Evidence map›Paper›PMID 36060543›Full record

ReviewFrontiers in digital health2022

Turning data into better mental health: Past, present, and future.

Nidal Moukaddam, Akane Sano, Ramiro Salas, Zakia Hammal, Ashutosh Sabharwal

Abstract readReview
In one paragraph

Review in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Predicting Craving-Related Emotions among Opioid Use Disorder Patients: Preliminary Results.... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks · 2025
    Article
  2. Article
  3. 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.

Nidal MoukaddamDepartment of Psychiatry, Baylor College of Medicine, Houston Texas, United States.
Akane SanoDepartment of Electrical and Computer Engineering, Rice University, Houston, Texas, United States.
Ramiro SalasDepartment of Psychiatry, Baylor College of Medicine, The Menninger Clinic, Michael E DeBakey VA Medical Center, Houston, Texas, United States.
Zakia HammalThe Robotics Institute Department in the School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States.
Ashutosh SabharwalDepartment of Electrical and Computer Engineering, Rice University, Houston, Texas, United States.

Funding

Efficient and Cost-Effective Multimodal System for Pain Management in Low Back PainR01NR018451 · NINR · CARNEGIE-MELLON UNIVERSITY · PI HAMMAL, ZAKIA · 2020 to 2025
$2.9M
CSRD VA I01 CX001937NINR NIH HHS R01 NR018451
6 · The paper itself

Abstract

In this mini-review, we discuss the fundamentals of using technology in mental health diagnosis and tracking. We highlight those principles using two clinical concepts: (1) cravings and relapse in the context of addictive disorders and (2) anhedonia in the context of depression. This manuscript is useful for both clinicians wanting to understand the scope of technology use in psychiatry and for computer scientists and engineers wishing to assess psychiatric frameworks useful for diagnosis and treatment. The increase in smartphone ownership and internet connectivity, as well as the accelerated development of wearable devices, have made the observation and analysis of human behavior patterns possible. This has, in turn, paved the way to understand mental health conditions better. These technologies have immense potential in facilitating the diagnosis and tracking of mental health conditions; they also allow the implementation of existing behavioral treatments in new contexts (e.g., remotely, online, and in rural/underserved areas), and the possibility to develop new treatments based on new understanding of behavior patterns. The path to understand how to best use technology in mental health includes the need to match interdisciplinary frameworks from engineering/computer sciences and psychiatry. Thus, we start our review by introducing bio-behavioral sensing, the types of information available, and what behavioral patterns they may reflect and be related to in psychiatric diagnostic frameworks. This information is linked to the use of functional imaging, highlighting how imaging modalities can be considered "ground truth" for mental health/psychiatric dimensions, given the heterogeneity of clinical presentations, and the difficulty of determining what symptom corresponds to what disease. We then discuss how mental health/psychiatric dimensions overlap, yet differ from, psychiatric diagnoses. Using two clinical examples, we highlight the potential agreement areas in assessment/management of anhedonia and cravings. These two dimensions were chosen because of their link to two very prevalent diseases worldwide: depression and addiction. Anhedonia is a core symptom of depression, which is one of the leading causes of disability worldwide. Cravings, the urge to use a substance or perform an action (e.g., shopping, internet), is the leading step before relapse. Lastly, through the manuscript, we discuss potential mental health dimensions.

Indexed as

anhedoniaapps and smartphonesbio-behavioral sensingcraving and relapsedepressionecological momentary assessmentmental health

Identifiers

PMID36060543
PMCPMC9428351

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.