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Acceptability of digital mental health interventions is a significant predictor of treatment-seeking behavior and engagement. However, acceptability has been conceptualized and operationalized in various ways, which decreases measurement precision and leads to heterogeneous conclusions about
This study aims to examine the psychometric validity and reliability of one of the first and most widely used measures of acceptability, the Attitudes Towards Psychological Online Interventions Questionnaire, among a Black American sample.
Participants (N=254) were recruited from a large southeastern university and the surrounding metropolitan area and completed the self-report measure via a web-based survey. A confirmatory factor analysis using mean and variance adjusted weighted least squares estimation was conducted to examine the validity of the underlying hierarchical 4-factor structure proposed by the original authors of the scale. An alternative, hierarchical 2-factor structure model and bifactor model were examined for comparative fit.
The findings indicated that the bifactor model demonstrated a superior fit (comparative fit index=0.96, Tucker-Lewis index=0.94, standardized root mean squared residual=0.03, and root mean square error of approximation=0.09) compared with both 2- and 4-factor hierarchical structure models.
The findings suggest that, within a Black American sample, there may be greater utility in interpreting the Attitudes Towards Psychological Online Interventions Questionnaire subscales as attitudinal constructs that are distinct from the global
Black communities face persistent barriers to mental health treatment, including cost, accessibility, and stigma [
Studies examining this research-to-practice gap have revealed a complex picture of user acceptance of digital mental health interventions. Although therapist-supported iCBT is generally rated as more acceptable than self-guided programs [
A problem in this budding literature is that the construct of acceptability has been defined in a variety of ways, which may contribute to heterogeneous results regarding consumer attitudes toward iCBT [
A total of 6 self-report measures of consumer acceptability of digital mental health interventions now exist, with evidence of their psychometric properties and factor structure [
Measures of acceptability toward digital mental health interventions.
Study | Title | Abbreviation | Intervention modality |
Clough et al [ |
e-Therapy Attitudes and Process Questionnaire | eTAP | All |
Gómez Penedo et al [ |
Working Alliance Inventory for Guided Internet Interventions | WAI-I | Guided interventions |
Miloff et al [ |
Virtual Therapist Alliance Scale | VTAS | Augmented and virtual reality |
Miragall et al [ |
Working Alliance Inventory Applied to Virtual and Augmented Reality | WAI-VAR | Augmented and virtual reality |
Schröder et al [ |
Attitudes Towards Psychological Online Interventions Questionnaire | APOI | All |
Teles et al [ |
Online Psychoeducational Intervention—Brief Attitudes Scale | OPI-BAS | Psychoeducation |
Further complicating matters are the dearth of acceptability research that is inclusive of ethnically or racially minoritized communities. In 1 meta-analysis, 62 of 64 randomized controlled trials examining the efficacy and acceptability of iCBT did not include (or did not report) racial minorities in their studies [
No research to date has evaluated the reliability or validity of the APOI scale among racially or ethnically minoritized communities, including Black Americans. This is highly problematic because even though Black communities may disproportionately benefit from the advantages afforded by iCBT and related digital mental health interventions, it is unknown whether the APOI demonstrates good psychometric properties in this population.
This study addresses this problem by assessing the psychometric properties of the APOI questionnaire in a sample of Black Americans. Using confirmatory factor analyses, this study examined whether the APOI demonstrates reliability and construct validity within a Black population. In this study, 2 measurement models were examined using 16 ordered categorical (ordinal) response items retained in the exploratory factor analysis of the APOI. The first model presents a 2-factor, hierarchical measurement model (positive and negative subfactors) distinct from the 4-factor hierarchical model proposed by Schröder et al [
Participants were self-identified Black or African American adults (N=254 participants). The participants ranged in age from 18 to 85 (mean 27.11, SD 13.40) years and were predominantly women (172.7/254, 68%), single (167.6/254, 66%), and highly educated (at least 70% had some college education; see
Demographics and clinical characteristics of participants.
Variables | Values | ||
Age (years; n=254), mean (SD) | 27.11 (13.40) | ||
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Male | 82 (32.3) | |
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Female | 172 (67.7) | |
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Heterosexual | 210 (83.3) | |
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Lesbian, gay, and bisexual | 36 (14.3) | |
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Self-identify | 6 (2.4) | |
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High school | 1 (0.4) | |
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Some college or currently in college | 173 (68.1) | |
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Graduate or professional degree | 5 (2.0) | |
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Nondegree student or other | 3 (1.2) | |
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Nonstudenta | 71 (28.0) | |
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Single | 166 (65.9) | |
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Serious dating or committed relationship | 55 (21.8) | |
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Married or civil union | 16 (6.4) | |
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Separated, divorced, or widowed | 15 (6.0) | |
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DASSb—total (n=243) | 29.58 (20.84) | |
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DASS—depression (n=250) | 8.99 (8.49) | |
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DASS—anxiety (n=249) | 8.35 (7.10) | |
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DASS—stress (n=250) | 11.96 (7.88) |
aReflects current noneducational status but does not indicate the highest level of education completed (ie, may include college graduates).
bDASS: Depression Anxiety Stress Scale.
Participants completed a survey developed via the Qualtrics web-based platform as part of an experimental study assessing the impact of treatment rationale on the acceptability of iCBT. Participants were randomly assigned via Qualtrics (1:1 allocation) to read either a treatment rationale or definition of iCBT (see the study by Ellis and Anderson [
All the data were collected on the web and will be made available upon request.
The APOI questionnaire [
The DASS-21 [
The variables used for the factor analysis are listed in
Confirmatory factor analyses were performed using Mplus (version 8.4; Muthén & Muthén) with a sample of Black American adults (N=254) to examine the cross-cultural equivalence of the factor structure derived from the final set of 16 items indicated in the study by Schröder et al [
In model 1, we examined a 2-factor, hierarchical confirmatory measurement model (2 first-order factors loading on 1 second-order global factor). We posited that the set of attitudes endorsed on the APOI would indicate a “positive attitudes towards internet-based treatments” latent factor as well as a “negative attitudes towards internet-based treatments” latent factor. Indicators drawn from the confidence in effectiveness (CON) and anonymity benefits (ABE) subscales comprise positive attitudes toward iCBT and were tested to examine statistically significant loading onto the “positive” latent factor. Indicators derived from the skepticism and perception of risk (SKE) and technologization threat (TET) subscales of the APOI comprise negative attitudes and were tested for statistically significant loading onto the “negative” latent factor. Both “positive” and “negative” first-order factors loaded onto the second-order global factor (termed
In model 2, we attempted a replication of the 4-factor, hierarchical confirmatory measurement model (4 first-order factors loading on 1 second-order global factor) proposed in the study by Schröder et al [
If neither hypothesized model 1 nor model 2 demonstrates adequate model fit, the modification fit indexes provided by the WLSMV estimation will be reviewed, and the comparative fit of a third alternative model (model 3) will be examined.
Attitudes Towards Psychological Online Interventions Questionnaire: subscale and item descriptionsa.
Measure name and scale or item label | Description | |
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Measures positive attitudes concerning the efficacy and credibility of therapist-assisted iCBTc | |
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CON1 | A therapist-assisted iCBT program can help me to recognize the issues that I have to challenge. |
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CON2 | I have the feeling that a therapist-assisted iCBT can help me. |
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CON3 | A therapist-assisted iCBT program can inspire me to better approach my problems. |
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CON4 | I believe that the concept of therapist-assisted iCBT programs makes sense. |
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Measures positive attitudes related to the privacy and confidentiality of using a therapist-assisted iCBT | |
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ABE1 | A therapist-assisted iCBT program is more confidential and discreet than visiting a therapist. |
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ABE2 | By using a therapist-assisted iCBT program, I can reveal my feelings more easily than with a therapist. |
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ABE3 | I would be more likely to tell my friends that I use a therapist-assisted iCBT program than that I visit a therapist. |
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ABE4 | By using a therapist-assisted iCBT program, I do not have to fear that someone will find out that I have psychological problems. |
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Measures negative attitudes concerning the efficacy and security of a therapist-assisted iCBT | |
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SKE1 | Using therapist-assisted iCBT programs, I do not expect long-term effectiveness. |
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SKE2 | Using therapist-assisted iCBT programs, I do not receive professional support. |
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SKE3 | It is difficult to implement the suggestions of a therapist-assisted iCBT effectively in everyday life. |
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SKE4 | Therapist-assisted iCBT programs could increase isolation and loneliness. |
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Measures negative attitudes related to the independent and remote nature of therapist-assisted iCBT | |
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TET1 | In crisis situations, a therapist can help me better than a therapist-assisted iCBT program. |
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TET2 | I learn skills to better manage my everyday life from a therapist rather than from a therapist-assisted iCBT program. |
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TET3 | I am more likely to stay motivated with a therapist than when using a therapist-assisted iCBT program. |
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TET4 | I do not understand therapeutic concepts as well with a therapist-assisted iCBT. |
aResponse scale (1=totally disagree to 5=totally agree).
bHigher scores represent greater acceptability.
ciCBT: internet-based cognitive behavioral therapy.
dHigher scores indicate lower acceptability.
Bivariate correlations between the 16 Attitudes Towards Psychological Online Interventions items.
Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
CONa1 | 1 | —b | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
CON2 | 0.74 | 1 | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
CON3 | 0.76 | 0.79 | 1 | — | — | — | — | — | — | — | — | — | — | — | — | — |
CON4 | 0.71 | 0.65 | 0.75 | 1 | — | — | — | — | — | — | — | — | — | — | — | — |
ABEc1 | 0.38 | 0.46 | 0.47 | 0.41 | 1 | — | — | — | — | — | — | — | — | — | — | — |
ABE2 | 0.37 | 0.42 | 0.43 | 0.44 | 0.72 | 1 | — | — | — | — | — | — | — | — | — | — |
ABE3 | 0.20 | 0.34 | 0.26 | 0.25 | 0.53 | 0.56 | 1 | — | — | — | — | — | — | — | — | — |
ABE4 | 0.38 | 0.41 | 0.40 | 0.45 | 0.61 | 0.58 | 0.66 | 1 | — | — | — | — | — | — | — | — |
SKEd1 | −0.05 | −0.10 | −0.07 | 0.01 | −0.27 | −0.31 | −0.15 | −0.17 | 1 | — | — | — | — | — | — | — |
SKE2 | −0.01 | −0.10 | −0.02 | 0.02 | −0.12 | −0.30 | −0.19 | −0.18 | 0.63 | 1 | — | — | — | — | — | — |
SKE3 | −0.15 | −0.21 | −0.15 | 0.03 | −0.19 | −0.26 | −0.22 | −0.15 | 0.71 | 0.72 | 1 | — | — | — | — | — |
SKE4 | −0.09 | −0.18 | −0.07 | 0.04 | −0.22 | −0.28 | −0.28 | −0.25 | 0.63 | 0.69 | 0.75 | 1 | — | — | — | — |
TETe1 | −0.44 | −0.42 | −0.50 | 0.58 | −0.42 | −0.41 | −0.28 | −0.33 | 0.24 | 0.21 | 0.24 | 0.22 | 1 | — | — | — |
TET2 | −0.36 | −0.39 | −0.42 | 0.33 | −0.43 | −0.45 | −0.39 | −0.43 | 0.41 | 0.34 | 0.41 | 0.45 | 0.63 | 1 | — | — |
TET3 | −0.39 | −0.34 | −0.41 | 0.36 | −0.47 | −0.38 | −0.34 | −0.41 | 0.38 | 0.25 | 0.30 | 0.38 | 0.66 | 0.72 | 1 | — |
TET4 | −0.22 | −0.22 | −0.29 | 0.18 | −0.45 | −0.50 | −0.33 | −0.40 | 0.54 | 0.41 | 0.48 | 0.51 | 0.39 | 0.68 | 0.62 | 1 |
aCON: confidence in effectiveness.
bNot applicable.
cABE: anonymity benefits.
dSKE: skepticism and perception of risk.
eTET: technologization threat.
Descriptive statistics of the 16 Attitudes Towards Psychological Online Interventions items.
CONa1 | CON2 | CON3 | CON4 | ABEb1 | ABE2 | ABE3 | ABE4 | SKEc1 | SKE2 | SKE3 | SKE4 | TETd1 | TET2 | TET3 | TET4 | |
Values, mean (SD) | 3.6 (1.0) | 3.4 (1.0) | 3.6 (1.0) | 3.7 (1.0) | 3.3 (1.0) | 3.2 (0.09) | 3.0 (1.0) | 3.2 (1.1) | 3.1 (1.2) | 3.3 (1.1) | 3.1 (1.1) | 3.2 (1.1) | 2.5 (1.0) | 2.7 (1.0) | 2.6 (1.0) | 2.9 (1.1) |
Skew | −0.41 | −0.15 | −0.51 | −0.50 | −0.03 | 0.04 | 0.01 | −0.08 | −0.09 | −0.19 | −0.07 | −0.13 | 0.26 | 0.03 | 0.18 | 0.11 |
Kurt | 0.07 | 0.24 | 0.34 | 0.16 | −0.02 | 0.09 | −0.12 | −0.14 | −0.50 | −0.34 | −0.18 | −0.33 | 0.30 | 0.16 | 0.07 | −0.06 |
aCON: confidence in effectiveness.
bABE: anonymity benefits.
cSKE: skepticism and perception of risk.
dTET: technologization threat.
Higher-order, 2-factor model depicting hierarchical relationship among indicators of 2 latent factors: positive and negative attitudes toward treatment loading on a global acceptability factor. ABE: anonymity benefits; CON: confidence in effectiveness; SKE: skepticism and perception of risk; TET: technologization threat. Note: threshold structure not shown.
Higher-order, 4-factor model depicting hierarchical relationship among indicators of 4 latent factors: confidence, anonymity benefits, skepticism, and technologization threat loading on a global acceptability factor. ABE: anonymity benefits; CON: confidence in effectiveness; SKE: skepticism and perception of risk; TET: technologization threat. Note: threshold structure not shown.
This study was conducted in compliance with The Georgia State University institutional review board protocol #H18341 and preregistered with the Open Science Framework [
A total of 268 participants were enrolled in the study and completed the survey. Of these, 14 participants were excluded because they did not complete the APOI questionnaire, thus yielding a sample of 254 participants. Participant ratings suggested mild symptoms of anxiety (mean 8.35, SD 7.10) and stress (mean 11.96, SD 7.88) and normal levels of depressive symptoms (mean 9.00, SD 8.49) according to standard thresholds of the DASS-21 [
The 2 proposed models explored the construct of acceptability as a hierarchical, 2-factor model comprising “positive attitudes” and “negative attitudes” toward therapist-assisted iCBT, or as a hierarchical, 4-factor model comprising 4 distinct domains of attitudes toward therapist-assisted iCBT (confidence in effectiveness, anonymity benefits, skepticism and perception of risk, and technologization threat). See
Neither model had a perfect absolute model fit according to the chi-square test (model 1: χ2103=1579.,
As models 1, 2, and 3 were nested, comparisons were conducted to verify the statistically improved model fit by examining the change in the chi-square statistic. As the scaled chi-square value for WLSMV cannot be used for traditional chi-square difference testing, the DIFFTEST option in Mplus (version 8.4) was used [
When examining the standardized factor loadings of the bifactor model, the absolute value of loadings for the categorical indicators ranged from 0.52 to 0.87 on their original 4 factors. Consistent with the findings of Schröder et al [
The relationship between the 16 ordinal indicators and the global acceptability factor was more complex, as the absolute value of the loadings ranged from 0.004 to 0.70. Although the factor loadings for both CON and ABE indicators were positively correlated with the global acceptability factor, only CON indicators demonstrated adequate strength (0.35-0.70), whereas loadings for ABE items ranged from 0.02 to 0.28, suggesting a relatively weak relationship with the global factor. One item of the ABE subscale (ABE3) “I would be more likely to tell my friends that I use a therapist-assisted iCBT program than that I visit a therapist” did not load significantly on the global factor (λ=0.016;
Overall, the results from the bifactor model structure of the APOI provide evidence that the 4 factors proposed by Schröder et al [
Goodness-of-fit indexes of models tested in confirmatory factor analysis.
Model name | Chi-square ( |
CFIa | TLIb | SRMRc | RMSEAd (95% CI) | Comparison | |||
ΔChi-square ( |
Note | ||||||||
2 factor | 1579.8 (103) | <.001 | 0.65 | 0.59 | 0.12 | 0.24 (0.23-0.25) | —e | — | — |
4 factorf | 595.3 (101) | <.001 | 0.88 | 0.86 | 0.08 | 0.14 (0.13-0.15) | 984.45 (2) | <.001 | Versus model 1 |
Bifactorf | 248.7 (82) | <.001 | 0.96 | 0.94 | 0.03 | 0.09 (0.08-0.10) | 346.57 (19) | <.001 | Versus model 2 |
aCFI: comparative fit index.
bTLI: Tucker-Lewis index.
cSRMR: standardized root mean squared residual.
dRMSEA: root mean square error of approximation.
eNot available.
fDIFFTEST command used for weighted least squares means and variance adjusted estimators to test differences in model fit.
Bifactor model depicting orthogonal relationship among indicators of 4 latent factors: confidence, anonymity benefits, skepticism, and technologization threat loading alongside a global acceptability factor. ABE: anonymity benefits; CON: confidence in effectiveness; SKE: skepticism and perception of risk; TET: technologization threat. Note: threshold structure not shown.
Model 3 (bifactor) standardized factor loadings with SEs.
Relation or variable | Estimate (SE) | |||||
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CON1 | 0.66 (0.06) | <.001 | ||
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CON2 | 0.83 (0.04) | <.001 | ||
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CON3 | 0.72 (0.06) | <.001 | ||
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CON4 | 0.52 (0.07) | <.001 | ||
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ABE1 | 0.77 (0.03) | <.001 | ||
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ABE2 | 0.83 (0.03) | <.001 | ||
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ABE3 | 0.75 (0.03) | <.001 | ||
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ABE4 | 0.75 (0.03) | <.001 | ||
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SKE1 | 0.79 (0.02) | <.001 | ||
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SKE2 | 0.75 (0.03) | <.001 | ||
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SKE3 | 0.87 (0.02) | <.001 | ||
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SKE4 | 0.81 (0.02) | <.001 | ||
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TET1 | 0.54 (0.06) | <.001 | ||
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TET2 | 0.81 (0.03) | <.001 | ||
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TET3 | 0.72 (0.04) | <.001 | ||
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TET4 | 0.86 (0.03) | <.001 | ||
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CON1 | 0.51 (0.07) | <.001 | ||
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CON2 | 0.35 (0.08) | <.001 | ||
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CON3 | 0.54 (0.08) | <.001 | ||
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CON4 | 0.70 (0.07) | <.001 | ||
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ABE1 | 0.28 (0.07) | <.001 | ||
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ABE2 | 0.18 (0.08) | .01 | ||
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ABE3 | 0.02 (0.08) | .83 | ||
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ABE4 | 0.22 (0.07) | .001 | ||
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SKE1 | 0.16 (0.06) | .01 | ||
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SKE2 | 0.20 (0.06) | .001 | ||
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SKE3 | 0.15 (0.06) | .02 | ||
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SKE4 | 0.15 (0.06) | .008 | ||
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TET1 | −0.64 (0.05) | <.001 | ||
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TET2 | −0.31 (0.07) | <.001 | ||
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TET3 | −0.39 (0.07) | <.001 | ||
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TET4 | <.01 (0.08) | .95 | ||
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Anonymity benefits | 0.54 (0.06) | <.001 | ||
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Skepticism and perception of risks | −0.30 (0.05) | <.001 | ||
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Technologization threat | −0.38 (0.06) | <.001 | ||
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Acceptability | 0.00 (—a) | — | ||
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Skepticism and perception of risks | −0.41 (0.06) | <.001 | ||
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Technologization threat | −0.61 (0.05) | <.001 | ||
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Acceptability | 0.00 (—) | — | ||
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Technologization threat | 0.70 (0.05) | <.001 | ||
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Acceptability | 0.00 (—) | — | ||
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Acceptability | 0.00 (—) | — |
aNot available.
Bifactor model depicting orthogonal relationship among indicators of 4 latent factors: confidence, anonymity benefits, skepticism, and technologization threat loading alongside a global acceptability factor. Standardized parameter estimates shown. ABE: anonymity benefits; CON: confidence in effectiveness; SKE: skepticism and perception of risk; TET: technologization threat. Note: threshold structure not shown.
The APOI demonstrated excellent internal consistency for the total scale (Cronbach α=.89) and retained good-to-excellent reliability across subscales (Cronbach α=.84 for ABE, .85 for TET, .87 for SKE, and .90 for CON). Across subscales, the corrected item-total correlations ranged from 0.59 to 0.83, with a mean adjusted correlation of 0.71 indicating good item discrimination within subscales. The corrected item‐total correlations for the APOI total scale ranged from 0.45 to 0.68, with a mean adjusted correlation of 0.55, indicating good item discrimination within the total scale.
This study evaluated the psychometric properties of the APOI questionnaire [
However, the original hierarchical, 4-factor model proposed by Schröder et al [
The heterogeneity of findings regarding model fit may be explained by the nature of the coefficients of the factor loadings and overall structure. Modeling both positive and negatively valenced factors onto a unitary, higher-order construct (ie, acceptability) can prove difficult, especially when variance exists among indicators of lower-order constructs. The factor loadings between the 16 indicators and global acceptability factor varied substantially. Several indicators loading on the ABE, SKE, and TET subscales exhibited relatively weak or null relations with acceptability or were in the opposite direction than expected. Items loaded on the ABE subscale, in particular, may indicate both facilitators and barriers to engagement with digital interventions, given the user’s conflicting perceptions of digital privacy and confidentiality [
Scholars have called for better conceptualizations of acceptability [
Furthermore, these data suggest that within a Black American population, there is greater utility in interpreting the APOI subscales as attitudinal constructs distinct from a global acceptability factor. However, given that the higher-order model is nested within the bifactor model [
This is the first study to investigate the psychometric properties of the APOI questionnaire among a racially minoritized population. This study is the first to provide evidence for the cross-cultural equivalence of APOI among Black Americans. This is a notable contribution to the literature, as the vast majority of randomized controlled trials examining the efficacy and acceptability of iCBT do not include (or do not report) racial minorities in their studies [
Despite the strengths of this study, there are some limitations that warrant attention. The study sample consisted of participants with minimal symptoms of depression, anxiety, or stress. This was distinct from the participants who reported moderate levels of depression in the study by Schröder et al [
Future research should modify the APOI to apply it to other digital mental health interventions (eg, virtual reality exposure therapies and massively open web-based interventions) and translate the measure into additional languages (eg, Spanish) to further examine cross-intervention and cross-cultural equivalency. Although the APOI demonstrated good internal consistency reliability within the present sample, test-retest reliability was not examined. Indeed, with the exception of the study by Clough et al [
The APOI questionnaire is a valid and reliable measure of attitudes toward therapist-assisted iCBT among Black Americans. However, some of the indicators were only weakly associated with the global factor of acceptability, and a bifactor model demonstrated better goodness-of-fit than the hierarchical, 4-factor structure proposed by the original authors. This provides strong evidence that the APOI demonstrates multidimensionality and that there is greater utility in interpreting APOI subscales as attitudinal constructs distinct from a global acceptability factor. Indeed, attitudes of acceptability comprise both positive and negative attitudes toward the uptake of digital mental health interventions and must be evaluated in tandem to effectively understand the nuanced attitudes consumers may hold toward these interventions. This is the first study to examine the psychometric properties of any measure of consumer attitudes toward digital mental health interventions among Black participants. Demonstrating the reliability, validity, and cultural equivalency of existing measures of attitudes toward these interventions is needed to improve our understanding of the drivers of and barriers to using digital treatments among minoritized communities. For the full potential of digital mental health interventions to improve equitable access to treatment to be realized, more adequate representation of minoritized communities in research on these interventions must be achieved.
Materials depict fit indices for all examined confirmatory factor analyses. Mplus (version 8.4) syntax is provided for all analyses.
Attitudes Towards Psychological Online Interventions
comparative fit index
Depression Anxiety Stress Scale-21 items
internet-based cognitive behavioral therapy
root mean square error of approximation
standardized root mean squared residual
Tucker-Lewis index
weighted least squares means and variance adjusted
The authors would like to thank Lee Branum-Martin, PhD, for consultation on structural equation modeling and confirmatory factor analyses. The data in this study are a secondary analysis by Ellis and Anderson [
DME devised the project, main conceptual ideas, and protocol outline and conducted all the statistical analyses; designed the figures and tables; and wrote the manuscript. Both DME and PLA contributed to the final version of this manuscript. PLA supervised the project.
None declared.