Evidence map›Paper›PMID 39753220›Full record

ArticleJMIR mental health2024

Assessing Health Technology Literacy and Attitudes of Patients in an Urban Outpatient Psychiatry Clinic: Cross-Sectional Survey Study.

Julia Tartaglia, Brendan Jaghab, Mohamed Ismail, Katrin Hänsel, Anna Van Meter, Michael Kirschenbaum, Michael Sobolev, John M Kane, Sunny X Tang

Abstract read
In one paragraph

Article in JMIR mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. 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

9 authors.

Julia TartagliaDepartment of Psychiatry, Northwell Health, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID 0009-0008-3826-5027
Brendan JaghabDepartment of Psychiatry, Northwell Health, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID 0009-0001-6601-5868
Mohamed IsmailDepartment of Psychiatry, Northwell Health, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID 0009-0007-6432-5663
Katrin HänselDepartment of Psychiatry, Northwell Health, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID 0000-0003-2407-8884
Anna Van MeterDepartment of Psychiatry, Northwell Health, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID 0000-0003-0012-206X
Michael KirschenbaumDepartment of Psychiatry, Northwell Health, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID 0000-0002-5365-1822
Michael SobolevDepartment of Psychiatry, Northwell Health, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID 0000-0002-8931-7682
John M KaneDepartment of Psychiatry, Northwell Health, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID 0000-0002-2628-9442
Sunny X TangDepartment of Psychiatry, Northwell Health, Zucker Hillside Hospital, Glen Oaks, NY, United States.ORCID 0000-0002-4001-8120

Funding

The Digital Phenotype of Bipolar Disorder: Harnessing Technology to Identify Bipolar Mood Symptoms - Revision - 1K23MH120505 · NIMH · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI VAN METER, ANNA ROBINSON · 2019 to 2024
$1.0M
Advancing Computational Linguistic Biomarkers of Disorganized Speech in PsychosisK23MH130750 · NIMH · FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH · PI TANG, SUNNY XIAOJING · 2022 to 2024
$586k
NIMH NIH HHS K23 MH120505NIMH NIH HHS K23 MH130750
6 · The paper itself

Abstract

backgroundDigital health technologies are increasingly being integrated into mental health care. However, the adoption of these technologies can be influenced by patients' digital literacy and attitudes, which may vary based on sociodemographic factors. This variability necessitates a better understanding of patient digital literacy and attitudes to prevent a digital divide, which can worsen existing health care disparities.

objectiveThis study aimed to assess digital literacy and attitudes toward digital health technologies among a diverse psychiatric outpatient population. In addition, the study sought to identify clusters of patients based on their digital literacy and attitudes, and to compare sociodemographic characteristics among these clusters.

methodsA survey was distributed to adult psychiatric patients with various diagnoses in an urban outpatient psychiatry program. The survey included a demographic questionnaire, a digital literacy questionnaire, and a digital health attitudes questionnaire. Multiple linear regression analyses were used to identify predictors of digital literacy and attitudes. Cluster analysis was performed to categorize patients based on their responses. Pairwise comparisons and one-way ANOVA were conducted to analyze differences between clusters.

resultsA total of 256 patients were included in the analysis. The mean age of participants was 32 (SD 12.6, range 16-70) years. The sample was racially and ethnically diverse: White (100/256, 38.9%), Black (39/256, 15.2%), Latinx (44/256, 17.2%), Asian (59/256, 23%), and other races and ethnicities (15/256, 5.7%). Digital literacy was high for technologies such as smartphones, videoconferencing, and social media (items with >75%, 193/256 of participants reporting at least some use) but lower for health apps, mental health apps, wearables, and virtual reality (items with <42%, 108/256 reporting at least some use). Attitudes toward using technology in clinical care were generally positive (9 out of 10 items received >75% positive score), particularly for communication with providers and health data sharing. Older age (P<.001) and lower educational attainment (P<.001) negatively predicted digital literacy scores, but no demographic variables predicted attitude scores. Cluster analysis identified 3 patient groups. Relative to the other clusters, cluster 1 (n=30) had lower digital literacy and intermediate acceptance of digital technology. Cluster 2 (n=50) had higher literacy and lower acceptance. Cluster 3 (n=176) displayed both higher literacy and acceptance. Significant between-cluster differences were observed in mean age and education level between clusters (P<.001), with cluster 1 participants being older and having lower levels of formal education.

conclusionsHigh digital literacy and acceptance of digital technologies were observed among our patients, indicating a generally positive outlook for digital health clinics. Our results also found that patients of older age and lower formal levels of educational attainment had lower digital literacy, highlighting the need for targeted interventions to support those who may struggle with adopting digital health tools.

Indexed as

Health LiteracyMental DisordersUrban PopulationAdolescentAdultAgedAmbulatory Care FacilitiesCross-Sectional StudiesFemaleHealth Knowledge, Attitudes, PracticeHumansMaleMiddle AgedOutpatientsPsychiatrySurveys and Questionnairesattitudescluster analysisdigital health technologydigital literacymental healthmobile phonepsychiatry

Identifiers

PMID39753220
PMCPMC11729776

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.