Positive Affect Treatment for Depression, Anxiety, and Low Positive Affect: A Randomized Clinical Trial

Alicia E Meuret; David Rosenfield; Emily Wang; Christina M Hough; Thomas Ritz; Michelle G Craske

doi:10.1001/jamanetworkopen.2026.7403

. 2026 Apr 24;9(4):e267403. doi: 10.1001/jamanetworkopen.2026.7403

Positive Affect Treatment for Depression, Anxiety, and Low Positive Affect

A Randomized Clinical Trial

Alicia E Meuret ^1,^✉, David Rosenfield ¹, Emily Wang ¹, Christina M Hough ², Thomas Ritz ¹, Michelle G Craske ^2,^3,^✉

¹Department of Psychology, Southern Methodist University, Dallas, Texas

²Department of Psychology, University of California, Los Angeles

³Psychiatry and Biobehavioral Sciences, University of California, Los Angeles

Accepted for Publication: February 10, 2026.

Published: April 24, 2026. doi:10.1001/jamanetworkopen.2026.7403

Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License, which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies. © 2026 Meuret AE et al. JAMA Network Open.

^✉

Corresponding Authors: Alicia E. Meuret, PhD, Department of Psychology, Southern Methodist University, PO Box 750442, Dallas, TX 75275 (ameuret@smu.edu); Michelle G. Craske, PhD, Department of Psychology, University of California, Los Angeles, 405 Hilgard Ave, Los Angeles, CA 90077 (mcraske@mednet.ucla.edu).

Author Contributions: Dr Rosenfield had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Meuret, Rosenfield, Ritz, Craske.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical review of the manuscript for important intellectual content: Meuret, Rosenfield, Hough, Ritz, Craske.

Statistical analysis: Meuret, Rosenfield, Ritz.

Obtained funding: Meuret, Rosenfield, Ritz, Craske.

Administrative, technical, or material support: Meuret, Wang, Ritz.

Supervision: Meuret, Ritz, Craske.

Conflict of Interest Disclosures: Drs Meuret, Rosenfield, Ritz, and Craske reported receiving grant support from the National Institutes of Health during the conduct of the study. No other disclosures were reported.

Funding/Support: This study was supported by grant R61MH115138 from the National Institute of Mental Health awarded to Drs Meuret, Ritz, and Craske.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 3.

Additional Contributions: The authors are grateful for the excellent work of the independent assessors, therapists, and research coordinators, especially Savannah Springfield, BA, Southern Methodist University; Shawn Wang, BA, University of California, Los Angeles; Gabe Fiol, BA, University of California, Los Angeles; and Claire Anderson, BA, Southern Methodist University. The authors also express their gratitude to their study consultants, Diego Pizzagalli, PhD, University of California, Irving; and Steven Hollon, PhD, Vanderbilt University; as well as to the members of the Data Safety Monitoring Board. None of these contributors were compensated outside of their employment.

^✉

Corresponding author.

PMCID: PMC13109798 PMID: 42030048

This randomized clinical trial assesses whether positive affect treatment engages reward systems more than negative affect treatment and whether alterations in reward and threat processing differentially mediate clinical outcomes among adults with severely low positive affect and moderate to severe depression or anxiety that is functionally impairing.

Key Points

Question

Does positive affect treatment (PAT) engage reward mechanisms more than negative affect treatment (NAT), and do intervention-specific mechanisms mediate clinical status in adults with depression, anxiety, and low positive affect?

Findings

In this randomized clinical trial of 98 adults, PAT produced greater improvements in clinical status than NAT. Of 7 self-reported reward and threat measures, 6 mediated clinical outcomes, whereas behavioral and physiological measures did not.

Meaning

Modulation of reward and threat processing, indexed by self-reported measures, may represent a shared mechanism of therapeutic improvement, with a reward-focused intervention yielding superior clinical status.

Abstract

Importance

Targeting impaired reward processing that underlies anhedonia and diminished positive affect is essential for reducing key risk factors in depression and anxiety, including suicidality and relapse. However, mechanistic research in this area remains limited.

Objectives

To assess whether a novel psychosocial intervention engages reward systems more than a mechanistically distinct comparison therapy, and whether alterations in reward and threat processing differentially mediate clinical outcomes.

Design, Setting, and Participants

This was an assessor-blinded, parallel-group, multisite, 2-arm randomized clinical superiority trial. Study recruitment was from December 2021 to January 2024, with final assessment in July 2024. Participants were recruited from academic outpatient treatment centers in Los Angeles, California, and Dallas, Texas, and included treatment-seeking adults with severely low positive affect and moderate to severe depression or anxiety that was functionally impairing. Analyses were conducted with intent-to-treat principles.

Intervention

Participants underwent 15 weekly individual therapy sessions of positive affect treatment (PAT) or negative affect treatment (NAT).

Main Outcomes and Measures

Clinical status was self-reported positive affect (using the Positive and Negative Affect Schedule–Positive subscale), interviewer-rated anhedonia (embedded within the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition), and self-reported depression and anxiety (using the Depression, Anxiety, and Stress Scale). Target measures were 14 self-reported, behavioral, and physiological measures of reward anticipation-motivation, response to reward attainment, reward learning, and threat. Analyses included mixed-effects multilevel models.

Results

In total, 98 participants (mean [SD] age, 32.8 [12.2] years; 65 [66.3%] female) were randomized to receive PAT (n = 51) or NAT (n = 47). Multivariate multilevel model analyses of the 3 clinical status variables as a multivariate outcome showed that clinical status improved more with PAT than NAT (b = −0.06 [95% CI, −0.11 to −0.01]; t₃₀₃₉ = 2.43; P = .02; d = 0.27) and that PAT had better (higher) scores on clinical status than NAT at the 1-month follow-up (b = −0.21 [95% CI, −0.41 to −0.02]; t₃₀₃₉ = 2.11; P = .04; d = 0.21). Improvements in reward anticipation-motivation (b = 0.02 [95% CI, 0.01-0.03]; t₁₃₀₇ = 4.36; P < .001; d = 0.40) and reward attainment (b = 0.04 [95% CI, 0.01-0.06]; t₁₄₀₅ = 3.16; P = .002; d = 0.18) targets were comparable for PAT and NAT. Of 7 reward and threat self-reported target measures, 6 mediated improvements in clinical status, but none of the behavioral or physiological measures did. There was limited evidence for moderated mediation.

Conclusions and Relevance

In this randomized clinical trial of 98 adults with severely low positive affect, depression, and anxiety, findings suggested that modulation of reward and threat processes was a central mechanism of therapeutic improvement, with a reward-focused intervention producing superior clinical outcomes.

Trial Registration

ClinicalTrials.gov Identifier: NCT05203861

Introduction

Anhedonia, or diminished positive affect, encompasses both motivational and affective deficits, including a reduced capacity to experience interest and pleasure in response to typically rewarding stimuli. Anhedonia has been recognized as a critical feature of major depressive disorder,^1,2,3,4,5 and is associated with chronicity and severity,⁶ diminished recovery or remission,^7,8 and greater functional impairment.^9,10 It is increasingly recognized as a transdiagnostic feature, occurring in the context of anxiety disorders,^3,11,12,13 posttraumatic stress disorder,^14,15 substance use disorders,¹⁶ schizophrenia,¹⁷ and neurological disorders.¹⁸ Anhedonia shares a robust association with suicidal behaviors.^19,20,21 Finally, low positive affect and anhedonia are impediments to treatment response.²² Consequently, substantial research efforts are aimed at elucidating its underlying mechanisms to inform treatment approaches.²¹

A leading neuroscience framework posits 3 distinct but interrelated phases that drive reward processing: wanting or anticipation of reward, liking or consumption of reward, and reward-based learning involving prediction error and feedback integration.^23,24 These reward processes are centrally regulated by the mesocorticolimbic circuit, including the anterior cingulate, orbitofrontal, and prefrontal cortices, ventral tegmental area, ventral striatum (nucleus accumbens), dorsal striatum (caudate and putamen), insula, amygdala, and thalamus.²⁵ Peripheral physiological measures can reflect these processes: heart rate acceleration has been shown to accompany pursuit and attainment of reward,^26,27 mediated by sympathetic excitation.²⁸ Disruptions in any of the reward phases can substantially impair a person’s ability to pursue, respond to, or learn from rewards and express or experience positive emotions. Such deficits are reflected in reduced self-reported, behavioral, and physiological activation in reward system–related measures and tasks^29,30,31,32 and are linked to depression and anxiety,^21,33,34,35 anhedonia, and low positive affect.^{36,37,38,39,40,41,42,43} Conventional pharmacological and psychological interventions have focused primarily on negative affect and demonstrate limited efficacy in achieving remission of anhedonia,^{7,44,45,46,47} thereby contributing to elevated risk of relapse.⁴⁸ These interventions fail to meet patients’ expressed need for restored positive affect,⁴⁹ well-being,⁵⁰ empowerment, and connectedness, dimensions essential for lasting recovery.⁵¹

In line with the US National Institute of Mental Health (NIMH) Research Domain Criteria initiative, we devised a novel psychosocial intervention, positive affect treatment (PAT),⁵² to explicitly target the 3 core phases of reward processing (reward anticipation-motivation, reward attainment, and reward learning). In 2 randomized clinical trials, PAT produced greater improvements in clinical status (composite of low positive affect, depression, anxiety,⁵² and interviewer-rated anhedonia⁵³) and target engagement⁵³ (changes in aforementioned reward processes) than a mechanistically distinct comparison intervention, negative affect treatment (NAT). The current study aimed to replicate the efficacy and target engagement of PAT and to test mediation. We hypothesized that reward measures would be significant mediators of clinical improvement in PAT, and, conversely, that threat measures would be significant mediators in NAT.

Methods

This multisite, assessor-blinded, parallel 2-arm, stratified by medication status, randomized (1:1) clinical superiority trial was designed in accordance with the NIMH experimental therapeutics approach. The study was reviewed and approved by the institutional review boards at participating sites, and all participants provided written informed consent. The trial protocol appears in Supplement 1. The trial was conducted and reported in accordance with the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline.

Participants

Participants were adults who met the following inclusion criteria: 18 to 65 years of age, English-speaking, low positive affect as indicated by a score of 24 or lower (corresponding to <18th percentile [1 SD] below a nonclinical reference sample)⁵⁴ on the Positive Affect subscale of the Positive and Negative Affect Schedule (PANAS-P),⁵⁵ moderate to severe depression or anxiety on the Depression, Anxiety, and Stress Scales (DASS),^56,57 clinically significant functional impairment,⁵⁸ willingness to refrain from initiating pharmacological or psychosocial interventions until 1-month follow-up (1MFU), and willingness to be video recorded (exclusion criteria and randomization strategy in Supplement 1). Participants self-reported their demographic characteristics, including race and ethnicity, which were collected in this study to more fully characterize the cohort. Selection categories included Alaska Native, Native American, or Indigenous; Asian; Black or African American; Hispanic or Latino non-White; Hispanic or Latino White; multiracial; Native Hawaiian or Other Pacific Islander; and White. This breakdown followed the National Institute of Mental Health guideline. An open-format self-description (“Other”) was also available.

Interventions

Interventions consisted of 15 weekly, 1-on-1 manualized treatment sessions delivered via telehealth: module 1 (sessions 2-7) emphasized behavioral change, module 2 (sessions 8-10) introduced cognitive strategies, module 3 (sessions 11-14) centered on either intentional cultivation of positive emotional states (PAT) or reducing arousal (NAT). Session 1 covered psychoeducation, and session 15 focused on relapse prevention. Therapists were randomly assigned to clients and intervention conditions, and both therapists and clients were blinded to intervention assignments until the first session.

PAT was designed to enhance key components of reward processing in individuals with low positive mood.^59,60 Reward anticipation-motivation is addressed by guiding individuals to plan pleasurable activities and envision positive future experiences. Reward consumption is facilitated through active engagement in enjoyable activities; vivid, present-focused mental rehearsal (savoring); cognitive strategies that redirect attention toward positive aspects of experience; and structured practices to cultivate positive emotions, such as loving-kindness and generosity. Reward learning is promoted by strengthening the association between positive behaviors and enhanced mood and by encouraging self-attribution for positive outcomes.

NAT was designed as a mechanistically distinct comparison condition to improve negative affect and threat responses. NAT incorporates several elements drawn from established evidence-based therapies for anxiety and depression, including exposure to feared or avoided situations, cognitive restructuring of distorted appraisals (eg, probability overestimation and self-blame), and respiratory training to reduce hypocapnia and physiological arousal, while intentionally excluding other components (eg, pleasant events scheduling) to maintain focus on targeting threat reactivity.

Assessments

Self-reported clinical status measures were collected before treatment, every therapy session, after treatment, and at the 1MFU. Measures of reward and threat targets and interviewer-rated anhedonia (a measure of clinical status) were assessed before treatment, at sessions 5 and 10, after treatment, and at the 1MFU. All measures were preregistered on ClinicalTrials.gov (eTable 1 in Supplement 2).

Clinical Status Measures

Positive affect during the past week was measured with the 10-item PANAS-P.⁵⁵ Anxious and depressive symptoms were assessed using the total score of the 21-item DASS (DASS-21).⁵⁶ Interviewers blinded to group assignments rated anhedonia (Interviewer-Rated Anhedonia) on interest, pleasure, and motivation during the past month.

Target Measures of Reward and Threat Processes

Consistent with the Research Domain Criteria initiative,⁶¹ measures were selected across multiple modalities. These modalities included self-reported, behavioral, and physiological measures.

Reward Anticipation-Motivation

We assessed reward anticipation-motivation using 4 measures. The 2 self-report measures were the Behavioral Activation Scale-Reward Drive (BAS-RD) subscale of the Behavioral Inhibition and Activation Scale (BIS/BAS)⁶² and the reward anticipation-motivation subscale (DARS-A-M) of the Dimensional Anhedonia Rating Scale (DARS).⁶³ The behavioral measure was the ratio of hard-effort choices to total choices of Effort Expenditure for Rewards Task (EEfRT).³² The physiological measure was the interbeat-interval shortening (heart rate acceleration) in the initial 30 seconds of reward trials of the Monetary Incentive Task.⁶⁴

Response to Reward Attainment

We assessed the response to reward attainment using 4 measures. The 2 self-report measures were the consummatory subscale (TEPS-C) of the Temporal Experience of Pleasure Scale (TEPS)⁶⁵ and the consummatory subscale of DARS (DARS-C).⁶³ The behavioral measure was the response time for bias in engagement for happy faces relative to neutral faces using the modified dot-probe task (DOTPROBE Happy-Engaged).⁶⁶ The physiological measure was the maximum interbeat-interval shortening (heart rate acceleration) to positive images relative to neutral images of the International Affective Picture System (IAPS).²⁷

Reward Learning

We used 1 behavioral task to assess reward learning. We investigated the response bias or preference for the stimulus paired with more frequent rewards in the Probabilistic Reward Task.³¹

Threat Processes

We assessed threat processes using 5 measures. The 3 self-report measures were the Anxiety Sensitivity Index (ASI),⁶⁷ the Probability and Cost Questionnaire (PCQ),⁶⁸ comprising probability and cost for feared events, and the Behavioral Inhibition Scale (BIS).⁶² The behavioral measure was the bias toward engagement with sad relative to neutral faces using DOTPROBE Sad-Engaged.⁶⁶ The physiological measure was the interbeat-interval shortening during a 3-minute mental arithmetic task⁶⁹ with fictive performance norms and distracting auditory stimuli relative to the last 3 minutes of an initial baseline.

Statistical Analysis

We used intent-to-treat, mixed-effects multilevel models (MLMs). Multivariate multilevel models (MMLMs) were used when multiple measures were analyzed (Supplement 1 and eMethods in Supplement 2 show power analysis and analytic details).

We used MMLMs to determine whether our multivariate clinical status outcome improved more in PAT than in NAT and whether the threat targets improved more in NAT than PAT. We hypothesized that reward and threat targets would mediate clinical improvement over time. Each target was tested separately using single mediator analyses and then tested in multimediator analyses, with target measures modeled as moderated mediators of change in clinical status. These analyses tested moderated mediation because it was expected that the targets would be differentially engaged in PAT vs NAT (ie, we expected that the “a” path in the longitudinal mediation analysis, which is the path from the time variable to the mediator, would be moderated by treatment group). The “b” paths in the mediation analyses were cross-lag relations between each target measure at each session (t) and the multivariate clinical status at the next session (t + 1), controlling for clinical status at the previous session (t). In all mediation analyses, the time-varying mediators (the individual measures of the target) were disaggregated into between-participant and within-participant components. Since individual differences among participants confounded the relations among the between-participant components of the mediators and clinical status, we examined only the mediation by the within-participant components of the individual mediators and showed them only in the figures. When the “a” path was indeed moderated by treatment group (ie, the “a” paths differed between treatment groups), we tested for mediation within each treatment group separately, and we tested whether the index of moderated mediation was significant. We used the distribution of products test (RMediation) to test the significance of each mediated pathway.

All analyses were conducted using SPSS software, version 29.0 (IBM Corporation, 2022). A 2-sided value of P < .05 was considered statistically significant.

Results

A total of 98 participants (mean [SD] age, 32.8 [12.2] years; 65 [66.3%] female and 33 [33.7%] male) were randomized to receive either PAT (n = 51) or NAT (n = 47) (Figure 1 and eAppendix 1 in Supplement 2). Race and ethnicity were self-reported as Alaska Native, Native American, or Indigenous (5 [5.1%]); Asian (24 [24.5%]); Black or African American (8 [8.2%]); Hispanic or Latino non-White (12 [12.2%]); Hispanic or Latino White (16 [16.3%]); multiracial (6 [6.1%]); White (60 [61.2%]); and other (10 [10.2%]). Additional baseline demographics and clinical characteristics are provided in the Table. Credibility, fidelity, and alliance are described in eAppendix 2 in Supplement 2. No serious adverse events were reported.

Table. Participant Demographic and Clinical Characteristics, Overall and by Treatment Group.

Characteristic	Participants, No. (%)
Characteristic	Total (N = 98)	PAT (n = 51)	NAT (n = 47)
Age, mean (SD), y	32.8 (12.2)	34.0 (14.0)	31.5 (9.8)
Sex^a
Female	65 (66.3)	31 (60.8)	34 (72.3)
Male	33 (33.7)	20 (39.2)	13 (27.7)
Race and ethnicity
Alaska Native, Native American, or Indigenous	5 (5.1)	3 (5.9)	2 (4.3)
Asian	24 (24.5)	12 (23.5)	12 (25.5)
Black or African American	8 (8.2)	4 (7.8)	4 (8.5)
Hispanic or Latino (non-White)	12 (12.2)	7 (13.7)	5 (10.6)
Hispanic or Latino (White)	16 (16.3)	8 (15.7)	8 (17.0)
Multiracial	6 (6.1)	3 (5.9)	3 (6.4)
White	60 (61.2)	30 (58.8)	30 (63.8)
Other^b	10 (10.2)	6 (11.8)	4 (8.5)
Marital status
Married	19 (19.4)	5 (9.8)	14 (29.8)
Single, in a relationship	33 (33.7)	19 (37.3)	14 (29.8)
Single, not in a relationship	34 (34.7)	19 (37.3)	15 (31.9)
Separated	1 (1.0)	1 (2.0)	0
Divorced	10 (10.2)	6 (11.8)	4 (8.5)
Widowed	1 (1.0)	1 (2.0)	0 (0.0)
Employed full- or part-time	75 (76.5)	34 (66.7)	41 (87.2)
Education level
High school diploma or GED	7 (7.1)	4 (7.8)	3 (6.4)
Some college or a 2-y degree	30 (30.6)	15 (29.4)	15 (31.9)
4-y College degree	42 (42.9)	23 (45.1)	19 (40.4)
Graduate degree	19 (19.4)	9 (17.6)	10(21.3)
Income, $
<10 000	7 (7.1)	3 (5.9)	4 (8.5)
10 001-30 000	8 (8.2)	5 (9.8)	3 (6.4)
30 001-50 000	14 (14.3)	6 (11.8)	8 (17.0)
50 001-70 000	19 (19.4)	12 (23.5)	7 (14.9)
>70 000	50 (51.0)	25 (49.0)	25 (53.2)
Current psychotropic medication	25 (25.5)	13 (25.5)	12 (25.5)
Any diagnosis	76 (77.6)	42 (82.4)	34 (72.3)
Depressive diagnosis	46 (46.9)	27 (52.9)	19 (40.4)
Major depressive disorder	40 (40.8)	23 (45.1)	17 (36.2)
Persistent depressive disorder	25 (25.5)	18 (35.3)	7 (14.9)
Anxiety diagnosis	60 (61.2)	31 (60.8)	29 (61.7)
Panic disorder	7 (7.1)	5 (9.8)	2 (4.3)
Agoraphobia	2 (2.0)	2 (3.9)	0
Social anxiety disorder	37 (37.8)	19 (37.3)	18 (38.3)
Generalized anxiety disorder	40 (40.8)	23 (45.1)	17 (36.2)
Specific phobia	8 (8.2)	4 (7.8)	4 (8.5)
Posttraumatic stress disorder	6 (6.1)	1 (2.0)	5 (10.6)
Obsessive-compulsive disorder	1 (1.0)	1 (2.0)	0
Attention-deficit/hyperactivity disorder	11(11.2)	8 (15.7)	3 (6.4)
Adjustment disorder	3 (3.1)	1 (2.0)	2 (4.3)

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Abbreviations: GED, General Educational Development; NAT, negative affect treatment; PAT, positive affect treatment.

^{^a}

Of the 3 participants who identified their sex as other, 1 identified as agender, 1 identified as nonbinary, and 1 did not report gender.

^{^b}

Of the 10 participants who identified their race and ethnicity as other, 5 identified as Hispanic or Latino, 3 identified as Middle Eastern, 1 identified as Southeast Asian, and 1 did not report race or ethnicity.

Treatment Efficacy

MMLM analyses of the 3 clinical status variables as a multivariate outcome showed that clinical status improved more with PAT than with NAT (b = −0.06 [95% CI, −0.11 to −0.01]; t₃₀₃₉ = 2.43; P = .02; d = 0.27) and that PAT had better (higher) scores on clinical status than NAT at the 1MFU (b = −0.21, [95% CI, −0.41 to −0.02]; t₃₀₃₉ = 2.11; P = .04; d = 0.21) (Figure 2). MLM analyses of the individual clinical status measures (eAppendix 2 and eFigure in Supplement 2) showed significantly greater improvements in PAT than NAT on the DASS-21 (b = 1.27 [95% CI, 0.61-1.93]; t₁₃₅₆ = 3.76; P < .001; d = 0.55), but not on interviewer-rated anhedonia (b = 0.16 [95% CI, −0.06 to 0.36]; t₃₃₇ = 1.45; P = .15; d = 0.39) or the PANAS-P (b = −0.38 [95% CI, −0.87 to 0.10]; t₁₃₅₆ = 1.55; P = .12; d = 0.26).

Figure 2. — Multivariate clinical status was composed of the Positive and Negative Affect Schedule Positive Subscale, Interviewer-Rated Anhedonia, and the Total Depression, Anxiety, and Stress Scale. Higher values reflect better outcome.

Target Engagement

Reward Anticipation-Motivation

The MMLM showed no difference between treatment groups in the slope of increase in the multivariate reward anticipation-motivation target (b = 0.00, [95% CI, −0.01 to 0.01]; t₁₃₀₇ = −0.02; P = .98; d = 0.00). But this target did increase significantly over time across the 2 treatment groups (b = 0.02 [95% CI, 0.01-0.03]; t₁₃₀₇ = 4.36; P < .001; d = 0.40).

Response to Reward Attainment

The MMLM showed no difference between treatment groups in the slope of increase in the multivariate response to reward attainment target (b = −0.01 [95% CI, −0.05 to 0.04]; t₁₄₀₅ = 0.32; P = .75; d = 0.03). It increased significantly over time across treatment groups (b = 0.04 [95% CI, 0.01-0.06]; t₁₄₀₅ = 3.16; P = .002; d = 0.18).

Reward Learning

There was no difference between groups in the increase in the Probabilistic Reward Task over time (b = −0.002 [95% CI, −0.010 to 0.002]; t₃₅₈ = −0.82; P = .41; d = 0.01). In addition, there was no increase in the Probabilistic Reward Task across treatment groups (b = −0.002, 95% CI, −0.006 to 0.002]; t₃₅₈ = −0.82; P = .41; d = 0.00).

Threat

The MMLM for the threat target showed that this target decreased significantly over time across groups (b = −0.09 [95% CI, −0.10 to −0.07]; t₂₀₄₁ = −9.07; P < .001; d = 0.40). However, the slope was steeper for NAT than for PAT (group by time interaction, b = −0.04 [95% CI, −0.08 to 0.00]; t₂₀₄₁ = −2.12; P = .03; d = 0.18), although the difference between groups at the 1MFU was not significant (b = −0.11 [95% CI, −0.25 to 0.02]; t₂₀₄₁ = −1.70; P = .09; d = 0.11).

We also performed sensitivity analyses for all the MMLMs using bayesian MLMs. In these sensitivity analyses, we added a random slope for individuals in addition to the random intercept for individuals, and we included a random intercept for therapist (some of the aforementioned MMLMs would not converge with a random intercept included because of the small number of clusters for therapist [n = 10] and the small variance between clusters). The significant (and nonsignificant) results in these bayesian sensitivity analyses matched the significant (and nonsignificant) results already presented, except that the group by time interaction for the threat target was not significant in the sensitivity analysis. Of particular importance for clinical status, the group by time interaction and the group main effect at the 1MFU were significant (95% credible intervals did not include 0) in these sensitivity analyses, supporting the results from our primary efficacy analysis.

Using these models, we then added exploratory analyses investigating site as a moderator of the treatment effects (ie, we added the group by time by site interaction and all its subcomponents). Site did not moderate any of the treatment effects.

Mediation

We examined each of our 4 targets as mediators of multivariate clinical status in separate analyses (1 group of analysis per target). A full report on all the a and b paths is given in eAppendix 2 in Supplement 2).

Reward Anticipation-Motivation as Mediator

Single Mediator Analyses

Treatment group moderated the change over time for both the BAS-RD (b = 0.10 [95% CI, 0.03-0.17]; t₂₈₅ = 2.85; P = .005; d = 0.40), and DARS-A-M (b = 0.14 [95% CI, 0.06-0.22]; t₂₈₄ = 3.37; P < .001; d = 0.55) (Figure 3A). Contrary to our expectations, deviations in the BAS-RD results significantly increased over time with NAT (b = 0.13 [95% CI, 0.08-0.18]; t₂₈₅ = 5.05; P < .001; d = 0.51) but not with PAT (b = 0.03 [95% CI, −0.02 to 0.08]; t₂₈₅ = 1.04; P = .30; d = 0.10). BAS-RD was not significantly related to clinical status at the next session (b = 0.11 [95% CI, −0.01 to 0.23]; t₅₉₅ = 1.83; P = .07; d = 0.15). BAS-RD did not mediate the effect of treatment on changes in clinical status with NAT (a × b = 0.014 [95% CI, 0.000-0.031]; P = .06) or with PAT (a × b = 0.003 [95% CI, −0.003 to 0.011]). The index of moderated mediation did not reach significance (a × b = 0.011 [95% CI, 0.00-0.028]; P = .06).

Figure 3. — Higher values on clinical status reflect better outcomes. Only path coefficients that are P < .10 are shown. Subscript t indicates assessment of that measure at time t, and subscript t + 1, assessment of that measure at time t + 1. Single indicates path coefficients from the single-mediator analyses; multi, from multimediator analyses (which enter all mediators simultaneously). The only paths that differ between single and multimediator analyses are the mediator to outcome (b) paths; the time to mediator (a) paths do not differ between single and multimediator analyses. Mean values of the mediators are not shown or evaluated since they are not causal. ASI indicates Anxiety Sensitivity Index; BAS, Behavioral Activation Scale; BIS, Behavioral Inhibition Scale; DARS-A-M, Dimensional Anhedonia Rating Scale, reward anticipation-motivation subscale; DARS-C, Dimensional Anhedonia Rating Scale, consummatory subscale; ΔHR, change in heart rate; EEfRT, Effort Expenditure for Rewards Task; IAPS-N, International Affective Picture System, negative images; IAPS-P, International Affective Picture System, positive images; MAT, mental arithmetic task; MIT, Monetary Incentive Task; PCQ, Probability and Cost Questionnaire; and TEPS-C, Temporal Experience of Pleasure Scale, consummatory subscale.

For the DARS-A-M, deviations significantly increased over time with both PAT and NAT (b = 0.11 [95% CI, 0.05-0.16]; t₂₈₄ = 3.72; P < .001; d = 0.43 and b = 0.24, [95% CI, 0.19-0.30]; t₂₈₄ = 8.44; P < .001; d = 0.98, respectively), although the slope of increase was significantly higher with NAT than PAT (as already reported). Although DARS-A-M was not significantly related to next session clinical status (b = 0.09 [95% CI, −0.01 to 0.19]; t₅₉₃ = 1.83; P = .07; d = 0.15), it was a mediator of changes in clinical status for PAT (a × b = 0.010 [95% CI, 0.000-0.024]) and for NAT (a × b = 0.022 [95% CI, 0.002-0.038]). Although the mediated effect was stronger with NAT than PAT, the index of moderated mediation did not reach significance (a × b = 0.011 [95% CI, 0.00-0.02]; P = .06).

Multimediator Analyses

We also conducted multimediator analyses. None of the individual measures were significantly related to clinical status over and above the other mediators.

Response to Reward Attainment as Mediator

Single Mediator Analyses

Treatment group moderated the change over time for DARS-C (b = 0.09 [95% CI, 0.02-0.17]; t₂₈₄ = 2.42; P = .02; d = 0.36) and for engagement with happy images in the modified dot-probe task (b = 0.13 [95% CI, 0.03-0.24]; t₂₉₆ = 2.46; P = .02; d = 0.52) (Figure 3B). Deviations in DARS-C increased significantly over time with both PAT and NAT (b = 0.11 [95% CI, 0.05-0.16]; t₂₈₄ = 3.97; P < .001; d = 0.44 and b = 0.20 [95% CI, 0.15-0.25]; t₂₈₄ = 7.35; P < .001; d = 0.80, respectively), but contrary to our expectations, the increase was statistically greater with NAT than PAT. Since DARS-C was significantly related to the next session clinical status (b = 0.14 [95% CI, 0.04-0.24]; t₅₉₃ = 2.63; P = .009; d = 0.22), DARS-C was a mediator of changes in clinical status for both PAT (a × b = 0.015 [95% CI, 0.003-0.031]) and for NAT (a × b = 0.028 [95% CI, 0.007-0.052]). The index of the moderated mediation was significant (a × b = 0.013 [95% CI, 0.001-0.031]). The modified dot-probe task was significantly related to the next session clinical status (b = 0.08 [95% CI, 0.00-0.15]; t₆₀₉ = 2.08; P = .04; d = 0.17), but mediation was not significant for either PAT (a × b = 0.005 [95% CI, −0.001 to 0.015]) or NAT (a × b = −0.005 [95% CI, −0.015 to 0.001]). TEPS-C was not moderated by treatment group (b = 0.00 [95% CI, −0.05 to 0.05]; t₂₈₄ = −0.01; P = .99; d = 0.00), but the change over time across groups was positive and significant (b = 0.04 [95% CI, 0.01-0.06]; t₂₈₄ = 3.06; P = .002; d = 0.36). Since deviations in TEPS-C were related to clinical status at the next session (b = 0.21 [95% CI, 0.04-0.38]; t₅₉₃ = 2.47; P = .01; d = 0.20), TEPS-C was a mediator of the overall change over time in clinical status across treatments (a × b = 0.008; 95% CI, 0.001-0.018]).

Multimediator Analyses

As we reported, deviations in DARS-C significantly increased over time with both PAT and NAT, and the increase was statistically greater for NAT than PAT. Since DARS-C was significantly related to next session clinical status in the multimediator analysis (b = 0.12 [95% CI, 0.01-0.23]; t₅₆₂ = 2.07; P = .04; d = 0.17), DARS-C was a significant mediator of changes in clinical status in both treatment groups in the multimediator analysis (PAT: a × b = 0.013 [95% CI, 0.001-0.029]; NAT: a × b = 0.024 [95% CI, 0.002-0.049]). However, the mediation was larger with NAT than PAT, with the index of moderated mediation being a × b = 0.011 (95% CI, 0.000-0.028).

Reward Learning as Mediator

Response bias on the Probabilistic Reward Task (the single measure of reward learning) did not significantly improve over time in either treatment (b = 0.00 [95% CI, −0.01 to 0.01]; t₃₀₁ = 0.13; P = .90; d = 0.00 for PAT and b = 0.00 [95% CI, −0.01 to 0.00]; t₃₀₁ = 1.08; P = .28; d = 0.12 for NAT) and was not related to clinical status (b = 0.002 [95% CI, −0.06 to 0.07]; t₆₀₇ = 0.06; P = .95; d = 0.00). Therefore, reward learning was not a mediator of clinical status for PAT (a × b = 0.000 [95% CI, −0.002 to 0.002]) and for NAT (a × b = 0.000 [95% CI, −0.003 to 0.003]).

Threat Process as a Mediator

Single Mediator Analyses

Although PCQ was not significantly moderated by treatment group (b = −0.06 [95% CI, −0.01 to 0.14]; t₂₆₂ = 1.77; P = .08; d = 0.26), PCQ scores decreased more with NAT (b = −0.13 [95% CI, −0.18 to −0.08]; t₂₆₂ = 5.03; P < .001; d = 0.52) than with PAT (b = −0.06 [95% CI, −0.12 to –0.01]; t₂₆₂ = −2.54; P = .01; d = 0.26). Deviations in PCQ scores were not significantly related to worse clinical status (b = −0.12; [95% CI, −0.25 to 0.00]; t₅₅₁ = −1.94; P = .053; d = 0.17) (Figure 3C). However, PCQ scores mediated the change in clinical status over time for both NAT (a × b = 0.016 [95% CI, 0.001-0.034]) and PAT (a × b = 0.008 [95% CI, 0.000-0.020]). Although the mediated effect was statistically stronger with NAT than PAT, the index of moderated mediation did not reach significance (a × b = 0.008; [95% CI, −0.023 to 0.001]; P = .09). By contrast, changes in ASI were not moderated by treatment group. Instead, ASI decreased significantly over time across the 2 treatment groups (b = −0.07 [95% CI, −0.11 to −0.04]; t₂₈₃ = −4.07; P < .001; d = 0.48), and since ASI was also significantly related to the clinical status (b = −0.23 [95% CI, −0.34, to −0.11]; t₅₉₃ = −3.72; P < .001; d = 0.31), it was a mediator of change in clinical outcome over time (a × b = 0.016 [95% CI, 0.006-0.029]) across both groups.

Multimediator Analyses

ASI was the only threat sensitivity mediator significantly related to clinical outcome in the multimediator analysis (b = −0.21 [95% CI, −0.35 to −0.06]; t₄₇₈ = 2.71; P = .007; d = 0.25). Since ASI decreased significantly across treatment groups over time, ASI mediated the change in clinical status over time (a × b = 0.015 [95% CI, 0.004-0.029]).

Discussion

The findings of this randomized clinical trial indicated that clinical status improved more rapidly with PAT than NAT, with superior outcomes at 1MFU, replicating prior findings.^52,53 This advantage (which was replicated in our sensitivity analyses) was driven primarily by greater reductions in depression and anxiety symptoms (DASS total score), whereas improvements in interviewer-rated anhedonia and self-reported positive affect (PANAS-P) did not differ between treatments. The PANAS-P result contrasts with the most comparable prior trial conducted by members of our team,⁵³ which used identical low PANAS-P entry criteria and observed greater increases in positive affect with PAT, while interviewer-rated anhedonia improved similarly across treatments. Study design and common treatment factors (eg, satisfaction, credibility, and fidelity) were consistent across trials. The primary methodological difference was delivery mode: fully virtual in the current trial vs mixed in-person and virtual delivery due to COVID-19 restrictions in the previous trial. In-person attendance may have potentiated PAT-specific effects. Attending in-person sessions requires overcoming motivational barriers, which may be therapeutic by enhancing a sense of accomplishment and agency and thereby improving reward sensitivity and positive affect. Future studies should directly compare in-person and virtual psychotherapy with respect to low positive affect and anhedonia. PAT and NAT both demonstrated improvements in reward anticipation-motivation and in response to reward attainment; however, PAT did not result in greater change than NAT, as previously shown.⁵³ Neither intervention improved the reward-learning target, as previously shown.⁵³ Both interventions improved the threat target, and as hypothesized, NAT led to significantly greater gains than PAT (although this effect was not replicated in the bayesian sensitivity analysis). Thus, there was only weak support for the specificity of effects on the targets for PAT and NAT.

Across the 14 potential mediators, the single mediator analyses revealed 4 substantial reward mediators (BAS-RD, DARS-A-M [reward anticipation-motivation], DARS-C, and TEPS-C [reward consumption]) and 2 significant threat mediators (PCQ and ASI), all of which were significant mediators of the overall improvement in clinical status in both treatment groups, except for BAS-RD which marginally mediated changes in clinical status in NAT but not in PAT. Although treatment moderated changes over time, as measured by the BAS-RD, DARS-A-M, DARS-C, and PCQ, the only mediator for which mediation differed significantly between treatment groups was the DARS-C, for which mediation was stronger with NAT than PAT. Multimediator analyses that simultaneously included all mediators of the respective reward or threat targets revealed 2 measures that significantly mediated improvements in clinical status over and above the other mediators: DARS-C (response to reward attainment target) and ASI (threat target). As with single mediator analyses, mediation was stronger with NAT than PAT for DARS-C but comparable for ASI. Although the significant self-report mediators shared method variance with clinical status measures, baseline correlations between the self-report mediators and the clinical status variables were low to moderate (eTable 2 in Supplement 2), suggesting that they were distinct from each other.

Single mediator and multimediator effects via our self-reported measures indicated that increases in reward and decreases in threat measures explained clinical outcomes, with mediational effects generally significant with both PAT and NAT. Detecting reliable mediators of change in psychotherapy for internalizing disorders remains challenging,^70,71 and except for covariation, is unexplored in interventions targeting positive affect or anhedonia.²¹ ASI represents a notable exception as a consistent mediator across cognitive behavioral therapy for affective disorders^{72,73,74,75,76} and was also a relevant individual mediator over and above other threat measures for both PAT and NAT in our study. PAT may engage alternative or downstream mechanisms that were not captured by our selected self-report reward measures or laboratory-based reward tasks. Examples of such mechanisms may include social connectedness, values-based engagement, and meaning-related processes. These processes may be more accurately assessed through momentary sampling of context-sensitive reward engagement or interpersonal reward processes in daily life. Within an experimental therapeutics framework, these findings underscore the need to refine mechanistic models and broaden measurement strategies for positive affect–based interventions.

Mechanistic conclusions regarding modulation of reward and threat processing should, therefore, be interpreted cautiously as all the reward and threat self-reported measures, except for BIS, emerged as mediators, whereas none of the behavioral (EEfRT hard ratio, happy-engaged or sad-engaged on the modified dot-probe task) or physiological measures (heart rate acceleration on the Monetary Incentive Task, mental arithmetic task, and IAPS) did so. Physiological measures of reward and threat targets showed minimal associations with treatment conditions or with clinical status.⁵³ Nonetheless, some support for the choice of our target engagement measures may be seen in the (1) significant improvements (path a) on a number of measures, including heart rate accelerations for positive IAPS images (for reward attainment), and a significant group by time interaction on the happy-engaged index from the modified dot-probe task, and (2) significant associations with change in clinical status in 2 measures (path b), the EEfRT hard ratio and the happy-engaged index from the modified dot-probe task in both single and multimediator analyses.

One reason for the lack of mediation by physiological or behavioral measures may be the distinct temporal dynamics of change, with self-report changes occurring earlier in treatment (eg, due to expectancy, self-awareness, or self-appraisal) than physiological or behavioral responses, which may change more slowly. Weaker alignment with the clinical outcome measure or low reliability (eg, internal consistency of trial-based measures)⁷⁷ may be other factors. Finally, we cannot exclude the possibility that these behavioral and physiological measures are not sufficiently sensitive or ecologically valid to capture changes over time in threat and reward processes, as most were developed for cross-sectional investigations. Our intensive target assessment protocol excluded central nervous system target measures, which may have yielded detectable changes. For example, nucleus accumbens and subgenual anterior cingulate cortex activation and connectivity in response to reward feedback was increased from before to after internet-based cognitive behavioral therapy and was correlated with reduced anhedonia.⁷⁸ On the other hand, central nervous system changes are not always detected following successful psychotherapy for anhedonia.⁷⁹ Within an experimental therapeutics framework, these findings underscore the need to refine mechanistic models and broaden measurement strategies for positive affect–based interventions.²²

Limitations

This study has limitations. Although our results suggest temporal precedence and control for autocorrelated effects, they do not establish causality or rule out unmeasured third variables. Cross-lag analyses are time-lag sensitive and may bias estimates⁸⁰; our results indicate temporal precedence but do not confirm causality or rule out all confounds. Our retrospective assessment of positive and negative affect may not fully capture patients’ internal experiences and their dynamic shifts over time. Experience sampling, such as ecological momentary assessment,^81,82 could reduce self-reference and memory bias, providing a more accurate index of therapeutic change. Future studies with larger samples could model positive and negative affect as latent variables using multilevel structural equation modeling to capture individual differences in change, consistent with dimensional models of psychopathology.⁸³

Conclusions

In this randomized clinical trial of 98 adults with severely low positive affect, depression, and anxiety, PAT produced greater clinical improvement than a mechanistically distinct psychotherapy targeting negative affect. Both transdiagnostic treatments improved reward-related processes, and several self-reported reward and threat processes mediated clinical gains, although mediation did not differ between the 2 interventions. These findings highlight the potential role of reward systems in therapeutic change in both psychological interventions for individuals with severely low positive affect (anhedonia), depression, and anxiety.

Supplement 1.

Trial Protocol and Statistical Analysis Plan

jamanetwopen-e267403-s001.pdf^{(601.6KB, pdf)}

Supplement 2.

eMethods. Interventions, Power Analyses, and Analytic Plan

eAppendix 1. Exclusions Prior to Randomization for Figure 1

eAppendix 2. Additional Results

eFigure. Growth Curve Models for PANAS-Positive, DASS Total, and Anhedonia Interview for PAT and NAT

eAppendix 3. Sample SPSS SYNTAX

eTable 1. Raw Means (SDs) for Each Reward Target Measure and Clinical Status Measure Across Pre-Treatment, Session 5, Session 10, Post-Treatment, Follow-Up (1 month after Post-treatment) in Participants Randomized to Positive Affect Treatment (PAT) and Negative Affect Treatment (NAT)

eTable 2. Correlation Matrix for Clinical Status and Reward Target Measures at Baseline, Collapsed Across Treatment Group

jamanetwopen-e267403-s002.pdf^{(611.4KB, pdf)}

Supplement 3.

Data Sharing Statement

jamanetwopen-e267403-s003.pdf^{(17.4KB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

Trial Protocol and Statistical Analysis Plan

jamanetwopen-e267403-s001.pdf^{(601.6KB, pdf)}

Supplement 2.

eMethods. Interventions, Power Analyses, and Analytic Plan

eAppendix 1. Exclusions Prior to Randomization for Figure 1

eAppendix 2. Additional Results

eFigure. Growth Curve Models for PANAS-Positive, DASS Total, and Anhedonia Interview for PAT and NAT

eAppendix 3. Sample SPSS SYNTAX

eTable 2. Correlation Matrix for Clinical Status and Reward Target Measures at Baseline, Collapsed Across Treatment Group

jamanetwopen-e267403-s002.pdf^{(611.4KB, pdf)}

Supplement 3.

Data Sharing Statement

jamanetwopen-e267403-s003.pdf^{(17.4KB, pdf)}

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PERMALINK

Positive Affect Treatment for Depression, Anxiety, and Low Positive Affect

Alicia E Meuret, PhD

David Rosenfield, PhD

Emily Wang, BS

Christina M Hough, PhD

Thomas Ritz, PhD

Michelle G Craske, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objectives

Design, Setting, and Participants

Intervention

Main Outcomes and Measures

Results

Conclusions and Relevance

Trial Registration

Introduction

Methods

Participants

Interventions

Assessments

Clinical Status Measures

Target Measures of Reward and Threat Processes

Reward Anticipation-Motivation

Response to Reward Attainment

Reward Learning

Threat Processes

Statistical Analysis

Results

Figure 1. Recruitment Flowchart.

Table. Participant Demographic and Clinical Characteristics, Overall and by Treatment Group.

Treatment Efficacy

Figure 2. Dot Plot of Changes in Multivariate Clinical Status Over Time for Participants Randomized to Positive Affect Treatment (PAT) or Negative Affect Treatment (NAT).

Target Engagement

Reward Anticipation-Motivation

Response to Reward Attainment

Reward Learning

Threat

Mediation

Reward Anticipation-Motivation as Mediator

Single Mediator Analyses

Figure 3. Path Diagrams of the Mediator Models.

Multimediator Analyses

Response to Reward Attainment as Mediator

Single Mediator Analyses

Multimediator Analyses

Reward Learning as Mediator

Threat Process as a Mediator

Single Mediator Analyses

Multimediator Analyses

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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