The relaxation effect of 360-degree nature videos presented with different immersion levels for patients with PTSD: A randomized, controlled, mixed-methods feasibility study

Status

Active, not recruiting

Phases

Unknown

Study type

Interventional

Source

DRKS

Registry ID

DRKS00020277

Enrollment

Registered

2020-06-29

Start date

2024-06-10

Completion date

Unknown

Last updated

2026-01-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

F43.1

Interventions

Group 1: Monoscopic 360° nature video via HMD + nature sounds (via headphones) Group 2: Monoscopic 360° nature video via PC screen + nature sounds (via headphones) Group 3: Control condition with onl

Eligibility

Sex/Gender

All

Age

18 Years to 62 Years

Inclusion criteria

Inclusion criteria: including only soldiers aged 18 or over with a primary diagnosis of PTSD (according to the ICD-10; F43.1), who will be treated at the Bundeswehr Hospital Hamburg

Exclusion criteria

Exclusion criteria: Those with psychosis, substance dependence, a change in psychiatric medication within the last month, suicidal intent, and motion sickness will be excluded. No further inclusion or exclusion criteria will be defined.

Design outcomes

Primary

Measure	Time frame
1) Self-reported instruments (PANAS, RSQ, VAS, SPES). To compare the three conditions, we will calculate pre-post mean differences and use them to calculate between-condition effect sizes (Cohen’s d). Furthermore, we will calculate between-condition Cohen’s d for spatial presence and construct a correlation matrix with all self-reported and psychophysiological measures using the rmcorr package in R. 2) Psychophysiological instruments skin conductance level (SCL), heart rate (HR), and heart rate variability (HRV). The raw SCL data will be preprocessed in the following four steps. In the first step, we will average the 32 SPS to one data point per second. Subsequently, we will remove all time intervals that did not directly belong to the experiment (e.g., time intervals during which the participants put on the HMD). In the third step, this dataset will be z-transformed to minimize interindividual differences. Accordingly, we will calculate 30 s intervals for by averaging the 30 data points. For HR and HRV the raw interbeat intervals (IBI) will be exported to the HRVTool. The preprocessing and cleaning procedures will be performed with the HRVTool. Afterwards, we will calculate 30s intervals for HR und HRV by averaging the 30 data points. The resulting values for SCL, HR, and HRV will be z-standardized to minimize interindividual differences. We will conduct and report the descriptive analyses for the z-transformed and untransformed raw scores; and will visualize the results with line graphs. 3) Qualitative data Transcription and subsequent categorization of the audio-recorded interviews will be conducted with the software MAXQDA. The transcription will be categorized, summarized, and reported using content-structuring content analysis.	—

Measure

Time frame

1) Self-reported instruments (PANAS, RSQ, VAS, SPES). To compare the three conditions, we will calculate pre-post mean differences and use them to calculate between-condition effect sizes (Cohen’s d). Furthermore, we will calculate between-condition Cohen’s d for spatial presence and construct a correlation matrix with all self-reported and psychophysiological measures using the rmcorr package in R. 2) Psychophysiological instruments skin conductance level (SCL), heart rate (HR), and heart rate variability (HRV). The raw SCL data will be preprocessed in the following four steps. In the first step, we will average the 32 SPS to one data point per second. Subsequently, we will remove all time intervals that did not directly belong to the experiment (e.g., time intervals during which the participants put on the HMD). In the third step, this dataset will be z-transformed to minimize interindividual differences. Accordingly, we will calculate 30 s intervals for by averaging the 30 data points. For HR and HRV the raw interbeat intervals (IBI) will be exported to the HRVTool. The preprocessing and cleaning procedures will be performed with the HRVTool. Afterwards, we will calculate 30s intervals for HR und HRV by averaging the 30 data points. The resulting values for SCL, HR, and HRV will be z-standardized to minimize interindividual differences. We will conduct and report the descriptive analyses for the z-transformed and untransformed raw scores; and will visualize the results with line graphs. 3) Qualitative data Transcription and subsequent categorization of the audio-recorded interviews will be conducted with the software MAXQDA. The transcription will be categorized, summarized, and reported using content-structuring content analysis.

—

Countries

Germany

Contacts

Public ContactThiemo Knaust

Zentrum für Seelische Gesundheit am Bundeswehrkrankenhaus in Hamburg

thiemo1knaust@bundeswehr.org040-6947-26410

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 14, 2026