Use of a mHealth Mobile Application to Reduce Stress in Adults with Autism: a Pre-Post Pilot Study of the Stress Autism Mate (SAM)

Participants

Initially, fifteen adults who met the inclusion criteria were included in the present study. The inclusion criteria were (a) being diagnosed with autism according to the DSM-5 and the guideline ‘diagnostics of autism’ of the Dutch Association of Psychiatry (NVVP); (b) having an IQ of above 85 according to the Wechsler Adult Intelligence Scale IV Dutch (WAIS-IV-NL), since the SAM app was developed with and for adults with an IQ above 85. All fifteen participants were receiving mental health care from Emerhese GGz Centraal, a mental health institution where all residents of the Netherlands can receive mental health care, since health insurance is mandatory in the Netherlands (Table 1).

Table 1 Demographic characteristics

The patient group consisted of six females and eight males with a mean age of respectively 46 years (SD 11.5) and 46 years (SD10.8), and a mean duration of treatment in months of 42.2 (SD 11.1) and 40.0 (SD 15.6). All participants were of Caucasian ethnicity.

Procedure SAM System

SAM, a mobile mHealth application, has been developed by a project group consisting of mental health researchers of the Netherlands Organization for applied scientific research (TNO), and practitioners of GGz Centraal Emerhese, Flevoland, in close alignment with the target group, fifteen patients of GGz Centraal Emerhese Flevoland.

The purpose of SAM is to support individuals with autism in stress recognition and self-management of stress in daily life, thereby improving well-being. The SAM app is easy to use, no special training is needed to use SAM. Considering the specific communication needs for adults with autism, SAM was designed to be easily customised. One can select different sets of colours for the feedback chart (i.e. traffic light colours or different shades of blue) and for the questionnaire multiple-choice options in written text or emoticons are available. SAM consists of unique features, which are explained separately below. The following link provide introductory videos about the SAM app: https://youtu.be/QBrad1Si4vA Please note that the video is spoken in Dutch, but English subtitles are available.

Questionnaire

SAM sets a questionnaire four times a day with an interval of four hours. The timing of the first questionnaire is chosen by the user and it takes two minutes to complete. The questionnaire starts with what activities the user has done in the past four hours and how the user felt during these activities. This is followed by two questions about whether the user had positive thoughts and felt energized during the past four hours. The questionnaire ends with ten multiple-choice questions about stress signals experienced in the past four hours (see Table 2). These questions are based on the results of the interviews with the target group. More information about the development of SAM can be found on our website: www.stressautismmate.nl and in the appendix.

Table 2 Ten stress signalling questions SAM app collected and validated in the focus groups

Every question-and-answer possibility in the SAM app is linked to a certain score, and the sum score corresponds to a certain stress level (i.e. no stress, little stress, stress, much stress). Based on cut-off values within the potential sum scores, the algorithm generates a report of the level of perceived stress (i.e. no stress, little stress, stress, much stress). SAM then verifies the results by asking if the measured stress level corresponds to the person’s perception. When there is a discrepancy between the stress level measured by SAM and the person’s own perception, this is registered on the overview page. This forms an input for the dialogue about stress signalling between the user and the practitioner or relative. In addition, we use the authentication data for the further development of SAM.

Personal Coping Advice

After the algorithm has calculated a stress level, the app provides a general as well as a personalized coping advice corresponding to the level of stress, as seen in Fig. 1. The personalized advice consists of stress management tips that are pre-set by the user: while installing the app, users are asked to select preformulated tips and/or to enter their own personal tips. Examples of general preformulated coping advice are ‘go for a walk,’ ‘do a breathing exercise’ or ‘listen to some music.’

Fig. 1figure 1

Screenshots feedback chart and measured stress level

Feedback Chart

At the end of every day and week, an overview of the daily stress level is generated in a feedback chart. This chart visualizes the stress level of every measurement moment and summarizes which activities contributed to feeling good or bad. By looking at the feedback chart, the user may discover stress patterns related to day-to-day activities. In this way, the user can retrospectively consider which activities or events caused or contributed to the experienced stress. Additionally, this chart could be a focus point in therapy if the user wishes to share the results with the therapist.

Study Design

This pilot study used a one-group pretest – post-test – follow-up, quasi-experimental design (Harris et al., 2006). During all phases of this study, participants continued with their regular daily activities and their usual treatment. There was no control group. This within-subject design allows each participant to potentially benefit from the intervention, potentially enhancing the feasibility of this study. The outcome parameters are (i) stress recognition and reduction, (ii) perceived stress, (iii) coping self-efficacy, and (iv) self-rated quality of life. Data was collected at three moments in time: at baseline right before the intervention (pre-test), after the four-week intervention phase (post), and after an eight-week follow-up phase (follow-up).

Baseline

At baseline, just before the start of the intervention phase, the participants were invited for an individual face-to-face appointment with the researcher at a local GGz Centraal location. The baseline questionnaire was completed during this appointment. After completing the questionnaire, the participant, together with the researcher, installed the SAM app on their mobile phone and went through the settings, followed by a detailed explanation of how the SAM app works. In case a participant would not be in possession of a suitable mobile phone on which the SAM app could run, a mobile phone would be made available by the researcher. This was, however, not the case.

Intervention Phase

The intervention phase lasted four weeks. This was based on the idea that a four-week period is acceptable in feasibility terms and burden on the participant. It should also be long enough for the expected effect of the intervention to occur. The expectation was that it would take users about two weeks to fully understand and integrate the SAM app into everyday life. In weeks three and four, the SAM app could influence the daily lives of the participants.

During the intervention phase, participants used SAM four times a day. At all times, a helpdesk was available for questions and technical problems. During the intervention phase, the researcher recorded that the participants completed ≥ 75% of the questionnaires in the SAM app. At the end of the four-week intervention phase, the post-test questionnaire was completed in a face-to-face appointment with the researcher.

Follow-up Phase

The follow-up phase lasted eight weeks. In these eight weeks, the participants did not use the SAM app. They continued with their regular daily activities and their usual treatment. At the end of the follow-up period, the final face-to-face interview took place in which the follow-up questionnaire was completed.

Measures

At all three measure moments, we used the same self-report questionnaires regarding perceived stress, coping self-efficacy and quality of life. All questionnaires were completed independently by the participants during individual face-to-face appointments at a GGz Centraal location. Characteristics regarding gender, age and duration of current treatment were collected from the electronic health record.

Stress Recognition and Reduction

Part 1 consists of four items about stress recognition and reduction created by the researchers. Respectively, 1 ‘To what extent are you capable of recognising a high degree of stress in yourself?’, 2 ‘To what extent are you capable of recognising a low degree of stress in yourself?’ 3 ‘To what extent are you aware of how to reduce stress on yourself?’, and 4 ‘To what extent are you able to actually reduce stress on yourself?’ Response scale ranging from 1–10 (not capable at all, very capable).

Perceived Stress

The reliable and validated Perceived Stress Scale (PSS) (Cohen et al., 1983; Hirvikoski & Blomqvist, 2015; Thoen et al., 2021) consists of ten items and is widely used to assess subjective stress in neurotypical adults as well as in adults with autism (Cronbach’s α = 0.92, McDonald’s ω = 0.93). An example item is ‘In the last month, how often have you been upset because of something that happened unexpectedly?’ Items used a five-point Likert scale anchored by “Never” and “Very often.” In case of the four positively stated items, the response score had to be reversed. Higher sum scores denote higher perceived stress.

Coping Self-Efficacy

The Coping Self-Efficacy Scale (CSES) (Chesney et al., 2006) is a reliable (Cronbach’s α = 0.91, McDonald’s ω = 0.91) and validated tool to measure the perceived coping efficacy of a person. Coping self-efficacy is defined as “one’s ability to perform specific coping behaviours.” (Chesney et al., 2006, p. 2) This widely used tool was chosen because it addresses specific coping abilities which are important for adults with autism. The CSES can be subdivided into three subscales: problem-focused coping, stop unpleasant emotions and thoughts, and get support from friends and family. The reliability Cronbach’s Alpha for the subscales is as follows: problem-focused coping α = 0.77, stop unpleasant emotions and thoughts α = 0.91 and get support from friends and family α = 0.88. An example items is ‘When you are not doing well, or when you have problems, are you able to make a plan of action and follow it when confronted with a problem?’ Response scale ranging from 0 to 10 (I am not able at all to I am very able).

Quality of Life

Since perceived stress can negatively influence quality of life, quality of life was assessed with one question of the World Health Organization Quality of Life (WHOQOL-BREF) questionnaire, namely ‘How would you rate your quality of life?.’ This item was scored with a five-point Likert scale anchored by “Very poor” and “Very good.” In this case, quality of life is defined as “Individuals’ perceptions of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” (World Health Organization, 1998, p. 551).

Data Analyses

The statistical analysis was done by using linear mixed effect models. The questionnaires were analysed by using the mean score for the self-developed stress recognition and reduction items, the sum score of the Perceived Stress Scale, the sum score of each subscale of the Coping Self-Efficacy Scale, and the mean score of the World Health Organization Quality of Life. The missing data is excluded from the data analysis.

In this within-subject design, all participants are their own control, so it is important to compare the questionnaire scores within everyone individually and to aggregate these results to make a statement at population level (Chen & Chen, 2014; Lillie et al., 2011; Zucker et al., 1997, 2010). Linear mixed effect models take within-subject variance into account (Hox, 2010), thereby providing a suitable analysis method for this study. For the analyses, R in combination with RStudio and the packages lme4 was used (Bates et al., 2015), lmerTest and tidyverse (Bates et al., 2015; Kuznetsova et al., 2017; Wickham et al., 2019). For each model, we used the measurement as fixed effect and the intercept or initial score as a random effect, differentiating for everyone. For the models with a significant effect of time (p < 0.05), we tested the assumptions of normality and heteroscedasticity of the residuals, using visualization. For the single question scales, we tested whether the use of an ordinal model using a logit-link improved assumption. As this was not the case, we decided on using a linear mixed effect model for these too.

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