Evidence map›Paper›PMID 30043234›Full record

ReviewCurrent psychiatry reports2018

Mapping Movement: Applying Motion Measurement Technologies to the Psychiatric Care of Older Adults.

Stephanie Collier, Patrick Monette, Katherine Hobbs, Edward Tabasky, Brent P Forester, Ipsit V Vahia

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current psychiatry reports, 2018. 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
2.8field-weighted citation impact, top 9% of its field
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, 29 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Motion mapping in humans as a biomarker for psychiatric disorders.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2019
    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

6 authors at 3 institutions in 1 country.

Stephanie CollierDivision of Geriatric Psychiatry, McLean Hospital, 115 Mill Street, Mail stop 234, Belmont, MA, 02451, USA.
Patrick MonetteDivision of Geriatric Psychiatry, McLean Hospital, 115 Mill Street, Mail stop 234, Belmont, MA, 02451, USA.
Katherine HobbsDivision of Geriatric Psychiatry, McLean Hospital, 115 Mill Street, Mail stop 234, Belmont, MA, 02451, USA.
Edward TabaskyDivision of Geriatric Psychiatry, McLean Hospital, 115 Mill Street, Mail stop 234, Belmont, MA, 02451, USA.
Brent P ForesterDivision of Geriatric Psychiatry, McLean Hospital, 115 Mill Street, Mail stop 234, Belmont, MA, 02451, USA.
Ipsit V VahiaHarvard Medical School, Boston, MA, USA. ivahia@partners.org.
McLean Hospital · USHarvard University · USSignature Healthcare Brockton Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewRecent advances in technology have changed the landscape of treatment for adults with mental illness. This review highlights technological innovations that may improve care for older adults with mental illness and neurocognitive disorders through the measurement and assessment of physical motion. These technologies include wearable sensors (such as smart watches and Fitbits), passive motion sensors, and smart home models that incorporate both active and passive motion technologies. RECENT

findingsClinicians have evaluated motion measurement technologies in older adults with depression, dementia, anxiety, and schizophrenia. Results from studies in dementia populations suggest that motion measurement technologies can assist clinicians in diagnosing dementia earlier through the evaluation of gait, balance, and postural kinematics. Motion detection technologies can also be used to identify mood episodes at an earlier stage by detecting subtle behavioral changes. Clinicians may use the objective data provided by technologies such as accelerometers to identify illnesses earlier, which may inform treatment decisions. The data may be used as a suitable surrogate marker for detecting depression in older adults, predicting the likelihood of falls, or quantifying physical activity in older adults with chronic mental illnesses or anxiety. Motion-based technologies also have the potential to detect physical activity for older adults residing in nursing homes. Wearable technologies are generally well tolerated in older adults, although the use of new technology and electronic health data could involve privacy and security concerns among this vulnerable population.

Indexed as

MovementWearable Electronic DevicesAccidental FallsAgedAnxiety DisordersDementiaDepressive DisorderExerciseHumansMental DisordersSchizophreniaDataDementiaMotionOlder adultsTechnology

Identifiers

PMID30043234
OpenAlexW2884440742

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.