Using Ecological Momentary Assessment to Examine Predictors of Motivation for Physical Activity in Older Adults Original Research
Main Article Content
Keywords
digital health, physical activity, physical function
Abstract
Introduction: Ecological Momentary Assessment (EMA) involves repeated assessments of patients’ real-time experiences. Smartphone delivery of EMA can monitor healthy behaviors, including physical activity (PA). PA is critical for maintaining mobility and independence in older adults. We aimed to utilize EMA for monitoring motivation for PA, while assessing PA and physical function (PF) as predictors.
Methods: We collected 179 EMA responses from 28 community-dwelling older adults (Mage = 72.67±6.55 years, 82.1% female) over one week. Motivation for PA was determined using a single question delivered by a smartphone, PA was monitored using accelerometers, and PF was assessed using dynamic balance (Timed-Up-and-Go) and lower limb power (Sit-to-Stand).
Results: Motivation for PA showed weak correlations with PA (ρ=0.187), dynamic balance (ρ=-0.157) and lower limb power (ρ=0.200). However, mixed effects models revealed that only better dynamic balance (p=0.001) predicted increased motivation.
Conclusions: This study revealed the feasibility of monitoring motivation through mobile delivery of EMA, validating the method for future interventions. Evaluation of motivation can assess older adults’ subjective attitudes towards interventions. Additionally, these findings have implications for intervention designs that aim to increase engagement in PA; exercises or methods designed to improve dynamic balance may provide more lasting improvements than focusing on increasing PA alone.
References
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12. Maher JP, Dunton GF. Editor’s choice: Dual-process model of older adults’ sedentary behavior: An ecological momentary assessment study. Psychology & Health. 2020;35(5):519–537.
13. Kakara R. Nonfatal and fatal falls among adults aged≥ 65 years—united states, 2020–2021. MMWR Morbidity and Mortality Weekly Report. 2023;72.
14. Adam CE, Fitzpatrick AL, Leary CS, Ilango SD, Phelan EA, Semmens EO. The impact of falls on activities of daily living in older adults: A retrospective cohort analysis. PLoS one. 2024;19(1):e0294017.
15. Appeadu MK, Bordoni B. Falls and fall prevention in older adults. Statpearls [internet]: StatPearls Publishing; 2023.
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21. Keadle SK, McKinnon R, Graubard BI, Troiano RP. Prevalence and trends in physical activity among older adults in the united states: A comparison across three national surveys. Preventive medicine. 2016;89:37–43.
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27. Domingos C, Correia Santos N, Pêgo JM. Association between self-reported and accelerometer-based estimates of physical activity in portuguese older adults. Sensors. 2021;21(7):2258.
28. Knittle K, Nurmi J, Crutzen R, Hankonen N, Beattie M, Dombrowski SU. How can interventions increase motivation for physical activity? A systematic review and meta-analysis. Health psychology review. 2018;12(3):211–230.
29. Willmott TJ, Pang B, Rundle-Thiele S. Capability, opportunity, and motivation: An across contexts empirical examination of the com-b model. BMC Public Health. 2021;21(1):1014.
30. Dacey M, Baltzell A, Zaichkowsky L. Older adults' intrinsic and extrinsic motivation toward physical activity. American journal of health behavior. 2008;32(6):570–582.
31. Meredith SJ, Cox NJ, Ibrahim K, et al. Factors that influence older adults’ participation in physical activity: A systematic review of qualitative studies. Age and ageing. 2023;52(8):afad145.
32. Arnautovska U, Fleig L, O'callaghan F, Hamilton K. Older adults’ physical activity: The integration of autonomous motivation and theory of planned behaviour constructs. Australian Psychologist. 2019;54(1):46–54.
33. Kebede AS, Ozolins L-L, Holst H, Galvin K. Digital engagement of older adults: Scoping review. Journal of Medical Internet Research. 2022;24(12):e40192.
34. Brunet J, Sabiston CM. Exploring motivation for physical activity across the adult lifespan. Psychology of sport and exercise. 2011;12(2):99–105.
35. Thiamwong L, Xie R, Park J-H, Lighthall N, Loerzel V, Stout J. Optimizing a technology-based body and mind intervention to prevent falls and reduce health disparities in low-income populations: Protocol for a clustered randomized controlled trial. JMIR research protocols. 2023;12(1):e51899.
36. Buschke H, Kuslansky G, Katz M, et al. Screening for dementia with the memory impairment screen. Neurology. 1999;52(2):231–231.
37. Choudhury R, Park J-H, Banarjee C, Thiamwong L, Xie R, Stout JR. Associations of mutually exclusive categories of physical activity and sedentary behavior with body composition and fall risk in older women: A cross-sectional study. International journal of environmental research and public health. 2023;20(4):3595.
38. ActiGraph LLC. Actigraph leap. 2025; https://theactigraph.com/actigraph-leap. Accessed August 24, 2024.
39. Rhudy MB, Dreisbach SB, Moran MD, Ruggiero MJ, Veerabhadrappa P. Cut points of the actigraph gt9x for moderate and vigorous intensity physical activity at four different wear locations. Journal of Sports Sciences. 2020;38(5):503–510.
40. Beauchet O, Fantino B, Allali G, Muir S, Montero-Odasso M, Annweiler C. Timed up and go test and risk of falls in older adults: A systematic review. The journal of nutrition, health & aging. 2011;15:933–938.
41. Jones CJ, Rikli RE, Beam WC. A 30-s chair-stand test as a measure of lower body strength in community-residing older adults. Research quarterly for exercise and sport. 1999;70(2):113–119.
42. Van Hees VT, Fang Z, Langford J, et al. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: An evaluation on four continents. Journal of applied physiology. 2014;117(7):738–744.
43. Van Hees VT, Gorzelniak L, Dean León EC, et al. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PloS one. 2013;8(4):e61691.
44. Bakrania K, Yates T, Rowlands AV, et al. Intensity thresholds on raw acceleration data: Euclidean norm minus one (enmo) and mean amplitude deviation (mad) approaches. PloS one. 2016;11(10):e0164045.
45. Suorsa K, Pulakka A, Leskinen T, et al. Comparison of sedentary time between thigh-worn and wrist-worn accelerometers. Journal for the Measurement of Physical Behaviour. 2020;3(3):234–243.
46. Harris PA, Taylor R, Minor BL, et al. The redcap consortium: Building an international community of software platform partners. Journal of biomedical informatics. 2019;95:103208.
47. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (redcap)—a metadata-driven methodology and workflow process for providing translational research informatics support. Journal of biomedical informatics. 2009;42(2):377–381.
48. de Winter JCF, Gosling SD, Potter J. Comparing the pearson and spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological Methods. 2016;21(3):273–290. doi: 10.1037/met0000079.
49. Bates D. Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:14065823. 2014.
50. Green P, MacLeod CJ. Simr: An r package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution. 2016;7(4):493–498.
51. Nilstomt A, Gustavsson J, Beckman L, et al. Physical activity from the perspective of older adults: A convergent mixed-method study. BMC geriatrics. 2024;24(1):768.
52. Van Lummel RC, Walgaard S, Pijnappels M, et al. Physical performance and physical activity in older adults: Associated but separate domains of physical function in old age. PloS one. 2015;10(12):e0144048.
53. Ferrand C, Martinent G, Bonnefoy M. Exploring motivation for exercise and its relationship with health-related quality of life in adults aged 70 years and older. Ageing & Society. 2014;34(3):411–427.
54. Jofré-Saldía E, Villalobos-Gorigoitía Á, Cofré-Bolados C, Ferrari G, Gea-García GM. Multicomponent training in progressive phases improves functional capacity, physical capacity, quality of life, and exercise motivation in community-dwelling older adults: A randomized clinical trial. International Journal of Environmental Research and Public Health. 2023;20(3):2755.
55. Thøgersen-Ntoumani C, Ntoumanis N. The role of self-determined motivation in the understanding of exercise-related behaviours, cognitions and physical self-evaluations. Journal of sports sciences. 2006;24(4):393–404.
56. Pohl P, Ahlgren C, Nordin E, Lundquist A, Lundin-Olsson L. Gender perspective on fear of falling using the classification of functioning as the model. Disability and Rehabilitation. 2015;37(3):214–222.
57. Timsina LR, Willetts JL, Brennan MJ, et al. Circumstances of fall-related injuries by age and gender among community-dwelling adults in the united states. PloS one. 2017;12(5):e0176561.
2. Zapata-Lamana R, Lalanza JF, Losilla J-M, Parrado E, Capdevila L. Mhealth technology for ecological momentary assessment in physical activity research: A systematic review. PeerJ. 2020;8:e8848.
3. Maher JP, Rebar AL, Dunton GF. Ecological momentary assessment is a feasible and valid methodological tool to measure older adults’ physical activity and sedentary behavior. Frontiers in psychology. 2018;9:1485.
4. Maher JP, Sappenfield K, Scheer H, Zecca C, Hevel DJ, Kennedy-Malone L. Feasibility and validity of assessing low-income, african american older adults’ physical activity and sedentary behavior through ecological momentary assessment. Journal for the Measurement of Physical Behaviour. 2021;4(4):343–352.
5. Stojanovic M, Venkatesan A, English T, Head D. Affective response to daily physical activity in younger and older adults. Psychology and Aging. 2024.
6. Hevel DJ, Dunton GF, Maher JP. Acute bidirectional relations between affect, physical feeling states, and activity-related behaviors among older adults: An ecological momentary assessment study. Annals of Behavioral Medicine. 2021;55(1):41–54.
7. Daniels K, Quadflieg K, Robijns J, et al. From steps to context: Optimizing digital phenotyping for physical activity monitoring in older adults by integrating wearable data and ecological momentary assessment. Sensors (Basel, Switzerland). 2025;25(3):858.
8. Yim SJ, Lui LM, Lee Y, et al. The utility of smartphone-based, ecological momentary assessment for depressive symptoms. Journal of Affective Disorders. 2020;274:602–609.
9. Eghdami S, Ahmadkhaniha HR, Baradaran HR, Hirbod-Mobarakeh A. Ecological momentary interventions for smoking cessation: A systematic review and meta-analysis. Social Psychiatry and Psychiatric Epidemiology. 2023;58(10):1431–1445.
10. Adams LB, Watts T, DeVinney A, et al. Acceptability and feasibility of a smartphone-based real-time assessment of suicide among black men: Mixed methods pilot study. JMIR formative research. 2024;8:e48992.
11. Maher JP, Dunton GF. Within-day time-varying associations between motivation and movement-related behaviors in older adults. Psychology of Sport and Exercise. 2020;47:101522.
12. Maher JP, Dunton GF. Editor’s choice: Dual-process model of older adults’ sedentary behavior: An ecological momentary assessment study. Psychology & Health. 2020;35(5):519–537.
13. Kakara R. Nonfatal and fatal falls among adults aged≥ 65 years—united states, 2020–2021. MMWR Morbidity and Mortality Weekly Report. 2023;72.
14. Adam CE, Fitzpatrick AL, Leary CS, Ilango SD, Phelan EA, Semmens EO. The impact of falls on activities of daily living in older adults: A retrospective cohort analysis. PLoS one. 2024;19(1):e0294017.
15. Appeadu MK, Bordoni B. Falls and fall prevention in older adults. Statpearls [internet]: StatPearls Publishing; 2023.
16. Lavedán A, Viladrosa M, Jürschik P, et al. Fear of falling in community-dwelling older adults: A cause of falls, a consequence, or both? PLoS one. 2018;13(3):e0194967.
17. Thomas E, Battaglia G, Patti A, et al. Physical activity programs for balance and fall prevention in elderly: A systematic review. Medicine. 2019;98(27):e16218.
18. Dipietro L, Campbell WW, Buchner DM, et al. Physical activity, injurious falls, and physical function in aging: An umbrella review. Medicine and science in sports and exercise. 2019;51(6):1303.
19. Borg GA. Psychophysical bases of perceived exertion. Medicine and science in sports and exercise. 1982;14(5):377–381.
20. Chen S, Malete L, Ling J. An examination of physical activity guidelines and health-related quality of life among us older adults. Preventive Medicine. 2022;156:106986.
21. Keadle SK, McKinnon R, Graubard BI, Troiano RP. Prevalence and trends in physical activity among older adults in the united states: A comparison across three national surveys. Preventive medicine. 2016;89:37–43.
22. Manini TM, Pahor M. Physical activity and maintaining physical function in older adults. British journal of sports medicine. 2009;43(1):28–31.
23. Cheung C, Talley K, McMahon S, Schorr E, Wyman J. Knowledge of physical activity guidelines and its association with physical activity and physical function in older adults. Activities, Adaptation & Aging. 2020;44(2):106–118.
24. Arvidsson D, Fridolfsson J, Börjesson M. Measurement of physical activity in clinical practice using accelerometers. Journal of internal medicine. 2019;286(2):137–153.
25. Banarjee C, Choudhury R, Park J-H, et al. Common physical performance tests for evaluating health in older adults: Cross-sectional study. Interactive Journal of Medical Research. 2024;13(1):e53304.
26. Patrizio E, Calvani R, Marzetti E, Cesari M. Physical functional assessment in older adults. The Journal of frailty & aging. 2021;10:141–149.
27. Domingos C, Correia Santos N, Pêgo JM. Association between self-reported and accelerometer-based estimates of physical activity in portuguese older adults. Sensors. 2021;21(7):2258.
28. Knittle K, Nurmi J, Crutzen R, Hankonen N, Beattie M, Dombrowski SU. How can interventions increase motivation for physical activity? A systematic review and meta-analysis. Health psychology review. 2018;12(3):211–230.
29. Willmott TJ, Pang B, Rundle-Thiele S. Capability, opportunity, and motivation: An across contexts empirical examination of the com-b model. BMC Public Health. 2021;21(1):1014.
30. Dacey M, Baltzell A, Zaichkowsky L. Older adults' intrinsic and extrinsic motivation toward physical activity. American journal of health behavior. 2008;32(6):570–582.
31. Meredith SJ, Cox NJ, Ibrahim K, et al. Factors that influence older adults’ participation in physical activity: A systematic review of qualitative studies. Age and ageing. 2023;52(8):afad145.
32. Arnautovska U, Fleig L, O'callaghan F, Hamilton K. Older adults’ physical activity: The integration of autonomous motivation and theory of planned behaviour constructs. Australian Psychologist. 2019;54(1):46–54.
33. Kebede AS, Ozolins L-L, Holst H, Galvin K. Digital engagement of older adults: Scoping review. Journal of Medical Internet Research. 2022;24(12):e40192.
34. Brunet J, Sabiston CM. Exploring motivation for physical activity across the adult lifespan. Psychology of sport and exercise. 2011;12(2):99–105.
35. Thiamwong L, Xie R, Park J-H, Lighthall N, Loerzel V, Stout J. Optimizing a technology-based body and mind intervention to prevent falls and reduce health disparities in low-income populations: Protocol for a clustered randomized controlled trial. JMIR research protocols. 2023;12(1):e51899.
36. Buschke H, Kuslansky G, Katz M, et al. Screening for dementia with the memory impairment screen. Neurology. 1999;52(2):231–231.
37. Choudhury R, Park J-H, Banarjee C, Thiamwong L, Xie R, Stout JR. Associations of mutually exclusive categories of physical activity and sedentary behavior with body composition and fall risk in older women: A cross-sectional study. International journal of environmental research and public health. 2023;20(4):3595.
38. ActiGraph LLC. Actigraph leap. 2025; https://theactigraph.com/actigraph-leap. Accessed August 24, 2024.
39. Rhudy MB, Dreisbach SB, Moran MD, Ruggiero MJ, Veerabhadrappa P. Cut points of the actigraph gt9x for moderate and vigorous intensity physical activity at four different wear locations. Journal of Sports Sciences. 2020;38(5):503–510.
40. Beauchet O, Fantino B, Allali G, Muir S, Montero-Odasso M, Annweiler C. Timed up and go test and risk of falls in older adults: A systematic review. The journal of nutrition, health & aging. 2011;15:933–938.
41. Jones CJ, Rikli RE, Beam WC. A 30-s chair-stand test as a measure of lower body strength in community-residing older adults. Research quarterly for exercise and sport. 1999;70(2):113–119.
42. Van Hees VT, Fang Z, Langford J, et al. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: An evaluation on four continents. Journal of applied physiology. 2014;117(7):738–744.
43. Van Hees VT, Gorzelniak L, Dean León EC, et al. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PloS one. 2013;8(4):e61691.
44. Bakrania K, Yates T, Rowlands AV, et al. Intensity thresholds on raw acceleration data: Euclidean norm minus one (enmo) and mean amplitude deviation (mad) approaches. PloS one. 2016;11(10):e0164045.
45. Suorsa K, Pulakka A, Leskinen T, et al. Comparison of sedentary time between thigh-worn and wrist-worn accelerometers. Journal for the Measurement of Physical Behaviour. 2020;3(3):234–243.
46. Harris PA, Taylor R, Minor BL, et al. The redcap consortium: Building an international community of software platform partners. Journal of biomedical informatics. 2019;95:103208.
47. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (redcap)—a metadata-driven methodology and workflow process for providing translational research informatics support. Journal of biomedical informatics. 2009;42(2):377–381.
48. de Winter JCF, Gosling SD, Potter J. Comparing the pearson and spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological Methods. 2016;21(3):273–290. doi: 10.1037/met0000079.
49. Bates D. Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:14065823. 2014.
50. Green P, MacLeod CJ. Simr: An r package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution. 2016;7(4):493–498.
51. Nilstomt A, Gustavsson J, Beckman L, et al. Physical activity from the perspective of older adults: A convergent mixed-method study. BMC geriatrics. 2024;24(1):768.
52. Van Lummel RC, Walgaard S, Pijnappels M, et al. Physical performance and physical activity in older adults: Associated but separate domains of physical function in old age. PloS one. 2015;10(12):e0144048.
53. Ferrand C, Martinent G, Bonnefoy M. Exploring motivation for exercise and its relationship with health-related quality of life in adults aged 70 years and older. Ageing & Society. 2014;34(3):411–427.
54. Jofré-Saldía E, Villalobos-Gorigoitía Á, Cofré-Bolados C, Ferrari G, Gea-García GM. Multicomponent training in progressive phases improves functional capacity, physical capacity, quality of life, and exercise motivation in community-dwelling older adults: A randomized clinical trial. International Journal of Environmental Research and Public Health. 2023;20(3):2755.
55. Thøgersen-Ntoumani C, Ntoumanis N. The role of self-determined motivation in the understanding of exercise-related behaviours, cognitions and physical self-evaluations. Journal of sports sciences. 2006;24(4):393–404.
56. Pohl P, Ahlgren C, Nordin E, Lundquist A, Lundin-Olsson L. Gender perspective on fear of falling using the classification of functioning as the model. Disability and Rehabilitation. 2015;37(3):214–222.
57. Timsina LR, Willetts JL, Brennan MJ, et al. Circumstances of fall-related injuries by age and gender among community-dwelling adults in the united states. PloS one. 2017;12(5):e0176561.
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Jul 23, 2025
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Using Ecological Momentary Assessment to Examine Predictors of Motivation for Physical Activity in Older Adults: Original Research. (2025). Research in Health and Medicine, 5(1). https://doi.org/10.53520/rdhs2025.104152
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Copyright, 2025 by the authors. This work is licensed under the Creative Commons Attribution 4.0 International License.