JMIR MENTAL HEALTH

Newson & Thiagarajan

Original Paper

Assessment of Population Well-Being With the Mental Health
Quotient (MHQ): Development and Usability Study
Jennifer Jane Newson, PhD; Tara C Thiagarajan, PhD
Sapien Labs, Arlington, VA, United States

Corresponding Author:
Jennifer Jane Newson, PhD
Sapien Labs
1201 Wilson Blvd, 27th floor
Arlington, VA, 22209
United States
Phone: 1 7039976657
Email: jennifer@sapienlabs.org

Abstract
Background: Existing mental health assessment tools provide an incomplete picture of symptom experience and create ambiguity,bias, and inconsistency in mental health outcomes. Furthermore, by focusing on disorders and dysfunction, they do not allow aview of mental health and well-being across a general population.
Objective: This study aims to demonstrate the outcomes and validity of a new web-based assessment tool called the Mental
Health Quotient (MHQ), which is designed for the general population. The MHQ covers the complete breadth of clinical mental
health symptoms and also captures healthy mental functioning to provide a complete profile of an individual’s mental health fromclinical to thriving.
Methods: The MHQ was developed based on the coding of symptoms assessed in 126 existing Diagnostic and Statistical Manualof Mental Disorders (DSM)–based psychiatric assessment tools as well as neuroscientific criteria laid out by Research Domain
Criteria to arrive at a comprehensive set of semantically distinct mental health symptoms and attributes. These were formulated
into questions on a 9-point scale with both positive and negative dimensions and developed into a web-based tool that takes
approximately 14 min to complete. As its output, the assessment provides overall MHQ scores as well as subscores for 6 categoriesof mental health that distinguish clinical and at-risk groups from healthy populations based on a nonlinear scoring algorithm.
MHQ items were also mapped to the DSM fifth edition (DSM-5), and clinical diagnostic criteria for 10 disorders were applied
to the MHQ outcomes to cross-validate scores labeled at-risk and clinical. Initial data were collected from 1665 adult respondents
to test the tool.
Results: Scores in the normal healthy range spanned from 0 to 200 for the overall MHQ, with an average score of approximately
100 (SD 45), and from 0 to 100 with average scores between 48 (SD 21) and 55 (SD 22) for subscores in each of the 6 mental
health subcategories. Overall, 2.46% (41/1665) and 13.09% (218/1665) of respondents were classified as clinical and at-risk,
respectively, with negative scores. Validation against DSM-5 diagnostic criteria showed that 95% (39/41) of those designatedclinical were positive for at least one DSM-5–based disorder, whereas only 1.14% (16/1406) of those with a positive MHQ score
met the diagnostic criteria for a mental health disorder.
Conclusions: The MHQ provides a fast, easy, and comprehensive way to assess population mental health and well-being;
identify at-risk individuals and subgroups; and provide diagnosis-relevant information across 10 disorders.
(JMIR Ment Health 2020;7(7):e17935) doi: 10.2196/17935
KEYWORDS
psychiatry; public health; methods; mental health; population health; social determinants of health; global health; behavioral
symptoms; diagnosis; symptom assessment; psychopathology; mental disorders; mhealth; depression; anxiety; attention deficit
disorder with hyperactivity; autistic disorder; internet

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Introduction
Background
According to the World Health Organization, mental health is“a state of well-being in which the individual realizes his or her
own abilities, can cope with the normal stresses of life, can work
productively and fruitfully, and is able to make a contribution
to his or her community” [1]. According to this definition, any
framework of mental health assessment should therefore not
only reflect the presence of dysfunction but also provide insight
into positive aspects of mental well-being, ensuring it isapplicable not only for clinical groups but also for the wider
population [2]. In addition, although personalized approaches
to mental health are essential in ensuring effective treatment
outcomes at the individual level [3-5], population-levelapproaches provide an understanding of the broader
geographical, cultural, and experiential factors that influencemental health on a macroscale [6,7]. This latter perspective
provides an opportunity to develop interventions that induce
large-scale shifts in population well-being and is becoming
increasingly important for understanding how to improve mental
health outcomes [8,9]. However, current approaches to mental
health assessment pose considerable challenges to these goals
and ideals.

Challenges in Mental Health Assessment
One major challenge is that the clinical heritage of mental healthassessment means that most tools are not designed for thegeneral population but are instead built around specificpsychiatric disorder categories based on the clinicalclassification systems of the Diagnostic and Statistical Manualof Mental Disorders (DSM) [10] or the International
Classification of Diseases (ICD) [11]. In this way, an assessment
can identify whether an individual exhibits symptoms pertaining
to a specific mental health disorder such as depression,
attention-deficit/hyperactivity disorder (ADHD), or alcohol
addiction but does not readily provide a perspective of theiroverall mental health. In contrast, the general population falls
along a continuum ranging from disordered to thriving andtherefore having a system that is predominantly focused on
disorders and dysfunction, without an equivalent understandingof well-being, presents a challenge to advancing theunderstanding of the borders between normal mental health andclinical disorder [12-15], especially because many mental healthsymptoms such as sadness, anxiety, and risk-taking also fallwithin the spectrum of normal mental functioning in the general
population. Understanding when such normal mental functions
cross the boundary to become symptoms requires an assessment
approach that is designed for the general population and thatencompasses the range from clinical dysfunction to positive
mental assets.
A second challenge is that existing mental health assessment
tools, despite being broadly based on symptom criteria definedby DSM or ICD classification systems, are highly
heterogeneous. Our recent analysis of 126 commonly usedmental health screening assessments revealed considerable
inconsistency in symptom assessment across different tools
focusing on the same disorder and substantial overlap between
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Newson & Thiagarajan
disorders [16]. Consequently, two assessments that target thesame population group, but which used different tools to assess
their experience of mental health problems, may deliver different
results because they are assessing a different set of symptoms(see also the study by Fried [17]). This creates ambiguity, bias,
and inconsistency in mental health determination and confuses
the development of effective treatments and interventions topromote well-being within the general population. Moreover,when examining assessment tools that span multiple disorders
and therefore aim to provide a broader perspective on mental
health, Newson et al [16] found that none of the 16
cross-disorder assessment tools that were analyzed covered thecomplete breadth of mental health symptoms and few considered
positive mental assets (see also the study by Allsopp et al [18]).
This suggests that existing cross-disorder tools fail to provide
a complete picture of mental health symptoms and positive
assets that would apply to both clinical and normal healthy
populations.

The Mental Health Quotient
To address these challenges, we have developed a new
web-based assessment tool called the Mental Health Quotient(MHQ) [19], which is designed for the general population andcovers the complete breadth of clinical mental health symptoms
as well as positive mental assets. It has been developed basedon an extensive review of the way mental health is assessed inclinical and research fields [16], and its purpose is to provide acomprehensive assessment of an individual’s mental healthprofile ranging from clinical to thriving, which is suitable forboth clinical and population-based assessments. Here, we
describe the development of the MHQ and provide preliminary
data from a cross-section of the population to illustrate its output.

Methods
Design and Development of the MHQ
Key Design Criteria
The key design criteria of the MHQ were that it had to be fastand easy to complete by the general population (take ≤15 min)and administered such that respondents felt confident inproviding honest responses that were reflective of the current
perception of the respondent’s mental health. The MHQ was
therefore designed to provide a view of respondent perception
within their individual life context, which is not absolute, thatis, what one person means by a severity rating of 8 could be
different from what someone else means in actual life outcomes
and can change over time. This is in line with how the majorityof mental health symptoms are typically assessed. In addition,as an output, it would have to provide an overall score of mental
health as well as scores along key macro dimensions. Taking
these requirements into consideration, the standard version of
the MHQ was developed to be taken on the web anonymously
and provide a score and full individual report that encourages
honest self-report.

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Developing a Complete Inventory of Mental Health andWell-Being Elements
The MHQ was developed based on a comprehensive review ofsymptoms assessed across 126 commonly used psychiatricassessment tools (Figure 1), spanning disorders of depression,anxiety, bipolar disorder, ADHD, post-traumatic stress disorder(PTSD), obsessive-compulsive disorder (OCD), addiction,schizophrenia, eating disorder, and autism spectrum disorder(ASD), and cross-disorder tools (see the study by Newson et al[16] for a complete list of assessment tools).
A total of 10,154 questions, taken from these 126 assessment
tools, were identified and coded based on a judgment of theirsemantic content and consolidated into a set of 43 symptom
categories by grouping similar preliminary symptom codings(see the study by Newson et al [16] for a more detailed
description). This approach was selected because diagnoses are
determined from self-reported symptoms that are based on asemantic description, rather than underlying biological factors.
Therefore, the objective was not to reduce the scale down into
independent items in terms of occurrence but to cover thebreadth of symptoms of mental health assessment based on theirsemantic description (as an example of this approach, fever andfatigue are semantically distinct but often co-occur). This setof symptom categories was then reviewed in the context of theNewson & Thiagarajan
Research Domain Criteria (RDoC) constructs and subconstructs
put forward by the National Institute of Mental Health [20-22],
and a few additions were made to ensure that the list of items
reflected the components within this non-DSM framework.
Next, we ensured that there were items within the MHQ thatreflected symptoms of neurological disorders (eg, dementia)that were not covered in the original review [16]. The resulting
categories were then restructured as follows. First, categories
that reflected purely physical symptoms (eg, urination problems)
were consolidated under the generalized item of Physical healthissues. Second, categories that reflected items that a naive
respondent might find difficult to differentiate (eg, delusions
and unwanted thoughts) were also consolidated. Third, wherea category reflected multiple symptoms or functions, it was split
into 2 (or 3) independent items to make it clear to the respondent
which function or symptom was being assessed (eg, sleep quality
vs nightmares). This resulted in 47 semantically distinct items(Textbox 1).
The resultant items from this review and reorganization werethen split into 2 formats: those mental functions that could
manifest as a spectrum from positive to negative, which we
called spectrum items (27 questions in all), and those symptoms
that purely represented detractions from overall mental health,which we called problem items (20 questions in all).

Figure 1. Diagram illustrating the method of development of the Mental Health Quotient. A total of 126 commonly used psychiatric assessment tools
covering 10 disorders (as well as those taking a cross-disorder approach) were reviewed and consolidated into 43 symptom categories. These categories,
together with additional symptom categories taken from a review of Research Domain Criteria constructs as well as dementia elements, were reorganized
into a final set of 47 items that were divided into spectrum and problem items for inclusion in the Mental Health Quotient. ADHD:
attention-deficit/hyperactivity disorder; ASD: autism spectrum disorder; DSM: Diagnostic and Statistical Manual of Mental Disorders; OCD:
obsessive-compulsive disorder; PTSD: post-traumatic stress disorder; RDoC: Research Domain Criteria.

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Textbox 1. List of spectrum and problem items.
Spectrum questions•

Adaptability to change•

Self-worth and confidence•

Creativity and problem solving•

Drive and motivation•

Stability and calmness•

Sleep quality•

Self-control and impulsivity•

Ability to learn•

Coordination•

Relationships with others•

Emotional resilience•

Planning and organization•

Physical intimacy•

Speech and language•

Memory•Social interactions and co-operation•

Decision making and risk-taking•

Curiosity, interest, and enthusiasm•

Energy level•

Emotional control•

Focus and concentration•

Appetite regulation•

Empathy•

Sensory sensitivity•

Self-image•

Outlook and optimism•

Selective attention

Problem questions•

Restlessness and hyperactivity•

Fear and anxiety•

Susceptibility to infection•

Aggression toward others•

Avoidance and withdrawal•

Unwanted, strange, or obsessive thoughts•

Mood swings•

Sense of being detached from reality•

Nightmares•

Addictions•

Anger and irritability•

Suicidal thoughts or intentions•

Experience of pain

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Guilt and blame•

Hallucinations•

Traumatic flashbacks•

Repetitive or compulsive actions•

Feelings of sadness, distress, and hopelessness•

Physical health issues•

Confusion or slowed thinking

Question Format
Questions were answered based on the current perception of
the respondent (“Please choose your answers based on yourcurrent perception of yourself”) and were formulated on a
9-point scale reflecting the consequence on one’s life
functioning and performance. Figure 2 shows an example of aspectrum question from the MHQ (on adaptability to change)
and an example of a problem question. Each question included
a broad category label as well as a one-sentence description of
the item for clarity.
The scale of spectrum questions was designed to reflect
functions that could be an asset for some individuals but a
problem for others. In this way, spectrum questions weredeveloped such that they did not relate to the presence orabsence of a function or symptom but instead focused on the

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Newson & Thiagarajan

impact that the item had on the individual across a range of
positive or negative possibilities. In the 9-point scale forspectrum items, 1 referred to “Is a real challenge and impacts
my ability to function effectively,” 9 referred to “It is a real
asset to my life and my performance,” and 5 referred to“Sometimes I wish it was better, but it’s ok.”
Problem questions were designed to reflect functions or
dysfunctions that typically had a negative impact on someone’s
life and could rarely be seen as a positive asset. Here, 1 on the
9-point scale referred to “Never causes me any problems,” 9referred to “Has a constant and severe impact on my ability tofunction effectively,” and 5 referred to “Sometimes causes me
difficulties or distress but I can manage.”
Within the spectrum and problems sections of the assessment
tool, questions were presented in a random order so as not tobe leading or priming for the subsequent question.

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Figure 2. Example of a spectrum question and a problem question. Each question was composed of an item category and a 1-sentence description ofthat item as well as a 1 to 9 rating scale with reference labels.

Demographic, Experiential, and Momentary Questions
Questions designed to collect demographic, experiential, andmomentary information were also included in the MHQ
assessment. These questions aimed to provide insight into thelife context and situation of the individual at the time of taking
the assessment to understand how they influence mental health.
Demographic questions were included to ask about the natureof a person’s daily occupation, geography, age, and gender.
Momentary assessments were designed to determine certain
aspects of the individual’s situation, as well as their physicaland mental state at the time of taking the assessment, including
alertness; mood; hours slept the previous night; time since last
meal; and any current physical symptoms such as headache,nausea, or pain. Experiential questions were included to ask
about life satisfaction, life trauma, whether they had a diagnosed
medical disorder, or whether they were currently seeking mental
health treatment. These questions were answered using
multiple-choice answer options, using 9-point rating scales, or
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using a text box, depending on the specific question type, andwere included to identify how these factors influence mental
health and well-being.

Scoring of the MHQ
Computing the MHQ
The MHQ was not computed as a simple average of raw scores,
given (1) there are both negative and positive aspects, (2) there
are differences in the seriousness of consequences of different
symptom types, and (3) consequences do not necessarily
increase linearly at higher values on the scale. Therefore, theraw scores were transformed in 2 steps, which included athreshold-based rescaling of the 9-point scale to a
positive-negative scale, followed by the application of adifferential nonlinear weighting of the negative scores to better
distinguish at-risk populations.
For all questions, a value N was determined as the rescaling
threshold to separate the scale into a positive side depicting a
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normal range and a negative side indicating clinical risk. For
problem questions, responses on the rating scale weretransformed to N – (rating response), where N was a threshold
number between 2 and 6 that was selected depending on theseriousness of the particular symptom and determined where
the scale split between positive and negative. Thus, if N was 2,
a rating response of 1 (representing the absence of the problem)
would be rescaled to a 1, and a rating response of 9 (representing
a constant and severe impact on the ability to function
effectively) would be rescaled to −7. If N was 4, a rating
response of 1 would be rescaled to 3, and a rating response of
9 would be rescaled to −5. For spectrum questions, the scores
were rescaled as (rating response) – N, where N was a number
between 2 and 6. Thus, if N was 3, a rating response of 1
(representing a constant and severe impact on the ability tofunction effectively) would be rescaled to a −2, and a rating
response of 9 (representing an asset to life and performance)
would be rescaled to 6. The specific values of N form part of a
proprietary MHQ algorithm, where lower numbers depict items
that have a greater negative consequence either to the individualor those around them when experienced at severe levels (eg,suicidal thoughts or intentions and aggression toward others).
In contrast, higher N values depict items that were evaluated ashaving a less negative consequence to the individual or which
are often found within a healthy population (eg, guilt and blame
and adaptability to change).
After this positive-negative rescaling, a differential nonlinear
weighting was applied to negative scores of different symptoms
to create greater distinction in the at-risk group. This weighting
value also forms part of the proprietary MHQ algorithm and,similar to N, was determined based on an evaluation of thenegative consequence of each symptom. For example, a rescaled
negative score of −7 for suicidal thoughts or intentions would
be weighted more negatively than a −7 for restlessness andhyperactivity and therefore result in a greater negative
amplification of the MHQ score. Similarly, a rescaled negativescore of −2 for energy levels would be weighted more negatively
than a −2 for creativity and problem solving and result in agreater negative amplification of the MHQ score.
The resulting rescaled and nonlinearly weighted scores acrossall problem and spectrum items were then summed to provide
an aggregate intermediate score. This intermediate score could
be either a negative or positive score, where negative scores

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Newson & Thiagarajan
identified those respondents who had or were at risk for aclinical mental health issue and positive scores represented anormal or healthy range of mental health. To compute the MHQ,
positive scores were then normalized to a scale between 0 and
200, whereas negative scores were normalized across a smaller
window of −1 to −100. The negative scale was chosen to besmaller to provide a mitigated number to minimize any
psychological distress that could be induced by receiving ahighly negative score. Thus, the overall MHQ score spans apossible range from −100 to +200, where negative scores reflect
clinical or clinically at-risk populations and positive scoresreflect the distribution of the normal healthy population. Thisscore range was also chosen to be similar to the way that IQ
scores are computed, where scores are centralized around 100.

MHQ Subscores
Subscores were also computed for 6 broad subcategories ofmental health: core cognition, complex cognition, mood andoutlook, drive and motivation, social self, and mind-body(Textbox 2).
To compute the subcategory scores, a weighted average of items
for each subcategory was calculated by weighting spectrum or
problem items core to the subcategory as 1 and spectrum or
problem items secondary to the subcategory as 0.5. Thisweighting algorithm was developed based on a review ofcognitive and neuroscience models of brain functioning andforms a part of the proprietary MHQ algorithm. For example,
the item stability and calmness was coded with a primary 1
weighting in the mood and outlook subcategory and a secondary
0.5 weighting in the mind and body subcategory to reflect its
dual components of emotion and physiological response,
whereas the item unwanted, strange, or obsessive thoughts wasdual coded with a primary weighting in the core cognition
subcategory and a secondary weighting in the mood and outlook
subcategory to reflect both the cognitive and emotional elementsof this item. In this regard, an item could be assigned to 2
different subcategories and occasionally to 3 different
subcategories. Overall, each subcategory comprised 10 to 24
items. The subcategory scores were then normalized to constrain
them to a smaller scale than the overall MHQ to distinguishthem from the overall score. Positive scores were normalized
to the range of 0 to 100, whereas negative scores werenormalized to the range of −1 to −50.

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Textbox 2. Descriptions of the 6 subcategories of mental health.
Core cognition•

The ability to function effectively and independently on a moment-to-moment basis. It includes brain functions such as attention, memory,learning, and self-control. Abnormal aspects of core cognition include severe or extreme forms of mental confusion, obsessive thoughts, sensory
sensitivity, compulsive behaviors, psychosis, and hallucinations

Complex cognition•

The ability to synthesize and make sense of complex sets of events and situations and display a longer-term perspective in thoughts and behavior.
It includes brain functions such as decision making, creativity, problem solving, planning, and adaptability to change. Abnormal forms of complex
cognition are associated with extreme risk-taking and severe intolerance to change

Mood and outlook•

The ability to manage and regulate emotions effectively and encompasses feelings of distress like fear, anxiety, anger, irritability, guilt, andsadness. It also includes the ability to have a constructive or optimistic outlook for the future. Abnormal forms of emotional functioning include
uncontrollable crying, night terrors, severe temper outbursts, extreme phobias, uncontrollable panic attacks, highly traumatic flashbacks, intensemania, or suicidal intentions

Drive and motivation•

The ability to work toward desired goals and to initiate, persevere, and complete activities in daily life. It is associated with interest, curiosity,and motivation and is also related to overall energy levels. Abnormal forms of drive and motivation include severe addictions that cause harmor extreme withdrawal from activities or social interaction

Social self•

The ability to interact with, relate to, and see oneself with respect to others. It includes factors like confidence, communication skills, self-worth,body image, empathy, and relationship building. Abnormal forms of social functioning include excessive unprovoked aggression, a strong senseof being detached from reality, or suicidal intentions

Mind-body•

The regulation of the balance between mind and body to ensure that any mental concerns do not manifest themselves as physical symptoms inthe body in a chronic or severe way. It includes functions like sleep, appetite, coordination, physical intimacy, and fatigue. Abnormal forms ofmind-body balance can include insomnia or chronic and severe pain, as well as a propensity for infection or frequent physical symptoms (eg,digestive issues) with no obvious physical cause

Mapping of the MHQ Against DSM-5 Criteria
Given that the MHQ items were derived from validated
DSM-based assessments and span the breadth of symptoms
assessed across 10 DSM-derived disorders, they can be readily
mapped to DSM criteria. Thus, to determine the diagnostic
status in relation to the MHQ score ranges, each of the 47 MHQ
question items was first mapped to the diagnostic criteria of 10
mental health disorders (depression, bipolar disorder, anxiety,
OCD, PTSD, schizophrenia, eating disorder, addiction, ADHD,
and ASD), as defined by the DSM fifth edition (DSM-5). For
example, the MHQ items of feelings of sadness, distress, andhopelessness and outlook and optimism were mapped onto thedepressed mood criteria for depression, whereas the MHQ itemsof unwanted, strange, or obsessive thoughts, self-control andimpulsivity, and emotional control were mapped onto theobsession criteria for OCD. Those below the negative threshold
N on the spectrum rating scale and above the negative threshold
N on the problem rating scale were considered to meet theseverity criteria of the DSM-5.
To arrive at the diagnostic indication, we then applied the
diagnostic criteria of the DSM-5 for these 10 disorders to the

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MHQ responses. These criteria stated the type of symptom (eg,interest, fear), the number of symptoms required (eg, must haveat least three), and whether any specific symptoms must bepresent (eg, depression must have either a depressed mood ormarkedly diminished interest) for a diagnosis of a clinicaldisorder. Together, this provided a view of (1) the percentage
of symptoms for a particular disorder that the individual exhibits(ie, the number of severe symptoms associated with that disorder
they report divided by the total number of symptoms associatedwith that disorder), (2) the percentage of an individual’s
symptoms associated with each of the 10 DSM-5–based disorderclassifications (ie, the number of severe symptoms they exhibit
associated with that disorder divided by the total number ofsevere symptoms they report), and (3) a diagnostic indicationfor each disorder based on criteria-derived algorithms. An
example of the MHQ output for the DSM-5 mapping ofsymptoms for one individual is shown in Table 1.
We note that the diagnosis is based on criteria of symptom
severity but excludes specifics of frequency and duration ofsymptoms not captured in the MHQ, which are sometimes partof the DSM-5 diagnostic criteria.

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Table 1. Example Mental Health Quotient output for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition mapping across 10 different
mental health disorders for one individual.
DisorderDisorder symptoms, n/N (%)

Individual’s symptoms (n=20), n (%)

Diagnostic indication

Depression

10/14 (71)

10 (50)

Positive

Anxiety

6/11 (55)

6 (30)

Negative

Bipolar11/15 (73)

11 (55)

Negative

PTSDa

9/20 (45)

9 (45)

Negative

OCDb

4/6 (67)

4 (20)

Negative

Schizophrenia

4/7 (57)

4 (20)

Negative

Eating disorder

3/3 (100)

3 (15)

Positive

Addiction

1/4 (25)

1 (5)NegativeADHD

4/8 (50)

4 (20)

Negative

ASDd

2/9 (22)

2 (10)

Negativeca

PTSD: post-traumatic stress disorder.

b

OCD: obsessive-compulsive disorder.

c

ADHD: attention-deficit/hyperactivity disorder.

d

ASD: autism spectrum disorder.

Reporting of the MHQ
The output of the MHQ was summarized both as scores as wellas into an optional detailed report with recommendations foraction that could be obtained by the respondent. Providing adetailed report ensured greater interest of the respondent toanswer questions thoughtfully and accurately. Figure 3 shows
an extract of an example MHQ results report detailing the MHQ
score and subscores. The first section offers an overall MHQ

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score and a recommendation based on that score. The following
sections offer scores for each of the 6 subcategories (Textbox
2) and recommendations based on each of those scores.
DSM-5-based mapping (eg, as shown in Table 1) is not includedin the current iteration of the individual output report, although
it may be included in the future. When the MHQ is used in aclinical setting, for instance, the DSM-5 mapping can beprovided to an individual’s physician to provide transdiagnostic
insight.

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Figure 3. Extract of an example Mental Health Quotient results report. The report details the overall Mental Health Quotient score and recommendations
based on that score. It also details each of the 6 subcategory scores as well as descriptions and recommendations based on each of those subcategory
scores. MHQ: Mental Health Quotient.

Results
Testing of the MHQ in the General Population
Participant and Protocol for Data Collection
A total of 1961 responses were collected in the study.
Respondents were recruited from the websites of Psychology
Today and Sapien Labs using a series of blog articles targetedat adults from July 2019 to February 2020 by providing links
to the study. The study received ethics approval from the HealthMedia Lab Institutional Review Board. Respondents took partby accessing the MHQ on the web [19] and completing the
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assessment. Those aged younger than 18 years were not eligible
to participate. On average, the assessment took 14 min tocomplete, with the typical time taken for completion being
between 8 min and 20 min (1315/1961, 67.06% of respondents).
In addition, 97.96% (1921/1961) of those taking part said thatthe assessment was easy to understand.
Data Cleaning and Exclusion Criteria
The following exclusion criteria were applied to the responses
for data cleaning purposes. First, the exclusion of all but thefirst of multiple assessments from the same internet protocol
address. Second, those respondents who took under 7 min (anJMIR Ment Health 2020 | vol. 7 | iss. 7 | e17935 | p. 10(page number not for citation purposes)

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indication that the questions were not actually read) or over 1
hour to complete the assessment (suggesting that the individual
was not focused on the response) were excluded. Third,
exclusion of individuals who found the assessment hard tounderstand (ie, responded no to the question, “Did you find thisassessment easy to understand?”). Fourth, respondents who
made unusual or unrealistic responses (eg, those who statedthey had not eaten for 16+ hours or who stated that they had

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slept for >16 hours) were excluded. We reasoned that while one
might sleep longer than 16 hours or fast for a day or more underunique circumstances, these responses might be considered to
be entered under distressed circumstances where thinking is
physiologically impaired and therefore invalid. This resulted in
the exclusion of 15.09% (296/1961) of responses (Table 2), and1665 responses were available for the final analysis.

Table 2. A breakdown of the percentage of responses excluded for each exclusion criterion (N=1961).
Exclusion criteria

Responses excluded, n (%)

Repeat responses from the same respondent

56 (2.85)

Time to complete <7 min (range 2-7 min)123 (6.27)

Time to complete >1 hour (range 1-23 hours)

47 (2.39)

Poor understanding of assessment

40 (2.04)

Over 16 hours since their last meal (range 17-52)

39 (1.98)

Over 16 hours sleep the previous night (range 31-85)

10 (0.51)

Respondent Profile
Overall, 61.14% (1018/1665) of the respondents were female,
36.58% (609/1665) were male, and 1.08% (18/1665) responded
as a nonbinary or third gender. In addition, 1.20% (20/1665) of
the respondents preferred not to reveal their gender. The age
distribution of respondents ranged from 18 years to 65 years
and above, with the highest number in the 25 to 34 years age
bracket (444/1665, 26.66%). Only 7.02% (117/1665) of the
respondents were aged 65 years and above. These specific age
ranges were selected to reflect major life periods above the ageof 18 years. For example, 18 to 24 years reflects early adulthood
and a period when many people are students, single, and areunlikely to have children, whereas 65 years and above reflects
the age at which many people retire from work.
Respondents from 90 different countries completed the survey.
The majority of the respondents were from the United States(797/1665, 47.87%), whereas a notable proportion of
respondents were from the United Kingdom (149/1665, 8.94%),
Canada (103/1665, 6.18%), and India (86/1665, 5.16%).

Overall MHQ ScoresFirst, we examined the overall MHQ scores across 1665
respondents. These scores ranged from −99 to +191 (on a scaleof −100 to +200), where 84.44% (1406/1665) of scores fellwithin the positive or normal healthy range and 15.55%(259/1665) fell within the negative range indicating clinicalrisk. The distribution is shown in Figure 4. The overall MHQ
scores had an average of 81 (median 94 and mode 139), while
the positive MHQ scores had an average of 101 (median 105
and mode 139) and the negative MHQ scores had an averageof −24 (median −15 and mode −4). To obtain an interpretative

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picture of these scores, we further grouped MHQ scores into 6
levels according to their score window (Figure 4). In the positivescore range, +151 to +200 was labeled as thriving (184/1665,
11.05% of the respondents), +101 to +150 was labeled as
succeeding (581/1665, 34.89% of the respondents), +51 to +100
was labeled managing (417/1665, 25.04% of the respondents),
and 0 to +50 was labeled enduring (224/1665, 13.45% of the
respondents). In the negative range, 13.09% (218/1665) of the
respondents fell in the − 1 to −50 score range labeled at-risk fora mental health disorder, whereas 2.46% (41/1665) ofrespondents fell in the −51 to −100 range, representing those
who would likely require immediate clinical intervention(labeled clinical). The proportion of respondents reporting
negative scores is therefore in line with the annual prevalence
rates of mental health disorders reported from other sources[23-25].
There were certain important characteristics of the MHQ score
distribution. First, the scale spanned both positive and negative
numbers, and the distribution was more heavily skewed to theleft compared with a simple average of the raw scores (Figure
4 in comparison with Figure 5). This reflects the characteristicsof the algorithm (negative thresholding and nonlinear weighting,see section Scoring of the MHQ), which creates a greater
distinction between people who have negative symptoms ofdifferent levels of seriousness and life consequence. Second,
there was a peak in the negative range in the bin immediately
to the left of 0. This arises because of the compression of thenegative scores to a smaller scale of 50% of the positive scale,such that each bin would be double what it would otherwise be.
The rationale for this differential was to mitigate stress to the
respondent.

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Figure 4. Distribution of Mental Health Quotient scores across 1665 respondents. Shows the percentage of respondents falling into Mental Health
Quotient score windows ranging from −100 to +200 and across each of the 6 MHQ score levels. Gray bars denote negative scores and black bars denotepositive scores. MHQ score levels are (from left to right) clinical (score range: −100 to −51), at-risk (−50 to −1), enduring (0-50), managing (51-100),
succeeding (101-150), and thriving (151-200). MHQ: Mental Health Quotient.

Figure 5. Distribution of raw scores depicting the percentage of respondents falling into different raw score brackets. Raw scores were calculated as
the sum of spectrum question rating responses and reverse-scored problem question rating responses (ie, where 1 is converted to a 9 and vice versa tomaintain a consistent positive-negative direction).

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Validation of MHQ Score Labels Against DSM-5
Diagnostic Criteria
To determine the validity of the MHQ scoring approach, weapplied DSM-5–mapped diagnostic criteria from 10 different
mental health disorders to the MHQ responses (see the Methods
section). This rule-based algorithm identified respondents who
met the criteria for a diagnosis of at least one mental healthdisorder, out of a possible 10 mental health disorders. We thenexamined the pattern of diagnoses across the different MHQ
levels from clinical to thriving. We found that 95% (39/41) ofindividuals with an MHQ score in the clinical range met thediagnostic criteria for at least one mental health disorder, and30.7% (67/218) of those in the at-risk range met the diagnostic
criteria for at least one mental health disorder. Those in theclinical and at-risk categories who did not meet the DSM-5
criteria for a disorder diagnosis nonetheless had a large numberof severe symptoms that spanned multiple disorders (an averageof 6 severe symptoms compared with an average of 1 in thepositive MHQ score group).
Within the positive score range (from 0 to 200), only 1.14%(16/1406) of the respondents met the DSM-5 criteria equivalent
to a disorder diagnosis, with 88% (14/16) of these being in theenduring category. Thus, MHQ scores exhibit both a low
false-positive rate within the clinical score range and a low
false-negative rate within the positive score range.

MHQ by Age and Gender

Newson & Thiagarajan
and females showed that a greater proportion of females reported
negative MHQ scores compared with males (177/1018, 17.39%
for females compared with 12.6% [77/609] for males; Figure6), with the greatest difference being in the mood and outlook
subcategory (204/1018, 20.04% of the female respondents wereat-risk or clinical compared with 92/609, 15.1% of the male
respondents), and mind-body (169/1018, 16.60% of the female
respondents were at-risk or clinical compared with 44/609, 7.2%of the male respondents) subcategories. Both subcategories
contain a large proportion of depressive symptoms; therefore,
this finding is in line with the gender differences reported
elsewhere [26-28]. In addition, MHQ scores differed
substantially by age, with older age brackets having increasingly
positive scores overall (Figure 7). MHQ scores of respondents
in the 18 to 24 years age range were sharply lower, with 23.7%(58/245) in the negative at-risk or clinical range and only 27.3%(67/245) in the succeeding or thriving range. The proportion of
respondents who were at-risk or clinical declined with age from
23.7% (58/245) to just 9.4% (11/117) in the 65+ years age
group, and the proportion of those succeeding or thriving (ie,scores above 100) increased with age from 27.3% (67/245) to69.2% (81/117). This pattern is in line with data from othersources [29]. This view by age and gender was not significantly
different between respondents from the United States alone
versus respondents from all other countries together. However,at this stage, because of the small representation from othercountries (maximum of 149/1665, 8.95% for any individual
country), a country-wise comparison was not possible.

Next, we show the initial results of overall MHQ scores bygender (Figure 6) and age (Figure 7). The distribution for males
Figure 6. Cumulative percentage of respondents across the Mental Health Quotient score range for male and female groups (N values for male andfemale groups shown in the legend). MHQ: Mental Health Quotient.

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Figure 7. Distribution of Mental Health Quotient scores across ages. Shows both the cumulative percentage of respondents across the Mental Health
Quotient score range for each age bracket (N values for each age bracket shown in the legend) and the linear increase in the proportion of succeedingor thriving (Mental Health Quotient scores above 100) and the decrease in the proportion of at-risk or clinical (Mental Health Quotient scores below 0)
from younger to older age groups. MHQ: Mental Health Quotient.

MHQ Subcategory Scores
Next, we show the distribution of MHQ subcategory scores
across each of the 6 subcategories of mental health (Figure 8).
The distribution structure is highly similar to the overall MHQ
across all categories, with a normal distribution in the positive
range and a skew in the negative range. The average values
across the entire score range for each subcategory were as
follows: core cognition 47 (median 54, mode 75); complex
cognition 49 (median 53, mode 51); drive and motivation 47(median 54, mode 74); mood and outlook 39 (median 43, mode−2); social self 39 (median 46, mode −1); and mind-body 40(median 45, mode 65). Within the positive score range, theaverage, median, and modal values were as follows: corecognition 54 (median 57, mode 63); complex cognition 54

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RenderX(median 56, mode 51); drive and motivation 55 (median 57,
mode 74); mood and outlook 49 (median 51, mode 80); social
self 53 (median 56, mode 75); and mind-body 48 (median 49,
mode 65). A few key aspects warrant mention: the social
self-subcategory, in particular, had a comparatively large
proportion of people in the negative range (374/1665, 22.46%
overall and 24/1665, 1.44% in the clinical range) followed bymood and outlook (302/1665, 18.14% overall, and 19/1665,
1.14% in the clinical range), indicating that challenges relating
to these aspects of mental health were highly prevalent in the
population of respondents (Figure 8). In contrast, the proportionof respondents facing serious challenges in their cognition (coreand complex), drive and motivation, and mind-body werecomparatively smaller.

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Newson & Thiagarajan

Figure 8. The distribution of Mental Health Quotient subscores for each of the 6 subcategories of mental health and the percentage of respondents foreach of the 6 subcategories of mental health for each Mental Health Quotient score level. These levels are (from left to right) clinical, at-risk, enduring,
managing, succeeding, and thriving. Numbers in the legend denote the Mental Health Quotient score range for each level.

Discussion

took, on average, only 14 min to complete, indicates that thetool is highly accessible to the general population.

A New Tool for Assessing Individual and Population
Mental Health and Well-Being

MHQ as a Unique and Comprehensive View of Both
Symptoms and Assets

The MHQ was uniquely developed based on an extensive review
of symptoms from 126 assessment tools across 10 different
mental health disorders as well as taking into account disorderagnostic approaches to mental health, such as RDoC [20-22].
In this regard, it represents the most comprehensive symptom
profiling available, overcoming many limitations and biases ofexisting tools that include only partial lists of symptoms andare often skewed toward feelings or behaviors [16]. The MHQ
also goes beyond a disorder-based approach (ie, a focus on
negative symptoms alone) with the inclusion of spectrum items
that consider a person’s mental abilities and assets. This aspect,rarely considered by existing mental health assessment tools,
is critical to existing views of mental well-being [1] andaddresses the growing realization that positive aspects of mental
health are essential for an integrated view of health [2,30].

The MHQ spans the breadth of mental health symptoms
associated with major psychiatric disorders in a standardized
and unbiased manner as well as assets and abilities importantfor overall mental well-being. The fact that 97.96% (1921/1961)
of respondents found the MHQ easy to understand, and that it

Together, this design approach allows respondents, on an
individual level, to obtain a holistic picture of both concerns
and abilities across their results profile, while at the populationlevel, it ensures that insights are not based on an incomplete orbiased picture of reported symptoms and functions.

Assessment is the first step in identifying individuals and groups
who are most at risk from mental health challenges as well as
understanding the overall mental well-being of a population.
However, existing mental health assessment tools exhibit several
limitations [16] that hinder both effective transdisorder diagnosis
and their application to the general population. Here, we present
the MHQ, a uniquely designed web-based assessment tool thatprovides both an individual view of mental health and clinicalrisk and, when aggregated, a population view of overall mental
well-being.

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JMIR MENTAL HEALTH

Insights Into Individual Mental HealthOn the one hand, the MHQ can be used to provide personalized
insight into an individual’s mental health in a manner that isdisorder agnostic and avoids the ambiguity of disorderclassification [18]. These insights are accompanied by feedback
that is generated based on the individual scoring profile. This
allows at-risk individuals to self-identify so that they can seek
appropriate support before reaching clinical levels of distressor impairment. For example, in this preliminary data set, 13.09%(218/1665) of the respondents were identified as being at-risk,
whereas 2.46% (41/1665) of the respondents likely required
immediate clinical intervention, of which 95% (39/41) met theDSM-5 criteria for a mental health disorder. It also provides amechanism for individuals within a normal healthy range toevaluate dimensions of their mental health and identify challengeareas so that they can take action (eg, make adjustments to theirlifestyle) to strengthen and preserve their well-being even if
they are not considered clinically at risk. Owing to its close
equivalence to diagnostic outcomes based on DSM-5 criteria,
the MHQ can also be used as a fast patient screen on admittanceto a hospital clinic, where individual scores and mappings to
DSM-5 disorder classifications, as shown in Table 1, can provide
an initial impression of a patient’s symptoms and diagnosis toguide faster paths to treatment.

Validation of the MHQ Against DSM-5 Diagnostic
Criteria and Known Epidemiology
The preliminary data presented here from just 1665 adultrespondents demonstrated that overall 15.56% (259/1665) of
the respondents were identified as being at-risk (218/1665,
13.09%) or requiring immediate clinical intervention (41/1665,
2.46%). Comparisons of MHQ scores against DSM-5 criteria
also revealed a low false-positive rate (2/41, 5%) within theclinical score range, where 95% (39/41) of the respondents met
the criteria for a diagnosis of at least one mental health disorder.
There was also a low false-negative rate (16/1406, 1.14%) within
the positive score range (from enduring to thriving), indicatingthat 98.86% (1390/1406) of respondents with a positive MHQ
score did not meet the criteria for a mental health disorderdiagnosis. The close alignment between MHQ scores and thedegree to which people meet DSM-5 diagnostic criteria
demonstrates its validity as a mental health assessment toolcapable of identifying at-risk individuals within a population
as well as providing a comprehensive cross-disorder clinicalview.
One limitation was that the MHQ mapping to the DSM-5, andthe subsequent diagnostic indication, only took into account theseverity of symptoms and not the duration or frequency of
symptoms required for some disorders, as these aspects do notform part of the MHQ. However, the MHQ is also able toidentify those people with a large number of severe clinicalsymptoms in need of help, whose symptoms do not fall
specifically into any particular disorder classification.
At the population level, the proportion of respondents reporting
negative scores is in line with annual prevalence rates of mental
health disorders reported from other sources [23-25]. In addition,
female respondents scored slightly more poorly, especially inthe mood and outlook and mind-body subcategories, both
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Newson & Thiagarajan
subcategories with a large proportion of depressive symptoms,
in line with gender differences reported elsewhere [26-28].
Finally, the data showed that individuals within the youngest
age bracket (18-24 years) were most at risk of experiencing
mental health challenges, which is also in line with data fromother sources [29]. Thus, the overall results of the MHQ are inline with other epidemiological estimates along various
dimensions, demonstrating its validity as an epidemiologicalmental health assessment tool.

Potential Applications of the MHQ
The MHQ was designed to be easy to implement in research
initiatives using large populations of individuals to obtaininsights into the profile of mental health challenges and positivewell-being. When used in a large-scale epidemiological context,
relating MHQ scores to a range of demographic, experiential,
and situational variables can support the development of relevantinterventions or policies that could induce larger-scale shifts inpopulation well-being. Furthermore, the MHQ can be usedwithin specific organizations, such as companies or universities,
to measure and track the overall mental health and well-beingof their workforce or student body, respectively; to support thedesign of tailored interventions suited to that specific group; toidentify at-risk individuals or subgroups; and to assess theimpact of any support programs. The MHQ can also be used ina clinical context as a first-line screening tool within bothprimary care and psychiatric clinics. From a research
perspective, the results obtained from the MHQ can also enablea better understanding of the relationship between individual
symptoms and symptom profiles and underlying biomarkers
and be used to examine the efficacy of new treatment regimes.

Identifying the Borders Between Abnormal andNormal Mental Health
The development of an assessment tool that covers the breadthof mental ill health through to positive functioning, and one thatis accessible to the general population, is also relevant for oneof the major discussion points pertaining to the diagnosis andclassification of mental disorders, namely, the distinction
between normal and abnormal mental health [12,14,15]. As
most negative mental states, such as sadness, despair, anxiety,fear, agitation, and anger, are not abnormalities per se but normal
responses to life’s ups and downs, being able to decipher
whether a person is responding normally to difficult
circumstances, or experiencing pathological levels of distressor impairment, is not straightforward [13]. One challenge
underpinning this debate relates to the fact that, currently, there
is a poor understanding of the state and diversity of mental
health across a normal population. Thus, if there is a poor
understanding of what the continuum of normal mental healthlooks like, how can we understand when it is starting to slide
into abnormal. Such a distinction is necessary not only to prevent
false positives in diagnosis, a label that can be unduly associatedwith stigma but also to ensure that people receive appropriate
treatment and that clinical research studies investigating
underlying etiologies select from appropriate sample pools. The
MHQ assessment tool has been constructed to capture thisbreadth of function from positive assets to extreme distress toestablish these distinctions.
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JMIR MENTAL HEALTH
Psychiatric disorders are among the most disabling healthconditions worldwide, creating a significant burden on
individuals and societies [31]. Assessments of mental healththat are accessible to the general population support the early
identification of at-risk individuals or subgroups and reveal
relevant risk factors. This, in turn, can help reduce the burden
by facilitating the development of relevant and effective
interventions and policies before symptoms escalate to clinicallevels. The importance of population accessible tools is further

Newson & Thiagarajanemphasized by the reported gap between those with severe
distress and impairment and those receiving the help and support
they need [32]. The MHQ aims to help realize the vital goalsof mental health prevention and support by providing a means
to measure and track population mental health. Going beyond
this, the MHQ ultimately seeks to enable a paradigm that canmanage and improve the lives and well-being of all people, andnot just those with a clinical disorder.

Acknowledgments
This work was supported by internal funding from Sapien Labs.

Authors' Contributions
JN and TT developed the assessment tool. JN drafted the manuscript. TT and JN revised the manuscript, approved the final
version, and agreed to be accountable for all aspects of the work.

Conflicts of Interest
The MHQ is both freely available on the web as well as available as a customizable and fee-for-use research tool from SapienLabs, which is a 501(c)(3) nonprofit organization. There is no direct financial benefit to the authors.

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Abbreviations
ADHD: attention-deficit/hyperactivity disorder
ASD: autism spectrum disorder
DSM: Diagnostic and Statistical Manual of Mental Disorders
ICD: International Classification of Diseases
MHQ: Mental Health Quotient
OCD: obsessive-compulsive disorder
PTSD: post-traumatic stress disorder
RDoC: Research Domain Criteria

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Newson & Thiagarajan

Edited by J Torous; submitted 23.01.20; peer-reviewed by M Bestek, N Lau; comments to author 16.04.20; revised version received
06.05.20; accepted 23.05.20; published 20.07.20
Please cite as:
Newson JJ, Thiagarajan TC
Assessment of Population Well-Being With the Mental Health Quotient (MHQ): Development and Usability StudyJMIR Ment Health 2020;7(7):e17935
URL: http://mental.jmir.org/2020/7/e17935/
doi: 10.2196/17935
PMID: 32706730©Jennifer Jane Newson, Tara C Thiagarajan. Originally published in JMIR Mental Health (http://mental.jmir.org), 20.07.2020.
This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, alink to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included.

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JMIR Ment Health 2020 | vol. 7 | iss. 7 | e17935 | p. 19(page number not for citation purposes)