1 Class 10 Ecological Momentary Assessment and Factor Analysis December 8, 2005 Anita L. Stewart Institute for Health & Aging University of California, San Francisco

2 Ecological Momentary Assessment (EMA) u A monitoring strategy to assess phenomena: –in person’s natural environment (ecological) –at the moment they occur (momentary) u Samples “points” in time –Subjects respond at those points/moments u Enables research on fluctuations in phenomena over time u Overcomes “retrospective” distortions Stone AA et al., Ecological momentary assessment in behavioral medicine. Ann Beh Med, 1999;16:

3 Types of Retrospective Reporting Bias u Forgetting experiences is not a linear function of time –Recent mood positively influences report of mood over prior week –Recall of one event/time influenced by subsequent events u Little known about cognitive processes of “averaging” experiences to report over a prior time period (e.g., 4 weeks)

4 Variety of EMA Methods for… u Sampling –by time - random, time of day –By event – e.g., occurrence of symptom, urge to smoke u Signaling the moment to subjects –Pre-programmed wristwatch –Cell phones –Palm pilot, electronic diary –Small computers

5 Variety of EMA Methods for… u Recording the data –Cell phones (enter numbers) –Paper –Tape recorder –Palm pilot, small computers u Summarizing the data –Many data points (e.g., 7 times/day x 7 days = 49 data points) –Mean, mode, minimum, maximum, range, variability –Per day, per morning, per afternoon, per evening

6 Pain and Symptoms: EMA Methods Especially Useful u Allows assessment of episodes of pain and symptoms u Older methods – diaries –Patients report pain or symptom on a “regular” schedule (e.g., 3x day) or when symptom changes u EMA – random intervals are sampled –More representative

7 Diurnal Patterns of Pain in Patients with Rheumatoid Arthritis u Patients signaled 7x day with wristwatch –Random times u Rated pain on a 0-10 scale u On 8 th day, at appointment, rated pain overall for prior week Stone AA et al, Retrospective reports of pain do not correspond well to momentary reports of pain over one week in rheumatoid arthritis patients. Arthritis and Rheumatism, 1995;28:S227.

8 Pain in RA Patients: Results u Predictors of overall pain reported on day 8 –Average daily rating –Maximum pain during week –Reports for day 6 (recent reports) –Morning pain u Dirurnal patterns –Only half exhibited worse pain in morning (contrary to belief) u Correlates of pain –Stressful events reported at each “beep”

9 EMA in Smoking Cessation Research u Coping with “temptation episodes” is important predictor of success in quitting u Natural history of coping during quit attempts is not well understood u Methodological limitations –Hard to recall coping strategies –Retrospective bias - memory affected by whether succeeded or not in quitting u EMA helps with these limitations

10 Study of Smoking and Craving for Cigarettes u EMA used to identify situational and emotional correlates of smoking –Heavy smokers –Light smokers (not nicotine dependent) u Smoking was more stimulus bound for the light smokers Shiffman S et al., First lapses to smoking: within subject analysis of real time reports. J Cons Clin Psychol., 1996;64:

11 Study of Coping with Urge to Smoke in Smoking Cessation Study u EMA – 3 days –Random prompts –After coping with an urge –After slipping (smoking) u Closed-ended and open-ended measures of intensity of smoking urge, location (e.g., home, work), and mood u 36 subjects reported 389 coping episodes and 1,047 responses to cope O’Connell KA et al., Coping in real time: using EMA to assess coping with the urge to smoke. Res Nurs Health, 1996;21:487-97

12 Study of Coping with Urge to Smoke in Smoking Cessation Study u EMA – 3 days –Random prompts –After coping with an urge –After slipping (smoking) u Closed-ended and open-ended measures of intensity of smoking urge, location (e.g., home, work), and mood O’Connell KA et al., Coping in real time: using EMA to assess coping with the urge to smoke. Res Nurs Health, 1996;21:487-97

13 Study of Coping with Urge to Smoke in Smoking Cessation Study (cont.) u 36 subjects reported 389 coping episodes and 1,047 responses to cope –3.6 coping episodes per day »2.7 coping responses per episode u 67% behavioral coping and 33% cognitive coping strategies u Summarized 9 most frequent coping responses

14 Some Limitations of EMA u Expensive to operationalize u Large amount of data generated –Requires statistical expertise

15 Factor Analysis u Steve Gregorich - slides

16 Example of Factor Analysis (Tiro et al., 2005) u Developed 22 survey items to measure attitudes and norms related to mammography screening u Hypothesized structure (Appendix): –Pros of regular mammography screening –Cons of regular mammography screening –Outcome expectations: consequences of regular mammography screening –Cancer worries: negative emotions related to cancer and screening –Subjective norms re screening of one’s social network u 1-5 scale ranging from strongly agree to strongly disagree

17 Methods u Sample of women divided into 2 random samples (sample 1 and sample 2) –Exploratory factor analysis in sample 1 –Confirmatory factor analysis in sample 2 –Invariance (equivalence) of factor structure between the two samples u Multitrait scaling analysis –Convergent and discriminant validity u 4 final scales

18 Select Number of Factors: EFA u Methods for choosing number of factors: –Scree plot examined –Eigenvalues > 1.0 u Four factors selected (Table 2) –pros, cons, subjective norms, and cancer worries u Slightly different from hypothesized structure –Outcome expectations factor not found –Two items loaded on different factor then hypothesized

19 Test Adequacy of Factor Structure in Sample 2: Confirmatory Factor Analysis u Model provided adequate fit (p. 559): NFI = 0.89 NNFI = 0.89 CFI = 0.90 RMSEA = u Modifying the model improved the fit: NFI = 0.92 NNFI = 0.92 CFI = 0.93 RMSEA = 0.055

20 Enough! u Thank you u Good luck in measuring your concepts! u Feel free to contact me in the future with measurement questions