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Voorbeeld 4 van de 88 pagina's
Samenvatting

Summary of all articles of the course Compassionate Technology (februari 2026)

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Voorbeeld 4 van de 88 pagina's

This document provides a structured and comprehensive summary of all articles included in the course Compassionate Technology, as taught in the Master’s program Positive Clinical Psychology and Technology (February 2026), based on the studyguide as giver by the teachers

Voorbeeld van de inhoud

Subject 1: compassion
Strauss, C., Lever Taylor, B., Gu, J., Kuyken, W., Baer, R., Jones, F., & Cavanagh, K.
(2016). What is compassion and how can we measure it? A review of definitions and
measures. Clinical Psychology Review, 47, 15–27.
https://doi.org/10.1016/J.CPR.2016.05.004
1. Why This Paper Is Important
Strauss et al. (2016) start from a fundamental scientific problem: compassion is widely valued across
society, yet poorly defined and inadequately measured.

Compassion is central in healthcare, education, and moral philosophy. It has evolutionary relevance
(Darwin argued that communities with more sympathetic members flourish better) and measurable
clinical consequences: it improves patient satisfaction, communication, stress regulation, and
recovery from psychopathology.

However, despite its importance:

 There is no consensus definition.
 There is no psychometrically robust measure.

Without a shared definition and reliable measures, we cannot properly compare studies or evaluate
compassion-based interventions. This is the central problem the paper addresses.


2. Aim and Structure of the Paper
The paper has two main goals:

1. To synthesize existing conceptualizations and propose an integrative definition of
compassion.
2. To conduct a systematic review of self- and observer-rated measures of compassion.

The authors draw from Buddhist philosophy, evolutionary psychology, clinical theory, and
organizational research, and evaluate nine existing measures using psychometric quality criteria.
Their conclusion: the field needs a new measure aligned with a coherent, multidimensional
definition.


3. The Challenge: No Consensus Definition
Across the literature compassion is generally described as being moved by suffering and wanting to
help. For example:

 Lazarus: “Being moved by another’s suffering and wanting to help.”
 Goetz: The feeling that arises when witnessing suffering and motivates helping.
 The Dalai Lama: Openness to suffering with a commitment to relieve it.

However, definitions differ in emphasis:

 Some stress emotional resonance.
 Others emphasize cognitive understanding.
 Some include regulatory components such as non-judgment or distress tolerance.
 Some focus mainly on action.

1

,This lack of consensus creates fragmentation in both theory and measurement.

4. From Three Facets to Five Elements
Early conceptualizations (Kanov et al., 2004) described compassion as three core facets:

 Noticing suffering
 Feeling concern
 Responding

Gilbert (2010) expanded this by conceptualizing compassion as an evolved motivational system and
adding:

 Distress tolerance
 Non-judgment

Distress tolerance refers to the ability to remain open to suffering without becoming overwhelmed
by one’s own discomfort. Without this regulatory component, empathy may lead to withdrawal
rather than helping.

Neff (2003), in her work on self-compassion, proposed three components:

 Kindness
 Mindfulness
 Common humanity

Common humanity reflects the idea that suffering is part of the shared human experience. Together,
these theoretical developments led Strauss et al. to propose a more comprehensive model.


5. The Five-Element Definition of Compassion
Based on this synthesis, Strauss et al. propose a five-element definition. Compassion is defined as a
cognitive, affective, and behavioral process consisting of:

1. Recognizing suffering.
2. Understanding the universality of suffering in human experience.
3. Feeling empathy for the person suffering and emotionally connecting with the distress
(emotional resonance).
4. Tolerating uncomfortable feelings that arise in response to suffering (such as distress,
anger, or fear), thereby remaining open and non-judgmental.
5. Motivation to act or acting to alleviate suffering.

This definition integrates emotional, cognitive, and behavioral components and applies to both self-
compassion and compassion for others. Importantly, no previous definition explicitly included all five
elements together.

6. Compassion Versus Empathy
A key conceptual clarification concerns the distinction between empathy and compassion.

Empathy can be divided into cognitive empathy (understanding another’s perspective) and affective
empathy (sharing another’s emotional state). Empathy does not necessarily involve action. One can
empathize with someone’s anger, joy, or fear without any inclination to reduce suffering.




2

,Compassion, by contrast, is specifically a response to suffering. It includes empathy but goes beyond
it. Compassion requires distress tolerance and motivation to alleviate suffering. It is therefore
empathy plus regulatory capacity and action orientation.

This distinction is clinically crucial. Empathy without distress tolerance may lead to empathic distress
and burnout. Compassion includes the regulatory processes that allow sustained helping behavior.


7. Are Self-Compassion and Compassion for Others the Same Construct?
This is an important theoretical debate addressed in the paper.

The paper also addresses whether self-compassion and compassion for others are the same
construct.

From a Buddhist perspective, separating self- and other-compassion is artificial; self-compassion is
considered a prerequisite for compassion toward others.

However, empirical research complicates this view:

 Neff and Pommier (2013) found no correlation between self-compassion and compassion
for others in undergraduate samples.
 Pommier (2010) reported similar findings.

This raises the question of whether the constructs are distinct or whether measurement limitations
explain weak correlations. The authors conclude that this remains unclear and requires further
investigation.



8. The Systematic Review of Measures
The second half of the paper systematically evaluates nine compassion measures. These include the
Compassionate Love Scale, the Santa Clara Brief Compassion Scale, the Self-Compassion Scale, the
Relational Compassion Scale, the Compassionate Care Assessment Tool, and the Schwartz Center
Compassionate Care Scale.

Each measure was evaluated on criteria including:

 Content validity
 Factor structure
 Internal consistency
 Test–retest reliability
 Convergent and discriminant validity
 Floor and ceiling effects
 Interpretability

The maximum possible quality score was 14. The highest score achieved by any measure was 7. No
scale comprehensively captured all five elements of compassion.

The Self-Compassion Scale and the Relational Compassion Scale performed relatively better, but
neither assessed all five elements. Many measures lacked test–retest reliability, had unclear factor
structures, or failed to examine floor and ceiling effects. Discriminant validity was often absent.




3

, The central conclusion is that no existing scale provides a psychometrically robust and
comprehensive measurement of compassion.


9. Why Measurement Matters
Without reliable and valid measures:

 We cannot confidently assess compassion levels.
 We cannot rigorously evaluate compassion-based interventions.
 Scientific progress remains limited.

Therefore, the authors call for:

 Development of a new measure aligned with the five-element definition.
 Empirical testing of the five-element model.
 Strong psychometric evaluation.



10. Final Conclusion
Strauss et al. (2016) make two foundational contributions:

1. They propose an integrated five-element definition of compassion, conceptualizing it as a
multidimensional process involving recognition, common humanity, emotional
resonance, regulation, and action.
2. They demonstrate that existing compassion measures are psychometrically insufficient
and fail to capture the full complexity of the construct.

Compassion is not merely a moral virtue or emotional reaction. It is a multidimensional, regulatory,
and action-oriented process — but current measurement tools do not adequately reflect this
complexity.




4

Inhoudsopgave

  1. 01 Subject 1: compassion 1
  2. 02 Strauss, C., Lever Taylor, B., Gu, J., Kuyken, W., Baer, R., Jones, F., & Cavanagh, K. (2016). What is compassion and how can we measure it? A review of definitions and measures. Clinical Psychology Review, 47, 15–27. https://doi.org/10.1016/J.CPR.2016.05.004 1
    1. 1. Why This Paper Is Important 1
    2. 2. Aim and Structure of the Paper 1
    3. 3. The Challenge: No Consensus Definition 1
    4. 4. From Three Facets to Five Elements 2
    5. 5. The Five-Element Definition of Compassion 2
    6. 6. Compassion Versus Empathy 2
    7. 7. Are Self-Compassion and Compassion for Others the Same Construct? 3
    8. 8. The Systematic Review of Measures 3
    9. 9. Why Measurement Matters 4
    10. 10. Final Conclusion 4
  3. 03 van Lotringen, C. M., Jeken, L., Westerhof, G. J., ten Klooster, P. M., Kelders, S. M., & Noordzij, M. L. (2021). Responsible Relations: A Systematic Scoping Review of the Therapeutic Alliance in Text-Based Digital Psychotherapy. Frontiers in Digital Health, 3(July). https://doi.org/10.3389/fdgth.2021.689750 5
    1. 1.Technology and the Therapeutic Alliance 5
    2. 2. What is the Therapeutic Alliance? 5
    3. 3. What is Text-Based Digital Psychotherapy? 5
    4. 4. The Promise of Text-Based Therapy 6
    5. 5. What Did the Review Find? 6
    6. 6. How Strong Was the Therapeutic Alliance? 7
    7. 7. Alliance and Outcome 7
    8. 8. Responsible Application of Text-Based Digital Psychotherapy 7
    9. 9. But: Important Limitations 8
    10. 10. Ethical and Cultural Limitations 8
  4. 04 Kemp, J., Zhang, T., Inglis, F., Wiljer, D., Sockalingam, S., Crawford, A., ... & Strudwick, G. (2020). Delivery of compassionate mental health care in a digital technology–driven age: scoping review. Journal of Medical Internet Research, 22(3), e16263. doi: 10.2196/16263 9
    1. 1. Why This Review Was Necessary 9
    2. 2. Working Definition of Compassion 9
    3. 3. Research Question 1: What Digital Technologies Are Used? 9
    4. 4. Research Question 2: How Do Digital Technologies Intersect With Compassion? 10
    5. 5. Research Question 3: Facilitators and Barriers 12
    6. 6. Comparison with Feijt et al. (2018) 13
    7. 7. Overall Conclusion 14
  5. 05 van Lotringen C, Lusi B, Westerhof G, Ludden G, Kip H, Kelders S, Noordzij M. The Role of Compassionate Technology in Blended and Digital Mental Health Interventions: Systematic Scoping Review. JMIR Ment Health 2023;10:e42403. URL: https://mental.jmir.org/2023/1/e42403. DOI: 10.2196/42403 14
    1. Why was this review needed? 14
    2. What is Compassion? (STRAUSS et al., 2016) 15
    3. The Three Compassionate Roles of Technology 15
    4. How Do DMHIs Support Each Element of Compassion? 17
    5. Compassionate Technology in Blended Treatment 18
    6. Critical Notes: Opportunities vs Risks 19
    7. Key Theoretical Insight 19
    8. Big Picture Conclusion 19
  6. 06 Morrow, E., Zidaru, T., Ross, F., Mason, C., Patel, K. D., Ream, M., & Stockley, R. (2023). Artificial intelligence technologies and compassion in healthcare: A systematic scoping review. Frontiers in Psychology, 13, 971044. https://doi.org/10.3389/fpsyg.2022.971044 20
    1. 1. Why Is AI Often Seen as Incompatible with Compassion? 20
    2. 2. Compassion as a Motivational System 21
    3. 3. Reconceptualizing Compassion as a Human–AI System of Intelligent Caring 21
    4. 4. Why See Compassion as a System? 22
    5. 5. How AI Technologies Enhance Compassion (From the Review Findings) 22
    6. 6. The Ethical Debate: Replication vs Transformation 23
    7. 7. Implications of the Human–AI System of Intelligent Caring 24
    8. 8. Limitations Acknowledged by the Authors 24
  7. 07 van Lotringen, C. M., Zirnheld, A., Kelders, S. M., Westerhof, G. J., & Noordzij, M. L. (2025). Compassion as a guiding framework for the implementation of digital mental health interventions: An interview study with clients and professionals. PLoS One, 20(10), e0320710. https://doi.org/10.1371/journal.pone.0320710 25
    1. 1. Compassion as a Guiding Framework for the Implementation of Digital Mental Health Interventions 25
    2. 2.Clients: What Do They Value? 26
    3. 3.Professionals: What Motivates Them? 26
    4. 4.Technology Through the Lens of Compassion 27
    5. 5.Matches and Mismatches With Organizational Motivation 27
    6. 6.Compassion as an Implementation Framework 27
  8. 08 Subject 2; Attitude of (mental) health professionals & implementation in clinical practice 28
  9. 09 Feijt, M. A., de Kort, Y. A., Bongers, I. M. & IJsselsteijn, W. A. Perceived Drivers and Barriers to the Adoption of eMental Health by Psychologists: The Construction of the Levels of Adoption of eMental Health Model. J. Med. Internet Res. 20, e153 (2018). https://www.jmir.org/2018/4/e153/ 28
    1. The Four Main Themes Identified 28
    2. 1. General characteristics of eMental health 28
    3. 2. Drivers for Adoption 29
    4. Accelerating the treatment process 29
    5. Increasing client engagement between sessions 29
    6. Intensifying therapy through more frequent contact 29
    7. Enhancing the therapeutic alliance 29
    8. 3. Barriers for Adoption 29
    9. Professional identity 29
    10. Ethical concerns 29
    11. Fear of increased responsibility 29
    12. Organizational pressures 30
    13. 4. Contextual Factors in Daily Clinical Practice 30
    14. The Theoretical Link: Rogers’ Diffusion of Innovations Theory (DIT) 30
    15. 1. Innovators 30
    16. 2. Early adopters 30
    17. 3. Early majority 30
    18. 4. Late majority 30
    19. 5. Laggards 30
    20. 1. No use – Skepticism and resistance; barriers dominate. 30
    21. 2. Minimal use – Some belief in benefits, but no integration into routine. 31
    22. 3. Passive use – eMental health becomes part of daily routines, but limited exploration. 31
    23. 4. Active use – High intrinsic motivation; active search for new tools. 31
    24. 5. Innovative use – Entrepreneurial behavior; initiating projects and envisioning systemic change. 31
    25. How Was the Model Constructed? 31
    26. 1. Thematic analysis of interview data 31
    27. 2. Identification of patterns in differences between participants 31
    28. 3. Mapping these patterns onto Rogers’ DIT 31
    29. 4. Developing five empirically grounded adoption levels 31
    30. 5. Communicative validation with participants 31
    31. Empirical grounding 31
    32. Theoretical anchoring 31
    33. Participant validation 31
    34. How Can the LAMH Model Be Used for Implementation? 31
    35. Level 1 → Increase awareness of benefits, reduce resistance 31
    36. Level 2 → Provide training and practical integration support 31
    37. Level 3 → Offer guiding principles and broaden knowledge 31
    38. Level 4 → Provide peer support and innovation opportunities 31
    39. Level 5 → Facilitate entrepreneurship and development 31
    40. Understanding the “Reluctance” Question 31
    41. Concern for therapeutic quality 31
    42. Ethical responsibility 31
    43. Fear of increased workload 31
    44. Lack of time and support 32
    45. Need for demonstrated benefits 32
    46. Professional identity 32
    47. Organizational constraints 32
    48. Risk perception 32
    49. Insufficient positive experience 32
  10. 10 Wiljer, D., Charow, R., Zhang, M., Lo, B., Sequeira, L., Shen, N., ... & Strudwick, G. (2025). Enabling Digital Compassion in Digital Health Environments: Modified eDelphi Study to Identify Interprofessional Competencies and Technology Attributes. Journal of Medical Internet Research, 27, e66547. https://doi.org/10.2196/66547 32
    1. Aim of the Study 32
    2. Identify essential digital compassion competencies for HCPs 32
    3. Identify digital compassion attributes for digital health technologies 32
    4. 4. The Digital Compassion Framework (Table 4) 33
    5. 58 professional competencies 33
    6. 15 technology attributes 33
    7. The 7 Domains 33
    8. 1. Digital literacy 33
    9. → As an HCP, you consciously explain digital tools to patients and reflect on how the medium shapes communication during each encounter. 33
    10. → As a policy advisor, you embed relational digital skills into training programs and competency frameworks, not just technical instruction. 33
    11. 2. Ethical implications 33
    12. → As an HCP, you assess digital access, literacy, and privacy constraints before recommending digital care. 33
    13. → As a policy advisor, you implement equity audits and ensure inclusive, accessible digital infrastructure. 33
    14. 3. Collaboration and co-design 33
    15. → As an HCP, you involve patients in decisions about whether and how digital tools are used in their treatment. 33
    16. → As a policy advisor, you include patient representatives in procurement and system design processes. 33
    17. 4. Patient preferences 33
    18. → As an HCP, you actively ask patients about their preferred communication mode and adapt accordingly. 34
    19. → As a policy advisor, you avoid “digital by default” policies and ensure flexibility across service delivery models. 34
    20. 5. Therapeutic relationship 34
    21. → As an HCP, you maintain eye contact through the camera, avoid multitasking, and verbally acknowledge emotions more explicitly in virtual sessions. 34
    22. → As a policy advisor, you provide training and guidelines on digital professionalism and virtual relational skills. 34
    23. 6. Patient safety 34
    24. → As an HCP, you explicitly assess emotional safety during virtual encounters and clarify crisis procedures. 34
    25. → As a policy advisor, you ensure that digital platforms include clear crisis-response protocols and secure escalation systems. 34
    26. 7. Technology safety 34
    27. → As an HCP, you use only secure platforms and clearly communicate privacy safeguards to patients. 34
    28. → As a policy advisor, you invest in interoperable, secure systems and mandate strict privacy standards. 34
    29. Technology Attributes 34
    30. 6. Table 6 – Linking to the Conceptual Model 35
    31. 1. Awareness of suffering 35
    32. 2. Mediated response 35
    33. 3. Online intervention 35
    34. 4. Training and coaching 35
    35. 5. Compassion-oriented technologies 35
    36. 6. Personalized response (AI potential) 35
    37. Key Discussion Insights 35
    38. Existing digital health competency frameworks focus on technical skills. 35
    39. They rarely address the digital therapeutic relationship. 35
    40. Compassion must be embedded across: 36
    41. Education 36
    42. Policy 36
    43. Design 36
    44. Organizational culture 36
    45. A cultural shift 36
    46. Compassion-centered co-design 36
    47. Ongoing evaluation 36
    48. Integration into digital literacy training 36
    49. How to Think About Operationalization 36
    50. 8. If You Were a Mental Health Professional… 36
    51. Explicitly acknowledging emotions during video sessions 36
    52. Clarifying communication expectations 36
    53. Checking digital access barriers 36
    54. Ensuring safety in virtual crisis situations 36
    55. Reflecting on how technology changes power dynamics 36
    56. 9. If You Were a Policy Advisor… 36
    57. Embedding digital compassion competencies into training standards 36
    58. Including patient partners in procurement 36
    59. Creating evaluation frameworks for compassion in digital tools 36
    60. Mandating equity assessments 36
    61. Investing in secure and interoperable systems 36
  11. 11 Reynolds, J., Griffiths, K. M., Cunningham, J. A., Bennett, K., & Bennett, A. (2015). Clinical practice models for the use of e-mental health resources in primary health care by health professionals and peer workers: a conceptual framework. JMIR Mental Health, 2(1), e4200. doi: 10.2196/mental.4200 37
    1. 1. Why Was This Paper Written? 37
    2. A clear conceptual framework 37
    3. Clearly differentiated practice models 37
    4. Guidance for training and policy 37
    5. 2. The Core Idea: Five Practice Models 37
    6. The role of the service provider 37
    7. The role of the e-mental health resource 37
    8. The intensity of provider involvement 37
    9. The required skills 37
    10. Referral models (Models 1–3) 37
    11. Integrative models (Models 4–5) 37
    12. 3. The Five Models Explained 38
    13. Competence in clinical assessment 38
    14. Knowledge of specific e-mental health programs 38
    15. Ability to evaluate appropriateness and risk 38
    16. Encouragement and motivation 39
    17. Monitoring progress 39
    18. Reinforcing task completion 39
    19. Providing clarification 39
    20. Offering emotional support 39
    21. Psychoeducation 39
    22. Self-monitoring 39
    23. Skills practice 39
    24. Homework exercises 39
    25. To address comorbid conditions 40
    26. As a supplement to intensive therapy 40
    27. To reinforce skills between sessions 40
    28. As relapse prevention 40
    29. Why Are These Models Necessary? 40
    30. Training programs lack focus. 40
    31. Implementation strategies remain vague. 40
    32. Research findings are difficult to compare. 40
    33. What Is Still Missing? 40
    34. Which model is most effective in routine primary care. 40
    35. Which model is safest. 41
    36. How much professional time is optimal. 41
    37. What training best supports uptake. 41
    38. The Core Continuum You Must Understand 41
    39. Minimal involvement → Promotion 41
    40. Referral with monitoring → Case management 41
    41. Structured human support → Coaching 41
    42. Therapist-led integration → Symptom-focused therapy 41
    43. Advanced multi-modal integration → Comprehensive therapy 41
  12. 12 Ramos, G., & Chavira, D. A. (2022). Use of technology to provide mental health care for racial and ethnic minorities: evidence, promise, and challenges. Cognitive and Behavioral Practice, 29(1), 15-40. https://doi.org/10.1016/j.cbpra.2019.10.004 42
    1. 1. Core Argument of the Paper 42
    2. Unequal distribution of providers 42
    3. Logistical barriers (transportation, work schedules, childcare) 42
    4. Attitudinal barriers (stigma, mistrust, language barriers, fear of discrimination) 42
    5. 2. Do BITs Improve Access? 42
    6. 3. The Five Clinical Recommendations 42
    7. Whether the BIT is appropriate for the presenting problem. 43
    8. Whether it supports treatment goals. 43
    9. Whether additional therapist monitoring is needed. 43
    10. Access to devices (phone, computer, tablet) 43
    11. Internet reliability 43
    12. Ability to afford connectivity 43
    13. Comfort and familiarity with technology 43
    14. Effectiveness of digital therapy 43
    15. Privacy 43
    16. Feeling that technology is impersonal 43
    17. Beliefs that therapy must be face-to-face to work 43
    18. Data security concerns 44
    19. Fear of surveillance 44
    20. Mistrust of institutions 44
    21. Use secure platforms 44
    22. Explain confidentiality clearly 44
    23. Ensure compliance with privacy regulations 44
    24. Consider whether additional monitoring or safety planning is required (e.g., in cases of suicidality) 44
    25. 4. Overall Position of the Authors 44
    26. Logistical barriers 44
    27. Geographic barriers 44
    28. Some stigma-related barriers 44
    29. Structural racism 44
    30. Socioeconomic inequalities 44
    31. Cultural mistrust 44
    32. Digital divides 44
  13. 13 Subject 3; current state of technology 45
  14. 14 Bauducco, S., Pillion, M., Bartel, K., Reynolds, C., Kahn, M., & Gradisar, M. (2024). A bidirectional model of sleep and technology use: a theoretical review of how much, for whom, and which mechanisms. Sleep Medicine Reviews, 76, 101933. https://doi.org/10.1016/j.smrv.2024.101933 45
    1. 1. From a Unidirectional to a Bidirectional Model 45
    2. 2. The Four Established Pathways (Technology → Sleep) 45
    3. 3. Reverse Mechanisms (Sleep → Technology) 46
    4. 4. Moderators: For Whom Is Technology Harmful? 46
    5. 5. Clinical Implications: Beyond “No Screens Before Bed” 46
    6. 6. Integration with Self-Regulation and Digital Behavior 47
  15. 15 Klein Tuente, S., Bogaerts, S., Bulten, E., Keulen-de Vos, M., Vos, M., Bokern, H., … Veling, W. (2020). Virtual Reality Aggression Prevention Therapy (VRAPT) versus Waiting List Control for Forensic Psychiatric Inpatients: A Multicenter Randomized Controlled Trial. Journal of Clinical Medicine, 9(7), 2258. https://doi.org/10.3390/jcm9072258 48
    1. Why is VR potentially very interesting for aggression prevention therapy? (3 reasons) 48
    2. What were the outcomes? 49
    3. What is the role of the therapist in VRAPT? 49
    4. Two interesting follow-up studies (based on the discussion) 50
    5. Overall takeaway for essay questions 50
  16. 16 Hanley, T., Prescott, J., & Gomez, K. U. (2019). A systematic review exploring how young people use online forums for support around mental health issues. Journal of Mental Health, 28(5), 566–576. https://doi.org/10.1080/09638237.2019.1630725 51
    1. Why do young people still use forums, and what does that teach us for new interventions? 52
    2. Strategic lesson for new technology-based interventions: 52
    3. Opportunities for professionals (3 themes) 52
    4. Challenges for professionals (3 themes) 53
    5. Bringing it together for essay questions (what you should be able to argue) 54
  17. 17 Josephine, K., Josefine, L., Philipp, D., David, E., & Harald, B. (2017). Internet- and mobile-based depression interventions for people with diagnosed depression: A systematic review and meta-analysis. Journal of Affective Disorders, 223, 28–40. https://doi.org/10.1016/J.JAD.2017.07.021 54
    1. 1. Why is this study unique? 54
    2. 2. Research Questions 55
    3. 3. Main Results 55
    4. 4. What does the Discussion emphasize? 56
    5. 6. What you should argue in an open essay question 57
  18. 18 Koelen, J., Klein, A., Wolters, N., Bol, E., De Koning, L., Roetink, S., ... & Wiers, R. (2024). Web-Based, Human-Guided, or Computer-Guided Transdiagnostic Cognitive Behavioral Therapy in University Students With Anxiety and Depression: Randomized Controlled Trial. JMIR Mental Health, 11, e50503. doi: 10.2196/50503 58
    1. The Three Conditions 58
    2. Delivery and Ethical Safeguards 59
    3. Main Findings 59
    4. Adherence and Engagement 60
    5. Interpretation in the Discussion 60
  19. 19 Subject 4; Innovative technology and new ideas in mental healthcare 61
  20. 20 Bond, R. R., Mulvenna, M. D., Potts, C., O’Neill, S., Ennis, E., & Torous, J. (2023). Digital transformation of mental health services. Npj Mental Health Research, 2(1), 13. https://doi.org/10.1038/s44184-023-00033-y 61
    1. 1. What is the article about? 61
    2. 2. Types of Digital Mental Health Technologies 62
    3. 4. Functions of Digital Mental Health 63
    4. 5. Six Benefits of Digital Interventions 63
    5. 6. Four Challenges of Digital Interventions 64
    6. 7. Stakeholder-Centred Design 64
    7. What are “Therapeutic Process Factors”? 65
    8. Why are Therapeutic Process Factors Important in Digital Mental Health? 65
    9. How Were Therapeutic Process Factors Implemented in Woebot? 66
    10. Conceptual Contribution of the Article 67
    11. What You Should Be Able to Explain for an Essay 67
    12. 1. Emotion Regulation as the Core Concept 68
    13. 2. The Autonomic Nervous System (ANS) 68
    14. 3. What is Biofeedback? 69
    15. 4. Traditional vs. Ambulatory Biofeedback vs. Biocueing 69
    16. 5. Study Aim 70
    17. 6. Main Findings 70
    18. 7. Feasibility 70
    19. 8. Limitations of the Evidence 71
    20. 9. Future Directions 71
  21. 21 Mohr, D. C., Zhang, M. & Schueller, S. M. Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annu. Rev. Clin. Psychol. 13, 23–47 (2017). https://doi-org.ezproxy2.utwente.nl/10.1146/annurev-clinpsy-032816-044949 71
    1. 1. What is “Personal Sensing” and Why Is It Important? 71
    2. 2. From Raw Data to Clinical Meaning: The Hierarchical Model (Figure 1) 72
    3. 3. Machine Learning: What It Actually Means 73
    4. 4. What Has Research Actually Achieved? 73
    5. 5. Why We Must Be Cautious 74
    6. 6. What Can Mental Healthcare Realistically Expect in the Next 2–3 Years? 75
  22. 22 Bossenbroek, R., Wols, A., Weerdmeester, J., Lichtwarck-Aschoff, A., Granic, I., & van Rooij, M. M. (2020). Efficacy of a virtual reality biofeedback game (DEEP) to reduce anxiety and disruptive classroom behavior: Single-case study. JMIR Mental Health, 7(3), e16066. https://doi.org/10.2196/16066 75
    1. What is the technological intervention and why is it appropriate for young individuals? 75
    2. Why is DEEP particularly appropriate for young individuals in special education? 76
    3. 2️. Why use a Single-Case Experimental Design (SCED)? 77
    4. Main Clinical Implications 78
    5. Clinical implication for special schools 79
  23. 23 Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 1-17. https://doi-org.ezproxy2.utwente.nl/10.1007/s12160-016-9830-8 80
    1. What is a JITAI? 80
    2. The Core Components of a JITAI (Figure 1) 80
    3. 3️ Engagement and Adherence 81
    4. 4️ Critical Perspective and Challenges 82
    5. 5️ Big Conceptual Take-Home Message 83
    6. What You Should Be Able to Do in an Essay 83
  24. 24 Blease, C., & Torous, J. (2023). ChatGPT and mental healthcare: balancing benefits with risks of harms. BMJ Ment Health, 26(1). https://doi.org/10.1136/bmjment-2023-300884 83
  25. 25 Sikström, S., Boehme, R. A., Mirström, M., Agbotsoka, T., Győri, G., Lasota, M., Tabesh, M., Stille, L., & Garcia, D. (2025). Generative AI-assisted clinical interviewing of mental health. Scientific Reports, 15(1), 37737. https://doi.org/10.1038/s41598-025-13429-x 85
    1. What the paper shows 86
    2. What the paper does not prove 86

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