Evidence map›Paper›PMID 39617767›Full record

ArticleScientific reports2024

Development of a remote therapeutic monitoring platform: applications for movement disorders.

Sreetharan Thankathuraipandian, Walter Greenleaf, Anahita Kyani, Tucker Tomlinson, Binesh Balasingh, Erika Ross, Yagna Pathak

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Trial
  2. Article
  3. Hereditary spastic paraplegia: from decades of therapy to future innovations.Therapeutic advances in neurological disorders · 2026
    Review
  4. Review
  5. Review
  6. Review
  7. Automated Monitoring and Clinical Notifications of Patient State for Neuropsychiatric Neuromodulation Studies.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sreetharan ThankathuraipandianAbbott Neuromodulation, Plano, TX, USA.
Walter GreenleafStanford University, Stanford, CA, USA.
Anahita KyaniAbbott Neuromodulation, Plano, TX, USA.
Tucker TomlinsonAbbott Neuromodulation, Plano, TX, USA.
Binesh BalasinghAbbott Neuromodulation, Plano, TX, USA.
Erika RossAbbott Neuromodulation, Plano, TX, USA.
Yagna PathakAbbott Neuromodulation, Plano, TX, USA. Yagna.Pathak@abbott.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital health tools are emerging as a promising solution for optimizing Deep Brain Stimulation (DBS) therapy for neurodegenerative diseases such as Parkinson's Disease (PD), addressing challenges of therapy maintenance, care access, and discrete assessments. Wearable technology and mobile health platforms can offer remote monitoring capabilities, allowing for real-time virtual programming with optimization of patients' therapy, use of digital biomarkers that can help identify the onset, symptoms, medication-related fluctuations and side-effects, and response to DBS treatment. Through an investigational remote monitoring application (RM app) integrating patient-reported outcomes (PROs) and objective data using wearables, we aimed to develop an accessible, data-driven digital tool to monitor patient symptoms and deliver low-burden and easy-to-access DBS therapy. The platform is designed to be compatible with several consumer-grade wearables and ensures patient data privacy and security. We conducted a feasibility study to test the tool's effectiveness in a large-scale DBS study, where subjects received a home-monitoring kit consisting of an iPhone and Apple Watch, pre-configured with the RM app to enable remote data collection. Compliance in this paper is defined as adherence to the RM app and Apple watch usage. Analysis of 67 subjects from the study demonstrated an average compliance rate of 63.3% for daily check-ins, 85.5% for monthly surveys and 61.9% for passive data with an average of 55 days of post-implant data. The study highlights the feasibility of frequent monitoring in the early post-implant period that could lead to optimal outcomes for patients. The platform developed can further optimize study execution and be extended to other chronic conditions treated with neuromodulation implants such as depression and pain.

Indexed as

Deep Brain StimulationMobile ApplicationsParkinson DiseaseTelemedicineAgedFeasibility StudiesFemaleHumansMaleMiddle AgedMonitoring, PhysiologicMovement DisordersPatient Reported Outcome MeasuresWearable Electronic Devices

Identifiers

PMID39617767
PMCPMC11609271

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.