Evidence map›Paper›PMID 30124453›Full record

ArticleJournal of Parkinson's disease2018

Precision Medicine in Parkinson's Disease - Exploring Patient-Initiated Self-Tracking.

Sara Riggare, Maria Hägglund

Abstract read
In one paragraph

Article in Journal of Parkinson's disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
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  6. Article
  7. Article
  8. Review
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  10. Article
  11. Article
  12. Article
  13. Review
  14. Deep Phenotyping of Parkinson's Disease.Journal of Parkinson's disease · 2020
    Review
  15. Article
  16. Patient, study thyself.BMC medicine · 2018
    Article
  17. Review
  18. Article
  19. Review
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

2 authors.

Sara RiggareDepartment for Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.
Maria HägglundDepartment for Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIndividually tailored healthcare, in the form of precision medicine, holds substantial potential for the future of medicine, especially for a complex disorder like Parkinson's disease (PD). Patient self-tracking is an under-researched area in PD.

objectiveThis study aimed to explore patient-initiated self-tracking in PD and discuss it in the context of precision medicine.

methodsThe first author used a smartphone app to capture finger-tapping data and also noted times for medication intakes.

resultsData were collected during four subsequent days. Only data from the first two days were complete enough to analyze, leading to the realization that the collection of data over a period of time can pose a significant burden to patients. From the first two days of data, a dip in finger function was observed around the time for the second medication dose of the day.

conclusionsPatient-initiated self-tracking enabled the first author to glean important insights about how her PD symptoms varied over the course of the day. Symptom tracking holds great potential in precision medicine and can, if shared in a clinical encounter, contribute to the learning of both patient and clinician. More work is needed to develop this field and extra focus needs to be given to balancing the burden of tracking for the patient against any expected benefit.

Indexed as

Diagnostic Self EvaluationMobile ApplicationsParkinson DiseasePrecision MedicineFemaleHumansMiddle AgedParkinson’s diseasepatient-initiatedprecision medicineself-trackingsymptom tracking

Identifiers

PMID30124453
PMCPMC6130409

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.