Development and Validation of a Lifestyle Behavior Tool in Overweight and Obese Women through Qualitative and Quantitative Approaches
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Defining Scope, Structure, and Content
Expert Evaluation
2.3. Pre-Testing (Study 1)
2.4. Pilot Study (Study 2)
2.5. Statistical Analysis
3. Results
3.1. Pre-Testing (Study 1)
3.1.1. Characteristics of the Participants
3.1.2. Perceived Acceptability of the 6P Tool
3.1.3. Perceived Relevance and Usefulness of the 6P Tool
3.1.4. Adjuncts to the 6P Tool
3.1.5. Perceived Acceptability of Mobile Health Messages
3.1.6. Perceived Usefulness of Mobile Health Messages
3.1.7. Suggestions for Improving Mobile Health Messages
3.2. Pilot Study (Study 2)
3.2.1. Characteristics of Participants
3.2.2. Internal Consistency of 6P
3.2.3. Associations of 6P Scores with Eating Behavior, Physical Activity, and BMI
3.2.4. Weight, Eating Behavior, and Physical Activity before and after the Intervention
3.2.5. 6P Assessment before and after Intervention
3.2.6. 6P Composite Scores before and after Intervention
3.2.7. 6P Diagnosis and Goal Selection
3.2.8. 6P Feedback and Evaluation
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Poston, L.; Caleyachetty, R.; Cnattingius, S.; Corvalán, C.; Uauy, R.; Herring, S.; Gillman, M.W. Preconceptional and maternal obesity: Epidemiology and health consequences. Lancet Diabetes Endocrinol. 2016, 4, 1025–1036. [Google Scholar] [CrossRef]
- GBD 2015 Obesity Collaborators. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N. Engl. J. Med. 2017, 377, 13–27. [Google Scholar] [CrossRef] [PubMed]
- Blüher, M. Obesity: Global epidemiology and pathogenesis. Nat. Rev. Endocrinol. 2019, 15, 288–298. [Google Scholar] [CrossRef] [PubMed]
- Godfrey, K.M.; Reynolds, R.M.; Prescott, S.L.; Nyirenda, M.; Jaddoe, V.W.; Eriksson, J.G.; Broekman, B.F. Influence of maternal obesity on the long-term health of offspring. Lancet Diabetes Endocrinol. 2017, 5, 53–64. [Google Scholar] [CrossRef] [Green Version]
- Voerman, E.; Santos, S.; Patro Golab, B.; Amiano, P.; Ballester, F.; Barros, H.; Bergström, A.; Charles, M.A.; Chatzi, L.; Chevrier, C.; et al. Maternal body mass index, gestational weight gain, and the risk of overweight and obesity across childhood: An individual participant data meta-analysis. PLoS Med. 2019, 16, e1002744. [Google Scholar] [CrossRef]
- Temel, S.; van Voorst, S.F.; Jack, B.W.; Denktaş, S.; Steegers, E.A. Evidence-based preconceptional lifestyle interventions. Epidemiol. Rev. 2014, 36, 19–30. [Google Scholar] [CrossRef] [Green Version]
- Hanson, M.; Barker, M.; Dodd, J.M.; Kumanyika, S.; Norris, S.; Steegers, E.; Stephenson, J.; Thangaratinam, S.; Yang, H. Interventions to prevent maternal obesity before conception, during pregnancy, and post partum. Lancet Diabetes Endocrinol. 2017, 5, 65–76. [Google Scholar] [CrossRef]
- Stephenson, J.; Heslehurst, N.; Hall, J.; Schoenaker, D.; Hutchinson, J.; Cade, J.E.; Poston, L.; Barrett, G.; Crozier, S.R.; Barker, M.; et al. Before the beginning: Nutrition and lifestyle in the preconception period and its importance for future health. Lancet 2018, 391, 1830–1841. [Google Scholar] [CrossRef]
- NCD Risk Factor Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet 2016, 387, 1377–1396. [Google Scholar] [CrossRef] [Green Version]
- Vandoni, M.; Codella, R.; Pippi, R.; Carnevale Pellino, V.; Lovecchio, N.; Marin, L.; Silvestri, D.; Gatti, A.; Magenes, V.C.; Regalbuto, C.; et al. Combatting Sedentary Behaviors by Delivering Remote Physical Exercise in Children and Adolescents with Obesity in the COVID-19 Era: A Narrative Review. Nutrients 2021, 13, 4459. [Google Scholar] [CrossRef]
- Ramage, S.; Farmer, A.; Eccles, K.A.; McCargar, L. Healthy strategies for successful weight loss and weight maintenance: A systematic review. Appl. Physiol. Nutr. Metab. 2014, 39, 1–20. [Google Scholar] [CrossRef]
- Kinmonth, A.L.; Wareham, N.J.; Hardeman, W.; Sutton, S.; Prevost, A.T.; Fanshawe, T.; Williams, K.M.; Ekelund, U.; Spiegelhalter, D.; Griffin, S.J. Efficacy of a theory-based behavioural intervention to increase physical activity in an at-risk group in primary care (ProActive UK): A randomised trial. Lancet 2008, 371, 41–48. [Google Scholar] [CrossRef]
- Rigby, R.R.; Mitchell, L.J.; Hamilton, K.; Williams, L.T. The Use of Behavior Change Theories in Dietetics Practice in Primary Health Care: A Systematic Review of Randomized Controlled Trials. J. Acad. Nutr. Diet. 2020, 120, 1172–1197. [Google Scholar] [CrossRef] [PubMed]
- Bacon, L.; Aphramor, L. Weight Science: Evaluating the Evidence for a Paradigm Shift. Nutr. J. 2011, 10, 9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Brown, R.E.; Kuk, J.L. Consequences of obesity and weight loss: A devil’s advocate position. Obes. Rev. 2015, 16, 77–87. [Google Scholar] [CrossRef]
- Gaesser, G.A.; Angadi, S.S.; Sawyer, B.J. Exercise and diet, independent of weight loss, improve cardiometabolic risk profile in overweight and obese individuals. Phys. Sportsmed. 2011, 39, 87–97. [Google Scholar] [CrossRef]
- Gaesser, G.; Tucker, W.; Jarrett, C.; Angadi, S. Fitness versus Fatness: Which Influences Health and Mortality Risk the Most? Curr. Sports Med. Rep. 2015, 14, 327–332. [Google Scholar] [CrossRef]
- Ross, R.; Blair, S.; de Lannoy, L.; Després, J.P.; Lavie, C.J. Changing the endpoints for determining effective obesity management. Prog. Cardiovasc. Dis. 2015, 57, 330–336. [Google Scholar] [CrossRef] [PubMed]
- Senge, P.M. The Art and Practice of the Learning Organization; Currency Doubleday: New York, NY, USA, 1990. [Google Scholar] [CrossRef]
- Flood, R.L. Fifth Discipline: Review and Discussion. Syst. Pract. Action Res. 1998, 11, 259–273. [Google Scholar] [CrossRef]
- Jones, N.A.; Ross, H.; Lynam, T.; Perez, P.; Leitch, A. Mental models: An interdisciplinary synthesis of theory and methods. Ecol. Soc. 2011, 16, 46. [Google Scholar] [CrossRef]
- Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
- Thompson, N.R.; Asare, M.; Millan, C.; Umstattd Meyer, M.R. Theory of Planned Behavior and Perceived Role Model as Predictors of Nutrition and Physical Activity Behaviors Among College Students in Health-Related Disciplines. J. Commun. Health 2020, 45, 965–972. [Google Scholar] [CrossRef] [PubMed]
- Ajzen, I.F.M. Understanding Attitudes and Predicting Social Behavior; Prentice-Hall: Englewood Cliffs, NY, USA, 1980. [Google Scholar]
- Armitage, C.J.; Conner, M. Efficacy of the Theory of Planned Behaviour: A meta-analytic review. Br. J. Soc. Psychol. 2001, 40, 471–499. [Google Scholar] [CrossRef] [Green Version]
- Arora, C.; Sinha, B.; Malhotra, A.; Ranjan, P. Development and Validation of Health Education Tools and Evaluation Questionnaires for Improving Patient Care in Lifestyle Related Diseases. J. Clin. Diagn. Res. 2017, 11, JE06–JE09. [Google Scholar] [CrossRef] [PubMed]
- Reethesh, S.R.; Ranjan, P.; Arora, C.; Kaloiya, G.S.; Vikram, N.K.; Dwivedi, S.N.; Jyotsna, V.P.; Soneja, M. Development and Validation of a Questionnaire Assessing Knowledge, Attitude, and Practices about Obesity among Obese Individuals. Indian J. Endocrinol. Metab. 2019, 23, 102–110. [Google Scholar] [PubMed]
- Burger, J.; Fleischer, J.; Jeitner, C.; Gochfeld, M. Environmental concerns and diet in Singapore. J. Toxicol. Environ. Health A 2003, 66, 1405–1419. [Google Scholar] [CrossRef] [PubMed]
- Clark, M.L.; Butler, L.M.; Koh, W.-P.; Wang, R.; Yuan, J.-M. Dietary fiber intake modifies the association between secondhand smoke exposure and coronary heart disease mortality among Chinese non-smokers inSingapore. Nutrition 2013, 29, 1304–1309. [Google Scholar] [CrossRef] [PubMed]
- Whitton, C.; Ma, Y.; Bastian, A.C.; Fen Chan, M.; Chew, L. Fast-food consumers in Singapore: Demographic profile, diet quality and weight status. Public Health Nutr. 2014, 17, 1805–1813. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bernard, J.Y.; Ng, S.; Natarajan, P.; Loy, S.L.; Aris, I.M.; Tint, M.T.; Chong, Y.S.; Shek, L.; Chan, J.; Godfrey, K.M.; et al. Associations of physical activity levels and screen time with oral glucose tolerance test profiles in Singaporean women of reproductive age actively trying to conceive: The S-PRESTO study. Diabet. Med. 2019, 36, 888–897. [Google Scholar] [CrossRef] [PubMed]
- Lai, J.S.; Soh, S.E.; Loy, S.L.; Colega, M.; Kramer, M.S.; Chan, J.K.Y.; Tan, T.C.; Shek, L.P.C.; Yap, F.K.P.; Tan, K.H.; et al. Macronutrient composition and food groups associated with gestational weight gain: The GUSTO study. Eur. J. Nutr. 2019, 58, 1081–1094. [Google Scholar] [CrossRef] [PubMed]
- Loy, S.L.; Loo, R.S.X.; Godfrey, K.M.; Chong, Y.S.; Shek, L.P.; Tan, K.H.; Chong, M.F.; Chan, J.K.Y.; Yap, F. Chrononutrition during Pregnancy: A Review on Maternal Night-Time Eating. Nutrients 2020, 12, 2783. [Google Scholar] [CrossRef] [PubMed]
- World Health Organization. Obesity and Overweight. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (accessed on 10 July 2021).
- Lee, B. Dietary guidelines in singapore. Asia Pac. J. Clin. Nutr. 2011, 203, 472–476. [Google Scholar]
- 8 Singapore Dietary Guidelines You Must Know. Available online: https://www.healthhub.sg/live-healthy/111/living_with_health_8_sets_diet_guidelines (accessed on 10 November 2021).
- Tylka, T.L.; Annunziato, R.A.; Burgard, D.; Daníelsdóttir, S.; Shuman, E.; Davis, C.; Calogero, R.M. The Weight-Inclusive versus Weight-Normative Approach to Health: Evaluating the Evidence for Prioritizing Well-Being over Weight Loss. J. Obes. 2014, 2014, 983495. [Google Scholar] [CrossRef]
- St-Onge, M.P.; Ard, J.; Baskin, M.L.; Chiuve, S.E.; Johnson, H.M.; Kris-Etherton, P.; Varady, K. Meal Timing and Frequency: Implications for Cardiovascular Disease Prevention: A Scientific Statement from the American Heart Association. Circulation 2017, 135, e96–e121. [Google Scholar] [CrossRef] [PubMed]
- Paoli, A.; Tinsley, G.; Bianco, A.; Moro, T. The Influence of Meal Frequency and Timing on Health in Humans: The Role of Fasting. Nutrients 2019, 11, 719. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shaw, E.; Leung, G.K.W.; Jong, J.; Coates, A.M.; Davis, R.; Blair, M.; Huggins, C.E.; Dorrian, J.; Banks, S.; Kellow, N.J.; et al. The Impact of Time of Day on Energy Expenditure: Implications for Long-Term Energy Balance. Nutrients 2019, 11, 2383. [Google Scholar] [CrossRef] [Green Version]
- Ross, R.; Chaput, J.P.; Giangregorio, L.M.; Janssen, I.; Saunders, T.J.; Kho, M.E.; Poitras, V.J.; Tomasone, J.R.; El-Kotob, R.; McLaughlin, E.C.; et al. Canadian 24-Hour Movement Guidelines for Adults aged 18-64 years and Adults aged 65 years or older: An integration of physical activity, sedentary behaviour, and sleep. Appl. Physiol. Nutr. Metab. 2020, 45 (Suppl. 2), S57–S102. [Google Scholar] [CrossRef] [PubMed]
- Wilson, K.; Senay, I.; Durantini, M.; Sánchez, F.; Hennessy, M.; Spring, B.; Albarracín, D. When it comes to lifestyle recommendations, more is sometimes less: A meta-analysis of theoretical assumptions underlying the effectiveness of interventions promoting multiple behavior domain change. Psychol. Bull. 2015, 141, 474–509. [Google Scholar] [CrossRef] [PubMed]
- Stunkard, A.J.; Messick, S. The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. J. Psychosom. Res. 1985, 29, 71–83. [Google Scholar] [CrossRef]
- Küçükerdönmez, Ö.; Akder, R.N.; Seçkiner, S.; Oksel, E.; Akpınar, Ş.; Köksal, E. Turkish version of the ‘sThree-Factor Eating Questionnaire-51’s for obese individuals: A validity and reliability study. Public Health Nutr. 2021, 24, 3269–3275. [Google Scholar] [CrossRef] [PubMed]
- Karlsson, J.; Persson, L.O.; Sjöström, L.; Sullivan, M. Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. Int. J. Obes. 2000, 24, 1715–1725. [Google Scholar] [CrossRef] [Green Version]
- Keränen, A.M.; Savolainen, M.J.; Reponen, A.H.; Kujari, M.L.; Lindeman, S.M.; Bloigu, R.S.; Laitinen, J.H. The effect of eating behavior on weight loss and maintenance during a lifestyle intervention. Prev. Med. 2009, 49, 32–38. [Google Scholar] [CrossRef] [PubMed]
- Craig, C.L.; Marshall, A.L.; Sjöström, M.; Bauman, A.E.; Booth, M.L.; Ainsworth, B.E.; Pratt, M.; Ekelund, U.; Yngve, A.; Sallis, J.F.; et al. International physical activity questionnaire: 12-country reliability and validity. Med. Sci. Sports Exerc. 2003, 35, 1381–1395. [Google Scholar] [CrossRef] [Green Version]
- Ryan, G.W.; Bernard, H.R. Techniques to Identify Themes. Field Methods 2003, 15, 85–109. [Google Scholar] [CrossRef] [Green Version]
- Ayres, L.; Kavanaugh, K.; Knafl, K.A. Within-Case and Across-Case Approaches to Qualitative Data Analysis. Qual. Health Res. 2003, 13, 871–883. [Google Scholar] [CrossRef] [PubMed]
- Streiner, D.L. Starting at the Beginning: An Introduction to Coefficient Alpha and Internal Consistency. J. Personal. Assess. 2003, 80, 99–103. [Google Scholar] [CrossRef] [PubMed]
- Greenwood, J.; Broadbent, J.; Fuller-Tyszkiewicz, M. Restrained eaters consume more food only if they are impulsive and male. Eat Behav. 2014, 15, 582–585. [Google Scholar] [CrossRef] [PubMed]
- Stice, E.; Sysko, R.; Roberto, C.A.; Allison, S. Are dietary restraint scales valid measures of dietary restriction? Additional objective behavioral and biological data suggest not. Appetite 2010, 54, 331–339. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shilts, M.K.; Horowitz, M.; Townsend, M.S. Goal setting as a strategy for dietary and physical activity behavior change: A review of the literature. Am. J. Health Promot. 2004, 19, 81–93. [Google Scholar] [CrossRef]
- Bandura, A. Social cognitive theory of self-regulation. Organ. Behav. Hum. Decis. Process. 1991, 50, 248–287. [Google Scholar] [CrossRef]
- Larson, N.; Story, M. A review of environmental influences on food choices. Ann. Behav. Med. 2009, 38 (Suppl. 1), S56–S73. [Google Scholar] [CrossRef] [PubMed]
- Teixeira, P.J.; Carraça, E.V.; Marques, M.M.; Rutter, H.; Oppert, J.-M.; De Bourdeaudhuij, I.; Lakerveld, J.; Brug, J. Successful behavior change in obesity interventions in adults: A systematic review of self-regulation mediators. BMC Medicine 2015, 13, 84. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Thomas, B.J.; Bishop, J.L. Dietary assessment methodology. In Manual of Dietetic Practice; British Dietetic Association: Birmingham, UK, 2007; pp. 32–40. [Google Scholar]
- Thompson, F.E.; Byers, T. Dietary assessment resource manual. J. Nutr. 1994, 124 (Suppl. 11), 2245s–2317s. [Google Scholar] [PubMed]
- England, C.Y.; Andrews, R.C.; Jago, R.; Thompson, J.L. A systematic review of brief dietary questionnaires suitable for clinical use in the prevention and management of obesity, cardiovascular disease and type 2 diabetes. Eur. J. Clin. Nutr. 2015, 69, 977–1003. [Google Scholar] [CrossRef] [Green Version]
- Shim, J.-S.; Oh, K.; Kim, H.C. Dietary assessment methods in epidemiologic studies. Epidemiol. Health 2014, 36, e2014009. [Google Scholar] [CrossRef]
- Hedrick, V.E.; Dietrich, A.M.; Estabrooks, P.A.; Savla, J.; Serrano, E.; Davy, B.M. Dietary biomarkers: Advances, limitations and future directions. Nutr. J. 2012, 11, 109. [Google Scholar] [CrossRef] [Green Version]
- Wing, R.R.; Tate, D.F.; Gorin, A.A.; Raynor, H.A.; Fava, J.L. A self-regulation program for maintenance of weight loss. N. Engl. J. Med. 2006, 355, 1563–1571. [Google Scholar] [CrossRef] [Green Version]
- Hollis, J.F.; Gullion, C.M.; Stevens, V.J.; Brantley, P.J.; Appel, L.J.; Ard, J.D.; Champagne, C.M.; Dalcin, A.; Erlinger, T.P.; Funk, K.; et al. Weight loss during the intensive intervention phase of the weight-loss maintenance trial. Am. J. Prev. Med. 2008, 35, 118–126. [Google Scholar] [CrossRef] [Green Version]
- Sheeran, P.; Webb, T.L.; Gollwitzer, P.M. The interplay between goal intentions and implementation intentions. Pers. Soc. Psychol. Bull. 2005, 31, 87–98. [Google Scholar] [CrossRef]
- Almoosawi, S.; Vingeliene, S.; Karagounis, L.G.; Pot, G.K. Chrono-nutrition: A review of current evidence from observational studies on global trends in time-of-day of energy intake and its association with obesity. Proc. Nutr. Soc. 2016, 75, 487–500. [Google Scholar] [CrossRef] [Green Version]
- Overdijkink, S.B.; Velu, A.V.; Rosman, A.N.; van Beukering, M.D.; Kok, M.; Steegers-Theunissen, R.P. The Usability and Effectiveness of Mobile Health Technology-Based Lifestyle and Medical Intervention Apps Supporting Health Care During Pregnancy: Systematic Review. JMIR Mhealth Uhealth 2018, 6, e109. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lau, Y.; Cheng, L.J.; Chi, C.; Tsai, C.; Ong, K.W.; Ho-Lim, S.S.T.; Wang, W.; Tan, K.L. Development of a Healthy Lifestyle Mobile App for Overweight Pregnant Women: Qualitative Study. JMIR Mhealth Uhealth 2018, 6, e91. [Google Scholar] [CrossRef] [PubMed]
- Thaler, R.H.; Sunstein, C.R. Nudge: Improving Decisions about Health, Wealth, and Happiness; Yale University Press: New Haven, CT, US, 2008; pp. 14–38. [Google Scholar]
- Hackman, C.L.; Knowlden, A.P. Theory of reasoned action and theory of planned behavior-based dietary interventions in adolescents and young adults: A systematic review. Adolesc. Health Med. Ther. 2014, 5, 101–114. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gibney, M.J.; Margetts, B.M.; Kearney, J.M.; Arab, L. Public Health Nutrition; Wiley: Hoboken, NJ, USA, 2013. [Google Scholar]
- Koplan, J.P.; Liverman, C.T.; Kraak, V.I. Preventing childhood obesity: Health in the balance: Executive summary. J. Am. Diet. Assoc. 2005, 105, 131–138. [Google Scholar] [CrossRef] [PubMed]
- Tunçalp, Ö.; Were, W.M.; MacLennan, C.; Oladapo, O.T.; Gülmezoglu, A.M.; Bahl, R.; Daelmans, B.; Mathai, M.; Say, L.; Kristensen, F.; et al. Quality of care for pregnant women and newborns-the WHO vision. Bjog 2015, 122, 1045–1049. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Torgerson, D.; Torgerson, C. Designing Randomised Trials in Health, Education and the Social Sciences: An Introduction; Springer: Berlin/Heidelberg, Germany, 2008; pp. 1–210. [Google Scholar]
Themes | Subthemes | Examples of Quotes from Participants |
---|---|---|
6P tool | ||
Perceived acceptability of the 6P tool | Comprehensive content and good language |
|
Suggestion for clearer definitions of food portion size | …the amount of rice, how do you define (what is) 1/3 of the rice, 2/3 of the rice, one whole plate of rice? …I think it would be better if you go by tablespoon (of rice) (04) | |
Mobile application as an ideal delivery platform |
| |
Weekly or daily frequency of administration is ideal |
| |
Perceived relevance and usefulness of the 6P tool | Increased knowledge about healthy eating and more aware of unhealthy eating habits |
|
Useful to track their eating habits | They (the 6P tool) can actually sort of like monitor how did we change our intake of food (over time) and things like this (12) | |
Adjuncts to the 6P tool | Suggestion for a feedback report | I want to see results (diagnosis) and recommendations at the end of filling the tool (07) |
Suggestion for a monitoring chart |
| |
6P mobile health messages | ||
Perceived acceptability of mobile health messages | Good content and language |
|
Sending it via a mobile application is ideal | It will be best (delivered) by (a) mobile application (14) | |
Weekly frequency of mobile health messages delivery | Weekly will be even better, because information retention would be there (07) | |
Perceived usefulness of mobile health messages | Serves as a good reminder of their goals | Each of these nudges serves as a reminder (for us to improve our eating habits). Just like my running app, when it is time to run, they will ask me if it is time to go for a run (06) |
Motivates them to improve eating habits | For me, if you have this kind of app or nudge, I feel (more) motivated (to work on my goal) because I know the direction in which to go (and how to improve) (05) | |
Provides useful dietary information | I think the most important information that has (been) given (to us through mobile health messages) is the (about the importance of eating) three main meals. I think most of us... feel like I still feel like I should skip meals to lose weight. So, I think this information is good for us to let women know that skipping meals does not mean that it will be a healthier choice (to lose weight) (15) | |
Suggestions for improving mobile health messages | Include more images |
|
Messages that list the type of foods to be taken and avoided | (It will be good to include) which food is good to take, which food should be avoided (15) |
Measures | r | p |
---|---|---|
BMI | 0.26 | 0.086 |
TFEQ- Cognitive restraint | −0.33 | 0.027 |
TFEQ- Disinhibition | 0.50 | <0.001 |
TFEQ- Hunger | 0.45 | 0.002 |
IPAQ- Total MET | a −0.52 | 0.002 |
Variable | Baseline | 1-Month Follow-Up | p a |
---|---|---|---|
Weight (kg) | 76.28 ± 11.29 | 76.37 ± 11.20 | 0.719 |
BMI (kg/m2) | 30.32 ± 4.09 | 30.36 ± 4.10 | 0.682 |
TFEQ-51 | |||
Cognitive restraint | 10.26 ± 4.10 | 11.82 ± 3.83 | 0.010 |
Disinhibition | 7.63 ± 2.87 | 6.68 ± 2.90 | 0.030 |
Hunger | 4.92 ± 2.88 | 4.50 ± 2.98 | 0.293 |
Physical activity based on IPAQ scoring | |||
Inactive | 10 (26.3) | 10 (26.3) | 0.931 |
Minimally active | 18 (47.4) | 17 (44.7) | |
High active (HEPA) | 10 (26.3) | 11 (29.0) | |
P1 Portion (amount of carbohydrates per meal, scored from 0–7) | 3.00 (2.00–4.50) | 2.63 (1.25–3.67) | 0.004 |
0 | 0 | 0 | 0.039 |
1–2 (recommended) | 6 (15.8) | 13 (34.2) | |
>2 | 32 (84.2) | 25 (65.8) | |
P2 Proportion (portion of vegetables per day, scored from 0–100%) | 46.88 (6.25–100.00) | 40.62 (4.69–100.00) | 0.777 |
<50% | 19 (50.0) | 20 (52.6) | 1.000 |
≥50% (recommended) | 19 (50.0) | 18 (47.4) | |
P3 Pleasure (total snacks and beverages per day) | 2.00 (0.50–3.00) | 2.00 (0.50–3.00) | 0.601 |
<3 (recommended) | 34 (89.5) | 32 (84.2) | 0.625 |
≥3 | 4 (10.5) | 6 (15.8) | |
P4 Phase (proportion of daily intake after 7 pm, scored from 0–100%) | 25.00 (0–55.00) | 20.00 (0–50.00) | 0.179 |
<50% (recommended) | 32 (84.2) | 34 (89.5) | 0.625 |
≥50% | 6 (15.8) | 4 (10.5) | |
P5 Physicality (total duration per week in mins) | 195.00 (60.00–1200.00) | 285.00 (75.00–840.00) | 0.264 |
<150 | 16 (42.1) | 13 (34.2) | 0.581 |
≥150 (recommended) | 22 (57.9) | 25 (65.8) | |
P6 Psychology (motivational level, scored from 1–10) | 6 (4–8) | 6 (3–8) | 0.653 |
≤4 | 8 (21.1) | 4 (10.5) | 0.344 |
≥5 (recommended) | 30 (78.9) | 34 (89.5) |
Baseline (n = 37) | Follow-Up (n = 38) | |||
---|---|---|---|---|
6P Assessment | Diagnosis | Goal Selection | Diagnosis | Goal Selection |
P1 Portion | n = 36 (97.3%) | n = 17 (45.9%) | n = 34 (89.5%) | n = 18 (47.4%) |
Lack of carbohydrate | 2 (5.4%) | 2 (5.3%) | ||
Overeating | 32 (86.5%) | 29 (76.3%) | ||
Lack of whole grain | 17 (45.9%) | 15 (40.5%) | ||
Eating too fast | 21 (56.8%) | 18 (47.4%) | ||
P2 Proportion | n = 35 (94.6%) | n = 13 (35.1%) | n = 35 (92.1%) | n = 15 (39.5%) |
Inadequate vegetable & fruit intake | 32 (86.5%) | 34 (89.5%) | ||
High fat intake | 21 (56.8%) | 20 (52.6%) | ||
P3 Pleasure | ||||
Frequent snacking and unhealthy drink | n = 33 (89.2%) | n = 14 (37.8%) | n = 31 (81.6%) | n = 13 (34.2%) |
Frequent snacking | 1 (2.7%) | 0 | ||
Unhealthy snack and drink | 26 (70.3%) | 15 (39.5%) | ||
Mindless snacking | 21 (56.8%) | 27 (71.1%) | ||
Alcohol intake | 4 (10.8%) | 5 (13.2%) | ||
Irregular intake | n = 37 (100.0%) | n = 8 (21.6%) | n = 35 (92.1%) | n = 6 (15.8%) |
Irregular meal intake | 37 (100.0%) | 35 (92.1%) | ||
Meal skipping | 28 (75.7%) | 22 (57.9%) | ||
P4 Phase | n = 21 (56.8%) | n = 6 (16.2%) | n = 19 (50.0%) | n = 6 (15.8%) |
Night eating | 16 (43.2%) | 13 (34.2%) | ||
Bedtime eating | 9 (24.3%) | 8 (21.1%) | ||
P5 Physicality | n = 11 (29.7%) | n = 10 (27.0%) | n = 14 (36.8%) | n = 10 (26.3%) |
Inadequate physical activity | 7 (18.9%) | 13 (34.2%) | ||
Activity intensity | 4 (10.8%) | 1 (2.6%) | ||
P6 Psychology | n = 8 (21.6%) | n = 3 (8.1%) | n = 4 (10.5%) | n = 3 (7.9%) |
Low motivation | 8 (21.6%) | 4 (10.5%) |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ku, C.W.; Loo, R.S.X.; Lim, C.J.E.; Tan, J.J.X.; Ho, J.E.W.; Han, W.M.; Ng, X.W.; Chan, J.K.Y.; Yap, F.; Loy, S.L. Development and Validation of a Lifestyle Behavior Tool in Overweight and Obese Women through Qualitative and Quantitative Approaches. Nutrients 2021, 13, 4553. https://doi.org/10.3390/nu13124553
Ku CW, Loo RSX, Lim CJE, Tan JJX, Ho JEW, Han WM, Ng XW, Chan JKY, Yap F, Loy SL. Development and Validation of a Lifestyle Behavior Tool in Overweight and Obese Women through Qualitative and Quantitative Approaches. Nutrients. 2021; 13(12):4553. https://doi.org/10.3390/nu13124553
Chicago/Turabian StyleKu, Chee Wai, Rachael Si Xuan Loo, Cheryl Jia En Lim, Jacinth J. X. Tan, Joey Ee Wen Ho, Wee Meng Han, Xiang Wen Ng, Jerry Kok Yen Chan, Fabian Yap, and See Ling Loy. 2021. "Development and Validation of a Lifestyle Behavior Tool in Overweight and Obese Women through Qualitative and Quantitative Approaches" Nutrients 13, no. 12: 4553. https://doi.org/10.3390/nu13124553
APA StyleKu, C. W., Loo, R. S. X., Lim, C. J. E., Tan, J. J. X., Ho, J. E. W., Han, W. M., Ng, X. W., Chan, J. K. Y., Yap, F., & Loy, S. L. (2021). Development and Validation of a Lifestyle Behavior Tool in Overweight and Obese Women through Qualitative and Quantitative Approaches. Nutrients, 13(12), 4553. https://doi.org/10.3390/nu13124553