Evidence map›Paper›PMID 42895797›Full record

ReviewJournal of obesity2026

Transforming Obesity Care With Digital Therapeutics: Mobile Health Apps, Clinical Evidence, and Regulatory Considerations.

Mayuri, Bidisha Bhattacharya, Kunal Kar, Pravin Pawar, Inderbir Singh, Tanikan Sangnim, Kampanart Huanbutta

Abstract readReview
In one paragraph

Review in Journal of obesity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

MayuriChitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India, chitkara.edu.in.ORCID https://orcid.org/0009-0002-5666-7931
Bidisha BhattacharyaChitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India, chitkara.edu.in.ORCID https://orcid.org/0009-0008-0183-6922
Kunal KarChitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India, chitkara.edu.in.ORCID https://orcid.org/0009-0009-7288-4347
Pravin PawarDr. Shivajirao Kadam College of Pharmacy, Sangli, Maharashtra, India.ORCID https://orcid.org/0000-0001-9541-1977
Inderbir SinghChitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India, chitkara.edu.in.ORCID https://orcid.org/0000-0002-1860-4246
Tanikan SangnimFaculty of Pharmaceutical Sciences, Burapha University, Chonburi, 20131, Thailand, buu.ac.th.ORCID https://orcid.org/0000-0002-9332-384X
Kampanart HuanbuttaDepartment of Manufacturing Pharmacy, College of Pharmacy, Rangsit University, Pathum Thani, 12000, Thailand, rsu.ac.th.ORCID https://orcid.org/0000-0001-8188-153X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Obesity remains a complex global public health crisis driven by environmental, biochemical, and behavioral factors, leading to severe metabolic comorbidities. Digital therapeutics (DTx) has emerged as a scalable, data-driven intervention, utilizing wearable sensors, mobile applications, and artificial intelligence (AI) to deliver personalized weight-management strategies. Rather than relying solely on traditional approaches, true DTx distinguishes it through rigorous clinical validation and targeted behavioral modification. Current evidence from clinical trials and real-world data indicates that while DTx effectively enhances patient engagement and promotes short-term behavioral change, sustaining long-term adherence and significant weight loss remains a critical challenge. Furthermore, the integration of AI models offers advanced personalization, though their efficacy must be balanced against potential algorithmic biases and traditional statistical approaches. Successful implementation of DTx into standard obesity care requires overcoming substantial barriers, particularly digital divide, data privacy concerns, and fragmented regulatory frameworks. Ultimately, realizing the full potential of DTx necessitates collaborative efforts to establish standardized regulations, ensure equitable access, and design evidence-based interventions that prioritize long-term patient engagement and clinical efficacy.

Indexed as

Mobile ApplicationsObesityArtificial IntelligenceDigital HealthHumansTelemedicineWearable Electronic Devicesartificial intelligencedigital therapeuticsmobile applicationsobesity managementsoftware as a medical device

Identifiers

PMID42895797
PMCPMC13679765

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.