OSCR

Aquahenosis: a non-pharmacological altered state of consciousness induced by floatation-REST in individuals with anxiety and depression.

Code ↔ Paper

8 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 8 matches
  1. [1] § Results › Group comparisons › Comparing altered states in Floatation-REST versus psychoactive drugs ↔ R/utils/radar_functions.R, lines 1–53 · score 0.79 · Audio Visual Synesthesia, Elementary Imagery, Impaired Control, Complex Imagery, Cognition, Bliss
  2. [2] § Results › Group comparisons › Comparing altered states in Floatation-REST versus psychoactive drugs ↔ R/01_data_cleaning.R, lines 230–317 · score 0.79 · Audio Visual Synesthesia, Elementary Imagery, Impaired Control, Complex Imagery, Cognition, Bliss
  3. [3] § Methods › Statistical tests ↔ R/utils/anova_functions.R, lines 6–71 · score 0.78 · generalized eta squared, Shapiro Wilk, Levene, Welch, Variance, ANOVA
  4. [4] § Results › Group comparisons › Comparing altered states in Floatation-REST versus psychoactive drugs ↔ R/utils/radar_functions.R, lines 1–53 · score 0.77 · Audio Visual Synesthesia, Blissful State, Complex Imagery, Spiritual Experience, perceptual, Disembodiment
  5. [5] § Results › Group comparisons › Comparing altered states in Floatation-REST versus psychoactive drugs ↔ R/01_data_cleaning.R, lines 230–317 · score 0.76 · Audio Visual Synesthesia, Blissful State, Complex Imagery, Spiritual Experience, perceptual, Disembodiment
  6. [6] § Methods › Materials › State and phenomenology measures ↔ R/01_data_cleaning.R, lines 189–228 · score 0.74 · Auditory Alterations, Anxious Ego Dissolution, Oceanic Boundlessness, Restructuralization, Vigilance, scores
  7. [7] § Results › Group comparisons › Exploratory mediation of the effects of Floatation-REST ↔ R/utils/table_functions.R, lines 20–106 · score 0.72 · Heart Intensity, Body Listening, Oceanic Boundlessness, mediation models, Regulation, sensitivity
  8. [8] § Results › Group comparisons › Exploratory mediation of the effects of Floatation-REST ↔ R/utils/table_functions.R, lines 109–188 · score 0.50 · ab, HAM, ASI, STAI, MADRS, R2

Paper

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The authors' code

R · 317 lines · 11 KB · no license · 3 matches

  1. # ============================================
  2. # 01_data_cleaning.R
  3. # Import data, compute derived variables, define analysis parameters
  4. # ============================================
  6. source("R/00_setup.R")
  8. # ============================================
  9. # 1. IMPORT RAW DATA
  10. # ============================================
  12. raw_data <- read.csv("data/raw_data.csv")
  13. only_complete_data <- read.csv("data/only_complete_data.csv")
  15. # Subset to first 70 rows
  16. raw_data <- raw_data[1:70, ]
  18. # Convert group variables to factors
  19. raw_data$binary_group_num <- as.factor(raw_data$binary_group_num)
  20. raw_data$pooled_group_num <- as.factor(raw_data$pooled_group_num)
  22. # ============================================
  23. # 2. COMPUTE PANAS MEANS AND DIFFERENCE SCORES
  24. # ============================================
  26. affects <- c(
  27. "posaffect_score", "negaffect_score", "cheerful", "attentive", "sluggish",
  28. "strong", "relaxed", "irritable", "delighted", "inspired", "calm", "afraid",
  29. "tired", "happy", "alert", "upset", "active", "guilty", "joyful", "nervous",
  30. "sleepy", "excited", "hostile", "proud", "jittery", "lively", "ashamed",
  31. "atease", "scared", "drowsy", "enthusiastic", "distressed", "determined",
  32. "interested", "hungry", "fatigue", "serenity_score", "joviality_score",
  33. "attentiveness_score1", "fatigue_score", "fear_score1"
  34. )
  36. for (a in affects) {
  37. pre_cols <- grep(paste0("prefloat_[1-6]_panasx_.*", a, ".*"), names(raw_data), value = TRUE)
  38. post_cols <- grep(paste0("postfloat_[1-6]_panasx_.*", a, ".*"), names(raw_data), value = TRUE)
  40. if (length(pre_cols) > 0) {
  41. raw_data[[paste0("prefloat_mean_panasx_", a)]] <-
  42. rowMeans(raw_data[, pre_cols, drop = FALSE], na.rm = TRUE)
  43. }
  44. if (length(post_cols) > 0) {
  45. raw_data[[paste0("postfloat_mean_panasx_", a)]] <-
  46. rowMeans(raw_data[, post_cols, drop = FALSE], na.rm = TRUE)
  47. }
  48. if (length(pre_cols) > 0 && length(post_cols) > 0) {
  49. raw_data[[paste0("diff_panasx_", a)]] <-
  50. raw_data[[paste0("postfloat_mean_panasx_", a)]] -
  51. raw_data[[paste0("prefloat_mean_panasx_", a)]]
  52. }
  53. }
  55. # ============================================
  56. # 3. COMPUTE STAI AND OTHER DIFFERENCE SCORES
  57. # ============================================
  59. raw_data <- raw_data %>%
  60. rowwise() %>%
  61. mutate(
  62. # STAI means
  63. prefloat_mean_stais = mean(c(prefloat_1_stais_state_score,
  64. prefloat_2_stais_state_score,
  65. prefloat_3_stais_state_score,
  66. prefloat_4_stais_state_score,
  67. prefloat_5_stais_state_score,
  68. prefloat_6_stais_state_score), na.rm = TRUE),
  70. postfloat_mean_stais = mean(c(postfloat_1_stais_state_score,
  71. postfloat_2_stais_state_score,
  72. postfloat_3_stais_state_score,
  73. postfloat_4_stais_state_score,
  74. postfloat_5_stais_state_score,
  75. postfloat_6_stais_state_score), na.rm = TRUE)
  76. ) %>%
  77. ungroup() %>%
  78. mutate(
  79. # Difference scores
  80. diff_mean_stais = postfloat_mean_stais - prefloat_mean_stais,
  81. diff_asi3r_total = postintervention_asi3r_score - baseline_asi3r_score,
  83. # Intensity and Pleasantness means
  84. breath_intensity_mean = rowMeans(
  85. select(., matches("postfloat_\\d+_fspost_17")), na.rm = TRUE),
  86. heart_intensity_mean = rowMeans(
  87. select(., matches("postfloat_\\d+_fspost_18")), na.rm = TRUE),
  88. gi_intensity_mean = rowMeans(
  89. select(., matches("postfloat_\\d+_fspost_19")), na.rm = TRUE),
  90. breath_pleasant_mean = rowMeans(
  91. select(., matches("postfloat_\\d+_fspost_50")), na.rm = TRUE),
  92. heart_pleasant_mean = rowMeans(
  93. select(., matches("postfloat_\\d+_fspost_51")), na.rm = TRUE),
  94. gi_pleasant_mean = rowMeans(
  95. select(., matches("postfloat_\\d+_fspost_52")), na.rm = TRUE)
  96. )
  98. # ============================================
  99. # 4. COMPUTE MAIA SUBSCALE MEANS
  100. # ============================================
  102. maia_subscales <- list(
  103. selfreg = 23:26,
  104. bodylisten = 27:29,
  105. noticing = 1:4,
  106. notdistracting = 5:7,
  107. notworrying = 8:10,
  108. attreg = 11:17,
  109. emoaware = 18:22,
  110. trusting = 30:32
  111. )
  113. maia_floats <- c(1, 4, 6)
  115. for (subscale_name in names(maia_subscales)) {
  117. items <- maia_subscales[[subscale_name]]
  119. expected_cols <- unlist(lapply(maia_floats, function(flt) {
  120. paste0("postfloat_", flt, "_maia_float_", items)
  121. }))
  123. existing_cols <- intersect(expected_cols, names(raw_data))
  125. if (length(existing_cols) > 0) {
  126. raw_data[existing_cols] <- lapply(raw_data[existing_cols], function(x) {
  127. as.numeric(as.character(x))
  128. })
  129. }
  131. # Per-float means
  132. for (flt in maia_floats) {
  133. float_cols <- paste0("postfloat_", flt, "_maia_float_", items)
  134. float_cols <- intersect(float_cols, names(raw_data))
  136. if (length(float_cols) > 0) {
  137. new_col <- paste0("maia_", subscale_name, "_float_", flt, "_mean")
  138. raw_data[[new_col]] <- rowMeans(raw_data[, float_cols, drop = FALSE], na.rm = TRUE)
  139. }
  140. }
  142. # Overall mean across floats
  143. mean_cols <- grep(paste0("^maia_", subscale_name, "_float_\\d+_mean$"),
  144. names(raw_data), value = TRUE)
  146. if (length(mean_cols) > 0) {
  147. overall_col <- paste0("maias_", subscale_name)
  148. raw_data[[overall_col]] <- rowMeans(raw_data[, mean_cols, drop = FALSE], na.rm = TRUE)
  149. }
  150. }
  152. # ============================================
  153. # 5. MERGE NEW COLUMNS INTO only_complete_data
  154. # ============================================
  156. raw_data$grp <- as.integer(raw_data$grp)
  157. only_complete_data$grp <- as.integer(only_complete_data$grp)
  159. cols_to_add <- setdiff(names(raw_data), names(only_complete_data))
  160. cols_to_add <- union("grp", cols_to_add)
  162. only_complete_data <- only_complete_data %>%
  163. left_join(raw_data %>% select(all_of(cols_to_add)), by = "grp")
  165. # Check for unmatched
  166. unmatched <- anti_join(only_complete_data %>% select(grp), raw_data %>% select(grp), by = "grp")
  167. if (nrow(unmatched) > 0) {
  168. message("Warning: some grp values in only_complete_data did not match raw_data: ",
  169. paste(unmatched$grp, collapse = ", "))
  170. }
  172. # ============================================
  173. # 6. COMPUTE SIDE EFFECTS AVERAGES (for phenom follow-up)
  174. # ============================================
  176. for (sec in 30:44) {
  177. cols <- paste0("postfloat_", 1:6, "_sec_", sec)
  178. cols <- intersect(cols, names(only_complete_data))
  179. only_complete_data[[paste0("average_sec_", sec)]] <- if (length(cols)) {
  180. rowMeans(only_complete_data[, cols, drop = FALSE], na.rm = TRUE)
  181. } else {
  182. NA_real_
  183. }
  184. }
  186. only_complete_data <- only_complete_data %>%
  187. mutate(mean_pos_effects = rowMeans(across(paste0("average_sec_", 30:44)), na.rm = TRUE))
  189. # ============================================
  190. # 7. DEFINE ANALYSIS PARAMETERS AND RENAME
  191. # ============================================
  193. # Outcome variables
  194. outcomes <- c(
  195. "diff_panasx_negaffect_score",
  196. "diff_mean_stais",
  197. "diff_hama",
  198. "diff_madrs",
  199. "diff_asi3r_total",
  200. "diff_panasx_posaffect_score"
  201. )
  203. # Mediator variables
  204. mediators <- c(
  205. "alt_postfloat_6_dasc_5d_oceanic_boundlessness_score",
  206. "alt_postfloat_6_dasc_5d_anxious_ego_dissolution_score",
  207. "maias_bodylisten",
  208. "maias_emoaware",
  209. "maias_selfreg",
  210. "breath_intensity_mean",
  211. "heart_intensity_mean",
  212. "gi_intensity_mean",
  213. "breath_pleasant_mean",
  214. "heart_pleasant_mean",
  215. "gi_pleasant_mean"
  216. )
  218. # Covariates
  219. covariates <- c("age")
  221. # 5D-ASC subscales
  222. subscales_5d <- c(
  223. "Oceanic Boundlessness" = "alt_postfloat_6_dasc_5d_oceanic_boundlessness_score",
  224. "Anxious Ego Dissolution" = "alt_postfloat_6_dasc_5d_anxious_ego_dissolution_score",
  225. "Visual Restructuralization" = "alt_postfloat_6_dasc_5d_visual_restructuralization_score",
  226. "Auditory Alterations" = "alt_postfloat_6_dasc_5d_auditory_alterations_score",
  227. "Reduction of Vigilance" = "alt_postfloat_6_dasc_5d_reduction_of_vigilance_score"
  228. )
  230. # 11-ASC subscales
  231. subscales_11d <- c(
  232. "Experience of Unity" = "alt_postfloat_6_dasc_11d_unity_score",
  233. "Spiritual Experience" = "alt_postfloat_6_dasc_11d_spiritual_score",
  234. "Blissful State" = "alt_postfloat_6_dasc_11d_bliss_score",
  235. "Insightfulness" = "alt_postfloat_6_dasc_11d_insight_score",
  236. "Disembodiment" = "alt_postfloat_6_dasc_11d_disembodiment_score",
  237. "Impaired Control and Cognition" = "alt_postfloat_6_dasc_11d_impaired_score",
  238. "Anxiety" = "alt_postfloat_6_dasc_11d_anxiety_score",
  239. "Complex Imagery" = "alt_postfloat_6_dasc_11d_com_imagery_score",
  240. "Elementary Imagery" = "alt_postfloat_6_dasc_11d_ele_imagery_score",
  241. "Audio-Visual Synesthesia" = "alt_postfloat_6_dasc_11d_synesthesiae_score",
  242. "Changed Meaning of Percepts" = "alt_postfloat_6_dasc_11d_percepts_score"
  243. )
  246. # Interoception subscales
  247. subscales_intero <- c(
  248. "Self-Regulation" = "maias_selfreg",
  249. "Body Listening" = "maias_bodylisten",
  250. "Noticing" = "maias_noticing",
  251. "Not Distracting" = "maias_notdistracting",
  252. "Not Worrying" = "maias_notworrying",
  253. "Attention Regulation" = "maias_attreg",
  254. "Emotional Awareness" = "maias_emoaware",
  255. "Trusting" = "maias_trusting",
  256. "Breath Intensity" = "breath_intensity_mean",
  257. "Heart Intensity" = "heart_intensity_mean",
  258. "GI Intensity" = "gi_intensity_mean",
  259. "Breath Pleasantness" = "breath_pleasant_mean",
  260. "Heart Pleasantness" = "heart_pleasant_mean",
  261. "GI Pleasantness" = "gi_pleasant_mean"
  262. )
  264. # Clinical outcomes subscales
  265. subscales_clinical <- c(
  266. "Negative Affect (PANAS)" = "diff_panasx_negaffect_score",
  267. "STAI-S" = "diff_mean_stais",
  268. "HAM-A" = "diff_hama",
  269. "MADRS" = "diff_madrs",
  270. "ASI-3R" = "diff_asi3r_total",
  271. "Positive Affect (PANAS)" = "diff_panasx_posaffect_score"
  272. )
  274. # PANAS subscales
  275. subscales_panas <- c(
  276. "Joviality" = "diff_panasx_joviality_score",
  277. "Attentiveness" = "diff_panasx_attentiveness_score1",
  278. "Fatigue" = "diff_panasx_fatigue_score",
  279. "Serenity" = "diff_panasx_serenity_score",
  280. "Fear" = "diff_panasx_fear_score1"
  281. )
  283. # Pre-post pairs for t-tests
  284. prepost_pairs <- list(
  285. "Negative Affect (PANAS)" = c("prefloat_mean_panasx_negaffect_score", "postfloat_mean_panasx_negaffect_score"),
  286. "Positive Affect (PANAS)" = c("prefloat_mean_panasx_posaffect_score", "postfloat_mean_panasx_posaffect_score"),
  287. "STAI-S" = c("prefloat_mean_stais", "postfloat_mean_stais"),
  288. "ASI-3R" = c("baseline_asi3r_score", "postintervention_asi3r_score"),
  289. "HAM-A" = c("baseline_hama_score", "postintervention_hama_score"),
  290. "MADRS" = c("baseline_madrs_score", "postintervention_madrs_score")
  291. )
  293. # ============================================
  294. # 8. SAVE CLEANED DATA AND PARAMETERS
  295. # ============================================
  297. cleaned_data <- list(
  298. raw_data = raw_data,
  299. only_complete = only_complete_data,
  301. # Analysis parameters
  302. outcomes = outcomes,
  303. mediators = mediators,
  304. covariates = covariates,
  306. # Subscale definitions
  307. subscales_11d = subscales_11d,
  308. subscales_5d = subscales_5d,
  309. subscales_intero = subscales_intero,
  310. subscales_clinical = subscales_clinical,
  311. subscales_panas = subscales_panas,
  312. prepost_pairs = prepost_pairs
  313. )
  315. saveRDS(cleaned_data, "data/cleaned_data.rds")
  317. cat("Data cleaning complete. Saved to data/cleaned_data.rds\n")

01_data_cleaning.R at commit 67b7ed5, no license · at the source

Overview

Authors: Theo Tobel1, Aidan Cone1, Emily Choquette2, McKenna Garland2, Micah A Johnson1, Keller Mink2, Caitlin Lynch1, Joel Frohlich1,3, Justin S Feinstein4, Nicco Reggente1, Sahib Khalsa2,5
ORCID iDs: Theo Tobel, Caitlin Lynch, Nicco Reggente
  1. Institute for Advanced Consciousness Studies, 2811 Wilshire Blvd #510, Santa Monica, CA 90403, United States
  2. Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK 74136, United States
  3. fMEG Center, University of Tübingen, Otfried-Müller-Straße 47, Tübingen, 72076, Germany
  4. Float Research Collective, 1254 S. Kihei Rd #1423, Kihei, Hawaii 96753, United States
  5. Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry, Geffen School of Medicine at UCLA, 760 Westwood Plaza, Los Angeles, CA 90095, United States
Institutions: Laureate Institute for Brain Research (United States); University of Tübingen (Germany); University of California, Los Angeles (United States)
Journal: Neuroscience of consciousness, volume 2026, issue 1, article niag044
Dates: received 10 January 2026; accepted 2 July 2026; published online 26 August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/nc/niag044 · PMID 42657182 · PMCID PMC13513766 · OpenAlex W7163997656
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/6mj8n_v2
Status: code verified
Categories: human (organism), depression (population), cognitive (subfield)
Methods: Connectivity, Spectral & time-frequency, Statistics
Keywords: States of Consciousness, Embodiment, Altered States
Journal subjects: Aware & Alive: Embodied and Phenomenological Perspectives on Consciousness
Topic: Psychedelics and Drug Studies (Clinical Psychology, Psychology), according to OpenAlex
Funding: NCCIH (R34AT009889)
Citations: not cited yet (Europe PMC); 83 references in the paper

Abstract

Floatation-REST (Reduced Environmental Stimulation Therapy) systematically alters the balance of sensory signals reaching the brain by combining neutral buoyancy, thermal and proprioceptive neutrality, attenuation of exteroceptive stimulation, and enhancement of cardiorespiratory signaling. Here we examined whether this non-pharmacological sensory perturbation induces altered states of consciousness and whether specific experiential dimensions are statistically related to changes in affect. In a secondary analysis of a randomized controlled feasibility trial, 75 treatment-seeking adults with anxiety and depression were assigned to six sessions of Floatation-REST with prescribed scheduling, Floatation-REST with preferred scheduling and duration, or a zero-gravity chair comparison condition. Altered states of consciousness were assessed using the 5-Dimensional Altered States of Consciousness Rating Scale, alongside measures of interoceptive awareness and affect. Compared to the chair condition, Floatation-REST was associated with increased interoceptive awareness of cardiorespiratory sensations and an altered state of consciousness characterized by Oceanic Boundlessness, Disembodiment, and Experience of Unity—a pattern we refer to as “aquahenosis”. Effects were strongest among participants who selected longer and more flexible float sessions. Experiential profiles selectively overlapped with those reported for psilocybin and ketamine along boundary-dissolution dimensions. These findings identify Floatation-REST as a tractable, non-pharmacological method for inducing specific altered states of consciousness and highlight Oceanic Boundlessness as an important mediator of the float-induced changes in positive affect.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

Institute-for-Advanced-Consciousness/Float

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 67b7ed5466078257438ed8ab5dd663fba6baed21, 14 June 2026
Languages: R (21)
Size: 35 files, 21 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, environment (renv.lock)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (10 files), broom (2 files), car (2 files), easystats (2 files), lavaan (2 files), ggpubr (1 file), rstatix (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
22 files
At the source: github.com/Institute-for-Advanced-Consciousness/Float

The paper's code and data availability statement is in the Data section.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 8 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Data availability

The data may be made available upon request to the corresponding author. The analysis code used in this study is publicly available at GitHub: https://github.com/Institute-for-Advanced-Consciousness/Float.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 3 keywords, 1 funder, 77 references.

Cite

This paper

Tobel, T., Cone, A., Choquette, E., Garland, M., Johnson, M. A., Mink, K., Lynch, C., Frohlich, J., Feinstein, J. S., Reggente, N., & Khalsa, S. (2026). Aquahenosis: a non-pharmacological altered state of consciousness induced by floatation-REST in individuals with anxiety and depression. Neuroscience of consciousness, 2026(1), niag044. https://doi.org/10.1093/nc/niag044

BibTeX

@article{tobel2026aquahenosis,
author = {Tobel, Theo and Cone, Aidan and Choquette, Emily and Garland, McKenna and Johnson, Micah A and Mink, Keller and Lynch, Caitlin and Frohlich, Joel and Feinstein, Justin S and Reggente, Nicco and Khalsa, Sahib},
title = {{Aquahenosis: a non-pharmacological altered state of consciousness induced by floatation-REST in individuals with anxiety and depression}},
journal = {Neuroscience of consciousness},
year = {2026},
month = aug,
volume = {2026},
number = {1},
pages = {niag044},
publisher = {Oxford University Press},
issn = {2057-2107},
doi = {10.1093/nc/niag044},
url = {https://doi.org/10.1093/nc/niag044},
pmid = {42657182},
pmcid = {PMC13513766}
}

RIS

TY - JOUR
AU - Tobel, Theo
AU - Cone, Aidan
AU - Choquette, Emily
AU - Garland, McKenna
AU - Johnson, Micah A
AU - Mink, Keller
AU - Lynch, Caitlin
AU - Frohlich, Joel
AU - Feinstein, Justin S
AU - Reggente, Nicco
AU - Khalsa, Sahib
TI - Aquahenosis: a non-pharmacological altered state of consciousness induced by floatation-REST in individuals with anxiety and depression
T2 - Neuroscience of consciousness
J2 - Neurosci Conscious
PY - 2026
DA - 2026/08/26
VL - 2026
IS - 1
SP - niag044
SN - 2057-2107
PB - Oxford University Press
DO - 10.1093/nc/niag044
UR - https://doi.org/10.1093/nc/niag044
LA - en
ER -

CSL-JSON

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"author": [
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{
"family": "Reggente",
"given": "Nicco"
},
{
"family": "Khalsa",
"given": "Sahib"
}
],
"container-title-short": "Neurosci Conscious",
"volume": "2026",
"issue": "1",
"page": "niag044",
"DOI": "10.1093/nc/niag044",
"PMID": "42657182",
"PMCID": "PMC13513766",
"ISSN": "2057-2107",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/nc/niag044",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
26
]
]
}
}

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