in stroke patients observed changes in clinical and patient-reported measures, but, perhaps in part due to inadequate adherence, failed to demonstrate changes in physical activity (average steps per day) in the home environment. [107, 108] Likewise, home intervention for increasing activity in people with PD observed improvements in strength, flexibility and a reduction in fear of falling, without noting changes is overall daily physical activity levels.[135] Studies in MS, however, indicated that Internet-based exercise interventions can help to increase physical activity (activity/ steps per day), and improve self-reported disease symptoms and self-efficacy over 6 months.[42, 68]
The few reviewed intervention studies using remote monitoring affirm that measuring activity levels of patients with minimal invasiveness in their natural environment has potential advantages over traditional self-reported and clinic-based measures. Self-reported measures are easy to obtain through questionnaires but are prone to recall bias. Performance-based measures in clinic can provide a useful snapshot of physical activity and may have prognostic value but are primarily measures of physical activity patients are capable of rather than how active patients actually are in their natural environment.[107, 108] Future intervention studies should continue measuring outcomes in multi-faceted ways as researchers gather more evidence of the relationship between the different categories of measures.
The accelerometer-based activity monitors used in many of the included studies are not primarily designed or marketed for consumer use, with current prices ranging from ~$200 to $600, which do not include software (~$2000) necessary for data analysis (S2 Table). Many commercially available monitors have not yet been evaluated in neurological populations. One recent study in healthy individuals showed no systematic bias when comparing step counts recorded via commercially available activity monitors (i.e. Fitbit) versus research grade accelerometers (ActiGraph).[162, 163] However, the accuracy of non-research grade activity monitors remains an active source of debate, [10, 19, 164–167] as does the failure of activity monitors to efficiently track many non-walking-based physical activities such as swimming, cycling, strength training and yoga.[168]
Lessons learned from this systematic review lead to several recommendations for translation of remote physical activity monitoring in neurological indications. 1) Remote physical activity monitoring research would benefit from standardization in reporting. We provide a checklist that might aid researchers and clinicians in future research and clinical use (Fig 2). 2) While remote monitoring devices and measurement protocols should be tested and validated in specific neurological conditions, solutions are likely to translate across neurological conditions that share patterns of functional impairment. 3) Activity monitors have the potential to be retooled with suites of variables specific to particular diagnostic indications. For example, a disease-specific remote monitoring suite for MS might include step and activity count, fall detection, upper extremity function and temperature sensors to correlate with possible heat-induced demyelination-related disability. Additional functionality could include reminders to exercise, take medication or keep to a schedule for bowel and bladder maintenance.[169] For all diagnostic groups, monitors could be tailored to track adherence to home exercise programs. If worn for longer periods of time, they could detect continuation of or changes in activity after specific punctate interventions (pharmacologic, medical, telehealth, or exercise-based) aimed to increase activity levels. Further studies are needed for longer periods of time (continuously for months/years) to determine the feasibility and responsiveness of activity monitoring devices for these purposes.
From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(6): e1000097. doi:10.1371/journal.pmed1000097 For more information, visit: www.prisma-statement.org.
Limitations of this review include the focus on adults with neurological disease; lessons learned do not necessarily extend to pediatric populations with these conditions. This review also focuses specifically on physical activity monitoring and by necessity does not analyze advances in non-voluntary activities that can also be assessed via remote monitoring, such as seizure detection and sleep. Because only 9 of the 134 studies were interventional, our review does not include a meta-analysis.
In conclusion, this review records emerging evidence to support the use of remote physical activity monitoring in neurological care and neurorehabilitation. Because some patients already regularly perform such monitoring on themselves using commercial wearable devices or through their smartphones, providers also need to become familiar with these technologies and strategies for interpretation and to consider this knowledge translation when planning future studies.
Supporting Information
S1 Table. Risk of Bias for Individual Studies.
(a = multiple sclerosis, b = stroke, c = Parkinson’s disease, d = Dementia/Alzheimer’s disease, and e = Multiple neurological disorders)
https://doi.org/10.1371/journal.pone.0154335.s002
(DOCX)
S2 Table. Summary of Common Monitors Used In Studies Monitoring Physical Activity for ≥ 24 Hours.
https://doi.org/10.1371/journal.pone.0154335.s003
(PDF)
S3 Table. Level of Evidence Intervention studies.
https://doi.org/10.1371/journal.pone.0154335.s004
(DOCX)
Author Contributions
Conceived and designed the experiments: VAJB DDA JMG. Analyzed the data: VJAB EP. Wrote the paper: VAJB BACC DDA JMG. Advised on search strategy: PT. Supervision of search strategy and checked data extraction: DDA JMG. Study Design: VAJB DDA JMG. Independently performed the systematic search: VAJB EP. Critical revision of the manuscript: VAJB DDA JMG PT BACC.
References
- 1. Trost SG, Tudor-Locke C. Advances in the science of objective physical activity monitoring: 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement. British journal of sports medicine. 2014;48(13):1009–10. Epub 2014/06/13. pmid:24920565.
- 2. Motl RW, Sandroff BM, Sosnoff JJ. Commercially available accelerometry as an ecologically valid measure of ambulation in individuals with multiple sclerosis. Expert review of neurotherapeutics. 2012;12(9):1079–88. Epub 2012/10/09. pmid:23039387.
- 3. Van Remoortel H, Giavedoni S, Raste Y, Burtin C, Louvaris Z, Gimeno-Santos E, et al. Validity of activity monitors in health and chronic disease: a systematic review. The international journal of behavioral nutrition and physical activity. 2012;9:84. Epub 2012/07/11. pmid:22776399; PubMed Central PMCID: PMCPmc3464146.
- 4. Stansfield B, Clemes S, John D, Meijer K, Melanson E, Rowlands A, et al. 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement (University of Massachusetts, Amherst, USA, June 17–19, 2013). Physiological measurement. 2014;35(11):E2179–81. Epub 2014/10/24. pmid:25340758.
- 5. Strath SJ, Kaminsky LA, Ainsworth BE, Ekelund U, Freedson PS, Gary RA, et al. Guide to the assessment of physical activity: Clinical and research applications: a scientific statement from the American Heart Association. Circulation. 2013;128(20):2259–79. Epub 2013/10/16. pmid:24126387.
- 6. Wang S, Luo X, Barnes D, Sano M, Yaffe K. Physical activity and risk of cognitive impairment among oldest-old women. The American journal of geriatric psychiatry: official journal of the American Association for Geriatric Psychiatry. 2014;22(11):1149–57. Epub 2013/07/09. pmid:23831179; PubMed Central PMCID: PMCPmc3864545.
- 7. Patel MS, Asch DA, Volpp KG. Wearable devices as facilitators, not drivers, of health behavior change. Jama. 2015;313(5):459–60. Epub 2015/01/09. pmid:25569175.
- 8. Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. Jama. 2015;313(6):625–6. Epub 2015/02/11. pmid:25668268.
- 9.
Group S. Documenting how dementia sufferers benefit from GPS. Medical News Today [Internet]. 2015.
- 10.
Garber M. The Ennui of the Fitbit. The Atlantic [Internet]. June 17 2015.
- 11.
World Health Organization. Global recommendations on physical activity for health. Geneva, Switzerland: WHO; 2010.
- 12. Plow M, Finlayson M, Motl RW, Bethoux F. Randomized controlled trial of a teleconference fatigue management plus physical activity intervention in adults with multiple sclerosis: rationale and research protocol. BMC neurology. 2012;12:122. Epub 2012/10/18. pmid:23072517; PubMed Central PMCID: PMCPmc3495833.
- 13. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, PT K. Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380:219–29.
- 14. Marrie RA, Horwitz RI. Emerging effects of comorbidities on multiple sclerosis. The Lancet Neurology. 2010;9(8):820–8. Epub 2010/07/24. pmid:20650403.
- 15. Butler EN, Evenson KR. Prevalence of physical activity and sedentary behavior among stroke survivors in the United States. Topics in stroke rehabilitation. 2014;21(3):246–55. Epub 2014/07/06. pmid:24985392; PubMed Central PMCID: PMCPmc4146341.
- 16. Zhu XC, Yu Y, Wang HF, Jiang T, Cao L, Wang C, et al. Physiotherapy intervention in Alzheimer's disease: systematic review and meta-analysis. Journal of Alzheimer's disease: JAD. 2015;44(1):163–74. Epub 2014/09/10. pmid:25201787.
- 17. van Nimwegen M, Speelman AD, Hofman-van Rossum EJ, Overeem S, Deeg DJ, Borm GF, et al. Physical inactivity in Parkinson's disease. Journal of neurology. 2011;258(12):2214–21. Epub 2011/05/27. pmid:21614433; PubMed Central PMCID: PMCPmc3225631.
- 18. Thelle DS. Assessment of physical activity and energy expenditure in epidemiological studies. European journal of epidemiology. 2007;22(6):351–2. Epub 2007/06/27. pmid:17593528.
- 19. Pedisic Z, Bauman A. Accelerometer-based measures in physical activity surveillance: current practices and issues. British journal of sports medicine. 2015;49(4):219–23. Epub 2014/11/06. pmid:25370153.
- 20. Allet L, Knols RH, Shirato K, de Bruin ED. Wearable systems for monitoring mobility-related activities in chronic disease: a systematic review. Sensors (Basel, Switzerland). 2010;10(10):9026–52. Epub 2010/01/01. pmid:22163393; PubMed Central PMCID: PMCPMC3230979.
- 21.
Cochrane Handbook for Systematic Reviews of Interventions. http://www.cochrane-handbook.org.: The Cochrane Collaboration; 2011.
- 22. Moher D, Liberati A, Tetzlaff J, Altman D. The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. BMJ. 2009;339(b):2535.
- 23. Balantrapu S, Sosnoff JJ, Pula JH, Sandroff BM, Motl RW. Leg spasticity and ambulation in multiple sclerosis. Multiple sclerosis international. 2014;2014:649390. Epub 2014/07/08. pmid:24999434; PubMed Central PMCID: PMCPmc4066854.
- 24. Cavanaugh JT, Gappmaier VO, Dibble LE, Gappmaier E. Ambulatory activity in individuals with multiple sclerosis. Journal of neurologic physical therapy: JNPT. 2011;35(1):26–33. Epub 2011/04/09. pmid:21475081.
- 25. Dlugonski D, Motl RW. Possible antecedents and consequences of self-esteem in persons with multiple sclerosis: preliminary evidence from a cross-sectional analysis. Rehabil Psychol. 2012;57(1):35–42. Epub 2012/03/01. pmid:22369115.
- 26. Dlugonski D, Motl RW, McAuley E. Increasing physical activity in multiple sclerosis: replicating Internet intervention effects using objective and self-report outcomes. Journal of rehabilitation research and development. 2011;48(9):1129–36. Epub 2012/01/12. pmid:22234717.
- 27. Dlugonski D, Pilutti LA, Sandroff BM, Suh Y, Balantrapu S, Motl RW. Steps per day among persons with multiple sclerosis: variation by demographic, clinical, and device characteristics. Arch Phys Med Rehabil. 2013;94(8):1534–9. Epub 2013/02/20. pmid:23419331.
- 28. Doerksen SE, Motl RW, McAuley E. Environmental correlates of physical activity in multiple sclerosis: a cross-sectional study. The international journal of behavioral nutrition and physical activity. 2007;4:49. Epub 2007/10/10. pmid:17922918; PubMed Central PMCID: PMCPmc2174510.
- 29. Filipovic Grcic P, Matijaca M, Bilic I, Dzamonja G, Lusic I, Caljkusic K, et al. Correlation analysis of visual analogue scale and measures of walking ability in multiple sclerosis patients. Acta neurologica Belgica. 2013;113(4):397–402. Epub 2013/03/16. pmid:23494833.
- 30. Filipovic Grcic P, Matijaca M, Lusic I, Capkun V. Responsiveness of walking-based outcome measures after multiple sclerosis relapses following steroid pulses. Medical science monitor: international medical journal of experimental and clinical research. 2011;17(12):Cr704–10. Epub 2011/12/02. pmid:22129902; PubMed Central PMCID: PMCPmc3628135.
- 31. Gijbels D, Alders G, Van Hoof E, Charlier C, Roelants M, Broekmans T, et al. Predicting habitual walking performance in multiple sclerosis: relevance of capacity and self-report measures. Multiple sclerosis (Houndmills, Basingstoke, England). 2010;16(5):618–26. Epub 2010/03/09. pmid:20207785.
- 32. Gosney JL, Scott JA, Snook EM, Motl RW. Physical activity and multiple sclerosis: validity of self-report and objective measures. Family & community health. 2007;30(2):144–50. Epub 2007/04/01. pmid:19241650.
- 33. Hale LA, Pal J, Becker I. Measuring free-living physical activity in adults with and without neurologic dysfunction with a triaxial accelerometer. Arch Phys Med Rehabil. 2008;89(9):1765–71. Epub 2008/09/02. pmid:18760161.
- 34. Klaren RE, Motl RW, Dlugonski D, Sandroff BM, Pilutti LA. Objectively quantified physical activity in persons with multiple sclerosis. Arch Phys Med Rehabil. 2013;94(12):2342–8. Epub 2013/08/03. pmid:23906692.
- 35. Klassen L, Schachter C, Scudds R. An exploratory study of two measures of free-living physical activity for people with multiple sclerosis. Clin Rehabil. 2008;22(3):260–71. Epub 2008/02/21. pmid:18285434.
- 36. Kos D, Nagels G, D'Hooghe MB, Duquet W, Ilsbroukx S, Delbeke S, et al. Measuring activity patterns using actigraphy in multiple sclerosis. Chronobiology International. 2007;24(2):345–56. WOS:000246297100010. pmid:17453852
- 37. Learmonth YC, Dlugonski DD, Pilutti LA, Sandroff BM, Motl RW. The reliability, precision and clinically meaningful change of walking assessments in multiple sclerosis. Multiple sclerosis (Houndmills, Basingstoke, England). 2013;19(13):1784–91. Epub 2013/04/17. pmid:23587605.
- 38. Learmonth YC, Motl RW, Sandroff BM, Pula JH, Cadavid D. Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC neurology. 2013;13:37. Epub 2013/04/27. pmid:23617555; PubMed Central PMCID: PMCPmc3651716.
- 39. Morris KS, McAuley E, Motl RW. Self-efficacy and environmental correlates of physical activity among older women and women with multiple sclerosis. Health Education Research. 2008;23(4):744–52. WOS:000257576700015. pmid:17962232
- 40. Motl RW, McAuley E, Sandroff BM. Longitudinal change in physical activity and its correlates in relapsing-remitting multiple sclerosis. Phys Ther. 2013;93(8):1037–48. Epub 2013 Apr 18. pmid:23599354
- 41. Motl RW, Snook EM, McAuley E, Scott SM, Gliottoni RC. Are Physical Activity and Symptoms Correlates of Functional Limitations and Disability in Multiple Sclerosis? Rehabil Psychol. 2007;52(4):463–9.
- 42. Motl RW, Dlugonski D. Increasing physical activity in multiple sclerosis using a behavioral intervention. Behavioral medicine (Washington, DC). 2011;37(4):125–31. Epub 2011/12/16. pmid:22168329.
- 43. Motl RW, Dlugonski D, Suh Y, Weikert M, Fernhall B, Goldman M. Accelerometry and its association with objective markers of walking limitations in ambulatory adults with multiple sclerosis. Arch Phys Med Rehabil. 2010;91(12):1942–7. Epub 2010/11/30. pmid:21112438; PubMed Central PMCID: PMCPmc3165019.
- 44. Motl RW, Fernhall B, McAuley E, Cutter G. Physical activity and self-reported cardiovascular comorbidities in persons with multiple sclerosis: evidence from a cross-sectional analysis. Neuroepidemiology. 2011;36(3):183–91. Epub 2011/05/21. pmid:21597305; PubMed Central PMCID: PMCPmc3214895.
- 45. Motl RW, Gappmaier E, Nelson K, Benedict RH. Physical activity and cognitive function in multiple sclerosis. Journal of sport & exercise psychology. 2011;33(5):734–41. Epub 2011/10/11. pmid:21984644.
- 46. Motl RW, Learmonth YC, Pilutti LA, Dlugonski D, Klaren R. Validity of minimal clinically important difference values for the multiple sclerosis walking scale-12? European neurology. 2014;71(3–4):196–202. Epub 2014/01/25. pmid:24457548.
- 47. Motl RW, McAuley E. Longitudinal analysis of physical activity and symptoms as predictors of change in functional limitations and disability in multiple sclerosis. Rehabil Psychol. 2009;54(2):204–10. Epub 2009/05/28. pmid:19469611.
- 48. Motl RW, McAuley E. Symptom cluster as a predictor of physical activity in multiple sclerosis: preliminary evidence. Journal of pain and symptom management. 2009;38(2):270–80. Epub 2009/03/31. pmid:19329276.
- 49. Motl RW, McAuley E. Pathways between physical activity and quality of life in adults with multiple sclerosis. Health Psychol. 2009;28(6):682–9. Epub 2009/11/18. pmid:19916636.
- 50. Motl RW, McAuley E. Association between change in physical activity and short-term disability progression in multiple sclerosis. Journal of rehabilitation medicine: official journal of the UEMS European Board of Physical and Rehabilitation Medicine. 2011;43(4):305–10. Epub 2011/02/10. pmid:21305247.
- 51. Motl RW, McAuley E, Dlugonski D. Reactivity in baseline accelerometer data from a physical activity behavioral intervention. Health Psychol. 2012;31(2):172–5. Epub 2011/10/26. pmid:22023436.
- 52. Motl RW, McAuley E, Klaren R. Reliability of physical-activity measures over six months in adults with multiple sclerosis: implications for designing behavioral interventions. Behavioral medicine (Washington, DC). 2014;40(1):29–33. Epub 2014/02/12. pmid:24512363.
- 53. Motl RW, McAuley E, Snook EM. Physical activity and quality of life in multiple sclerosis: Possible roles of social support, self-efficacy, and functional limitations. Rehabil Psychol. 2007;52(2):143–51. WOS:000246433900003.
- 54. Motl RW, McAuley E, Snook EM, Gliottoni RC. Does the relationship between physical activity and quality of life differ based on generic versus disease-targeted instruments? Annals of behavioral medicine: a publication of the Society of Behavioral Medicine. 2008;36(1):93–9. Epub 2008/08/23. pmid:18719976; PubMed Central PMCID: PMCPmc2893359.
- 55. Motl RW, McAuley E, Snook EM, Gliottoni RC. Physical activity and quality of life in multiple sclerosis: intermediary roles of disability, fatigue, mood, pain, self-efficacy and social support. Psychology, health & medicine. 2009;14(1):111–24. Epub 2008/12/17. pmid:19085318; PubMed Central PMCID: PMCPmc2893350.
- 56. Motl RW, McAuley E, Snook EM, Scott JA. Validity of physical activity measures in ambulatory individuals with multiple sclerosis. Disability and rehabilitation. 2006;28(18):1151–6. Epub 2006/09/13. pmid:16966236.
- 57. Motl RW, McAuley E, Wynn D, Suh Y, Weikert M, Dlugonski D. Symptoms and physical activity among adults with relapsing-remitting multiple sclerosis. The Journal of nervous and mental disease. 2010;198(3):213–9. Epub 2010/03/11. pmid:20215999.
- 58. Motl RW, Pilutti L, Sandroff BM, Dlugonski D, Sosnoff JJ, Pula JH. Accelerometry as a measure of walking behavior in multiple sclerosis. Acta neurologica Scandinavica. 2013;127(6):384–90. Epub 2012/12/18. pmid:23240822.
- 59. Motl RW, Pilutti LA, Learmonth YC, Goldman MD, Brown T. Clinical importance of steps taken per day among persons with multiple sclerosis. PloS one. 2013;8(9):e73247. Epub 2013/09/12. pmid:24023843; PubMed Central PMCID: PMCPmc3762863.
- 60. Motl RW, Sandroff BM, Suh Y, Sosnoff JJ. Energy cost of walking and its association with gait parameters, daily activity, and fatigue in persons with mild multiple sclerosis. Neurorehabilitation and neural repair. 2012;26(8):1015–21. Epub 2012/04/03. pmid:22466791.
- 61. Motl RW, Schwartz CE, Vollmer T. Continued validation of the Symptom Inventory in multiple sclerosis. Journal of the neurological sciences. 2009;285(1–2):134–6. Epub 2009/07/14. pmid:19592041.
- 62. Motl RW, Snook EM, McAuley E, Gliottoni RC. Symptoms, self-efficacy, and physical activity among individuals with multiple sclerosis. Research in Nursing & Health. 2006;29(6):597–606. WOS:000242523800010.
- 63. Motl RW, Snook EM, McAuley E, Scott JA, Douglass ML. Correlates of physical activity among individuals with multiple sclerosis. Annals of behavioral medicine: a publication of the Society of Behavioral Medicine. 2006;32(2):154–61. Epub 2006/09/16. pmid:16972813.
- 64. Motl RW, Snook EM, McAuley E, Scott JA, Hinkle ML. Demographic correlates of physical activity in individuals with multiple sclerosis. Disability and rehabilitation. 2007;29(16):1301–4. Epub 2007/07/27. pmid:17654005.
- 65. Motl RW, Snook EM, Wynn DR, Vollmer T. Physical activity correlates with neurological impairment and disability in multiple sclerosis. The Journal of nervous and mental disease. 2008;196(6):492–5. Epub 2008/06/17. pmid:18552627.
- 66. Motl RW, Zhu W, Park Y, McAuley E, Scott JA, Snook EM. Reliability of scores from physical activity monitors in adults with multiple sclerosis. Adapted physical activity quarterly: APAQ. 2007;24(3):245–53. Epub 2007/10/06. pmid:17916920.
- 67. Pilutti LA, Dlugonski D, Pula JH, Motl RW. Weight status in persons with multiple sclerosis: implications for mobility outcomes. Journal of obesity. 2012;2012:868256. Epub 2012/10/11. pmid:23050129; PubMed Central PMCID: PMCPmc3463173.
- 68. Pilutti LA, Dlugonski D, Sandroff BM, Klaren R, Motl RW. Randomized controlled trial of a behavioral intervention targeting symptoms and physical activity in multiple sclerosis. Mult Scler J. 2014;20(5):594–601. WOS:000333690400013.
- 69. Ranadive SM, Yan H, Weikert M, Lane AD, Linden MA, Baynard T, et al. Vascular dysfunction and physical activity in multiple sclerosis. Medicine and science in sports and exercise. 2012;44(2):238–43. Epub 2011/07/22. pmid:21775908.
- 70. Rietberg MB, van Wegen EE, Kollen BJ, Kwakkel G. Do Patients With Multiple Sclerosis Show Different Daily Physical Activity Patterns From Healthy Individuals? Neurorehabilitation and neural repair. 2014;28(6):516–23. Epub 2014/02/12. pmid:24515924.
- 71. Rietberg MB, van Wegen EE, Uitdehaag BM, de Vet HC, Kwakkel G. How Reproducible Is Home-Based 24-Hour Ambulatory Monitoring of Motor Activity in Patients With Multiple Sclerosis? Arch Phys Med Rehabil. 2010;91(10):1537–41. WOS:000282720300008. pmid:20875511
- 72. Sandroff B, Pilutti L, Dlugonski D, Motl RW. Physical activity and information processing speed in persons with multiple sclerosis: A prospective study. Mental Health and Physical Activity. 2013;6:205–11.
- 73. Sandroff BM, Dlugonski D, Weikert M, Suh Y, Balantrapu S, Motl RW. Physical activity and multiple sclerosis: new insights regarding inactivity. Acta neurologica Scandinavica. 2012;126(4):256–62. Epub 2012/01/04. pmid:22211941.
- 74. Sandroff BM, Motl RW. Comparison of ActiGraph activity monitors in persons with multiple sclerosis and controls. Disability and rehabilitation. 2013;35(9):725–31. Epub 2013/04/06. pmid:23557239.
- 75. Scott SM, Hughes AR, Galloway SD, Hunter AM. Surface EMG characteristics of people with multiple sclerosis during static contractions of the knee extensors. Clinical physiology and functional imaging. 2011;31(1):11–7. Epub 2010/09/03. pmid:20807227.
- 76. Shammas L, Zentek T, Haaren Bv, Schlesinger S, Hey S, Rashid A. Home-based system for physical activity monitoring in patients with multiple sclerosis (Pilot study). Biomed Eng Online. 2014;13(10). Epub Feb 6, 2014.
- 77. Snook EM, Motl RW. Physical activity behaviors in individuals with multiple sclerosis: roles of overall and specific symptoms, and self-efficacy. Journal of pain and symptom management. 2008;36(1):46–53. Epub 2008/03/26. pmid:18362058.
- 78. Snook EM, Motl RW, Gliottoni RC. The effect of walking mobility on the measurement of physical activity using accelerometry in multiple sclerosis. Clin Rehabil. 2009;23(3):248–58. Epub 2009/02/17. pmid:19218299.
- 79. Sosnoff JJ, Goldman MD, Motl RW. Real-life walking impairment in multiple sclerosis: preliminary comparison of four methods for processing accelerometry data. Multiple sclerosis (Houndmills, Basingstoke, England). 2010;16(7):868–77. Epub 2010/06/11. pmid:20534642.
- 80. Ward CL, Suh Y, Lane AD, Yan H, Ranadive SM, Fernhall B, et al. Body composition and physical function in women with multiple sclerosis. Journal of rehabilitation research and development. 2013;50(8):1139–47. Epub 2014/01/25. pmid:24458900.
- 81. Weikert M, Motl RW, Suh Y, McAuley E, Wynn D. Accelerometry in persons with multiple sclerosis: measurement of physical activity or walking mobility? Journal of the neurological sciences. 2010;290(1–2):6–11. Epub 2010/01/12. pmid:20060544.
- 82. Weikert M, Suh Y, Lane A, Sandroff B, Dlugonski D, Fernhall B, et al. Accelerometry is associated with walking mobility, not physical activity, in persons with multiple sclerosis. Medical engineering & physics. 2012;34(5):590–7. Epub 2011/10/05. pmid:21968005.
- 83. Lamers I, Kerkhofs L, Raats J, Kos D, Van Wijmeersch B, Feys P. Perceived and actual arm performance in multiple sclerosis: relationship with clinical tests according to hand dominance. Multiple sclerosis (Houndmills, Basingstoke, England). 2013;19(10):1341–8. Epub 2013/02/15. pmid:23407701.
- 84. Learmonth YC, Motl RW, Sandroff BM, Pula JH, Cadavid D. Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC neurology. 2013;13. WOS:000318848600001.
- 85.
Services. UDoHaH. Physical Activity Guidelines for Americans http://www.health.gov/paguidelines/report/: U.S. Department of Health and Human Services. Washington, DC: US Department of Health and Human Services; 2008 [cited 2015 02/11/15].
- 86. Alzahrani MA, Ada L, Dean CM. Duration of physical activity is normal but frequency is reduced after stroke: an observational study. Journal of physiotherapy. 2011;57(1):47–51. Epub 2011/03/16. pmid:21402330.
- 87. Alzahrani MA, Dean CM, Ada L. Ability to negotiate stairs predicts free-living physical activity in community-dwelling people with stroke: an observational study. The Australian journal of physiotherapy. 2009;55(4):277–81. Epub 2009/11/26. pmid:19929771.
- 88. Alzahrani MA, Dean CM, Ada L, Dorsch S, Canning CG. Mood and Balance are Associated with Free-Living Physical Activity of People after Stroke Residing in the community. Stroke research and treatment. 2012;2012:470648. Epub 2011/10/21. pmid:22013550; PubMed Central PMCID: PMCPmc3195499.
- 89. Askim T, Bernhardt J, Churilov L, Fredriksen KR, Indredavik B. Changes in physical activity and related functional and disability levels in the first six months after stroke: a longitudinal follow-up study. Journal of rehabilitation medicine: official journal of the UEMS European Board of Physical and Rehabilitation Medicine. 2013;45(5):423–8. Epub 2013/04/11. pmid:23571658.
- 90. Baert I, Feys H, Daly D, Troosters T, Vanlandewijck Y. Are patients 1 year post-stroke active enough to improve their physical health? Disability and rehabilitation. 2012;34(7):574–80. Epub 2011/10/11. pmid:21981331.
- 91. Barak S, Wu SS, Dai Y, Duncan PW, Behrman AL. Adherence to accelerometry measurement of community ambulation poststroke. Phys Ther. 2014;94(1):101–10. Epub 2013/09/14. pmid:24029299; PubMed Central PMCID: PMCPmc3892677.
- 92. Bowden MG, Balasubramanian CK, Behrman AL, Kautz SA. Validation of a speed-based classification system using quantitative measures of walking performance poststroke. Neurorehabilitation and neural repair. 2008;22(6):672–5. Epub 2008/10/31. pmid:18971382; PubMed Central PMCID: PMCPmc2587153.
- 93. Danks KA, Roos MA, McCoy D, Reisman DS. A step activity monitoring program improves real world walking activity post stroke. Disability and rehabilitation. 2014;36(26):2233–6. Epub 2014/03/29. pmid:24670193; PubMed Central PMCID: PMCPmc4350931.
- 94. de Niet M, Bussmann JB, Ribbers GM, Stam HJ. The stroke upper-limb activity monitor: its sensitivity to measure hemiplegic upper-limb activity during daily life. Arch Phys Med Rehabil. 2007;88(9):1121–6. Epub 2007/09/11. pmid:17826456.
- 95. Dobkin BH, Xu X, Batalin M, Thomas S, Kaiser W. Reliability and validity of bilateral ankle accelerometer algorithms for activity recognition and walking speed after stroke. Stroke; a journal of cerebral circulation. 2011;42(8):2246–50. Epub 2011/06/04. pmid:21636815.
- 96. Frazer SW, Hellebrand WE, Keijsers NL. Variation and achievement of ambulatory activity among patients with chronic stroke. Journal of rehabilitation medicine: official journal of the UEMS European Board of Physical and Rehabilitation Medicine. 2013;45(9):848–53. Epub 2013/07/05. pmid:23824138.
- 97. Fulk GD, Reynolds C, Mondal S, Deutsch JE. Predicting home and community walking activity in people with stroke. Arch Phys Med Rehabil. 2010;91(10):1582–6. Epub 2010/09/30. pmid:20875518.
- 98. Gebruers N, Truijen S, Engelborghs S, De Deyn PP. Predictive value of upper-limb accelerometry in acute stroke with hemiparesis. Journal of rehabilitation research and development. 2013;50(8):1099–106. Epub 2014/01/25. pmid:24458895.
- 99. Gebruers N, Truijen S, Engelborghs S, De Deyn PP. Prediction of upper limb recovery, general disability, and rehabilitation status by activity measurements assessed by accelerometers or the Fugl-Meyer score in acute stroke. American journal of physical medicine & rehabilitation / Association of Academic Physiatrists. 2014;93(3):245–52. Epub 2014/01/09. pmid:24398579.
- 100. Gebruers N, Truijen S, Engelborghs S, Nagels G, Brouns R, De Deyn PP. Actigraphic measurement of motor deficits in acute ischemic stroke. Cerebrovascular diseases (Basel, Switzerland). 2008;26(5):533–40. Epub 2008/10/07. pmid:18836264.
- 101. Haeuber E, Shaughnessy M, Forrester LW, Coleman KL, Macko RF. Accelerometer monitoring of home- and community-based ambulatory activity after stroke. Arch Phys Med Rehabil. 2004;85(12):1997–2001. Epub 2004/12/18. pmid:15605339.
- 102. Knarr B, Roos MA, Reisman DS. Sampling frequency impacts measurement of walking activity after stroke. Journal of rehabilitation research and development. 2013;50(8):1107–12. Epub 2014/01/25. pmid:24458896.
- 103. Lang CE, Wagner JM, Edwards DF, Dromerick AW. Upper extremity use in people with hemiparesis in the first few weeks after stroke. Journal of neurologic physical therapy: JNPT. 2007;31(2):56–63. Epub 2007/06/15. pmid:17558358.
- 104. Lemmens RJ, Timmermans AA, Janssen-Potten YJ, Pulles SA, Geers RP, Bakx WG, et al. Accelerometry measuring the outcome of robot-supported upper limb training in chronic stroke: a randomized controlled trial. PloS one. 2014;9(5):e96414. Epub 2014/05/16. pmid:24823925; PubMed Central PMCID: PMCPmc4019639.
- 105. Manns PJ, Baldwin E. Ambulatory activity of stroke survivors: measurement options for dose, intensity, and variability of activity. Stroke; a journal of cerebral circulation. 2009;40(3):864–7. Epub 2009/01/20. pmid:19150867.
- 106. Moore JL, Roth EJ, Killian C, Hornby TG. Locomotor training improves daily stepping activity and gait efficiency in individuals poststroke who have reached a "plateau" in recovery. Stroke; a journal of cerebral circulation. 2010;41(1):129–35. Epub 2009/11/17. pmid:19910547.
- 107. Michael K, Goldberg AP, Treuth MS, Beans J, Normandt P, Macko RF. Progressive Adaptive Physical Activity in Stroke Improves Balance, Gait, and Fitness: Preliminary Results. Topics in stroke rehabilitation. 2009;16(2):133–9. PMC2934904. pmid:19581199
- 108. Mudge S, Barber PA, Stott NS. Circuit-based rehabilitation improves gait endurance but not usual walking activity in chronic stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2009;90(12):1989–96. Epub 2009/12/09. pmid:19969159.
- 109. Michielsen ME, Selles RW, Stam HJ, Ribbers GM, Bussmann JB. Quantifying nonuse in chronic stroke patients: a study into paretic, nonparetic, and bimanual upper-limb use in daily life. Arch Phys Med Rehabil. 2012;93(11):1975–81. Epub 2012/04/03. pmid:22465403.
- 110. Mudge S, Stott NS. Test—retest reliability of the StepWatch Activity Monitor outputs in individuals with chronic stroke. Clin Rehabil. 2008;22(10–11):871–7. Epub 2008/10/29. pmid:18955419.
- 111. Mudge S, Stott NS. Timed walking tests correlate with daily step activity in persons with stroke. Arch Phys Med Rehabil. 2009;90(2):296–301. Epub 2009/02/25. pmid:19236983.
- 112. Rand D, Eng JJ. Disparity between functional recovery and daily use of the upper and lower extremities during subacute stroke rehabilitation. Neurorehabilitation and neural repair. 2012;26(1):76–84. Epub 2011/06/23. pmid:21693771; PubMed Central PMCID: PMCPmc3233607.
- 113. Rand D, Eng JJ, Tang PF, Hung C, Jeng JS. Daily physical activity and its contribution to the health-related quality of life of ambulatory individuals with chronic stroke. Health Qual Life Outcomes. 2010;8:80. Epub 2010/08/05. pmid:20682071; PubMed Central PMCID: PMCPmc2927504.
- 114. Rand D, Eng JJ, Tang PF, Jeng JS, Hung C. How active are people with stroke?: use of accelerometers to assess physical activity. Stroke; a journal of cerebral circulation. 2009;40(1):163–8. Epub 2008/10/25. pmid:18948606.
- 115. Reiterer V, Sauter C, Klosch G, Lalouschek W, Zeitlhofer J. Actigraphy—a useful tool for motor activity monitoring in stroke patients. European neurology. 2008;60(6):285–91. Epub 2008/10/01. pmid:18824856.
- 116. Robinson CA, Shumway-Cook A, Ciol MA, Kartin D. Participation in community walking following stroke: subjective versus objective measures and the impact of personal factors. Phys Ther. 2011;91(12):1865–76. Epub 2011/10/18. pmid:22003172.
- 117. Roos MA, Rudolph KS, Reisman DS. The structure of walking activity in people after stroke compared with older adults without disability: a cross-sectional study. Phys Ther. 2012;92(9):1141–7. Epub 2012/06/09. pmid:22677293; PubMed Central PMCID: PMCPmc3432950.
- 118. Seitz RJ, Hildebold T, Simeria K. Spontaneous arm movement activity assessed by accelerometry is a marker for early recovery after stroke. Journal of neurology. 2011;258(3):457–63. Epub 2010/10/19. pmid:20953792.
- 119. Shim S, Kim H, Jung J. Comparison of upper extremity motor recovery of stroke patients with actual physical activity in their daily lives measured with accelerometers. Journal of physical therapy science. 2014;26(7):1009–11. Epub 2014/08/21. pmid:25140084; PubMed Central PMCID: PMCPmc4135185.
- 120. Strommen AM, Christensen T, Jensen K. Quantitative measurement of physical activity in acute ischemic stroke and transient ischemic attack. Stroke; a journal of cerebral circulation. 2014;45(12):3649–55. Epub 2014/11/06. pmid:25370584.
- 121. Thrane G, Emaus N, Askim T, Anke A. Arm use in patients with subacute stroke monitored by accelerometry: association with motor impairment and influence on self-dependence. Journal of rehabilitation medicine: official journal of the UEMS European Board of Physical and Rehabilitation Medicine. 2011;43(4):299–304. Epub 2011/02/25. pmid:21347506.
- 122. Uswatte G, Foo WL, Olmstead H, Lopez K, Holand A, Simms LB. Ambulatory monitoring of arm movement using accelerometry: an objective measure of upper-extremity rehabilitation in persons with chronic stroke. Arch Phys Med Rehabil. 2005;86(7):1498–501. Epub 2005/07/09. pmid:16003690.
- 123. Uswatte G, Giuliani C, Winstein C, Zeringue A, Hobbs L, Wolf SL. Validity of accelerometry for monitoring real-world arm activity in patients with subacute stroke: evidence from the extremity constraint-induced therapy evaluation trial. Arch Phys Med Rehabil. 2006;87(10):1340–5. Epub 2006/10/07. pmid:17023243.
- 124. Uswatte G, Hobbs Qadri L. A behavioral observation system for quantifying arm activity in daily life after stroke. Rehabil Psychol. 2009;54(4):398–403. Epub 2009/11/26. pmid:19929121; PubMed Central PMCID: PMCPmc2799120.
- 125. van der Pas SC, Verbunt JA, Breukelaar DE, van Woerden R, Seelen HA. Assessment of arm activity using triaxial accelerometry in patients with a stroke. Arch Phys Med Rehabil. 2011;92(9):1437–42. Epub 2011/09/01. pmid:21878214.
- 126. Fulk GD, Combs SA, Danks KA, Nirider CD, Raja B, Reisman DS. Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury. Phys Ther. 2014;94(2):222–9. Epub 2013/09/21. pmid:24052577.
- 127. Cancela J, Pastorino M, Arredondo MT, Nikita KS, Villagra F, Pastor MA. Feasibility study of a wearable system based on a wireless body area network for gait assessment in Parkinson's disease patients. Sensors (Basel, Switzerland). 2014;14(3):4618–33. Epub 2014/03/13. pmid:24608005; PubMed Central PMCID: PMCPmc4003960.
- 128. Cavanaugh JT, Ellis TD, Earhart GM, Ford MP, Foreman KB, Dibble LE. Capturing ambulatory activity decline in Parkinson's disease. Journal of neurologic physical therapy: JNPT. 2012;36(2):51–7. Epub 2012/05/18. pmid:22592060; PubMed Central PMCID: PMCPmc3934648.
- 129. Chastin SF, Baker K, Jones D, Burn D, Granat MH, Rochester L. The pattern of habitual sedentary behavior is different in advanced Parkinson's disease. Movement disorders: official journal of the Movement Disorder Society. 2010;25(13):2114–20. Epub 2010/08/20. pmid:20721926.
- 130. Dontje ML, de Greef MH, Speelman AD, van Nimwegen M, Krijnen WP, Stolk RP, et al. Quantifying daily physical activity and determinants in sedentary patients with Parkinson's disease. Parkinsonism & related disorders. 2013;19(10):878–82. Epub 2013/06/19. pmid:23769178.
- 131. El-Gohary M, Pearson S, McNames J, Mancini M, Horak F, Mellone S, et al. Continuous monitoring of turning in patients with movement disability. Sensors (Basel, Switzerland). 2013;14(1):356–69. Epub 2014/01/01. pmid:24379043; PubMed Central PMCID: PMCPmc3926561.
- 132. Ellis T, Cavanaugh JT, Earhart GM, Ford MP, Foreman KB, Fredman L, et al. Factors associated with exercise behavior in people with Parkinson disease. Phys Ther. 2011;91(12):1838–48. Epub 2011/10/18. pmid:22003171; PubMed Central PMCID: PMCPmc3229047.
- 133. Ford MP, Malone LA, Walker HC, Nyikos I, Yelisetty R, Bickel CS. Step activity in persons with Parkinson's disease. Journal of physical activity & health. 2010;7(6):724–9. Epub 2010/11/23. pmid:21088302.
- 134. Garcia Ruiz PJ, Sanchez Bernardos V. Evaluation of ActiTrac (ambulatory activity monitor) in Parkinson's Disease. Journal of the neurological sciences. 2008;270(1–2):67–9. Epub 2008/03/08. pmid:18325537.
- 135. Hideyuki N, Hitoshi T. Analysis of 24-h Physical Effects of Home Exercise on Physical Function and Activity in Home Care Patients with Parkinson's Disease. Journal of physical therapy science. 2014;26:1701–6.
- 136. Iluz T, Gazit E, Herman T, Sprecher E, Brozgol M, Giladi N, et al. Automated detection of missteps during community ambulation in patients with Parkinson's disease: a new approach for quantifying fall risk in the community setting. Journal of neuroengineering and rehabilitation. 2014;11:48. Epub 2014/04/04. pmid:24693881; PubMed Central PMCID: PMCPmc3978002.
- 137. Lord S, Godfrey A, Galna B, Mhiripiri D, Burn D, Rochester L. Ambulatory activity in incident Parkinson's: more than meets the eye? Journal of neurology. 2013;260(12):2964–72. Epub 2013/08/01. pmid:23900754.
- 138. Moore ST, Dilda V, Hakim B, Macdougall HG. Validation of 24-hour ambulatory gait assessment in Parkinson's disease with simultaneous video observation. Biomedical engineering online. 2011;10:82. Epub 2011/09/23. pmid:21936884; PubMed Central PMCID: PMCPmc3184280.
- 139. Hideyuki N, Hitoshi T. Analysis of 24-h Physical Activities of Patients with Parkinson's Disease at Home. Journal of physical therapy science. 2011;23(3):509–13.
- 140. Pan W, Ohashi K, Yamamoto Y, Kwak S. Power-law temporal autocorrelation of activity reflects severity of parkinsonism. Movement disorders: official journal of the Movement Disorder Society. 2007;22(9):1308–13. Epub 2007/05/01. pmid:17469191.
- 141. Rochester L, Jones D, Hetherington V, Nieuwboer A, Willems AM, Kwakkel G, et al. Gait and gait-related activities and fatigue in Parkinson's disease: what is the relationship? Disability and rehabilitation. 2006;28(22):1365–71. Epub 2006/10/31. pmid:17071567.
- 142. Wallen MB, Dohrn IM, Stahle A, Franzen E, Hagstromer M. Comparison of pedometer and accelerometer derived steps in older individuals with Parkinson's disease or osteoporosis under free-living conditions. Journal of aging and physical activity. 2014;22(4):550–6. Epub 2013/12/07. pmid:24306767.
- 143. Wallen MB, Nero H, Franzen E, Hagstromer M. Comparison of two accelerometer filter settings in individuals with Parkinson's disease. Physiological measurement. 2014;35(11):2287–96. Epub 2014/10/24. pmid:25340812.
- 144. Weiss A, Herman T, Giladi N, Hausdorff JM. Objective assessment of fall risk in Parkinson's disease using a body-fixed sensor worn for 3 days. PloS one. 2014;9(5):e96675. Epub 2014/05/08. pmid:24801889; PubMed Central PMCID: PMCPmc4011791.
- 145. White DK, Wagenaar RC, Del Olmo ME, Ellis TD. Test-retest reliability of 24 hours of activity monitoring in individuals with Parkinson's disease in home and community. Neurorehabilitation and neural repair. 2007;21(4):327–40. Epub 2007/03/21. pmid:17369513.
- 146. Yoneyama M, Kurihara Y, Watanabe K, Mitoma H. Accelerometry-based gait analysis and its application to Parkinson's disease assessment- part 2: a new measure for quantifying walking behavior. IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society. 2013;21(6):999–1005. Epub 2013/06/26. pmid:23797284.
- 147. Busse ME, Pearson OR, Van Deursen R, Wiles CM. Quantified measurement of activity provides insight into motor function and recovery in neurological disease. Journal of neurology, neurosurgery, and psychiatry. 2004;75(6):884–8. Epub 2004/05/18. pmid:15146006; PubMed Central PMCID: PMCPmc1739073.
- 148. David R, Mulin E, Friedman L, Le Duff F, Cygankiewicz E, Deschaux O, et al. Decreased daytime motor activity associated with apathy in Alzheimer disease: an actigraphic study. The American journal of geriatric psychiatry: official journal of the American Association for Geriatric Psychiatry. 2012;20(9):806–14. Epub 2011/10/15. pmid:21997602.
- 149. Erickson KI, Barr LL, Weinstein AM, Banducci SE, Akl SL, Santo NM, et al. Measuring physical activity using accelerometry in a community sample with dementia. Journal of the American Geriatrics Society. 2013;61(1):158–9. Epub 2013/01/15. pmid:23311557; PubMed Central PMCID: PMCPmc3567918.
- 150. Gietzelt M, Feldwieser F, Govercin M, Steinhagen-Thiessen E, Marschollek M. A prospective field study for sensor-based identification of fall risk in older people with dementia. Informatics for health & social care. 2014;39(3–4):249–61. Epub 2014/08/26. pmid:25148560.
- 151. Gietzelt M, Wolf KH, Kohlmann M, Marschollek M, Haux R. Measurement of accelerometry-based gait parameters in people with and without dementia in the field: a technical feasibility study. Methods of information in medicine. 2013;52(4):319–25. Epub 2013/06/29. pmid:23807731.
- 152. Greiner C, Makimoto K, Suzuki M, Yamakawa M, Ashida N. Feasibility study of the integrated circuit tag monitoring system for dementia residents in Japan. American journal of Alzheimer's disease and other dementias. 2007;22(2):129–36. Epub 2007/06/05. pmid:17545140.
- 153. Hoffmeyer A, Yordanova K, Teipel S, Kirste T. Sensor Based Monitoring for People with Dementia: Searching for Movement Markers in Alzheimer's Disease for a Early Diagnostic. 2012. In: Constructuring Ambient Intelligence [Internet]. [137–45].
- 154. James BD, Boyle PA, Bennett DA, Buchman AS. Total daily activity measured with actigraphy and motor function in community-dwelling older persons with and without dementia. Alzheimer disease and associated disorders. 2012;26(3):238–45. Epub 2011/09/29. pmid:21946015; PubMed Central PMCID: PMCPmc3251727.
- 155. Kirste T, Hoffmeyer A, Koldrack P, Bauer A, Schubert S, Schroder S, et al. Detecting the effect of Alzheimer's disease on everyday motion behavior. Journal of Alzheimer's disease: JAD. 2014;38(1):121–32. Epub 2013/10/01. pmid:24077435.
- 156. Yuki A, Lee S, Kim H, Kozakai R, Ando F, Shimokata H. Relationship between physical activity and brain atrophy progression. Medicine and science in sports and exercise. 2012;44(12):2362–8. Epub 2012/07/11. pmid:22776876.
- 157. Nagels G, Engelborghs S, Vloeberghs E, Lemmens W, Pickut BA, De Deyn PP. Correlation between actigraphy and nurses' observation of activity in dementia. International journal of geriatric psychiatry. 2007;22(1):84–6. Epub 2006/12/16. pmid:17171751.
- 158. Nagels G, Engelborghs S, Vloeberghs E, Van Dam D, Pickut BA, De Deyn PP. Actigraphic measurement of agitated behaviour in dementia. International journal of geriatric psychiatry. 2006;21(4):388–93. Epub 2006/03/15. pmid:16534768.
- 159. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. Journal of psychiatric research. 1975;12(3):189–98. Epub 1975/11/01. pmid:1202204.
- 160. Hassett L, Moseley A, Harmer A, van der Ploeg HP. The Reliability, Validity, and Feasibility of Physical Activity Measurement in Adults With Traumatic Brain Injury: An Observational Study. The Journal of head trauma rehabilitation. 2014. Epub 2014/04/12. pmid:24721810.
- 161. Subramony SH, Kedar S, Murray E, Protas E, Xu H, Ashizawa T, et al. Objective home-based gait assessment in spinocerebellar ataxia. Journal of the neurological sciences. 2012;313(1–2):95–8. Epub 2011/10/25. pmid:22018764.
- 162. Tully MA, McBride C, Heron L, Hunter RF. The validation of Fibit Zip physical activity monitor as a measure of free-living physical activity. BMC research notes. 2014;7(1):952. Epub 2014/12/30. pmid:25539733; PubMed Central PMCID: PMCPmc4307145.
- 163. Cadmus-Bertram LA, Marcus BH, Patterson RE, Parker BA, Morey BL. Randomized Trial of a Fitbit-Based Physical Activity Intervention for Women. American journal of preventive medicine. 2015;49(3):414–8. Epub 2015/06/15. pmid:26071863.
- 164. Clemes SA, Matchett N, Wane SL. Reactivity: an issue for short-term pedometer studies? British journal of sports medicine. 2008;42(1):68–70. Epub 2008/01/08. pmid:18178685.
- 165. Dobkin BH, Dorsch A. The Promise of mHealth: Daily Activity Monitoring and Outcome Assessments by Wearable Sensors. Neurorehabilitation and neural repair. 2011;25(9):788–98. WOS:000295802400001. pmid:21989632
- 166. John D, Freedson P. ActiGraph and Actical physical activity monitors: a peek under the hood. Medicine and science in sports and exercise. 2012;44(1 Suppl 1):S86–9. Epub 2011/12/23. pmid:22157779; PubMed Central PMCID: PMCPmc3248573.
- 167. Higgins JP. Smartphone Applications for Patients' Health and Fitness. The American journal of medicine. 2015. Epub 2015/06/21. pmid:26091764.
- 168. Mammen G, Gardiner S, Senthinathan A, McClemont L, Stone M, Faulkner G. Is this Bit Fit? Measuring the Quality of the Fitbit Step-Counter. 2012;5(4):10. Epub 2012-12-30.
- 169. Checchi KD, Huybrechts KF, Avorn J, Kesselheim AS. Electronic medication packaging devices and medication adherence: a systematic review. Jama. 2014;312(12):1237–47. Epub 2014/09/24. pmid:25247520; PubMed Central PMCID: PMCPmc4209732.