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e-Mental health apps targeting depression have gained increased attention in mental health care. Daily self-assessment is an essential part of e-mental health apps. The Self-administered Psycho-TherApy-SystemS (SELFPASS) app is a self-management app to manage depressive and comorbid anxiety symptoms of patients with a depression diagnosis. A self-developed item pool with 40 depression items and 12 anxiety items is included to provide symptom-specific suggestions for interventions. However, the psychometric properties of the item pool have not yet been evaluated.
The aim of this study is to investigate the validity and reliability of the SELFPASS item pool.
A weblink with the SELFPASS item pool and validated mood assessment scales was distributed to healthy subjects and patients who had received a diagnosis of a depressive disorder within the last year. Two scores were derived from the SELFPASS item pool: SELFPASS depression (SP-D) and SELFPASS anxiety (SP-A). Reliability was examined using Cronbach α. Construct validity was assessed through Pearson correlations with the Patient Health Questionnaire-9 (PHQ-9), the General Anxiety Disorder Scale-7 (GAD-7), and the WHO-5-Wellbeing-Scale (WHO-5). Logistic regression analysis was performed as an indicator for concurrent criterion validity of SP-D and SP-A. Factor analysis was performed to provide information about the underlying factor structure of the item pool. Item-scale correlations were calculated in order to determine item quality.
A total of 284 participants were included, with 192 (67.6%) healthy subjects and 92 (32.4%) patients. Cronbach α was set to .94 for SP-D and α=.88 for SP-A. We found significant positive correlations between SP-D and PHQ-9 scores (
The SELFPASS item pool showed good psychometric properties in terms of reliability, construct, and criterion validity. The item pool is an appropriate source for daily mood tracking in future e-mental health apps among patients with depression. Our study provides general recommendations for future developments as well as recommendations within the item pool.
e-Mental health apps targeting depression and anxiety play an increasing role in mental health care ranging from smartphone-based monitoring apps to extensive treatment applications [
Regular mood tracking and symptom monitoring is a necessary step to identify sudden fluctuations that may hint at a suicidal crisis. The purpose of mood tracking in e-mental health apps for depression lies in the enhanced opportunities for self-reflection on mood and on their patterns and potential triggers of symptom aggravation [
Regarding anxiety apps, there is only scarce data on the availability and technical realization of mood tracking thus far. The last comprehensive study [
Digital versions of self-report scales regarding psychiatric symptoms show a comparable reliability to paper-pencil versions [
The PHQ-9 is a brief depression screening instrument that has been validated across a variety of medical conditions [
Recently since the introduction of the mixed anxiety-depressive disorder (F41.2) category in the ICD-10 in 1992, the complexity of differential diagnostics of anxiety and depression became evident [
Somatic symptoms play an essential role in depressive symptomatology over the lifespan. Recent results have pointed out that depressive symptoms tend to shift with increasing age from a mood-related profile of symptoms to rather vegetative somatic symptoms including psychomotor agitation, gastrointestinal problems, or sleep disturbances [
Scale development is a defined procedure that has been described comprehensively [
The aim of this study is the development of an item pool of mood-related questions for daily self-assessment in an e-mental health app to cover the main aspects of depressive symptoms in accordance with the diagnostic criteria of ICD-10 and comorbid symptoms of anxiety. We investigated the psychometric properties’ reliability, construct validity, criterion validity, and item-scale correlation of the item pool, which may be used for future self-management apps with suggestions for symptom-specific interventions. Thus, we aim to develop recommendations for the integration of mood-tracking items in future e-mental health apps.
The mobile app Self-administered Psycho-TherApy-SystemS (SELFPASS) was developed in a German study that received federal funding. This app was designed to improve the self-management of patients with depression on the basis of an individualized daily mood score. The target group comprises patients diagnosed with depression, who often wait a long time for a face-to-face psychotherapy [
An item pool of 52 depression- and anxiety-related questions was developed to cover the main aspects of depressive symptoms and to provide suggestions for a pool of individualized interventions. Out of all items, 40 questions refer to depressive and 12 to anxiety symptoms. All questions are presented in
All questions were rephrased. Each symptom was assessed through 4 different items in accordance with recommendations in the literature regarding test construction [
We used a cross-sectional, web-based survey design to investigate the validity and reliability of the SELFPASS item pool. The study population consisted of 2 groups. The first group included healthy subjects, who reported not having any affective disorder within in the last 3 years. The second group included patients, who have received a diagnosis of any depressive disorder within the last year. We excluded patients with bipolar disorder, a psychosis, or suicidal ideation. Ethical approval for this study was granted by the Ethics Commission of the Medical Faculty of Heidelberg University (S-031/2020).
The survey was made available on the internet via the soscisurvey.de [
Patients were recruited within the Heidelberg University Hospital. They were contacted personally, via email or by post, and received a weblink to the study. Thus, the presence of a physicians’ diagnosis could be ensured, which is a prerequisite to assess criterion validity. Exclusion criteria were applied in advance.
We provided 4 major parts of mood-related questions in random order: the German versions of the PHQ-9, GAD-7, WHO-5-Wellbeing-Scale (WHO-5) [
The PHQ-9 is a 9-item tool to assess depressive symptoms. The responses are rated on a 4-point Likert scale ranging from 0=not at all to 3=nearly every day. GAD-7 assesses 7 anxiety items on a scale from 0=not at all to 3=nearly every day. Both the PHQ-9 and GAD-7 consider a cut-off score of 10, with higher values indicating MDD [
WHO-5 consists of 5 items, rated on a scale from 0=no time to 5=all of the time, for subjective well-being of the participant, with a high score indicating higher well-being. It was initially introduced by the World Health Organization (WHO) in 1998 as a first step in a 10-item screening process of depression and is usually followed by a diagnostic interview [
In this study, all psychometric instruments are used for construct validation, which refers to the alignment of the concepts measured by the instrument with their theoretical construct [
Statistical analysis was carried out in multiple steps using SPSS (version 24; IBM Corp) [
We excluded those data sets with a relative speed index of >1.75 as well as above 10% missing data per participant following pragmatic considerations and recommendations of the literature [
After descriptive analysis of all participants (including means, SDs, and frequencies) we calculated subscales among the 40 SELFPASS depression (SP-D) and the 12 SELFPASS anxiety (SP-A) items.
We used Cronbach α to determine internal consistency of the subscales. Cronbach α>.80 was considered a threshold for acceptance [
Convergent validity was assessed using Pearson correlation coefficients of SP-D with PHQ-9 and SP-A with GAD-7. Pearson correlation coefficients were also computed for WHO-5 and SP-D and for the sum of SP-A and SP to assess discriminant validity. Following Cohen’s [
Logistic regression analysis was performed to analyze the power of SP-D and SP-A to distinguish between patients and healthy subjects. The results will serve as criterion validation. We considered the
We carried out an explorative factor analysis to investigate the underlying factor structure of the items. At first, we applied the analysis to the whole sample, and a more detailed investigation studied the factor structure within the data of only the patients. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was interpreted as acceptable if its value exceeded 0.50 [
Finally, we correlated the items’ values with the respective scale value to obtain the individual item-scale correlation for the analysis of the discriminatory power of each item. We applied the rule of thumb to remove items with a correlation below
A level of
In total, 329 participants responded to the web-based questionnaire from end-March to mid-August 2020. After excluding respondents with a relative speed index of >1.75 (n=31) and more than 10% of missing items (n=4), as well as healthy subjects with a psychiatric diagnosis (n=10), the final sample comprised 284 participants. The sample consisted of 192 (67.6%) healthy subjects and 92 (32.4%) patients. The demographic characteristics of the participants are shown in
Demographic characteristics of the study sample (N=284).
Characteristics | Participants | ||||||
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Healthy subjects (n=192) | Patients (n=92) | Total | ||||
Age (years), mean (SD) | 29.02 (9.70) | 40.46 (14.91) | 32.73 (12.8) | ||||
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Male | 43 (22.4) | 38 (41.3) | 81 (28.5) | |||
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Female | 148 (77.1) | 53 (57.6) | 201 (70.8) | |||
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Other | 1 (0.5) | 1 (1.1) | 2 (0.7) | |||
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Single | 162 (84.4) | 46 (50.0) | 208 (73.2) | |||
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Married | 19 (9.9) | 31 (33.7) | 50 (17.6) | |||
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Divorced | 7 (3.6) | 7 (7.6) | 14 (4.9) | |||
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Widowed | 0 (0) | 2 (2.2) | 2 (0.7) | |||
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Separated | 0 (0) | 5 (5.4) | 5 (1.8) | |||
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Other | 4 (2.1) | 1 (1.1) | 5 (1.8) | |||
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No degree | 0 (0) | 3 (3.3) | 3 (1.1) | |||
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High school | 64 (33.3) | 37 (40.2) | 101 (35.6) | |||
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College | 123 (64.1) | 47 (51.1) | 170 (59.9) | |||
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Dissertation/PhD | 5 (2.6) | 2 (2.2) | 7 (2.5) | |||
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Other | 0 (0) | 3 (3.3) | 3 (1.1) | |||
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Self-employed | 3 (1.6) | 1 (1.1) | 4 (1.4) | |||
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Worker | 5 (2.6) | 9 (9.8) | 14 (4.9) | |||
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Civil servant | 3 (1.6) | 3 (3.3) | 6 (2.1) | |||
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Employee | 51 (26.6) | 37 (40.2) | 88 (31.0) | |||
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Not working | 3 (1.6) | 11 (12.0) | 14 (4.9) | |||
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Student/pupil | 123 (64.1) | 17 (18.5) | 140 (49.3) | |||
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Retired | 3 (1.6) | 5 (5.4) | 8 (2.8) | |||
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Other | 1 (0.5) | 9 (9.8) | 10 (3.5) |
The mean scores of all psychometric scales and the SELFPASS subscales are presented in
Scores of healthy subjects and patients on the psychometric scales.
Scale | Healthy subjects (n=192), mean (SD) | Patients (n=92), mean (SD) |
SELFPASSa for depression | 53.33 (24.62) | 87.40 (29.27) |
SELFPASS for anxiety | 18.63 (9.65) | 31.37 (11.62) |
Overall Self-administered Psycho-TherApy-SystemS | 71.96 (32.37) | 118.77 (38.63) |
Patient Health Questionnaire-9 | 5.39 (3.95) | 10.40 (5.23) |
General Anxiety Disorder Scale-7 | 5.03 (3.88) | 8.76 (4.53) |
WHO-5-Wellbeing-Scale | 14.46 (4.86) | 8.57 (5.47) |
a SELFPASS: Self-administered Psycho-TherApy-SystemS.
The internal consistency of the 40-item SP-D subscale and the 12-item SP-A subscale was assessed from a Cronbach α of .94 for SP-D (n=240) and .88 for SP-A (n=275).
Pearson correlation analysis to determine the correlation between the scores of SELFPASSa subscales with those of the Patient Health Questionnaire-9, General Anxiety Disorder Scale-7, and WHO-5-Wellbeing-Scale.
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Pearson correlation coefficient | |||||
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SELFPASS for depression | SELFPASS for anxiety | Overall SELFPASS | |||
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Patient Health Questionnaire-9 | 0.87b | 0.74b |
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General Anxiety Disorder Scale-7 | 0.70b | 0.80b |
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WHO-5-Wellbeing-Scale | –0.80b | –0.69b | –0.80b |
a SELFPASS: Self-administered Psycho-TherApy-SystemS.
b
The results of the logistic regression analysis for patients and healthy subjects are presented in
Results of logistic regression analysis indicating the probability of being part of the patient group on the basis of the scores of the SELFPASS depression and anxiety subscalesa.
Predictor | β (SE) | Odds ratio (95% CI) | |
Constant | –4.15 (0.49) | <.001 | 0.02 |
SELFPASS for depression | 0.03 (0.01) | <.001 | 1.03 (1.01-1.05) |
SELFPASS for anxiety | 0.05 (0.02) | .01 | 1.05 (1.01-1.10) |
aSELFPASS: Self-administered Psycho-TherApy-SystemS; Omnibus test:
A principal axis analysis with the whole sample revealed a 2-factor solution after scree plot analysis. The 2 factors accounted for 36.70% of the variance, and oblique rotation was performed. The intercorrelation of the 2 factors was
The patient subgroup showed a KMO measure of 0.70. A principal axis analysis with the patient sample accounted for 32.77% of the variance. After excluding the critical items, 35.90% of the variance was accounted for. The intercorrelation of the 2 factors was
The items as well as the loadings of the items in the whole sample and the patient subsample are presented in
Items, associated symptoms, and results of the principal axis factor analysis of the whole sample after oblique rotation (N=284), and of the patients’ sample (n=92).
Item | Item formulation | Symptom | Factor 1 |
Factor 1 |
Factor 2 |
Factor 2 |
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SP1 | I feel depressed, sad or hopeless. | CSb1 | 0.73 | 0.73 |
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SP2 | I easily burst into tears. | CS1 | 0.40 | 0.42 |
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SP3 | I am cheerful and in good spirits. | CS1 | 0.87 | 0.82 |
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SP4 | I feel easy and carefree. | CS1 | 0.88 | 0.77 |
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SP5 | I have much less desire and enjoyment for things I usually like to do. | CS2 | 0.76 | 0.72 |
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SP6 | I have no interest in people around me. | CS2 | 0.33 | 0.37 |
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SP7 | I can laugh at funny moments. | CS2 | 0.54 | 0.54 |
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SP8 | I can enjoy pleasant things and be happy about them. | CS2 | 0.63 | 0.53 |
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SP9 | I feel exhausted and sluggish. | CS3 | 0.82 | 0.68 |
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SP10 | I can't force myself to do anything. | CS3 | 0.61 | 0.47 |
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SP11 | Decision making is easy for me. | CS3 | 0.48 | 0.34 |
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SP12 | I am full of drive and energy. | CS3 | 0.86 | 0.83 |
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SP13 | I have problems in concentrating on something. | ASc1 | 0.74 | 0.72 |
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SP14 | My thoughts keep on slipping away. | AS1 | 0.63 | 0.64 |
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SP15 | I can dwell on one thing with my full concentration. | AS1 | 0.69 | 0.59 |
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SP16 | I am not easily distracted. | AS1 | 0.54 | 0.49 |
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SP17 | I am just not good enough. | AS2 | 0.54 | 0.40 |
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SP18 | Others can do things much better than I can. | AS2 | 0.35 | 0.35 |
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SP19 | I am satisfied with myself. | AS2 | 0.76 | 0.60 |
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SP20d | I take care of my appearance. | AS2 | 0.20 |
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0.32 | ||||||
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SP21 | I should have done things much differently in the past. | AS3 | 0.43 | 0.40 |
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SP22 | I have made mistakes. It´s not surprising I feel bad. | AS3 | 0.47 | 0.51 |
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SP23d | I am not perfect. But who is? | AS3 | 0.29 | 0.24 |
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SP24d | I don’t deserve to feel bad. | AS3 | -0.03 | -0.23 |
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SP25d | It can only get worse. | AS4 | 0.19 | 0.14 | 0.19 |
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SP26 | The future has nothing to offer for me. | AS4 | 0.53 | 0.62 |
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SP27 | I am looking forward to the future. | AS4 | 0.67 | 0.67 |
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SP28 | Time heals all wounds. Everything will be alright. | AS4 | 0.61 | 0.46 |
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SP29 | Sometimes I think it would be better to be dead. | AS5 |
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0.59 | 0.45 | ||||||
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SP30 | I think a lot about death. | AS5 |
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0.33 | 0.41 |
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SP31 | I think about putting hands on myself. | AS5 |
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0.56 | 0.31 | ||||||
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SP32 | I have already thought about how to kill myself. | AS5 |
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0.63 | 0.46 | ||||||
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SP33d | I sleep too much. | AS6 | 0.16 |
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0.35 | ||||||
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SP34 | I have trouble falling asleep and/or wake up constantly. | AS6 | 0.55 | 0.40 |
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SP35 | My sleep was restful and sufficient. | AS6 | 0.76 |
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0.45 | ||||||
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SP36 | I slept well. | AS6 | 0.74 | 0.44 |
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SP37 | I feel a constant hunger or appetite for food. | AS7 |
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-0.06 | 0.08 |
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SP38 | I don’t feel like eating anything. | AS7 | 0.33 | 0.30 |
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SP39 | I have a good appetite. | AS7 | 0.28 |
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0.27 | ||||||
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SP40 | I eat enough and I follow a balanced diet. | AS7 | 0.42 |
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0.43 | ||||||
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SP41d | I hope that I don’t get sick. | CAe |
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0.18 | 0.07 | -0.36 | ||||||
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SP42 | Sometimes I have an oppressive feeling in my stomach. | CA | 0.56 | 0.55 |
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SP43 | I am worried that something terrible will happen. | CA | 0.37 | 0.59 |
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SP44 | Sometimes I start panicking suddenly. | CA | 0.52 | 0.58 |
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SP45 | When I’m worried, I still can keep my control. | CA | 0.56 | 0.46 |
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SP46 | Disturbing thoughts run through my mind. | CA | 0.52 | 0.66 |
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SP47 | I’m calm. | CA | 0.79 | 0.78 |
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SP48 | When I think of my current affairs, I get anxious. | CA | 0.59 | 0.61 |
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SP49 | I feel safe and secure | CA | 0.77 | 0.69 |
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SP50 | I’m worried about something going wrong soon. | CA |
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0.48 | 0.43 |
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SP51 | Sometimes I feel tightness in my chest. | CA | 0.66 | 0.63 |
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SP52 | Sometimes I can’t breathe properly. | CA | 0.53 | 0.59 |
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Eigen value |
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16.95 | 14.27 | 5.65 | 4.10 |
aSELFPASS: Self-administered Psycho-TherApy-SystemS.
bCS: core symptom.
cAS: additional symptom.
ditems that should be excluded or reformulated.
eCA: comorbid anxiety.
This study aimed to investigate the psychometric properties of an item pool of mood-related questions for daily self-assessment, which cover symptoms of depression and comorbid anxiety to make suggestions for symptom-specific interventions. The item pool was developed for the use within the e-mental health app SELFPASS and for future developments. Through a web-based cross-sectional survey design, the instrument emerged as reliable and valid. The psychometric properties are shown in a representative study population of healthy subjects and patients with a diagnosis of depression within the past year. Considering that average PHQ-9 scores of 10.4 indicate a moderate severity of depression [
Both subscales assessing symptoms of depression (SP-D) and anxiety (SP-A) showed high correlations with standardized psychometric instruments (PHQ-9 and GAD-7). This demonstrates a high inherent construct validity. The WHO-5 as an indicator of subjective well-being was negatively correlated with SP-D and SP-A, which in turn reveals good discriminatory construct validity. The negative association of the WHO-5 with depression and anxiety scales has already been shown in other validation studies [
An exploratory factor analysis indicated an underlying 2-factor structure of the item pool that covered a mood-related factor on the one hand and a somatic factor on the other hand in the whole sample. The factor structure of the patient sample even increased this structure, including more items in the second factor, which were related to paying attention to appearance, suicidal thoughts, sleep, appetite, and the fear of becoming sick. There was 1 item that did not fully seem to fit to this interpretation (“I’m worried about something going wrong soon”). A potential explanation might lie in the timing of investigation, as many patients were concerned with becoming infected with SARS-CoV-2 at that time [
A closer investigation of screening instruments for depression shows that validation studies of the PHQ-9 among different populations; for example, in palliative care, these instruments show a comparable 2-fold factor structure of 1 factor focusing on cognitive and affective aspects and another one relating to somatic symptoms [
Enhancing the quality of e-mental health apps, especially with regard to a successful crisis management in case of symptom exacerbation, has already been identified as a necessary step to reduce dropout rates and increase adherence to digital interventions [
The SELFPASS item pool is suitable for daily mood assessment in any kind of web-based intervention or e-mental health app. It is designed for highly frequent repetitive use providing approximately 6 items every day out of the total item pool based on the results of the previous days. Following this purpose, we recommend reformulating items 20, 23, 24, 25, 33, and 41 for appropriate use and future validation studies. For any other use, the items may be dropped as well. Although item 31, 32, 37, and 39 also showed a lack of quality after item analysis, we recommend retaining these items as they assess important depressive symptoms.
Based on our experience with the development and validation of the SELFPASS item pool, some general recommendations may be provided to ensure optimization of the items (
Recommendations for the future use of the SELFPASSa item pool.
Topic | General recommendation | Recommendation for use within the SELFPASS item pool |
Item presentation | Provide a diversion in item presentation to increase adherence. | Ask for 2 main symptoms, 2 additional symptoms and two anxiety symptoms per day. If 1 symptom exceeds a critical score, pursue this symptom with an alternative item. |
Symptom coverage | Cover somatic, cognitive, and emotional aspects of symptomatology. | Provide a random choice of daily items following the rules described above to cover a broad range of symptoms. |
Crisis management | Include crisis management in case of positive answers to suicidal ideation. | Same as the general recommendation. |
Discriminatory power | Evaluate each question with regard to whether it might be able to sufficiently differentiate between healthy subjects and patients. Hence, symptoms including increased appetite and sleep were excluded by the Beck Depression Inventory [ |
Reformulate items 20, 23, 24, 25, 33, and 41. A closer focus on the manifestation of these symptoms in patients with depressive and anxiety symptoms is recommended. |
External validation | Provide validation of single assessments, such as BMI, as matching self-assessments of body weight or sleep parameters delivered by a sensor as control for self-assessed sleep quality. | Same as the general recommendation. |
Variation | Provide positive and negative directions of items. | Same as the general recommendation. |
Targeted use | Be aware of the target population of the questionnaire. | The item pool addresses patients with a pre-existing diagnosis of depression for daily monitoring of their symptoms. |
aSELFPASS: Self-administered Psycho-TherApy-SystemS.
With the introduction of the ICD-11, some diagnostic criteria for depressive episodes will change, which should be considered for further use of the SELFPASS item pool. It is expected that the previous 3 main symptoms of depression will be reduced to 2. Fatigue and lack of drive are then considered additional symptoms [
Moreover, much evidence has been provided that ecological momentary assessments delivered through mobile data have the potential to become behavioral markers for mental health symptoms; for example, movement profiles collected from GPS data and circadian sleep rhythms recorded through phone usage [
Beside the somatic symptoms outlined above and considered in our item pool, it should be noted that depression is often accompanied by several somatic conditions including obesity, cardiovascular diseases, diabetes, pain, or even multimorbidity [
There are some limitations to this study, which should be considered. First, the data were collected during the 2020 COVID-19 pandemic, which might have had an impact on the mental health of the participants [
The SELFPASS item pool is a valid and reliable source for daily self-assessment in e-mental health apps. It follows diagnostic standards of depression and comorbid anxiety symptoms. The item pool is a valuable source of questions for daily mood tracking in future e-mental health apps. Further developments have to focus on optimizing the wording of single items as well as the adaptations that are expected from ICD-11 in the near future.
Self-administered Psycho-TherApy-SystemS (SELFPASS) question catalogue (German original).
Self-administered Psycho-TherApy-SystemS (SELFPASS) question catalogue (English translation).
Results of the item analysis of SELFPASS depression (SP-D) and SELFPASS anxiety (SP-A): Means, SD, Item-scale-correlation, missing values (N, %) and number significant inter-item correlations per item (N). SELFPASS: Self-administered Psycho-TherApy-SystemS.
Beck Depression Inventory
General Anxiety Disorder Scale-7
Hospital Anxiety and Depression Scale
International Classification of Diseases
Kaiser-Meyer-Olkin
Patient Health Questionnaire-9
Self-administered Psycho-TherApy-SystemS
SELFPASS anxiety
SELFPASS depression
State-Trait Anxiety Inventory
World Health Organization
WHO-5-Wellbeing-Scale
This study was part of the Self-administered Psycho-TherApy-SystemS (SELFPASS) project, which was funded by the Federal Ministry of Education and Research (FKZ 13GW0157B). We thank Ali Zafar, MA, MSc, for revising the English translation of the item pool. We further thank Joshua Barniske for revising the manuscript.
None declared.