DOI: 10.5061/dryad.hqbzkh1wb
title: Data from: Duration and economic value of a walking-in-nature therapy program: Implications for conservation

description: Nature exposure, such as visiting protected areas, provides mental health
 benefits that reduce healthcare costs and improve productivity, with
 global values in the trillions. Countries are bringing nature-based
 programs into mainstream mental healthcare via nature therapies. This
 study quantifies the scale, duration, and economic value of mental health
 benefits from a long-established nature therapy program and implications
 for conservation. Using a Before-After-Control-Impact design, we evaluated
 a 12-week nature walking program with social mechanisms for therapeutic
 adherence. Mental health was assessed using the Personal Wellbeing Index,
 a measure of subjective well-being, with participants and controls from
 the same subpopulation. Measurements occurred at program start, end, and
 12 weeks post-intervention. Economic benefits were calculated using the
 financial value of quality-adjusted life-years. The nature-based therapy
 program improved the mental well-being of participants during the program
 and for at least three months afterwards. While controls showed well-being
 improvements when they reported having physically exercised (despite not
 being instructed to), program participants exhibited an additional
 Personal Wellbeing Index increase of 5.1%. Training in nature was a
 critical component, leading to the highest increase in mental health
 benefits and doubling of their duration (up to 12 months). The mean total
 economic benefit per participant who followed the program design in full
 was c.AU$4,000. Total economic contribution via mental health, adjusted
 for socioeconomic and demographic factors, participation patterns,
 post-program fade-out, and the national number of participants each year,
 is therefore c.AU$20 million per annum. 5. Mental health benefits of
 nature visits fade once people stop visiting parks. To maximise their
 contribution to political and economic support for protected areas,
 therefore, the focus for future research and practice should be on social
 mechanisms to promote lifelong park visit habits.
description: # Data from: Duration and economic value of a walking-in-nature therapy
 program: Implications for conservation Dataset DOI:
 [10.5061/dryad.hqbzkh1wb](https://doi.org/10.5061/dryad.hqbzkh1wb) ##
 Description of the data and file structure We designed a
 Before-After-Control-Impact experiment. Both treatment and control
 participants were recruited within members of a group who self-identify as
 “wild women” on dedicated social media accounts. These women are
 affiliated with Coastrek® via its parent company Wild Women on Top
 (WWoT®), a women’s outdoor hiking and mountaineering organisation. We
 identified 4000 current and active Wild Women on Top members contactable
 by email and invited them to take part in this research. Members enrolled
 to take part in the Melbourne 2022 event formed our treatment group,
 whereas those who had taken part in previous programs or planned to join
 future programs but were not signed up to the Melbourne 2022 event, formed
 our control group. Our Before-After-Control-Impact design relies on the
 same individuals completing multiple surveys, and both treatment and
 control groups were surveyed with the same questionnaire three times at
 fixed time points. To minimise drop-out, we conducted surveys at only
 three time-points. The first, T~1~, was immediately prior to the beginning
 of the 12-week program, Week 0. Participants can sign up at any time
 before this, but that is the date at which they are committed and start
 active involvement. The second, T~2~, was at the end of the 12-week
 program, Week 12. That is immediately after the participants had completed
 the final event of the program (30-60 km team hike along a national park).
 They were asked to complete this survey within a week of the event. The
 12-week interval T~1 ~- T~2~, the period of the therapy program itself,
 thus spans the greatest anticipated improvement in wellbeing. The third,
 T~3~, Week 24, was 12 weeks after the end of the program, chosen as a
 fade-out period equal in length to the program itself. ### Files and
 variables #### File: Chauvenet_df_final_15Sept2025.csv **Description:** 
 ##### Variables * ID: Unique identifier for participants. * PWI: Personal
 Wellbeing Index score in each surveys at Time 1, 2 and 3. * time:
 Continuous time variable representing surveys at Time 1, 2 and 3. *
 Relationship.ord: Categorical variable representing marital status of
 participants.  * children.ord: Categorical variable representing parental
 status of participants.  * Age.ord: Categorical variable representing age
 of participants.  * trainteam.ord: Categorical variable representing
 training team status of participants.  * trainloc.ord: Categorical
 variable representing training location status of participants.  *
 exercise.ord: Categorical variable representing training intensity status
 of participants.  * subcat2.ord: Categorical variable representing
 treatment (yes) or control (no) status of participants. * PWI_diff:
 Proportional change in PWI between T1 and T2, and T2 and T3.  * PWI_lag:
 Previous PWI. * Survey.ord:  Categorical variable representing surveys at
 Time 1, 2 and 3. #### File: CHAUVE_1.RMD **Description:**  R code to load
 and prepare the survey data, and run the two Generalised Additive Models
 presented in the paper. Code to make predictions with both models, and to
 perform the economic valuation is also included.  ## Code/software
 Software used: R version 4.4.2 (2024-10-31) Platform:
 aarch64-apple-darwin20 Running under: macOS Sequoia 15.6.1 Loaded
 packages:  * attached base packages: stats, graphics, GrDevices, utils,
 datasets, methods, base - other attached packages: itsadug_2.4.1;
 plotfunctions_1.4; glue_1.8.0;  ggtext_0.1.2; AICcmodavg_2.3-3;
 infer_1.0.7;  mgcv_1.9-1; nlme_3.1-167 paletteer_1.6.0; ggnewscale_0.5.0;
 FactoMineR_2.11; factoextra_1.0.7; effects_4.2-2; carData_3.0-5;
 ggpubr_0.6.0; rstatix_0.7.2; lubridate_1.9.4; forcats_1.0.0;
 stringr_1.5.1; purrr_1.0.4; readr_2.1.5 tibble_3.2.1 ggplot2_3.5.1;
 tidyverse_2.0.0; data.table_1.17.0; gridExtra_2.3; viridisLite_0.4.2;
 reshape_0.8.9; MuMIn_1.48.4; tidyr_1.3.1; corrplot_0.95;
 RColorBrewer_1.1-3; ncf_1.3-2; MASS_7.3-64; colorRamps_2.3.4; dplyr_1.1.4;
 lme4_1.1-36; Matrix_1.7-2; raster_3.6-32; sp_2.2-0 ## Access information
 Other publicly accessible locations of the data: * Not applicable Data was
 derived from the following sources: * Not applicable ## Human subjects
 data We received explicit consent from participants to publish the
 de-identified data in the public domain. Unique identifiers were used to
 anonymise the responses.