Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: a mixed-methods systematic review

In the paper

“Individuals with a neurological condition might find it difficult to interact with technology due to physical or cognitive limitations, and visually conspicuous wearables may increase disease stigmatization [3].”

What the sources say

• Supported

  Section 3.4.2, “Management that hinders,” says participants thought motor and cognition deficits could make it difficult to replace batteries, connect a USB cable, or put wearables on and take them off. This directly supports the quoted claim about physical or cognitive limitations making technology difficult to use. (LLM, conf=0.92)

  cites [3] Ozanne A, Johansson D, Hallgren Graneheim U, Malmgren K, Bergquist F, Alt Murphy M (2017) Wearables in epilepsy and Parkinson’s disease—a focus group study. Acta Neurol Scand. https://doi.org/10.1111/ane.12798 source

In the paper

“Parkinson’s disease (PD) and stroke are the two neurological conditions, where accelerometry-based technology has been applied most [1].”

What the sources say

• Supported

  In Results > Study quality (page not specified), the review says studies evaluating accelerometry-based methods were mainly in Parkinson’s disease (six studies), followed by stroke (three), with none involving other neurological conditions. This supports the citing sentence’s comparison within the scope of the review. (LLM, conf=0.88)

  cites [1] Steins D, Dawes H, Esser P, Collett J (2014) Wearable accelerometry-based technology capable of assessing functional activities in neurological populations in community settings: a systematic review. J Neuroeng Rehabil 11:36. https://doi.org/10.1186/1743-0003-11-36 source

In the paper

“In stroke, upper extremity activity [40–51], walking, and physical activity levels were investigated in several studies using step and activity counts [52–62].”

What the sources say

• Not checked yetcites [40] Gebruers N, Truijen S, Engelborghs S, Nagels G, Brouns R, De Deyn PP (2008) Actigraphic measurement of motor deficits in acute ischemic stroke. Cerebrovasc Dis 26(5):533–540. https://doi.org/10.1159/000160210 source
• Not checked yetcites [41] Gebruers N, Truijen S, Engelborghs S, De Deyn PP (2013) Predictive value of upper-limb accelerometry in acute stroke with hemiparesis. J Rehabil Res Dev 50(8):1099–1106. https://doi.org/10.1682/jrrd.2012.09.0166 source
• Not checked yetcites [42] Le Heron C, Fang K, Gubbi J, Churilov L, Palaniswami M, Davis S, Yan B (2014) Wireless accelerometry is feasible in acute monitoring of upper limb motor recovery after ischemic stroke. Cerebrovasc Dis 37(5):336–341. https://doi.org/10.1159/000360808 source
• Not checked yetcites [43] Thrane G, Emaus N, Askim T, Anke A (2011) Arm use in patients with subacute stroke monitored by accelerometry: association with motor impairment and influence on self-dependence. J Rehabil Med 43(4):299–304. https://doi.org/10.2340/16501977-0676 source
• Not checked yetcites [44] Lang CE, Wagner JM, Edwards DF, Dromerick AW (2007) Upper extremity use in people with hemiparesis in the first few weeks after stroke. J Neurol Phys Ther 31(2):56–63. https://doi.org/10.1097/NPT.0b013e31806748bd source
• Not checked yetcites [45] de Niet M, Bussmann JB, Ribbers GM, Stam HJ (2007) The stroke upper-limb activity monitor: its sensitivity to measure hemiplegic upper-limb activity during daily life. Arch Phys Med Rehabil 88(9):1121–1126. https://doi.org/10.1016/j.apmr.2007.06.005 source
• Not checked yetcites [46] van der Pas SC, Verbunt JA, Breukelaar DE, van Woerden R, Seelen HA (2011) Assessment of arm activity using triaxial accelerometry in patients with a stroke. Arch Phys Med Rehabil 92(9):1437–1442. https://doi.org/10.1016/j.apmr.2011.02.021 source
• Supported

  The Introduction and Subjects and Methods describe measuring upper-extremity activity in stroke patients with accelerometers worn on both wrists. This supports the upper-extremity portion of the sentence, to which citations [40–51] are attached; the walking and step-count portion is attached to a different citation group. (LLM, conf=0.91)

  cites [47] Shim S, Kim H, Jung J (2014) Comparison of upper extremity motor recovery of stroke patients with actual physical activity in their daily lives measured with accelerometers. J Phys Ther Sci 26(7):1009–1011. https://doi.org/10.1589/jpts.26.1009 source
• Not checked yetcites [48] Uswatte G, Foo WL, Olmstead H, Lopez K, Holand A, Simms LB (2005) Ambulatory monitoring of arm movement using accelerometry: an objective measure of upper-extremity rehabilitation in persons with chronic stroke. Arch Phys Med Rehabil 86(7):1498–1501 source
• Not checked yetcites [49] Uswatte G, Giuliani C, Winstein C, Zeringue A, Hobbs L, Wolf SL (2006) 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 87(10):1340–1345. https://doi.org/10.1016/j.apmr.2006.06.006 source

In the paper

“In addition, good agreement was found between sway and spatiotemporal gait measures from wearables and other established technologies [23, 31, 32].”

What the sources say

• Partly supported

  The Discussion reports moderate-to-good correlations between wearable accelerometer and force-plate measures of postural sway, but says not all measures correlated and identifies exceptions. The paper does not assess agreement for spatiotemporal gait measures; that part of the multi-citation sentence cannot be supported by this source. (LLM, conf=0.84)

  cites [23] Mancini M, Salarian A, Carlson-Kuhta P, Zampieri C, King L, Chiari L, Horak FB (2012) ISway: a sensitive, valid and reliable measure of postural control. J Neuroeng Rehabil 9:59. https://doi.org/10.1186/1743-0003-9-59 sourceExperimental — accuracy not yet measured
• Not checked yetcites [31] Lord S, Rochester L, Baker K, Nieuwboer A (2008) Concurrent validity of accelerometry to measure gait in Parkinsons Disease. Gait Posture 27(2):357–359. https://doi.org/10.1016/j.gaitpost.2007.04.001 source
• Not checked yetcites [32] Esser P, Dawes H, Collett J, Feltham MG, Howells K (2012) Validity and inter-rater reliability of inertial gait measurements in Parkinson’s disease: a pilot study. J Neurosci Methods 205(1):177–181. https://doi.org/10.1016/j.jneumeth.2012.01.005 source

In the paper

“PD participants further expressed that it was stressful to fasten the sensors during an off state [64, 69].”

What the sources say

• Not checked yetcites [64] Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW (2016) Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients. Telemed J E Health 22(1):63–69. https://doi.org/10.1089/tmj.2015.0026 source
• Partly supported

  In Results and Discussion, “Interview and Complementary Questions” (page not supplied), one participant expressed concern about difficulty attaching the device in an OFF state. A separate participant described the device as causing stress and nervousness, but the paper does not explicitly link that stress to fastening sensors during an OFF state. (LLM, conf=0.84)

  cites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 sourceExperimental — accuracy not yet measured

In the paper

“and sedentary or upright and walking behaviour measures have been shown to be sensitive to longitudinal changes [55, 60, 62].”

What the sources say

• Not checked yetcites [55] Askim T, Bernhardt J, Churilov L, Fredriksen KR, Indredavik B (2013) Changes in physical activity and related functional and disability levels in the first 6 months after stroke: a longitudinal follow-up study. J Rehabil Med 45(5):423–428. https://doi.org/10.2340/16501977-1137 source
• Supported

  The paper reports longitudinal changes after stroke: time walking and sedentary time changed significantly from T1 to T2 (Discussion, p. 7), while time walking, time upright, and the number of walking bouts increased from T1 to T2 (Results, p. 2). It also notes that some other walking measures did not change significantly, so the claim is supported as referring to measures that detect change, not every measure studied. (LLM, conf=0.90)

  cites [60] Sanchez MC, Bussmann J, Janssen W, Horemans H, Chastin S, Heijenbrok M, Stam H (2015) Accelerometric assessment of different dimensions of natural walking during the first year after stroke: recovery of amount, distribution, quality and speed of walking. J Rehabil Med. https://doi.org/10.2340/16501977-1994 source
• Not checked yetcites [62] Tieges Z, Mead G, Allerhand M, Duncan F, van Wijck F, Fitzsimons C, Greig C, Chastin S (2015) Sedentary behavior in the first year after stroke: a longitudinal cohort study with objective measures. Arch Phys Med Rehabil 96(1):15–23. https://doi.org/10.1016/j.apmr.2014.08.015 source

In the paper

“In total, 56 studies (50 reporting quantitative and 6 reporting qualitative data) were included for data extraction and synthesis.”

• Also stated in: § Results · plain

  “Thus, 50 studies reporting quantitative data and 6 studies reporting qualitative data were included for further data extraction and synthesis (Fig. 1).”

In the paper

“In epilepsy, wearables are used to detect and differentiate seizures in hospital settings.”

• Also stated in: § Studies reporting quantitative data · plain

  “In epilepsy, wrist-worn sensors with built-in accelerometers were used for detection and classification of seizures in hospital settings [14–18].”

What the sources say

• Not checked yetcites [14] Nijsen TM, Arends JB, Griep PA, Cluitmans PJ (2005) The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy. Epilepsy Behav 7(1):74–84. https://doi.org/10.1016/j.yebeh.2005.04.011 source
• Not checked yetcites [15] Beniczky S, Polster T, Kjaer TW, Hjalgrim H (2013) Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study. Epilepsia 54(4):e58–e61. https://doi.org/10.1111/epi.12120 source
• Not checked yetcites [16] Velez M, Fisher RS, Bartlett V, Le S (2016) Tracking generalized tonic-clonic seizures with a wrist accelerometer linked to an online database. Seizure 39:13–18. https://doi.org/10.1016/j.seizure.2016.04.009 source
• Not checked yetcites [17] Bayly J, Carino J, Petrovski S, Smit M, Fernando DA, Vinton A, Yan B, Gubbi JR, Palaniswami MS, O’Brien TJ (2013) Time-frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures. Epilepsia 54(8):1402–1408. https://doi.org/10.1111/epi.12207 source
• Not checked yetcites [18] Gubbi J, Kusmakar S, Rao A, Yan B, O’Brien T, Palaniswami M (2015) Automatic detection and classification of convulsive psychogenic non-epileptic seizures using a wearable device. IEEE J Biomed Health Inform. https://doi.org/10.1109/jbhi.2015.2446539 source

In the paper

“In stroke upper extremity activity, walking and physical activity have been studied in laboratory and during free activities.”

In the paper

“In PD, the focus is on quantification of cardinal motor symptoms and medication-evoked adverse symptoms in both laboratory and free-living environment.”

In the paper

“All studies in epilepsy were conducted in a hospital environment.”

In the paper

“In PD, wearables were used to detect and quantify cardinal motor symptoms including bradykinesia [19], tremor [20, 21], and postural sway [22, 23] as well as medication-evoked adverse symptoms such as dyskinesia [24–27] and motor fluctuations [28, 29].”

What the sources say

• Not checked yetcites [19] Dunnewold RJ, Hoff JI, van Pelt HC, Fredrikze PQ, Wagemans EA, van Hilten BJ (1998) Ambulatory quantitative assessment of body position, bradykinesia, and hypokinesia in Parkinson’s disease. J Clin Neurophysiol 15(3):235–242 source
• Not checked yetcites [20] Scanlon BK, Levin BE, Nation DA, Katzen HL, Guevara-Salcedo A, Singer C, Papapetropoulos S (2013) An accelerometry-based study of lower and upper limb tremor in Parkinson’s disease. J Clin Neurosci 20(6):827–830. https://doi.org/10.1016/j.jocn.2012.06.015 source
• Not checked yetcites [21] Heldman DA, Espay AJ, LeWitt PA, Giuffrida JP (2014) Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson’s disease. Parkinsonism Relat Disord 20(6):590–595. https://doi.org/10.1016/j.parkreldis.2014.02.022 source
• Not checked yetcites [22] Mancini M, Carlson-Kuhta P, Zampieri C, Nutt JG, Chiari L, Horak FB (2012) Postural sway as a marker of progression in Parkinson’s disease: a pilot longitudinal study. Gait Posture 36(3):471–476. https://doi.org/10.1016/j.gaitpost.2012.04.010 source
• Not checked yetcites [23] Mancini M, Salarian A, Carlson-Kuhta P, Zampieri C, King L, Chiari L, Horak FB (2012) ISway: a sensitive, valid and reliable measure of postural control. J Neuroeng Rehabil 9:59. https://doi.org/10.1186/1743-0003-9-59 source

In the paper

“In PD, wearables were used to detect and quantify cardinal motor symptoms including bradykinesia [19], tremor [20, 21], and postural sway [22, 23] as well as medication-evoked adverse symptoms such as dyskinesia [24–27] and motor fluctuations [28, 29].”

What the sources say

• Not checked yetcites [24] Lopane G, Mellone S, Chiari L, Cortelli P, Calandra-Buonaura G, Contin M (2015) Dyskinesia detection and monitoring by a single sensor in patients with Parkinson’s disease. Mov Disord. https://doi.org/10.1002/mds.26313 source
• Not checked yetcites [25] Pulliam CL, Burack MA, Heldman DA, Giuffrida JP, Mera TO (2014) Motion sensor dyskinesia assessment during activities of daily living. J Parkinsons Dis 4(4):609–615. https://doi.org/10.3233/jpd-140348 source
• Not checked yetcites [26] Ramsperger R, Meckler S, Heger T, van Uem J, Hucker S, Braatz U, Graessner H, Berg D, Manoli Y, Serrano JA, Ferreira JJ, Hobert MA, Maetzler W, team S-Ps (2016) Continuous leg dyskinesia assessment in Parkinson’s disease-clinical validity and ecological effect. Parkinsonism Relat Disord 26:41–46. https://doi.org/10.1016/j.parkreldis.2016.02.007 source
• Not checked yetcites [27] Mera TO, Burack MA, Giuffrida JP (2013) Objective motion sensor assessment highly correlated with scores of global levodopa-induced dyskinesia in Parkinson’s disease. J Parkinsons Dis 3(3):399–407. https://doi.org/10.3233/JPD-120166 source
• Not checked yetcites [28] Horne MK, McGregor S, Bergquist F (2015) An objective fluctuation score for Parkinson’s disease. PLoS One 10(4):e0124522. https://doi.org/10.1371/journal.pone.0124522 source
• Not checked yetcites [29] Rodriguez-Molinero A, Sama A, Perez-Martinez DA, Perez Lopez C, Romagosa J, Bayes A, Sanz P, Calopa M, Galvez-Barron C, de Mingo E, Rodriguez Martin D, Gonzalo N, Formiga F, Cabestany J, Catala A (2015) Validation of a portable device for mapping motor and gait disturbances in Parkinson’s disease. JMIR mHealth uHealth 3(1):e9. https://doi.org/10.2196/mhealth.3321 source

In the paper

“Different measures derived from wearables quantifying tremor, dyskinesia, postural sway, and spatiotemporal gait characteristics discriminated well between individuals with PD and healthy controls [20, 22, 24, 26, 31]; dyskinesia measures discriminated also between patients with and without dyskinesia [24, 26].”

What the sources say

• Not checked yetcites [20] Scanlon BK, Levin BE, Nation DA, Katzen HL, Guevara-Salcedo A, Singer C, Papapetropoulos S (2013) An accelerometry-based study of lower and upper limb tremor in Parkinson’s disease. J Clin Neurosci 20(6):827–830. https://doi.org/10.1016/j.jocn.2012.06.015 source
• Not checked yetcites [22] Mancini M, Carlson-Kuhta P, Zampieri C, Nutt JG, Chiari L, Horak FB (2012) Postural sway as a marker of progression in Parkinson’s disease: a pilot longitudinal study. Gait Posture 36(3):471–476. https://doi.org/10.1016/j.gaitpost.2012.04.010 source
• Not checked yetcites [24] Lopane G, Mellone S, Chiari L, Cortelli P, Calandra-Buonaura G, Contin M (2015) Dyskinesia detection and monitoring by a single sensor in patients with Parkinson’s disease. Mov Disord. https://doi.org/10.1002/mds.26313 source
• Not checked yetcites [26] Ramsperger R, Meckler S, Heger T, van Uem J, Hucker S, Braatz U, Graessner H, Berg D, Manoli Y, Serrano JA, Ferreira JJ, Hobert MA, Maetzler W, team S-Ps (2016) Continuous leg dyskinesia assessment in Parkinson’s disease-clinical validity and ecological effect. Parkinsonism Relat Disord 26:41–46. https://doi.org/10.1016/j.parkreldis.2016.02.007 source
• Not checked yetcites [31] Lord S, Rochester L, Baker K, Nieuwboer A (2008) Concurrent validity of accelerometry to measure gait in Parkinsons Disease. Gait Posture 27(2):357–359. https://doi.org/10.1016/j.gaitpost.2007.04.001 source

In the paper

“Video observations, clinical scales, and other technologies such as gait analysis were often used as standard reference to validate variables derived from wearables.”

In the paper

“Moderate-to-strong correlations were reported between dyskinesia detected from wearables and clinical ratings [24–26].”

What the sources say

• Not checked yetcites [24] Lopane G, Mellone S, Chiari L, Cortelli P, Calandra-Buonaura G, Contin M (2015) Dyskinesia detection and monitoring by a single sensor in patients with Parkinson’s disease. Mov Disord. https://doi.org/10.1002/mds.26313 source
• Not checked yetcites [25] Pulliam CL, Burack MA, Heldman DA, Giuffrida JP, Mera TO (2014) Motion sensor dyskinesia assessment during activities of daily living. J Parkinsons Dis 4(4):609–615. https://doi.org/10.3233/jpd-140348 source
• Not checked yetcites [26] Ramsperger R, Meckler S, Heger T, van Uem J, Hucker S, Braatz U, Graessner H, Berg D, Manoli Y, Serrano JA, Ferreira JJ, Hobert MA, Maetzler W, team S-Ps (2016) Continuous leg dyskinesia assessment in Parkinson’s disease-clinical validity and ecological effect. Parkinsonism Relat Disord 26:41–46. https://doi.org/10.1016/j.parkreldis.2016.02.007 source

In the paper

“Postural sway measures derived from wearables have been examined for test–retest reliability (ICC 0.55–0.86) [23] and the mediolateral sway and jerk were shown to be sensitive to detect progression of postural instability in PD over time [22].”

What the sources say

• Not checked yetcites [23] Mancini M, Salarian A, Carlson-Kuhta P, Zampieri C, King L, Chiari L, Horak FB (2012) ISway: a sensitive, valid and reliable measure of postural control. J Neuroeng Rehabil 9:59. https://doi.org/10.1186/1743-0003-9-59 source

In the paper

“Postural sway measures derived from wearables have been examined for test–retest reliability (ICC 0.55–0.86) [23] and the mediolateral sway and jerk were shown to be sensitive to detect progression of postural instability in PD over time [22].”

What the sources say

• Not checked yetcites [22] Mancini M, Carlson-Kuhta P, Zampieri C, Nutt JG, Chiari L, Horak FB (2012) Postural sway as a marker of progression in Parkinson’s disease: a pilot longitudinal study. Gait Posture 36(3):471–476. https://doi.org/10.1016/j.gaitpost.2012.04.010 source

In the paper

“One study reported no significant correlation between step counts derived from arm worn sensors and manual observational step counting, while an inconsistent but moderate-to-strong correlation (r = 0.56–0.85) for measuring energy expenditure was noted with indirect calorimetry [59].”

What the sources say

• Not checked yetcites [59] Vanroy C, Vissers D, Cras P, Beyne S, Feys H, Vanlandewijck Y, Truijen S (2014) Physical activity monitoring in stroke: senseWear Pro2 activity accelerometer versus Yamax Digi-Walker SW-200 pedometer. Disabil Rehabil 36(20):1695–1703. https://doi.org/10.3109/09638288.2013.859307 source

In the paper

“Different measures derived from wearables quantifying tremor, dyskinesia, postural sway, and spatiotemporal gait characteristics discriminated well between individuals with PD and healthy controls [20, 22, 24, 26, 31]; dyskinesia measures discriminated also between patients with and without dyskinesia [24, 26].”

What the sources say

• Not checked yetcites [24] Lopane G, Mellone S, Chiari L, Cortelli P, Calandra-Buonaura G, Contin M (2015) Dyskinesia detection and monitoring by a single sensor in patients with Parkinson’s disease. Mov Disord. https://doi.org/10.1002/mds.26313 source
• Not checked yetcites [26] Ramsperger R, Meckler S, Heger T, van Uem J, Hucker S, Braatz U, Graessner H, Berg D, Manoli Y, Serrano JA, Ferreira JJ, Hobert MA, Maetzler W, team S-Ps (2016) Continuous leg dyskinesia assessment in Parkinson’s disease-clinical validity and ecological effect. Parkinsonism Relat Disord 26:41–46. https://doi.org/10.1016/j.parkreldis.2016.02.007 source

In the paper

“In stroke, good agreement was found between step counts derived from wearables compared to step counts from 3D gait analysis [53] or video-based counts [54].”

What the sources say

• Not checked yetcites [53] Mudge S, Stott NS, Walt SE (2007) Criterion validity of the StepWatch activity monitor as a measure of walking activity in patients after stroke. Arch Phys Med Rehabil 88(12):1710–1715. https://doi.org/10.1016/j.apmr.2007.07.039 source
• Not checked yetcites [54] Fulk GD, Combs SA, Danks KA, Nirider CD, Raja B, Reisman DS (2014) Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury. Phys Ther 94(2):222–229. https://doi.org/10.2522/ptj.20120525 source

In the paper

“One study reported no significant correlation between step counts derived from arm worn sensors and manual observational step counting, while an inconsistent but moderate-to-strong correlation (r = 0.56–0.85) for measuring energy expenditure was noted with indirect calorimetry [59].”

What the sources say

• Not checked yetcites [59] Vanroy C, Vissers D, Cras P, Beyne S, Feys H, Vanlandewijck Y, Truijen S (2014) Physical activity monitoring in stroke: senseWear Pro2 activity accelerometer versus Yamax Digi-Walker SW-200 pedometer. Disabil Rehabil 36(20):1695–1703. https://doi.org/10.3109/09638288.2013.859307 source

In the paper

“Differentiation of psychogenic non-epileptic seizures from epileptic seizures showed a sensitivity of 93–100% with different machine learning approaches, while the specificity ranged from 75 to 91% [17, 18].”

What the sources say

• Not checked yetcites [17] Bayly J, Carino J, Petrovski S, Smit M, Fernando DA, Vinton A, Yan B, Gubbi JR, Palaniswami MS, O’Brien TJ (2013) Time-frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures. Epilepsia 54(8):1402–1408. https://doi.org/10.1111/epi.12207 source
• Not checked yetcites [18] Gubbi J, Kusmakar S, Rao A, Yan B, O’Brien T, Palaniswami M (2015) Automatic detection and classification of convulsive psychogenic non-epileptic seizures using a wearable device. IEEE J Biomed Health Inform. https://doi.org/10.1109/jbhi.2015.2446539 source

In the paper

“No studies investigated the test–retest reliability or responsiveness in free activities at hospital settings.”

In the paper

“Upper extremity activity measures derived from accelerometers discriminated well between persons with stroke and healthy controls [41, 42, 44, 50] as well as between patients with different impairment levels [40, 41].”

What the sources say

• Not checked yetcites [41] Gebruers N, Truijen S, Engelborghs S, De Deyn PP (2013) Predictive value of upper-limb accelerometry in acute stroke with hemiparesis. J Rehabil Res Dev 50(8):1099–1106. https://doi.org/10.1682/jrrd.2012.09.0166 source
• Not checked yetcites [42] Le Heron C, Fang K, Gubbi J, Churilov L, Palaniswami M, Davis S, Yan B (2014) Wireless accelerometry is feasible in acute monitoring of upper limb motor recovery after ischemic stroke. Cerebrovasc Dis 37(5):336–341. https://doi.org/10.1159/000360808 source
• Not checked yetcites [44] Lang CE, Wagner JM, Edwards DF, Dromerick AW (2007) Upper extremity use in people with hemiparesis in the first few weeks after stroke. J Neurol Phys Ther 31(2):56–63. https://doi.org/10.1097/NPT.0b013e31806748bd source
• Not checked yetcites [50] Michielsen ME, Selles RW, Stam HJ, Ribbers GM, Bussmann JB (2012) Quantifying nonuse in chronic stroke patients: a study into paretic, nonparetic, and bimanual upper-limb use in daily life. Arch Phys Med Rehabil 93(11):1975–1981. https://doi.org/10.1016/j.apmr.2012.03.016 source

In the paper

“In epilepsy, stereotypical movement patterns for motor seizures were detected with three-axes accelerometers in 95% of the motor seizures identified with video-EEG [14].”

What the sources say

• Not checked yetcites [14] Nijsen TM, Arends JB, Griep PA, Cluitmans PJ (2005) The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy. Epilepsy Behav 7(1):74–84. https://doi.org/10.1016/j.yebeh.2005.04.011 source

In the paper

“Upper extremity activity measures derived from accelerometers discriminated well between persons with stroke and healthy controls [41, 42, 44, 50] as well as between patients with different impairment levels [40, 41].”

What the sources say

• Not checked yetcites [40] Gebruers N, Truijen S, Engelborghs S, Nagels G, Brouns R, De Deyn PP (2008) Actigraphic measurement of motor deficits in acute ischemic stroke. Cerebrovasc Dis 26(5):533–540. https://doi.org/10.1159/000160210 source
• Not checked yetcites [41] Gebruers N, Truijen S, Engelborghs S, De Deyn PP (2013) Predictive value of upper-limb accelerometry in acute stroke with hemiparesis. J Rehabil Res Dev 50(8):1099–1106. https://doi.org/10.1682/jrrd.2012.09.0166 source

In the paper

“One study reported 40 false alarms in 16 out of 73 patients [15], and another study found 81 false alarms reported in 17 patients out of a sample of 30 [16].”

What the sources say

• Not checked yetcites [16] Velez M, Fisher RS, Bartlett V, Le S (2016) Tracking generalized tonic-clonic seizures with a wrist accelerometer linked to an online database. Seizure 39:13–18. https://doi.org/10.1016/j.seizure.2016.04.009 source

In the paper

“Differentiation of psychogenic non-epileptic seizures from epileptic seizures showed a sensitivity of 93–100% with different machine learning approaches, while the specificity ranged from 75 to 91% [17, 18].”

What the sources say

• Not checked yetcites [17] Bayly J, Carino J, Petrovski S, Smit M, Fernando DA, Vinton A, Yan B, Gubbi JR, Palaniswami MS, O’Brien TJ (2013) Time-frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures. Epilepsia 54(8):1402–1408. https://doi.org/10.1111/epi.12207 source
• Not checked yetcites [18] Gubbi J, Kusmakar S, Rao A, Yan B, O’Brien T, Palaniswami M (2015) Automatic detection and classification of convulsive psychogenic non-epileptic seizures using a wearable device. IEEE J Biomed Health Inform. https://doi.org/10.1109/jbhi.2015.2446539 source

In the paper

“Moderate correlations were found between arm activity measures (activity counts) and clinical assessments in individuals with acute stroke [40–42, 44].”

What the sources say

• Not checked yetcites [40] Gebruers N, Truijen S, Engelborghs S, Nagels G, Brouns R, De Deyn PP (2008) Actigraphic measurement of motor deficits in acute ischemic stroke. Cerebrovasc Dis 26(5):533–540. https://doi.org/10.1159/000160210 source
• Not checked yetcites [41] Gebruers N, Truijen S, Engelborghs S, De Deyn PP (2013) Predictive value of upper-limb accelerometry in acute stroke with hemiparesis. J Rehabil Res Dev 50(8):1099–1106. https://doi.org/10.1682/jrrd.2012.09.0166 source
• Not checked yetcites [42] Le Heron C, Fang K, Gubbi J, Churilov L, Palaniswami M, Davis S, Yan B (2014) Wireless accelerometry is feasible in acute monitoring of upper limb motor recovery after ischemic stroke. Cerebrovasc Dis 37(5):336–341. https://doi.org/10.1159/000360808 source
• Not checked yetcites [44] Lang CE, Wagner JM, Edwards DF, Dromerick AW (2007) Upper extremity use in people with hemiparesis in the first few weeks after stroke. J Neurol Phys Ther 31(2):56–63. https://doi.org/10.1097/NPT.0b013e31806748bd source

In the paper

“Test–retest reliability for step counts and energy expenditure (ICC = 0.61–0.98) was also reported [59].”

What the sources say

• Not checked yetcites [59] Vanroy C, Vissers D, Cras P, Beyne S, Feys H, Vanlandewijck Y, Truijen S (2014) Physical activity monitoring in stroke: senseWear Pro2 activity accelerometer versus Yamax Digi-Walker SW-200 pedometer. Disabil Rehabil 36(20):1695–1703. https://doi.org/10.3109/09638288.2013.859307 source

In the paper

“One study reported 40 false alarms in 16 out of 73 patients [15], and another study found 81 false alarms reported in 17 patients out of a sample of 30 [16].”

What the sources say

• Not checked yetcites [15] Beniczky S, Polster T, Kjaer TW, Hjalgrim H (2013) Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study. Epilepsia 54(4):e58–e61. https://doi.org/10.1111/epi.12120 source

In the paper

“More recent studies demonstrated detection sensitivity ranging from 90 to 92% for convulsive seizures [15, 16], but the false positive events varied between the studies.”

What the sources say

• Not checked yetcites [15] Beniczky S, Polster T, Kjaer TW, Hjalgrim H (2013) Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study. Epilepsia 54(4):e58–e61. https://doi.org/10.1111/epi.12120 source
• Not checked yetcites [16] Velez M, Fisher RS, Bartlett V, Le S (2016) Tracking generalized tonic-clonic seizures with a wrist accelerometer linked to an online database. Seizure 39:13–18. https://doi.org/10.1016/j.seizure.2016.04.009 source

In the paper

“A more recent study showed that a commercial proprietary algorithm could discriminate between individuals with and without motor fluctuations, and detect changes in fluctuations before and after deep brain stimulation [28].”

What the sources say

• Not checked yetcites [28] Horne MK, McGregor S, Bergquist F (2015) An objective fluctuation score for Parkinson’s disease. PLoS One 10(4):e0124522. https://doi.org/10.1371/journal.pone.0124522 source

In the paper

“The results showed that acceleration of extremities and immobility measures was effective to discriminate individuals with PD from controls [19].”

What the sources say

• Not checked yetcites [19] Dunnewold RJ, Hoff JI, van Pelt HC, Fredrikze PQ, Wagemans EA, van Hilten BJ (1998) Ambulatory quantitative assessment of body position, bradykinesia, and hypokinesia in Parkinson’s disease. J Clin Neurophysiol 15(3):235–242 source

In the paper

“A more recent study showed that a commercial proprietary algorithm could discriminate between individuals with and without motor fluctuations, and detect changes in fluctuations before and after deep brain stimulation [28].”

What the sources say

• Not checked yetcites [28] Horne MK, McGregor S, Bergquist F (2015) An objective fluctuation score for Parkinson’s disease. PLoS One 10(4):e0124522. https://doi.org/10.1371/journal.pone.0124522 source

In the paper

“Quantification of missteps and risk of falling was shown to discriminate non-fallers and fallers [35, 36].”

What the sources say

• Not checked yetcites [35] Weiss A, Herman T, Giladi N, Hausdorff JM (2014) Objective assessment of fall risk in Parkinson’s disease using a body-fixed sensor worn for 3 days. PLoS One 9(5):e96675. https://doi.org/10.1371/journal.pone.0096675 source
• Not checked yetcites [36] Iluz T, Gazit E, Herman T, Sprecher E, Brozgol M, Giladi N, Mirelman A, Hausdorff JM (2014) Automated detection of missteps during community ambulation in patients with Parkinson’s disease: a new approach for quantifying fall risk in the community setting. J Neuroeng Rehabil 11:48. https://doi.org/10.1186/1743-0003-11-48 source

In the paper

“In one study, the walking activity measured with ankle accelerometers in hospital showed low correlation with stroke severity, but interestingly, a greater level of asymmetry was detected for individuals with stroke during their daily walking at hospital compared to laboratory gait analysis [61].”

What the sources say

• Not checked yetcites [61] Prajapati SK, Gage WH, Brooks D, Black SE, McIlroy WE (2011) A novel approach to ambulatory monitoring: investigation into the quantity and control of everyday walking in patients with subacute stroke. Neurorehabil Neural Repair 25(1):6–14. https://doi.org/10.1177/1545968310374189 source

In the paper

“Clinical scales were commonly used to determine relationships between wearables and clinical assessments (Fig. 2 and Table 3).”

In the paper

“In PD, acceleration-based assessment of bradykinesia in free-living settings was already described in 1998 [19].”

What the sources say

• Not checked yetcites [19] Dunnewold RJ, Hoff JI, van Pelt HC, Fredrikze PQ, Wagemans EA, van Hilten BJ (1998) Ambulatory quantitative assessment of body position, bradykinesia, and hypokinesia in Parkinson’s disease. J Clin Neurophysiol 15(3):235–242 source

In the paper

“In one study, the walking activity measured with ankle accelerometers in hospital showed low correlation with stroke severity, but interestingly, a greater level of asymmetry was detected for individuals with stroke during their daily walking at hospital compared to laboratory gait analysis [61].”

What the sources say

• Not checked yetcites [61] Prajapati SK, Gage WH, Brooks D, Black SE, McIlroy WE (2011) A novel approach to ambulatory monitoring: investigation into the quantity and control of everyday walking in patients with subacute stroke. Neurorehabil Neural Repair 25(1):6–14. https://doi.org/10.1177/1545968310374189 source

In the paper

“In stroke, arm activity measures discriminated effectively between individuals with stroke and healthy controls [45], and between different motor impairment levels [45, 47, 48].”

What the sources say

• Not checked yetcites [45] de Niet M, Bussmann JB, Ribbers GM, Stam HJ (2007) The stroke upper-limb activity monitor: its sensitivity to measure hemiplegic upper-limb activity during daily life. Arch Phys Med Rehabil 88(9):1121–1126. https://doi.org/10.1016/j.apmr.2007.06.005 source

In the paper

“Over a 1-year period, a decline in physical activity levels was detected using accelerometers in individuals with PD [37].”

What the sources say

• Not checked yetcites [37] Cavanaugh JT, Ellis TD, Earhart GM, Ford MP, Foreman KB, Dibble LE (2012) Capturing ambulatory activity decline in Parkinson’s disease. J Neurol Phys Ther 36(2):51–57. https://doi.org/10.1097/NPT.0b013e318254ba7a source

In the paper

“Moderate-to-strong correlations were found between accelerometer measures (threshold-based counts per time unit) and clinical upper extremity scales in chronic stroke [46, 49].”

What the sources say

• Not checked yetcites [46] van der Pas SC, Verbunt JA, Breukelaar DE, van Woerden R, Seelen HA (2011) Assessment of arm activity using triaxial accelerometry in patients with a stroke. Arch Phys Med Rehabil 92(9):1437–1442. https://doi.org/10.1016/j.apmr.2011.02.021 source
• Not checked yetcites [49] Uswatte G, Giuliani C, Winstein C, Zeringue A, Hobbs L, Wolf SL (2006) 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 87(10):1340–1345. https://doi.org/10.1016/j.apmr.2006.06.006 source

In the paper

“A poor-to-moderate correlation was reported between measures from accelerometers (e.g., step counts and activity counts) and unified Parkinson’s disease rating scale [38, 39].”

What the sources say

• Not checked yetcites [38] Skidmore FM, Mackman CA, Pav B, Shulman LM, Garvan C, Macko RF, Heilman KM (2008) Daily ambulatory activity levels in idiopathic Parkinson disease. J Rehabil Res Dev 45(9):1343–1348 source
• Not checked yetcites [39] Nero H, Benka Wallen M, Franzen E, Conradsson D, Stahle A, Hagstromer M (2016) Objectively assessed physical activity and its association with balance, physical function and dyskinesia in Parkinson’s disease. J Parkinsons Dis 6(4):833–840. https://doi.org/10.3233/jpd-160826 source

In the paper

“Five studies in stroke and one in PD have reported compliance regarding the use of wearables (Fig. 3a).”

In the paper

“Measures of activity levels (e.g., amount of time spent in an upright position) showed changes over time both during the acute and subacute stage of stroke [55].”

What the sources say

• Not checked yetcites [55] Askim T, Bernhardt J, Churilov L, Fredriksen KR, Indredavik B (2013) Changes in physical activity and related functional and disability levels in the first 6 months after stroke: a longitudinal follow-up study. J Rehabil Med 45(5):423–428. https://doi.org/10.2340/16501977-1137 source

In the paper

“In stroke, arm activity measures discriminated effectively between individuals with stroke and healthy controls [45], and between different motor impairment levels [45, 47, 48].”

What the sources say

• Not checked yetcites [45] de Niet M, Bussmann JB, Ribbers GM, Stam HJ (2007) The stroke upper-limb activity monitor: its sensitivity to measure hemiplegic upper-limb activity during daily life. Arch Phys Med Rehabil 88(9):1121–1126. https://doi.org/10.1016/j.apmr.2007.06.005 source
• Not checked yetcites [47] Shim S, Kim H, Jung J (2014) Comparison of upper extremity motor recovery of stroke patients with actual physical activity in their daily lives measured with accelerometers. J Phys Ther Sci 26(7):1009–1011. https://doi.org/10.1589/jpts.26.1009 source
• Not checked yetcites [48] Uswatte G, Foo WL, Olmstead H, Lopez K, Holand A, Simms LB (2005) Ambulatory monitoring of arm movement using accelerometry: an objective measure of upper-extremity rehabilitation in persons with chronic stroke. Arch Phys Med Rehabil 86(7):1498–1501 source

In the paper

“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].”

What the sources say

• Not checked yetcites [63] Barak S, Wu SS, Dai Y, Duncan PW, Behrman AL (2014) Adherence to accelerometry measurement of community ambulation poststroke. Phys Ther 94(1):101–110. https://doi.org/10.2522/ptj.20120473 source

In the paper

“Threshold-based activity counts of arm activity were also shown to be reliable (r = 0.81–0.9) in test–retest [49].”

What the sources say

• Not checked yetcites [49] Uswatte G, Giuliani C, Winstein C, Zeringue A, Hobbs L, Wolf SL (2006) 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 87(10):1340–1345. https://doi.org/10.1016/j.apmr.2006.06.006 source

In the paper

“Measures based on gait (e.g., step counts and step rate) over 1- or 3-day periods showed good test–retest reliability (ICC = 0.83–0.99) [52].”

What the sources say

• Not checked yetcites [52] Mudge S, Stott NS (2008) Test–retest reliability of the StepWatch activity monitor outputs in individuals with chronic stroke. Clin Rehabil 22(10–11):871–877. https://doi.org/10.1177/0269215508092822 source

In the paper

“The amount of time spend walking, standing, and number of walking bouts were also shown to be sensitive to change over a 12-week period after stroke [60].”

What the sources say

• Not checked yetcites [60] Sanchez MC, Bussmann J, Janssen W, Horemans H, Chastin S, Heijenbrok M, Stam H (2015) Accelerometric assessment of different dimensions of natural walking during the first year after stroke: recovery of amount, distribution, quality and speed of walking. J Rehabil Med. https://doi.org/10.2340/16501977-1994 source

In the paper

“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].”

What the sources say

• Not checked yetcites [63] Barak S, Wu SS, Dai Y, Duncan PW, Behrman AL (2014) Adherence to accelerometry measurement of community ambulation poststroke. Phys Ther 94(1):101–110. https://doi.org/10.2522/ptj.20120473 source

In the paper

“Acceptable wearing time was reported to be 7 days for patients with PD [64].”

What the sources say

• Not checked yetcites [64] Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW (2016) Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients. Telemed J E Health 22(1):63–69. https://doi.org/10.1089/tmj.2015.0026 source

In the paper

“The average percentage of missing data attributable to technical errors in the reported studies was 10% (range 6–14%).”

In the paper

“Four studies reported technical errors including device failures, disconnection between sensors, and data storage problems (Fig. 3b).”

In the paper

“Persons with epilepsy reported that they would agree to use a seizure registration device, and 65% would want to use it permanently [67].”

What the sources say

• Not checked yetcites [67] Hoppe C, Feldmann M, Blachut B, Surges R, Elger CE, Helmstaedter C (2015) Novel techniques for automated seizure registration: Patients’ wants and needs. Epilepsy Behav 52(Pt A):1–7. https://doi.org/10.1016/j.yebeh.2015.08.006 source

In the paper

“Four studies reported that human factors, such as the device being removed and/or used incorrectly, were the predominant reasons for incomplete data.”

In the paper

“Persons with epilepsy reported that they would agree to use a seizure registration device, and 65% would want to use it permanently [67].”

What the sources say

• Not checked yetcites [67] Hoppe C, Feldmann M, Blachut B, Surges R, Elger CE, Helmstaedter C (2015) Novel techniques for automated seizure registration: Patients’ wants and needs. Epilepsy Behav 52(Pt A):1–7. https://doi.org/10.1016/j.yebeh.2015.08.006 source

In the paper

“Missing data, as reported in 12 studies, was attributed to technical errors and/or human factors.”

In the paper

“The average of missing data resulted from both human factors and technical errors was 19% (range 6–24%).”

In the paper

“Missing data attributable to human factors were on average 12% (range 4–24%).”

In the paper

“An intervention study with stroke showed that participants wore accelerometers 76–89% of waking hours in a 3-day measurement [48, 49].”

What the sources say

• Not checked yetcites [48] Uswatte G, Foo WL, Olmstead H, Lopez K, Holand A, Simms LB (2005) Ambulatory monitoring of arm movement using accelerometry: an objective measure of upper-extremity rehabilitation in persons with chronic stroke. Arch Phys Med Rehabil 86(7):1498–1501 source
• Not checked yetcites [49] Uswatte G, Giuliani C, Winstein C, Zeringue A, Hobbs L, Wolf SL (2006) 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 87(10):1340–1345. https://doi.org/10.1016/j.apmr.2006.06.006 source

In the paper

“In general, individuals with epilepsy, PD, and stroke were positive towards using wearables, such as body-worn small separate sensor units [64], gloves [65], smart glasses [66], and “intelligent” clothes [67].”

What the sources say

• Not checked yetcites [64] Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW (2016) Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients. Telemed J E Health 22(1):63–69. https://doi.org/10.1089/tmj.2015.0026 source
• Not checked yetcites [65] Simone LK, Sundarrajan N, Luo X, Jia Y, Kamper DG (2007) A low cost instrumented glove for extended monitoring and functional hand assessment. J Neurosci Methods 160(2):335–348. https://doi.org/10.1016/j.jneumeth.2006.09.021 source
• Not checked yetcites [66] Zhao Y, Heida T, van Wegen EE, Bloem BR, van Wezel RJ (2015) E-health support in people with Parkinson’s disease with smart glasses: a survey of user requirements and expectations in the Netherlands. J Parkinsons Dis. https://doi.org/10.3233/JPD-150568 source

In the paper

“Some felt self-consciousness using when they could be seen by others, especially during summer [64, 69].”

What the sources say

• Not checked yetcites [64] Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW (2016) Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients. Telemed J E Health 22(1):63–69. https://doi.org/10.1089/tmj.2015.0026 source
• Not checked yetcites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 source

In the paper

“Some participants also expressed feelings of stress and awkwardness towards the very idea of wearing a technological device [69].”

What the sources say

• Not checked yetcites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 source

In the paper

“The participants found that wrist-worn sensors were easy to put on and take off [64]; however, in other studies, some participants with stroke felt that extra help would be needed to put the wrist sensors on but the sensors were comfortable to wear during daily activities [65, 68, 69].”

What the sources say

• Not checked yetcites [65] Simone LK, Sundarrajan N, Luo X, Jia Y, Kamper DG (2007) A low cost instrumented glove for extended monitoring and functional hand assessment. J Neurosci Methods 160(2):335–348. https://doi.org/10.1016/j.jneumeth.2006.09.021 source
• Not checked yetcites [68] Mountain G, Wilson S, Eccleston C, Mawson S, Hammerton J, Ware T, Zheng H, Davies R, Black N, Harris N, Stone T, Hu H (2010) Developing and testing a telerehabilitation system for people following stroke: issues of usability. J Eng Des 21(2):223–236. https://doi.org/10.1080/09544820903333792 source
• Not checked yetcites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 source

In the paper

“Participants with stroke and PD described that wearables did not impact their daily activities [64, 65, 68, 69].”

What the sources say

• Not checked yetcites [64] Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW (2016) Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients. Telemed J E Health 22(1):63–69. https://doi.org/10.1089/tmj.2015.0026 source
• Not checked yetcites [65] Simone LK, Sundarrajan N, Luo X, Jia Y, Kamper DG (2007) A low cost instrumented glove for extended monitoring and functional hand assessment. J Neurosci Methods 160(2):335–348. https://doi.org/10.1016/j.jneumeth.2006.09.021 source
• Not checked yetcites [68] Mountain G, Wilson S, Eccleston C, Mawson S, Hammerton J, Ware T, Zheng H, Davies R, Black N, Harris N, Stone T, Hu H (2010) Developing and testing a telerehabilitation system for people following stroke: issues of usability. J Eng Des 21(2):223–236. https://doi.org/10.1080/09544820903333792 source
• Not checked yetcites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 source

In the paper

“The participants found that wrist-worn sensors were easy to put on and take off [64]; however, in other studies, some participants with stroke felt that extra help would be needed to put the wrist sensors on but the sensors were comfortable to wear during daily activities [65, 68, 69].”

What the sources say

• Not checked yetcites [65] Simone LK, Sundarrajan N, Luo X, Jia Y, Kamper DG (2007) A low cost instrumented glove for extended monitoring and functional hand assessment. J Neurosci Methods 160(2):335–348. https://doi.org/10.1016/j.jneumeth.2006.09.021 source
• Not checked yetcites [68] Mountain G, Wilson S, Eccleston C, Mawson S, Hammerton J, Ware T, Zheng H, Davies R, Black N, Harris N, Stone T, Hu H (2010) Developing and testing a telerehabilitation system for people following stroke: issues of usability. J Eng Des 21(2):223–236. https://doi.org/10.1080/09544820903333792 source
• Not checked yetcites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 source

In the paper

“The participants found that wrist-worn sensors were easy to put on and take off [64]; however, in other studies, some participants with stroke felt that extra help would be needed to put the wrist sensors on but the sensors were comfortable to wear during daily activities [65, 68, 69].”

What the sources say

• Not checked yetcites [64] Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW (2016) Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients. Telemed J E Health 22(1):63–69. https://doi.org/10.1089/tmj.2015.0026 source

In the paper

“Feeling “embarrassed” and that the sensors might “look funny” were described by participants with stroke [65].”

What the sources say

• Not checked yetcites [65] Simone LK, Sundarrajan N, Luo X, Jia Y, Kamper DG (2007) A low cost instrumented glove for extended monitoring and functional hand assessment. J Neurosci Methods 160(2):335–348. https://doi.org/10.1016/j.jneumeth.2006.09.021 source

In the paper

“They worried that unpredictable technical errors would lead to confusion about how to handle the wearables [68].”

What the sources say

• Not checked yetcites [68] Mountain G, Wilson S, Eccleston C, Mawson S, Hammerton J, Ware T, Zheng H, Davies R, Black N, Harris N, Stone T, Hu H (2010) Developing and testing a telerehabilitation system for people following stroke: issues of usability. J Eng Des 21(2):223–236. https://doi.org/10.1080/09544820903333792 source

In the paper

“and in PD, this was even more challenging during an off state [64, 69].”

What the sources say

• Not checked yetcites [64] Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW (2016) Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients. Telemed J E Health 22(1):63–69. https://doi.org/10.1089/tmj.2015.0026 source
• Not checked yetcites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 source

In the paper

“Participants with stroke felt a need for clear instructions on how to use the device, including both how to wear and how to operate it [68].”

What the sources say

• Not checked yetcites [68] Mountain G, Wilson S, Eccleston C, Mawson S, Hammerton J, Ware T, Zheng H, Davies R, Black N, Harris N, Stone T, Hu H (2010) Developing and testing a telerehabilitation system for people following stroke: issues of usability. J Eng Des 21(2):223–236. https://doi.org/10.1080/09544820903333792 source

In the paper

“They experienced that keeping and placing the sensors at correct positions were difficult [64, 68, 69]”

What the sources say

• Not checked yetcites [64] Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW (2016) Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients. Telemed J E Health 22(1):63–69. https://doi.org/10.1089/tmj.2015.0026 source
• Not checked yetcites [68] Mountain G, Wilson S, Eccleston C, Mawson S, Hammerton J, Ware T, Zheng H, Davies R, Black N, Harris N, Stone T, Hu H (2010) Developing and testing a telerehabilitation system for people following stroke: issues of usability. J Eng Des 21(2):223–236. https://doi.org/10.1080/09544820903333792 source
• Not checked yetcites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 source

In the paper

“Individuals with epilepsy, PD, and stroke reported a wide spectrum of expectations in terms of usability of wearables [65–69].”

What the sources say

• Not checked yetcites [65] Simone LK, Sundarrajan N, Luo X, Jia Y, Kamper DG (2007) A low cost instrumented glove for extended monitoring and functional hand assessment. J Neurosci Methods 160(2):335–348. https://doi.org/10.1016/j.jneumeth.2006.09.021 source
• Not checked yetcites [66] Zhao Y, Heida T, van Wegen EE, Bloem BR, van Wezel RJ (2015) E-health support in people with Parkinson’s disease with smart glasses: a survey of user requirements and expectations in the Netherlands. J Parkinsons Dis. https://doi.org/10.3233/JPD-150568 source
• Not checked yetcites [67] Hoppe C, Feldmann M, Blachut B, Surges R, Elger CE, Helmstaedter C (2015) Novel techniques for automated seizure registration: Patients’ wants and needs. Epilepsy Behav 52(Pt A):1–7. https://doi.org/10.1016/j.yebeh.2015.08.006 source
• Not checked yetcites [68] Mountain G, Wilson S, Eccleston C, Mawson S, Hammerton J, Ware T, Zheng H, Davies R, Black N, Harris N, Stone T, Hu H (2010) Developing and testing a telerehabilitation system for people following stroke: issues of usability. J Eng Des 21(2):223–236. https://doi.org/10.1080/09544820903333792 source
• Not checked yetcites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 source

In the paper

“Participants worried that the sensors would get wet while washing dishes or showering [64, 69].”

What the sources say

• Not checked yetcites [64] Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW (2016) Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients. Telemed J E Health 22(1):63–69. https://doi.org/10.1089/tmj.2015.0026 source
• Not checked yetcites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 source

In the paper

“Participants with PD and stroke described that wearables should be easy to learn and use [65, 66, 68, 69].”

What the sources say

• Not checked yetcites [65] Simone LK, Sundarrajan N, Luo X, Jia Y, Kamper DG (2007) A low cost instrumented glove for extended monitoring and functional hand assessment. J Neurosci Methods 160(2):335–348. https://doi.org/10.1016/j.jneumeth.2006.09.021 source
• Not checked yetcites [66] Zhao Y, Heida T, van Wegen EE, Bloem BR, van Wezel RJ (2015) E-health support in people with Parkinson’s disease with smart glasses: a survey of user requirements and expectations in the Netherlands. J Parkinsons Dis. https://doi.org/10.3233/JPD-150568 source
• Not checked yetcites [68] Mountain G, Wilson S, Eccleston C, Mawson S, Hammerton J, Ware T, Zheng H, Davies R, Black N, Harris N, Stone T, Hu H (2010) Developing and testing a telerehabilitation system for people following stroke: issues of usability. J Eng Des 21(2):223–236. https://doi.org/10.1080/09544820903333792 source
• Not checked yetcites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 source

In the paper

“PD participants wanted wearables to assist with physiotherapy training, to improve gait and balance problems [66].”

What the sources say

• Not checked yetcites [66] Zhao Y, Heida T, van Wegen EE, Bloem BR, van Wezel RJ (2015) E-health support in people with Parkinson’s disease with smart glasses: a survey of user requirements and expectations in the Netherlands. J Parkinsons Dis. https://doi.org/10.3233/JPD-150568 source

In the paper

“Persons with epilepsy wanted features that would allow improved diagnosis and seizure management [67].”

What the sources say

• Not checked yetcites [67] Hoppe C, Feldmann M, Blachut B, Surges R, Elger CE, Helmstaedter C (2015) Novel techniques for automated seizure registration: Patients’ wants and needs. Epilepsy Behav 52(Pt A):1–7. https://doi.org/10.1016/j.yebeh.2015.08.006 source

In the paper

“Wearables need to be small and non-obtrusive [64, 65, 68, 69]”

What the sources say

• Not checked yetcites [64] Fisher JM, Hammerla NY, Rochester L, Andras P, Walker RW (2016) Body-worn sensors in Parkinson’s disease: evaluating their acceptability to patients. Telemed J E Health 22(1):63–69. https://doi.org/10.1089/tmj.2015.0026 source
• Not checked yetcites [65] Simone LK, Sundarrajan N, Luo X, Jia Y, Kamper DG (2007) A low cost instrumented glove for extended monitoring and functional hand assessment. J Neurosci Methods 160(2):335–348. https://doi.org/10.1016/j.jneumeth.2006.09.021 source
• Not checked yetcites [68] Mountain G, Wilson S, Eccleston C, Mawson S, Hammerton J, Ware T, Zheng H, Davies R, Black N, Harris N, Stone T, Hu H (2010) Developing and testing a telerehabilitation system for people following stroke: issues of usability. J Eng Des 21(2):223–236. https://doi.org/10.1080/09544820903333792 source
• Not checked yetcites [69] Cancela J, Pastorino M, Tzallas AT, Tsipouras MG, Rigas G, Arredondo MT, Fotiadis DI (2014) Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors. Sensors (Basel) 14(9):17235–17255. https://doi.org/10.3390/s140917235 source

In the paper

“In PD, the focus was on quantification of dyskinesia, tremor, and bradykinesia, and in stroke, the focus was on upper extremity activity, gait, and physical activity.”

In the paper

“Despite an increasing number of studies using wearables in clinical applications, only half of the eligible studies identified were of sufficient reporting quality.”

In the paper

“In epilepsy, the wearables were primarily used to detect and differentiate seizures.”

In the paper

“In PD, the focus was on quantification of dyskinesia, tremor, and bradykinesia, and in stroke, the focus was on upper extremity activity, gait, and physical activity.”

In the paper

“This systematic review illustrates how wearables have been used to monitor movement and disease-related signs in epilepsy, PD, and stroke in different environments, including laboratory, hospital, and free-living.”

In the paper

“The validity of measures derived from wearables was to some extent addressed in several studies, but the reliability and responsiveness have only been studied in PD and stroke.”

In the paper

“The validity of measures derived from wearables was to some extent addressed in several studies, but the reliability and responsiveness have only been studied in PD and stroke.”

In the paper

“Several studies in PD and stroke using wearables during free activities or in free-living conditions reported moderate-to-strong correlations between measures derived from wearables and clinical scales.”

In the paper

“The qualitative thematic analysis of studies which reported users’ experiences and acceptance rendered three main analytic themes: acceptable integration in daily life, lack of confidence in technology, and the need to consider individualization.”

In the paper

“As a reflection of this, we found no studies on epilepsy based on measurements during free-living conditions.”

In the paper

“The lack of data on clinical utility of using wearables in free-living conditions is a gap that needs to be filled.”

In the paper

“Predominantly acceleration signals were used in hospital and free-living settings, even though gyroscope signals have shown promise for increasing the sensitivity and specificity when measuring dyskinesia and postural instability in laboratory settings [23–26].”

In the paper

“One thing common to the three disorders, however, is that during free-living monitoring, the comparison methods are often subjective and retrospective.”

In the paper

“The lack of confidence in handling the new technology was also one of the main themes that emerged from our thematic synthesis.”

In the paper

“Interestingly, we found that in studies where the monitoring time was longer, better adherence to wearables was reported.”

In the paper

“A positive acceptance towards the use of wearables emerged as one of the main themes from our thematic synthesis and technical support and feedback were considered important factors for increasing motivation and confidence in the use of wearables.”

In the paper

“Human factors contributed to between 4 and 24% of data loss in the included studies.”

In the paper

“A positive acceptance towards the use of wearables emerged as one of the main themes from our thematic synthesis and technical support and feedback were considered important factors for increasing motivation and confidence in the use of wearables.”

In the paper

“Individuals with a neurological condition might find it difficult to interact with technology due to physical or cognitive limitations, and visually conspicuous wearables may increase disease stigmatization [3].”

What the sources say

• Not checked yetcites [3] Ozanne A, Johansson D, Hallgren Graneheim U, Malmgren K, Bergquist F, Alt Murphy M (2017) Wearables in epilepsy and Parkinson’s disease—a focus group study. Acta Neurol Scand. https://doi.org/10.1111/ane.12798 source

In the paper

“Wearables may provide information of clinical features of interest in epilepsy, PD and stroke, but knowledge regarding the clinical utility for supporting clinical decision making remains to be established.”

In the paper

“Wearables may provide information of clinical features of interest in epilepsy, PD and stroke, but knowledge regarding the clinical utility for supporting clinical decision making remains to be established.”

In the paper

“A meta-analysis was considered unfeasible for the quantitative studies due to large variation of study aims and designs.”

In the paper

“To overcome these problems, advanced algorithm development is often required to reach sufficient accuracy.”

In the paper

“In stroke, the step counts and measures of upper extremity and physical activity were shown to be reliable [48, 49, 52, 56–59]”

What the sources say

• Not checked yetcites [48] Uswatte G, Foo WL, Olmstead H, Lopez K, Holand A, Simms LB (2005) Ambulatory monitoring of arm movement using accelerometry: an objective measure of upper-extremity rehabilitation in persons with chronic stroke. Arch Phys Med Rehabil 86(7):1498–1501 source
• Not checked yetcites [49] Uswatte G, Giuliani C, Winstein C, Zeringue A, Hobbs L, Wolf SL (2006) 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 87(10):1340–1345. https://doi.org/10.1016/j.apmr.2006.06.006 source
• Not checked yetcites [52] Mudge S, Stott NS (2008) Test–retest reliability of the StepWatch activity monitor outputs in individuals with chronic stroke. Clin Rehabil 22(10–11):871–877. https://doi.org/10.1177/0269215508092822 source
• Not checked yetcites [56] Hale LA, Pal J, Becker I (2008) Measuring free-living physical activity in adults with and without neurologic dysfunction with a triaxial accelerometer. Arch Phys Med Rehabil 89(9):1765–1771. https://doi.org/10.1016/j.apmr.2008.02.027 source
• Not checked yetcites [57] Rand D, Eng JJ, Tang PF, Jeng JS, Hung C (2009) How active are people with stroke? Use of accelerometers to assess physical activity. Stroke 40(1):163–168. https://doi.org/10.1161/strokeaha.108.523621 source
• Not checked yetcites [58] Haeuber E, Shaughnessy M, Forrester LW, Coleman KL, Macko RF (2004) Accelerometer monitoring of home- and community-based ambulatory activity after stroke. Arch Phys Med Rehabil 85(12):1997–2001 source
• Not checked yetcites [59] Vanroy C, Vissers D, Cras P, Beyne S, Feys H, Vanlandewijck Y, Truijen S (2014) Physical activity monitoring in stroke: senseWear Pro2 activity accelerometer versus Yamax Digi-Walker SW-200 pedometer. Disabil Rehabil 36(20):1695–1703. https://doi.org/10.3109/09638288.2013.859307 source