{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/157717"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/157717","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Investigating Interventions in Fine-grained Contexts for Habit Formation","abstract":"Behavior change is important, yet hard to sustain. Habits are automatic responses to specific contextual cues, and can help sustain behavior change. Fine-grained specific contexts are commonly used in habit formation, but interventions in automatically-detected fine-grained contexts have rarely been explored for habit formation. We investigate habit-formation using interventions in fine-grained mobile, physical-world and digital, computer-based contexts, making three key contributions for each: a survey to identify behavior change needs, a prototype system designed to deliver fine-grained context-specific interventions, and a study to investigate habit-formation using interventions in fine-grained contexts, compared to interventions in less fine-grained contexts. We use the Self-report Habit Index (SRHI) and Self-Report Behavioral Automaticity Index (SRBAI) to measure habit formation and habit automaticity, respectively. For mobile, physical-world behavior change, the survey of needs (N=53 participants) indicated that participants want diverse and personalized behavior change support in diverse and specific contexts. We created a wearable device with on-device deep learning for interventions in personalized and privacy-preserving egocentric visual contexts. In a 4-week pilot study (N=10), interventions in egocentric visual contexts led to more percentage increase in average habit formation (SRHI) and automaticity (SRBAI) than interventions in coarse-grained contexts based on time, geolocation, and physical activity. The percentage increase in median habit formation was also more for the fine-grained egocentric context group, whereas the percentage increase in median habit automaticity was similar between the two groups. For both groups, the habits persisted in the post-study evaluations 1 and 10 weeks later, without interventions. For computer-usage behavior change, the survey of needs (N=68) indicated that participants want to reduce excessive/unnecessary use, e.g., social media, and found off-the-screen breaks helpful. We created a Chrome extension to deliver interventions based on specific web activities, and conducted a 6+2-week study (N=31, 6 weeks of interventions and 2 weeks of post-study without interventions). After 6 weeks, interventions in fine-grained website-entry-based contexts led to more percentage increase in mean and median habit formation and automaticity than interventions in coarse-grained interval-based or random contexts. After the additional two-week post-study, without interventions, the website-entry group had the largest percentage increase in mean SRHI/SRBAI, whereas the interval-based group had the largest percentage increase in median SRHI/SRBAI. Qualitative results from both studies indicated that interventions in fine-grained contexts were delivered at more opportune moments and were less disruptive. We discuss the limitations of our research and present a first step towards investigating interventions in fine-grained contexts for habit formation, potentially for sustainable behavior change, without long-term dependence on technology.","abstract_html":"Behavior change is important, yet hard to sustain. Habits are automatic responses to specific contextual cues, and can help sustain behavior change. Fine-grained specific contexts are commonly used in habit formation, but interventions in automatically-detected fine-grained contexts have rarely been explored for habit formation. We investigate habit-formation using interventions in fine-grained mobile, physical-world and digital, computer-based contexts, making three key contributions for each: a survey to identify behavior change needs, a prototype system designed to deliver fine-grained context-specific interventions, and a study to investigate habit-formation using interventions in fine-grained contexts, compared to interventions in less fine-grained contexts. We use the Self-report Habit Index (SRHI) and Self-Report Behavioral Automaticity Index (SRBAI) to measure habit formation and habit automaticity, respectively. For mobile, physical-world behavior change, the survey of needs (N=53 participants) indicated that participants want diverse and personalized behavior change support in diverse and specific contexts. We created a wearable device with on-device deep learning for interventions in personalized and privacy-preserving egocentric visual contexts. In a 4-week pilot study (N=10), interventions in egocentric visual contexts led to more percentage increase in average habit formation (SRHI) and automaticity (SRBAI) than interventions in coarse-grained contexts based on time, geolocation, and physical activity. The percentage increase in median habit formation was also more for the fine-grained egocentric context group, whereas the percentage increase in median habit automaticity was similar between the two groups. For both groups, the habits persisted in the post-study evaluations 1 and 10 weeks later, without interventions. For computer-usage behavior change, the survey of needs (N=68) indicated that participants want to reduce excessive/unnecessary use, e.g., social media, and found off-the-screen breaks helpful. We created a Chrome extension to deliver interventions based on specific web activities, and conducted a 6+2-week study (N=31, 6 weeks of interventions and 2 weeks of post-study without interventions). After 6 weeks, interventions in fine-grained website-entry-based contexts led to more percentage increase in mean and median habit formation and automaticity than interventions in coarse-grained interval-based or random contexts. After the additional two-week post-study, without interventions, the website-entry group had the largest percentage increase in mean SRHI/SRBAI, whereas the interval-based group had the largest percentage increase in median SRHI/SRBAI. Qualitative results from both studies indicated that interventions in fine-grained contexts were delivered at more opportune moments and were less disruptive. We discuss the limitations of our research and present a first step towards investigating interventions in fine-grained contexts for habit formation, potentially for sustainable behavior change, without long-term dependence on technology.","abstract_has_math":false,"creators":["Khan, Mina"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Program in Media Arts and Sciences (Massachusetts Institute of Technology)","school":null,"contributors":[],"advisors":["Maes, Pattie"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-09","date_published":"2024-09","updated_at":"2026-07-22T22:21:37Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/157717","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Maes, Pattie"]},{"key":"dc:contributor.department","label":"Department","values":["Program in Media Arts and Sciences (Massachusetts Institute of Technology)"]},{"key":"dc:creator","label":"Author","values":["Khan, Mina"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-12-02T21:14:30Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-12-02T21:14:30Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-09"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctoral","Doctor of Philosophy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/157717"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Behavior change is important, yet hard to sustain. Habits are automatic responses to specific contextual cues, and can help sustain behavior change. Fine-grained specific contexts are commonly used in habit formation, but interventions in automatically-detected fine-grained contexts have rarely been explored for habit formation. We investigate habit-formation using interventions in fine-grained mobile, physical-world and digital, computer-based contexts, making three key contributions for each: a survey to identify behavior change needs, a prototype system designed to deliver fine-grained context-specific interventions, and a study to investigate habit-formation using interventions in fine-grained contexts, compared to interventions in less fine-grained contexts. We use the Self-report Habit Index (SRHI) and Self-Report Behavioral Automaticity Index (SRBAI) to measure habit formation and habit automaticity, respectively. For mobile, physical-world behavior change, the survey of needs (N=53 participants) indicated that participants want diverse and personalized behavior change support in diverse and specific contexts. We created a wearable device with on-device deep learning for interventions in personalized and privacy-preserving egocentric visual contexts. In a 4-week pilot study (N=10), interventions in egocentric visual contexts led to more percentage increase in average habit formation (SRHI) and automaticity (SRBAI) than interventions in coarse-grained contexts based on time, geolocation, and physical activity. The percentage increase in median habit formation was also more for the fine-grained egocentric context group, whereas the percentage increase in median habit automaticity was similar between the two groups. For both groups, the habits persisted in the post-study evaluations 1 and 10 weeks later, without interventions. For computer-usage behavior change, the survey of needs (N=68) indicated that participants want to reduce excessive/unnecessary use, e.g., social media, and found off-the-screen breaks helpful. We created a Chrome extension to deliver interventions based on specific web activities, and conducted a 6+2-week study (N=31, 6 weeks of interventions and 2 weeks of post-study without interventions). After 6 weeks, interventions in fine-grained website-entry-based contexts led to more percentage increase in mean and median habit formation and automaticity than interventions in coarse-grained interval-based or random contexts. After the additional two-week post-study, without interventions, the website-entry group had the largest percentage increase in mean SRHI/SRBAI, whereas the interval-based group had the largest percentage increase in median SRHI/SRBAI. Qualitative results from both studies indicated that interventions in fine-grained contexts were delivered at more opportune moments and were less disruptive. We discuss the limitations of our research and present a first step towards investigating interventions in fine-grained contexts for habit formation, potentially for sustainable behavior change, without long-term dependence on technology."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Investigating Interventions in Fine-grained Contexts for Habit Formation"]}]}],"canonical_facts":{"dc:contributor.advisor":["Maes, Pattie"],"dc:contributor.department":["Program in Media Arts and Sciences (Massachusetts Institute of Technology)"],"dc:creator":["Khan, Mina"],"dc:date.accessioned":["2024-12-02T21:14:30Z"],"dc:date.available":["2024-12-02T21:14:30Z"],"dc:date.issued":["2024-09"],"dc:description.abstract":["Behavior change is important, yet hard to sustain. Habits are automatic responses to specific contextual cues, and can help sustain behavior change. Fine-grained specific contexts are commonly used in habit formation, but interventions in automatically-detected fine-grained contexts have rarely been explored for habit formation. We investigate habit-formation using interventions in fine-grained mobile, physical-world and digital, computer-based contexts, making three key contributions for each: a survey to identify behavior change needs, a prototype system designed to deliver fine-grained context-specific interventions, and a study to investigate habit-formation using interventions in fine-grained contexts, compared to interventions in less fine-grained contexts. We use the Self-report Habit Index (SRHI) and Self-Report Behavioral Automaticity Index (SRBAI) to measure habit formation and habit automaticity, respectively. For mobile, physical-world behavior change, the survey of needs (N=53 participants) indicated that participants want diverse and personalized behavior change support in diverse and specific contexts. We created a wearable device with on-device deep learning for interventions in personalized and privacy-preserving egocentric visual contexts. In a 4-week pilot study (N=10), interventions in egocentric visual contexts led to more percentage increase in average habit formation (SRHI) and automaticity (SRBAI) than interventions in coarse-grained contexts based on time, geolocation, and physical activity. The percentage increase in median habit formation was also more for the fine-grained egocentric context group, whereas the percentage increase in median habit automaticity was similar between the two groups. For both groups, the habits persisted in the post-study evaluations 1 and 10 weeks later, without interventions. For computer-usage behavior change, the survey of needs (N=68) indicated that participants want to reduce excessive/unnecessary use, e.g., social media, and found off-the-screen breaks helpful. We created a Chrome extension to deliver interventions based on specific web activities, and conducted a 6+2-week study (N=31, 6 weeks of interventions and 2 weeks of post-study without interventions). After 6 weeks, interventions in fine-grained website-entry-based contexts led to more percentage increase in mean and median habit formation and automaticity than interventions in coarse-grained interval-based or random contexts. After the additional two-week post-study, without interventions, the website-entry group had the largest percentage increase in mean SRHI/SRBAI, whereas the interval-based group had the largest percentage increase in median SRHI/SRBAI. Qualitative results from both studies indicated that interventions in fine-grained contexts were delivered at more opportune moments and were less disruptive. We discuss the limitations of our research and present a first step towards investigating interventions in fine-grained contexts for habit formation, potentially for sustainable behavior change, without long-term dependence on technology."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/157717"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Investigating Interventions in Fine-grained Contexts for Habit Formation"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral","Doctor of Philosophy"]},"updated_at":"2026-07-22T22:21:37Z"}