Wearability Assessment of a Wearable System for Parkinson’s Disease Remote Monitoring Based on a Body Area Network of Sensors
Open accessAbstract
Wearable technologies for health monitoring have become a reality in the last few years. So far, most research studies have focused on assessments of the technical performance of these systems, as well as the validation of the clinical outcomes. Nevertheless, the success in the acceptance of these solutions depends not only on the technical and clinical effectiveness, but on the final user acceptance. In this work the compliance of a telehealth system for the remote monitoring of Parkinson’s disease (PD) patients is presented with testing in 32 PD patients. This system, called PERFORM, is based on a Body Area Network (BAN) of sensors which has already been validated both from the technical and clinical point for view. Diverse methodologies (REBA, Borg and CRS scales in combination with a body map) are employed to study the comfort, biomechanical and physiological effects of the system. The test results allow us to conclude that the acceptance of this system is satisfactory with all the levels of effect on each component scoring in the lowest ranges. This study also provided useful insights and guidelines to lead to redesign of the system to improve patient compliance.
AI snapshot
Written from the paper’s full text.
TL;DR
This study assessed wearability and patient acceptance of the PERFORM Body Area Network system for remote Parkinson’s disease monitoring in 32 PD patients. Using REBA, Borg CR-10, and Comfort Rating Scales, all wearability components scored in the lowest effect ranges, though attachment, anxiety, privacy, and data-logger weight were identified as areas for redesign.
Key findings
- The overall wearability level was “Low,” with average scores: perceived exertion 0.71 (Borg CR-10), posture 0 (REBA), localized pain/discomfort 0.11 (Borg CR-10), emotion 1.4, attachment 2.5, harm 0.15, perceived change 0.67, movement 0.59, and anxiety 1.63 (all CRS).
- Most patients reported no exertion or discomfort; only one 71-year-old female reported moderate physical stress, and eight of 32 patients reported extremely weak discomfort/pain at overall body or specific areas such as mid torso-waist, mid/lower back, wrists, and ankles.
- The data logger was the most penalized component, with mid torso-waist, mid back, and lower back effects attributed to its weight and size.
- Attachment was described as the weakest point: straps caused discomfort and made self-wearing difficult, especially wrist sensors; anxiety was linked to lack of feedback about correct placement/functioning, and privacy/appearance concerns were raised by six of 32 patients.
Methods
- Wearability assessment of the PERFORM BAN system in 32 PD patients meeting criteria including diagnosis of PD, age 40–70, ambulatory, motor fluctuations, stable dopaminergic treatment, and caregiver support; exclusions included dementia, hallucinations, or significant systemic disease.
- Evaluation combined REBA for posture/movement, Borg CR-10 with body map for perceived exertion and localized pain/discomfort, Comfort Rating Scales for six comfort dimensions, and an informal patient interview.
- Posture was scored before and after wearing the device, using the difference between REBA scores to isolate wearable-induced postural effects; results were translated into a homogeneous five-level effect scale and summarized as wearability levels.
Limitations
- The paper states that the evaluation would benefit from extending the analysis to a larger number of patients, though it notes the 32-patient sample is large compared with other wearability studies cited.
- The paper states the proposed methodology is not applicable when implanted or invasive sensors are used, since other parameters such as weight, battery operation, and placement must be evaluated.
- The text makes evident that findings rely on subjective self-report scales, observer-based REBA scoring, and informal interviews, with no control or comparator group described.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 27 cited sources checked · 0 supported · 0 flagged
- Findingown data
The average emotion score was 1.4 on the CRS scale.
“average emotion score (1.4 on the CRS scale)”
Quote in the paperNot a substantial claim - Findingown data
The average perceived exertion score was 0.71 on the Borg CR-10 scale.
“average perceived exertion score (0.71 on the Borg CR-10 scale)”
Quote in the paperSubstantial claim - Findingown data
The average localized pain/discomfort score was 0.11 on the Borg CR-10 scale.
“average localized pain/discomfort score (0.11 on the Borg CR-10 scale)”
Quote in the paperSubstantial claim - Findingown data
The average perceived change score was 0.67 on the CRS scale.
“average perceived change score (0.67 on the CRS scale)”
Quote in the paperNot a substantial claim - Findingown data
The average anxiety score was 1.63 on the CRS scale.
“average anxiety score (1.63 on the CRS scale)”
Quote in the paperNot a substantial claim
- Findingown data
The average attachment score was 2.5 on the CRS scale.
“average attachment score (2.5 on the CRS scale)”
Quote in the paperNot a substantial claim - Findingown data
The average harm score was 0.15 on the CRS scale.
“average harm score (0.15 on the CRS scale)”
Quote in the paperSubstantial claim - Findingown data
The average posture score was 0 on the REBA scale.
“average posture score (0 based on the REBA scale)”
Quote in the paperNot a substantial claim - Findingown data
The average movement score was 0.59 on the CRS scale.
“average movement score (0.59 on the CRS scale)”
Quote in the paperNot a substantial claim - Findingown data
The general level of effect of the system is Low since all scores are Low for each category of assessment.
“The results of the wearability assessment are summarized in Table 7 and indicate that the general level of effect of the system is “Low” since all scores are “Low” for each category of assessment.”
Quote in the paperSubstantial claim - Findingown data
The majority of the patients did not feel very tense wearing the device.
“The majority of the patients did not feel very very tense wearing the device.”
Quote in the paperNot a substantial claim - Findingown data
Most participants agree that they would not mind wearing the device if it was invisible.
“However, most of the participants agree that they would not mind wearing the device if it was invisible as depicted in the Figure 8c.”
Quote in the paperNot a substantial claim - Findingown data
Some patients had forgotten they were wearing the device after a while.
“In fact they have forgotten of wearing it after a while.”
Quote in the paperNot a substantial claim - Findingown data
Apart from appearance aspects, the subset of patients with appearance and emotional issues experienced no particular problem with the device.
“Apart from appearance aspects, they experienced no particular problem with the device.”
Quote in the paperNot a substantial claim - Findingown data
Informal interviews confirmed that the use of straps leads to unpleasant feelings in terms of comfort.
“it has been confirmed with the informal interviews that the use of the straps lead to some unpleasant feelings in terms of comfort”
Quote in the paperSubstantial claim - Findingown data
Most patients replied that they would need some help from their caregivers to wear and attach the device properly on their own.
“As far as the ability to wear and attach the device properly on their own, most patients replied that they would need some help from their caregivers.”
Quote in the paperNot a substantial claim - Findingown data
According to users, attachment is the weakest point of the system.
“About the attachment which according to the users is the weakest point of the system, it has been confirmed with the informal interviews that the use of the straps lead to some unpleasant feelings in terms of comfort but also, it makes very difficult to users the self-wearing of the device, especially the wrist sensors.”
Quote in the paperSubstantial claim - Findingown data
Eight out of 32 patients felt extremely weak discomfort/pain at the overall assessment of their body and at some parts of their body such as Mid Torso-Waist, Mid and Lower Back, Right and Left Wrist, Right and Left ankle.
“Only a few patients (eight out of 32 patients) felt extremely weak discomfort/pain at the overall assessment of their body and at some parts of their body such as: Mid Torso-Waist, Mid and Lower Back, Right and Left Wrist, Right and Left ankle.”
Quote in the paperSubstantial claim - Findingown data
Some patients would not mind wearing the device both in and outside their home.
“There were also patients who wouldn't mind wearing it both in and outside their home.”
Quote in the paperNot a substantial claim - Findingown data
The attachment method and the requirement of tightening the straps to ensure good technical performance has a moderate impact on the comfort of the patients.
“also the attachment method, and the requirement of tighten the straps to ensure a good technical performance of the system, has a moderate impact in the comfort of the patients (Figure 9a).”
Quote in the paperNot a substantial claim - Findingown data
The wearable device did not have any impact on the patients' posture.
“As indicated in Figure 7, the interpretation of this outcome is that the wearable device did not have any impact on the patients' posture.”
Quote in the paperSubstantial claim - Findingown data
The majority of patients did not feel any exertion caused by the device.
“As indicated in Figure 6, the majority of patients did not feel any exertion caused by the device.”
Quote in the paperSubstantial claim - Findingown data
A moderate feeling of physical stress was reported by only one 71 year old female patient.
“A moderate feeling of physical stress was reported by only one 71 year old female patient.”
Quote in the paperNot a substantial claim - Findingown data
Most of the rest of the patients had absolutely no substantial problem with the device.
“As far as the rest of the patients are concerned, most of them had absolutely no substantial problem with the device.”
Quote in the paperNot a substantial claim - Findingown data
The majority of the patients did not feel any discomfort/pain on almost any parts of their body.
“The responses reported in Table 5 reveal that the majority of the patients did not feel any discomfort/pain on almost any parts of their body.”
Quote in the paperSubstantial claim - Findingreasoning
All affected areas of discomfort/pain are coded as Low according to the Level of effect described in Table 1.
“According to Level of effect described in Table 1, all the affected areas are coded as “Low”.”
Quote in the paperNot a substantial claim - Findingown data
Patients have some difficulties related to the attachment of the device to their body.
“The graphs in Figure 9 indicate that patients have some difficulties related to the attachment of the device to their body.”
Quote in the paperNot a substantial claim - Findingreasoning
A constant moderate concern is shown in all questions regarding the Anxiety space.
“A constant moderate concerning is shown in all the questions regarding the Anxiety space (Figure 13).”
Quote in the paperNot a substantial claim - Findingown data
Use of straps makes self-wearing of the device very difficult for users, especially the wrist sensors.
“but also, it makes very difficult to users the self-wearing of the device, especially the wrist sensors.”
Quote in the paperSubstantial claim - Findingown data
The straps methods led to some difficulty to wear the device without a caregiver's help.
“Also, the straps methods led to some difficult to wear the device without a care giver help.”
Quote in the paperNot a substantial claim - Findingown data
No participant reported a problem with allergic reactions caused by the device.
“As it is shown no problem was encountered by any of the participants.”
Quote in the paperNot a substantial claim - Findingown data
The meeting with users raised appearance and emotional issues among a substantial group of patients, six out of 32.
“The meeting with the users raised some appearance and emotional issues among a substantial group of patients (six out of 32).”
Quote in the paperNot a substantial claim - Premisecitation
The PERFORM system shows an accuracy of 93.73% for the classification of levodopa-induced dyskinesia (LID) severity.
“The system shows an accuracy 93.73% for the classification of levodopa-induced dyskinesia (LID) severity [1]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [1] Tsipouras, 2010, Automated Levodopa-induced dyskinesia assessment, Conf. Proc. IEEE Eng. Med. Biol. Soc.
Not checked in the free run.
- Premisecitation
The PERFORM system shows an accuracy of 87% for tremors.
“87% for tremors [3]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3] Rigas, 2012, Assessment of Tremor Activity in the Parkinson's Disease Using a Set of Wearable Sensors, IEEE Trans. Inf. Technol. Biomed. source
Not checked in the free run.
- Premisecitation
The PERFORM system shows an accuracy of 86% for bradykinesia severity.
“86% for bradykinesia severity [2]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [2] Pastorino, 2011, Assessment of bradykinesia in Parkinson's disease patients through a multi-parametric system, Conf. Proc. IEEE Eng. Med. Biol. Soc.
Not checked in the free run.
- Premisecitation
The PERFORM system has already been validated from both technical and clinical points of view.
“This system, called PERFORM, is based on a Body Area Network (BAN) of sensors which has already been validated both from the technical and clinical point for view.”
- Also stated in: § Introduction · plain
“This work presents a wearability assessment which complements the papers [1–4] that we have already published describing the technical performance for the detection and quantification of PD system using a BAN of sensors.”
Quote in the paperNot a substantial claim0 of 4 sources checked- cites [1] Tsipouras, 2010, Automated Levodopa-induced dyskinesia assessment, Conf. Proc. IEEE Eng. Med. Biol. Soc.
Not checked in the free run.
- cites [2] Pastorino, 2011, Assessment of bradykinesia in Parkinson's disease patients through a multi-parametric system, Conf. Proc. IEEE Eng. Med. Biol. Soc.
Not checked in the free run.
- cites [3] Rigas, 2012, Assessment of Tremor Activity in the Parkinson's Disease Using a Set of Wearable Sensors, IEEE Trans. Inf. Technol. Biomed. source
Not checked in the free run.
- cites [4] Cancela, 2011, Gait assessment in Parkinson's disease patients through a network of wearable accelerometers in unsupervised environments, Conf. Proc. IEEE Eng. Med. Biol. Soc.
Not checked in the free run.
- Also stated in: § Introduction · plain
- Interpretationown data
Each individual component of the wearability assessment can be labeled as a low level effect according to the guidelines provided for each of the methodologies.
“Each individual component of the wearability assessment can be labeled as a low level effect according to the guidelines provided for each of the methodologies.”
Quote in the paperSubstantial claim - Premisecitation
The PERFORM system is a wearable platform for Parkinson's disease remote monitoring and management, based on a Body Area Network (BAN) of sensors.
“This system, called PERFORM, is based on a Body Area Network (BAN) of sensors which has already been validated both from the technical and clinical point for view.”
- Also stated in: § The PERFORM System · plain
“The PERFORM System is a wearable platform for PD remote monitoring and management [9,15].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [9] Cancela, 2014, Feasibility Study of a Wearable System Based on a Wireless Body Area Network for Gait Assessment in Parkinson's Disease Patients, Sensors source
Not checked in the free run.
- cites [15] Tsipouras, M.G., Tzallas, A.T., Karvounis, E.C., Tsalikakis, D.G., Cancela, J., Pastorino, M., Waldmeyer, M.T.A., Konitsiotis, S., and Fotiadis, D.I. (2014, January 1–4). A wearable system for long-term ubiquitous monitoring of common motor symptoms in patients with Parkinson's disease. Valencia, Spain. source
Not checked in the free run.
- Also stated in: § The PERFORM System · plain
- Interpretationreasoning
The moderate scores in the mid torso-waist, mid back and lower back lead to identifying the weight of the data logger device as the part of the BAN more penalized by the users in terms of physical impact and energy cost.
“The moderate scores in the mid torso-waist, mid back and lower back lead to identify the weight of the data logger device as the part of the BAN more penalized by the users in terms of physical impact and energy cost.”
Quote in the paperSubstantial claim - Study design and conductcitation
The compliance assessment used already validated methodologies, specifically the REBA, Borg, and CRS scales in combination with a body map when needed.
“To carry out this compliance assessment different already validated methodologies have been emplaced, specifically the REBA, Borg and CRS scales have been used in combination with a body map when needed.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [21] Borg, 1990, Psychophysical scaling with applications in physical work and the perception of exertion, Scand. J. Work Environ. Heal. source
Not checked in the free run.
- cites [22] Hignett, S., and McAtamney, L. Rapid Entire Body Assessment (REBA). Available online: http://www. ncbi.nlm.nih.gov/pubmed/10711982.
Not checked in the free run.
- cites [23] Knight, 2005, A tool to assess the comfort of wearable computers, Hum. Factors J. Hum. Factors Ergon. Soc. source
Not checked in the free run.
- Interpretationown data
The results of the localized pain/discomfort confirm the suggestion that the prototype does suppose a substantial physical load for the users.
“The results of the localized pain/discomfort confirm this suggestion.”
Quote in the paperSubstantial claim - Interpretationreasoning
The most remarkable effects in the Mid Torso-Waist, Mid Back and Lower Back are a direct consequence of the data logger impact.
“The most remarkable effects in the Mid Torso-Waist, Mid Back and Lower Back are a direct consequence of the data logger impact (the heaviest and largest device of the BAN).”
Quote in the paperNot a substantial claim- Rests on an assumption: The data logger is the heaviest and largest device of the BAN.
- Study design and conductcitation
The number of patients used in the study is large enough compared to other wearability studies and evaluations of monitoring systems for Parkinson's disease and other movement disorder diseases.
“To our knowledge the number of patients used in our study is large enough compared to other wearability studies [26–29] and evaluation of monitoring systems for the Parkinson's disease and other movement disorder diseases.”
Quote in the paperNot a substantial claim0 of 4 sources checked- cites [26] Knight, J.F., Williams, D.D., Arvanitis, T.N., Chris, B., Wichmann, A., Wittkaemper, M., Herbst, I., and Sotiriou, S. (2005, January 3–7). Wearability assessment of a mobile augmented reality system. Ghent, Flanders.
Not checked in the free run.
- cites [27] Vincke, 2010, A New Electronic Monitoring Device to Measure Medication Adherence: Usability of the Helping Hand (TM), Sensors source
Not checked in the free run.
- cites [28] Tharion, 2013, Acceptability and usability of an ambulatory health monitoring system for use by military personnel, IIE Trans. Occup. Ergon. Hum. Factors source
Not checked in the free run.
- cites [29] Abbate, 2014, Usability Study of a Wireless Monitoring System among Alzheimer's Disease Elderly Population, Int. J. Telemed. Appl.
Not checked in the free run.
- Interpretationreasoning
The moderate impact of the perceived change results suggests that users expect to receive more feedback from the system about correct functioning or at least a procedure to know if the system is working properly.
“Likewise, the moderate impact of the perceived change results suggests that the users expect to receive more feedback from the system about the correct functioning or at least a procedure to know if the system is working properly.”
Quote in the paperSubstantial claim
- May combine two claims. 12
- The quote only partly states this claim. 1
- Classified as probably not a substantial claim. 16
May combine two claims. The quote only partly states this claim.
Findingown dataIn 32 PD patients, the acceptance of the PERFORM system is satisfactory, with all levels of effect on each component scoring in the lowest ranges.
“The test results allow us to conclude that the acceptance of this system is satisfactory with all the levels of effect on each component scoring in the lowest ranges.”
- Also stated in: § The PERFORM System · boosted
“A total of 32 PD patients have been involved in this study, showing an excellent acceptance of the BAN system in wearability terms.”
Quote in the paperSubstantial claim- Also stated in: § The PERFORM System · boosted
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataThe subset of patients with appearance and emotional issues would feel much more comfortable wearing an invisible device or if the wearing time were limited to the home environment.
“They would feel much more comfortable wearing an invisible device or if the wearing time were limited to the home environment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataA common concern raised during the interview was whether the devices were waterproof because daily activities could wet the devices, especially the wrist sensors.
“Also, a common concern raised during the interview was if the devices were waterproof since some daily and very common activities could wet the devices, especially the ones on the wrists, for example when washing the hands and washing dishes.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataNone of the patients had any concern about harm caused by the device, with answers ranging from Not at all and Not very much.
“The feedback received is that none of the patients had such a concern, ranging in their answers from Not at all and Not very much.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe system did not restrict, obstruct or block patients' daily movement and activities.
“Finally, the answer to the (Figure 9c) question shows that the system does not restrict the daily movement and activities of the patients.”
- Also stated in: § Comfort · plain
“In general, all participants in the study agree that the system did not obstruct or block them in everyday activities neither limit their activities.”
- Also stated in: § Interview and Complementary Questions · hedged
“On the other hand, the device would not prevent them from doing their daily tasks.”
Quote in the paperSubstantial claim- Also stated in: § Comfort · plain
May combine two claims.
Findingown dataPatients in the study had concerns about the social impact of wearing the device and what other people might think about them.
“The social impact from wearing the sensors and being seen by other people is a sensible concern for the PD patients involved in the study.”
- Also stated in: § Comfort · hedged
“Again the social component appears again since some patient expressed their concerns about what other people could think about them (Figure 11b).”
- Also stated in: § Interview and Complementary Questions · plain
“This subset of patients had some concerns about others' opinions when wearing the device.”
Quote in the paperNot a substantial claim- Also stated in: § Comfort · hedged
May combine two claims.
Findingnone givenThe second posture evaluation had similar results for every single body part of each patient respectively, resulting in similar REBA scores.
“This second posture evaluation had similar results for every single body part of each patient respectively; obviously resulting to similar REBA scores.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe Borg CR-10 scale is used to measure perceived exertion and is usually employed to measure perceived exertion while exercising.
“The method selected is the BORG CR-10 scales for perceived exertion [18] usually employed to measure the perceived exertion while exercising [21].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [18] Knight, J.F., Deen-Williams, D., Arvanitis, T.N., Baber, C., Sotiriou, S., Anastopoulou, S., and Gargalakos, M. (2006, January 11–14). Assessing the wearability of wearable computers. Montreux, Switzerland. source
Not checked in the free run.
- cites [21] Borg, 1990, Psychophysical scaling with applications in physical work and the perception of exertion, Scand. J. Work Environ. Heal. source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationAssessing the wearability of a wearable system involves an evaluation of physiological, biomechanical, and comfort effects.
“Assessing the wearability of a given wearable systems could thus involve an evaluation of the physiological, biomechanical and comfort effects.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [18] Knight, J.F., Deen-Williams, D., Arvanitis, T.N., Baber, C., Sotiriou, S., Anastopoulou, S., and Gargalakos, M. (2006, January 11–14). Assessing the wearability of wearable computers. Montreux, Switzerland. source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationWearability directly impacts the usability of a wearable computer system; if the wearer is uncomfortable or placed under too much stress, the usability of the system will be reduced.
“Wearability directly impacts the usability of a wearable computer system. If the wearer is uncomfortable or placed under too much stress then the usability of the system will be reduced.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [18] Knight, J.F., Deen-Williams, D., Arvanitis, T.N., Baber, C., Sotiriou, S., Anastopoulou, S., and Gargalakos, M. (2006, January 11–14). Assessing the wearability of wearable computers. Montreux, Switzerland. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe REBA method is designed to be sensitive to the type of unpredictable working postures found in healthcare and other service industries.
“The REBA method has been developed to fill a perceived need for a practitioner's field tool, specifically designed to be sensitive to the type of unpredictable working postures found in healthcare and other service industries [22].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [22] Hignett, S., and McAtamney, L. Rapid Entire Body Assessment (REBA). Available online: http://www. ncbi.nlm.nih.gov/pubmed/10711982.
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe wearability evaluation of a Body Area Network is mostly based on the work of Knight and Baber.
“The wearability evaluation of BAN is mostly based on the work of Knight and Baber [18].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [18] Knight, J.F., Deen-Williams, D., Arvanitis, T.N., Baber, C., Sotiriou, S., Anastopoulou, S., and Gargalakos, M. (2006, January 11–14). Assessing the wearability of wearable computers. Montreux, Switzerland. source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationFive Wearability Levels proposed by Knight and Baber allow homogeneous interpretation of different metrics and scales to provide a final wearability score.
“From the levels of effect, five Wearability Levels (WL) have been proposed by Knight and Baber, as shown in Table 2 [18]. They allow the homogenous interpretation of the different metrics and scales in an easy and direct way providing a final score rating the wearability of the system.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [18] Knight, J.F., Deen-Williams, D., Arvanitis, T.N., Baber, C., Sotiriou, S., Anastopoulou, S., and Gargalakos, M. (2006, January 11–14). Assessing the wearability of wearable computers. Montreux, Switzerland. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningPart of the concern about social impact could come from the public disclosure of suffering a disease only by wearing a device.
“As it was mentioned above, part of this concern could come from the public disclosure of suffering a disease only by wearing a device.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationFeelings of anxiety indicate levels of discomfort arising from concerns about safety and reliability and from feelings of insecurity.
“Feelings of anxiety indicate levels of discomfort arising from concerns about safety and reliability and from feelings of insecurity [23].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] Knight, 2005, A tool to assess the comfort of wearable computers, Hum. Factors J. Hum. Factors Ergon. Soc. source
Not checked in the free run.
May combine two claims.
Interpretationown dataBoth the emotion and anxiety results suggest a significant concern of the patients about their privacy; most patients do not like what other people could think about them when they wear the device, especially in public spaces.
“Regarding the comfort analysis, both the emotion and anxiety results suggest a significant concern of the patients about their privacy, most of the patients do not like what other people could think about them when they wear the device, especially in public spaces.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe way patients wear and attach the devices or sensors properly on their own is difficult and uncomfortable, and they might need some help from their caregivers.
“The way that patients wear and attach the devices or sensors properly on their own is difficult and uncomfortable and they might need some help from their caregivers.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataLow scores for perceived exertion and posture suggest that even though the prototype is larger and heavier than a commercial device, it does suppose a substantial physical load for the users.
“Starting this analysis with the energy cost and the biomechanical spaces the low scores for the perceived exertion and the posture values suggest that even the prototype is larger and heavier than a commercial device it does suppose a substantial physical load for the users.”
Quote in the paperNot a substantial claim- Rests on an assumption: The prototype is larger and heavier than a commercial device.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe Comfort Rating Scales (CRS) is a tool for comfort assessment of wearable devices that breaks down comfort analysis into six components.
“Knight and Baber developed a tool for the comfort assessment of wearable devices called the CRS [23] which break down the comfort analysis into six different components.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] Knight, 2005, A tool to assess the comfort of wearable computers, Hum. Factors J. Hum. Factors Ergon. Soc. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenThe anxiety caused to patients by the device is generated by a lack of feedback to the patient about the correct placement and functioning of the device.
“The anxiety caused to patients by the device is generated by a lack of feedback to patient about the correct placement and functioning of the device.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationLevels of both emotion and anxiety may be reduced with time and experience as the wearer becomes accustomed to the device.
“Ultimately, levels of both emotion and anxiety may be reduced with time and experience as the wearer becomes accustomed to the device [23].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Knight, 2005, A tool to assess the comfort of wearable computers, Hum. Factors J. Hum. Factors Ergon. Soc. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe 0-20 range was considered large enough to elicit a range of responses that could be used for detailed analysis.
“According to Knight and Baber this range was considered large enough to elicit a range of responses that could be used for detailed analysis.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] Knight, 2005, A tool to assess the comfort of wearable computers, Hum. Factors J. Hum. Factors Ergon. Soc. source
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationreasoningThe proposed methodology is not applicable when implanted or invasive sensors are used, since other parameters must be taken into account.
“The proposed methodology is not applicable when implanted or invasive sensors are used, since other parameters must be taken into account.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductreasoningUsing the difference between REBA scores without and with the wearable device helps focus the posture analysis on the effects imposed by the wearable itself.
“Thus, in order to identify these changes we use as score for our evaluation the difference between the REBA scores the patient gained (i) without and (ii) with the wearable device. This helps us to focus the posture analysis on the effects imposed by the wearable itself.”
Quote in the paperSubstantial claim- Rests on an assumption: The REBA evaluation in PD patients is meaningful if the observer differentiates between postural changes caused by the disease and any postural changes caused by the wearable itself.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenThe analysis performed can be extended to the wearability analysis of similar systems for movement disorders monitoring or other diseases which require the use of sensors for continuous monitoring of physiological parameters.
“The analysis performed can be extended to the wearability analysis of similar systems for movement disorders monitoring or other diseases which require the use of sensors for the continuous monitoring of physiological parameters.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Wearable health monitoring systems can be highly intrusive and uncomfortable for patients, which could cause the application to fail even if it benefits health.
“The use of these systems can be highly intrusive and uncomfortable for the patients and this could lead the application to fail, even when it has been proven that it benefits patients' health.”
The paper relies on this without support.
Most works during the prototyping phase focus exclusively on the technical performance of the system and ignore or bypass the wearability and compliance analysis.
“Nevertheless, during the prototyping phase most works focus exclusively on the technical performance of the system and ignore or bypass the wearability and compliance analysis.”
The paper relies on this without support.
The REBA evaluation in PD patients is meaningful if the observer differentiates between postural changes caused by the disease and any postural changes caused by the wearable itself.
“As it was previously mentioned, the REBA evaluation in PD patients is meaningful if the observer differentiates between the postural changes caused by the disease and any postural changes caused by the wearable itself.”
The paper relies on this without support.
The data logger is the heaviest and largest device of the BAN.
“(the heaviest and largest device of the BAN)”
The paper relies on this without support.
The prototype is larger and heavier than a commercial device.
“even the prototype is larger and heavier than a commercial device”
The paper relies on this without support.
A wearable device is an extra load attached to the human body and could have a direct effect on the energy consumed by the body and on posture.
“As it was already mentioned a wearable device is an extra load that gets attached to the human body. Thus, it could have a direct effect both on the energy consumed by the body as the muscles need more energy in order to produce the force to counteract the weight of the device and the resulting movement and posture changes.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Some participants with stroke felt that extra help would be needed to put the wrist sensors on.
“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].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Some participants felt self-consciousness using wearables when they could be seen by others, especially during summer.
“Some felt self-consciousness using when they could be seen by others, especially during summer [64, 69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Some participants expressed feelings of stress and awkwardness towards the very idea of wearing a technological device.
“Some participants also expressed feelings of stress and awkwardness towards the very idea of wearing a technological device [69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
The sensors were comfortable to wear during daily activities for some participants with stroke.
“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].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Participants with stroke and Parkinson's disease described that wearables did not impact their daily activities.
“Participants with stroke and PD described that wearables did not impact their daily activities [64, 65, 68, 69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Participants with Parkinson's disease and stroke experienced that keeping and placing the sensors at correct positions was difficult.
“They experienced that keeping and placing the sensors at correct positions were difficult [64, 68, 69]”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Participants worried that the sensors would get wet while washing dishes or showering.
“Participants worried that the sensors would get wet while washing dishes or showering [64, 69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
In Parkinson's disease, keeping and placing the sensors at correct positions was more challenging during an off state.
“and in PD, this was even more challenging during an off state [64, 69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Participants with stroke wanted repeated instructions, confirmation, supervised practice, and external support for technical problems in follow-up sessions to improve their confidence.
“They wanted repeated instructions, confirmation, supervised practice, and external support for technical problems in follow-up sessions to improve their confidence [68, 69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Parkinson's disease participants expressed that it was stressful to fasten the sensors during an off state.
“PD participants further expressed that it was stressful to fasten the sensors during an off state [64, 69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Wearables need to be small and non-obtrusive according to participants.
“Wearables need to be small and non-obtrusive [64, 65, 68, 69]”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Both epilepsy and Parkinson's disease participants described waterproof design as a desirable feature of wearables.
“Both epilepsy and PD participants further described desirable features of wearables, including the possibility of real-time analysis of data, getting reminders to take drugs and waterproof design [66, 67, 69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Both epilepsy and Parkinson's disease participants described the possibility of real-time analysis of data as a desirable feature of wearables.
“Both epilepsy and PD participants further described desirable features of wearables, including the possibility of real-time analysis of data, getting reminders to take drugs and waterproof design [66, 67, 69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Participants with Parkinson's disease and stroke described that wearables should be easy to learn and use.
“Participants with PD and stroke described that wearables should be easy to learn and use [65, 66, 68, 69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Individuals with epilepsy, Parkinson's disease, and stroke reported a wide spectrum of expectations in terms of usability of wearables.
“Individuals with epilepsy, PD, and stroke reported a wide spectrum of expectations in terms of usability of wearables [65–69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Both epilepsy and Parkinson's disease participants described getting reminders to take drugs as a desirable feature of wearables.
“Both epilepsy and PD participants further described desirable features of wearables, including the possibility of real-time analysis of data, getting reminders to take drugs and waterproof design [66, 67, 69].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [69]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
A participant falls outside the stated age eligibility range
MinorThe eligibility criteria limit participants to ages 40-70, but the results describe a 71-year-old patient in the study.
“aged between 40 and 70 years old”
“A moderate feeling of physical stress was reported by only one 71 year old female patient.”
Cites
Higgins, S. M. F., Macken, A. P., Cullen, W., Saunders, J., Dunne, C. P., & O’Gorman, C. S. (2013) · PubMed · doi:10.34961/9953 · Cited by 4,806 · see citing works
Rapid entire body assessment (REBA)
Hignett, S., & McAtamney, L. (2000) · Applied Ergonomics · doi:10.1016/s0003-6870(99)00039-3 · Cited by 2,260 · see citing works
Psychophysical scaling with applications in physical work and the perception of exertion
Borg, G. (1990) · Scandinavian Journal of Work, Environment & Health · doi:10.5271/sjweh.1815 · Cited by 1,817 · see citing works
The Unified Parkinson's Disease Rating Scale (UPDRS): Status and recommendations
Movement Disorder Society Task Force on Rating Scales for Parkinson's Disease (2003) · Movement Disorders · doi:10.1002/mds.10473 · Cited by 1,811 · see citing works
Gemperle, F., Kasabach, C., Stivoric, J., Bauer, M., & Martin, R. (2002) · International Symposium on Wearable Computers · doi:10.1109/iswc.1998.729537 · Cited by 452 · see citing works
Genotype-phenotype correlations between GBA mutations and Parkinson disease risk and onset
Gan‐Or, Z., Giladi, N., Rozovski, U., Shifrin, C., Rosner, S., Gurevich, T., Bar‐Shira, A., & Orr‐Urtreger, A. (2008) · Neurology · doi:10.1212/01.wnl.0000304039.11891.29 · Cited by 359 · see citing works
Assessment of Tremor Activity in the Parkinson’s Disease Using a Set of Wearable Sensors
Rigas, G., Tzallas, A. T., Tsipouras, M. G., Bougia, P., Tripoliti, E. E., Baga, D., Fotiadis, D. I., Tsouli, S. G., & Konitsiotis, S. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2011.2182616 · Cited by 241 · see citing works
Self-citationPolisena, J., Coyle, D., Coyle, K., & McGill, S. (2009) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462309990201 · Cited by 184 · see citing works
A tool to assess the comfort of wearable computers
Knight, J. F., & Baber, C. (2005) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1518/0018720053653875 · Cited by 164 · see citing works
The LRRK2 G2019S mutation in Ashkenazi Jews with Parkinson disease
Orr‐Urtreger, A., Shifrin, C., Rozovski, U., Rosner, S., Bercovich, D., Gurevich, T., Yagev-More, H., Bar‐Shira, A., & Giladi, N. (2007) · Neurology · doi:10.1212/01.wnl.0000277637.33328.d8 · Cited by 155 · see citing works
Fransson‐Hall, C., Gloria, R., Kilbom, Å., Winkel, J., Karlqvist, L., & Wiktorin, C. (1995) · Applied Ergonomics · doi:10.1016/0003-6870(95)00003-u · Cited by 135 · see citing works
SIMPLE RATING METHODS FOR ESTIMATION OF PERCEIVED EXERTION
Borg, G. A. V. (1977) · Elsevier eBooks · doi:10.1016/b978-0-08-021373-6.50010-6 · Cited by 132 · see citing works
Parkinson disease: etiology, pathogenesis and future of gene therapy
Shastry, B. S. (2001) · Neuroscience Research · doi:10.1016/s0168-0102(01)00254-1 · Cited by 115 · see citing works
Smoking and tea consumption delay onset of Parkinson's disease
Kandinov, B., Giladi, N., & Korczyn, A. D. (2008) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2008.02.011 · Cited by 99 · see citing works
Assessing the Wearability of Wearable Computers
Knight, J., Deen-Williams, D., Arvanitis, T., Baber, C., Sotiriou, S., Anastopoulou, S., & Gargalakos, M. (2006) · 2006 10th IEEE International Symposium on Wearable Computers · doi:10.1109/iswc.2006.286347 · Cited by 67 · see citing works
Cancela, J., Pastorino, M., Arredondo, M., Nikita, K., Villagra, F., & Pastor, M. (2014) · Sensors · doi:10.3390/s140304618 · Cited by 63 · see citing works
Self-citationUsability Study of a Wireless Monitoring System among Alzheimer’s Disease Elderly Population
Abbate, S., Avvenuti, M., & Light, J. (2014) · International Journal of Telemedicine and Applications · doi:10.1155/2014/617495 · Cited by 59 · see citing works
Assessment of bradykinesia in Parkinson's disease patients through a multi-parametric system
Pastorino, M., Cancela, J., Arredondo, M. T., Pansera, M., Pastor‐Sanz, L., Villagra, F., Pastor, M. A., & Martin, J. A. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090516 · Cited by 53 · see citing works
Self-citationCancela, J., Pastorino, M., Arredondo, M. T., Pansera, M., Pastor‐Sanz, L., Villagra, F., Pastor, M. A., & González, Á. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090423 · Cited by 52 · see citing works
Self-citationAutomated Levodopa-induced dyskinesia assessment
Tsipouras, M. G., Tzallas, A. T., Rigas, G., Bougia, P., Fotiadis, D. I., & Konitsiotis, S. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626130 · Cited by 44 · see citing works
Self-citationWider, C. W., & Wszołek, Z. K. (2008) · Neurodegenerative Diseases · doi:10.1159/000113680 · Cited by 44 · see citing works
Acceptability and usability of an ambulatory health monitoring system for use by military personnel
Tharion, W. J., Buller, M. J., Potter, A. W., Karis, A. J., Goetz, V., & Hoyt, R. W. (2013) · IIE Transactions on Occupational Ergonomics and Human Factors · doi:10.1080/21577323.2013.838195 · Cited by 27 · see citing works
A New Electronic Monitoring Device to Measure Medication Adherence: Usability of the Helping Hand™
De Bleser, L., Vincke, B., Dobbels, F., Happ, M. B., Maes, B., Vanhaecke, J., & De Geest, S. (2010) · Sensors · doi:10.3390/s100301535 · Cited by 26 · see citing works
Tugwell, C. (2007) · Cited by 18 · see citing works
Tsipouras, M. G., Tzallas, A. T., Karvounis, E. C., Tsalikakis, D. G., Cancela, J., Pastorino, M., Waldmeyer, M. T. A., Konitsiotis, S., & Fotiadis, D. I. (2014) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) · doi:10.1109/bhi.2014.6864332 · Cited by 13 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Baber, C., Wilson, J.R., and Corlett, E.N. (2005). Evaluation in Human-Computer Interaction, Taylor & Francis.
Cited by
Sorted by citations
Telehealth and patient satisfaction: a systematic review and narrative analysis
Kruse, C. S., Krowski, N., Rodriguez, B., Tran, L., Vela, J., & Brooks, M. (2017) · BMJ Open · doi:10.1136/bmjopen-2017-016242 · Cited by 1,350 · see citing works
Free‐living monitoring of Parkinson's disease: Lessons from the field
Din, S. D., Godfrey, A., Mazzà, C., Lord, S., & Rochester, L. (2016) · Movement Disorders · doi:10.1002/mds.26718 · Cited by 377 · see citing works
Interactive wearable systems for upper body rehabilitation: a systematic review
Wang, Q., Markopoulos, P., Yu, B., Chen, W., & Timmermans, A. A. A. (2017) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-017-0229-y · Cited by 350 · see citing works
A review of wearable technology in medicine
Iqbal, M. H., Aydın, A., Brunckhorst, O., Prokar Dasgupta, A., & Ahmed, K. (2016) · Journal of the Royal Society of Medicine · doi:10.1177/0141076816663560 · Cited by 189 · see citing works
Johansson, D., Malmgren, K., & Alt Murphy, M. (2018) · Journal of Neurology · doi:10.1007/s00415-018-8786-y · Cited by 188 · see citing works
Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19
Sun, S., Folarin, A. A., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Cummins, N., Matcham, F., Dalla Costa, G., Simblett, S., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/19992 · Cited by 256 · see citing works
PERFORM: A System for Monitoring, Assessment and Management of Patients with Parkinson’s Disease
Tzallas, A. T., Tsipouras, M. G., Rigas, G., Tsalikakis, D., Karvounis, E. C., Chondrogiorgi, M., Psomadellis, F., Cancela, J., Pastorino, M., Arredondo, M. T., et al. (2014) · Sensors · doi:10.3390/s141121329 · Cited by 170 · see citing works
Self-citationThe Influence of Wearables on Health Care Outcomes in Chronic Disease: Systematic Review
Mattison, G., Canfell, O., Forrester, D., Dobbins, C., Smith, D., Töyräs, J., & Sullivan, C. (2022) · Journal of Medical Internet Research · doi:10.2196/36690 · Cited by 171 · see citing works
Wearable inertial sensors for human movement analysis: a five-year update
Picerno, P., Iosa, M., D’Souza, C., Benedetti, M. G., Paolucci, S., & Morone, G. (2021) · Expert Review of Medical Devices · doi:10.1080/17434440.2021.1988849 · Cited by 146 · see citing works
Mobile Phones in Research and Treatment: Ethical Guidelines and Future Directions
Carter, A., Liddle, J., Hall, W., & Chenery, H. J. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4538 · Cited by 133 · see citing works
Rodgers, M. M., Alon, G., Pai, V. M., & Conroy, R. S. (2019) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/2055668319839607 · Cited by 119 · see citing works
Hybrid Integration of Wearable Devices for Physiological Monitoring
Zhang, Y., Zheng, X. T., Zhang, X., Pan, J., & Thean, A. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00471 · Cited by 122 · see citing works
Continuous home monitoring of Parkinson’s disease using inertial sensors: A systematic review
Sica, M., Tedesco, S. A., Crowe, C., Kenny, L., Moore, K., Timmons, S., Barton, J. J., O’Flynn, B., & Komaris, D.-S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0246528 · Cited by 111 · see citing works
Brodie, M. A. D., Lord, S. R., Coppens, M. J. M., Annegarn, J., & Delbaere, K. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2433935 · Cited by 95 · see citing works
Understanding, Addressing, and Analysing Digital Eye Strain in Virtual Reality Head-Mounted Displays
Hirzle, T., Fischbach, F., Karlbauer, J., Jansen, P., Gugenheimer, J., Rukzio, E., & Bulling, A. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3492802 · Cited by 89 · see citing works
Stamford, J. A., Schmidt, P. N., & Friedl, K. E. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2464354 · Cited by 86 · see citing works
Kamei, T., Kanamori, T., Yamamoto, Y., & Edirippulige, S. (2020) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x20937573 · Cited by 83 · see citing works
Nguyen, H., Lebel, K., Bogard, S., Goubault, É., Boissy, P., & Duval, C. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2745418 · Cited by 75 · see citing works
Bayés, À., Samà, A., Prats, A., Pérez‐López, C., Crespo, M. C., Moreno, J. M., Alcaine, S., Rodríguez‐Molinero, A., Mestre, B., Quispe, P., et al. (2017) · Gait & Posture · doi:10.1016/j.gaitpost.2017.09.031 · Cited by 70 · see citing works
Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson’s Disease
Marcante, A., Marco, R. D., Gentile, G., Pellicano, C., Assogna, F., Pontieri, F. E., Spalletta, G., Macchiusi, L., Gatsios, D. A., Giannakis, A., et al. (2020) · Sensors · doi:10.3390/s21010128 · Cited by 70 · see citing works
Self-citationAutomatic Spiral Analysis for Objective Assessment of Motor Symptoms in Parkinson’s Disease
Memedi, M., Sadikov, A., Groznik, V., Žabkar, J., Možina, M., Bergquist, F., Johansson, A., Haubenberger, D., & Nyholm, D. (2015) · Sensors · doi:10.3390/s150923727 · Cited by 70 · see citing works
Antonini, A., Reichmann, H., Gentile, G., Garon, M., Tedesco, C., Frank, A., Falkenburger, B., Konitsiotis, S., Tsamis, K. I., Rigas, G., et al. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1080752 · Cited by 69 · see citing works
Self-citationHirzle, T., Cordts, M., Rukzio, E., Gugenheimer, J., & Bulling, A. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445361 · Cited by 67 · see citing works
Keogh, A., Argent, R., Anderson, A., Caulfield, B., & Johnston, W. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00931-2 · Cited by 64 · see citing works
Botros, A., Schütz, N., Camenzind, M., Urwyler, P., Bolliger, D., Vanbellingen, T., Kistler, R., Bohlhalter, S., Müri, R. M., Mosimann, U. P., et al. (2019) · Sensors · doi:10.3390/s19235169 · Cited by 62 · see citing works
Assessing Motor Fluctuations in Parkinson’s Disease Patients Based on a Single Inertial Sensor
Pérez‐López, C., Samà, A., Rodríguez-Martín, D., Catalá, A., Cabestany, J., Aróstegui, J. M. M., de Mingo-Fernández, E., & Rodríguez‐Molinero, A. (2016) · Sensors · doi:10.3390/s16122132 · Cited by 56 · see citing works
Minen, M. T., Corner, S. E., Berk, T., Levitan, V., Friedman, S., Adhikari, S., & Seng, E. (2021) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2020.12.008 · Cited by 52 · see citing works
Rovini, E., Maremmani, C., & Cavallo, F. (2018) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2018.0035 · Cited by 50 · see citing works
Asakawa, T., Sugiyama, K., Nozaki, T., Sameshima, T., KOBAYASHI, S., Wang, L. Y., Hong, Z., Chen, S., Li, C., & Namba, H. (2019) · Neurologia medico-chirurgica · doi:10.2176/nmc.ra.2018-0045 · Cited by 49 · see citing works
Li, Z., Zhang, R., Lee, C., & Lee, Y. (2020) · Sensors · doi:10.3390/s20164414 · Cited by 49 · see citing works
A Comprehensive Review of the Recent Developments in Wearable Sweat-Sensing Devices
Ibrahim, N. F. A., Sabani, N. B., Johari, S., Manaf, A. A., Wahab, A. A., Zakaria, Z., & Noor, A. M. (2022) · Sensors · doi:10.3390/s22197670 · Cited by 49 · see citing works
Cancela, J., Fico, G., & Arredondo, M. T. (2015) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-15-s3-s7 · Cited by 43 · see citing works
Self-citationMonitoring of motor and non-motor symptoms of Parkinson's disease through a mHealth platform
Cancela, J., Villanueva-Mascato, S., Gatsios, D. A., Rigas, G., Marcante, A., Gentile, G., Biundo, R., Giglio, M., Chondrogiorgi, M., Vilzmann, R., et al. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590789 · Cited by 42 · see citing works
Self-citationAreia, C. M., Young, L., Vollam, S., Ede, J., Santos, M., Tarassenko, L., & Watkinson, P. (2020) · JMIR mhealth and uhealth · doi:10.2196/20214 · Cited by 42 · see citing works
A Randomized Crossover Pilot Study of Telemedicine Delivered via iPads in Parkinson’s Disease
Sekimoto, S., Oyama, G., Hatano, T., Sasaki, F., Nakamura, R., Jo, T., Shimo, Y., & Hattori, N. (2019) · Parkinson s Disease · doi:10.1155/2019/9403295 · Cited by 40 · see citing works
Jeong, S. C., & Choi, B. (2022) · SAGE Open · doi:10.1177/21582440221134798 · Cited by 45 · see citing works
Laar, A., de Lima, A. L. S., Maas, B. R., Bloem, B. R., & de Vries, N. M. (2023) · Clinical Parkinsonism & Related Disorders · doi:10.1016/j.prdoa.2023.100188 · Cited by 39 · see citing works
Kenny, L., Moore, K., Riordan, C. O., Fox, S., Barton, J. J., Tedesco, S. A., Sica, M., Crowe, C., Alamäki, A., Condell, J. V., et al. (2022) · JMIR Formative Research · doi:10.2196/27418 · Cited by 41 · see citing works
Zhang, Z., Hong, R., Lin, A., Su, X., Jin, Y., Gao, Y., Peng, K., Li, Y., Zhang, T., Zhi, H., et al. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00959-4 · Cited by 36 · see citing works
A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors
Haider, F., Salim, F. A., Postma, D. B. W., van Delden, R. W., Reidsma, D., van Beijnum, B.-J. F., & Luz, S. F. (2020) · Multimodal Technologies and Interaction · doi:10.3390/mti4020033 · Cited by 32 · see citing works
The effectiveness of Google GLASS as a vital signs monitor in surgery: A simulation study
Iqbal, M. H., Aydın, A., Lowdon, A., Ahmed, H. I., Muir, G., Khan, M. S., Prokar Dasgupta, A., & Ahmed, K. (2016) · International Journal of Surgery · doi:10.1016/j.ijsu.2016.11.013 · Cited by 31 · see citing works
Lee, R., James, C. L., Edwards, S., Skinner, G., Young, J. L., & Snodgrass, S. J. (2021) · Sensors · doi:10.3390/s21196377 · Cited by 29 · see citing works
Motor Control Training for the Shoulder with Smart Garments
Wang, Q., Baets, L. D., Timmermans, A. A. A., Chen, W., Giacolini, L., Matheve, T., & Markopoulos, P. (2017) · Sensors · doi:10.3390/s17071687 · Cited by 30 · see citing works
Wearables for Neurologic Conditions
Minen, M. T., & Stieglitz, E. J. (2020) · Neurology Clinical Practice · doi:10.1212/cpj.0000000000000971 · Cited by 28 · see citing works
Henschke, J., Kaplick, H., Wochatz, M., & Engel, T. (2022) · Health Science Reports · doi:10.1002/hsr2.772 · Cited by 27 · see citing works
Antonini, A., Gentile, G., Giglio, M., Marcante, A., Gage, H., Touray, M., Fotiadis, D. I., Gatsios, D. A., Konitsiotis, S., Timotijević, L., et al. (2018) · Trials · doi:10.1186/s13063-018-2767-4 · Cited by 27 · see citing works
Self-citationJanitor ergonomics and injuries in the safe workload ergonomic exposure project (SWEEP) study
Schwartz, A., Gerberich, S. G., Kim, H. S., Ryan, A. D., Church, T. R., Albin, T. J., McGovern, P. M., Arthur, E. E., Green, D. R., & Arauz, R. F. (2019) · Applied Ergonomics · doi:10.1016/j.apergo.2019.102874 · Cited by 26 · see citing works
Enhancing volleyball training: empowering athletes and coaches through advanced sensing and analysis
Salim, F. A., Postma, D. B. W., Haider, F., Luz, S. F., van Beijnum, B.-J. F., & Reidsma, D. (2024) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2024.1326807 · Cited by 25 · see citing works
Bendig, J., Spanz, A., Leidig, J., Frank, A. K., Stahr, M., Reichmann, H., Loewenbrück, K. F., & Falkenburger, B. H. (2022) · JMIR Formative Research · doi:10.2196/39954 · Cited by 27 · see citing works
Murphy, M. A., Bergquist, F., Hagström, B., Hernández, N., Johansson, D., Ohlsson, F., Sandsjö, L., Wipenmyr, J., & Malmgren, K. (2019) · BMC Biomedical Engineering · doi:10.1186/s42490-019-0002-3 · Cited by 21 · see citing works
A multiple motion sensors index for motor state quantification in Parkinson's disease
Aghanavesi, S., Westin, J., Bergquist, F., Nyholm, D., Askmark, H., Aquilonius, S. M., Constantinescu, R., Medvedev, A. V., Spira, J., Ohlsson, F., et al. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105309 · Cited by 18 · see citing works
Virbel-Fleischman, C., Rétory, Y., Hardy, S., Huiban, C., Corvol, J., & Grabli, D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265438 · Cited by 19 · see citing works
A wearable cardioverter defibrillator with a low false alarm rate
Poole, J. E., Gleva, M. J., Birgersdotter-Green, U. M., Branch, K. R., Doshi, R. N., Salam, T., Crawford, T., Willcox, M. E., Sridhar, A. M., Mikdadi, G., et al. (2022) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.15417 · Cited by 17 · see citing works
Wearable Device Technology in Healthcare—Exploring Constraining and Enabling Factors
Krey, M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-0637-6_1 · Cited by 14 · see citing works
Salim, F. A., Haider, F., Postma, D. B. W., van Delden, R. W., Reidsma, D., Luz, S. F., & van Beijnum, B.-J. F. (2020) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2020-0012 · Cited by 12 · see citing works
Morgan, C., Tonkin, E. L., Craddock, I. J., & Whone, A. L. (2022) · JMIR Human Factors · doi:10.2196/36370 · Cited by 12 · see citing works
Review: Emerging Trends in Healthcare Adoption of Wireless Body Area Networks
Rangarajan, A. (2016) · Biomedical Instrumentation & Technology · doi:10.2345/0899-8205-50.4.264 · Cited by 11 · see citing works
Designing socially acceptable mHealth technologies for Parkinson's disease self-management
Grosjean, S., Bate, E., & Mestre, T. (2020) · Finnish Journal of eHealth and eWelfare · doi:10.23996/fjhw.95231 · Cited by 11 · see citing works
Evaluating the efficacy of an active compression brace on orthostatic cardiovascular responses
Moein, H., Jhalli, R., Blaber, A. P., Claydon, V. E., & Menon, C. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187885 · Cited by 10 · see citing works
Clinical application of a body area network-based smart bracelet for pre-hospital trauma care
Han, W., Yuan, J.-Y., Li, R., Yang, L., Fang, J., Fan, H., & Hou, S. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1190125 · Cited by 9 · see citing works
Sendra, A., Grosjean, S., & Bonneville, L. (2022) · Qualitative Health Communication · doi:10.7146/qhc.v1i1.124110 · Cited by 8 · see citing works
Morgan-Jones, P., Jones, A. E., Busse, M. E., Mills, L., Pallmann, P., Drew, C. J. G., Arnesen, A., & Wood, F. (2022) · JMIR Formative Research · doi:10.2196/36870 · Cited by 9 · see citing works
Intelligent algorithms powered smart devices for atrial fibrillation discrimination
Xie, L., Wang, L., Mo, D., Zhang, Z., & Liang, M. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107480 · Cited by 7 · see citing works
Evaluation of Dominant and Non-Dominant Hand Movements For Volleyball Action Modelling
Haider, F., Salim, F. A., Naghashi, V., Yengec-Tasdemir, S. B., Tengiz, I., Cengiz, K., Postma, D. B. W., van Delden, R. W., Reidsma, D., van Beijnum, B.-J. F., et al. (2019) · doi:10.1145/3351529.3360651 · Cited by 7 · see citing works
Incorporating the Qualitative Variable Comfort into the Design of a Wearable Body Sensor System
Falch, L., & de Silva, C. W. (2020) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2020.3004190 · Cited by 6 · see citing works
A Shape Memory Alloy-Based Compression Therapy Prototype Tested With Individuals in Seated Position
Moein, H., Wu, A., & Menon, C. (2017) · Journal of Medical Devices · doi:10.1115/1.4037441 · Cited by 6 · see citing works
Telemedicine for the Care of Neuromuscular Disorders
Grogan, J., & Simmons, Z. (2020) · Current Treatment Options in Neurology · doi:10.1007/s11940-020-00625-5 · Cited by 6 · see citing works
Choi, J. H., Ma, H., Kim, Y. J., & Lee, U. (2016) · International Journal of Bio-Science and Bio-Technology · doi:10.14257/ijbsbt.2016.8.2.01 · Cited by 6 · see citing works
Lee, H., Lee, S., Salado, L., Estrada, J., White, J. C., Muthukumar, V., Lee, S., & Mohapatra, S. (2018) · Asian/Pacific Island Nursing Journal · doi:10.31372/20180304.1027 · Cited by 6 · see citing works
Finger tracking for wearable VR glove using flexible rack mechanism
Thilakarathna, R., & Phlernjai, M. (2025) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2024.03.001 · Cited by 5 · see citing works
Analysis and Improvement of Tennis Motion Recognition Algorithm Based on Human Body Sensor Network
Pan, N., Wang, Y., & Tong, J. (2022) · Mobile Information Systems · doi:10.1155/2022/8188571 · Cited by 5 · see citing works
Wu, J., Zang, W., Fang, M., Xiao, N., Zhang, X., Wang, S., & Zhang, Q. (2026) · Journal of Medical Internet Research · doi:10.2196/85914 · Cited by 4 · see citing works
Wearability assessment of an equipment for spine posture monitoring
Voinea, G.-D., & Butnariu, S. (2017) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb.2017.7995352 · Cited by 4 · see citing works
Investigating the Impact of Possession-Way of a Smartphone on Action Recognition
Kim, Z. M., Jeong, Y.-S., Oh, H., Oh, K.-J., Lim, C.-G., Iraqi, Y., & Choi, H. (2016) · Sensors · doi:10.3390/s16060812 · Cited by 3 · see citing works
Holder, T., Nichols, C., Summers, E., Roberts, D. L., & Bozkurt, A. Y. (2024) · Animals · doi:10.3390/ani14243628 · Cited by 2 · see citing works
Closed-Loop Deep Brain Stimulation for Parkinson’s Disease
Eitan, R., Bergman, H., & Israel, Z. H. (2018) · Springer eBooks · doi:10.1007/978-3-319-23693-3_10 · Cited by 2 · see citing works
Wearability and Usability Assessment of Cliff: An Automatized Zipper
Baharom, M. Z., Delbressine, F. L. M., Feijs, L. M. G., & Toeters, M. (2020) · Journal of Industrial and Intelligent Information · doi:10.18178/jiii.8.1.1-9 · Cited by 2 · see citing works
Designing smart garments for rehabilitation
Wang, Q. (2018) · TU/e Research Portal · Cited by 2 · see citing works
USE_WR 1.0: A New Comprehensive Instrument for Subjective Usability Evaluation for Wearable Robots
Park, J., Lee, S., & Kim, M. (2025) · IEEE Access · doi:10.1109/access.2025.3606883 · Cited by 1 · see citing works
Choi, J. H., Kim, Y. J., Ma, H., & Lee, U. (2015) · Advanced science and technology letters · doi:10.14257/astl.2015.116.49 · Cited by 1 · see citing works
Telemedicine and digital health solutions for managing movement disorders emergencies
Ungureanu, L., Filip, R., Ciopleiaș, B., & Falup‐Pecurariu, C. (2023) · International review of movement disorders · doi:10.1016/bs.irmvd.2023.08.006 · Cited by 1 · see citing works
Unsupervised gait detection using biomechanical restrictions
Hotta, S., Inomata, A., Sasamoto, Y., Washizawa, S., & Caulfield, B. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037735 · Cited by 1 · see citing works
Technological Devices as an Opportunity for People with Parkinson
Marešová, P. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-74935-8_6 · Cited by 1 · see citing works
The Development and the Wearability Assessment of Cliff: An Automatized Zipper
Baharom, M. Z., Delbressine, F. L. M., Toeters, M., & Feijs, L. M. G. (2018) · International Journal of Mechanical Engineering and Robotics Research · doi:10.18178/ijmerr.7.5.448-457 · Cited by 1 · see citing works
Patel, J. S., Lai, P. T. S., Dormer, D., Gullapelli, R., Wu, H., & Jones, J. (2021) · PubMed Central · Cited by 1 · see citing works
Franco, D. M., Gonzalez, G. A., Hines, K., Mouchtouris, N., Sharan, A. D., & Harrop, J. S. (2022) · Neurosurgery · doi:10.1227/neu.0000000000001947 · Cited by 1 · see citing works
Deep Learning Prediction of Parkinson’s Disease using Remotely Collected Structured Mouse Trace DataPreprint
Zawad, M. R. S., Tumpa, Z. N., Sollis, L. J., Parab, S., & Washington, P. Y. (2024) · medRxiv · doi:10.1101/2024.10.27.24316195 · Cited by 1 · see citing works
Study of Free-Radical Processes during Rotenone Modeling of Parkinson Disease
Ghazaryan, N. A., Ghulikyan, L. A., Chavushyan, V. A., & Ayvazyan, N. M. (2016) · Journal of Biophysical Chemistry · doi:10.4236/jbpc.2016.71001
Pubudu, F., Li, S., Lee, Y. S., & Pham, T. (2021) · doi:10.1002/9781119515104.biblio
Grosjean, S., Bate, E., & Mestre, T. (2019) · doi:10.2196/preprints.16909
Detection of Walking Features Using Mobile Health and Deep Learning
Lee, S., & Lee, H. (2022) · Applied Sciences · doi:10.3390/app12115444
Thilak, K. D., Kalaiselvi, K., Bhuvaneswari, R., Prakash, U. M., Kumaresan, K., & Matheen, M. A. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-1115-8.ch012
Comfort Prediction Method for Wearable Devices: Current Progress and Future Direction
Lin, W., & Chen, Z. (2024) · Advances in Engineering Technology Research · doi:10.56028/aetr.9.1.868.2024
Keogh, A., Argent, R., Anderson, A., Caulfield, B., & Johnston, W. (2021) · Figshare · doi:10.6084/m9.figshare.16625327
Using XGBoost for early detection of parkinson's disease from patient movement data
Chunawala, P., Dingankar, S. U., Jadhav, V., Parida, P. K., Vishakha, & Gill, A. K. (2026) · AIP conference proceedings · doi:10.1063/5.0323365
Virbel-Fleischman, C. (2021) · theses.fr (ABES) · doi:10.70675/21583008z772az4c86z822dza5cca46513d5
Zawad, M. R. S., Tumpa, Z. N., Sollis, L. J., Parab, S., & Washington, P. Y. (2026) · JMIR Formative Research · doi:10.2196/81368
- Authors
- Cancela, J., Pastorino, M., Tzallas, A., Tsipouras, M., Rigas, G., Arredondo, M., & Fotiadis, D.
- Affiliations
- Universidad Politécnica de Madrid (ES)
- University of Ioannina (GR)
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
- Life Supporting Technologies, Universidad Politécnica de Madrid, Avenida Complutense 30, Madrid 28040, Spain
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2014-09-16
- Volume
- 14
- Issue
- 9
- Pages
- 17235-17255
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC4208222
- OpenAlex
- W2068361901
- Semantic Scholar
- 171a4b6c4b1ba85f37c54be40d91d3476dc472b2
- Cited by
- 97
- References
- 25
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 7 · 2023: 4 · 2022: 15 · 2021: 11 · 2020: 13 · 2019: 9 · 2018: 8 · 2017: 9 · 2016: 9 · 2015: 6 · 2014: 1
- FWCI
- 4.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (215952)
- Universidad de Navarra
- University of Ioannina
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- Parkinson's disease, wearable sensors, body area networks, remote patient monitoring, telemedicine, user acceptance, wearability, comfort evaluation, REBA, Borg scale, medication adherence, system redesign
- MeSH terms
- Aged, Computer Communication Networks, Equipment Design, Female, Humans, Male, Parkinson Disease, Patient Acceptance of Health Care, User-Computer Interface, Patient Satisfaction, Telemedicine, Monitoring, Ambulatory, Equipment Failure Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review