The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
PopTherapy: Coping with Stress through Pop-Culture
Abstract
Stress is considered to be a modern day “global epidemic"; so given the widespread nature of this problem, it would be beneficial if solutions that help people to learn how to cope better with stress were scalable beyond what individual or group therapies can provide today. Therefore, in this work,
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“Finally, one of the four studies classified as a controlled intervention [79] was rated as having a “poor” quality (2 out of 14 points) as it did not follow most of the criteria for controlled interventions (i.e., randomization, large sample size, blind allocation, or assessment).”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [79]
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Machine learning in mental health: a scoping review of methods and applications
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019) · Psychological Medicine · doi:10.1017/s0033291719000151 · Cited by 1,047
Thieme, A., Belgrave, D., & Doherty, G. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3398069 · Cited by 406
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“In the latter, Paredes et al [14] used RL to select interventions to assist mildly depressed individuals, showing that RL-selected interventions were more effective than those selected using other strategies.”
Pocket Skills: A Conversational Mobile Web App To Support Dialectical Behavioral Therapy
Schroeder, J., Wilkes, C., Rowan, K., Toledo, A., Paradiso, A., Czerwinski, M., Mark, G., & Linehan, M. M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173972 · Cited by 175
Self-citationSelf-tracking for Mental Wellness
Kelley, C., Lee, B., & Wilcox, L. G. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025750 · Cited by 178
“Biofeedback displays in the form of games have been found to help reduce stress levels more quickly [10], but representations matter: displaying someone’s stress data does make them more aware of their stress levels, but can also exacerbate their stress [33].”
BackgroundThieme, A., Hanratty, M., Lyons, M., Palacios, J. E., Marques, R. F., Morrison, C. P., & Doherty, G. J. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3564752 · Cited by 197
Smartphone Applications Utilizing Biofeedback Can Aid Stress Reduction
Dillon, A. C., Kelly, M., Robertson, I. H., & Robertson, D. A. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00832 · Cited by 165
Design Opportunities for Mental Health Peer Support Technologies
O'Leary, K., Bhattacharya, A., Munson, S. A., Wobbrock, J. O., & Pratt, W. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998349 · Cited by 154
Machine Learning Techniques in Adaptive and Personalized Systems for Health and Wellness
Oyebode, O., Fowles, J. R., Steeves, D., & Orji, R. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2089085 · Cited by 166
“Paredes et al. developed a recommender system aimed at matching interventions to the personal traits of individual users and their temporal context using RF model (Paredes et al., 2014).”
BackgroundMethodInfluentialHealth Recommender Systems: Systematic Review
Croon, R. D., Houdt, L. V., Htun, N. N., Štiglic, G., Abeele, V. V., & Verbert, K. (2021) · Journal of Medical Internet Research · doi:10.2196/18035 · Cited by 138
Designing emotionally sentient agents
McDuff, D., & Czerwinski, M. (2018) · Communications of the ACM · doi:10.1145/3186591 · Cited by 132
Self-citationEMMA: an emotion-aware wellbeing chatbot
Ghandeharioun, A., McDuff, D., Czerwinski, M., & Rowan, K. (2019) · 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2019.8925455 · Cited by 126
“In positive computing [36] literature, there have been efforts around personalizing interventions toward the users’ preferences (e.g., [12], [37]) and using sensor data to derive the timing of interventions (e.g., [13], [38]).”
BackgroundMethodResultInfluentialSelf-citationParedes, P. E., Zhou, Y., Hamdan, N. A.-H., Balters, S., Murnane, E. L., Ju, W. G., & Landay, J. A. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3191760 · Cited by 124
“Furthermore, designing a suite of interventions that take into consideration novelty effects could help overcome habituation in order to maintain motivation and adherence over the long term [52].”
BackgroundSelf-citationTeaching and Developing Social and Emotional Skills with Technology
Slovák, P., & Fitzpatrick, G. (2015) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2744195 · Cited by 124
“…skills taught as part of SEL, they nonetheless raise the question about strategies for coping with stress in everyday life for a general population [Paredes et al. 2014], and Mamykina et al. [2008] designed MAHI, a mobile-based scaffolding system for newly diagnosed diabetes patients that extends…”
BackgroundGual-Montolio, P., Jaén, I., Martínez-Borba, V., Castilla, D., & Suso-Ribera, C. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19137737 · Cited by 93
Howe, E. S., Suh, J., Morshed, M. B., McDuff, D., Rowan, K., Hernandez, J., Abdin, M., Ramos, G., Tran, T., & Czerwinski, M. P. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502027 · Cited by 90
“engagement and long-term outcomes for technology-delivered interventions [42, 45].”
BackgroundInfluentialSelf-citationRotating Online Behavior Change Interventions Increases Effectiveness But Also Increases Attrition
Kovacs, G., Wu, Z., & Bernstein, M. S. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274364 · Cited by 81
“Techniques that personalize interventions via multi-armed bandits show positive treatment effects, suggesting that the approach may hold promise, but this existing work cannot separate the effects of personalization from the effects of rotation [60, 76, 82].”
BackgroundInfluentialZhang, R., Ringland, K. E., Paan, M., Mohr, D. C., & Reddy, M. C. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445771 · Cited by 81
“People with depression tended to hold a negative perception of themselves and seeing negative feedback might exacerbate their symptoms [49, 83].”
BackgroundMachine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey
Shishvan, O. R., Zois, D., & Soyata, T. (2018) · IEEE Access · doi:10.1109/access.2018.2866049 · Cited by 73
“Another example is provided in [189], where a contextual MAB formulation is proposed that matches stress-coping interventions to individuals and their temporal”
BackgroundSmartphone-Based Psychotherapeutic Micro-Interventions to Improve Mood in a Real-World Setting
Meinlschmidt, G., Lee, J.-H., Stalujanis, E., Belardi, A., Oh, M., Jung, E. K., Kim, H.-C., Alfano, J., Yoo, S.-S., & Tegethoff, M. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01112 · Cited by 68
“We thereby applied micro-interventions in form of psychotherapeutic techniques that have already been used as components of face-to-face psychotherapy (see Paredes et al., 2014), guided by short video-clips of 5min duration.”
BackgroundMethodInfluentialForman, E. M., Kerrigan, S. G., Butryn, M. L., Juarascio, A. S., Manasse, S. M., Ontañón, S., Dallal, D. H., Crochiere, R. J., & Moskow, D. M. (2018) · Journal of Behavioral Medicine · doi:10.1007/s10865-018-9964-1 · Cited by 70
“randomly recommended interventions, by selecting mobile phone-based stress management techniques based on patterns of previous response (Paredes et al., 2014).”
MethodChikersal, P., Belgrave, D. C. M., Doherty, G. J., Enrique, Á., Palacios, J. E., Richards, D., & Thieme, A. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376341 · Cited by 68
“[67] applied reinforcement learning for recommending stress coping interventions tailored to the person.”
BackgroundDesigning opportune stress intervention delivery timing using multi-modal data
Sano, A., Johns, P., & Czerwinski, M. P. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273623 · Cited by 57
“The interventions were grouped into 4 categories (positive psychology, cognitive behavioral, meta-cognitive and somatic practices) and subdivided each category into “individual” and “social” (activities that people perform alone and with others) interventions (5 (interventions per each subcategory) x 2 (individual or…”
BackgroundResultSelf-citationKornfield, R., Meyerhoff, J., Studd, H., Bhattacharjee, A., Williams, J. J., Reddy, M. C., & Mohr, D. C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502046 · Cited by 62
“Therefore, as some past work has also noted [88, 97], an important direction will be to fnd ways that user preferences can be defned and used to shape content, while balancing this against the risk of habituation.”
BackgroundMartins, A., Londral, A. R. M., Nunes, I. L., & Lapão, L. V. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105385 · Cited by 58
Lauffenburger, J. C., Yom‐Tov, E., Keller, P. A., McDonnell, M. E., Crum, K. L., Bhatkhande, G., Sears, E. S., Hanken, K. E., Bessette, L. G., Fontanet, C. P., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01028-5 · Cited by 43
Participatory Design of Technologies to Support Recovery from Substance Use Disorders
Schmitt, Z., & Yarosh, S. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274425 · Cited by 41
“depression [26,66], stress [55,59,73,74] general anxiety [96], bipolar disorder [8,9,62], mental illness [70], and ADHD [24].”
BackgroundIs Your Time Well Spent? Reflecting on Knowledge Work More Holistically
Guillou, H., Chow, K., Fritz, T., & McGrenere, J. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376586 · Cited by 43
“There is a breadth of research that investigates worker productivity in the digital age, but it predominantly focuses separately on either productivity or specific aspects of well-being [7, 21, 37].”
BackgroundAlhasani, M., Mulchandani, D., Oyebode, O., Baghaei, N., & Orji, R. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.777567 · Cited by 43
“Hence, it is unsurprising that mobile health (mHealth) apps that offer personalized content or services were found to be effective by target users (76–79), including apps for managing stress (80).”
BackgroundPREVENTER, a Selection Mechanism for Just-in-Time Preventive Interventions
Jaimes, L. G., Llofriu, M., & Raij, A. B. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2490062 · Cited by 39
“[36] present PopTherapy a system to deliver JITAIs for stress.”
BackgroundResultInfluentialHealthAware: An advice system for stress, sleep, diet and exercise
Sano, A., Johns, P., & Czerwinski, M. P. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344623 · Cited by 38
“We summarized participants’ sleep, diet and exercise behaviors based on their survey responses and Fitbit measurements (Table 2).”
BackgroundSelf-citationMilitello, L. K., Sobolev, M., Okeke, F., Adler, D. A., & Nahum-Shani, I. B. (2021) · JMIR Formative Research · doi:10.2196/30606 · Cited by 39
“This parallels other research suggesting that prompts impact near-time engagement with the app [30,77] but are not sustained over time [29,43,77,78].”
BackgroundResultSmith, C. E., Lane, W., Miller, H. J., Kluver, D., Terveen, L., & Yarosh, S. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479561 · Cited by 39
“…positive thoughts to best support an individual’s continued progress—e.g., affirmation of progress, congratulations, additional questions to provoke more insight, or possibly even nudges to do other beneficial offline activities and wellness or stress management practices (as in [71], for example).”
BackgroundInteractive Technologies for Emotion Regulation Training: A Scoping Review
Sadka, O., & Antle, A. N. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102906 · Cited by 41
Mauriello, M. L., Tantivasadakarn, N., Mora-Mendoza, M. A., Lincoln, E. T., Hon, G., Nowruzi, P., Simon, D., Hansen, L., Goenawan, N. H., Kim, J., et al. (2021) · JMIR Formative Research · doi:10.2196/25294 · Cited by 40
Self-citationMorshed, M. B., Hernandez, J., McDuff, D., Suh, J., Howe, E. S., Rowan, K., Abdin, M., Ramos, G., Tran, T., & Czerwinski, M. P. (2022) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii55700.2022.9953824 · Cited by 35
Self-citationmStress: A mobile recommender system for just-in-time interventions for stress
Clarke, S., Jaimes, L. G., & Labrador, M. A. (2017) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2017.8015367 · Cited by 35
“[12] and used mobile applications and websites as the interventions that were given to the users.”
MethodZois, D. (2016) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2016.7845446 · Cited by 35
“Stress management interventions [5]: In this line of work, a contextual MAB formulation is proposed that matches stress–coping interventions to individuals and their temporal circumstances over time.”
BackgroundMethodSleepBandits: Guided Flexible Self-Experiments for Sleep
Daskalova, N., Yoon, J., Wang, Y., Araujo, C., Beltrán, G., Nugent, N. R., McGeary, J. E., Williams, J. J., & Huang, J. J. S. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376584 · Cited by 34
“They have been applied to testing in educational games [31], identifying effective explanations and feedback messages [51, 52], activities for mental health [36], and interventions for behavior change [26].”
A Resilient and Hierarchical IoT-Based Solution for Stress Monitoring in Everyday Settings
Jiang, S., Firouzi, F., Chakrabarty, K., & Elbogen, E. B. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3122015 · Cited by 35
Bhattacharjee, A., Williams, J. J., Meyerhoff, J., Kumar, H., Mariakakis, A. T., & Kornfield, R. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580774 · Cited by 38
“Activities like these have been found to improve moods and reduce momentary stress [24, 94].”
BackgroundChow, K., Fritz, T., Holsti, L., Barbic, S. P., & McGrenere, J. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3609330 · Cited by 33
“PopTherapy [103] delivers micro-interventions for users to practice CBT- and other therapy-based skills with the goal of reducing stress through the repurposing of popular websites (e.”
BackgroundEvaluating In-Car Movements in the Design of Mindful Commute Interventions: Exploratory Study
Paredes, P. E., Hamdan, N. A.-H., Clark, D., Cai, C. J., Ju, W. G., & Landay, J. A. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6983 · Cited by 29
“Furthermore, effective stress technology interventions [5,28] could be enhanced using multimodal actuation [29] and leverage entertainment systems to increase engagement [20].”
BackgroundSelf-citationPokémon GO Within the Context of Family Health: Retrospective Study
Militello, L. K., Hanna, N., & Nigg, C. R. (2018) · JMIR Pediatrics and Parenting · doi:10.2196/10679 · Cited by 30
Conservation of Procrastination
Kovacs, G., Gregory, D. M., Ma, Z., Wu, Z., Emami, G., Ray, J., & Bernstein, M. S. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300560 · Cited by 29
“Similarly, PopTherapy is a mobile phone app that studied micro-interventions for coping with stress [49].”
BackgroundApproaches for tailoring between-session mental health therapy activities
Oewel, B., Areán, P. A., & Agapie, E. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642856 · Cited by 33
“For example, text messages are adapted based on the user’s changes in location [48], proximity to others [48], screen status [88], and proximity to home or work [88].”
BackgroundPersonalization Paradox in Behavior Change Apps
Zhu, J., Dallal, D. H., Gray, R. C., Villareale, J., Ontañón, S., Forman, E. M., & Arigo, D. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449190 · Cited by 33
“Among existing work on PA personalization, an emergent approach is to use artificial intelligence (AI) techniques to automatically personalize digital interventions [18, 45, 65].”
IntelligentPooling: practical Thompson sampling for mHealth
Tomkins, S., Liao, P., Klasnja, P., & Murphy, S. A. (2021) · Machine Learning · doi:10.1007/s10994-021-05995-8 · Cited by 29
“The second approach completely pools users’ data, that is one algorithm is used on all users so as to learn a common treatment policy both in bandit algorithms (Paredes et al. 2014; Yom-Tov et al. 2017), and in full reinforcement learning algorithms (Clarke et al.”
BackgroundReinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
Deliu, N., Williams, J. J., & Chakraborty, B. (2024) · International Statistical Review · doi:10.1111/insr.12583 · Cited by 32
“Successful applications of LinUCB in mHealth can be found in [94] and [38].”
BackgroundPersonalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical ActivityPreprint
Liao, P., Greenewald, K. H., Klasnja, P., & Murphy, S. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03539 · Cited by 24
“authors uses a contextual bandit learning algorithm combined with a Softmax approach to select the actions (daily suggestion) with the goal of maximizing increased minutes of activity. Paredes et al. [33] employed a contextual bandit learning algorithm combined with a Upper Condence Bound approach to select among 10 t…”
BackgroundMethodTian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398 · Cited by 25
Kovacs, G., Wu, Z., & Bernstein, M. S. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445695 · Cited by 28
“Some studies allow users to choose their own intervention [100, 110, 114], but this paper is the frst system to study changes in user intervention preferences as refected by repeated intervention choices over time.”
BackgroundAvrahami, D., Williams, K., Lee, M. L., Tokunaga, N., Tjahjadi, Y., & Marlow, J. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376369 · Cited by 20
“Several prior systems offered mood tracking, emotional logging, and reflection [21, 20, 42, 40, 22, 32, 31].”
Kitson, A., Slovák, P., & Antle, A. N. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642488 · Cited by 19
“Specifically, somatic sensing and feedback technology, such as wear-ables [70, 91] and AI recommender systems [85, 96, 105], could be embedded in a user’s everyday life (see 2.4.5) by utilizing on-the-spot and experiential delivery mechanisms (no transfer needed) that are tailored (see 2.4.4) to…”
BackgroundInfluentialLauffenburger, J. C., Yom‐Tov, E., Keller, P. A., McDonnell, M. E., Bessette, L. G., Fontanet, C. P., Sears, E. S., Kim, E., Hanken, K. E., Buckley, J. J., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-052091 · Cited by 21
Tong, X., Mauriello, M. L., Mora-Mendoza, M. A., Prabhu, N., Kim, J. P., & Paredes, P. E. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581319 · Cited by 18
“’s MAB implementation [53] for personalized recommendations in our HSO system.”
BackgroundMethodResultInfluentialSelf-citationTrends in Mobile Cyber-Physical Systems for health Just-in time interventions
Jaimes, L. G., Calderón, J. M., Lopez, J. G., & Raij, A. B. (2015) · IEEE SoutheastCon · doi:10.1109/secon.2015.7132887 · Cited by 17
“Recommendation systems based on these techniques include PopTherapy [30], MoodWings [24], and Calma [18].”
BackgroundMethodLewis, R., Ferguson, C., Wilks, C. R., Jones, N. C., & Picard, R. W. (2022) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3491101.3519840 · Cited by 19
“PopTherapy is another relevant system, that uses contextual bandits to recommend interventions derived from popular web apps to users (e.g., learn about active constructive responding by watching this YouTube video with a friend) with the goal to reduce their stress levels [32].”
BackgroundMethodMitigating Issues With/of/for True Personalization
B, H. O.-K. A., Pohjolainen, S., & Agyei, E. E. Y. F. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.844817 · Cited by 16
“…support systems have demonstrated strategies which use personalized messaging, rotating interventions, and multi-armed bandits to gain feedback from users whilst trying out different intervention strategies (Paredes et al., 2014; Dempsey et al., 2015; Rabbi et al., 2015b; Kovacs et al., 2018).”
BackgroundEfficacy and acceptability of digital stress management micro-interventions
Johnson, J. A., Zawadzki, M. J., Materia, F. T., White, A. C., & Smyth, J. M. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.084 · Cited by 15
Eagle, T., Mehrotra, A., Sharma, A., Zuniga, A., & Whittaker, S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555155 · Cited by 16
“Recommendations can be based on users’ daily activities [57], personality or current context [108].”
BackgroundDeveloping a Mobile App for Young Adults with Nonsuicidal Self-Injury: A Prototype Feedback Study
Kruzan, K. P., Reddy, M. C., Washburn, J. J., & Mohr, D. C. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192316163 · Cited by 14
WorkFit: Designing Proactive Voice Assistance for the Health and Well-Being of Knowledge Workers
Ahire, S., Simon, B., & Rohs, M. (2024) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3640794.3665561 · Cited by 15
A Lifelogging Platform Towards Detecting Negative Emotions in Everyday Life using Wearable Devices
Dobbins, C., Fairclough, S. H., Lisboa, P. J. G., & Gonzalez-Navarro, F. F. (2018) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2018.8480180 · Cited by 15
“When a person enters a stressful state, they encounter what is commonly known as the “fight or flight” response, a physiological reaction that results from a perceived threat [16].”
BackgroundIncorporating Artificial Intelligence into Medical Cyber Physical Systems: A Survey
Shishvan, O. R., Zois, D., & Soyata, T. (2019) · Springer eBooks · doi:10.1007/978-3-030-27844-1_8 · Cited by 14
“In [70], a stress reduction system is introduced that uses a contextual MAB formulation to detect the relationship among different interventions to cope with stress and their outcome on different individuals for a given context.”
MethodFrom Monitoring to Assisting: A Systematic Review towards Healthier Workplaces
Lopes, L. S., Rodrigues, A., Cabral, D., & Campos, P. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192316197 · Cited by 13
“[63] developed a mobile recommender system that uses Machine Learning (ML) to deliver personalized micro-interventions to workers.”
BackgroundMethodResultInfluentialNaturalistic Enactment to Elicit and Recognize Caregiver State Anxiety
Miranda, D., Favela, J., Ibarra, C., & Hernandez-Cruz, N. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0551-0 · Cited by 13
Personalized real-time anomaly detection and health feedback for older adults
Parvin, P., Chessa, S., Kaptein, M. C., & Paternò, F. (2019) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-190536 · Cited by 13
“For example, they have been used for stress reduction [32], learning action selection for the student [21], modern service economy [38], suggesting personalized news articles on Yahoo [45] and serving advertisement in Google [24].”
Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing
Vaid, S. S., & Harari, G. M. (2019) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-31620-4_5 · Cited by 12
Persson, D. R., Zhukouskaya, K., Wegener, A.-M. K., Jørgensen, L. K., Bardram, J. E., & Bækgaard, P. (2023) · JMIR Formative Research · doi:10.2196/49738 · Cited by 11
“Participants noted skepticism toward the idea that 1 “solution” could serve every need or preference, a notion also supported in mHealth literature [34,51].”
BackgroundAnalysing IoT Data for Anxiety and Stress Monitoring: A Systematic Mapping Study and Taxonomy
Paula, L. D. S., Dias, L. P. S., Francisco, R., & Barbosa, J. L. V. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2132361 · Cited by 10
“To predict the expected reduction in stress level if a particular intervention, Paredes et al. (2014) trained ensembles of regression trees using the Random Forest algorithm.”
BackgroundMethodHonka, A. M., Nieminen, H., Similä, H., Kaartinen, J., & van Gils, M. (2022) · IEEE Access · doi:10.1109/access.2022.3218776 · Cited by 11
“In addition, both supervised and unsupervised machine learning have been utilized in HRSs [5], including random forests [28], reinforcement learning [24], [35], and neural networks [26].”
BackgroundMethodInfluentialZhou, N., Devleminck, S., & Geurts, L. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713483 · Cited by 10
“Additionally, some studies focused on the promotion of emotional awareness through self-reporting of emotions and/or through monitoring bio-signals in mobile applications [27, 42, 86, 88] or tangible objects [62].”
InfluentialOn-road Stress Analysis for In-car Interventions During the Commute
Balters, S., Bernstein, M., & Paredes, P. E. (2019) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3290607.3312824 · Cited by 10
“Various ideas for in-car stress interventions have been proposed such as soothing temperatures and music, bio-feedback interfaces [5], and chatbots [8]; and first proof-ofconcepts have been validated, e.”
BackgroundSelf-citationDeveloping Skills for Social and Emotional Wellbeing
Slovák, P., Wadley, G., Coyle, D., Thieme, A., Yamashita, N., Lederman, R., Schutt, S., & Doces, M. (2015) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2702613.2702654 · Cited by 10
Paredes, P. E., Ko, R., Calle-Ortiz, E., Canny, J., Hartmann, B., & Niemeyer, G. O. (2016) · Designing Interactive Systems Conference · doi:10.1145/2901790.2901832 · Cited by 10
“[17] described how affective interventions suffer from novelty effects despite their efficacy.”
BackgroundSelf-citationSong, S., Yamashita, N., & Kim, J. (2020) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3421937.3421973 · Cited by 9
“Many researchers have proposed systems that detect user stress with diverse technology and analysis, includingmobility patterns [16], the human voice [39], passive smartphone sensor data [66], less invasive sensing approach [5], patterns of smartphone app usage [25], as well as such stress treatment as self-tracking a…”
BackgroundThe evaluation of health recommender systems: A scoping review
Ananthakrishnan, A., Milne‐Ives, M., Cong, C., Shankar, R., Lakey, B., Alexánder, J., Tapuria, A., Marchal, A., Joy, E., & Meinert, E. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105697 · Cited by 10
Zhou, X., Rau, P. P., & Liu, X. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449077 · Cited by 9
“DISs have been applied successfully to deal with many health issues, such as stress management [37,43], healthy eating [14], and sleep [7].”
BackgroundEffectiveness of Mental Health Care by Using Machine Learning on Manufacturing Worker
Lim, J., Lee, S., Noh, J., Lee, W., Su, P., & Yoon, Y. (2023) · International Journal of Precision Engineering and Manufacturing-Smart Technology · doi:10.57062/ijpem-st.2023.0066 · Cited by 9
Valtchanov, D., & Hancock, M. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702510 · Cited by 9
“In human-computer interaction (HCI), the importance of identifying and alleviating stress has been recognized, and various different sensing and mobile intervention technologies have been developed to combat the “stress epidemic” [19].”
BackgroundJoshi, A. C., Ghogare, A. S., & Madavi, P. B. (2025) · Industrial Psychiatry Journal · doi:10.4103/ipj.ipj_363_24 · Cited by 8
“20-27] A “low‑risk” rating indicates that these studies used reliable methods to categorize patients into different treatment groups, ensuring the validity of their results.”
PITCH: Designing Agentic Conversational Support for Planning and Self-reflection
Abbas, A., Wohn, C., Hu, D., Rho, E. H. R., & Lee, S. W. (2025) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3719160.3736634 · Cited by 11
“Similarly, another study explored CBT-based micro-intervention in popular smartphone apps for stress management [93].”
A survey of cyber-physical system implementations of real-time personalized interventions
Steele, R. J., Hillsgrove, T., Khoshavi, N., & Jaimes, L. G. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03263-0 · Cited by 8
“Reinforcement learning methods are not the only type of ML used here; ensemble learning methods such as random forest have also been used for intervention selection (Paredes et al. 2014).”
MethodCounterfactual inference with latent variable and its application in mental health care
Marchezini, G. F., Lacerda, A. M., Pappa, G. L., Meira, W., de Miranda, D. M., Romano‐Silva, M. A., Costa, D. D. S., & Diniz, L. M. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-021-00818-9 · Cited by 10
“In the context of mental health, Paredes et al. (2014) dealt with personalized clinical interventions.”
BackgroundIntelligent Pooling in Thompson Sampling for Rapid Personalization in Mobile Health
Tomkins, S., Liao, P., Yeung, S. Y., Klasnja, P., & Murphy, S. A. (2019) · Cited by 7
Roy, T., & Nahid, M. H. (2022) · International Conference on Computing Advancements (ICCA) · doi:10.1145/3542954.3543015 · Cited by 8
“The literature identified some sequential decisionmaking apps, such as physical activity detection for obesity prevention, stroke rehabilitation, spherocytosis treatment, and stress management interventions[32, 32, 43, 51, 80].”
BackgroundSereneMind: Design and Evaluation of a Persuasive Mobile App for Managing Stress Among Adults
Alhasani, M., Oyebode, O., & Orji, R. (2023) · SeGAH ... IEEE ... International Conference on Serious Games and Applications for Health/IEEE International Conference on Serious Games and Applications for Health · doi:10.1109/segah57547.2023.10253762 · Cited by 7
“Personalizing contents or interventions will not only be effective in reducing stress but also to boosts user motivation and engagement with the app [46].”
BackgroundBhattacharjee, A., Chen, P., Mandal, A., Hsu, A., O’Leary, K., Mariakakis, A. T., & Williams, J. J. (2023) · JMIR Formative Research · doi:10.2196/47360 · Cited by 7
“In our work, we draw on insights from clinical psychology and human-computer interaction literature on how to design brief RQAs that are helpful for people to manage psychological well-being and adopt healthy behaviors [13-15].”
BackgroundMethodEvaluating a New Approach to Data Fusion in Wearable Physiological Sensors for Stress Monitoring
Rodrigues, C. G., Fröhlich, W. D. R., Jabroski, A. G., Rigo, S. J., Rodrigues, A. K., & de Castro, E. K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61380-8_37 · Cited by 7
Chandrasekaran, A., Bielicke, L., Shah, D., Janakiraman, H., & Mauriello, M. L. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713269 · Cited by 9
“While displaying stress data can enhance users’ awareness of their stress levels, it may also inadvertently amplify stress[81, 83].”
Using LLMs for self-care: User and counsellor perspectives
Wester, J., de Jong, S., Pohl, H., & van Berkel, N. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103589 · Cited by 6
Jang, S., Seo, Y., Choi, W., & Lee, U. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714208 · Cited by 6
“PopTherapy [87] developed a personalization technique to decide treatment options based on tailoring variables by collecting user and contextual data.”
Improving Fairness in Adaptive Social Exergames via Shapley Bandits
Gray, R. C., Villareale, J., Fox, T. B., Dallal, D. H., Ontañón, S., Arigo, D., Jabbari, S., & Zhu, J. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584050 · Cited by 5
“Among existing work on PA personalization, an emergent approach is to use AI techniques to personalize digital interventions automatically [39, 79, 101].”
MethodSmartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing
Vaid, S. S., & Harari, G. M. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_6 · Cited by 5
Empirical Likelihood for Contextual BanditsPreprint
Karampatziakis, N., Langford, J. C., & Mineiro, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03323 · Cited by 5
“Contextual Bandits [3, 17] are now in widespread practical use ([19, 7, 25]).”
BackgroundRapidly Personalizing Mobile Health Treatment Policies with Limited DataPreprint
Tomkins, S., Liao, P., Klasnja, P., Yeung, S. Y., & Murphy, S. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.09971 · Cited by 5
“The second paradigm is learning one treatment policy for all users both in bandit algorithms (Bouneffouf et al., 2012; Paredes et al., 2014; Yom-Tov et al., 2017), and in full reinforcement learning algorithms (Clarke et al., 2017; Zhou et al., 2018).”
Zhu, J., Dallal, D. H., Gray, R. C., Villareale, J., Ontañón, S., Forman, E. M., & Arigo, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.10020 · Cited by 5
“Among existing work on PA personalization, an emergent approach is to use artificial intelligence (AI) techniques to automatically personalize digital interventions [18, 45, 65].”
Investigating the Role of Situational Disruptors in Engagement with Digital Mental Health Tools
Bhattacharjee, A., Williams, J. J., Beltzer, M., Meyerhoff, J., Kumar, H., Song, H., Mohr, D. C., Mariakakis, A. T., & Kornfield, R. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757487 · Cited by 8
“…it is important to recognize that not all DMH tools require such extended durations; for example, some are designed as single-session interventions lasting around 10 to 20 minutes, focusing on teaching a specific micro-skill or providing immediate support for stress management [14, 155, 176].”
Technology's Role in Fostering Therapist-Client Collaboration and Engagement with Goals
Oewel, B., Azizan, N., Areán, P. A., & Agapie, E. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3687055 · Cited by 4
“Numerous technologies aim to provide predictive information about people’s mental health based on signals such as location or social proximity [12, 25, 94, 122].”
Metadata
- Authors
- Paredes, P., Giald-Bachrach, R., Czerwinski, M., Roseway, A., Rowan, K., & Hernandez, J.
- Affiliations
- University of California, Berkeley (US)
- Microsoft (United States) (US)
- MIT Media Lab (US)
- Corresponding author
- Paredes, Pablo
- Venue
- Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare
- Publisher
- ICST
- Published
- 2014
- ISSN
- 2153-1633 (ISSN-L)
- OpenAlex
- W2016372372
- Cited by
- 152
- References
- 23
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 12 · 2023: 10 · 2022: 41 · 2021: 14 · 2020: 10 · 2019: 13 · 2018: 11 · 2017: 7 · 2016: 5 · 2015: 8
- FWCI
- 17.33
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Pervasive Computing Technologies for Healthcare (publishedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Communication › Media Influence and Health
- Social Sciences › Psychology › Clinical Psychology › Art Therapy and Mental Health
- Keywords
- stress coping, popular culture, stress management
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