Figure - available from: Advances in Neurodevelopmental Disorders
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Screenshots feedback chart and measured stress level
Source publication
Objectives
Adults with autism often need support to detect their stress and to apply adequate coping strategies for dealing with daily stress. The personalized mobile application Stress Autism Mate (SAM) is developed for and by adults with autism to detect and cope with daily life stress. SAM measures stress four times daily, generates an overview...
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Sustaining long-term user engagement with mobile health (mHealth) interventions while preserving their high efficacy remains an ongoing challenge in real-world well-being applications. To address this issue, we introduce a new algorithm, the Personalized, Context-Aware Recommender (PCAR), for intervention selection and evaluate its performance in a...
Citations
... An effective stress-reducing app for autistic adults is the Stress Autism Mate (SAM) (30,31). The purpose of this app is to improve stress recognition in adults with autism as well as help them to improve their coping skills. ...
... In addition, the app offers stress-reducing tips that can help users to lower their stress levels immediately. In a pilot study of 15 adults with autism, Hoeberichts et al. (30) found that four weeks of using the SAM app resulted in a reduction in stress levels and an improvement in QoL. A more recent single case experimental design study in 34 adults with autism also concluded that SAM was able to reduce stress levels, although no changes were found with regard to quality of life (31). ...
... Therefore, to address this gap, this study evaluated an adaptation of the SAM app specifically tailored to adolescents: Stress Autism Mate Junior (SAM Junior), More specifically, we aimed to assess the effect of the app on autistic adolescents' perceived stress, coping styles and QoL after four weeks of use and a four-week follow-up phase. Based on the studies by Hoeberichts et al. (30,31), we hypothesized that the SAM Junior app decreases perceived stress and maladaptive coping styles of participants while increasing their adaptive coping styles and QoL. ...
Objective
Studies indicate that stress levels of autistic adolescents may be particularly high. Therefore, support is needed to help them deal with their stressors. Stress Autism Mate (SAM) Junior, a mobile self-help tool, was designed in co-creation with adolescents with autism to help reduce daily stress levels. The app is based on the SAM app, which was previously shown to be effective in reducing stress in autistic adults. This study aimed to evaluate the effectiveness of the SAM Junior app in reducing perceived stress and maladaptive coping styles, and increasing adaptive coping styles and quality of life in adolescents with autism.
Methods
A total of 24 Dutch adolescents with autism participated in this Single Case Experimental Design study. Sixteen of them (9 girls and 7 boys; Mage = 15.0 years, SD = 1.9) completed all research phases. Data were collected at four time points separated by four weeks: Control, pre-test, post-test and follow-up. Linear mixed-effects models were used to analyze the data.
Results
At post-test, use of the SAM Junior app had no significant effects on participants’ perceived stress (B = 0.31; 95% CI [-1.59, 2.22], p = .73), adaptive coping (B = -1.38; 95% CI [-5.69, 2.94], p = .51), maladaptive coping (B = -0.63; 95% CI [-4.56, 3.30], p = .74) and quality of life (B = -4.13; 95% CI [-12.19, 3.94], p = .29). These non-significant effects persisted at follow-up.
Discussion
Current preliminary results do not show effectiveness of the SAM Junior app to support adolescents with autism. Using the app as intended, without professional supervision, may have been too complex for this population. Further research is needed to determine the potential effects of the SAM Junior app with more certainty.
... In a previous pilot study (22), a mobile application called 'Stress Autism Mate' (SAM) aimed at supporting adults with autism in identifying and effectively managing their stress levels was developed and tested. SAM uses questionnaires four times a day to assess activities and stress levels and provides personalised stress reduction advice and visual feedback charts based on researchbased algorithms. ...
... A1: control phase with TAU; B: intervention phase with use of SAM; A2: follow-up with TAU. involvement in research or familiarity with SAM (22); (c) inability to understand the Dutch language; and (d) inability to use a smartphone. All 36 participants in the study were being treated at Emerhese, a centre of expertise for autism in the Netherlands. ...
... SAM is freely available in app stores across Europe. More detailed information on the features and development of SAM can be found in the previous publication by Hoeberichts et al. (22), and at https:// www.stressautismmate.nl/. ...
Introduction
The mobile health application “Stress Autism Mate” (SAM) was designed to support adults with autism in identifying and managing daily stress. SAM measures stress four times daily, provides a daily and weekly stress overview, and provides personalised stress reduction advice. This study aimed to assess the effectiveness of SAM over four weeks in reducing perceived stress and internalised stigma, and enhancing coping self-efficacy, quality of life, and resilience among adults with autism.
Methods
Using an A1-B-A2 single-case experimental design, the effect of using SAM on adults with autism was assessed. The phases consisted of A1; treatment as usual (TAU), B; introducing SAM, and finally A2; follow-up with TAU and without the use of SAM. Each phase lasted four weeks, and data were collected via questionnaires before and after each phase. Linear mixed models were used for data analysis.
Results
Results show significant reductions in perceived stress levels, increased coping self-efficacy, and improved perceived health and psychological well-being after using SAM. Furthermore, increased resilience, and decreased internalised stigma were reported after follow-up.
Discussion
In conclusion, this study highlights SAM as a valuable tool for empowering adults with autism to reduce stress and internalised stigmaand to improve coping self-efficacy, psychological well-being, and resilience.
... Onderzoek heeft diverse positieve effecten van inclusieve technologie aangetoond voor bijvoorbeeld het cognitief ondersteunen van mensen bij assemblage en logistiek werk (De Looze et al., 2023), de toeleiding naar werk via virtual reality (Lanser et al., 2021) en ondersteunen bij het omgaan met stress (Hoeberichts et al., 2023). Inclusieve technologie verhoogt en vermeerdert hiermee de zelfstandigheid, autonomie en het welbevinden van mensen, en vergroot tegelijkertijd hun productiviteit, nauwkeurigheid en inzetbaarheid (Knelange et al., 2023). ...
... In prior studies [26][27][28][29][30][31][32][33][34][35], the authors presented ASD IoT applications based on machine learning and deep learning approaches. These applications imple- These classification methods [36][37][38][39][40][41][42][43] for Autism have higher accuracy of AD-CN and AD-MCI and CN-MCI with the single dataset. ...
The incidence of Autism Spectrum Disorder (ASD) among children, attributed to genetics and environmental factors, has been increasing daily. ASD is a non-curable neurodevelopmental disorder that affects children’s communication, behavior, social interaction, and learning skills. While machine learning has been employed for ASD detection in children, existing ASD frameworks offer limited services to monitor and improve the health of ASD patients. This paper presents a complex and efficient ASD framework with comprehensive services to enhance the results of existing ASD frameworks. Our proposed approach is the Federated Learning-enabled CNN-LSTM (FCNN-LSTM) scheme, designed for ASD detection in children using multimodal datasets. The ASD framework is built in a distributed computing environment where different ASD laboratories are connected to the central hospital. The FCNN-LSTM scheme enables local laboratories to train and validate different datasets, including Ages and Stages Questionnaires (ASQ), Facial Communication and Symbolic Behavior Scales (CSBS) Dataset, Parents Evaluate Developmental Status (PEDS), Modified Checklist for Autism in Toddlers (M-CHAT), and Screening Tool for Autism in Toddlers and Children (STAT) datasets, on different computing laboratories. To ensure the security of patient data, we have implemented a security mechanism based on advanced standard encryption (AES) within the federated learning environment. This mechanism allows all laboratories to offload and download data securely. We integrate all trained datasets into the aggregated nodes and make the final decision for ASD patients based on the decision process tree. Additionally, we have designed various Internet of Things (IoT) applications to improve the efficiency of ASD patients and achieve more optimal learning results. Simulation results demonstrate that our proposed framework achieves an ASD detection accuracy of approximately 99% compared to all existing ASD frameworks.
Background
Work-related stress and burnout remain common problems among employees, leading to impaired health and higher absenteeism. The use of mobile health apps to promote well-being has grown substantially; however, the impact of such apps on reducing stress and preventing burnout is limited.
Objective
This study aims to assess the effectiveness of STAPP@Work, a mobile-based stress management intervention, on perceived stress, coping self-efficacy, and the level of burnout among mental health employees.
Methods
The study used a single-case experimental design to examine the use of STAPP@Work among mental health employees without a known diagnosis of burnout (N=63). Participants used the app for 1 week per month repeatedly for a period of 6 months. Using a reversal design, the participants used the app 6 times to assess replicated immediate (1 week after use) and lasting (3 weeks after use) effects. The Perceived Stress Scale, the Coping Self-Efficacy Scale, and the Burnout Assessment Tool were used to measure the outcomes. Linear mixed models were used to analyze the data.
Results
After 6 months of app use for 1 week per month, the participants showed a statistically significant decrease in perceived stress (b=–0.38, 95% CI –0.67 to –0.09; P=.01; Cohen d=0.50) and burnout symptoms (b=–0.31, 95% CI –0.51 to –0.12; P=.002; Cohen d=0.63) as well as a statistically significant improvement in problem-focused coping self-efficacy (b=0.42, 95% CI 0-0.85; P=.049; Cohen d=0.42). Long-term use of the app provided consistent reductions in burnout symptoms over time, including in the level of exhaustion and emotional impairment.
Conclusions
The use of an app-based stress management intervention has been shown to reduce burnout symptoms and enhance coping self-efficacy among mental health workers. Prevention of burnout and minimization of work-related stress are of utmost importance to protect employee health and reduce absenteeism.
