Evidence map›Paper›PMID 30021710›Full record

ArticleJournal of medical Internet research2018

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study.

Jingbo Meng, Syed Ali Hussain, David C Mohr, Mary Czerwinski, Mi Zhang

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

13 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024
    Article
  3. The Perceived Utility of Smartphone and Wearable Sensor Data in Digital Self-tracking Technologies for Mental Health.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023
    Article
  4. Review
  5. Using Health Forum Data to Assess User Needs for mHealth App Development: A Feasibility Study on Alzheimer's Disease.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023
    Article
  6. Developing a Mobile App for Young Adults with Nonsuicidal Self-Injury: A Prototype Feedback Study.International journal of environmental research and public health · 2022
    Article
  7. Review
  8. Article
  9. Article
  10. Understanding Participant Needs for Engagement and Attitudes towards Passive Sensing in Remote Digital Health Studies.International Conference on Pervasive Computing Technologies for Healthcare : [proceedings]. International Conference on Pervasive Computing Technologies for Healthcare · 2020
    Article
  11. "Energy is a Finite Resource": Designing Technology to Support Individuals across Fluctuating Symptoms of Depression.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2020
    Article
  12. Article
  13. 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

5 authors at 3 institutions in 1 country.

Jingbo MengDepartment of Communication, Michigan State University, East Lansing, MI, United States.ORCID 0000-0001-9120-2908
Syed Ali HussainDepartment of Communication, Michigan State University, East Lansing, MI, United States.ORCID 0000-0002-2704-4551
David C MohrFeinberg School of Medicine, Northwestern University, Chicago, IL, United States.ORCID 0000-0002-5443-7596
Mary CzerwinskiMicrosoft Research, Redmond, WA, United States.ORCID 0000-0003-0881-401X
Mi ZhangDepartment of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, United States.ORCID 0000-0001-7002-6757
Michigan State University · USMicrosoft (United States) · USNorthwestern University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundToday, college students are dealing with depression at some of the highest rates in decades. As the primary mental health service provider, university counseling centers are limited in their capacity and efficiency to provide mental health care due to time constraints and reliance on students' self-reports. A mobile behavioral-sensing platform may serve as a solution to enhance the efficiency and accessibility of university counseling services.

objectiveThe main objectives of this study are to (1) understand the usefulness of a mobile sensing platform (ie, iSee) in improving counseling services and assisting students' self-management of their depression conditions, and (2) explore what types of behavioral targets (ie, meaningful information extracted from raw sensor data) and feedback to deliver from both clinician and students' perspectives.

methodsWe conducted semistructured interviews with 9 clinicians and 12 students with depression recruited from a counseling center at a large Midwestern university. The interviews were 40-50 minutes long and were audio recorded and transcribed. The interview data were analyzed using thematic analysis with an inductive approach. Clinician and student interviews were analyzed separately for comparison. The process of extracting themes involved iterative coding, memo writing, theme revisits, and refinement.

resultsFrom the clinician perspective, the mobile sensing platform helps to improve counseling service by providing objective evidence for clinicians and filling gaps in clinician-patient communication. Clinicians suggested providing students with their sensed behavioral targets organized around personalized goals. Clinicians also recommended delivering therapeutic feedback to students based on their sensed behavioral targets, including positive reinforcement, reflection reminders, and challenging negative thoughts. From the student perspective, the mobile sensing platform helps to ease continued self-tracking practices. Students expressed their need for integrated behavioral targets to understand correlations between behaviors and depression. They also pointed out that they would prefer to avoid seeing negative feedback.

conclusionsAlthough clinician and student participants shared views on the advantages of iSee in supporting university counseling, they had divergent opinions on the types of behavioral targets and feedback to be provided via iSee. This exploratory work gained initial insights into the design of a mobile sensing platform for depression management and informed a more conclusive research project for the future.

Indexed as

AdultDepressionFemaleHumansMaleMental Health ServicesQualitative ResearchTechnologyYoung Adultcounselingdepressionmental healthmobile sensinguser-centered design

Identifiers

PMID30021710
PMCPMC6068382
OpenAlexW2806867909

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.