Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Sep 1:7:e40197.
doi: 10.2196/40197.

Utility of Smartphone-Based Digital Phenotyping Biomarkers in Assessing Treatment Response to Transcranial Magnetic Stimulation in Depression: Proof-of-Concept Study

Radhika Suneel Kelkar #  1 , Danielle Currey #  2 , Srilakshmi Nagendra  1 , Urvakhsh Meherwan Mehta  1 , Vanteemar S Sreeraj  1 , John Torous  2 , Jagadisha Thirthalli  1
Affiliations

Utility of Smartphone-Based Digital Phenotyping Biomarkers in Assessing Treatment Response to Transcranial Magnetic Stimulation in Depression: Proof-of-Concept Study

Radhika Suneel Kelkar et al. JMIR Form Res. .

Abstract

Background: Identifying biomarkers of response to transcranial magnetic stimulation (TMS) in treatment-resistant depression is a priority for personalizing care. Clinical and neurobiological determinants of treatment response to TMS, while promising, have limited scalability. Therefore, evaluating novel, technologically driven, and potentially scalable biomarkers, such as digital phenotyping, is necessary.

Objective: This study aimed to examine the potential of smartphone-based digital phenotyping and its feasibility as a predictive biomarker of treatment response to TMS in depression.

Methods: We assessed the feasibility of digital phenotyping by examining the adherence and retention rates. We used smartphone data from passive sensors as well as active symptom surveys to determine treatment response in a naturalistic course of TMS treatment for treatment-resistant depression. We applied a scikit-learn logistic regression model (l1 ratio=0.5; 2-fold cross-validation) using both active and passive data. We analyzed related variance metrics throughout the entire treatment duration and on a weekly basis to predict responders and nonresponders to TMS, defined as ≥50% reduction in clinician-rated symptom severity from baseline.

Results: The adherence rate was 89.47%, and the retention rate was 73%. The area under the curve for correct classification of TMS response ranged from 0.59 (passive data alone) to 0.911 (both passive and active data) for data collected throughout the treatment course. Importantly, a model using the average of all features (passive and active) for the first week had an area under the curve of 0.7375 in predicting responder status at the end of the treatment.

Conclusions: The results of our study suggest that it is feasible to use digital phenotyping data to assess response to TMS in depression. Early changes in digital phenotyping biomarkers, such as predicting response from the first week of data, as shown in our results, may also help guide the treatment course.

Keywords: TMS; depression; digital phenotyping; mobile phone; outcome; predictive biomarker; smartphone; theta burst stimulation; transcranial magnetic stimulation; treatment response.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: J Torous is the editor in chief of JMIR Mental Health. All other authors declare no other conflicts of interest.

Similar articles

See all similar articles

Cited by

References

    1. Kobayashi M, Pascual-Leone A. Transcranial magnetic stimulation in neurology. Lancet Neurol. 2003 Mar;2(3):145–56. doi: 10.1016/s1474-4422(03)00321-1.S1474442203003211 - DOI - PubMed
    1. Cohen SL, Bikson M, Badran B, George M. A visual and narrative timeline of US FDA milestones for Transcranial Magnetic Stimulation (TMS) devices. Brain Stimul. 2022;15(1):73–75. doi: 10.1016/j.brs.2021.11.010. https://linkinghub.elsevier.com/retrieve/pii/S1935-861X(21)00825-1 S1935-861X(21)00825-1 - DOI - PMC - PubMed
    1. Marwaha S, Palmer E, Suppes T, Cons E, Young A, Upthegrove R. Novel and emerging treatments for major depression. The Lancet. 2023 Jan;401(10371):141–153. doi: 10.1016/S0140-6736(22)02080-3. doi: 10.1016/s0140-6736(22)02080-3. - DOI - DOI - PubMed
    1. Lefaucheur JP, André-Obadia N, Antal A, Ayache S, Baeken C, Benninger D, Cantello RM, Cincotta M, de Carvalho M, De Ridder D, Devanne H, Di Lazzaro Vincenzo, Filipović Saša R, Hummel Friedhelm C, Jääskeläinen Satu K, Kimiskidis Vasilios K, Koch Giacomo, Langguth Berthold, Nyffeler Thomas, Oliviero Antonio, Padberg Frank, Poulet Emmanuel, Rossi Simone, Rossini Paolo Maria, Rothwell John C, Schönfeldt-Lecuona Carlos, Siebner Hartwig R, Slotema Christina W, Stagg Charlotte J, Valls-Sole Josep, Ziemann Ulf, Paulus Walter, Garcia-Larrea Luis. Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS) Clin Neurophysiol. 2014 Nov;125(11):2150–2206. doi: 10.1016/j.clinph.2014.05.021. doi: 10.1016/j.clinph.2014.05.021.S1388-2457(14)00296-X - DOI - DOI - PubMed
    1. Ge Ruiyang, Downar J, Blumberger D, Daskalakis Z, Vila-Rodriguez F. Functional connectivity of the anterior cingulate cortex predicts treatment outcome for rTMS in treatment-resistant depression at 3-month follow-up. Brain Stimul. 2020;13(1):206–214. doi: 10.1016/j.brs.2019.10.012. https://linkinghub.elsevier.com/retrieve/pii/S1935-861X(19)30419-X S1935-861X(19)30419-X - DOI - PubMed

LinkOut - more resources