eMind Blog

June 14, 2022

What is eMindLog®?


eMindLog®: Mobile Self-Measurement of Depression and Anxiety

                                         Philip T Ninan, M.D.*

Depression and anxiety have surged during the COVID-19 pandemic (Santomauro 2021). They are the biggest drivers of misery in the U.S. and globally (Helliwell 2017). The report concludes the most economical way to reduce misery is to successfully treat depressive and anxiety disorders. Such care requires assessment of severity, both at baseline and subsequently for change. Measurement-based care demonstrates superior outcomes compared to standard practice (Fortney 2017).  However, common scales have significant limitations - they assess symptoms as though they are on a flat 'Euclidean' plane, lack granularity, and are not built for tracking over time. Alternates such as digital phenotype measures (Insel 2017) are derived data which have yet to demonstrate biomarker potential (Cohen 2020).

Mental Health and Ill-health. When mentally healthy, the mind perceives with minimal bias, modulates emotions, controls the process and content of thoughts, and manages behaviors. These mental dimensions are integrated in a higher order flow that is smooth and untroubled. We can soar.

Mental ill-health is the loss of such integration, modulation, control, and choice. The seat of our distress is in the mind, not the brain. In both anxiety and depression, the mind shifts to a lower order of integration. Negative emotions, such as anxiety, anger, sadness, and lack of pleasure, dominate. Perception becomes selectively constrained by emotions. The content of thinking becomes captive, binary, and polarized , and the process turbulent or imbued with spurious certainty. Behavior (including interpersonal interactions) is conditioned and reflexive reactions to negative emotions. Freedom of choice is lost. In these states of mind, higher order integration is taken offline, and largely unavailable to the individual.

eMindLog® is developed as an optimal measurement tool to assess anxiety, depression, and related symptoms. It is a comprehensive, source assessment for purposes of screening, quantifying severity, and tracking longitudinally. It brings value to individuals in distress, healthcare systems (including for veterans), educational institutions and enterprises.

eMindLog®is a self-report measure of depression, anxiety, and associated features. A daily component (17 items) measures emotions, thinking and behavior. A weekly component (also 17 items) assesses associated symptoms and quality of life/functioning. Experiences that commonly fluctuate are assessed daily, while those more easily averaged over longer periods are assessed weekly. This combination of daily/weekly appraisals minimizes the assessment burden without compromising the necessary information being gathered. For answers, users choose a number from 0-10, with prescriptive guidance - none (0), mild (1-3), moderate (4-6), severe (7-9) and extreme (10). A regular user requires 3-5 minutes to complete each daily or weekly assessment.

Four index scores for anxiety, sadness, anger, and lack of pleasure are the key gauges. Each incorporate component emotions, thoughts, and behaviors. These are weighted equivalently, permitting score comparisons and ratios between indexes. Additional scores measure stress, quality of life and functioning (disability). Algorithms calculate thresholds for clinical anxiety and depression, reflective of standard diagnostic categories.

eMindLog's® unique combination of features deliver superior value. Three validation studies demonstrate excellent reliability and a strong convergent validity against standard references (Penders 2017).

·      eMindLog® is foundationally based on psychological constructs, enhanced with neuroscience knowledge, as well as developments in related fields such as information theory and AI. Such an underlying  theoretical structure provides scores (e.g., anxiety, anger, sadness, and anhedonia indexes) that can be contrasted with each other as they are equivalently weighted.

·      eMindLog® describes items in easily understandable terms (what is included, with non-overlapping boundaries between items) to enhance precision and accuracy of assessment.

·      Scoring in numbers is enhanced visually and with descriptive guidance reflecting severity. Such a discretized tripartite analogue approach enhances validity (Sheehan 2008). Inferences can be drawn within a database without the necessity of population norms.

·      eMindLog® distinguishes emotions, thoughts, and behaviors, reflective of the ‘joints’ nature divides brain systems. It leverages the inter-dependent relations between emotions, thoughts, and behaviors to derive blended index scores.

·      Subjective experiences mediated by threat versus reward brain systems are measured independently.

·      The relationship between components can be explored, providing a complex, multidimensional mental map as against the single total score of conventional measures. For example, the relationships between symptoms and stress, interpersonal relations, quality of life and function can be explored. This provides a nuanced and comprehensive picture for clinicians for use in measurement-based care.

·      Algorithms provide imputations of diagnostic thresholds (such as meeting 5 of 9 Major Depression criteria), including tracking their initiation and termination.

·      It is personal and private, with user ownership. The user can choose to share their data for specific purposes. It empowers the user to be an active partner in decisions, including with their healthcare provider.

·      eMindLog®permits tracking the flow of depressive and anxiety experiences over time. Information is graphed for easy tracking. Inherent in the display of information is the meaning of a number – categorized as ‘mild’, ‘moderate’ or ‘severe’.

·      eMindLog® covertly educates the user, encouraging self-reflection, and provides guidance for choice among interventions.

·      eMindLog® brings distinctive value to mental/behavioral health. The individual provides their information in a standardized way, empowering them with greater self-awareness to be partners in their own care. The busy provider can glance at a graph and obtain the information necessary to guide treatment decisions.


·      for individuals, a measurement and tracking tool to quantify their depressive and anxiety experiences, vital signs of the mind.

·      for healthcare entities, an electronic health record as a guidance for severity, diagnosis and measurement-based care.

·      for developers of novel interventions for depression and anxiety, a precise and validated tool to measure outcome.

·      for corporations, a valuable tool for employee assistance programs.

·      for enterprises interested in assessing depression and anxiety and aggregate big data, a tool for population-based data in behavioral health, and as a metric for ESG indexes (environment, social and governance).


Cohen AS, Schwartz E, Le T, Cowan T, Cox C, Tucker T, Foltz P, Holmlund, TB, Elvevåg B.      Validating digital phenotyping technologies for clinical use: the critical importance of “resolution”. World Psychiatry 19:1;114-115, 2020. DOI:10.1002/wps.20703        

Fortney JC, Unützer J, Wrenn G, Pyne JM, Smith GR, Schoenbaum M, Harbin HT. A Tipping Point for Measurement-Based Care. Psychiatric Services 2017, Vol. 68, No. 2, pp. 179 - 188      

Helliwell J, Layard R, & Sachs J. (2017). World Happiness Report 2017, New York: Sustainable Development Solutions Network.

Insel TR. Digital Phenotyping Technology for a New Science of Behavior. JAMA 2017.   doi:10.1001/jama.2017.11295

Penders TM, Wuensch KL, Ninan PT. eMindLog: Self Measurement of Anxiety and Depression            Using Mobile Technology. JMIR Res Protoc 2017;6(5):e98. doi:10.2196/resprot.7447.            https://www.researchprotocols.org/2017/5/e98/

Santomauro DF, COVID-19 Mental Disorders Collaborators. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet 2021; 398: 1700–12 https://doi.org/10.1016/ S0140-6736(21)02143-7

Sheehan KH, Sheehan DV. Assessing treatment effects in clinical trials with the Discan metric of the Sheehan Disability Scale. International Clinical Psychopharmacology 2008, 23:70–83

* Philip T Ninan, MD (pninan@emindscience.com), is the Founder of eMind Science Corp, license holder for eMindLog®.

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