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Record W3185073343 · doi:10.1002/mdc3.13313

Expiratory Muscle Strength Training in Patients with Parkinson's Disease: A Pilot Study of Mobile Monitoring Application

2021· article· en· W3185073343 on OpenAlexaboutno aff
Martin Srp, Rebeka Korteová, Radim Kliment, Robert Jech, Evžen Růžička, Martina Hoskovcová

Bibliographic record

VenueMovement Disorders Clinical Practice · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsnot available
FundersAgentura Pro Zdravotnický Výzkum České RepublikyČeské Vysoké Učení Technické v Praze
KeywordsMedicineAudio feedbackPhysical medicine and rehabilitationPhysical therapyParkinson's diseaseBiofeedbackExhalationDiseaseAnesthesia

Abstract

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Expiratory muscle strength training (EMST) studies have reported significant improvements in maximum expiratory strength, cough efficacy, and swallowing function in patients with Parkinson's disease (PD).1 Currently, EMST is usually performed in short and intensive training periods.1 However, information about detraining outcomes highlights the need for the development of long-term maintenance home-based programs to sustain training gains following intensive periods of EMST, especially considering the progressive nature of PD.2 Nevertheless, low long-term adherence to home exercise is an important issue in many patient groups and may compromise treatment outcomes.3 Therefore, we developed a mobile phone-based visual feedback (MPVF) application to keep patients motivated to continue EMST following intensive periods of training. With the help of a specially developed algorithm, the application can evaluate real-time data using a microphone attached to an expiratory handheld device (Fig. 1). Supplement S1 provides detailed information about the algorithm. The first step in the research process of examining whether the feedback application is effective in long-term EMST adherence is to find out whether a MPVF application is suitable for patients with PD who may experience difficulties in using a smartphone due to motor and cognitive problems related to age and PD itself. Therefore, we conducted a pilot study to investigate the usability of MPVF in EMST in patients with PD. A total of 12 patients (Table S1) with PD performed an intensive home-based EMST with MPVF for 2 weeks. Peak cough flow, maximum expiratory pressure, and maximum inspiratory pressure were measured. The usability of the MPVF was also assessed using a semistructured interview. Supplement S2 provides detailed information about the methods. The median of adherence for completing the prescribed exercises in the training period was 90.5% (between 174 and 431 completed EMST maneuvers). A total of 2 weeks of intensive EMST were sufficient to significantly improve the participants' maximum expiratory pressure and voluntary peak cough flow (Table S2). The improvement is quantitatively comparable with the results of other intensive EMST studies with longer durations, for example.4, 5 When interpreting such rapid improvement, the impact of visual feedback should be considered. It can be assumed that visual feedback increased training effort compared with regular training without immediate control. All participants appreciated EMST with MPVF (12/12). They found it motivating (11/12), comprehensible (11/12), and user friendly (10/12). Even participants (n = 3) with mild cognitive impairment (Montreal Cognitive Assessment scores 24–25) who coincidentally had no previous experience with smartphones were able to use the application without difficulties or help from another person. With respect to the suggestions for improvement from a semistructured interview, the patients might be divided into 2 groups. The first group proposed simplifying the application as much as possible. The second group preferred more advanced options of presenting training data, such as in overview graphs and charts. To meet the needs of both groups, the application should be able to work in basic and advanced modes in the future. These findings indicate that EMST coupled with MPVF is feasible and potentially useful in patients with PD. We thank Tomas Sieger, PhD, from the Czech Technical University in Prague for his advice on the method of instantaneous sound level calculation. We also thank Ota Gal, PhD, from the Department of Neurology and Centre of Clinical Neuroscience, General University Hospital in Prague for manuscript review and critique. (1) Research Project: A. Conception, B. Organization, C. Execution; (2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript Preparation: A. Writing of the First Draft, B. Review and Critique. M.S.: 1A, 1B, 1C, 2A, 2B, 2C, 3A R. Korteová: 1A, 1B, 1C, 2C R. Kliment: 1A, 3A R.J.: 3B E.R.: 1A, 2C, 3B M.H.: 1A, 3B This study was approved by the Ethics Committee of the General University Hospital in Prague (No. 1613/19 S-IV). All participants signed an informed consent. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. This work was supported by The EU Joint Programme–Neurodegenerative Disease Research 8F19003iCARE-PD and Ministry of Health of the Czech Republic Grants No NV19-04-00233 and NU20-04-0337. The authors declare that there are no conflicts of interest relevant to this work. The authors declare that there are no additional disclosures to report. Supplement S1 The MPVF algorithm. Supplement S2 The methods. Table S1 Demographic and clinical characteristics. Table S2 Median and interquartile range (IQR) of maximum respiratory pressure and voluntary cough values across pre- and posttesting time points. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.073
GPT teacher head0.445
Teacher spread0.372 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2021
Admission routes1
Has abstractyes

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